Leading Edge
Perspective
Embracing cancer complexity: Hallmarks
of systemic disease
Charles Swanton,1,2,41,* Elsa Bernard,1,3,41 Chris Abbosh,4,42 Fabrice Andre´,3,5,6 Johan Auwerx,7 Allan Balmain,8
Dafna Bar-Sagi,9 Rene´ Bernards,10 Susan Bullman,11 James DeGregori,12 Catherine Elliott,13 Ayelet Erez,14
Gerard Evan,15,16 Mark A. Febbraio,17 Andre´s Hidalgo,18,19 Mariam Jamal-Hanjani,2
(Author list continued on next page)
1The Francis Crick Institute, London, UK
2Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
ll3INSERM U981, Gustave Roussy, Villejuif, France
4AstraZeneca, Cambridge, UK
5Department of Medical Oncology, Gustave Roussy, Villejuif, France
6Paris Saclay University, Kremlin-Bicetre, France
7Laboratory of Integrative Systems Physiology, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
8UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA
9NYU Langone Health, New York, NY, USA
10Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, Amsterdam, the Netherlands
(Affiliations continued on next page)SUMMARYThe last 50 years have witnessed extraordinary developments in understanding mechanisms of carcinogen-
esis, synthesized as the hallmarks of cancer. Despite this logical framework, our understanding of the molec-
ular basis of systemicmanifestations and the underlying causes of cancer-related death remains incomplete.
Looking forward, elucidating how tumors interact with distant organs and how multifaceted environmental
and physiological parameters impinge on tumors and their hosts will be crucial for advances in preventing
andmore effectively treating human cancers. In this perspective, we discuss complexities of cancer as a sys-
temic disease, including tumor initiation and promotion, tumor micro- and immunemacro-environments, ag-
ing, metabolism and obesity, cancer cachexia, circadian rhythms, nervous system interactions, tumor-
related thrombosis, and the microbiome. Model systems incorporating human genetic variation will be
essential to decipher the mechanistic basis of these phenomena and unravel gene-environment interactions,
providing a modern synthesis of molecular oncology that is primed to prevent cancers and improve patient
quality of life and cancer outcomes.INTRODUCTION
Looking back 50 years, whenCell launched as a new journal, it is
evident that the landscape of cancer research has changed
dramatically. During this time, reductionist cell biology utilizing
elegant and simple model systems has formed the mainstay of
scientific discovery, producing extraordinary insights into the
regulation of the cell cycle, apoptosis, cell motility, invasion,
and immune dysregulation, leading to significant progress in
the diagnosis and treatment of cancer; 5-year survival rates
have increased from 35% in the 1950s to 69.7% by 2017
(SEER Cancer Statistics Review 1975–2018). Despite this prog-
ress, cancer remains a leading cause of death worldwide.
We are in the midst of a renaissance where knowledge about
cancer biology and genetics is exploding, with >1,000 genes
identified that are altered in tumors, either genetically by recur-
rent mutations, or epigenetically, resulting in changes in theirregulation and expression.1 This milestone has facilitated
ever more precise delineation of the molecular circuitry, cellular
constitutions, and heterogeneous population dynamics of
mutant cancer cells, as well as the mechanisms of tumor pro-
gression andmetastatic dissemination in concert with ostensibly
normal—but functionally corrupted—cells of distinctive origins.
Such knowledge, which tends to be focused on the cancer cell
itself and its local microenvironment, has enabled new genera-
tions of mechanism-guided therapeutic drugs and treatment
regimens that have benefited some patients with certain forms
of cancer. Frustratingly, few of these innovative new therapeutic
strategies are broadly beneficial across the spectrum of human
cancers and, withmany avenues for tumors to evolve resistance,
even fewer significantly prolong overall survival.
The multi-dimensional archaeology of cancer, combined with
advancing technologies, creates a dizzyingly vast scope for ‘‘big
data’’ down to the single-cell level. These technologies aim toCell 187, March 28, 2024 ª 2024 Published by Elsevier Inc. 1589
Johanna A. Joyce,20 Matthew Kaiser,13 Katja Lamia,21 Jason W. Locasale,22,23 Sherene Loi,24,25 Ilaria Malanchi,1
Miriam Merad,26 Kathryn Musgrave,27,28 Ketan J. Patel,29 Sergio Quezada,30 Jennifer A. Wargo,31 Ashani Weeraratna,32
Eileen White,33,34 Frank Winkler,35,36 John N. Wood,37 Karen H. Vousden,1,* and Douglas Hanahan38,39,40,*
11Human Biology Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
12Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
13Cancer Research UK, London, UK
14Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
15The Francis Crick Institute, London, UK
16Kings College London, London, UK
17Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia
18Department of Immunobiology, Yale University, New Haven, CT 06519, USA
19Area of Cardiovascular Regeneration, Centro Nacional de Investigaciones Cardiovasculares, 28029 Madrid, Spain
20Department of Oncology, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
21Department of Molecular Medicine, Scripps Research Institute, La Jolla, CA, USA
22Department of Pharmacology and Cancer Biology, Duke University, Durham, NC, USA
23Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, USA
24Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
25The Sir Department of Medical Oncology, The University of Melbourne, Parkville, VIC, Australia
26Department of immunology and immunotherapy, Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY,
USA
27Translational and Clinical Research Institute, Newcastle University, Newcastle, UK
28Department of Haematology, The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
29MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
30Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
31Department of Surgical Oncology, Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX,
USA
32Sidney Kimmel Cancer Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
33Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
34Ludwig Princeton Branch, Ludwig Institute for Cancer Research, Princeton, NJ, USA
35Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
36Clinical Cooperation Unit Neuro-oncology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg,
Germany
37Molecular Nociception Group, WIBR, University College London, London, UK
38Lausanne Branch, Ludwig Institute for Cancer Research, Lausanne, Switzerland
39Swiss institute for Experimental Cancer Research (ISREC), EPFL, Lausanne, Switzerland
40Agora Translational Cancer Research Center, Lausanne, Switzerland
41These authors contributed equally
42Present address: Saga Diagnostics, Boston, MA, USA
*Correspondence: charles.swanton@crick.ac.uk (C.S.), karen.vousden@crick.ac.uk (K.H.V.), douglas.hanahan@epfl.ch (D.H.)
https://doi.org/10.1016/j.cell.2024.02.009
ll
Perspectivecharacterize cancers in ever-increasing detail, shedding light on
the neo-Darwinian development and progression of cancers
from different cells of origin that face tissue-specific barriers
requiring distinctive adaptations reflected in their multistep
tumorigenesis. Appreciation of this sobering diversity and
complexity has been growing for decades, looming as a poten-
tially insurmountable challenge for the ‘‘war on cancer.’’ 24 years
ago, in the millennium issue of Cell, an attempt to reconcile this
daunting diversity with the growing knowledge about mecha-
nisms of cancer was published.2 The hallmarks of cancer posited
that virtually all tumors acquire a common set of qualitatively
distinct functional capabilities that collectively enable cancer
cells to proliferate expansively while orchestrating the formation
of tumors that grow and often disseminate. The corollary
concept was that a common pair of phenotypic characteris-
tics—genomic instability and mutation, along with inflamma-
tion—facilitate their acquisition. Thus, the immense complexity
of cancer pathogenesis could be distilled, it was argued, as
different solutions to the same challenge, namely acquiring the
same set of hallmark capabilities during tumorigenesis and ma-1590 Cell 187, March 28, 2024lignant progression. This simple concept, refined in subsequent
years,3–5 has resonated through cancer medicine to the present
day, indicating that it has some utility as a conceptual organizing
principle.
However, this modern synthesis of cancer biology does not
fully consider the broader interactions of an evolving tumor
with distant organs of the host, nor the impact of host patho-
physiology, germline genetic diversity, and environmental expo-
sures, on cancer initiation and evolution. The reassuring
simplicity of the hallmarks is clearly insufficient to fully under-
stand the multifarious manifestations of disease mechanisms.
As such, there is a clear need to develop new therapeutic strate-
gies that improve both the quality and length of life for patients
suffering from cancer, tackling some of the most life-threatening
conditions such as cancer cachexia, thrombosis, and paraneo-
plastic syndromes and, importantly, opportunities to intervene
at the earliest stages of cancer initiation in new prevention stra-
tegies. An expanding repertoire of (multi-omic) research tools
and refined model systems are now poised to address cancer
as a systemic disease consequent to the complex interplay
ll
Perspectivebetween the diversity of the host genome, chance events, and a
legacy of human behavior resulting in complex environmental
exposures.
Aging is the number one prognostic factor for most tumors,
given that over 90% of patients diagnosed with cancer are
over the age of 50. In addition to this major contributor to cancer
complexity, and indeed a route for actionable preventive strate-
gies, is the environment in which we live, estimated from epide-
miological studies to influence the development of up to 80% of
human cancers.6 The term ‘‘environment’’ encompasses a wide
range of exogenous factors, including industrial pollutants in the
air or in our diet, as well as occupational or medical exposures to
toxic substances or certain types of radiation and pathogenic
viral and bacterial infection. It also includes what have come to
be known as ‘‘lifestyle factors’’: diet, alcohol consumption, ciga-
rette smoking, sun exposure, and sedentary behavior, all of
which impact cancer risk. Remarkably, it is becoming increas-
ingly clear that these disparate environmental agents increase
cancer risk by affecting the same cancer hallmark characteris-
tics described above—genomic mutations, altered metabolism,
chromosome instability, and inflammation.
In this perspective, a culmination of four Cancer-Research-
UK-sponsored Marshall Symposia, we contemplate—as illus-
trated in Figure 1—the new frontier that complements the hall-
marks of cancer by embracing the complexity of human cancer
pathogenesis, using technological advances to unravel the
diverse interplay between an evolving tumor within an aging
environment and distant organ systems in genetically diverse
populations who have engaged in distinct behaviors and
been exposed to heterogeneous environmental exposures.
Importantly, we argue that over the next 50 years, we will need
to acknowledge the limitations of inbred animal models in highly
controlled environments to fully recapitulate such complexity.
Addressing this challenge will require well-conceived studies in
human subjects as a vehicle for advancing discovery, interfaced
with increasingly sophisticated ex vivo and animal models of
cancer complexities, in order to advance therapeutic interven-
tion and early interception to more effectively prevent, diagnose
and treat cancer and its systemic manifestations.
Any review with a visionary goal to chart future directions for
cancer research in the next 50 years might well consider other
salient topics that are not highlighted in Figure 1. Prominent
among these is the role of biological sex in cancer susceptibility,
development, and therapeutic responses. Apart from the impor-
tance of sex hormones in the development of breast, ovarian,
and prostate cancers, biological sex is associated with distinct
incidences of a range of other cancer types, including bladder,
kidney and esophagus,7,8 all of which are higher in males.
Increasing evidence supports a role for sex-chromosome gene
effects9 and crosstalk between sex hormones and other systems
involved in inflammation and immunity,10,11 effects that have
clear implications for cancer treatment and therapeutic re-
sponses. For example, understanding why lung cancer in never
smokers is more common in females will require unraveling the
role of the environment, genetics, and human behavior. This field
is ripe for deeper analysis in the coming decades.
It would be remiss to discuss cancer complexity without
mentioning how the ever-increasing pace of developing artificialintelligence (AI) algorithms may impact our ability to process the
vast amounts of data being generated, and to understand how
the emergent properties of interconnected networks of genes
can help us to understand the progression of cancer from initi-
ated cells to metastasis at the single-cell level. Ultimately, we
anticipate a timewhen these newmethodswill be applied tomul-
tiple dimensions of cancer prevention and treatment, ranging
from analysis of the clonal architecture of normal tissues, to illu-
minating signs of increased risk, and to the prediction of stem
cell plasticity states that lead to drug resistance and poor patient
survival. Even when these clouds of complexity are understood
and AI is primed to improve outcomes for patients, the role of
‘‘bad luck’’ in cancer-initiating mutations may never be fully miti-
gated. Finally, a focus on cancer complexity should not detract
from reductionist approaches to understanding cancer’s hall-
marks, particularly with respect to the dynamic, evolving nature
of the disease over space and time. Deep longitudinal sampling
studies integrated with autopsy programs to decipher the
co-evolution of the tumor within its microenvironment during
the disease course and at distant metastatic sites, exemplified
by studies such as renal, lung, and breast TRACERx, lung
TRACERx EVO, and the UK national PEACE autopsy program,
are primed to help with these endeavors to create ‘‘dynamic tu-
mor atlases’’ across cancer subtypes. However, space consid-
erations do not allow us to consider these important areas in
the depth they deserve.
Here, we lay out a roadmap delineating a selection of over-
arching themes—clouds of complexity—looming on the horizon
of cancer biology and medicine that, if mechanistically ad-
dressed, promise to improve patient outcomes and quality of life.
MULTISTEP TUMORIGENESIS: UNDERSTANDING
TUMOR PROMOTION AND PROGRESSION
It has long been appreciated that cancers develop via pathways
of stepwise tumorigenesis and malignant progression, and,
more recently, during adaptive resistance to therapy. In part,
these stepwise transitions and stages reflect the acquisition
and refinement of hallmark capabilities, enabled in particular
via the prominent phenotypic characteristics of genome insta-
bility and gene mutation, and tissue inflammation.2,4
Cancer genetics has taught us that mutations in specific regu-
latory genes—termed oncogenes, of which the RAS genes are
prototypes—are essential drivers of this disease. In concert with
loss-of-function mutations in tumor suppressor genes, these
complementary genetic events can lead to the acquisition of the
multiple cancer hallmark capabilities found in most human can-
cers. Indeed, this paradigm has been strongly supported by
elegant mousemodels of cancer in which germline or somatic ge-
netic modifications can combine to unleash the complete set of
phenotypes associated with human cancers, in the absence of
any evident environmental carcinogenic factors. However,
new knowledge has revealed that ostensibly ‘‘normal’’ tissues
throughout the bodyharbor amultitude of oncogenic driver events
comprising a patchwork of mutant cells that persist and can
expand as asymptomatic clones during aging.12,13 This provoca-
tive result informs us that mutations in ‘‘driving’’ oncogenes may
benecessary but not alwayssufficient for tumorigenesis (Figure2).Cell 187, March 28, 2024 1591
Figure 1. The ‘‘clouds of complexity’’
impinging on the frontiers of cancer biology
and cancer medicine
Although the hallmarks of cancer have provided an
overarching conceptual rationale for the myriad
manifestations encompassing cancer as a dis-
ease, below this simplicity lies a dizzying diversity
in mechanistic effects and phenotypes, both inside
tumors and system-wide in the affected individual.
Thus, above the horizon are clouds of complexity
that, we argue in this perspective, are important
and incompletely understood. Below the horizon
lie mechanistic effectors—the building blocks of
cancer—governing the inception and progression
of cancer, which are also incompletely under-
stood. Elucidating both dimensions of cancer as a
systemic disease will be instrumental for ground-
breaking innovations in prevention and enduring
treatment of human cancer.
ll
PerspectiveIt is estimated that approximately 40% of the population will
develop cancer in their lifetime, and given that a typical human
has 30 trillion cells, these observations prompt the question:
why is cancer so rare at the single-cell level?
The historical concept that chemically induced skin carcino-
genesis, mimicking natural environmental insults, involves ‘‘initi-
ating’’ and ‘‘promoting’’ events14 has been conceptually refined
by the realization that many normal tissues are replete with cells
containing latent mutations in oncogenes and tumor suppressor
genes.12,13 Certainly, some environmental carcinogens act syn-
ergistically as both mutagens and inducers of inflammation (typi-
fied by tobacco smoke) to mutationally initiate and promote can-
cer. This suggestion, first made in the 1940s, is one that has
important implications for cancer prevention and unproven as-
sumptions regarding the long-term safety of e-cigarettes.15
However, Riva and colleagues recently demonstrated
that many known or suspected initiating carcinogens do not
appear to cause mutations themselves, but rather may act by
stimulating other hallmarks—including but not limited to chronic
inflammation—that awaken and trigger clonal expansion of
cells carrying latent oncogenic mutations.16 The concept that
non-mutational factors—e.g., wound healing, chronic inflamma-
tion, and exposure to chemicals in the environment—can stimu-
late the growth and selection of cells containing such activating
mutations in oncogenes or inactivating mutations in tumor1592 Cell 187, March 28, 2024suppressor genes has been documented
in mouse cancer models,16–20 in human
lung cancers linked to air pollution,21
and in mesothelioma through asbestos
exposure,22 and is implicated in other
human cancers, including those arising
in the esophagus,23 pancreas,24 and
colon.25
A logical and overlooked implication of
these important insights is that we lack
biological assays to assess the potential
‘‘tumor-promoting activity’’ of existing or
new chemical matter introduced into the
environment. We foresee the need to
chart the entirety of multistage pathwaysof tumorigenesis across tissues, to clarify the mechanisms by
which environmental or endogenous physiological promoters
can trigger stem/progenitor-cell-like properties in pre-initiated
cells harboring oncogenic mutations, acting through diverse in-
flammatory/wounding pathways across different tissues and
cell types to alter clonal selection. The roster of suspects that
may act as environmental tumor promoters includes, but is
certainly not limited to, microplastics, glyphosate,26 per/poly-
fluoroalkyl substances (PFASs), hot liquids,27 and infectious
agents such as H. pylori (Figure 2). Moreover, serious questions
remain unansweredwith respect to the long-term safety of e-cig-
arettes and the realistic possibility that human exposure to vap-
ing substances could promote tumor initiation, independent of
DNA mutagenesis, in much the same way as air pollution is
thought to act. Endogenous or lifestyle factors, including diet,
stress, sleep deprivation, sedentary behavior, and circadian
rhythm disruptions, many of which can impact the microbiome,
are also likely to play formative roles.
We envisage that defining encyclopedias of environmental
(and physiological) promoter-induced inflammatory and
other reactive responses that orchestrate tumorigenesis will
facilitate the development of crucially important technical capa-
bilities for early detection of incipient neoplasia that can distin-
guish lesions likely to progress to malignancy from those that
will not.
Figure 2. The interplay between cancer
driver mutations and environmental or
endogenous tumor promoters in cancer risk
Mutations are essential for cancers to develop and
may arise as a result of spontaneous errors in
normal DNA replication, during aging of quiescent
cells, or by exposure to mutagens in the environ-
ment. Obesity, dietary factors, and inflammation
may also contribute to mutation burden indirectly,
for example, through generation of reactive oxy-
gen species (ROS), but this is unlikely to make a
major contribution to overall mutation numbers.
Mutations may remain dormant in normal tissues
for long periods, unless the tissue is repeatedly
exposed to an inducer of inflammation or tissue
wounding, causing selection of cells carrying
specific mutations, leading to clonal outgrowth.
The particular mutations selected depend onmany
factors including the tissue or cell of origin, the host
genetic background, or the nature of the specific
promoting factor. Known or suspected exogenous
promoting factors are shown as examples, as are
potential endogenous or lifestyle-associated pro-
moting factors.
ll
PerspectiveRigorously assaying for non-mutagenic tumor promoters will
require improved technological capabilities, including single-cell
RNA and DNA sequencing, aswell as spatial transcriptomics/pro-
teomics/metabolomics, to unveil heterotypic cellular interactions
and actionable pathways suitable for molecular cancer prevention
efforts. While the foundation of cancer may lie in mutant onco-
genes and tumor suppressor genes, it is increasingly evident
that tumor progression involves non-mutational epigenetic
programming,4as reflected in thedynamicheterogeneityapparent
in many tumors, and as such, the epigenomes of both cancer
cells and the diverse cells of the tumor microenvironment
(TME) will necessarily require illumination at all stages. There is
evidence for ‘‘epigenetic memory’’ of prior exposure to inflamma-
tion-inducing agents, which we envisage could subsequently
contribute to tumorigenesis.28 While past exposure to mutagens
can be identified by whole-genome sequencing of human cells
and tissues that revealsmutational signaturesofdistinctive carcin-
ogens,29 there is currently no technology for identifyingpastorcur-
rent exposure to tumor promoters. The technical development of
such tools will enable molecular cancer prevention efforts aimed
to dampen inflammatory tumor promoter activities, further refined
by deep appreciation of the broader macro-environmental com-
plexities of the host, as elaborated in the topical sections that
follow.
TUMORIGENESIS AND WOUND HEALING: TWO SIDES
OF THE SAME COIN
While the complexity and uniqueness of each human cancer is
undeniable, there are also remarkable commonalities across
cancers. Some of these commonalities are general—all cancers
engage the same cell cycle machinery and almost all exhibit p53pathway inactivation, and promiscuous
activation of both Myc and the Ras/phos-
phatidylinositol 3-kinase (PI3K) pathway.
Furthermore, cancers of specific typesor tissues of origin share signature tumor/stromal phenotypes,
even when driven by different oncogenic mutations, and these
differ profoundly from adenocarcinomas arising in other organs,
even when driven by the same oncogenic mutations. Dvorak
offered us the first clue as to the source of these organ-specific
neoplastic constraints with his proposal that tumors are unre-
solved wounds,30 and there are clear parallels between the hall-
marks of cancer and the hallmarks of wound healing.31 More
lately this has been directly confirmed by showing that activation
of the same core oncogenic mutations—KRasG12D and Myc—
in adult tissues (lung and pancreas) directly and immediately
drives formation of distinct adenocarcinomas whose tumor-stro-
mal phenotypes perfectly match those of their spontaneous hu-
man pancreatic or lung adenocarcinoma counterparts.32,33
Thus, the principal determinant of cancer phenotypes is not their
unique panoply of oncogenic mutations but their organ/tissue of
origin. In contemporary parlance, the same oncogenic pathways
are hacking into the unique endogenous wound repair programs
of each target tissue/cell type.
This is a potent conceptual advance for two reasons. First, it
speaks to the widely held presumption that cancer’s hallmarks
(inflammation, immune suppression, promiscuous proliferation,
suppression of apoptosis, etc.) are neomorphic traits selected
through tumor evolution because they benefit tumor progres-
sion.2 However, if cancers are merely persistent hacks of normal
regenerative programs, then most of cancer’s ‘‘hallmarks’’
actually evolved to optimize tissue repair, not neoplastic subter-
fuge. Second, wound repair has major components—an initial
regenerative phase, which is followed by resolution, a discrete
morphogenic program that reorganizes the inchoate tissue and
reasserts homeostatic architecture, cellularity, and function.
Emerging evidence indicates that, in wound healing, the switchCell 187, March 28, 2024 1593
ll
Perspectivefrom regeneration to resolution is triggered by a decrease in
mitogenic signaling. Provocatively, it has long been known
from genetic and pharmacological studies that blockade of
oncogenic signaling triggers rapid regression of tumors (both
cancer cells and stroma)—at least initially. This is typically
attributed to ‘‘oncogene addiction,’’ a phenomenon lacking a
coherent mechanistic explanation. However, the similarity be-
tween post-injury wound resolution, due to cessation of mito-
genic signaling, and regression of tumors upon oncogene
blockade suggests that a close examination of injury resolution
could yield entirely novel strategies for cancer treatment.
TME: UNRAVELING AND TARGETING CELLULAR
NICHES
The inflammation induced by cancer promoters and resultant
multistep tumorigenesis discussed above are intertwined with
stage-specific alterations in the complex multicellular TME and
its associated extracellular milieu, which progressively co-
evolves with the cancer cells. Emerging evidence indicates
that alterations in tissue integrity associated with age, tobacco
exposure, or environmental pollutants among others (see aging
and cancer: cellular fitness, microenvironment dynamics, and
evolution over time section) can permit clonal expansions of
normal somatic cells harboring oncogenic mutations. The TME
is evidently involved in regulating disease progression and
modulating the response to a broad range of cancer thera-
pies.31,34 Within the TME, cellular niches provide unique habitats
that influence tumor behavior, treatment response, and immune
surveillance. Understanding the complex interactions within
these spatial niches is essential for developing more effective
cancer treatments. Moreover, there is potential that by identi-
fying and targeting the key cell types, processes, or signaling
pathwayswithin particular niches, the effects of such therapeutic
interventions can be amplified. We briefly summarize below
knowledge about cellular niches within the TME and highlight
questions that warrant investigation over the next decades.
Cellular niches in the TME consist of diverse cell populations,
including cancer cells, immune cells, fibroblasts, vasculature,
fat, nerve cells, and extracellular matrix (ECM) components.
Each niche may contribute to tumor growth, invasion, and
metastasis in distinct ways by supporting the specific features
of cancer cells that inhabit them (Figure 3). For instance, cancer
cells with high stemness potential reside within stem-cell-like
niches,32 which support their self-renewal and resistance to con-
ventional therapies, leading to disease relapse.33,35 Immune cell
niches can either promote or suppress anti-tumor immune re-
sponses, thereby profoundly influencing treatment outcomes.36
The spatial heterogeneity of other TME components such as fi-
broblasts in the tumor stroma, can contribute to desmoplasia,
angiogenesis, and immune modulation.37
The importance of cancer niches can be particularly appreci-
ated in the context of metastatic colonization, where metastatic
cells must recreate a supportive environment upon seeding a
new organ. Notably, the fact that metastases may erupt years
after primary tumor resection, indicates that metastasis-
competent cells can reside in distant organs in a non-prolifer-
ating, dormant state. The existence of dormant niche compo-1594 Cell 187, March 28, 2024nents that sustain the quiescent but viable state of metastatic
cells, with the propensity to enable outgrowth, is reported.38,39
However, the properties of these dormant niches and how they
change over time to re-awaken cancer cells are still largely un-
known. Certainly, tissue aging has implications for dormancy
reversion.40 As we discuss in the following sections, aging
as well as progressive systemic changes that are directly
linked to metabolic and inflammatory perturbations caused by
cancer-derived factors may contribute to evolving niches
(Figure 3).
Significant technological progress has been made in charac-
terizing cellular niches within the TME, revealing their dynamic
and complex nature. Advanced imaging techniques, single-cell
sequencing, spatial transcriptomics, multiplex high-resolution
imaging techniques, and niche-labeling strategies are allowing
researchers to identify distinct cellular populations and map
their spatial organization within tumors.41 This has enabled, for
instance, identification of a tissue-intrinsic regenerative program
activated at early stages of metastatic niche initiation,41 the
phenotypic states inherent to particular tumor niches linked to
certain genetic abnormalities or between patients with primary
or metastatic cancers,42,43 the tumor-stromal composition
changes associated with cancer progression,44 as well as the
features in the TME that can be correlated with clinical outcome
or predict therapy responses.45–47 Based on such multi-omics
profiles, new classifications of tumors are emerging.48 Although
we expect that many tumors will ultimately be amenable to TME
modulation in the future, it is conceivable that certain cancers will
prove especially challenging, such as liver metastases in pa-
tients with microsatellite-stable colorectal cancers or glioblas-
toma (GBM). Notably, these are also organs subject to immune
suppression under steady-state conditions, which likely contrib-
utes an additional barrier to mounting an effective immune
response.
Looking forward, it will be important to determine the
biology underlying TME spatial diversity; how hypoxia, nutrient
availability, metabolites, and ECM composition influence the
behavior of cellular niches. Understanding the intricate inter-
play between the distinct cellular niches within tumors is
essential, for example, to discriminate between cancer-pro-
moting and cancer-restrictive niches. Investigating the tempo-
ral evolution of these cellular niches will provide insights into
how the TME evolves both during cancer progression and
following therapeutic intervention. Longitudinal studies
capturing the dynamics of cellular populations within niches
over time, for example, from sequential patient biopsies com-
bined with multimodal imaging strategies in preclinical
models,49 will be key to unravel the spatiotemporal heteroge-
neity of tumors.
We envisage that charting the dynamic evolution of the TME
and its heterogeneous niches will reveal new avenues to
manipulate TME states to interfere with tumor growth and pro-
gression, metastatic outgrowth, and adaptive resistance to
therapy. Understanding the key determinants of dormant
niches that can maintain metastatic cells in a quiescent state
for decades, and the subsequent alterations that lead to a
switch toward metastatic outgrowth, will be an essential
requirement for developing strategies to detect and eliminate
Figure 3. Tumor micro- and macro-environ-
ments
Representation of the complex tumor ecosystem.
Interactions between diverse components of the
tumor micro- and macro-environments are de-
picted, involving influences of the host, as well as
external factors, on tumor growth and metastatic
dissemination.
ll
Perspectivedormant cells. Generating refined model systems to investigate
these questions will be paramount to gaining mechanistic
knowledge around metastatic dormancy and other niche phe-
notypes. Exploiting vulnerabilities within cellular niches holds
great potential for the development of effective precision-based
therapeutic strategies through the identification of specific
markers or pathways that are essential for niche maintenance
and tumor progression. Beyond the TME is the tumor
‘‘macro-environment’’ of the host, also depicted in Figure 3,
which encompasses a number of the complexities discussed
in the following sections.
THE IMMUNE MACRO-ENVIRONMENT: HOMEOSTATIC
CONTROL AND HACKING SYSTEMS PHYSIOLOGY
The effects of tumor-induced perturbations of the immune sys-
tem extend beyond the local tumor-immune environment,
involving pronounced alterations to the systemic immune land-
scape during tumorigenesis. Paracrine molecules producedby cancer cells, immune cells, and non-
immune stromal cells throughout tumor
progression and released into the cardio-
vascular system have actions beyond the
local TME (Figure 4). Several mechanisms
leading to the release of immuno-modula-
tory molecules during cancer progression
have been identified. First, the tumor itself
can act in an autocrine manner on neigh-
boring tumor and non-tumor cells, leading
to the activation of both immune and non-
immune cells and the release of additional
molecules that add to the TME’s secre-
tome.50 Second, cellular senescence pro-
grams that are commonly active in malig-
nant and non-malignant cells can be
exacerbated with chemotherapy agents,
resulting in a secretory phenotype marked
by high production of pro-inflammatory
cytokines such as interleukin (IL)-6 and
IL-8.51–54 Third, genetically unstable tu-
mor cells activate DNA damage sensors
that produce type-1 interferons or acti-
vate inflammasomes and IL-1b produc-
tion, which can act peripherally on
stromal cells and bone marrow progeni-
tors.50,55–58 Fourthly, microbial elements
influenced by patients’ diet and environ-
ment can lead to the activation of cellsexpressing pattern recognition receptors and the release of in-
flammatory molecules such as IL-6, IL-1b, and tumor necrosis
factor (TNF).59–65 Lastly, stress-induced metabolites produced
by genetically unstable tumor cells or surrounding stromal and
immune cells can also be actively released into the circulatory
system.66–69 These systemic cues are sensed by hematopoietic
progenitors during the course of tumor development, leading to
dysregulated myelopoiesis and expansion of aberrant myeloid
cells that contribute to the dampening of local and systemic
anti-tumor immunity, and to the shaping of a pro-tumorigenic
TME (Figure 4).70,71
The co-option of developmental and regenerative programs
by tumor cells further contributes to changes in the systemic
immune environment, as exemplified by the cooperation be-
tween MYC and KRAS in driving an immunosuppressive micro-
environment, marked by the exclusion of systemic T and B cell
infiltrates in highly proliferative, invasive carcinomas. Deactiva-
tion of MYC triggers tumor regression that is accompanied by
the rapid reversal of these immunological changes, involvingCell 187, March 28, 2024 1595
Figure 4. Local and systemic drivers of protumorigenic myeloid dysregulation
(1) Molecules produced during tumor initiation that contribute to the recruitment and activation of macrophages that play a key role in the initiation of the in-
flammatory cascade and the release of inflammatory molecules in the blood circulation.
(2) Release of inflammatory molecules in the blood circulation that drives the enhancement and dysregulation of myelopoiesis.
(3) Epigenetic remodeling of myeloid progenitors in the bone marrow that contributes to the induction of nodes of molecular suppressive programs along the
myeloid lineage.
(4) Additional drivers produced in the local tumor microenvironment that contribute to further enhancement of myeloid suppression that further dampen anti-
tumor immunity and promote tumor proliferation and growth. The list of molecules provided in each category is not exhaustive and is only provided as examples.
ll
Perspectivea program that parallels the resolution phase of wound healing
responses.72
Additionally, the characteristics of systemic immune re-
sponses in tumor-burdened hosts are heavily influenced by
changes in physiological homeostatic controls, such as circa-
dian control, endocrine response to stress and metabolism,
and physical activity-induced changes in the circulatory system.
Understanding how the systemic immune response changes
during cancer progression, and how systemic immune functions
are altered as the antigenic and phenotypic diversity of a tumor
increases, and whether adaptive immunosuppression brought
about by tumor evolution contributes to cancer-related death,
are all important topics for future research.
These complexities suggest that the design of effective im-
mune-based therapies will require a comprehensive under-
standing of themolecular underpinnings of the interplay between
local and systemic immunity. Broadly, these analyses will
comprise the identification of the systemic drivers of pathogenic
myelopoiesis through deep and dynamic profiling of cancer pa-
tients’ secretome, as well as charting the chromatin and tran-
scriptomic programs along the myeloid lineage, starting from
early hematopoietic progenitors in the bone marrow to tumor-
associated myeloid cells in order to identify initiating nodes of
myeloid dysregulation. The results may guide the development
of novel strategies to target mediators of systemic immune
dysfunction. Concurrent assessments of systemic cancer pa-
tients’ secretomes and their clinical features, along with an
understanding of tumor somatic evolution, could form the foun-
dation for a therapeutic framework to guide patient immune-
stratification and immunotherapeutic decision-making.
Importantly, multistep tumorigenesis, malignant progression,
and the evolving phenotypes of cancer-promoting micro- and1596 Cell 187, March 28, 2024systemic macro-environments are also modulated by a plethora
of other complexities, as delineated below.
AGING AND CANCER: CELLULAR FITNESS,
MICROENVIRONMENT DYNAMICS, AND EVOLUTION
OVER TIME
Most cancers occur in people over the age of 60, and by 2050,
it is projected that over 2 billion of the world’s population will
be over 60 years old.73 The age-related rise in cancer can be
attributed to various factors, including the accumulation of
chronic genetic damage, epigenetic drift, alterations in tissue
microenvironments (including increased senescent cells), and
changes in adaptive and innate immunity.74 These factors
can alter tissue homeostasis and thus fitness, enabling selec-
tion for proliferative expansion of mutant cells responsive to
such altered landscapes. However, given the frequency with
which they are observed in normal tissues, it can be surmised
that mutation-driven clonal expansions rarely evolve to
become life-threatening malignancies in aging tissue microen-
vironments.75 It is critical to view the association of cancers
with old age through the lens of evolutionary biology—animals
have evolved strategies to maintain tissue functions and avoid
disease, maximizing reproductive success. These mecha-
nisms wane in post-reproductive periods. Therefore, we
need to appreciate how young/healthy tissue microenviron-
ments maximize the somatic fitness of stem and progenitor
cells, preventing the persistence or expansion of cells with
potentially malignant mutations—youth is tumor suppressive.
Unfortunately, aged tissues progressively lose their ability to
limit cancer evolution.75,76 Notably, inherited progeria syn-
dromes often (but not always) exhibit accelerated and
ll
Perspectiveaugmented cancer incidence,77 which we envisage results
from progeria-associated tissue dysfunction in the context of
increased frequency of mutations.
Recent studies have revealed that aging is characterized by
an increase in clonal expansions of somatic cells harboring
oncogenic mutations in histologically normal tissue,78 which
can vary widely in their pathogenic potential, as has been
most clearly shown for clonal hematopoiesis, which is associ-
ated with increased risk for leukemias and solid cancers.79 A
challenge will be to better understand how healthy tissues
impede tissue-impairing clonal expansion and the phenotypic
evolution of somatic cells that leads to cancer pathogenesis,
and how environmental carcinogens and lifestyle factors in
concert with natural aging affect these protective mechanisms
(see multistep tumorigenesis: understanding tumor promotion
and progression section). These expansions are associated
with conditions that change tissue landscapes, including in-
flammatory bowel disease,80–82 sun exposure for the skin,83
alcohol and smoking for the esophagus,84 air pollution and
smoking exposures for the lung,21 and obesity and smoking
for clonal hematopoiesis.85 Although it is increasingly accepted
that healthy lifestyles promote tissue repair/maintenance and
cancer protection and that unhealthy lifestyles engender
chronic inflammation, poor tissue repair, and increased risk of
cancer and other diseases,86,87 we do not understand how
healthy lifestyles impact the earliest stages of carcinogenesis,
including such mutation-driven clonal expansions. The forth-
coming knowledge may support the development of interven-
tions that can block abnormal clonal expansions in aging tis-
sues, with the aim of reducing cancer incidence as well as
physiological aging. Understanding how non-mutagenic carcin-
ogens (analogous to the tumor-promoting potential of pollutant
particulate matter) impact this process and drive the expansion
of cells with stem-like potential harboring oncogenic mutations
will be instrumental. We also need to characterize suspected
feedback loops whereby aging promotes particular mutation-
driven clonal expansions, which then further contribute to tis-
sue aging (which itself promotes oncogenesis). Analyzing clonal
mutational patterns in tissue samples (e.g., blood and acces-
sible epithelial tissues) from clinical trials of candidate anti-ag-
ing agents and from individuals of different ages, lifestyles (e.g.,
exercise), and diet could reveal strategies to limit the clonal
evolution that can lead to aging-associated tissue decline and
cancer.80,81
Aging also influences the ECM, a core constituent of the TME
secreted by tumor-associated fibroblasts and other non-tumor
cells.76 The ECM modulates tumor cell behavior through me-
chano-transduction. Aged fibroblasts release molecules that
induce significant changes in tumor cells, impacting signaling
pathways, reactive oxygen species (ROS) response, and
metabolism,74,76 as well as emergence from proliferative
dormancy.40 Indeed, it is possible that the regulatory processes
that augment selection for somatic clones carrying cancer-
associated mutations during aging overlap with those that
awaken cells from the dormant state to instruct metastatic
colonization (see the above discussion on dormant metastatic
niches).40,88 While fibroblast senescence may be involved in
both proliferative expansions, it is important to distinguish be-tween senescence and aging, as senescence can occur across
the lifespan, having both pro-tumor and anti-tumor effects, as
described in the next section. Targeting actionable elements
in the microenvironment of aging tissues, beyond senescence,
has the potential to inhibit metastasis and overcome therapy
resistance in elderly cancer patients. This pursuit will require
sophisticated bioengineering techniques, AI-based pathology,
and exploration of intersecting factors such as biological sex
and stress with age. Specifically, spatial transcriptomics
layered with sequencing and the 3D reconstruction and anno-
tation of the TME using AI-based pathology techniques such
as CODA,89 quantitative analysis of multiplex immunohisto-
chemistry using techniques like AstroPath,89,90 as well as bioin-
formatics tools for gene behavior analysis to predict cell-cell
communication will be critical to understand how the aging
TME impacts the expansion of cells with stem-like potential
leading to tumor initiation or the awakening of dormant meta-
static cells.91 The ultimate goal is to identify agents that induce
tissue landscapes to assume phenotypic states that limit all
stages of cancer evolution, from early clonal expansions to
metastatic outgrowths. Applications could range from early
prevention (e.g., interventions that maintain more youthful land-
scapes) to therapies (e.g., adjuvant/neoadjuvant therapies that
reduce metastatic outgrowth) that can be used to reduce can-
cer burden and mortality in a rapidly aging population. Among
these, leveraging the expanding knowledge base and insights
about the complexities connecting (partially age-related) cell
senescence to cancer are likely to lay the groundwork, as dis-
cussed below.
SENESCENCE AND CANCER: CELL STATE
TRANSITIONS AND THERAPEUTIC APPROACHES
Senescence is a stress response characterized by a stable
cessation of proliferation, with changes in cellular morphology,
gene expression, chromatin states, as well as increased
secretion of cytokines. The unambiguous identification of se-
nescent cells is complicated by a lack of gold standard
markers of the senescent state. Moreover, there are several
closely related cell states, such as quiescence, dormancy,
diapause, and drug-tolerant-persister (DTP) cells, which share
features with senescent cells. For example, the senescence
marker senescence-associated b-galactosidase is also ex-
pressed in some DTPs. Moreover, while the dogma in the
senescence field is that the proliferation arrest of senescent
cells is irreversible (which is not the case for the related states
mentioned above), there is growing evidence that senescence
is also reversible.92 Multiple gene expression signatures can
identify senescent cells, but there is significant variability in
gene expression among senescent cancer cells derived from
different tissues.93
In cancer cells, senescence can be triggered by genotoxic
stress (resulting from chemotherapy or radiotherapy; referred
to as ‘‘therapy-induced senescence’’), oxidative stress, or hy-
peractivated mitogenic signaling. A proliferation arrest can be
considered beneficial for cancer therapy in the short term.
However, the persistence of senescent cancer cells can be un-
favorable in the long term due to the creation of an inflamedCell 187, March 28, 2024 1597
ll
Perspectivemicroenvironment, thereby acting as a tumor promoter (see tu-
mor promotion section). It is increasingly feasible, and poten-
tially beneficial, to kill senescent cancer cells, fibroblasts,
and collaterally affected normal cells, via so-called senolytic
therapy, aiming to avoid undesired phenotypic effects of
senescence in cancer. Indeed, evidence from mouse models
suggests that several of the side effects of chemotherapeutic
agents are caused by the induction of senescence in normal
cells, such that the removal of senescent cells in chemo-
therapy-treated mice reduced bone marrow suppression and
improved renal function.94 Aging studies have also shown an
increase in senescent cells—principally fibroblasts—over
time, and senolytic clearing of such senescent cells in animal
models delays aging-associated disorders, including cancer
(see aging section).95,96
Based on these considerations, a ‘‘one-two punch’’ approach
to cancer therapy has been proposed, consisting of sequential
treatment with a senescence-inducing therapy, followed by a se-
nolytic therapy.92 Conceptually, sequential treatment should be
highly synergistic, while reducing the toxicities of combination
therapy. A practical complication in applying this approach is
the heterogeneity of cancer cell senescence pathways, making
it challenging to find senolytic drugs that act broadly. For
instance, the BH3 mimetic drug navitoclax (ABT-263), which is
used widely as a senolytic drug in aging research, is only seno-
lytic in a fraction of senescent cancer cells. Recent data indicate
that activation of death receptor signaling with agonistic anti-
bodies has broader senolytic activity in cancer cells.97 An alter-
native approach to senolysis is the exploitation of the immune
cells attracted to the senescent tumor mass by secreted cyto-
kines that are components of the senescence-associated secre-
tory program (SASP). In preclinical models of pancreatic cancer,
induction of senescence resulted in recruitment of CD8+ T cells
into tumors, resulting in sensitivity to checkpoint immuno-
therapy.98
Despite these initial advances, many questions remain to be
answered. Successful exploitation of the ‘‘one-two punch’’
pro-senescence therapeutic approach will require an under-
standing of which combinations of pro-senescence plus seno-
lytic drugs are most active, and of the context dependency of
such drug pairs. Moreover, we need to better understand the
complexity of the infiltrating immune cells in senescent tumors
and how to exploit their presence therapeutically. There is,
however, another layer of complexity, namely that maintaining
senescent cells can be demonstrably beneficial in certain tu-
mors, perhaps reflecting variabilities in their SASP4; this di-
chotomy of function requires further investigation in regard
to fine-tuning therapeutic targeting of senescence. Finally, in
the aging human brain, neuronal cells abundantly express
senescence markers.99 It will therefore be crucial to under-
stand whether a therapeutic window can be defined for
aged patients with cancer in the context of senolytic therapy.
A successful proof-of-concept clinical study using pro-senes-
cence and senolytic therapies will act as a catalyst for this
approach to cancer therapy. Intertwined with aging and
senescence are the complex effects of metabolic variation,
in cancer cells, in tumors, and in the host, as elabo-
rated below.1598 Cell 187, March 28, 2024METABOLISM, DIET, AND CANCER: SYSTEMIC
EFFECTS AND THERAPEUTIC OPPORTUNITIES
The widespread dysregulation of metabolism and cellular ener-
getics in cancer suggests that altered metabolism is not merely
an ancillary consequence of tumorigenesis but is rather a
selected requirement for tumor initiation and progression. While
metabolic changes intrinsic to cancer cells support tumorigen-
esis, the complex metabolic crosstalk between the tumor and
the host—both within the immediate TME and across distant
organs—highlights our emerging appreciation of cancer as a
systemic disease (Figure 5). Early during carcinogenesis, can-
cer cells are selected for metabolic reprogramming that maxi-
mizes the production and supply of nutrients from the microen-
vironment to satisfy the metabolic demands required for
aberrant growth.100 However, the reciprocal relationship be-
tween tumor metabolism and systemic metabolism remains
largely unexplored. We focus in this section on our increasing
appreciation of these intertwining complexities and their poten-
tial to provide new approaches to prevent, detect, and treat
cancer.
Effects of cancer on the body
Cancer cells subvert non-cancer cells (e.g., fibroblasts, adipo-
cytes, immune cells, and neurons) in the TME toprovidemetabolic
support and supplement nutrient-poor tumor environments,101,102
wherein avid nutrient uptake and metabolic waste production
can suppress anti-cancer immune responses.103–105 In the
macro-environment, tumors alter the host’s systemic metabolism
both directly102 (by the tumor secreting particular signaling
molecules or altering nutrient availability) or indirectly106 (e.g.,
via the immune system’s response to the tumor). The reprogram-
ming of metabolism in distant organs, such as the liver107 and
brain,108 alters whole-body metabolism to promote cancer-
related systemic manifestations, including metastasis, resistance
to anti-cancer therapy, cancer-associated cachexia (CAC), and
death (Figure 5).109,110 Since these alterations in systemic meta-
bolism start early, understanding them may guide and enable
the design of strategies aimed to preserve host metabolism and
restrict systemic manifestations of late-stage cancers.
A clear example of the metabolic effect of cancer on the body
is the induction of the active catabolic state of CAC, a complex,
debilitating wasting syndrome that limits fitness and the effec-
tiveness of therapies, resulting in poor patient outcomes.111,112
CAC likely represents corruption of a normal and transient
wound healing process, which cannot be resolved in the context
of cancer.113 The mechanisms by which tumor progression and
metastasis cause systemic changes that facilitate the loss of
muscle and/or fat remains largely unknown but may reflect a
cancer/host interplay that causes a systemic metabolic imbal-
ance favoring the tumor at the expense of the host. Conse-
quently, interventions that prevent or reverse CACmay also limit
tumor growth.114 A deeper understanding of distinct metabolic,
inflammatory, molecular, and neuro-endocrine drivers of CAC
and their origins from the host or the evolving tumor may open
new therapeutic avenues that extend beyond treating the causes
and consequences of CAC to hampering the growth of primary
and metastatic tumor.114
Figure 5. Cancer as a systemic disease
Reciprocal relationships between cancer cell metabolism, the metabolism of heterotypic cells of the tumor microenvironment, and systemic metabolism. These
metabolic crosstalks modulate cancer progression and could lead to cancer-associated cachexia. Detecting these metabolic alterations provides clinical op-
portunities for early detection and disease monitoring.
ll
PerspectiveObesity, physical activity, and cancer
Metabolic disorders as well as variations in healthy physiolog-
ical states impacted by factors like exercise can influence the
development and progression of cancers and are likely to
involve interplay between the host’s genome, diet, physical
state, physiological status, and microbiome.115 Approximately
4%–8% of all cancers are attributed directly to obesity,116
but this statistic is set to increase rapidly as obesity rates
grow, particularly in low- and middle-income countries.
Although strategies to reduce obesity and increase regular
physical activity are obvious interventions with potential to pre-
vent or treat cancer, the mechanistic link between obesity and
cancer remains unclear. Outstanding questions include the
following: is obesity per se or the accompanying metabolic
perturbation responsible for increased cancer incidence?
Does adipocyte expansion indiscriminately release factors
that are pro-oncogenic? Do some individuals have a specific
pathophysiologic response to obesity that makes them more
susceptible to cancer? And, what is the role of the gut micro-
biome (discussed in more detail below)?
Regular physical activity has been implicated in the preven-
tion and/or improved cancer-specific survival for several can-cer types,117 ostensibly functioning not only by reducing
body mass index but also by lowering levels of hormones,
suppressing inflammation, improving immune function, and
altering intermediary metabolism.118 The emergence of new
technologies for identifying molecular effectors of physical
activity at the organism level119,120 will provide unprece-
dented opportunities to define physiological responses to
physical activity that can be leveraged for cancer prevention
and control.
Cancer and host metabolism in detection and therapy
Metabolic interventions that impact the cancer cells, the TME, or
systemic host metabolism—through limitation of nutrient sup-
port of cancers or by boosting anti-tumor-host responses—
may each provide new therapeutic opportunities. However,
targeting the metabolic alterations in cancer has proven to be
challenging, due to many factors including potential toxicities
of targeting pathways that are essential in normal cells and the
difficulty of making selective drugs for highly abundant enzymes,
as well as the metabolic flexibility and plasticity of cancer cells.
New approaches to overcome these limitations will be needed,
including strategies to influence the pathologic activities ofCell 187, March 28, 2024 1599
ll
Perspectivemetabolic enzymes while maintaining their function in normal
organs.121
Cancers’ abilities to alter systemic and tissue-specific meta-
bolism also suggest new opportunities for early disease detec-
tion through changes in the levels or types of circulating or
excreted metabolites.122 Redox metabolism, urea cycle meta-
bolism, sulfur metabolism (including bile acid production), as-
pects of one-carbon and nucleotide metabolism, and possibly
even tricarboxylic acid (TCA) cycle function or glycolysis, could
bemonitored systematically using increasingly refined technolo-
gies to inform on the metabolism of the host, the tumor, and their
interplay.
Dietary interventions are another promising avenue, most
likely to be effective in augmenting cancer therapy.123,124
Fasting or fasting-mimicking diets (FMDs) can improve cancer
outcomes for many organ-specific tumors in both rodents and
humans,125,126 evidently functioning through the regulation of
various regulatory pathways, including reduced insulin and leptin
signaling, and attenuated inflammation (which are also downre-
gulated by exercise).
Precision nutrition—selective alterations of specific dietary
carbohydrates, amino acids, and lipids—offers another way to
target cancer vulnerabilities. Reducing the intake of non-essen-
tial amino acids such as serine, glycine, and methionine can
retard tumor growth and sensitize treatment-resistant cancer
cells.127,128 Additionally, manipulating the balance of saturated
and unsaturated lipids can directly affect membrane composi-
tion and thereby impair the survival of cancer cells.129 Moreover,
low-carbohydrate diets that limit insulin production can increase
the efficacy of PIK3CA-targeted therapies in certain cancers.130
While several of these dietary approaches are now being
explored in clinical settings, further understanding of how diet af-
fects normal physiology and anti-cancer responses is still
emerging123,124,30 and can be anticipated to guide further appli-
cations of this strategy.
The success of such metabolic interventions depends on a
deeper mechanistic understanding of the requirements imposed
on cancer cells by their origin, genetics, and environment,
coupled with the impact of therapy.123,131 We also need to
understand the interplay and effects of metabolism and its phys-
iological and pharmacological modulations on many of the sys-
temic host factors that govern cancer progression, as discussed
elsewhere in this review, including circadian rhythms, as dis-
cussed below.
CIRCADIAN RHYTHMS AND CANCER: CLOCKS AS
CRITICAL REGULATORS OF PHYSIOLOGY AND
DISEASE
A poorly understood aspect of tumor-host dynamics involves the
potential temporal effects on cancer cell behavior and response
to therapy. Circadian rhythms describe 24-h oscillations of light,
temperature, and other aspects of the earthly environment, as
well as daily fluctuations in gene expression and physiology
driven by genetically encoded molecular oscillators (‘‘circadian
clocks’’) present in virtually all mammalian cells. Disruption of
circadian rhythms caused by nightshift work, travel across mul-
tiple time zones, and residence at the western edge of time1600 Cell 187, March 28, 2024zones, are all associated with increased cancer incidence.132
Recent work has uncovered mechanisms that may contribute
to these associations.
Mammalian circadian clocks are based on a transcription-
translation feedback loop in which a heterodimer of the tran-
scription factors CLOCK and BMAL1 drives the expression
of its repressors, PERIODs (PER1–3) and CRYPTOCHROMEs
(CRY1 and 2). PERs and CRYs accumulate and inactivate
CLOCK-BMAL1 after which PERs and CRYs are tagged for pro-
teasomal degradation, renewing the cycle. Thousands of genes
exhibit daily oscillations of expression in every organ that has
been examined, such that over half the genome is rhythmically
expressed somewhere in the body.133 Several mechanisms
contribute to transcriptional oscillations including transactivation
by CLOCK-BMAL1, and to repression of diverse transcription
factors by PERs and CRYs. Intriguingly, both PER2 and CRY2
have been shown to influence P53, the most frequently inacti-
vated tumor suppressor in cancer.134,135 CRY2 also stimulates
turnover of the oncoprotein c-MYC in rapidly proliferating
cells,136 although its deletion does not appear to affect MYC in
healthy spleens in vivo.137
Molecular connections between clocks and proteins that are
well-known to influence cancer, such as P53 and c-MYC,
encourage speculation that disruption of these pathways might
explain enhanced cancer incidence associated with circadian
abnormalities. However, recent work suggests alternativemech-
anisms, including disruption of anti-tumor immunity and of
cellular stress responses.138,139 Genetic deletion of individual
clock components has varied impacts on tumor growth inmouse
models,132,140,141 so it seems unlikely that a particular mecha-
nism based on disrupting a core clock component will universally
explain increased cancer incidence caused by circadian disrup-
tion. Using transformed murine cells to initiate tumors results in
striking differences in tumor growth depending on the time of
day at which cells were implanted,139 implicating circadian regu-
lation of anti-tumor-immune responses. Exposure of host mice
to chronic circadian disruption exacerbated tumor growth and
eliminated the impact of engraftment time, which suggests that
impaired immune responses may contribute to increased tumor
growth upon circadian disruption. Chronic circadian disruption
also increased tumor burden in genetically engineered mouse
models of KRAS-driven lung adenocarcinoma.138,142 Unbiased
analysis of gene expression in lung tumors revealed that circa-
dian disruption increases expression of heat shock factor 1
(HSF1), which has been linked to enhanced tumorigenesis in a
variety of contexts.143 Additional research is needed to deter-
mine whether reduced immune surveillance and/or elevated
HSF1 is required for enhanced tumorigenesis caused by chronic
circadian disruption.
Multiple mechanisms likely contribute to enhanced tumori-
genesis observed in people exposed to circadian disruption.
Circadian disruption also influences cancer-associated pheno-
types, including metastasis and cachexia.144,145 Circadian
disruption acts as a tumor-promoting lifestyle factor through
unknown mechanisms, likely including sleep deprivation and
metabolic disturbances. Continuing research is needed to
delineate the contributions of the mechanisms discussed here
and the additional effects of circadian disruption on the etiology
ll
Perspectiveof diverse tumor types. Few clinical trials include multiple
dosing times and ‘‘circadian logic’’ in their design, although
there are clues that such regimens can affect chemotherapy
outcomes,146 and the response to checkpoint blockade in mel-
anoma, where emerging evidence from the MEMOIR study
suggests that adaptive immune responses are less robust in
the evenings.147 Recording the time of day at which biopsies
are collected, and when treatments are delivered during clinical
trials, would also markedly improve the potential to advance
understanding in this area. Another systemic regulator of the
interplay between the host and tumor phenotypes, likely inter-
twined with circadian rhythms, involves the nervous system,
as considered below.
INFLUENCES OF THE NERVOUS SYSTEM: EMERGING
ROLES OF NEURO-CANCER CROSSTALK
Neurons, glial cells, and nerves: A critical component of
the cancer ecosystem
In recent years, both the central and peripheral nervous sys-
tem have emerged as critical components of tumor-host inter-
actions. Exciting discoveries have established neural innerva-
tion and signaling subversion as potential new factors of the
tumor macro- and microenvironment, both in brain tumors
and most other cancers, making it possible that neuronal-can-
cer interactions might eventually become another hallmark of
cancer. Much as it governs physiological wound healing, tis-
sue development, and organogenesis, as well as homeostasis
and plasticity throughout life, the nervous system seems to
play instrumental roles in the regulation of cancers.5,148 Spe-
cifically, synaptic and paracrine neuronal activity, but also can-
cer-intrinsic neural features, can govern cancer initiation,
growth, dissemination, and treatment resistance; conversely,
tumors can negatively affect the nervous system and even
reprogram it, leading to detrimental feedback loops.5,148 More-
over, the nervous system influences cancer biology and can-
cer therapy response through dysregulation of the immune
system, alterations to angiogenesis, and broader systemic
effects.
A salient example is GBM (gliomas), wherein pathways of
normal neurodevelopment are hijacked to interconnect cancer
cells into a network, a syncytium involving cell-cell communica-
tions that foster tumor phenotypes.149 This cancer network re-
ceives direct input from bona fide glutamatergic (excitatory)
neuron-glioma synapses that activate the network, driving tumor
growth.150 The cell population is constantly stimulated by an
autonomous rhythmic activity generated by pacemaker-like can-
cer cells within the network, recapitulating neurodevelopmental
processes.151 GBM dissemination—perineural invasion—in the
brain is governed by this cancer network via its neuron-glioma
associations, as well as other co-opted neuronal mecha-
nisms.152 Remarkably, in the human brain, GBM also remodels
neural circuits, such as in regions of language representation,
developing functional interconnectivity between the cancer
and the normal brain, which is associated with decreased patient
survival.153
Outside the brain, tumors secrete growth factors (e.g., nerve
growth factor, NGF) that attract innervation by peripheral nervesand can even reprogram them. This innervation by sympathetic,
parasympathetic, and sensory nerves has been demonstrated to
drive tumor growth, invasion, metastasis, and therapeutic resis-
tance in most cancer sites throughout the body, largely via para-
crine secretion of neural factors. New therapeutic avenues that
are based on these neuro-cancer interactions are currently being
explored.148
Cancer pain
An important example of how tumors and cancer therapies can
negatively impact a patient’s nervous system involves the sensa-
tion of pain. Debilitating bone pain associated withmetastases in
prostate, breast, and lung cancers can severely impact quality of
life. Opioid analgesics remain the mainstay of cancer pain
management, but the long-term consequences of tolerance,
dependence, and hyperalgesia are problematic.154 The potent
analgesic lidocaine has both pain-killing and anti-tumorigenic
activity as well as effects on immune responses, consistent
with a vital role for the neuro-immune cancer axis in approaches
to treatment.155 Clinical trials of new approaches to pain treat-
ment focused on blocking input into the central nervous system
have promise, for example, using antibodies that neutralize im-
mune-derived mediators such as NGF or TNF to alleviate cancer
pain (NCT02609828). Similarly, channel-blocking small mole-
cules are also under evaluation in phase 3 trials aimed at
reducing cancer-associated pain.156 Pain control is an increas-
ingly important dimension to managing and treating cancer,
with encouraging promise.
The road to translation
Future research in translational cancer neuroscience will need
to address three main points: (1) mapping the world of neuro-
cancer interactions (e.g., synaptic, paracrine, and systemic),
including the classes of neurotransmitters, neurotrophins, neu-
ropeptides, and hormones that are relevant for distinct tumor
entities and disease stages, combining molecular imaging,
circulating biomarkers and morphological, neurophysiological,
and molecular profiling of freshly resected tumor tissues, for
which bespoke methodologies need to be developed; (2)
developing informative biomarkers to monitor neuro-cancer
interactions in a given patient; (3) establishing optimal combi-
nation therapy partners, e.g., immunotherapies, as part of
the emerging field of neuro-immuno-oncology. Since more
than 100 drugs are approved in neurology, psychiatry, and
internal medicine that target neural signaling pathways, drug
repurposing may prove productive, as exemplified by clinical
trials targeting stimulatory neuron-cancer synapses of the
AMPA receptor subtype with the antiepileptic drug perampa-
nel,150,157 or targeting neural-like cancer cell networks with
meclofenamate (EudraCT 2021-000708-39).149 Moreover,
drug development aimed at early neurodevelopmental regula-
tors usurped by cancer cells and at cancer-specific neural in-
teractions present promising avenues. While interfering with
normal neurotransmission can lead to side effects that need
to be monitored, understanding the mediators and receptors
specifically implicated in driving tumorigenesis should produce
therapeutic targets based on more precise mechanistic in-
sights.Cell 187, March 28, 2024 1601
ll
PerspectiveIn summary, the recent discovery of intimate neuro-cancer in-
terfaces opens new horizons in our broader understanding of
cancer, including the possibility that modulating cognitive or
other neural states might impair crucial cancer phenotypes.
Future research will determine whether this emerging knowledge
can be leveraged for the development of new therapeutic stra-
tegies.
PARITY: LESSONS FOR TUMOR PROTECTION
The aforementioned examples of how the host can modulate
tumor development and malignant progression have largely
focused on tumor-promoting intersections. The converse
consideration is the potential for human behavior and environ-
mental exposures to protect against tumor initiation. Recent
work has shed light on the role of sex hormones such as andro-
gens and the endocrine system in such interactions and re-
vealed mechanisms via which they govern anti-tumor immunity
and T cell checkpoint responses. This crosstalk is consistent
with knowledge about autoimmunity and infectious diseases,
where, e.g., biological sex is associated with apparent differ-
ences in incidence and magnitude of vaccine-mediated im-
mune responses. A clear example of this is parity and the dura-
tion of breastfeeding, which are well established to have
positive health benefits for both mother and child. Pregnancy
and breastfeeding have been associated with protection from
breast and ovarian cancer, though the risk does rise in the short
term, and a lower risk from all-cause mortality later in life.158
The risk of breast cancer is reduced by 4% for every 12 months
of breastfeeding in addition to the 7% decrease in risk
observed for each birth.159 Breastfeeding particularly reduces
the risk of triple-negative breast cancers (TNBCs).160 The
mechanisms behind breast cancer protection associated with
pregnancy are thought to be related to the maturation of the
breast epithelial cells, making them less susceptible to transfor-
mation, and to a reduction in circulating estrogen through
amenorrhea.
Previous studies have established a positive prognostic effect
from high levels of T cell infiltration in early-stage breast cancers,
particularly in TNBC.161 For instance, a small subset of T cells
with a tissue-resident phenotypewere shown to be highly prolifer-
ative and cytotoxic, essential qualities for a durable anti-tumor
response.162 Recently, using mouse models, the critical role of
T cells with a resident memory (TRM) phenotype in immune pro-
tection frommammary tumor rechallenge has been reported, sug-
gesting that TRMs may confer protection against breast can-
cer.163 Healthy, non-cancer affected breast tissue contains a
multiplicity of immune cells, including myeloid, natural killer (NK),
B, and T cells that do not express high levels of T cell-inhibitory
checkpoint molecules.162,164 Given this epidemiological data,
future work should seek evidence for an immunological mecha-
nism that might explain the relationship between breastfeeding,
pregnancy, and resultant long-term breast cancer protection.
Gene expression patterns in the human breast significantly
change years after pregnancy, notably with an upregulation of
immune-related genes.165 Pregnancy itself causes dramatic
changes in the gut and vaginal microbiome of the mother through
the three trimesters.166 It is also well accepted that breast milk1602 Cell 187, March 28, 2024contains a rich microbiome that changes throughout the stages
of lactation.167 In colostrum samples, Weisella, Leuconostoc,
Staphylococcus, Streptococcus, and Lactococcus are the pre-
dominant species. In contrast, at 1 and 6 months, microbes that
are typically found in the oral cavity (e.g., Veillonella, Leptotrichia,
and Prevotella) are significantly increased in breast milk.168 Over-
all, breast milk can contain up to 600 different bacterial species,
which could ultimately influence the immune repertoire of both
mother and child.169
Research into the inflammatory and microbial changes during
pregnancy and post-pregnancy, as well the composition of
breast milk, has mainly focused on the benefits to the infant
but has not yet deeply investigated how these effects may
benefit the mother in the long term, as well as contribute to im-
mune surveillance of the local breast microenvironment. Further
research into sex hormones, their change at various reproduc-
tive stages, in both females and males, and how they alter
anti-tumor immunity may help elucidate underlying molecular
mechanisms governing cancer protection as well as progres-
sion, not only in hormone-responsive organs.
THROMBOINFLAMMATION AND CANCER
Thromboembolism is the obstruction of a blood vessel by the
pathological formation of a clot. It is a leading cause of death
in people with cancer and is associated with a poor prognosis
that cannot be explained by cancer stage.170,171 Individuals
with cancer have a 9-fold higher risk of thrombosis than the gen-
eral population and cancer-associated thromboembolism is
growing in incidence.172
An underlying predisposition to thrombosis relates to a
dysregulated thromboinflammatory response that involves the
interplay between coagulation and inflammatory mediators
(Figure 6).173 Neutrophils, the most abundant myeloid leuko-
cytes, promote thromboinflammation through the release of
neutrophil-extracellular traps (NETs), a negatively charged com-
bination of histones, proteolytic enzymes, and DNA that acti-
vates thrombosis.174 Several cancer types are also known to
secrete tissue factor, the primary activator of coagulation in vivo.
Higher plasma levels of tissue factor are associated with a worse
cancer stage, poorer outcomes, and a risk of thrombosis.175
Thromboinflammation potentially promotes tumor growth and
spread. Tumor cell-platelet aggregates are hypothesized to pro-
mote tumor migration.176 An elevated platelet count is a marker
of poor prognosis as well as a risk for thromboembolism. Murine
models of GBM demonstrate that platelets bind to tumor cells
through podoplanin and, importantly, elevated plasma levels of
podoplanin are associated with a higher risk of thromboembo-
lism and lower survival rates in patients.176,177
Studies in mice and humans have established associations
between the number and type of intra-tumoral neutrophils and
disease progression.178 The neutrophil phenotype is influenced
by the environment andmany types of cancer elicit potent immu-
nosuppressive and pro-angiogenic functions of neutrophils that
promote tumor growth and/or dissemination.179,180 Balancing
this view, emerging evidence also reveals anti-tumoral activities
of neutrophils and tumor-derived factors that act by promoting
the education and activation of cytotoxic T cells.181,182
Figure 6. Thromboinflammation and cancer
The interplay between the coagulation and
innate immunity systems plays important roles
throughout the development and growth of a tu-
mor. Early interactions between neutrophils and
tumor cells, including neutrophil-extracellular traps
(NETs) produced by neutrophils, promote dormant
tumor cell awakening, tumor survival and immune
escape. Later aggregations between platelets and
tumor cells promote tumor survival intravascularly
and tumor spread. Activation of coagulation
through tumor-specific mechanisms, such as the
release of tissue factor, increases the risk of can-
cer-associated thrombosis.
ll
PerspectiveBeyond these functions, the release of NETs has been shown
to promote intravascular tumor cell entrapment, which facilitates
metastasis, by physically shielding tumors from the actions of
cytotoxic T cells, or awakening dormant cancer cells by remod-
eling their local microenvironment (Figure 6).183 Conversely,
NETs can also be cytotoxic and have been shown to kill tumor
cells by the action of associated proteolytic enzymes and
histones.
Managing thrombosis
Many factors can alter thrombotic risk during treatment of pa-
tients with cancer, including surgery, chemotherapy, infection,
and intravenous inoculation of therapeutic drugs. A large-scale
cohort study that serially measures multiple biomarkers in peo-
ple with cancer receiving therapy is needed to help identify risk
biomarkers that could be used to define a change in an individ-
ual’s risk over time. Future assay development should look for
reliable global markers of thrombotic risk that would allow a
more flexible and personalized approach. This leads us to the
question: once we can identify the risk, can we do anything to
mitigate it?
The use of low-dose anticoagulation (thromboprophylaxis) to
prevent venous thromboembolism is effective in higher-risk can-
cer populations. However, there is insufficient evidence to sup-
port the use of thromboprophylaxis in all people with cancer
due to the risk of bleeding caused by this treatment. Clinical
scores, the most validated being the Khorana score, can identify
some but not all at higher risk.184 Newer anticoagulants such as
factor XI inhibitors, which aim to ‘‘uncouple’’ haemostasis from
thrombosis are currently being trialed for treatment of cancer
thrombosis (NCT05171075). Future randomized controlled trials
should investigate their effectiveness in preventing thrombosis.
To improve outcomes for people with cancer, we need both to
understand the role of thromboinflammation, and to develop
methods to monitor thrombotic risk throughout cancer manage-
ment so as to improve both prevention and treatment strategies.The intersection of clonal
hematopoiesis in inflammation,
thrombosis, and tumor progression
Somatic mutations in hematopoietic stem
cells are linked to increased NET forma-
tion and the promotion of thrombosis,
both in the context of hematologic malig-
nancies and clonal hematopoiesis (e.g.,JAK2V617F in myeloproliferative neoplasms or in clonal hemato-
poiesis).185
Clonal hematopoiesis of indeterminate potential (CHIP) is a
phenomenon of aging defined by the presence of leukemia-
associated somatic mutations in the hematopoietic stem cells
of individuals without apparent hematologic clonal disorders.85
These mutated hematopoietic stem cells give rise to altered im-
mune progenies that influence host immunity.186 The most
frequent CHIP mutations are recognized to promote myeloid-
derived inflammation through increased production of inflamma-
tory cytokines.
Furthermore, CHIP is linked to non-hematologic cancer in
several ways. CHIP is associated with an increased risk of devel-
oping solid tumors, such as lung cancer, kidney cancer, lym-
phoma, and sarcoma.187 CHIP is observed in 20%–30% of
patients with cancer overall, is shaped by anti-cancer thera-
pies,188 and is associated with an increased risk of death through
the progression of the primary non-hematologic tumors.189
However, little is known about the relationships between im-
mune dysfunction in CHIP and anti-tumor immunity during tumor
initiation, progression, and response to therapies.190 Future
work delving into the tumor-immune interface within the context
of CHIP are poised to unveil prognostic biomarkers and poten-
tially anti-inflammatory interventions to attenuate disease initia-
tion and progression.
MICROBIOMES AND CANCER: DISEASE
PATHOGENESIS, THERAPY RESPONSE, AND
THERAPEUTIC TARGETING
Another environmental complexity that transcends the host per
se, in addition to those discussed above, involves the human
microbiome, composed of microbes and their genomes.191 It is
increasingly well established that the microbiome variably pro-
foundly impacts disease pathogenesis and therapy outcomes
in human malignancies,192 with polymorphic microbiomesCell 187, March 28, 2024 1603
ll
Perspectiverecently highlighted as a hallmark-enabling phenotypic charac-
teristic of cancer.4 There is increasing evidence for the impact
of gut and other tissue microbiota on the pathogenesis of cancer
as well as therapy responses, along with intriguing implications
for intra-tumoral microbiota.
The role of gut microbes on pathogenesis and therapy
response
Microbes within the gut have diverse effects on immunity and
cancer via a number of different mechanisms. They can directly
modulate cellular constituents promoting carcinogenesis, for
example, in colorectal cancer,193 and influence systemic and
anti-tumor immunity.194 The importance of the gut microbiota
in shaping responses to immune checkpoint blockade was first
shown in preclinical models59 and was quickly followed by land-
mark studies in human cohorts,61,62,195 now bolstered by a num-
ber of studies demonstrating prognostic associations of the gut
microbiota with human cancers.192 Importantly, we are gaining
more clarity into the taxa and functional characteristics of gut mi-
crobiota that are associated with response and resistance to
immunotherapy, with data suggesting that the presence of unfa-
vorable taxa in the gut is associated with systemic inflammation
and impaired anti-tumor immunity along with increased risk for
adverse events.196 Recent studies have also suggested that di-
etary intake can impact the gut microbiota and immunotherapy
response and toxicity,197–199 thus providing a tractable angle
for therapeutic intervention.
The role of intra-tumoral microbes on pathogenesis and
therapy response
Nucleic acid sequencing and imaging analyses have revealed
the presence of intra-tumoral microbes in various human can-
cers.200–204 Microbial composition and load have been associ-
ated in certain tumor types with treatment responses and patient
survival.203,205 Advances in multi-omics approaches have pro-
vided mechanistic insight into the role of intra-tumoral microbes
within the oral and colorectal TME.204 Initially, intra-tumoral mi-
crobiome studies focused on gastrointestinal cancers and
microbe-accessible mucosal sites. However, in 2020, two land-
mark studies reported tumor-type-specific bacterial commu-
nities in many solid cancer types,206,207 suggesting diagnostic
potential involving bacterial nucleic acids in cancer patients’
blood.207 One study reported that intra-tumoral bacteria were
predominantly intracellular in non-gastrointestinal cancers.206
Similarly, cancer-type-specific intra-tumoral fungal communities
have been reported.208,209 However, recent data reanalysis
has raised important concerns about the prevalence of tumor-
type-specific microbiomes, in particular inside cancer cells
highlighting microbial contamination in datasets, human DNA
contamination of microbial references, and machine learning is-
sues.210,211 Bacterial imaging reanalysis questioned bacterial
localization within breast cancer epithelial cells,206 instead
suggesting their presence in macrophages and interstitial
stroma. Reproducing findings on intra-tumoral fungi in pancre-
atic cancers has also faced challenges.212–214 Thus, while there
is compelling evidence for the presence of tumor-infecting
microbes in specific cancer types, such as colorectal can-
cers,200,201,204,215 there is now controversy regarding the gener-1604 Cell 187, March 28, 2024ality of a bona fide tumor-type-specific microbiome across hu-
man cancer types. Further research is needed to determine
whether the presence of intra-tumoral bacterial and fungal com-
munities is unique to specific cases or is amorewidespread phe-
nomenon, and in which types of cancer they are most prevalent,
and functionally impactful.
Targeting gut and tumormicrobes for cancer treatment,
interception, and prevention
A growing body of evidence supports the modulation of micro-
biota as a strategy to enhance outcomes to cancer treatment,192
including fecal microbiota transplants216,217 or inoculation with
well-defined, mechanism-guided microbial species.218 Other
strategies include elimination of tumor-promoting species within
the TME via targeted antibiotic approaches.219 As we move for-
ward, it will be imperative to develop standardized approaches
for assessing pathogenic (and beneficial) microbes acting at a
distance from the gut and mucosal tissues they naturally popu-
late, as well as within tumors. We must enhance our mechanistic
understanding of how gut and intra-tumoral microbes directly
and indirectly shape cancer pathogenesis, and thereby impact
patient outcomes. A corollary opportunity is to leverage this in-
formation to identify microbial-based biomarkers for cancer pre-
vention or prognostics, to aid in the design and testing of thera-
peutic strategies aimed to modulate microbes and thereby
better treat cancer, and ultimately even to prevent or intercept
malignant transformation.
FUTURE STRATEGIES AIMED TO LEVERAGE CANCER
COMPLEXITIES
Experimental systems for modeling cancer and its
complexity
Genetically engineered (and traditional cancer cell transplant)
mouse models of human cancer have for 40 years illuminated
mechanisms of multistep tumorigenesis and malignant pro-
gression, and increasingly, responses and adaptive resistance
to therapies, the latter enabled by the possibility of assembling
age- and disease-matched cohorts to conduct preclinical tri-
als.220–222 However, the spectrum of cancer complexities dis-
cussed in this review cannot be fully addressed in such
models, including, for example, assessing the impact of
gene-environment (GxE) interactions. The study of large hu-
man cohorts, such as the UK Biobank and ‘‘All of Us,’’ has
made us aware of the significant impact of confounding fac-
tors, including population structure, ethnic background, and
environmental exposures, on the outcomes of genetic ana-
lyses and therapeutic targeting. This simple lesson, which is
by its nature largely observational, has not yet been effectively
embraced by bio-medical experimentalists. It is therefore not
surprising that experiments in inbred animals, aimed to sup-
port the causal and mechanistic links between genotypes
and phenotypes suggested by human genetics, can fail to
translate into humans. Such genetically constrained experi-
mental models have raised debate about the limitations of an-
imal models for cancer research and have fueled the notion
that humans are the best model to study human disease,
despite the reality that performing experiments to fully address
ll
Perspectiveunderlying mechanisms are very difficult if not impossible in
humans.223 Looking forward, in order to address the chal-
lenges of cancer complexities and the translation of their illu-
mination to better treatments for human cancers, we envisage
increasing refinement and utilization of other model systems,
both in genetically diverse mouse models and in ex vivo
models involving human cells and tissues.
In one approach tomodeling GxE effects in cancer, mouse ge-
netic diversity can be embraced. Such diversity can be achieved
through studies in genetic reference populations (GRP), such as
the BXD and the Collaborative Cross GRPs224,225 or in outbred
mice, such as the diversity outbred mice,226 that collectively
capture 
90% of the genetic variation in laboratory mice, sur-
passing the genetic diversity present in humans. Such geneti-
cally diverse mice can be used not only to study genetic
perturbations that are protective or predisposing to cancer
development (e.g., generating F1 animals of a GRP crossed
with a strain carrying an oncogene) as well as to study environ-
mental modifiers ranging from dietary challenges, stressors, to
pharmacological interventions.227 Such longitudinal population
studies in mice have the prospect to (1) model the genetic diver-
sity present in human populations, (2) study GxE interactions, (3)
allow access to internal tumors and tissues for molecular charac-
terization, and (4) map genes responsible for a given phenotype
through quantitative trait locus (QTL) analysis. An unresolved
technical challenge for the future will be to develop efficient
means for genetic engineering of outbred cancer models to pro-
duce the remarkable diversity of organ-specific tumors carrying
the same oncogenic mutations as cognate human cancers in co-
horts amenable to preclinical trials. Certainly, the cancer field
has much to gain by embracing genetic diversity to model tumor
initiation and progression, and to use thesemodels for preclinical
trials of new cancer drugs and mechanism-guided combina-
tions, advancing knowledge and its application to cancer
medicine.
Beyond all the myriad variations of mouse models of human
cancer, we envisage that ex vivo systems involving human cells
and tissues will be increasingly refined and creatively applied to
elucidate the mechanisms underlying cancer complexity and
develop ‘‘precision/personalized’’ assays both to test therapeu-
tic hypotheses and to guide treatment decisions for individual
cancer patients. Once it became evident that tissue stem cells,
alone or with stromal cells, could be propagated ex vivo as tis-
sue-like structures in 3D matrix,228,229 organoid cultures have
grown in use as a bridge between simplistic 2D cultures and
the complexity of in vivo human biology. Organoids offer ap-
proaches to propagate tumors ex vivo in three dimensions, rep-
resenting the pathological features of the parental tumor, there-
fore expanding the number of patient-derived model systems
amenable to experimentation aiming to complement observa-
tions from mouse models. Organoids can be established from
either adult tissue- or cancer-cell-specific stem cells as well as
pluripotent stem cells (either induced or embryonic),230 although
the latter have the additional challenge of faithfully recapitulating
the biochemical signals and spatial organization of tissue/tumor
development.
Notwithstanding the challenge of intratumor heterogeneity
and tissue sampling bias where subclonal events may not bewell represented in ex vivo propagated tumor models from pa-
tients, patient-derived organoids have been shown to maintain
tumor clonal events,231 and their transcriptomic signatures can
be similar to the tumors from which they were derived.232
Recent progress has been made in developing organoids to
more effectively mirror the TME observed in human cancers,
with lymphocyte/tumor organoid co-culture methods under
development,233 as are means to create a functional vascula-
ture. Another limitation of the organoid approach in modeling
cancer is the potential lack of cancer-specific ECM and the
physical microenvironment (stiffness, interstitial fluid pressure,
vascularization, etc.). Currently most organoid culture ap-
proaches utilize Matrigel, collagen, or a mixture of the two.
Leveraging the field of bioengineering, many synthetic materials
are being developed that allow the generation of more
controlled environments enabling the modulation of tissue stiff-
ness.234 We envisage future development of ‘‘tumoroids’’ that
more fully recapitulate all of these salient features of the bona
fide TME.
Tumor-derived explants and organ-on-chip technologies
offer additional solutions to these challenges, which may
enhance cancer drug discovery and development by permitting
appropriate and diverse human models of cancer with repre-
sentative TMEs. Organ-on-chip technologies provide more
elaborate models of disease, employing microfluidics to model
tissue interfaces and to begin to recapitulate the complex TME,
allowing the investigation of tumor/stromal interactions and
anti-cancer drug testing235–237 Patient-derived tumor explants
are initiated by culturing fragments or thick (vibratome) sections
of fresh tumor tissues, permitting the short-term maintenance
of the histopathological features of the parental tumors. Since
tumor spatial morphology can be maintained, such approaches
may offer the ability to combine ex vivo drug testing with
biomarker analysis to identify predictors of drug response,
and to decipher the impact of the TME on drug response and
resistance.238
Patient-derived xenograft (PDX) mice also facilitate in vivo
testing of drugs in the context of a human tumor in an immuno-
deficient background; partially ‘‘humanized’’ PDX mice bearing
some features of the human immune system have further
extended their use to evaluate immunotherapies,239 although
further refinement will be needed to fully model the complexity
and polymorphic diversity of human immune response to human
tumors in mouse hosts. Moreover, PDX models are cumber-
some, expensive, and, in many cases, unable to replicate
the complexity and heterogeneity of human tumors and their mi-
croenvironments. Patient-derived tumor fragments (PDTFs)
address some of these issues by allowing ex vivo testing of the
impact of novel drugs in multiple tumor fragments obtained
from patients.240,241 The PDTF platform allows perturbation of
fresh or frozen tumor fragments retaining the microenvironment
of the original tumor, albeit only briefly. Crucially, the PDTF
response ex vivo to checkpoint inhibition was shown in one study
to be highly concordant with the clinical response of the same
patient to anti-programmed cell death protein 1 (anti-PD1)
agents, illustrating the potential value of this platform.242 While
still limited in some respects (such as short-term viability of the
fragments and loss of innate immune cells and functionalCell 187, March 28, 2024 1605
ll
Perspectivevasculature), we envisage that further improvements in this plat-
form will significantly contribute to our understanding of re-
sponses (and resistance) to cancer therapeutics within the
complexity and heterogeneity of human tumors.
Exploiting the other side of oncogene addiction
Studying resistance to targeted therapies over the past two de-
cades has revealed that re-activation of the drug-inhibited
signaling pathway by secondary mutations is a dominant mech-
anism of resistance. By inhibiting signals to which the cancer cell
is addicted, we can steer the cancer cell to evolve into the direc-
tion of higher signaling. This begs the question whether we can
also steer cancer cells in the opposite direction, of evolving to-
ward a less malignant phenotype through reduced oncogenic
signaling. In considering this concept, it is important to realize
that cancer cells strive to optimize rather than maximize mito-
genic signaling. Consistent with this, emerging evidence indi-
cates that further stimulation of mitogenic signaling in cancer
cells can be as toxic as the inhibition of these signals, by further
activating cellular stress response pathways.243 Indeed, a com-
bination of hyperactivation of oncogenic signaling and inhibition
of the associated stress responses is very effective in animal
models of colon cancer. Remarkably, resistance to this combi-
nation was associated with downregulation of oncogenic
signaling and loss of oncogenic capacity.244 An obvious concern
in such a therapeutic approach is the presence of many appar-
ently normal cells in the aging soma that harbor oncogenic mu-
tations (see tumor promotion section). Such cells may be stimu-
lated to expand by drugs that further activate oncogenic
signaling. However, in animal models of precancerous intestinal
adenomas, a further increase in Wnt signaling by treatment with
lithium chloride was associated with a reduction in adenoma for-
mation rather than an increase, indicating that also in a pre-can-
cer state, there is a ‘‘goldilocks’’ scenario in which more onco-
genic signal is not better.245 Consistently, epidemiological
studies show that long-term treatment of bipolar disorder pa-
tients with lithium chloride is associated with a reduced risk of
colorectal cancer.246 Although the risks and hurdles of this par-
adoxical approach must be addressed, steering cancer cells to-
ward a less malignant phenotype through deliberate hyperacti-
vation of oncogenic signaling holds promise for future cancer
therapy.
TRANSLATING DISCOVERIES TO PATIENTS: NEXT
GENERATION CLINICAL TRIALS
Many basic science discoveries inform us about the early steps
of tumorigenesis. There is, therefore, an opportunity to develop
clinical trials testing drugs in early-stage cancers and preven-
tion platforms. Two distinct types of preoperative trials repre-
sent attractive designs for drug development and clinical trans-
lation. First, neoadjuvant clinical trials evaluate the efficacy of
drugs administered with therapeutic intent several months
before surgery. Those trials also allow for the investigation of
the mechanisms of resistance.247 Second, window-of-opportu-
nity trials are not designed to assess drug efficacy but are
particularly appealing to explore the mechanisms of action
and the pharmacodynamics of new drugs administered for a1606 Cell 187, March 28, 2024short duration (typically a few days) prior to the initiation of can-
cer therapy.248 Moreover, in a prevention setting, the categori-
zation of studies as primary, secondary, and tertiary is an
appealing framework that may prove useful for future drug
development if endorsed by scientific societies.249 Primary
chemoprevention studies describe the use of medicines to
prevent cancer in high-risk individuals. Precise definitions of
clinical, genomic, demographic, and environmental factors
capable of highlighting at-risk patient populations will be
required to enable such studies. This capability might be
achieved through combined epidemiological and preclinical
studies, facilitated by improving capabilities for clinical data
collection, including digital AI-facilitated tumor pathology.21,250
Novel biological endpoints that capture treatment effects asso-
ciated with impaired cancer development will be necessary as
early indicators of treatment benefit. Secondary chemopreven-
tion studies refer to evaluating potential interventions in pa-
tients with established pre-malignant conditions. There is a
need for prospective randomized evaluation of promising
agents from the preclinical space in this therapeutic setting
for multiple cancer indications.251 Tertiary chemoprevention
studies involve approaches to reduce the risk of cancer
relapse, locally or at other organ sites. In this category, circu-
lating tumor DNA will have an increasing role as a tool to
non-invasively detect minimal residual disease (MRD) as a
biomarker and an early endpoint of therapeutic efficacy, as
this technology is capable of detecting and determining treat-
ment response in micrometastatic disease that is not identifi-
able by clinical imaging.252–256
Since cancer is a disease driven by biological mechanisms,
there is a need to establish new classification systems based
on the biology of the disease, in addition to the current ones
based on the organ of origin. Multi-omic classifications could
dramatically accelerate the development of drugs targeting
biological pathways through organ-agnostic clinical trials.257
The previous sections of this review have shown that cancer
is a complex disease driven by multiple and interrelated bio-
logical mechanisms. These complexities should therefore be
integrated into portraits that recapitulate the complete biology
of each cancer patient. Trials testing the clinical utility of these
multiplex, comprehensive molecular portraits of cancer are be-
ing developed,258 although at present validating the utility of
individual components of the multiplex analyses on an
extended number of patients is a challenge. Moreover, the
future of cancer therapy is almost certainly one of combinato-
rial multi-targeting with distinctive classes of cancer drugs that
disrupt tumor-driving mechanisms as well as systemic mani-
festations. Therefore, as knowledge progresses about molec-
ular and cellular mechanisms driving the complexities of can-
cer and their pathogenic effects, these and other innovative
clinical (and preclinical) therapeutic trial designs will be instru-
mental in translating the results to the benefit of cancer pa-
tients.
CONCLUDING REMARKS
Improving the quantity of life for cancer patients remains an over-
arching goal of cancer research. Looking forward, improving
ll
Perspectivethe quality and duration of life (‘‘cancer without disease’’),
decreasing the burden of symptoms, and improving healthcare
sustainability, affordability, and accessibility face monumental
yet ultimately tractable challenges, enabled by embracing and
better understanding the complex biology of the disease, locally
and systemically, as elaborated herein.
Despite progress in our understanding of the disease, it is
estimated that cancer incidence will increase by 20%–50%
over the next 20 years, reflecting in part aging populations,
increased obesity and the multi-faceted environmental factors
impinging on the inception and malignant progression of can-
cer, a reality mandating ongoing investment and redoubled ef-
forts to address its complexities. Furthermore, the incidence of
early-onset cancers is increasing,259 for reasons that are un-
clear. An overarching issue related to the new era of addressing
cancer’s complexities involves the systemic manifestations of
the disease and the impact of environmental exposures and in-
fluences—from well-recognized and cryptic pollution to un-
healthy diets, microbiota, and beyond—to the course of
multistep tumorigenesis, malignant progression, and therapy
resistance across the spectrum of human cancers. Success-
fully elucidating and addressing these complexities will benefit
from the recruitment into the cancer research enterprise of sci-
entists and clinical academics with expertise in areas outside of
oncology such as data science, mathematical modeling, neuro-
science, inflammation and autoimmunity, aging, metabolism,
endocrinology, and muscle physiology. This expansion in
expertise and capabilities will require new funding and
research opportunities that cross disciplines and international
borders and move beyond the traditional (largely governmental)
funding mechanisms, analogous to the philanthropically-based
CRUK/NCI Cancer Grand Challenges Program, specifically
aimed at encouraging multi-disciplinary collaborations to
address the plethora of questions delineated in this review.
Furthermore, at a time when commitments to clinical academic
training and career paths thereafter is under threat around the
world, there is a pressing need to reinvigorate clinical academic
MD/PhD training programs, along with post-MD research
fellowships in lieu of formal PhDs, as well as innovative initia-
tives and funding mechanisms to ‘‘buy’’ protected time for
physician-scientists to substantively engage in translational
research.
Understandably, much effort has been focused on achieving
‘‘cures’’ for this disease; while an appropriate strategic goal,
the reality of neo-Darwinian adaptation of cancer cells,
enabled by genome instability and mutational evolution as
well as epigenetic plasticity, suggest that all forms of cancer
may never be completely curable. Optimistically, however,
elucidating and targeting the mechanisms that enable cancer
cells’ phenotypic plasticity and flexibility through tumorigen-
esis, malignant progression, and adaptive resistance to ther-
apy may provide opportunities to transform cancer into a
chronic, largely asymptomatic disease, increasing survival
and improving the quality of life of patients with cancer. Con-
nections between aging and cancer extend beyond the accu-
mulation of mutations. Understanding how organ/tissue and
tumor landscapes change with age and with diverse environ-
mental exposures, and how such changes influence all stagesof cancer evolution, from selection of initiated clones to metas-
tasis, will be essential for winning more battles and maybe
even wars on cancer.
Over the next two decades, as the world is faced with an ag-
ing population in which cancer will touch at least one in three
and global incidence is set to rise, this review may help to pro-
vide a logical framework for initiatives aimed to address the
clouds of complexity described herein, aiming to increasingly
prevent the inception of cancer, to reduce the incidence of
symptomatic cancer, as well as to diagnose cancers earlier,
collectively alleviating suffering and prolonging the quality
and quantity of life for those suffering from this daunting
disease.
ACKNOWLEDGMENTS
CRUK thanks Alison Howe for her generosity in supporting the creation of the
CRUK Marshall Symposium series and Lucy Stuart for coordinating the orga-
nization of this workshop. J.A. was supported by Ecole Polytechnique Fe´de´r-
ale de Lausanne (EPFL), and grants from the European Research Council
(ERC-AdG-787702) and the Swiss National Science Foundation (SNSF
31003A_179435). A.B. is supported by National Cancer Institutes (NCI) grant
R35CA210018; Barbara Bass Bakar Professorship of Cancer Genetics; Can-
cer Research UK/NCI PROMINENT Cancer Grand Challenge Award (Project
Number: OT2CA278665 and CGCATF-2021/100006), D.B.-S. NIH/National
Cancer Institute (NCI) grant R35CA210263. R.B. was supported by ERC-
787925. J.D. was supported by R01AG067584. A.E. was supported by
ERC818943, ISF860/18, and ICRF 837124. M.A.F. was supported by an Inves-
tigator Grant APP1194141 from the National Health &Medical Research Coun-
cil of Australia (NHMRC). A.H. was supported by 1R01AI165661. J.A.J. was
supported by the Swiss National Science Foundation Advanced Grant
TMAG-3_209224, Ludwig Institute for Cancer Research, and the University
of Lausanne. K.L. was supported by CA271500. I.M. was supported by ERC
CoGH2020-725492; Francis Crick Institute/Wellcome Trust CC2051; Francis
Crick Institute/UK Medical Research Council CC2051; Francis Crick
Institute/Cancer Research UK CC2051. K.H.V. was supported by Cancer
Research UK grant C596/A26855; Francis Crick Institute/Wellcome Trust
CC2073; Francis Crick Institute/UK Medical Research Council CC2073; Fran-
cis Crick Institute/Cancer Research UK. E.W. was supported by the CRUK/
NCI Cancer Grand Challenge CANCAN award, the Ludwig Institute for Cancer
Research, NCI grants CA163591 and CA243547, and the Fifth Generation
Foundation. F.W. was supported by the Deutsche Forschungsgemeinschaft
(SFB 1389, UNITE Glioblastoma). D.H. was supported by the Ludwig Institute
for Cancer Research.
In memory of Professor Chris Marshall FRS 1949–2015.
DECLARATION OF INTERESTS
C.A. reports employment at Saga Diagnostics and reports options to own
stock in the company. C.A. and C.S. are listed as inventors on a European pat-
ent application relating to assay technology to detect tumor recurrence (PCT/
GB2017/053289). This patent has been licensed to commercial entities and,
under their terms of employment, C.A. and C.S. are due a revenue share of
any revenue generated from such license(s). C.A. and C.S. declare a patent
application (PCT/US2017/028013) for methods to detect lung cancer. C.A.
and C.S. are named inventors on a patent application to determine methods
and systems for tumor monitoring (PCT/EP2022/077987). C.A. and C.S. are
named inventors on a provisional patent protection related to a ctDNA detec-
tion algorithm.
J.A.J. has received honoraria for speaking at research symposia organized
by Bristol Meyers Squibb and Glenmark Pharmaceuticals and previously
served on the SAB of Pionyr Immunotherapeutics.
D.H. is a founder and member of the BoD and SAB of Opna Bio, and amem-
ber of the SABs of Pfizer, Cellestia Biotech, 4D Molecular Therapeutics, and
AtG Therapeutics.Cell 187, March 28, 2024 1607
ll
PerspectiveF.A. reports travel/accommodation/expenses from AstraZeneca,
GlaxoSmithKline, Novartis, Pfizer, and Roche, and his institution has received
research funding from AstraZeneca, Daiichi Sankyo, Lilly, Novartis, Pfizer, and
Roche. C.M. Consultant/Advisory fees from Amgen, Astellas, AstraZeneca,
Bayer, BeiGene, BMS, Celgene, Debiopharm, Genentech, Ipsen, Janssen,
Lilly, MedImmune, MSD, Novartis, Pfizer, Roche, Sanofi, Orion. Principal/
sub-Investigator of Clinical Trials for AbbVie, Aduro, Agios, Amgen, Argen-x,
Astex, AstraZeneca, Aveo pharmaceuticals, Bayer, Beigene, Blueprint,
BMS, Boeringer Ingelheim, Celgene, Chugai, Clovis, Daiichi Sankyo, Debio-
pharm, Eisai, Eos, Exelixis, Forma, Gamamabs, Genentech, Gortec, GSK,
H3 biomedecine, Incyte, InnatePharma, Janssen, KuraOncology, Kyowa, Lilly,
Loxo, Lysarc, Lytix Biopharma, Medimmune, Menarini, Merus,MSD, Nanobio-
tix, Nektar Therapeutics, Novartis, Octimet, Oncoethix, Oncopeptides AB,
Orion, Pfizer, Pharmamar, Pierre Fabre, Roche, Sanofi, Servier, Sierra
Oncology, Taiho, Takeda, Tesaro, and Xencor.
R.B. is founder of Oncosence.
M.A.F. is founder and shareholder of Celesta Therapeutics.
A.H. is a paid consultant for Calida Therapeutics.
M.J.-H. has consulted for, and is amember of, the Achilles Therapeutics Sci-
entific Advisory Board and Steering Committee, has received speaker hono-
raria from Pfizer, Astex Pharmaceuticals, Oslo Cancer Cluster, Bristol Myers
Squibb, and is listed as a co-inventor on a European patent application relating
to methods to detect lung cancer PCT/US2017/028013), this patent has been
licensed to commercial entities and, under terms of employment, M.J.-H. is
due a share of any revenue generated from such license(s).
J.W.L. advises Restoration Foodworks, Nanocare Technologies, and
Cornerstone Pharmaceuticals.
S.L. received research funding to institution from Novartis, Bristol
Myers Squibb, MSD, Puma Biotechnology, Eli Lilly, Nektar Therapeutics,
AstraZeneca/Daiichi Sankyo, and Seattle Genetics. S.L. has acted as consul-
tant (not compensated) to Seattle Genetics, Novartis, Bristol Myers Squibb,
MSD, AstraZeneca/Daiichi Sankyo, Eli Lilly, Pfizer, Gilead Therapeutics, Nek-
tar Therapeutics, PUMA Biotechnologies and Roche-Genentech. S.L. has
acted as consultant (paid to institution) to Novartis, GlaxoSmithKline,
Roche-Genentech, AstraZeneca/Daiichi Sankyo, Pfizer, Gilead Therapeutics,
Seattle Genetics, MSD, Tallac Therapeutics, Eli Lilly, and Bristol Myers Squibb.
I.M. is a consultant of LIfT BioSciences.
K.M. has received honoraria from LEO Pharma, Pfizer and Bayer PLC. She
has received research funding from LEO Pharma and Bayer PLC.
S.Q. is a founder, CSO, and holds stock options in Achilles Therapeutics.
C.S. acknowledges grants from AstraZeneca, Boehringer Ingelheim, Bris-
tol Myers Squibb, Pfizer, Roche-Ventana, Invitae (previously Archer Dx Inc—
collaboration in minimal residual disease sequencing technologies), Ono
Pharmaceutical, and Personalis. He is Chief Investigator for the AZ MeRmaiD
1 and 2 clinical trials and is the Steering Committee Chair. He is also Co-
Chief Investigator of the NHS Galleri trial funded by GRAIL and a paid mem-
ber of GRAIL’s Scientific Advisory Board. He receives consultant fees from
Achilles Therapeutics (also SAB member), Bicycle Therapeutics (also a
SAB member), Genentech, Medicxi, China Innovation Centre of Roche (CI-
CoR) formerly Roche Innovation Centre—Shanghai, Metabomed (until July
2022), Relay Therapeutics, and the Sarah Cannon Research Institute. C.S.
has received honoraria from Amgen, AstraZeneca, Bristol Myers Squibb,
GlaxoSmithKline, Illumina, MSD, Novartis, Pfizer, and Roche-Ventana. C.S.
has previously held stock options in Apogen Biotechnologies and GRAIL,
and currently has stock options in Epic Bioscience, Bicycle Therapeutics,
and has stock options and is co-founder of Achilles Therapeutics. Patents:
C.S. declares a patent application (PCT/US2017/028013) for methods to
lung cancer); targeting neoantigens (PCT/EP2016/059401); identifying patent
response to immune checkpoint blockade (PCT/EP2016/071471), deter-
mining HLA LOH (PCT/GB2018/052004); predicting survival rates of patients
with cancer (PCT/GB2020/050221); identifying patients who respond to can-
cer treatment (PCT/GB2018/051912); methods for lung cancer detection
(US20190106751A1). C.S. is an inventor on a European patent application
(PCT/GB2017/053289) relating to assay technology to detect tumor recur-
rence. This patent has been licensed to a commercial entity and under their
terms of employment C.S. is due a revenue share of any revenue generated
from such license(s).1608 Cell 187, March 28, 2024K.H.V. is on the board of directors and shareholder of Bristol Myers Squibb
and on the science advisory board (with stock options) of PMV Pharma, RAZE
Therapeutics, Volastra Pharmaceuticals and Kovina Therapeutics. She is on
the SAB of Ludwig Cancer and a co-founder and consultant of Faeth Thera-
peutics. She has been in receipt of research funding from Astex Pharmaceu-
ticals and AstraZeneca and contributed to CRUK Cancer Research Technol-
ogy filing of patent application WO/2017/144877.
J.W. is an inventor on a US patent application (PCT/US17/53.717) submitted
by the University of TexasMDAnderson Cancer Center which covers methods
to enhance immune checkpoint blockade responses by modulating the micro-
biome, reports compensation for speaker’s bureau and honoraria from Ime-
dex, Dava Oncology, Omniprex, Illumina, Gilead, PeerView, Physician Educa-
tion Resource, MedImmune, Exelixis and Bristol Myers Squibb, and has
served as a consultant/advisory board member for Roche/Genentech, Novar-
tis, AstraZeneca, GlaxoSmithKline, Bristol Myers Squibb, Micronoma, OSE
therapeutics, Merck, and Everimmune. Dr. Wargo receives stock options
from Micronoma and OSE therapeutics.
A.W. is on the board of Regain Therapeutics.REFERENCES
1. Wishart, D.S. (2015). Is Cancer a Genetic Disease or a Metabolic Dis-
ease? EBiomedicine 2, 478–479.
2. Hanahan, D., and Weinberg, R.A. (2000). The hallmarks of cancer. Cell
100, 57–70.
3. Hanahan, D., and Weinberg, R.A. (2011). Hallmarks of cancer: the next
generation. Cell 144, 646–674.
4. Hanahan, D. (2022). Hallmarks of Cancer: New Dimensions. Cancer Dis-
cov. 12, 31–46.
5. Hanahan, D., and Monje, M. (2023). Cancer hallmarks intersect with
neuroscience in the tumor microenvironment. Cancer Cell 41, 573–580.
6. Brennan, P., and Davey-Smith, G. (2022). Identifying Novel Causes of
Cancers to Enhance Cancer Prevention: New Strategies Are Needed.
J. Natl. Cancer Inst. 114, 353–360.
7. Clocchiatti, A., Cora, E., Zhang, Y., and Dotto, G.P. (2016). Sexual dimor-
phism in cancer. Nat. Rev. Cancer 16, 330–339.
8. Haupt, S., Caramia, F., Klein, S.L., Rubin, J.B., and Haupt, Y. (2021). Sex
disparities matter in cancer development and therapy. Nat. Rev. Cancer
21, 393–407.
9. Li, J., Lan, Z., Liao, W., Horner, J.W., Xu, X., Liu, J., Yoshihama, Y., Jiang,
S., Shim, H.S., Slotnik, M., et al. (2023). Histone demethylase KDM5D up-
regulation drives sex differences in colon cancer. Nature 619, 632–639.
10. O¨zdemir, B.C., and Dotto, G.P. (2019). Sex Hormones and Anticancer
Immunity. Clin. Cancer Res. 25, 4603–4610.
11. Vellano, C.P., White, M.G., Andrews, M.C., Chelvanambi, M., Witt, R.G.,
Daniele, J.R., Titus, M., McQuade, J.L., Conforti, F., Burton, E.M., et al.
(2022). Androgen receptor blockade promotes response to BRAF/
MEK-targeted therapy. Nature 606, 797–803.
12. Martincorena, I., and Campbell, P.J. (2015). Somatic mutation in cancer
and normal cells. Science 349, 1483–1489.
13. Fowler, J.C., and Jones, P.H. (2022). SomaticMutation: What Shapes the
Mutational Landscape of Normal Epithelia? Cancer Discov. 12,
1642–1655.
14. Berenblum, I., and Shubik, P. (1947). The role of croton oil applications,
associated with a single painting of a carcinogen, in tumour induction
of the mouse’s skin. Br. J. Cancer 1, 379–382.
15. Friedewald,W.F., and Rous, P. (1944). THE INITIATINGANDPROMOTING
ELEMENTS IN TUMOR PRODUCTION: AN ANALYSIS OF THE EFFECTS
OF TAR, BENZPYRENE, AND METHYLCHOLANTHRENE ON RABBIT
SKIN. J. Exp. Med. 80, 101–126.
16. Riva, L., Pandiri, A.R., Li, Y.R., Droop, A., Hewinson, J., Quail, M.A., Iyer,
V., Shepherd, R., Herbert, R.A., Campbell, P.J., et al. (2020). The
ll
Perspectivemutational signature profile of known and suspected human carcinogens
in mice. Nat. Genet. 52, 1189–1197.
17. Guerra, C., Schuhmacher, A.J., Can˜amero, M., Grippo, P.J., Verdaguer,
L., Pe´rez-Gallego, L., Dubus, P., Sandgren, E.P., and Barbacid, M.
(2007). Chronic pancreatitis is essential for induction of pancreatic ductal
adenocarcinoma by K-Ras oncogenes in adult mice. Cancer Cell 11,
291–302.
18. Bailleul, B., Surani, M.A., White, S., Barton, S.C., Brown, K., Blessing, M.,
Jorcano, J., and Balmain, A. (1990). Skin hyperkeratosis and papilloma
formation in transgenic mice expressing a ras oncogene from a supra-
basal keratin promoter. Cell 62, 697–708.
19. Alonso-Curbelo, D., Ho, Y.J., Burdziak, C., Maag, J.L.V., Morris, J.P.,
Chandwani, R., Chen, H.A., Tsanov, K.M., Barriga, F.M., Luan, W.,
et al. (2021). A gene-environment-induced epigenetic program initiates
tumorigenesis. Nature 590, 642–648.
20. Wong, T.N., Ramsingh, G., Young, A.L., Miller, C.A., Touma, W., Welch,
J.S., Lamprecht, T.L., Shen, D., Hundal, J., Fulton, R.S., et al. (2015). Role
of TP53 mutations in the origin and evolution of therapy-related acute
myeloid leukaemia. Nature 518, 552–555.
21. Hill, W., Lim, E.L., Weeden, C.E., Lee, C., Augustine, M., Chen, K., Kuan,
F.C., Marongiu, F., Evans, E.J., Moore, D.A., et al. (2023). Lung adenocar-
cinoma promotion by air pollutants. Nature 616, 159–167.
22. Kadariya, Y., Menges, C.W., Talarchek, J., Cai, K.Q., Klein-Szanto, A.J.,
Pietrofesa, R.A., Christofidou-Solomidou, M., Cheung, M., Mossman,
B.T., Shukla, A., et al. (2016). Inflammation-Related IL1b/IL1R Signaling
Promotes the Development of Asbestos-Induced Malignant Mesotheli-
oma. Cancer Prev. Res. (Phila) 9, 406–414.
23. Moody, S., Senkin, S., Islam, S.M.A., Wang, J., Nasrollahzadeh, D., Cor-
tez Cardoso Penha, R., Fitzgerald, S., Bergstrom, E.N., Atkins, J., He, Y.,
et al. (2021). Mutational signatures in esophageal squamous cell carci-
noma from eight countries with varying incidence. Nat. Genet. 53,
1553–1563.
24. Lowenfels, A.B., Maisonneuve, P., Cavallini, G., Ammann, R.W., Lan-
kisch, P.G., Andersen, J.R., Dimagno, E.P., Andre´n-Sandberg, A., and
Domello¨f, L. (1993). Pancreatitis and the risk of pancreatic cancer. Inter-
national pancreatitis study group. N. Engl. J. Med. 328, 1433–1437.
25. Collins, R.H., Jr., Feldman, M., and Fordtran, J.S. (1987). Colon cancer,
dysplasia, and surveillance in patients with ulcerative colitis. A critical re-
view. N. Engl. J. Med. 316, 1654–1658.
26. George, J., Prasad, S., Mahmood, Z., and Shukla, Y. (2010). Studies on
glyphosate-induced carcinogenicity in mouse skin: a proteomic
approach. J. Proteomics 73, 951–964.
27. Luo, H., and Ge, H. (2022). Hot Tea Consumption and Esophageal Can-
cer Risk: A Meta-Analysis of Observational Studies. Front. Nutr. 9,
831567.
28. Naik, S., Larsen, S.B., Gomez, N.C., Alaverdyan, K., Sendoel, A., Yuan,
S., Polak, L., Kulukian, A., Chai, S., and Fuchs, E. (2017). Inflammatory
memory sensitizes skin epithelial stem cells to tissue damage. Nature
550, 475–480.
29. Alexandrov, L.B., Kim, J., Haradhvala, N.J., Huang, M.N., Tian Ng, A.W.,
Wu, Y., Boot, A., Covington, K.R., Gordenin, D.A., Bergstrom, E.N., et al.
(2020). The repertoire of mutational signatures in human cancer. Nature
578, 94–101.
30. Kanarek, N., Petrova, B., and Sabatini, D.M. (2020). Dietary modifications
for enhanced cancer therapy. Nature 579, 507–517.
31. Bejarano, L., Jord
ao, M.J.C., and Joyce, J.A. (2021). Therapeutic Target-
ing of the Tumor Microenvironment. Cancer Discov. 11, 933–959.
32. Plaks, V., Kong, N., andWerb, Z. (2015). The cancer stem cell niche: how
essential is the niche in regulating stemness of tumor cells? Cell Stem
Cell 16, 225–238.
33. Debaugnies, M., Rodrı´guez-Acebes, S., Blondeau, J., Parent, M.A.,
Zocco, M., Song, Y., de Maertelaer, V., Moers, V., Latil, M., Dubois, C.,et al. (2023). RHOJ controls EMT-associated resistance to chemo-
therapy. Nature 616, 168–175.
34. de Visser, K.E., and Joyce, J.A. (2023). The evolving tumor microenviron-
ment: From cancer initiation to metastatic outgrowth. Cancer Cell 41,
374–403.
35. Can˜ellas-Socias, A., Cortina, C., Hernando-Momblona, X., Palomo-
Ponce, S., Mulholland, E.J., Turon, G., Mateo, L., Conti, S., Roman, O.,
Sevillano, M., et al. (2022). Metastatic recurrence in colorectal cancer
arises from residual EMP1+ cells. Nature 611, 603–613.
36. Kim, I.S., Gao, Y., Welte, T., Wang, H., Liu, J., Janghorban,M., Sheng, K.,
Niu, Y., Goldstein, A., Zhao, N., et al. (2019). Immuno-subtyping of breast
cancer reveals distinct myeloid cell profiles and immunotherapy resis-
tance mechanisms. Nat. Cell Biol. 21, 1113–1126.
37. Lavie, D., Ben-Shmuel, A., Erez, N., and Scherz-Shouval, R. (2022). Can-
cer-associated fibroblasts in the single-cell era. Nat. Cancer 3, 793–807.
38. Dai, J., Cimino, P.J., Gouin, K.H., 3rd, Grzelak, C.A., Barrett, A., Lim,
A.R., Long, A., Weaver, S., Saldin, L.T., Uzamere, A., et al. (2022). Astro-
cytic laminin-211 drives disseminated breast tumor cell dormancy in
brain. Nat. Cancer 3, 25–42.
39. Di Martino, J.S., Nobre, A.R., Mondal, C., Taha, I., Farias, E.F., Fertig,
E.J., Naba, A., Aguirre-Ghiso, J.A., and Bravo-Cordero, J.J. (2022). A tu-
mor-derived type III collagen-rich ECM niche regulates tumor cell
dormancy. Nat. Cancer 3, 90–107.
40. Fane, M.E., Chhabra, Y., Alicea, G.M., Maranto, D.A., Douglass, S.M.,
Webster, M.R., Rebecca, V.W., Marino, G.E., Almeida, F., Ecker, B.L.,
et al. (2022). Stromal changes in the aged lung induce an emergence
from melanoma dormancy. Nature 606, 396–405.
41. Ombrato, L., Nolan, E., Kurelac, I., Mavousian, A., Bridgeman, V.L.,
Heinze, I., Chakravarty, P., Horswell, S., Gonzalez-Gualda, E., Mata-
cchione, G., et al. (2019). Metastatic-niche labelling reveals parenchymal
cells with stem features. Nature 572, 603–608.
42. Klemm, F., Maas, R.R., Bowman, R.L., Kornete, M., Soukup, K., Nassiri,
S., Brouland, J.P., Iacobuzio-Donahue, C.A., Brennan, C., Tabar, V., et al.
(2020). Interrogation of the Microenvironmental Landscape in Brain Tu-
mors Reveals Disease-Specific Alterations of Immune Cells. Cell 181,
1643–1660.e17.
43. A´lvarez-Prado, A´.F., Maas, R.R., Soukup, K., Klemm, F., Kornete, M.,
Krebs, F.S., Zoete, V., Berezowska, S., Brouland, J.P., Hottinger, A.F.,
et al. (2023). Immunogenomic analysis of human brain metastases re-
veals diverse immune landscapes across genetically distinct tumors.
Cell Rep. Med. 4, 100900.
44. Risom, T., Glass, D.R., Averbukh, I., Liu, C.C., Baranski, A., Kagel, A.,
McCaffrey, E.F., Greenwald, N.F., Rivero-Gutie´rrez, B., Strand, S.H.,
et al. (2022). Transition to invasive breast cancer is associated with pro-
gressive changes in the structure and composition of tumor stroma. Cell
185, 299–310.e18.
45. Jackson, H.W., Fischer, J.R., Zanotelli, V.R.T., Ali, H.R., Mechera, R.,
Soysal, S.D., Moch, H., Muenst, S., Varga, Z., Weber, W.P., et al.
(2020). The single-cell pathology landscape of breast cancer. Nature
578, 615–620.
46. Schu¨rch, C.M., Bhate, S.S., Barlow, G.L., Phillips, D.J., Noti, L., Zlobec,
I., Chu, P., Black, S., Demeter, J., McIlwain, D.R., et al. (2020). Coordi-
nated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the
Colorectal Cancer Invasive Front. Cell 183, 838.
47. Sammut, S.J., Crispin-Ortuzar, M., Chin, S.F., Provenzano, E., Bardwell,
H.A., Ma, W., Cope, W., Dariush, A., Dawson, S.J., Abraham, J.E., et al.
(2022). Multi-omic machine learning predictor of breast cancer therapy
response. Nature 601, 623–629.
48. Liu, Z., Zhao, Y., Kong, P., Liu, Y., Huang, J., Xu, E., Wei, W., Li, G.,
Cheng, X., Xue, L., et al. (2023). Integrated multi-omics profiling yields
a clinically relevant molecular classification for esophageal squamous
cell carcinoma. Cancer Cell 41, 181–195.e9.Cell 187, March 28, 2024 1609
ll
Perspective49. Croci, D., Santalla Me´ndez, R., Temme, S., Soukup, K., Fournier, N., Zo-
mer, A., Colotti, R., Wischnewski, V., Flo¨gel, U., van Heeswijk, R.B., et al.
(2022). Multispectral fluorine-19 MRI enables longitudinal and noninva-
sive monitoring of tumor-associated macrophages. Sci. Transl. Med.
14, eabo2952.
50. Dmitrieva-Posocco, O., Dzutsev, A., Posocco, D.F., Hou, V., Yuan, W.,
Thovarai, V., Mufazalov, I.A., Gunzer, M., Shilovskiy, I.P., Khaitov,
M.R., et al. (2019). Cell-Type-Specific Responses to Interleukin-1 Control
Microbial Invasion and Tumor-Elicited Inflammation in Colorectal Can-
cer. Immunity 50, 166–180.e7.
51. Dou, Z., Ghosh, K., Vizioli, M.G., Zhu, J., Sen, P., Wangensteen, K.J.,
Simithy, J., Lan, Y., Lin, Y., Zhou, Z., et al. (2017). Cytoplasmic chromatin
triggers inflammation in senescence and cancer. Nature 550, 402–406.
52. Milanovic, M., Fan, D.N.Y., Belenki, D., Da¨britz, J.H.M., Zhao, Z., Yu, Y.,
Do¨rr, J.R., Dimitrova, L., Lenze, D., Monteiro Barbosa, I.A., et al. (2018).
Senescence-associated reprogramming promotes cancer stemness.
Nature 553, 96–100.
53. Cai, Z., Aguilera, F., Ramdas, B., Daulatabad, S.V., Srivastava, R., Kotzin,
J.J., Carroll, M., Wertheim, G., Williams, A., Janga, S.C., et al. (2020). Tar-
geting Bim via a lncRNA Morrbid Regulates the Survival of Preleukemic
and Leukemic Cells. Cell Rep. 31, 107816.
54. Biavasco, R., Lettera, E., Giannetti, K., Gilioli, D., Beretta, S., Conti, A.,
Scala, S., Cesana, D., Gallina, P., Norelli, M., et al. (2021). Oncogene-
induced senescence in hematopoietic progenitors features myeloid
restricted hematopoiesis, chronic inflammation and histiocytosis. Nat.
Commun. 12, 4559.
55. Tu, S., Bhagat, G., Cui, G., Takaishi, S., Kurt-Jones, E.A., Rickman, B.,
Betz, K.S., Penz-Oesterreicher, M., Bjorkdahl, O., Fox, J.G., et al.
(2008). Overexpression of interleukin-1beta induces gastric inflammation
and cancer and mobilizes myeloid-derived suppressor cells in mice.
Cancer Cell 14, 408–419.
56. Deng, L., Liang, H., Xu, M., Yang, X., Burnette, B., Arina, A., Li, X.D., Mau-
ceri, H., Beckett, M., Darga, T., et al. (2014). STING-Dependent Cytosolic
DNA Sensing Promotes Radiation-Induced Type I Interferon-Dependent
Antitumor Immunity in Immunogenic Tumors. Immunity 41, 843–852.
57. Woo, S.R., Fuertes, M.B., Corrales, L., Spranger, S., Furdyna, M.J.,
Leung, M.Y., Duggan, R., Wang, Y., Barber, G.N., Fitzgerald, K.A.,
et al. (2014). STING-dependent cytosolic DNA sensing mediates innate
immune recognition of immunogenic tumors. Immunity 41, 830–842.
58. Ahn, J., Xia, T., Rabasa Capote, A., Betancourt, D., and Barber, G.N.
(2018). Extrinsic Phagocyte-Dependent STING Signaling Dictates the
Immunogenicity of Dying Cells. Cancer Cell 33, 862–873.e5.
59. Ve´tizou, M., Pitt, J.M., Daille`re, R., Lepage, P., Waldschmitt, N., Flament,
C., Rusakiewicz, S., Routy, B., Roberti, M.P., Duong, C.P., et al. (2015).
Anticancer immunotherapy by CTLA-4 blockade relies on the gut micro-
biota. Science 350, 1079–1084.
60. Sivan, A., Corrales, L., Hubert, N., Williams, J.B., Aquino-Michaels, K.,
Earley, Z.M., Benyamin, F.W., Lei, Y.M., Jabri, B., Alegre, M.L., et al.
(2015). Commensal Bifidobacterium promotes antitumor immunity and
facilitates anti-PD-L1 efficacy. Science 350, 1084–1089.
61. Gopalakrishnan, V., Spencer, C.N., Nezi, L., Reuben, A., Andrews, M.C.,
Karpinets, T.V., Prieto, P.A., Vicente, D., Hoffman, K., Wei, S.C., et al.
(2018). Gut microbiome modulates response to anti-PD-1 immuno-
therapy in melanoma patients. Science 359, 97–103.
62. Matson, V., Fessler, J., Bao, R., Chongsuwat, T., Zha, Y., Alegre, M.L.,
Luke, J.J., and Gajewski, T.F. (2018). The commensal microbiome is
associated with anti-PD-1 efficacy in metastatic melanoma patients. Sci-
ence 359, 104–108.
63. Xing, C., Wang, M., Ajibade, A.A., Tan, P., Fu, C., Chen, L., Zhu, M., Hao,
Z.Z., Chu, J., Yu, X., et al. (2021). Microbiota regulate innate immune
signaling and protective immunity against cancer. Cell Host Microbe
29, 959–974.e7.1610 Cell 187, March 28, 202464. Tsay, J.-C.J., Wu, B.G., Sulaiman, I., Gershner, K., Schluger, R., Li, Y.,
Yie, T.A., Meyn, P., Olsen, E., Perez, L., et al. (2021). Lower Airway Dys-
biosis Affects Lung Cancer Progression. Cancer Discov. 11, 293–307.
65. Villemin, C., Six, A., Neville, B.A., Lawley, T.D., Robinson, M.J., and Bak-
dash, G. (2023). The heightened importance of the microbiome in cancer
immunotherapy. Trends Immunol. 44, 44–59.
66. Zhao, H., Teng, D., Yang, L., Xu, X., Chen, J., Jiang, T., Feng, A.Y., Zhang,
Y., Frederick, D.T., Gu, L., et al. (2022). Myeloid-derived itaconate sup-
presses cytotoxic CD8+ T cells and promotes tumour growth. Nat.
Metab. 4, 1660–1673.
67. Low, V., Li, Z., and Blenis, J. (2022). Metabolite activation of tumorigenic
signaling pathways in the tumor microenvironment. Sci. Signal. 15,
eabj4220.
68. Juarez, D., and Fruman, D.A. (2021). Targeting the Mevalonate Pathway
in Cancer. Trends Cancer 7, 525–540.
69. Chen, Y.J., Li, G.N., Li, X.J., Wei, L.X., Fu, M.J., Cheng, Z.L., Yang, Z.,
Zhu, G.Q., Wang, X.D., Zhang, C., et al. (2023). Targeting IRG1 reverses
the immunosuppressive function of tumor-associated macrophages and
enhances cancer immunotherapy. Sci. Adv. 9, eadg0654.
70. Messmer, M.N., Netherby, C.S., Banik, D., and Abrams, S.I. (2015). Tu-
mor-induced myeloid dysfunction and its implications for cancer immu-
notherapy. Cancer Immunol. Immunother. 64, 1–13.
71. Gabrilovich, D.I., and Nagaraj, S. (2009). Myeloid-derived suppressor
cells as regulators of the immune system. Nat. Rev. Immunol. 9, 162–174.
72. Kortlever, R.M., Sodir, N.M., Wilson, C.H., Burkhart, D.L., Pellegrinet, L.,
Brown Swigart, L., Littlewood, T.D., and Evan, G.I. (2017). Myc Cooper-
ates with Ras by Programming Inflammation and Immune Suppression.
Cell 171, 1301–1315.e14.
73. Rahib, L., Wehner, M.R., Matrisian, L.M., and Nead, K.T. (2021). Esti-
mated Projection of US Cancer Incidence and Death to 2040. JAMA
Netw. Open 4, e214708.
74. Fane, M., and Weeraratna, A.T. (2020). How the ageing microenviron-
ment influences tumour progression. Nat. Rev. Cancer 20, 89–106.
75. Rozhok, A.I., and DeGregori, J. (2015). Toward an evolutionary model of
cancer: Considering the mechanisms that govern the fate of somatic mu-
tations. Proc. Natl. Acad. Sci. USA 112, 8914–8921.
76. Chhabra, Y., and Weeraratna, A.T. (2023). Fibroblasts in cancer: unity in
heterogeneity. Cell 186, 1580–1609.
77. Hoeijmakers, J.H.J. (2009). DNA damage, aging, and cancer. N. Engl. J.
Med. 361, 1475–1485.
78. Marongiu, F., and DeGregori, J. (2022). The sculpting of somatic muta-
tional landscapes by evolutionary forces and their impacts on aging-
related disease. Mol. Oncol. 16, 3238–3258.
79. Ahmad, H., Jahn, N., and Jaiswal, S. (2023). Clonal Hematopoiesis and
Its Impact on Human Health. Annu. Rev. Med. 74, 249–260.
80. Olafsson, S., McIntyre, R.E., Coorens, T., Butler, T., Jung, H., Robinson,
P.S., Lee-Six, H., Sanders, M.A., Arestang, K., Dawson, C., et al. (2020).
Somatic Evolution in Non-neoplastic IBD-Affected Colon. Cell 182, 672–
684.e11.
81. Nanki, K., Fujii, M., Shimokawa, M., Matano, M., Nishikori, S., Date, S.,
Takano, A., Toshimitsu, K., Ohta, Y., Takahashi, S., et al. (2020). Somatic
inflammatory gene mutations in human ulcerative colitis epithelium. Na-
ture 577, 254–259.
82. Kakiuchi, N., Yoshida, K., Uchino, M., Kihara, T., Akaki, K., Inoue, Y., Ka-
wada, K., Nagayama, S., Yokoyama, A., Yamamoto, S., et al. (2020).
Frequent mutations that converge on the NFKBIZ pathway in ulcerative
colitis. Nature 577, 260–265.
83. Fowler, J.C., King, C., Bryant, C., Hall, M.W.J., Sood, R., Ong, S.H., Earp,
E., Fernandez-Antoran, D., Koeppel, J., Dentro, S.C., et al. (2021). Selec-
tion of Oncogenic Mutant Clones in Normal Human Skin Varies with Body
Site. Cancer Discov. 11, 340–361.
ll
Perspective84. Yokoyama, A., Kakiuchi, N., Yoshizato, T., Nannya, Y., Suzuki, H., Take-
uchi, Y., Shiozawa, Y., Sato, Y., Aoki, K., Kim, S.K., et al. (2019). Age-
related remodelling of oesophageal epithelia by mutated cancer drivers.
Nature 565, 312–317.
85. Jaiswal, S., and Ebert, B.L. (2019). Clonal hematopoiesis in human aging
and disease. Science 366, eaan4673.
86. Kerr, J., Anderson, C., and Lippman, S.M. (2017). Physical activity,
sedentary behaviour, diet, and cancer: an update and emerging new ev-
idence. Lancet Oncol. 18, e457–e471.
87. Furman, D., Campisi, J., Verdin, E., Carrera-Bastos, P., Targ, S., France-
schi, C., Ferrucci, L., Gilroy, D.W., Fasano, A., Miller, G.W., et al. (2019).
Chronic inflammation in the etiology of disease across the life span. Nat.
Med. 25, 1822–1832.
88. Turrell, F.K., Orha, R., Guppy, N.J., Gillespie, A., Guelbert, M., Starling,
C., Haider, S., and Isacke, C.M. (2023). Age-associated microenviron-
mental changes highlight the role of PDGF-C in ER+ breast cancer met-
astatic relapse. Nat. Cancer 4, 468–484.
89. Kiemen, A.L., Braxton, A.M., Grahn,M.P., Han, K.S., Babu, J.M., Reichel,
R., Jiang, A.C., Kim, B., Hsu, J., Amoa, F., et al. (2022). CODA: quantita-
tive 3D reconstruction of large tissues at cellular resolution. Nat. Methods
19, 1490–1499.
90. Berry, S., Giraldo, N.A., Green, B.F., Cottrell, T.R., Stein, J.E., Engle, E.L.,
Xu, H., Ogurtsova, A., Roberts, C., Wang, D., et al. (2021). Analysis of
multispectral imaging with the AstroPath platform informs efficacy of
PD-1 blockade. Science 372, eaba2609.
91. Drapela, S., and Gomes, A.P. (2023). The aging lung reawakens dormant
tumor cells. Nat. Cancer 4, 442–443.
92. Wang, L., Lankhorst, L., and Bernards, R. (2022). Exploiting senescence
for the treatment of cancer. Nat. Rev. Cancer 22, 340–355.
93. Jochems, F., Thijssen, B., De Conti, G., Jansen, R., Pogacar, Z., Groot,
K., Wang, L., Schepers, A., Wang, C., Jin, H., et al. (2021). The Cancer
SENESCopedia: A delineation of cancer cell senescence. Cell Rep. 36,
109441.
94. Demaria, M., O’Leary,M.N., Chang, J., Shao, L., Liu, S., Alimirah, F., Koe-
nig, K., Le, C., Mitin, N., Deal, A.M., et al. (2017). Cellular Senescence
Promotes Adverse Effects of Chemotherapy and Cancer Relapse. Can-
cer Discov. 7, 165–176.
95. Baker, D.J., Wijshake, T., Tchkonia, T., LeBrasseur, N.K., Childs, B.G.,
van de Sluis, B., Kirkland, J.L., and van Deursen, J.M. (2011). Clearance
of p16Ink4a-positive senescent cells delays ageing-associated disor-
ders. Nature 479, 232–236.
96. Baker, D.J., Childs, B.G., Durik, M., Wijers, M.E., Sieben, C.J., Zhong, J.,
Saltness, R.A., Jeganathan, K.B., Verzosa, G.C., Pezeshki, A., et al.
(2016). Naturally occurring p16(Ink4a)-positive cells shorten healthy life-
span. Nature 530, 184–189.
97. Wang, L., Jin, H., Jochems, F., Wang, S., Lieftink, C., Martinez, I.M., De
Conti, G., Edwards, F., de Oliveira, R.L., Schepers, A., et al. (2022). cFLIP
suppression and DR5 activation sensitize senescent cancer cells to se-
nolysis. Nat. Cancer 3, 1284–1299.
98. Ruscetti, M., Morris, J.P., 4th, Mezzadra, R., Russell, J., Leibold, J., Ro-
messer, P.B., Simon, J., Kulick, A., Ho, Y.-J., Fennell, M., et al. (2020).
Senescence-Induced Vascular Remodeling Creates Therapeutic Vulner-
abilities in Pancreas Cancer. Cell 181, 424–441.e21.
99. Kang, C., Xu, Q., Martin, T.D., Li, M.Z., Demaria, M., Aron, L., Lu, T.,
Yankner, B.A., Campisi, J., and Elledge, S.J. (2015). The DNA damage
response induces inflammation and senescence by inhibiting autophagy
of GATA4. Science 349, aaa5612.
100. Li, F., and Simon, M.C. (2020). Cancer Cells Don’t Live Alone: Metabolic
Communication within Tumor Microenvironments. Dev. Cell 54,
183–195.
101. Kimmelman, A.C., and White, E. (2017). Autophagy and Tumor Meta-
bolism. Cell Metab. 25, 1037–1043.102. Al-Zoughbi, W., Al-Zhoughbi, W., Huang, J., Paramasivan, G.S., Till, H.,
Pichler, M., Guertl-Lackner, B., and Hoefler, G. (2014). Tumor macroen-
vironment and metabolism. Semin. Oncol. 41, 281–295.
103. Li, X., Yang, Y., Zhang, B., Lin, X., Fu, X., An, Y., Zou, Y., Wang, J.X.,
Wang, Z., and Yu, T. (2022). Lactate metabolism in human health and dis-
ease. Signal Transduct. Target. Ther. 7, 305.
104. Hui, S., Ghergurovich, J.M., Morscher, R.J., Jang, C., Teng, X., Lu, W.,
Esparza, L.A., Reya, T., Le Zhan, Yanxiang Guo, J., et al. (2017). Glucose
feeds the TCA cycle via circulating lactate. Nature 551, 115–118.
105. Faubert, B., Li, K.Y., Cai, L., Hensley, C.T., Kim, J., Zacharias, L.G., Yang,
C., Do, Q.N., Doucette, S., Burguete, D., et al. (2017). LactateMetabolism
in Human Lung Tumors. Cell 171, 358–371.e9.
106. Du, Z., Zhang, H., Feng, Y., Zhan, D., Li, S., Tu, C., Liu, J., and Wang, J.
(2023). Tumour-derived small extracellular vesicles contribute to the
tumour progression through reshaping the systemic immune macroen-
vironment. Br. J. Cancer 128, 1249–1266.
107. Goldman, O., Adler, L.N., Hajaj, E., Croese, T., Darzi, N., Galai, S., Tishler,
H., Ariav, Y., Lavie, D., Fellus-Alyagor, L., et al. (2023). Early Infiltration of
Innate Immune Cells to the Liver Depletes HNF4a and Promotes Extrahe-
patic Carcinogenesis. Cancer Discov. 13, 1616–1635.
108. Burfeind, K.G., Zhu, X., Norgard, M.A., Levasseur, P.R., Huisman, C.,
Buenafe, A.C., Olson, B., Michaelis, K.A., Torres, E.R., Jeng, S., et al.
(2020). Circulating myeloid cells invade the central nervous system to
mediate cachexia during pancreatic cancer. eLife 9, e54095.
109. Argile´s, J.M., Lo´pez-Soriano, F.J., Stemmler, B., and Busquets, S.
(2023). Cancer-associated cachexia - understanding the tumour macro-
environment and microenvironment to improve management. Nat. Rev.
Clin. Oncol. 20, 250–264.
110. Pryce, B.R., Wang, D.J., Zimmers, T.A., Ostrowski, M.C., and Guttridge,
D.C. (2023). Cancer cachexia: involvement of an expanding macroenvir-
onment. Cancer Cell 41, 581–584.
111. Martin, L., Birdsell, L., Macdonald, N., Reiman, T., Clandinin, M.T.,
McCargar, L.J., Murphy, R., Ghosh, S., Sawyer, M.B., and Baracos,
V.E. (2013). Cancer cachexia in the age of obesity: skeletal muscle deple-
tion is a powerful prognostic factor, independent of body mass index.
J. Clin. Oncol. 31, 1539–1547.
112. Baracos, V.E., Martin, L., Korc, M., Guttridge, D.C., and Fearon, K.C.H.
(2018). Cancer-associated cachexia. Nat. Rev. Dis. Primers 4, 17105.
113. Ferrer, M., Anthony, T.G., Ayres, J.S., Biffi, G., Brown, J.C., Caan, B.J.,
Cespedes Feliciano, E.M., Coll, A.P., Dunne, R.F., Goncalves, M.D.,
et al. (2023). Cachexia: A systemic consequence of progressive, unre-
solved disease. Cell 186, 1824–1845.
114. Neyroud, D., Laitano, O., Dasgupta, A., Lopez, C., Schmitt, R.E.,
Schneider, J.Z., Hammers, D.W., Sweeney, H.L., Walter, G.A., Doles,
J., et al. (2023). Blocking muscle wasting via deletion of the muscle-spe-
cific E3 ligase MuRF1 impedes pancreatic tumor growth. Commun. Biol.
6, 519.
115. Ternes, D., Tsenkova, M., Pozdeev, V.I., Meyers, M., Koncina, E., Atatri,
S., Schmitz, M., Karta, J., Schmoetten, M., Heinken, A., et al. (2022). The
gut microbial metabolite formate exacerbates colorectal cancer progres-
sion. Nat. Metab. 4, 458–475.
116. Pati, S., Irfan, W., Jameel, A., Ahmed, S., and Shahid, R.K. (2023).
Obesity and Cancer: A Current Overview of Epidemiology, Pathogenesis,
Outcomes, and Management. Cancers 15, 485.
117. Schmitz, K.H., Campbell, A.M., Stuiver, M.M., Pinto, B.M., Schwartz,
A.L., Morris, G.S., Ligibel, J.A., Cheville, A., Galva˜o, D.A., Alfano, C.M.,
et al. (2019). Exercise is medicine in oncology: engaging clinicians to
help patients move through cancer. CA Cancer J. Clin. 69, 468–484.
118. McTiernan, A. (2008). Mechanisms linking physical activity with cancer.
Nat. Rev. Cancer 8, 205–211.
119. Moore, T.M., Lee, S., Olsen, T., Morselli, M., Strumwasser, A.R., Lin, A.J.,
Zhou, Z., Abrishami, A., Garcia, S.M., Bribiesca, J., et al. (2023).Cell 187, March 28, 2024 1611
ll
PerspectiveConserved multi-tissue transcriptomic adaptations to exercise training in
humans and mice. Cell Rep. 42, 112499.
120. Wei, W., Riley, N.M., Lyu, X., Shen, X., Guo, J., Raun, S.H., Zhao, M.,
Moya-Garzon, M.D., Basu, H., Sheng-Hwa Tung, A., et al. (2023). Organ-
ism-wide, cell-type-specific secretome mapping of exercise training in
mice. Cell Metab. 35, 1261–1279.e11.
121. Liberti, M.V., Dai, Z., Wardell, S.E., Baccile, J.A., Liu, X., Gao, X., Baldi,
R., Mehrmohamadi, M., Johnson, M.O., Madhukar, N.S., et al. (2017).
A Predictive Model for Selective Targeting of the Warburg Effect through
GAPDH Inhibition with a Natural Product. Cell Metab. 26, 648–659.e8.
122. Altea-Manzano, P., Cuadros, A.M., Broadfield, L.A., and Fendt, S.M.
(2020). Nutrient metabolism and cancer in the in vivo context: ametabolic
game of give and take. EMBO Rep. 21, e50635.
123. Tajan, M., and Vousden, K.H. (2020). Dietary Approaches to Cancer
Therapy. Cancer Cell 37, 767–785.
124. Locasale, J.W. (2022). Diet and Exercise in Cancer Metabolism. Cancer
Discov. 12, 2249–2257.
125. Di Biase, S., Lee, C., Brandhorst, S., Manes, B., Buono, R., Cheng, C.W.,
Cacciottolo, M., Martin-Montalvo, A., de Cabo, R., Wei, M., et al. (2016).
Fasting-Mimicking Diet Reduces HO-1 to Promote T Cell-Mediated Tu-
mor Cytotoxicity. Cancer Cell 30, 136–146.
126. Valdemarin, F., Caffa, I., Persia, A., Cremonini, A.L., Ferrando, L., Taglia-
fico, L., Tagliafico, A., Guijarro, A., Carbone, F., Ministrini, S., et al. (2021).
Safety and Feasibility of Fasting-Mimicking Diet and Effects on Nutri-
tional Status and CirculatingMetabolic and Inflammatory Factors in Can-
cer Patients Undergoing Active Treatment. Cancers 13, 4013.
127. Maddocks, O.D.K., Athineos, D., Cheung, E.C., Lee, P., Zhang, T., van
den Broek, N.J.F., Mackay, G.M., Labuschagne, C.F., Gay, D., Kruiswijk,
F., et al. (2017). Modulating the therapeutic response of tumours to die-
tary serine and glycine starvation. Nature 544, 372–376.
128. Gao, X., Sanderson, S.M., Dai, Z., Reid, M.A., Cooper, D.E., Lu, M., Ri-
chie, J.P., Ciccarella, A., Calcagnotto, A., Mikhael, P.G., et al. (2019). Di-
etary methionine influences therapy in mouse cancer models and alters
human metabolism. Nature 572, 397–401.
129. Lien, E.C., Westermark, A.M., Zhang, Y., Yuan, C., Li, Z., Lau, A.N., Sapp,
K.M., Wolpin, B.M., and Vander Heiden, M.G. (2021). Low glycaemic di-
ets alter lipid metabolism to influence tumour growth. Nature 599,
302–307.
130. Hopkins, B.D., Pauli, C., Du, X., Wang, D.G., Li, X., Wu, D., Amadiume,
S.C., Goncalves, M.D., Hodakoski, C., Lundquist, M.R., et al. (2018).
Suppression of insulin feedback enhances the efficacy of PI3K inhibitors.
Nature 560, 499–503.
131. Taylor, S.R., Falcone, J.N., Cantley, L.C., and Goncalves, M.D. (2022).
Developing dietary interventions as therapy for cancer. Nat. Rev. Cancer
22, 452–466.
132. Pariollaud, M., and Lamia, K.A. (2020). Cancer in the Fourth Dimension:
What Is the Impact of Circadian Disruption? Cancer Discov. 10,
1455–1464.
133. Zhang, R., Lahens, N.F., Ballance, H.I., Hughes, M.E., and Hogenesch,
J.B. (2014). A circadian gene expression atlas in mammals: implications
for biology and medicine. Proc. Natl. Acad. Sci. USA 111, 16219–16224.
134. Gotoh, T., Vila-Caballer, M., Santos, C.S., Liu, J., Yang, J., and Finkiel-
stein, C.V. (2014). The circadian factor Period 2 modulates p53 stability
and transcriptional activity in unstressed cells. Mol. Biol. Cell 25,
3081–3093.
135. Chan, A.B., Parico, G.C.G., Fribourgh, J.L., Ibrahim, L.H., Bollong, M.J.,
Partch, C.L., and Lamia, K.A. (2021). CRY2missensemutations suppress
P53 and enhance cell growth. Proc. Natl. Acad. Sci. USA 118,
e2101416118.
136. Huber, A.L., Papp, S.J., Chan, A.B., Henriksson, E., Jordan, S.D., Kriebs,
A., Nguyen, M., Wallace, M., Li, Z., Metallo, C.M., et al. (2016). CRY2 and
FBXL3 Cooperatively Degrade c-MYC. Mol. Cell 64, 774–789.1612 Cell 187, March 28, 2024137. Liu, Z., Selby, C.P., Yang, Y., Lindsey-Boltz, L.A., Cao, X., Eynullazada,
K., and Sancar, A. (2020). Circadian regulation of c-MYC in mice. Proc.
Natl. Acad. Sci. USA 117, 21609–21617.
138. Pariollaud, M., Ibrahim, L.H., Irizarry, E., Mello, R.M., Chan, A.B., Altman,
B.J., Shaw, R.J., Bollong, M.J., Wiseman, R.L., and Lamia, K.A. (2022).
Circadian disruption enhances HSF1 signaling and tumorigenesis in
Kras-driven lung cancer. Sci. Adv. 8, eabo1123.
139. Wang, C., Barnoud, C., Cenerenti, M., Sun, M., Caffa, I., Kizil, B., Bill, R.,
Liu, Y., Pick, R., Garnier, L., et al. (2023). Dendritic cells direct circadian
anti-tumour immune responses. Nature 614, 136–143.
140. Chun, S.K., Fortin, B.M., Fellows, R.C., Habowski, A.N., Verlande, A.,
Song, W.A., Mahieu, A.L., Lefebvre, A.E.Y.T., Sterrenberg, J.N., Velez,
L.M., et al. (2022). Disruption of the circadian clock drivesApc loss of het-
erozygosity to accelerate colorectal cancer. Sci. Adv. 8, eabo2389.
141. Qu, M., Zhang, G., Qu, H., Vu, A., Wu, R., Tsukamoto, H., Jia, Z., Huang,
W., Lenz, H.J., Rich, J.N., et al. (2023). Circadian regulator
BMAL1::CLOCK promotes cell proliferation in hepatocellular carcinoma
by controlling apoptosis and cell cycle. Proc. Natl. Acad. Sci. USA 120,
e2214829120.
142. Papagiannakopoulos, T., Bauer, M.R., Davidson, S.M., Heimann, M.,
Subbaraj, L., Bhutkar, A., Bartlebaugh, J., Vander Heiden, M.G., and
Jacks, T. (2016). Circadian Rhythm Disruption Promotes Lung Tumori-
genesis. Cell Metab. 24, 324–331.
143. Alasady, M.J., and Mendillo, M.L. (2020). The Multifaceted Role of HSF1
in Tumorigenesis. Adv. Exp. Med. Biol. 1243, 69–85.
144. Diamantopoulou, Z., Castro-Giner, F., Schwab, F.D., Foerster, C., Saini,
M., Budinjas, S., Strittmatter, K., Krol, I., Seifert, B., Heinzelmann-
Schwarz, V., et al. (2022). The metastatic spread of breast cancer accel-
erates during sleep. Nature 607, 156–162.
145. Verlande, A., Chun, S.K., Goodson, M.O., Fortin, B.M., Bae, H., Jang, C.,
and Masri, S. (2021). Glucagon regulates the stability of REV-ERBa to
modulate hepatic glucose production in a model of lung cancer-associ-
ated cachexia. Sci. Adv. 7, eabf3885.
146. Innominato, P.F., Ballesta, A., Huang, Q., Focan, C., Chollet, P., Kara-
boue´, A., Giacchetti, S., Bouchahda, M., Adam, R., Garufi, C., et al.
(2020). Sex-dependent least toxic timing of irinotecan combined with
chronomodulated chemotherapy for metastatic colorectal cancer: Ran-
domized multicenter EORTC 05011 trial. Cancer Med. 9, 4148–4159.
147. Qian, D.C., Kleber, T., Brammer, B., Xu, K.M., Switchenko, J.M., Jano-
paul-Naylor, J.R., Zhong, J., Yushak, M.L., Harvey, R.D., Paulos, C.M.,
et al. (2021). Effect of immunotherapy time-of-day infusion on overall sur-
vival among patients with advanced melanoma in the USA (MEMOIR): a
propensity score-matched analysis of a single-centre, longitudinal study.
Lancet Oncol. 22, 1777–1786.
148. Winkler, F., Venkatesh, H.S., Amit, M., Batchelor, T., Demir, I.E., Deneen,
B., Gutmann, D.H., Hervey-Jumper, S., Kuner, T., Mabbott, D., et al.
(2023). Cancer neuroscience: state of the field, emerging directions.
Cell 186, 1689–1707.
149. Osswald, M., Jung, E., Sahm, F., Solecki, G., Venkataramani, V., Blaes,
J., Weil, S., Horstmann, H., Wiestler, B., Syed, M., et al. (2015). Brain
tumour cells interconnect to a functional and resistant network. Nature
528, 93–98.
150. Venkataramani, V., Tanev, D.I., Strahle, C., Studier-Fischer, A., Fank-
hauser, L., Kessler, T., Ko¨rber, C., Kardorff, M., Ratliff, M., Xie, R., et al.
(2019). Glutamatergic synaptic input to glioma cells drives brain tumour
progression. Nature 573, 532–538.
151. Hausmann, D., Hoffmann, D.C., Venkataramani, V., Jung, E., Horschitz,
S., Tetzlaff, S.K., Jabali, A., Hai, L., Kessler, T., Azorin, D.D., et al.
(2023). Autonomous rhythmic activity in glioma networks drives brain
tumour growth. Nature 613, 179–186.
152. Venkataramani, V., Yang, Y., Schubert, M.C., Reyhan, E., Tetzlaff, S.K.,
Wißmann, N., Botz, M., Soyka, S.J., Beretta, C.A., Pramatarov, R.L.,
ll
Perspectiveet al. (2022). Glioblastoma hijacks neuronal mechanisms for brain inva-
sion. Cell 185, 2899–2917.e31.
153. Krishna, S., Choudhury, A., Keough, M.B., Seo, K., Ni, L., Kakaizada, S.,
Lee, A., Aabedi, A., Popova, G., Lipkin, B., et al. (2023). Glioblastoma re-
modelling of human neural circuits decreases survival. Nature 617,
599–607.
154. Haroun, R., Wood, J.N., and Sikandar, S. (2022). Mechanisms of cancer
pain. Front. Pain Res. (Lausanne) 3, 1030899.
155. Zhang, C., Xie, C., and Lu, Y. (2021). Local Anesthetic Lidocaine andCan-
cer: Insight Into Tumor Progression and Recurrence. Front. Oncol. 11,
669746.
156. Mullard, A. (2022). Vertex’s Nav1.8 inhibitor passes phase II pain point.
Nat. Rev. Drug Discov. 21, 327.
157. Heuer, S., Burghaus, I., Gose, M., Kessler, T., Sahm, F., Vollmuth, P.,
Venkataramani, V., Hoffmann, D., Schlesner, M., Ratliff, M., et al.
(2024). PerSurge (NOA-30) phase II trial of perampanel treatment
around surgery in patients with progressive glioblastoma. BMC Cancer
24, 135. PMID: 38279087; PMCID: PMC10811925. https://doi.org/10.
1186/s12885-024-11846-1.
158. Britt, K.L., Cuzick, J., and Phillips, K.A. (2020). Key steps for effective
breast cancer prevention. Nat. Rev. Cancer 20, 417–436.
159. Collaborative Group on Hormonal Factors in Breast Cancer (2002).
Breast cancer and breastfeeding: collaborative reanalysis of individual
data from 47 epidemiological studies in 30 countries, including 50302
women with breast cancer and 96973 women without the disease. Lan-
cet 360, 187–195.
160. Islami, F., Liu, Y., Jemal, A., Zhou, J., Weiderpass, E., Colditz, G., Bof-
fetta, P., and Weiss, M. (2015). Breastfeeding and breast cancer risk by
receptor status–a systematic review and meta-analysis. Ann. Oncol.
26, 2398–2407.
161. Loi, S., Drubay, D., Adams, S., Pruneri, G., Francis, P.A., Lacroix-Triki,
M., Joensuu, H., Dieci, M.V., Badve, S., Demaria, S., et al. (2019). Tu-
mor-Infiltrating Lymphocytes and Prognosis: A Pooled Individual Patient
Analysis of Early-Stage Triple-Negative Breast Cancers. J. Clin. Oncol.
37, 559–569.
162. Savas, P., Virassamy, B., Ye, C., Salim, A., Mintoff, C.P., Caramia, F., Sal-
gado, R., Byrne, D.J., Teo, Z.L., Dushyanthen, S., et al. (2018). Single-cell
profiling of breast cancer T cells reveals a tissue-resident memory subset
associated with improved prognosis. Nat. Med. 24, 986–993.
163. Virassamy, B., Caramia, F., Savas, P., Sant, S., Wang, J., Christo, S.N.,
Byrne, A., Clarke, K., Brown, E., Teo, Z.L., et al. (2023). Intratumoral
CD8+ T cells with a tissue-resident memory phenotype mediate local im-
munity and immune checkpoint responses in breast cancer. Cancer Cell
41, 585–601.e8.
164. Pal, B., Chen, Y., Vaillant, F., Capaldo, B.D., Joyce, R., Song, X., Bryant,
V.L., Penington, J.S., Di Stefano, L., Tubau Ribera, N., et al. (2021). A sin-
gle-cell RNA expression atlas of normal, preneoplastic and tumorigenic
states in the human breast. EMBO J. 40, e107333.
165. Asztalos, S., Gann, P.H., Hayes, M.K., Nonn, L., Beam, C.A., Dai, Y., Wi-
ley, E.L., and Tonetti, D.A. (2010). Gene expression patterns in the human
breast after pregnancy. Cancer Prev. Res. (Phila) 3, 301–311.
166. Koren, O., Goodrich, J.K., Cullender, T.C., Spor, A., Laitinen, K.,
Ba¨ckhed, H.K., Gonzalez, A., Werner, J.J., Angenent, L.T., Knight, R.,
et al. (2012). Host remodeling of the gut microbiome and metabolic
changes during pregnancy. Cell 150, 470–480.
167. Hunt, K.M., Foster, J.A., Forney, L.J., Schu¨tte, U.M., Beck, D.L., Abdo,
Z., Fox, L.K., Williams, J.E., McGuire, M.K., and McGuire, M.A. (2011).
Characterization of the diversity and temporal stability of bacterial com-
munities in human milk. PLoS One 6, e21313.
168. Cabrera-Rubio, R., Collado, M.C., Laitinen, K., Salminen, S., Isolauri, E.,
and Mira, A. (2012). The human milk microbiome changes over lactation
and is shaped bymaternal weight andmode of delivery. Am. J. Clin. Nutr.
96, 544–551.169. Camacho-Morales, A., Caba, M., Garcı´a-Jua´rez, M., Caba-Flores, M.D.,
Viveros-Contreras, R., and Martı´nez-Valenzuela, C. (2021). Breastfeed-
ing Contributes to Physiological Immune Programming in the Newborn.
Front. Pediatr. 9, 744104.
170. Khorana, A.A., Francis, C.W., Culakova, E., Kuderer, N.M., and Lyman,
G.H. (2007). Thromboembolism is a leading cause of death in cancer pa-
tients receiving outpatient chemotherapy. J. Thromb. Haemost. 5,
632–634.
171. Khorana, A.A., Francis, C.W., Culakova, E., Fisher, R.I., Kuderer, N.M.,
and Lyman, G.H. (2006). Thromboembolism in hospitalized neutropenic
cancer patients. J. Clin. Oncol. 24, 484–490.
172. Mulder, F.I., Horva´th-Puho´, E., van Es, N., van Laarhoven, H.W.M., Ped-
ersen, L., Moik, F., Ay, C., Bu¨ller, H.R., and Sørensen, H.T. (2021). Venous
thromboembolism in cancer patients: a population-based cohort study.
Blood 137, 1959–1969.
173. Engelmann, B., andMassberg, S. (2013). Thrombosis as an intravascular
effector of innate immunity. Nat. Rev. Immunol. 13, 34–45.
174. Fuchs, T.A., Brill, A., Duerschmied, D., Schatzberg, D., Monestier, M.,
Myers, D.D., Wrobleski, S.K., Wakefield, T.W., Hartwig, J.H., and Wag-
ner, D.D. (2010). Extracellular DNA traps promote thrombosis. Proc.
Natl. Acad. Sci. USA 107, 15880–15885.
175. Kasthuri, R.S., Taubman, M.B., and Mackman, N. (2009). Role of tissue
factor in cancer. J. Clin. Oncol. 27, 4834–4838.
176. Anvari, S., Osei, E., and Maftoon, N. (2021). Interactions of platelets with
circulating tumor cells contribute to cancer metastasis. Sci. Rep.
11, 15477.
177. Tawil, N., Bassawon, R., Meehan, B., Nehme, A., Montermini, L., Gay-
den, T., De Jay, N., Spinelli, C., Chennakrishnaiah, S., Choi, D., et al.
(2021). Glioblastoma cell populations with distinct oncogenic programs
release podoplanin as procoagulant extracellular vesicles. Blood Adv.
5, 1682–1694.
178. Xue, R., Zhang, Q., Cao, Q., Kong, R., Xiang, X., Liu, H., Feng, M., Wang,
F., Cheng, J., Li, Z., et al. (2022). Liver tumour immune microenvironment
subtypes and neutrophil heterogeneity. Nature 612, 141–147.
179. Ballesteros, I., Rubio-Ponce, A., Genua, M., Lusito, E., Kwok, I., Ferna´n-
dez-Calvo, G., Khoyratty, T.E., van Grinsven, E., Gonza´lez-Herna´ndez,
S., Nicola´s-A´vila, J.A´., et al. (2020). Co-option of Neutrophil Fates by Tis-
sue Environments. Cell 183, 1282–1297.e18.
180. Maas, R.R., Soukup, K., Fournier, N., Massara, M., Galland, S., Kornete,
M., Wischnewski, V., Lourenco, J., Croci, D., A´lvarez-Prado, A´.F., et al.
(2023). The local microenvironment drives activation of neutrophils in hu-
man brain tumors. Cell 186, 4546–4566.e27.
181. Ponzetta, A., Carriero, R., Carnevale, S., Barbagallo, M., Molgora, M.,
Perucchini, C., Magrini, E., Gianni, F., Kunderfranco, P., Polentarutti,
N., et al. (2019). Neutrophils Driving Unconventional T Cells Mediate
Resistance against Murine Sarcomas and Selected Human Tumors.
Cell 178, 346–360.e24.
182. Cui, C., Chakraborty, K., Tang, X.A., Zhou, G., Schoenfelt, K.Q., Becker,
K.M., Hoffman, A., Chang, Y.F., Blank, A., Reardon, C.A., et al. (2021).
Neutrophil elastase selectively kills cancer cells and attenuates tumori-
genesis. Cell 184, 3163–3177.e21.
183. Adrover, J.M., McDowell, S.A.C., He, X.Y., Quail, D.F., and Egeblad, M.
(2023). NETworking with cancer: The bidirectional interplay between can-
cer and neutrophil extracellular traps. Cancer Cell 41, 505–526.
184. Khorana, A.A., Kuderer, N.M., Culakova, E., Lyman, G.H., and Francis,
C.W. (2008). Development and validation of a predictive model for
chemotherapy-associated thrombosis. Blood 111, 4902–4907.
185. Wolach, O., Sellar, R.S., Martinod, K., Cherpokova, D., McConkey, M.,
Chappell, R.J., Silver, A.J., Adams, D., Castellano, C.A., Schneider,
R.K., et al. (2018). Increased neutrophil extracellular trap formation pro-
motes thrombosis in myeloproliferative neoplasms. Sci. Transl. Med.
10, eaan8292.Cell 187, March 28, 2024 1613
ll
Perspective186. Belizaire, R., Wong, W.J., Robinette, M.L., and Ebert, B.L. (2023). Clonal
haematopoiesis and dysregulation of the immune system. Nat. Rev. Im-
munol. 23, 595–610.
187. Kar, S.P., Quiros, P.M., Gu, M., Jiang, T., Mitchell, J., Langdon, R., Iyer,
V., Barcena, C., Vijayabaskar, M.S., Fabre, M.A., et al. (2022). Genome-
wide analyses of 200,453 individuals yield new insights into the causes
and consequences of clonal hematopoiesis. Nat. Genet. 54, 1155–1166.
188. Bolton, K.L., Ptashkin, R.N., Gao, T., Braunstein, L., Devlin, S.M., Kelly,
D., Patel, M., Berthon, A., Syed, A., Yabe, M., et al. (2020). Cancer ther-
apy shapes the fitness landscape of clonal hematopoiesis. Nat. Genet.
52, 1219–1226.
189. Coombs, C.C., Zehir, A., Devlin, S.M., Kishtagari, A., Syed, A., Jonsson,
P., Hyman, D.M., Solit, D.B., Robson, M.E., Baselga, J., et al. (2017).
Therapy-Related Clonal Hematopoiesis in Patients with Non-hematolog-
ic Cancers Is Common and Associated with Adverse Clinical Outcomes.
Cell Stem Cell 21, 374–382.e4.
190. Lee, M., Li, J., Li, J., Fang, S., Zhang, J., Vo, A.T.T., Han, W., Zeng, H.,
Isgandarova, S., Martinez-Moczygemba, M., et al. (2021). Tet2 Inactiva-
tion Enhances the Antitumor Activity of Tumor-Infiltrating Lymphocytes.
Cancer Res. 81, 1965–1976.
191. Budden, K.F., Shukla, S.D., Rehman, S.F., Bowerman, K.L., Keely, S.,
Hugenholtz, P., Armstrong-James, D.P.H., Adcock, I.M., Chotirmall,
S.H., Chung, K.F., et al. (2019). Functional effects of the microbiota in
chronic respiratory disease. Lancet Respir. Med. 7, 907–920.
192. Sepich-Poore, G.D., Zitvogel, L., Straussman, R., Hasty, J., Wargo, J.A.,
and Knight, R. (2021). The microbiome and human cancer. Science 371,
eabc4552.
193. Wong, S.H., and Yu, J. (2019). Gutmicrobiota in colorectal cancer: mech-
anisms of action and clinical applications. Nat. Rev. Gastroenterol. Hep-
atol. 16, 690–704.
194. Baruch, E.N., Wang, J., and Wargo, J.A. (2021). Gut Microbiota and Anti-
tumor Immunity: Potential Mechanisms for Clinical Effect. Cancer Immu-
nol. Res. 9, 365–370.
195. Routy, B., Le Chatelier, E., Derosa, L., Duong, C.P.M., Alou, M.T., Dail-
le`re, R., Fluckiger, A., Messaoudene, M., Rauber, C., Roberti, M.P.,
et al. (2018). Gut microbiome influences efficacy of PD-1-based immuno-
therapy against epithelial tumors. Science 359, 91–97.
196. McCulloch, J.A., Davar, D., Rodrigues, R.R., Badger, J.H., Fang, J.R.,
Cole, A.M., Balaji, A.K., Vetizou, M., Prescott, S.M., Fernandes, M.R.,
et al. (2022). Intestinal microbiota signatures of clinical response and im-
mune-related adverse events in melanoma patients treated with anti-PD-
1. Nat. Med. 28, 545–556.
197. Spencer, C.N., McQuade, J.L., Gopalakrishnan, V., McCulloch, J.A., Ve-
tizou, M., Cogdill, A.P., Khan, M.A.W., Zhang, X., White, M.G., Peterson,
C.B., et al. (2021). Dietary fiber and probiotics influence the gut
microbiome and melanoma immunotherapy response. Science 374,
1632–1640.
198. Lam, K.C., Araya, R.E., Huang, A., Chen, Q., Di Modica, M., Rodrigues,
R.R., Lope`s, A., Johnson, S.B., Schwarz, B., Bohrnsen, E., et al. (2021).
Microbiota triggers STING-type I IFN-dependent monocyte reprogram-
ming of the tumor microenvironment. Cell 184, 5338–5356.e21.
199. Simpson, R.C., Shanahan, E.R., Batten, M., Reijers, I.L.M., Read, M.,
Silva, I.P., Versluis, J.M., Ribeiro, R., Angelatos, A.S., Tan, J., et al.
(2022). Diet-driven microbial ecology underpins associations between
cancer immunotherapy outcomes and the gut microbiome. Nat. Med.
28, 2344–2352.
200. Kostic, A.D., Gevers, D., Pedamallu, C.S., Michaud, M., Duke, F., Earl,
A.M., Ojesina, A.I., Jung, J., Bass, A.J., Tabernero, J., et al. (2012).
Genomic analysis identifies association of Fusobacterium with colorectal
carcinoma. Genome Res. 22, 292–298.
201. Bullman, S., Pedamallu, C.S., Sicinska, E., Clancy, T.E., Zhang, X., Cai,
D., Neuberg, D., Huang, K., Guevara, F., Nelson, T., et al. (2017). Analysis1614 Cell 187, March 28, 2024of Fusobacterium persistence and antibiotic response in colorectal can-
cer. Science 358, 1443–1448.
202. Geller, L.T., Barzily-Rokni, M., Danino, T., Jonas, O.H., Shental, N., Nej-
man, D., Gavert, N., Zwang, Y., Cooper, Z.A., Shee, K., et al. (2017). Po-
tential role of intratumor bacteria in mediating tumor resistance to the
chemotherapeutic drug gemcitabine. Science 357, 1156–1160.
203. Riquelme, E., Zhang, Y., Zhang, L., Montiel, M., Zoltan, M., Dong, W.,
Quesada, P., Sahin, I., Chandra, V., San Lucas, A., et al. (2019). Tumor
MicrobiomeDiversity andComposition Influence Pancreatic Cancer Out-
comes. Cell 178, 795–806.e12.
204. Galeano Nin˜o, J.L., Wu, H., LaCourse, K.D., Kempchinsky, A.G., Bar-
yiames, A., Barber, B., Futran, N., Houlton, J., Sather, C., Sicinska, E.,
et al. (2022). Effect of the intratumoral microbiota on spatial and cellular
heterogeneity in cancer. Nature 611, 810–817.
205. Serna, G., Ruiz-Pace, F., Hernando, J., Alonso, L., Fasani, R., Landolfi,
S., Comas, R., Jimenez, J., Elez, E., Bullman, S., et al. (2020). Fusobac-
terium nucleatum persistence and risk of recurrence after preoperative
treatment in locally advanced rectal cancer. Ann. Oncol. 31, 1366–1375.
206. Nejman, D., Livyatan, I., Fuks, G., Gavert, N., Zwang, Y., Geller, L.T.,
Rotter-Maskowitz, A., Weiser, R., Mallel, G., Gigi, E., et al. (2020). The hu-
man tumor microbiome is composed of tumor type-specific intracellular
bacteria. Science 368, 973–980.
207. Poore, G.D., Kopylova, E., Zhu, Q., Carpenter, C., Fraraccio, S., Wandro,
S., Kosciolek, T., Janssen, S., Metcalf, J., Song, S.J., et al. (2020). Micro-
biome analyses of blood and tissues suggest cancer diagnostic
approach. Nature 579, 567–574.
208. Narunsky-Haziza, L., Sepich-Poore, G.D., Livyatan, I., Asraf, O., Martino,
C., Nejman, D., Gavert, N., Stajich, J.E., Amit, G., Gonza´lez, A., et al.
(2022). Pan-cancer analyses reveal cancer-type-specific fungal ecolo-
gies and bacteriome interactions. Cell 185, 3789–3806.e17.
209. Dohlman, A.B., Klug, J., Mesko, M., Gao, I.H., Lipkin, S.M., Shen, X., and
Iliev, I.D. (2022). A pan-cancer mycobiome analysis reveals fungal
involvement in gastrointestinal and lung tumors. Cell 185, 3807–
3822.e12.
210. Gihawi, A., Ge, Y., Lu, J., Puiu, D., Xu, A., Cooper, C.S., Brewer, D.S.,
Pertea, M., and Salzberg, S.L. (2023). Major data analysis errors invali-
date cancer microbiome findings. mBio 14, e0160723.
211. Gihawi, A., Cooper, C.S., and Brewer, D.S. (2023). Caution regarding the
specificities of pan-cancer microbial structure. Microb. Genom. 9,
mgen001088.
212. Aykut, B., Pushalkar, S., Chen, R., Li, Q., Abengozar, R., Kim, J.I., Shad-
aloey, S.A., Wu, D., Preiss, P., Verma, N., et al. (2019). The fungal myco-
biome promotes pancreatic oncogenesis via activation of MBL. Nature
574, 264–267.
213. Fletcher, A.A., Kelly, M.S., Eckhoff, A.M., and Allen, P.J. (2023). Revisiting
the intrinsic mycobiome in pancreatic cancer. Nature 620, E1–E6.
214. Xu, F., Saxena, D., Pushalkar, S., and Miller, G. (2023). Reply to: Revisit-
ing the intrinsic mycobiome in pancreatic cancer. Nature 620, E7–E9.
215. Dejea, C.M., Fathi, P., Craig, J.M., Boleij, A., Taddese, R., Geis, A.L., Wu,
X., DeStefano Shields, C.E., Hechenbleikner, E.M., Huso, D.L., et al.
(2018). Patients with familial adenomatous polyposis harbor colonic bio-
films containing tumorigenic bacteria. Science 359, 592–597.
216. Baruch, E.N., Youngster, I., Ben-Betzalel, G., Ortenberg, R., Lahat, A.,
Katz, L., Adler, K., Dick-Necula, D., Raskin, S., Bloch, N., et al. (2021).
Fecal microbiota transplant promotes response in immunotherapy-re-
fractory melanoma patients. Science 371, 602–609.
217. Davar, D., Dzutsev, A.K., McCulloch, J.A., Rodrigues, R.R., Chauvin,
J.M., Morrison, R.M., Deblasio, R.N., Menna, C., Ding, Q., Pagliano, O.,
et al. (2021). Fecal microbiota transplant overcomes resistance to anti-
PD-1 therapy in melanoma patients. Science 371, 595–602.
218. Dizman, N., Meza, L., Bergerot, P., Alcantara, M., Dorff, T., Lyou, Y.,
Frankel, P., Cui, Y., Mira, V., Llamas, M., et al. (2022). Nivolumab plus
ll
Perspectiveipilimumab with or without live bacterial supplementation in metastatic
renal cell carcinoma: a randomized phase 1 trial. Nat. Med. 28, 704–712.
219. Holt, R.A. (2023). Oncomicrobial vaccines: The potential for a Fusobac-
terium nucleatum vaccine to improve colorectal cancer outcomes. Cell
Host Microbe 31, 141–145.
220. Maertens, O., McCurrach, M.E., Braun, B.S., De Raedt, T., Epstein, I.,
Huang, T.Q., Lauchle, J.O., Lee, H., Wu, J., Cripe, T.P., et al. (2017). A
Collaborative Model for Accelerating the Discovery and Translation of
Cancer Therapies. Cancer Res. 77, 5706–5711.
221. Olson, B., Li, Y., Lin, Y., Liu, E.T., and Patnaik, A. (2018). Mouse Models
for Cancer Immunotherapy Research. Cancer Discov. 8, 1358–1365.
222. Drapkin, B.J., and Rudin, C.M. (2021). Advances in Small-Cell Lung Can-
cer (SCLC) Translational Research. Cold Spring Harb. Perspect. Med. 11,
a038240.
223. Nadeau, J.H., and Auwerx, J. (2019). The virtuous cycle of human ge-
netics and mouse models in drug discovery. Nat. Rev. Drug Discov.
18, 255–272.
224. Churchill, G.A., Airey, D.C., Allayee, H., Angel, J.M., Attie, A.D., Beatty, J.,
Beavis, W.D., Belknap, J.K., Bennett, B., Berrettini, W., et al. (2004). The
Collaborative Cross, a community resource for the genetic analysis of
complex traits. Nat. Genet. 36, 1133–1137.
225. Ashbrook, D.G., Arends, D., Prins, P., Mulligan, M.K., Roy, S., Williams,
E.G., Lutz, C.M., Valenzuela, A., Bohl, C.J., Ingels, J.F., et al. (2021). A
platform for experimental precision medicine: The extended BXD mouse
family. Cell Syst. 12, 235–247.e9.
226. Churchill, G.A., Gatti, D.M., Munger, S.C., and Svenson, K.L. (2012). The
Diversity Outbred mouse population. Mamm. Genome 23, 713–718.
227. Halliwill, K.D., Quigley, D.A., Kang, H.C., Del Rosario, R., Ginzinger, D.,
and Balmain, A. (2016). Panx3 links body mass index and tumorigenesis
in a genetically heterogeneousmousemodel of carcinogen-induced can-
cer. Genome Med. 8, 83.
228. Ootani, A., Li, X., Sangiorgi, E., Ho, Q.T., Ueno, H., Toda, S., Sugihara, H.,
Fujimoto, K., Weissman, I.L., Capecchi, M.R., et al. (2009). Sustained
in vitro intestinal epithelial culture within a Wnt-dependent stem cell
niche. Nat. Med. 15, 701–706.
229. Pastu1a, A., Middelhoff, M., Brandtner, A., Tobiasch, M., Ho¨hl, B., Nuber,
A.H., Demir, I.E., Neupert, S., Kollmann, P., Mazzuoli-Weber, G., et al.
(2016). Three-Dimensional Gastrointestinal Organoid Culture in Combi-
nation with Nerves or Fibroblasts: A Method to Characterize the Gastro-
intestinal Stem Cell Niche. Stem Cells Int. 2016, 3710836.
230. Yan, H.H.N., Chan, A.S., Lai, F.P.-L., and Leung, S.Y. (2023). Organoid
cultures for cancer modeling. Cell Stem Cell 30, 917–937.
231. Lee, S.H., Hu, W., Matulay, J.T., Silva, M.V., Owczarek, T.B., Kim, K.,
Chua, C.W., Barlow, L.J., Kandoth, C., Williams, A.B., et al. (2018). Tumor
Evolution and Drug Response in Patient-Derived Organoid Models of
Bladder Cancer. Cell 173, 515–528.e17.
232. Li, X., Francies, H.E., Secrier, M., Perner, J., Miremadi, A., Galeano-Dal-
mau, N., Barendt, W.J., Letchford, L., Leyden, G.M., Goffin, E.K., et al.
(2018). Organoid cultures recapitulate esophageal adenocarcinoma het-
erogeneity providing a model for clonality studies and precision thera-
peutics. Nat. Commun. 9, 2983.
233. Dijkstra, K.K., Cattaneo, C.M., Weeber, F., Chalabi, M., van de Haar, J.,
Fanchi, L.F., Slagter, M., van der Velden, D.L., Kaing, S., Kelderman, S.,
et al. (2018). Generation of Tumor-Reactive TCells byCo-cultureof Periph-
eral Blood Lymphocytes and Tumor Organoids. Cell 174, 1586–1598.e12.
234. LeSavage, B.L., Suhar, R.A., Broguiere, N., Lutolf, M.P., and Heilshorn,
S.C. (2022). Next-generation cancer organoids. Nat. Mater. 21, 143–159.
235. Ingber, D.E. (2018). Developmentally inspired human ‘‘organs on chips.’’.
Development 145, dev156125.
236. Sontheimer-Phelps, A., Hassell, B.A., and Ingber, D.E. (2019). Modelling
cancer in microfluidic human organs-on-chips. Nat. Rev. Cancer 19,
65–81.237. Ma, C., Peng, Y., Li, H., and Chen, W. (2021). Organ-on-a-Chip: A New
Paradigm for Drug Development. Trends Pharmacol. Sci. 42, 119–133.
238. Powley, I.R., Patel, M., Miles, G., Pringle, H., Howells, L., Thomas, A.,
Kettleborough, C., Bryans, J., Hammonds, T., MacFarlane, M., et al.
(2020). Patient-derived explants (PDEs) as a powerful preclinical platform
for anti-cancer drug and biomarker discovery. Br. J. Cancer 122,
735–744.
239. Chiorazzi, M., Martinek, J., Krasnick, B., Zheng, Y., Robbins, K.J., Qu, R.,
Kaufmann, G., Skidmore, Z., Juric, M., Henze, L.A., et al. (2023). Autolo-
gous humanized PDX modeling for immuno-oncology recapitulates fea-
tures of the human tumor microenvironment. J. Immunother. Cancer 11,
e006921.
240. Thommen, D.S. (2022). Tumour avatars to model patients’ responses to
immunotherapy. Nat. Rev. Cancer 22, 660.
241. Roelofsen, L.M., Voabil, P., de Bruijn, M., Herzig, P., Zippelius, A., Schu-
macher, T.N., and Thommen, D.S. (2023). Protocol for ex vivo culture of
patient-derived tumor fragments. Star Protoc. 4, 102282.
242. Voabil, P., de Bruijn, M., Roelofsen, L.M., Hendriks, S.H., Brokamp, S.,
van den Braber, M., Broeks, A., Sanders, J., Herzig, P., Zippelius, A.,
et al. (2021). An ex vivo tumor fragment platform to dissect response to
PD-1 blockade in cancer. Nat. Med. 27, 1250–1261.
243. Dias, M.H., and Bernards, R. (2021). Playing cancer at its own game: acti-
vating mitogenic signaling as a paradoxical intervention. Mol. Oncol. 15,
1975–1985.
244. Dias, M.H., Friskes, A., Wang, S., Fernandes Neto, J.M., van Gemert, F.,
Mourragui, S., Kuiken, H.J., Mainardi, S., Alvarez-Villanueva, D., Lieftink,
C., et al. (2023). Paradoxical activation of oncogenic signaling as a cancer
treatment strategy. Preprint at bioRxiv. https://doi.org/10.1101/2023.02.
06.527335.
245. van Neerven, S.M., de Groot, N.E., Nijman, L.E., Scicluna, B.P., van Driel,
M.S., Lecca, M.C., Warmerdam, D.O., Kakkar, V., Moreno, L.F., Vieira
Braga, F.A., et al. (2021). Apc-mutant cells act as supercompetitors in in-
testinal tumour initiation. Nature 594, 436–441.
246. Ge, W., and Jakobsson, E. (2019). Systems Biology Understanding of the
Effects of Lithium on Cancer. Front. Oncol. 9, 296.
247. Bardia, A., and Baselga, J. (2013). Neoadjuvant therapy as a platform for
drug development and approval in breast cancer. Clin. Cancer Res. 19,
6360–6370.
248. Marron, T.U., Galsky, M.D., Taouli, B., Fiel, M.I., Ward, S., Kim, E., Yan-
kelevitz, D., Doroshow, D., Guttman-Yassky, E., Ungar, B., et al. (2022).
Neoadjuvant clinical trials provide a window of opportunity for cancer
drug discovery. Nat. Med. 28, 626–629.
249. Reynolds, A.R., Moschetta, M., Yohannes, A.R., Walcott, F., Ashford, M.,
Szucs, Z., Sarbajna, T., Hadfield, J., Harrison, E., Challis, B.G., et al.
(2023). A View on Drug Development for Cancer Prevention. Cancer Dis-
cov. 13, 1058–1083.
250. Inan, O.T., Tenaerts, P., Prindiville, S.A., Reynolds, H.R., Dizon, D.S.,
Cooper-Arnold, K., Turakhia, M., Pletcher, M.J., Preston, K.L., Krumholz,
H.M., et al. (2020). Digitizing clinical trials. NPJ Digit. Med. 3, 101.
251. Alkhayyat, M., Kumar, P., Sanaka, K.O., and Thota, P.N. (2021). Chemo-
prevention in Barrett’s esophagus and esophageal adenocarcinoma.
Therap. Adv. Gastroenterol. 14, 17562848211033730.
252. Gale, D., Heider, K., Ruiz-Valdepenas, A., Hackinger, S., Perry, M., Mar-
sico, G., Rundell, V., Wulff, J., Sharma, G., Knock, H., et al. (2022). Resid-
ual ctDNA after treatment predicts early relapse in patients with early-
stage non-small cell lung cancer. Ann. Oncol. 33, 500–510.
253. Abbosh, C., Frankell, A.M., Harrison, T., Kisistok, J., Garnett, A., John-
son, L., Veeriah, S., Moreau, M., Chesh, A., Chaunzwa, T.L., et al.
(2023). Tracking early lung cancer metastatic dissemination in
TRACERx using ctDNA. Nature 616, 553–562.
254. Powles, T., Assaf, Z.J., Davarpanah, N., Banchereau, R., Szabados, B.E.,
Yuen, K.C., Grivas, P., Hussain, M., Oudard, S., Gschwend, J.E., et al.Cell 187, March 28, 2024 1615
ll
Perspective(2021). ctDNA guiding adjuvant immunotherapy in urothelial carcinoma.
Nature 595, 432–437.
255. Lipsyc-Sharf, M., de Bruin, E.C., Santos, K., McEwen, R., Stetson, D.,
Patel, A., Kirkner, G.J., Hughes, M.E., Tolaney, S.M., Partridge, A.H.,
et al. (2022). Circulating Tumor DNA and Late Recurrence in High-Risk
Hormone Receptor-Positive, Human Epidermal Growth Factor Receptor
2-Negative Breast cancer. J. Clin. Oncol. 40, 2408–2419.
256. Tie, J., Cohen, J.D., Lahouel, K., Lo, S.N., Wang, Y., Kosmider, S., Wong,
R., Shapiro, J., Lee, M., Harris, S., et al. (2022). Circulating Tumor DNA
Analysis Guiding Adjuvant Therapy in Stage II Colon Cancer. N. Engl. J.
Med. 386, 2261–2272.1616 Cell 187, March 28, 2024257. Andre´, F. (2018). Developing Anticancer Drugs in Orphan Molecular En-
tities - A Paradigm under Construction. N. Engl. J. Med. 378, 763–765.
258. Andre, F., Filleron, T., Kamal, M., Mosele, F., Arnedos, M., Dalenc, F., Sa-
blin, M.P., Campone, M., Bonnefoi, H., Lefeuvre-Plesse, C., et al. (2022).
Genomics to select treatment for patients with metastatic breast cancer.
Nature 610, 343–348.
259. Koh, B., Tan, D.J.H., Ng, C.H., Fu, C.E., Lim, W.H., Zeng, R.W., Yong,
J.N., Koh, J.H., Syn, N., Meng, W., et al. (2023). Patterns in Cancer Inci-
dence Among People Younger Than 50 Years in the US, 2010 to 2019.
JAMA Netw. Open 6, e2328171.