Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery.

Significance:

Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.

Tumorigenesis and malignant progression embody a Darwinian process of dynamic evolution, involving selective pressures encountered by proliferatively expansive “outlaw cells” facing barriers and limitations intended to preserve tissue homeostasis and prevent the emergence of inappropriate phenotypes. The stepwise evolution that ultimately circumvents these protective mechanisms is apparent in the epidemiology and histopathology characteristics of human cancer, and in the realization that distinctive capabilities dubbed the hallmarks of cancer (1, 2) underlay the process of tumor progression. The appearance of such hallmarks can be seen as the result of an evolutionary process: Tumor phenotypes that can be considered as “enabling characteristics,” such as genomic instability or epigenetic reprogramming, facilitate tumor cell diversification and the Darwinian selection of the set of hallmark traits instrumental for tumorigenesis and malignant progression. At the DNA level, genome instability leads to driver (and passenger) mutations, here intended as changes in the DNA sequence, that convey functional capabilities that are fundamental to the disease. Beyond mutations altering the activities of classic oncogenes and tumor suppressors, mutations in chromatin remodeling factors mechanistically contribute to tumor evolution by broadly affecting gene expression, contributing to the variation among which hallmark-enabling changes can be phenotypically selected during tumor evolution. Even in the absence of mutated chromatin remodeling factors, nonmutational epigenetic reprogramming can diversify the cancer cell populations (3). We should note that whereas epigenetic alterations broadly encompass any heritable molecular alteration that leads to a phenotypic change without modifying the DNA sequence, in this review, the term will largely refer to chromatin-based changes including modifications of DNA methylation, histone posttranslational modifications, and chromatin accessibility, which are directly associated with altered transcriptional programs. Cellular plasticity and epigenetic reprogramming guide the ordered phenotypical events underlying embryogenesis, organogenesis, mammalian development, and tissue homeostasis. As such, these processes are expected to play a role in cancer, by promoting lineage plasticity and adaptive responses to external stimuli. Nongenetic triggers of cellular plasticity can often be found in cell–cell interactions, in particular, those among cancer cells and the tumor microenvironment (TME). Cancer evolution is indeed also enabled by interactions with surrounding and infiltrating stromal and immune cells, which themselves change and evolve in response to tumor–TME interactions. Genetic mutations, epigenetic reprogramming, and cell interactions within the TME are general promoters of change, although arguably not the only ones that enable tumor evolution (Fig. 1). Herein we review recent findings illuminating the role of these enabling characteristics in tumorigenesis, malignant progression, and adaptive resistance to therapy, and the mechanisms through which alterations confer selective advantages to the tumors they create.

Figure 1.

The many faces of cancer evolution. Enabling characteristics such as genetic instability, epigenetic reprogramming, and interactions between tumor cells and the TME enable cancer evolutionary mechanisms such as the emergence and selection of genetic variants, cancer cell plasticity, and re-education of the TME.

Figure 1.

The many faces of cancer evolution. Enabling characteristics such as genetic instability, epigenetic reprogramming, and interactions between tumor cells and the TME enable cancer evolutionary mechanisms such as the emergence and selection of genetic variants, cancer cell plasticity, and re-education of the TME.

Close modal

Mutagenesis and Genetic Cancer Drivers

Dividing cells accumulate mutations throughout a lifetime. Whereas most somatic mutations have neutral phenotypic effects, occasionally, mutations confer a selective advantage to the cell, potentially leading to clonal expansion of the mutated lineage and, eventually, mutated cells may develop into a tumor. The interplay between mutagenesis and selection has allowed us to model cancer evolution according to neo-Darwinian principles of species evolution.

Mutations emerge and are distributed across the genome as the result of DNA damage and inaccuracies in replication and repair processes. DNA lesions induced by damaging agents or defective endogenous processes are for the most part efficiently removed or repaired. However, occasionally damage leads to mutations, such as single-base substitutions (SBS). SBS may not be resolved within a single-cell cycle. Consequently, DNA lesions in individual DNA strands can segregate unrepaired into daughter cells for multiple cellular generations, resulting in chromosome-scale strand asymmetry, termed lesion segregation (ref. 4; Fig. 2A). This novel mechanism has recently been shown to be a feature of DNA-damaging agents and shed new light into the mechanisms of action of known mutagens (4–6), including ultraviolet radiation and chemotherapy agents, and the etiology of multiple tumor types. The propagation of strand-specific lesions through multiple cell generations provides an engine for genetic diversity. Pervasive lesions can act as a template in successive rounds of replication, meaning that different incorrectly (or correctly) paired nucleotides can be incorporated opposite an individual lesion. This results in the observation of multiple alleles at the same position in the resultant cell population. For example, diethylnitrosamine (DEN) is known to produce the long-lived thymine adduct O4-ethyl-deoxythymidine on the sugar-phosphate backbone of DNA. These small mutagenic ethyl-adducts most commonly result in T>A SBS, but T>C and T>G SBS are also observed. This phenomenon is referred to as multiallelic variation (ref. 4; Fig. 2A). Multiallelic sites, which are often and mistakenly filtered out during variant calling, provide the opportunity to disentangle DNA damage and DNA repair mechanisms, and reveal, for example, that DNA substitution patterns across the genome are largely shaped by the influence of DNA accessibility and repair efficiency, rather than gradients of DNA damage (bioRxiv: 2022.06.10.495644). From an evolutionary perspective, multiallelic sites violate the infinite site assumption, a commonly held assumption in genetics and evolutionary models that forbids recurrent mutations at the same site (7), and, thus, require to rethink of traditional cancer evolutionary models that rely on such assumption.

Figure 2.

Emergence and selection of somatic mutations. A, The propagation of DNA lesions that are not resolved into mutations within one cell cycle leads to lesion segregation and multiallelic variation. B, Initiating genetic mutations observed in cancer cells can be found in normal cells at the tumor site allowing us to trace the origin of the tumor. C, Genetic instability during tumor progression leads to the acquisition and selection of tumor-advantageous variants. Variant selection can be studied by modeling the recurrence of somatic mutations across large tumor cohorts, developing machine learning approaches to predict variant oncogenicity, and investigating whether the selection of specific variants depends on either preexisting alterations or the cell of origin of the tumor.

Figure 2.

Emergence and selection of somatic mutations. A, The propagation of DNA lesions that are not resolved into mutations within one cell cycle leads to lesion segregation and multiallelic variation. B, Initiating genetic mutations observed in cancer cells can be found in normal cells at the tumor site allowing us to trace the origin of the tumor. C, Genetic instability during tumor progression leads to the acquisition and selection of tumor-advantageous variants. Variant selection can be studied by modeling the recurrence of somatic mutations across large tumor cohorts, developing machine learning approaches to predict variant oncogenicity, and investigating whether the selection of specific variants depends on either preexisting alterations or the cell of origin of the tumor.

Close modal

DNA damage and, therefore, mutagenesis are fostered by endogenous mutational processes, for example, associated with aging and faulty DNA-repair mechanisms, and by exogenous mutagens, such as UV radiation, tobacco consumption, or dietary habits. In cancer cells, the resulting mutations accumulate over time, sometimes gradually, and others in a more punctuated or even catastrophic manner (8, 9), especially in stress responses (10, 11). Mutagens often act on specific nucleotide contexts, thus generating specific mutational signatures. It is therefore possible to infer the causes of DNA mutations from mutational signatures, which has become a powerful tool in the investigation of human cancers. Somatic mutations can also be used as a “barcode” to trace the developmental history of a cell and, in the case of cancer, to trace cancer evolution to its origins (ref. 8; Fig. 2B). By extending this search from cancer cells to surrounding normal tissues, one may be able to trace the earliest origin of cancers and its relationship to human development. This approach has been applied to some childhood cancers, revealing, for example, that the commonest childhood kidney cancer, Wilms tumor, often originates in clonal expansions residing in normal kidney tissue (12). This discovery, replicated in other tumor types (13–16), indicates that there may be a window of opportunity for preventing tumor formation, by interfering with the process that connects the earliest clonal expansion with malignant transformation. Ultimately, to faithfully recapitulate the series of multistep clonal expansions that led to the tumor and intervene in this dynamic process, it is critical to understand what triggered a clonal expansion, i.e., determine which mutations provided a selective advantage (Fig. 2C).

Mutations providing a selective advantage to the tumor cell are typically referred to as “driver mutations” or “cancer drivers.” Cancer driver mutations not only alter the function of the corresponding protein, but they do so in a way that promotes cancer-enabling features such as tumor initiation, progression, invasion, or resistance to therapy. Whereas the identification of cancer drivers requires the possibility of monitoring the dynamics of an evolutionary process, the initial discoveries of driver mutations and cancer genes relied on targeted functional approaches. In the past two decades, significant advances in genomics technologies have allowed us to unbiasedly assess the mutational status of the entire genome of thousands of tumors, and with that came the challenge of disentangling between driver mutations and mutations that do not contribute to tumor phenotypes, so-called neutral or “passengers.” When analyzing mutation occurrences across multiple tumors, the task is to determine whether the mutational pattern observed in a gene or DNA region can be explained by neutral mutagenesis; hence, the mutation would be classified as passenger, or if, by contrast, it constitutes a signal of positive selection, as would be expected by a driver mutation (17). Approaches based on this principle largely relied on estimating the expected frequency of a given mutation or mutated gene under the hypothesis of neutral evolution, to then statistically assess when observed frequencies exceed expectation (Fig. 2C). A critical challenge for these approaches is the correct estimation of the mutational probability of each nucleotide under neutral mutagenesis (17–19). As already mentioned, DNA accessibility and repair efficiency have been shown to be key determinants of local mutation rates in our genome (refs. 20, 21; bioRxiv: 2022.09.14.507952), but additional studies are required to improve our models of the background mutation rate under neutrality, especially in the noncoding genome (22). That notwithstanding, a general approach, even without using prior knowledge, can effectively rediscover well-known cancer genes, and it has been adopted to identify new cancer genes in several large-scale cancer genomics studies, eventually leading to the generation of multiple catalogs of cancer drivers (17, 22, 23). Recently, with increasing data availability, new machine-learning approaches have emerged to predict the pathogenicity of individual mutations independently of their actual occurrence (refs. 24, 25; Fig. 2C). Overall, these approaches have shown that only a minor fraction of the mutations observed in a tumor can be classified as drivers, typically less than 10 per tumor (18). However, these estimates should be taken with caution, as the phenotypic consequences of a mutation are unlikely to be binary (driver or passenger), but they rather describe a gradient, and they may be transient and/or dependent on the emergence of specific conditions.

Even with a well-annotated map of mutational processes and cancer drivers, the route to tumor initiation and progression remains sometimes elusive. Indeed, the discovery of ubiquitous clonal selection of cancer drivers in normal tissue mosaicism (26–28) confirmed that human tumorigenesis requires specific combinations of genetic and, possibly, nongenetic alterations (29). Analyses of large tumor cohorts have shown that these combinations are often nonrandom (30, 31): certain sets of mutations are more frequently observed in the same tumor than expected by chance (cooccurring mutations), while others are rarely or never found together (mutually exclusive mutations). Nonrandom comutation patterns could help draw evolutionary trajectories, from the emergence and accumulation of mutations in normal cells to tumor initiation, progression, and metastatic invasion (Fig. 2C). Intriguingly, comutation patterns only emerged among driver mutations, whereas no significant cooccurrence or mutual exclusivity was observed among synonymous SBS or mutations of unknown significance (32). These results indicated that comutation patterns provide evidence of selection. Importantly, such evidence is independent of individual mutation frequencies and, thus, could be used as orthogonal features in the search for new cancer drivers.

Overall, mutagenesis and selection are not only key mechanisms of cancer evolution but have become critical ingredients in modern cancer therapy. The discovery of driver mutations has fueled the development of targeted treatments to selectively kill only cells harboring specific oncogenic mutations and, as a consequence, introduced the need for accurate DNA mutation sequencing in the clinic. In this context, unraveling tumor evolutionary dynamics will be critical to predict treatment response based on the combination of mutations observed in a patient, and anticipate and monitor the emergence of new mutations leading to treatment resistance.

Lineage Identity: Hardwired Constrains on Tumor Evolution

Somatic mutations in most cancer-driver genes are detected only in a small fraction of tumor types (23), and inherited genetic cancer predisposition variants typically affect only a limited set of tissues (33). Hence, cell identity, defined by tissue-specific transcriptional programs, has a profound impact on the phenotypic output of oncogenic mutations (Fig. 2C). For example, the transcription factor SOX10 specifies a developmental stage in the neural crest–melanoblast–melanocyte trajectory in which BRAF, an oncogene mutated in 50% of cutaneous melanomas, is capable of cellular transformation and tumor initiation (34). Interestingly, acral melanomas, which develop on the palms and feet, depend on a different set of lineage transcription factors and mutations (35). Lineage factors can thus establish a permissive cellular state that facilitates oncogenesis by some genetically activated pathways but not others. In line with this, the renal lineage factor PAX8 is required for oncogenic signaling by common kidney cancer-associated genetic alterations (36). Inactivation of the VHL tumor suppressor is the initiating genetic event in 90% of clear cell renal cell carcinomas (ccRCC). VHL loss leads to HIF2A stabilization, which supports tumor development and metastatic progression through tissue-specific expression of oncogenic drivers, such as the cell-cycle regulator CCND1 (37, 38). The ability of the VHL–HIF2A pathway to regulate CCND1 mRNA expression is dependent on PAX8, suggesting a possible explanation for the tissue-restricted tumor-suppressive role of VHL. A PAX8-dependent lineage factor program is also required for MYC expression from ccRCC metastasis-associated genetic amplicons (36). This functional convergence of lineage factors and oncogenic programs in the regulation of universally important cancer driver genes such as CCND1 and MYC may open opportunities for inhibiting canonical oncogenic programs in a tissue- and cancer type–specific manner.

Indeed, cancer cells are often exquisitely sensitive to the inhibition of lineage transcription factors (39). Although transcription factors have long been viewed as “undruggable,” their gene-regulatory activities typically depend on specific cofactor interactions that can be exploited for drug development (40, 41). In addition, pathways controlling the dynamic transcriptional, posttranscriptional, and posttranslational regulation of transcription factors provide alternative entry points for pharmacologic interference. For example, the transcription of MYC is exquisitely sensitive to BET bromodomain inhibition in various hematopoietic malignancies (42, 43), while PROTACs and molecular glues enable the rapid elimination of specific transcription factors through proteasomal degradation (44, 45). To systematically explore such alternative molecular targets, a more detailed understanding of the pathways that control the expression and turnover of specific transcription factors is highly desirable. Time-resolved FACS-based CRISPR/Cas9 mutagenesis screens have recently been used to systematically characterize regulators of the essential transcription factor MYC in diverse cellular contexts (46). Besides context-specific regulators of MYC, these screens uncovered a conserved pathway that controls the nuclear import of proteasomes, and thereby the proteasomal turnover of transcription factors and other nuclear proteins. Extending this screening approach to other oncogenic transcription factors will further help to distinguish broadly relevant from factor- and context-specific regulators and cofactors.

Somatic Mutations Through the Lens of Chromatin 3D Structures

The development of chromosome conformation capture technologies has allowed us to study cancer somatic point mutations and structural variants in a new light, by exploring their effect on the three-dimensional (3D) structure of the chromatin in the nucleus as well as the impact of 3D organization on the functional consequences of the mutation. Chromosome conformation capture technologies determine the frequency with which two genomic loci are found in close 3D proximity, also referred to as contact or interaction frequency. Using high-throughput chromosome conformation capture (Hi-C), it is possible to derive genome-wide maps of contact frequencies to estimate features of chromatin folding and segregation (47). These approaches have revealed that, during the interphase, chromatin is organized into a hierarchy of structural elements, ranging from a few kilobase-long loops to submegabase topologically associating domains (TAD) and compartment domains, which, at a broader scale, cluster into a handful of compartments, each characterized by specific epigenetic features (48–51).

The potential for chromatin 3D structures to explain or even determine the oncogenic capacity of genetic alterations is probably most evident in TADs. TADs represent genomic regions that display increased interaction frequencies and where loops between cis-regulatory elements (CRE) and their target genes are usually contained (52). As such, TADs facilitate proper CRE–promoter and promoter–promoter interactions, and insulation between adjacent TADs reduces improper or disease-promoting interactions. In their role of scaffolds for regulatory interactions, TADs shed new light on the impact of hotspot mutations of the PRC2 catalytic unit EZH2, which generally lead to increased histone-3 lysine 27 trimethylation (H3K27me3) genome wide. Interestingly, the spreading of H3K27me3 in tumors with EZH2 gain-of-function mutations was found frequently confined within the boundaries of selected TADs, resulting in the downregulation of all genes in the domain. When multiple tumor suppressors were contained in the same domain, this downregulation synergistically drove tumorigenesis (53). Structural variants affecting TAD boundaries can induce novel interactions between CREs and genes that were previously isolated (refs. 54, 55; Fig. 3A). Such aberrant interactions have been associated with developmental disorders (56, 57) and cancer (58–61). Additionally, alteration of TAD boundaries may occur by preventing or reducing the binding of the chromatin architectural protein CTCF, which is essential for TAD formation and maintenance (62, 63). Indeed, high-density CTCF binding at TAD boundaries ensures insulations between adjacent TADs. DNA hypermethylation of CTCF binding sites in IDH-mutant gliomas or SDH-mutant gastrointestinal stromal tumors reduced protein binding and TAD insulation leading to spurious activation of oncogene expression (64, 65). Similarly, lower levels of CTCF in childhood acute lymphoblastic leukemia (ALL) led to reduced TAD insulation and consequent gene misregulation (66), consistent with CTCF being a quantitative modulator of gene activation across TADs (67).

Figure 3.

From somatic mutations to epigenetic reprogramming. A, Somatic mutations targeting CTCF binding sites can impair CTCF binding and insulation between chromatin loops or topologically associating domains (TAD). Loss of insulation leads to aberrant interactions between gene promoters and cis-regulatory elements (CRE). B, Somatic mutations of histone modifiers may lead to altered histone modification (e.g., methylation and acetylation). Aberrant histone marks have been associated with missegregated chromatin compartments and compartment repositioning, as well as the emergence of a state of “phenotypic inertia,” which allows tumor cells to tolerate oncogenic stress and ultimately adapt.

Figure 3.

From somatic mutations to epigenetic reprogramming. A, Somatic mutations targeting CTCF binding sites can impair CTCF binding and insulation between chromatin loops or topologically associating domains (TAD). Loss of insulation leads to aberrant interactions between gene promoters and cis-regulatory elements (CRE). B, Somatic mutations of histone modifiers may lead to altered histone modification (e.g., methylation and acetylation). Aberrant histone marks have been associated with missegregated chromatin compartments and compartment repositioning, as well as the emergence of a state of “phenotypic inertia,” which allows tumor cells to tolerate oncogenic stress and ultimately adapt.

Close modal

More recently, CTCF levels were shown to be significantly reduced in cells undergoing whole-genome doubling (WGD), causing loss of insulation at TAD boundaries (68). Interestingly, CTCF downregulation was a result of a more general inability of upscale protein synthesis in WGD cells (69). Indeed, beyond CTCF, WGD cells exhibited reduced histone-3 lysine 9 trimethylation (H3K9me3), a marker of heterochromatin and a driver of chromatin compartmentalization. Lower H3K9me3 led to reduced segregation of chromatin compartments in WGD cells and longitudinal analyses of tumors originating from WGD cells showed that regions losing compartment segregation ultimately were ultimately found associated with a different compartment, having altered their overall spectrum of chromatin contacts, and exhibiting new histone posttranslational modifications and CRE–promoter loops associated with oncogene activation (68). Compartment repositioning of genomic regions has been more frequently observed across cell types and cell states than changes in TAD boundaries (70, 71) and has been reported in cancer in association with altered histone modifications (refs. 72, 73; Fig. 3B). Given the high prevalence of cancer mutations targeting chromatin remodeling factors (74), it will be interesting to explore the dynamic interplay between mutated histone modifiers, epigenetic changes, and subcompartment repositioning, and whether it can further explain the selective advantage provided by these mutations. Overall, these results suggest that modifications of chromatin 3D features are not simple bystanders or consequences of epigenetic alterations but could themselves initiate epigenetic and transcriptional reprogramming. Longitudinal analyses tracing chromatin structural changes in single cells will shed new light on the role of chromatin plasticity in tumor evolution.

Beyond their effect on chromatin 3D structures, recurrent mutations of chromatin remodeling factors induce genome-wide epigenetic changes that could favor cancer cell plasticity. This hypothesis was recently functionally tested via large-scale CRISPR-based mutagenesis (75). Inactivation of over 100 epigenetic regulators of diverse functions converged into a common phenotype characterized by increased tolerance to environmental stress, which is selected during tumor growth. In this particular case, disruption of epigenetic control does not appear to enhance the phenotypic plasticity of cancer cells but rather prevents cells from mounting an efficient stress response at the transcriptional level. The resulting cell state was thus characterized by phenotypic inertia (ref. 75; Fig. 3B), defined as the inability of the cell to halt proliferation and activate an apoptotic program in response to unfavorable environments.

Overall, cell epigenetic features, such as DNA methylation and histone posttranslational modifications, characterize physiologic states during normal cell differentiation and can be co-opted to promote malignant cell transformation (76). Interestingly, the oncogenic capacity of somatic mutations has been shown to depend on such cell states, as exemplified by differential oncogenic competence of melanocytes, neural crest, and melanoblasts in melanoma development (34, 77). Whether driven by genetic variants or nongenetic mechanisms, the emergence of heterogeneous phenotypes in a cell population enables tumor adaptation and evolution.

From Genetic Mutations to Cancer Cell States

Intratumor heterogeneity and its relationship with cancer evolution have been often investigated in terms of genetic mutations, which can be ubiquitously observed across all cancer cells or present only on a subset of them, determining distinct subpopulations. In recent years, the notion of heterogeneous “cancer cell states” in otherwise genetically identical cells has gained traction. Evolving cancer cell states characterize the progression of several tumor types and can emerge independently of specific genetic mutations through cancer cell plasticity. Cell plasticity often ensues in response to epigenetic and transcriptional reprogramming associated with the progression of the disease, tumor–TME interactions, micro-RNA (miRNA) regulation, and treatment administration (Fig. 4A). In contrast to lineage-defined cell states, which characterize developmental stages of normal cell lineages, cancer cell states have been associated with phenotypic properties of cancer cells, which can be transient, induced, and reversible (78). Investigating functional diversity within transformed cells is a complex and possibly unfeasible endeavor. Therefore, the existence of heterogeneous states is routinely inferred from the transcriptional and/or epigenetic diversity of individual cells within a tumor, with different “states” potentially integrating static (genetic) and dynamic and reversible (epigenetic and transcriptional) determinants (79–81). Cancer cell states have been largely inferred algorithmically from single-cell RNA-sequencing (scRNA-seq) data sets by describing mRNA expression of ∼20,000 genes through a much smaller number of nonredundant transcriptional programs (82, 83). Such transcriptional programs were either themselves defined as cell states, or clustered into cell states, consistent with the notion that cell phenotypes are the result of concurrent activation of different biological processes. The notion that cancer phenotypes are encoded by combinations of genetic and nongenetic modifications motivated the further deve­lopment of new single-cell multiomics technologies that integrate genotypes, transcriptomes, and epigenetic profiling (refs. 84–87; Fig. 4A). The integration of transcriptional activity and chromatin states and accessibility in single cells provide a more comprehensive description of cell states allowing to infer a fingerprint for transcription factor activity and cell state transitions (88–90). Joint high-throughput capture of genotypes and transcriptomes revealed a complex interplay between somatic mutations and cell states (91) with somatic mutations associated with cell progenitor-type specific aberrations, years before malignant transformation (92, 93). Stochastic, heritable DNA methylation changes can also be leveraged as native lineage barcoding marks for high-resolution phylogenetic reconstruction of single cells in primary human cancer samples (86). Excitingly, the ability to obtain high-resolution evolutionary trees together with the concomitant transcriptional annotation of leaves allowed the direct measurement in human tissues of the heritability of cell states (94).

Figure 4.

Cancer cell plasticity and cancer cell states. A, Histone modifications, miRNA, and interactions with the TME induce epigenetic and transcriptional reprogramming that alters cancer cell identity. This process defines cancer cell plasticity transitions among phenotypically heterogeneous cell states that can be inferred from single-cell molecular profiles. B, Examples of such cell state transitions have been reported in skin melanoma, where mesenchymal and stem-like states have been associated with metastatic and tumorigenic capacity, respectively, in lung adenocarcinoma, where disease progression was associated with morphologic differences and transition from an alveolar-type 2-like state (AT2-like) to a dedifferentiated and proliferative cell state; and in breast cancer, where resistance to therapy was shown associated with cell dormancy and random awakening mediated by epigenetic reprogramming.

Figure 4.

Cancer cell plasticity and cancer cell states. A, Histone modifications, miRNA, and interactions with the TME induce epigenetic and transcriptional reprogramming that alters cancer cell identity. This process defines cancer cell plasticity transitions among phenotypically heterogeneous cell states that can be inferred from single-cell molecular profiles. B, Examples of such cell state transitions have been reported in skin melanoma, where mesenchymal and stem-like states have been associated with metastatic and tumorigenic capacity, respectively, in lung adenocarcinoma, where disease progression was associated with morphologic differences and transition from an alveolar-type 2-like state (AT2-like) to a dedifferentiated and proliferative cell state; and in breast cancer, where resistance to therapy was shown associated with cell dormancy and random awakening mediated by epigenetic reprogramming.

Close modal

Cancer Cell States in Tumor Progression and Treatment

The existence of cancer cell states has many implications: (i) genetically identical cells may be phenotypically heterogeneous and thus implement different functions to foster disease progression and treatment resistance; (ii) treatment and microenvironmental conditions are likely to impact cancer cell properties and, thus, drive state transitions; (iii) opportunities exist to reprogram cells to a therapy-sensitive state. To test these hypotheses, there is a need to develop models that recapitulate not only genetic diversity in the tumor but also the presence and transitions of dynamic states.

Examples of cell plasticity transitions have been recently reported in several tumor types. In this context, micro-RNA regulation of gene expression has been shown to determine extensive transcriptional reprogramming in the development and progression of pancreatic neuroendocrine tumors (PNET; Fig. 4A). The RIP-Tag mouse models undergo stereotypic development of PNET through distinctive histologic stages, arising from the insulin-producing beta cells of the pancreatic islets in response to the targeted expression of the SV40 large T antigen (Tag) oncoprotein, which abrogates the functions of the RB and TP53 tumor suppressor protein (95). Leveraging the possibility to physically isolate and individually molecularly profile premalignant lesions as well as solid tumors and liver metastases, the micro-RNA transcriptome revealed that sets of microRNAs were up- and downregulated during each of the stepwise transitions in this tumorigenesis pathway (96). Additionally, pancreatic tumors could be subclassified by micro-RNA (miR) signatures into comparatively benign (noninvasive) islets tumors (IT) and invasive and metastasis-like tumors (MLP). Focusing on the miR signature that defines malignant progression from IT to MLP, functional perturbation has revealed that many of the miRNAs in the signature of malignant progression from IT to MLP enable hallmark capabilities: upregulation of miR137 stimulates invasive growth (97), whereas the miR23b cluster enables metastatic seeding, in part by suppressing expression of the ALK7 receptor (98). Additionally, one facet of cellular plasticity—dedifferentiation—has been exceptionally revealed to be a distinctive step, separable from increases in aberrant proliferation, in the course of malignant progression. Upregulation of miR181cd, a component of the MLP signature, instructs dedifferentiation along the pathway by which endocrine progenitor cells normally differentiate into mature insulin-producing beta cells, by altering the expression of developmental transcription factors that orchestrate a progenitor-like phenotype in MLP tumors (99). Although the mechanisms of this dynamic regulation of dozens of microRNAs during tumorigenesis and malignant progression remain to be elucidated, there is no evidence to ascribe its basis to mutational evolution of the cancer cell genome, which rather most logically involves nonmutational epigenetic reprogramming, fertile ground for continuing investigation.

Similarly to PNET, lung adenocarcinoma progression also involves a transition through histologically defined stages, with the lepidic and solid histology associated with the most indolent and most aggressive manifestations of the disease, respectively (100). Histopathology-guided multiregion sampling of lung adenocarcinoma has shown that histologically distinct regions are predominantly characterized by epigenetic and transcriptional differences (101). These differences trace a transition from a lepidic-like cell state, characterized by retained expression of alveolar-type 2 (AT2) lineage markers and transcription factors (NKX2-1 and FOXA2), toward a less differentiated state that no longer expresses AT2 markers and activating proliferation and stemness factors such as FOXM1 and MYBL2 (Fig. 4B). When enhanced proliferation is not the major selective force, tumors may evolve in unexpected manners. This is often the case when therapeutic intervention leads to the formation of persister cells, a cell state characterized by treatment tolerance and transient inability to proliferate. In a hormone-dependent breast cancer model, dormant persister cells are directly induced by cytostatic treatment (102). A long-term evolutionary experiment has now revealed that the dormant persister pool is stochastically generated (bioRxiv: 2021.04.21.440779, 2022.02.15.480537) via the accumulation of heterochromatin modifications. The epigenome of dormant cells is then reversed in awakening clones, leading to tumor relapse and overt drug resistance (Fig. 4B). In skin melanoma, drug-tolerant persister cells harboring a neural crest stem cell phenotype can emerge through drug-induced reprogramming (103), rather than selection of a preexisting cell population, and that this population promotes emergence of drug resistance through a nongenetic mechanism. These findings further support epigenetic mechanisms as major drivers of tumor evolution in these tumor types (104–106).

Cell states, just like mutations, are potential therapeutic targets (79) and may become the main targets of tumors with currently no actionable mutations. An interesting example is a high mesenchymal cell state driven by increased expression of a specific master regulator, ZEB1, which, on the one hand, is associated with aggressiveness and resistance to therapy and, on the other, generates unique therapeutic vulnerabilities (107). Along the same line, the high transcriptional diversity of pancreatic ductal adenocarcinoma has been linked to microenvironmental changes, which affect sensitivity to therapy (ref. 108; bioRxiv: 2022.12.12.520054). Because cell states are also driven by local microenvironmental conditions, it follows the need to integrate such state diversity with spatial information within the tumor, a task now made possible by advances in spatial omics approaches (109, 110). A recent study leveraged time-resolved single-cell analyses and spatial transcriptomics, to establish a high-resolution four-dimensional (space and time) map of the drug-naïve melanoma ecosystem and study its evolution under therapy (111). This map revealed the presence of 6 evolutionarily conserved melanoma transcriptional states: melanocytic, mesenchymal-like, neural crest-like, antigen presentation, and stress, associated either with a p53 or hypoxia response. These cell states exhibited distinctive tissue localization and their presence was associated with divergent responses to immune-checkpoint blockade. Moreover, these cell states decoupled the proliferative and metastatic potential of melanoma cells. Indeed, single-cell tracing revealed that the mesenchymal population are responsible for metastasis initiation while they do not support primary tumor growth, which is instead associated with a stem-like population forming a perivascular niche (ref. 111; bioRxiv: 2022.08.11.502598; Fig. 4B). The observation that oncogenic competence can depend on specific microenvironmental signals warrants the development of therapeutic strategies that could interfere with such signals and their potential for cancer cell plastic reprogramming.

Overall, multiple mechanisms can induce epigenetic and transcriptional reprogramming within tumor cells, independently of genetic mutations. Transcriptional reprogramming in turn leads to the emergence of heterogeneous cell states which may undergo Darwinian selection. This heterogeneity appears associated with the tumor's spatial architecture, temporal progression, and resistance to therapy.

Coevolution in the TME

Detailed analyses of cancer and noncancerous cell populations that compose the tumor ecosystem revealed that heterogeneity and evolution are not concepts restricted to cancer cells. As already alluded to in the previous sections, mutation-induced cancer cell signaling and plastic cancer cell state transition inevitably affect and are affected by the TME. Indeed, a plethora of factors directly modulate immune and stromal cell features and, with evolutionary changes occurring in tumor cells, the TME and interactions between the TME and cancer cells also change, often contributing critically to therapy resistance (112–114).

Examples of coevolving tumor cells and TME under treatment have been recently shown in melanoma and glioblastoma (Fig. 5A). Indeed, when BRAF-mutated melanomas acquire resistance to MAPK pathway-targeted therapies (RAFi/MEKi), tumors simultaneously acquire cross-resistance to immunotherapy (115). This cross-resistance is surprising given the entirely different mode of action of these therapies. Functional analyses on immune-competent mouse models revealed that cross-resistance was mediated by an immune-evasive TME lacking antigen-presenting cells. This immune-evasive TME was cancer cell–instructed, and it evolved from an increased transcriptional output of the reactivated MAPK pathway in tumors that bypassed MAPK-signaling inhibition, rather than from selective pressure to overcome immune response. Similarly, adapting cancer cells posttreatment can shape the evolution of subpopulations of tumor-associated macrophages (TAM; refs. 116–118). In treatment-resistant glioblastoma, glioma cells shifted their subtype toward a mesenchymal-like state and induced transcriptional reprogramming of TAMs, turning them into “food providers.” These timed and regulated alterations of TAM metabolic capacities reactivated developmental properties of brain scavenger cells (119) leading to the coevolution of TAMs and glioblastoma cells to enable proliferation and immunosuppression fueling relapse posttreatment (120).

Figure 5.

Interactions with an evolving TME. A, Along with tumor cells, the surrounding TME also evolves often acquiring protumor features. Examples include reeducation of macrophages in treatment-resistant glioma, phenotypically heterogeneous populations of cancer-associated fibroblasts, treatment-induced generation of a tumor-evasive TME, which is associated with dual resistance to targeted and immunotherapies in melanoma, and interactions between tumor-associated neutrophils (TAN) and lung cancer cells, which toward a less proliferative, but hypoxia responsive cell state. B, Spatially resolved molecular profiles provide a critical tool to investigate tumor–TME interactions.

Figure 5.

Interactions with an evolving TME. A, Along with tumor cells, the surrounding TME also evolves often acquiring protumor features. Examples include reeducation of macrophages in treatment-resistant glioma, phenotypically heterogeneous populations of cancer-associated fibroblasts, treatment-induced generation of a tumor-evasive TME, which is associated with dual resistance to targeted and immunotherapies in melanoma, and interactions between tumor-associated neutrophils (TAN) and lung cancer cells, which toward a less proliferative, but hypoxia responsive cell state. B, Spatially resolved molecular profiles provide a critical tool to investigate tumor–TME interactions.

Close modal

Systematic and unbiased interrogations of heterotypic cellular interactions within the TME have been enabled by scRNA-seq and, more recently, spatial transcriptomics and proteomics approaches. In particular, the recent application of image mass cytometry and other spatial proteomics or transcriptomics technologies has allowed to spatially resolve cell type admixing within the TME and, thus, to define TME niches as markers of disease progression and response to therapy (121–126). Granular subclassification of cell types determined phenotypic heterogeneity for multiple immune cell types (127, 128). For instance, recent studies have shown that cancer-associated fibroblasts diversify into states with distinct functions, ranging from immunomodulatory fibroblasts (iCAF) that can suppress lymphocyte activation, to myofibroblasts that remodel extracellular collagen matrices (129–132). Importantly, spatial “omics” technologies can now bridge the gap between the identification of cancer cell states and spatial TME niches, to determine when an association and interactions exist between the two (Fig. 5B). For example, macrophages and lymphocytes were found associated with the acquisition of an interferon-activated phenotype characterized by noncanonical expression of antigen presentation pathways in nearby cancer cells (83). In lung adenocarcinoma, cancer cell states emerging with the progression of the disease interacted with vastly different TMEs, with transitions between immune cold, inflamed, and excluded environments being observed within the same tumor, in correspondence with distinct cancer cell states (101). In particular, tumor cells exhibiting a response-to-hypoxia and migratory phenotype were found colocalizing with tumor-associated neutrophils (bioRxiv: 2023.01.10.523386; Fig. 5AB). Consistently, cells in this state with expression of neutrophil recruiting chemokines, such as CXCL1 and CXCL3, further indicating that interactions between tumor cells and the TME shape cancer cell state emergence and transition (bioRxiv: 2023.01.10.523386). Intriguingly, the physical properties of the TME can themselves determine epigenetic adaption. Indeed, interstitial pressure-driven fluid flow was shown to induce an autocrine signaling circuit comprised of secreted glutamate that activates the NMDA receptor, stimulating invasive tumor growth (133, 134).

The advent of technologies capable of probing single-cell spatial organization and interactions at high resolution and throughput will provide critical clues into TME components at different stages of the disease and how these can determine cancer cell plasticity. Ultimately, charting this evolving landscape of tumor and nontumor cells will enable the development of therapeutic strategies targeting cellular interactions and cancer–TME niches (135). The new frontier lays in identifying the appropriate longitudinal model, especially in the clinical setting, which will allow enough temporal resolution to match these technological advances.

Although cancer evolution has mainly been viewed through a genetic lens, mutations are just one face of the complex prism of forces that underpin tumor evolution (8, 136, 137). Nonmutational transcriptional and epigenetic reprogramming and interactions among tumor cells and the TME equally provide numerous opportunities for diversification that may favor cancer phenotypes. Even more, environmental pressures and treatment can induce adaptive responses, establishing non-Darwinian evolutionary routes (10, 138). Examples reviewed in this manuscript highlight how different evolutionary mechanisms are not acting in an independent or, even less, mutually exclusive manner. Genetic variants can induce and modulate cancer cell plasticity, which, in turn, can favor mutagenesis or determine the oncogenic impact of specific mutations. Similarly, interactions among stromal and tumor cells can induce transcriptional reprogramming, leading to the emergence of heterogeneous cancer cell states exhibiting different immunogenicity and, thus, establishing different immune microenvironments. To decipher the interactions among biological processes that have been historically the focus of different scientific communities requires fostering conversations and collaborations crossing the barriers among such communities. As in the old parable of “the blind men and the elephant,” the incomplete experience of a single field will not be sufficient to capture the whole picture.

Toward this goal, the increasing complexity of high-throughput technologies and data has enabled to integrate multiple layers of molecular information within each cell, which could allow to directly measure how different evolutionary mechanisms concurrently alter cell phenotypes. These data sets could be used to generate novel hypotheses, but they require rigorous statistical approaches to be correctly interpreted and need to be coupled with appropriate experimental models. Indeed, on the one hand, data-driven approaches have the power to decode the complexity of human tumors but provide limited mechanistic insight. On the other hand, experimental models allow us to decipher the mechanisms generating specific observations but are often limited in the number of variables they can model and remain proxies of human disease.

In this review, we only touched upon some of the many facets of cancer evolutionary mechanisms. We have, for example, not discussed the reprogramming of cellular metabolism that enables tumors to meet their nutrient requirements and opportunistically adapt to nutrient scarcity. Multiple metabolic pathways have been implicated in tumor growth, and metabolic reprogramming has been associated with cell differentiation and interactions with the TME (139–141). Currently, several trials are incorporating metabolic intervention in combination with classic approaches, highlighting the patient benefit of exploring this facet of tumor evolution. Analogously, the composition of the human gut microbiota has been shown to be a critical effector of response to cancer immunotherapy and possibly influencing response to other treatments. Interactions between cancer cells and the microbiota are mediated by secreted small molecules and have been further implicated in tumorigenesis and progression (142, 143). Metabolic reprogramming and microbiota interactions are just two examples of additional mechanisms that are likely to promote and influence the evolution of the disease. Along with different mechanisms, different evolutionary models should be evaluated to explain experimental and clinical observations. A recent study showed that rather than the emergence and selection of new alterations, air pollution–driven inflammation enables the oncogenic potential of preexisting mutations in the lung driving lung adenocarcinoma in never smokers (144). These observations are reminiscent of genetic studies investigating the role of evolution of preexisting variation in a population (145, 146). In cancer, such “preexisting variation” could be embodied by somatic mutations accumulated before tumor formation, as well as germline variants, and their phenotypic effect being neutral in the absence of specific endogenous (e.g., cancer cell states) or exogenous (e.g., TME interactions) conditions.

As we deepen our understanding of cancer evolution from a fundamental scientific view, we may be able to translate some actionable lessons into clinical interventions. Critical questions in this direction are whether we can monitor, anticipate, and, ultimately, interfere with the evolution of the disease (147–149). To address these questions, large-scale clinical research studies have been implemented by the TRAcking Cancer Evolution through therapy (Rx; TRACERx) consortium, which, for example already provided actionable classifications of renal carcinoma based on evolutionary trajectories detected in patients (150, 151), and reveal unexpected genetic and nongenetic routes of lung cancer evolution (152, 153). A deeper understanding of tumor evolutionary mechanisms and trajectories offers the opportunity to design strategies to interfere with them and optimize the chances of therapeutic success (11, 154–156).

Targeted deep DNA sequencing is now routinely performed in most cancer hospitals and often at multiple timepoints. The scale and resolution of these data sets could represent a critical resource to trace recurrent cancer evolutionary trajectories. In particular, with the advent of modern artificial intelligence approaches in precision medicine, data scale and complexity will become an asset rather than a limitation (157). Initiatives toward increasing the spectrum of molecular data generated in the clinic, standardizing and sharing data collection, and creating cancer registries matching anonymized molecular data and clinical annotation will ultimately transform our ability to predict the evolution of the disease in patients.

S.J. Aitken reports grants from the Medical Research Council during the conduct of the study. S. Behjati reports grants from Wellcome Trust during the conduct of the study. A.C. Obenauf reports other support from Boehringer Ingelheim outside the submitted work. E. Oricchio reports grants from the Swiss National Foundation and Swiss Cancer League during the conduct of the study. P. Scaffidi reports grants from the Francis Crick Institute during the conduct of the study. G. Tonon reports grants from the Italian Association for Cancer Research (AIRC) during the conduct of the study; personal fees from Adaptive Biotechnologies outside the submitted work; in addition, G. Tonon has a patent for single-cell technology GET-seq licensed to Millipore. S. Vanharanta reports grants from Sigrid Jusêlius Foundation during the conduct of the study. J. Zuber reports personal fees from Quantro Therapeutics GmbH, Mirimus Inc., and grants and nonfinancial support from Boehringer Ingelheim outside the submitted work. No disclosures were reported by the other authors.

We acknowledge the European Molecular Biology Organization (EMBO) for their support in facilitating the EMBO workshop “The Many Faces of Cancer Evolution” that convened the contributors of this perspective. The discussions from this assembly were instrumental in the development of this piece.

1.
Hanahan
D
,
Weinberg
RA
.
The hallmarks of cancer
.
Cell
2000
;
100
:
57
70
.
2.
Hanahan
D
,
Weinberg
RA
.
Hallmarks of cancer: the next generation
.
Cell
2011
;
144
:
646
74
.
3.
Hanahan
D
.
Hallmarks of cancer: new dimensions
.
Cancer Discov
2022
;
12
:
31
46
.
4.
Aitken
SJ
,
Anderson
CJ
,
Connor
F
,
Pich
O
,
Sundaram
V
,
Feig
C
, et al
.
Pervasive lesion segregation shapes cancer genome evolution
.
Nature
2020
;
583
:
265
70
.
5.
Kucab
JE
,
Zou
X
,
Morganella
S
,
Joel
M
,
Nanda
AS
,
Nagy
E
, et al
.
A compendium of mutational signatures of environmental agents
.
Cell
2019
;
177
:
821
36
.
6.
de Kanter
JK
,
Peci
F
,
Bertrums
E
,
Rosendahl Huber
A
,
van Leeuwen
A
,
van Roosmalen
MJ
, et al
.
Antiviral treatment causes a unique mutational signature in cancers of transplantation recipients
.
Cell Stem Cell
2021
;
28
:
1726
39
.
7.
Kimura
M
.
The number of heterozygous nucleotide sites maintained in a finite population due to steady flux of mutations
.
Genetics
1969
;
61
:
893
903
.
8.
Yates
LR
,
Campbell
PJ
.
Evolution of the cancer genome
.
Nat Rev Genet
2012
;
13
:
795
806
.
9.
Davis
A
,
Gao
R
,
Navin
N
.
Tumor evolution: linear, branching, neutral or punctuated?
Biochim Biophys Acta Rev Cancer
2017
;
1867
:
151
61
.
10.
Russo
M
,
Crisafulli
G
,
Sogari
A
,
Reilly
NM
,
Arena
S
,
Lamba
S
, et al
.
Adaptive mutability of colorectal cancers in response to targeted therapies
.
Science
2019
;
366
:
1473
80
.
11.
Fitzgerald
DM
,
Hastings
PJ
,
Rosenberg
SM
.
Stress-induced mutagenesis: implications in cancer and drug resistance
.
Annu. Rev Cancer Biol
2017
;
1
:
119
40
.
12.
Coorens
THH
,
Treger
TD
,
Al-Saadi
R
,
Moore
L
,
Tran
MGB
,
Mitchell
TJ
, et al
.
Embryonal precursors of Wilms tumor
.
Science
2019
;
366
:
1247
51
.
13.
Khabirova
E
,
Jardine
L
,
Coorens
THH
,
Webb
S
,
Treger
TD
,
Engelbert
J
, et al
.
Single-cell transcriptomics reveals a distinct developmental state of KMT2A-rearranged infant B-cell acute lymphoblastic leukemia
.
Nat Med
2022
;
28
:
743
51
.
14.
Oliver
TRW
,
Chappell
L
,
Sanghvi
R
,
Deighton
L
,
Ansari-Pour
N
,
Dentro
SC
, et al
.
Clonal diversification and histogenesis of malignant germ cell tumours
.
Nat Commun
2022
;
13
:
4272
.
15.
Coorens
THH
,
Farndon
SJ
,
Mitchell
TJ
,
Jain
N
,
Lee
S
,
Hubank
M
, et al
.
Lineage-independent tumors in bilateral neuroblastoma
.
N Engl J Med
2020
;
383
:
1860
5
.
16.
Custers
L
,
Khabirova
E
,
Coorens
THH
,
Oliver
TRW
,
Calandrini
C
,
Young
MD
, et al
.
Somatic mutations and single-cell transcriptomes reveal the root of malignant rhabdoid tumours
.
Nat Commun
2021
;
12
:
1407
.
17.
Martínez-Jiménez
F
,
Muiños
F
,
Sentís
I
,
Deu-Pons
J
,
Reyes-Salazar
I
,
Arnedo-Pac
C
, et al
.
A compendium of mutational cancer driver genes
.
Nat Rev Cancer
2020
;
20
:
555
72
.
18.
Martincorena
I
,
Raine
KM
,
Gerstung
M
,
Dawson
KJ
,
Haase
K
,
Van Loo
P
, et al
.
Universal patterns of selection in cancer and somatic tissues
.
Cell
2017
;
171
:
1029
41
.
19.
Cannataro
VL
,
Gaffney
SG
,
Townsend
JP
.
Effect sizes of somatic mutations in cancer
.
J Natl Cancer Inst
2018
;
110
:
1171
7
.
20.
Supek
F
,
Lehner
B
.
Scales and mechanisms of somatic mutation rate variation across the human genome
.
DNA Repair (Amst)
2019
;
81
:
102647
.
21.
Gonzalez-Perez
A
,
Sabarinathan
R
,
Lopez-Bigas
N
.
Local determinants of the mutational landscape of the human genome
.
Cell
2019
;
177
:
101
14
.
22.
Rheinbay
E
,
Nielsen
MM
,
Abascal
F
,
Wala
JA
,
Shapira
O
,
Tiao
G
, et al
.
Analyses of non-coding somatic drivers in 2,658 cancer whole genomes
.
Nature
2020
;
578
:
102
11
.
23.
Bailey
MH
,
Tokheim
C
,
Porta-Pardo
E
,
Sengupta
S
,
Bertrand
D
,
Weerasinghe
A
, et al
.
Comprehensive characterization of cancer driver genes and mutations
.
Cell
2018
;
173
:
371
85
.
24.
Muiños
F
,
Martínez-Jiménez
F
,
Pich
O
,
Gonzalez-Perez
A
,
Lopez-Bigas
N
.
In silico saturation mutagenesis of cancer genes
.
Nature
2021
;
596
:
428
32
.
25.
Dietlein
F
,
Wang
AB
,
Fagre
C
,
Tang
A
,
Besselink
NJM
,
Cuppen
E
, et al
.
Genome-wide analysis of somatic noncoding mutation patterns in cancer
.
Science
2022
;
376
:
eabg5601
.
26.
Moore
L
,
Cagan
A
,
Coorens
THH
,
Neville
MDC
,
Sanghvi
R
,
Sanders
MA
, et al
.
The mutational landscape of human somatic and germline cells
.
Nature
2021
;
597
:
381
6
.
27.
Abascal
F
,
Harvey
LMR
,
Mitchell
E
,
Lawson
ARJ
,
Lensing
SV
,
Ellis
P
, et al
.
Somatic mutation landscapes at single-molecule resolution
.
Nature
2021
;
593
:
405
10
.
28.
Herms
A
,
Jones
PH
.
Somatic mutations in normal tissues: new perspectives on early carcinogenesis
.
Annu Rev Cancer Biol
2023
;
7
:
189
205
.
29.
Vogelstein
B
,
Kinzler
KW
.
The path to cancer: three strikes and you're out
.
N Engl J Med
2015
;
373
:
1895
8
.
30.
Ciriello
G
,
Miller
ML
,
Aksoy
BA
,
Senbabaoglu
Y
,
Schultz
N
,
Sander
C
.
Emerging landscape of oncogenic signatures across human cancers
.
Nat Genet
2013
;
45
:
1127
33
.
31.
Mina
M
,
Raynaud
F
,
Tavernari
D
,
Battistello
E
,
Sungalee
S
,
Saghafinia
S
, et al
.
Conditional selection of genomic alterations dictates cancer evolution and oncogenic dependencies
.
Cancer Cell
2017
;
32
:
155
68
.
32.
Mina
M
,
Iyer
A
,
Tavernari
D
,
Raynaud
F
,
Ciriello
G
.
Discovering functional evolutionary dependencies in human cancers
.
Nat Genet
2020
;
52
:
1198
207
.
33.
Fletcher
O
,
Houlston
RS
.
Architecture of inherited susceptibility to common cancer
.
Nat Rev Cancer
2010
;
10
:
353
61
.
34.
Baggiolini
A
,
Callahan
SJ
,
Montal
E
,
Weiss
JM
,
Trieu
T
,
Tagore
MM
, et al
.
Developmental chromatin programs determine oncogenic competence in melanoma
.
Science
2021
;
373
:
eabc1048
.
35.
Weiss
JM
,
Hunter
MV
,
Cruz
NM
,
Baggiolini
A
,
Tagore
M
,
Ma
Y
, et al
.
Anatomic position determines oncogenic specificity in melanoma
.
Nature
2022
;
604
:
354
61
.
36.
Patel
SA
,
Hirosue
S
,
Rodrigues
P
,
Vojtasova
E
,
Richardson
EK
,
Ge
J
, et al
.
The renal lineage factor PAX8 controls oncogenic signalling in kidney cancer
.
Nature
2022
;
606
:
999
1006
.
37.
Vanharanta
S
,
Shu
W
,
Brenet
F
,
Hakimi
AA
,
Heguy
A
,
Viale
A
, et al
.
Epigenetic expansion of VHL-HIF signal output drives multiorgan metastasis in renal cancer
.
Nat Med
2013
;
19
:
50
6
.
38.
Wykoff
CC
,
Sotiriou
C
,
Cockman
ME
,
Ratcliffe
PJ
,
Maxwell
P
,
Liu
E
, et al
.
Gene array of VHL mutation and hypoxia shows novel hypoxia-induced genes and that cyclin D1 is a VHL target gene
.
Br J Cancer
2004
;
90
:
1235
43
.
39.
Pacini
C
,
Dempster
JM
,
Boyle
I
,
Gonçalves
E
,
Najgebauer
H
,
Karakoc
E
, et al
.
Integrated cross-study datasets of genetic dependencies in cancer
.
Nat Commun
2021
;
12
:
1661
.
40.
Uttarkar
S
,
Frampton
J
,
Klempnauer
K-H
.
Targeting the transcription factor Myb by small-molecule inhibitors
.
Exp Hematol
2017
;
47
:
31
35
.
41.
Grembecka
J
,
He
S
,
Shi
A
,
Purohit
T
,
Muntean
AG
,
Sorenson
RJ
, et al
.
Menin-MLL inhibitors reverse oncogenic activity of MLL fusion proteins in leukemia
.
Nat Chem Biol
2012
;
8
:
277
84
.
42.
Zuber
J
,
Shi
J
,
Wang
E
,
Rappaport
AR
,
Herrmann
H
,
Sison
EA
, et al
.
RNAi screen identifies Brd4 as a therapeutic target in acute myeloid leukaemia
.
Nature
2011
;
478
:
524
8
.
43.
Delmore
JE
,
Issa
GC
,
Lemieux
ME
,
Rahl
PB
,
Shi
J
,
Jacobs
HM
, et al
.
BET bromodomain inhibition as a therapeutic strategy to target c-Myc
.
Cell
2011
;
146
:
904
17
.
44.
Farnaby
W
,
Koegl
M
,
Roy
MJ
,
Whitworth
C
,
Diers
E
,
Trainor
N
, et al
.
BAF complex vulnerabilities in cancer demonstrated via structure-based PROTAC design
.
Nat Chem Biol
2019
;
15
:
672
80
.
45.
Krönke
J
,
Udeshi
ND
,
Narla
A
,
Grauman
P
,
Hurst
SN
,
McConkey
M
, et al
.
Lenalidomide causes selective degradation of IKZF1 and IKZF3 in multiple myeloma cells
.
Science
2014
;
343
:
301
5
.
46.
de Almeida
M
,
Hinterndorfer
M
,
Brunner
H
,
Grishkovskaya
I
,
Singh
K
,
Schleiffer
A
, et al
.
AKIRIN2 controls the nuclear import of proteasomes in vertebrates
.
Nature
2021
;
599
:
491
6
.
47.
Lieberman-Aiden
E
,
van Berkum
NL
,
Williams
L
,
Imakaev
M
,
Ragoczy
T
,
Telling
A
, et al
.
Comprehensive mapping of long-range interactions reveals folding principles of the human genome
.
Science
2009
;
326
:
289
93
.
48.
Bonev
B
,
Cavalli
G
.
Organization and function of the 3D genome
.
Nat Rev Genet
2016
;
17
:
661
78
.
49.
Dixon
JR
,
Selvaraj
S
,
Yue
F
,
Kim
A
,
Li
Y
,
Shen
Y
, et al
.
Topological domains in mammalian genomes identified by analysis of chromatin interactions
.
Nature
2012
;
485
:
376
80
.
50.
Nora
EP
,
Lajoie
BR
,
Schulz
EG
,
Giorgetti
L
,
Okamoto
I
,
Servant
N
, et al
.
Spatial partitioning of the regulatory landscape of the X-inactivation centre
.
Nature
2012
;
485
:
381
5
.
51.
Rao
SSP
,
Huntley
MH
,
Durand
NC
,
Stamenova
EK
,
Bochkov
ID
,
Robinson
JT
, et al
.
A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping
.
Cell
2014
;
159
:
1665
80
.
52.
Symmons
O
,
Uslu
VV
,
Tsujimura
T
,
Ruf
S
,
Nassari
S
,
Schwarzer
W
, et al
.
Functional and topological characteristics of mammalian regulatory domains
.
Genome Res
2014
;
24
:
390
400
.
53.
Donaldson-Collier
MC
,
Sungalee
S
,
Zufferey
M
,
Tavernari
D
,
Katanayeva
N
,
Battistello
E
, et al
.
EZH2 oncogenic mutations drive epigenetic, transcriptional, and structural changes within chromatin domains
.
Nat Genet
2019
;
51
:
517
28
.
54.
Lupiáñez
DG
,
Spielmann
M
,
Mundlos
S
.
Breaking TADs: how alterations of chromatin domains result in disease
.
Trends Genet
2016
;
32
:
225
37
.
55.
Spielmann
M
,
Lupiáñez
DG
,
Mundlos
S
.
Structural variation in the 3D genome
.
Nat Rev Genet
2018
;
19
:
453
67
.
56.
Franke
M
,
Ibrahim
DM
,
Andrey
G
,
Schwarzer
W
,
Heinrich
V
,
Schöpflin
R
, et al
.
Formation of new chromatin domains determines pathogenicity of genomic duplications
.
Nature
2016
;
538
:
265
9
.
57.
M Real
F
,
Haas
SA
,
Franchini
P
,
Xiong
P
,
Simakov
O
,
Kuhl
H
, et al
.
The mole genome reveals regulatory rearrangements associated with adaptive intersexuality
.
Science
2020
;
370
:
208
14
.
58.
Northcott
PA
,
Lee
C
,
Zichner
T
,
Stütz
AM
,
Erkek
S
,
Kawauchi
D
, et al
.
Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma
.
Nature
2014
;
511
:
428
34
.
59.
Weischenfeldt
J
,
Dubash
T
,
Drainas
AP
,
Mardin
BR
,
Chen
Y
,
Stütz
AM
, et al
.
Pan-cancer analysis of somatic copy-number alterations implicates IRS4 and IGF2 in enhancer hijacking
.
Nat Genet
2017
;
49
:
65
74
.
60.
Hnisz
D
,
Weintraub
AS
,
Day
DS
,
Valton
A-L
,
Bak
RO
,
Li
CH
, et al
.
Activation of proto-oncogenes by disruption of chromosome neighborhoods
.
Science
2016
;
351
:
1454
8
.
61.
Yang
M
,
Safavi
S
,
Woodward
EL
,
Duployez
N
,
Olsson-Arvidsson
L
,
Ungerbäck
J
, et al
.
13q12.2 deletions in acute lymphoblastic leukemia lead to upregulation of FLT3 through enhancer hijacking
.
Blood
2020
;
136
:
946
56
.
62.
Nora
EP
,
Goloborodko
A
,
Valton
A-L
,
Gibcus
JH
,
Uebersohn
A
,
Abdennur
N
, et al
.
Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization
.
Cell
2017
;
169
:
930
44
.
63.
Huang
H
,
Zhu
Q
,
Jussila
A
,
Han
Y
,
Bintu
B
,
Kern
C
, et al
.
CTCF mediates dosage- and sequence-context-dependent transcriptional insulation by forming local chromatin domains
.
Nat Genet
2021
;
53
:
1064
74
.
64.
Flavahan
WA
,
Drier
Y
,
Liau
BB
,
Gillespie
SM
,
Venteicher
AS
,
Stemmer-Rachamimov
AO
, et al
.
Insulator dysfunction and oncogene activation in IDH mutant gliomas
.
Nature
2016
;
529
:
110
4
.
65.
Flavahan
WA
,
Drier
Y
,
Johnstone
SE
,
Hemming
ML
,
Tarjan
DR
,
Hegazi
E
, et al
.
Altered chromosomal topology drives oncogenic programs in SDH-deficient GISTs
.
Nature
2019
;
575
:
229
33
.
66.
Yang
M
,
Vesterlund
M
,
Siavelis
I
,
Moura-Castro
LH
,
Castor
A
,
Fioretos
T
, et al
.
Proteogenomics and Hi-C reveal transcriptional dysregulation in high hyperdiploid childhood acute lymphoblastic leukemia
.
Nat Commun
2019
;
10
:
1519
.
67.
Anania
C
,
Acemel
RD
,
Jedamzick
J
,
Bolondi
A
,
Cova
G
,
Brieske
N
, et al
.
In vivo dissection of a clustered-CTCF domain boundary reveals developmental principles of regulatory insulation
.
Nat Genet
2022
;
54
:
1026
36
.
68.
Lambuta
RA
,
Nanni
L
,
Liu
Y
,
Diaz-Miyar
J
,
Iyer
A
,
Tavernari
D
, et al
.
Whole-genome doubling drives oncogenic loss of chromatin segregation
.
Nature
2023
;
615
:
925
33
.
69.
Gemble
S
,
Wardenaar
R
,
Keuper
K
,
Srivastava
N
,
Nano
M
,
Macé
A-S
, et al
.
Genetic instability from a single S phase after whole-genome duplication
.
Nature
2022
;
604
:
146
51
.
70.
Dixon
JR
,
Jung
I
,
Selvaraj
S
,
Shen
Y
,
Antosiewicz-Bourget
JE
,
Lee
AY
, et al
.
Chromatin architecture reorganization during stem cell differentiation
.
Nature
2015
;
518
:
331
6
.
71.
Liu
Y
,
Nanni
L
,
Sungalee
S
,
Zufferey
M
,
Tavernari
D
,
Mina
M
, et al
.
Systematic inference and comparison of multi-scale chromatin sub-compartments connects spatial organization to cell phenotypes
.
Nat Commun
2021
;
12
:
2439
.
72.
Sungalee
S
,
Liu
Y
,
Lambuta
RA
,
Katanayeva
N
,
Donaldson Collier
M
,
Tavernari
D
, et al
.
Histone acetylation dynamics modulates chromatin conformation and allele-specific interactions at oncogenic loci
.
Nat Genet
2021
;
53
:
650
62
.
73.
Rosencrance
CD
,
Ammouri
HN
,
Yu
Q
,
Ge
T
,
Rendleman
EJ
,
Marshall
SA
, et al
.
Chromatin hyperacetylation impacts chromosome folding by forming a nuclear subcompartment
.
Mol Cell
2020
;
78
:
112
26
.
74.
Koschmann
C
,
Nunez
FJ
,
Mendez
F
,
Brosnan-Cashman
JA
,
Meeker
AK
,
Lowenstein
PR
, et al
.
Mutated chromatin regulatory factors as tumor drivers in cancer
.
Cancer Res
2017
;
77
:
227
33
.
75.
Loukas
I
,
Simeoni
F
,
Milan
M
,
Inglese
P
,
Patel
H
,
Goldstone
R
, et al
.
Selective advantage of epigenetically disrupted cancer cells via phenotypic inertia
.
Cancer Cell
2023
;
41
:
70
87
.
76.
Wainwright
EN
,
Scaffidi
P
.
Epigenetics and cancer stem cells: unleashing, hijacking, and restricting cellular plasticity
.
Trends Cancer
2017
;
3
:
372
86
.
77.
Kaufman
CK
,
Mosimann
C
,
Fan
ZP
,
Yang
S
,
Thomas
AJ
,
Ablain
J
, et al
.
A zebrafish melanoma model reveals emergence of neural crest identity during melanoma initiation
.
Science
2016
;
351
:
aad2197
.
78.
Ciriello
G
,
Magnani
L
.
The many faces of cancer evolution.
iScience
2021
;
24
:
102403
.
79.
Hahn
WC
,
Bader
JS
,
Braun
TP
,
Califano
A
,
Clemons
PA
,
Druker
BJ
, et al
.
An expanded universe of cancer targets
.
Cell
2021
;
184
:
1142
55
.
80.
Gavish
A
,
Tyler
M
,
Greenwald
AC
,
Hoefflin
R
,
Simkin
D
,
Tschernichovsky
R
, et al
.
Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours
.
Nature
2023
;
618
:
598
606
.
81.
Karthaus
WR
,
Hofree
M
,
Choi
D
,
Linton
EL
,
Turkekul
M
,
Bejnood
A
, et al
.
Regenerative potential of prostate luminal cells revealed by single-cell analysis
.
Science
2020
;
368
:
497
505
.
82.
Kinker
GS
,
Greenwald
AC
,
Tal
R
,
Orlova
Z
,
Cuoco
MS
,
McFarland
JM
, et al
.
Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity
.
Nat Genet
2020
;
52
:
1208
18
.
83.
Barkley
D
,
Moncada
R
,
Pour
M
,
Liberman
DA
,
Dryg
I
,
Werba
G
, et al
.
Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment
.
Nat Genet
2022
;
54
:
1192
201
.
84.
van Galen
P
,
Hovestadt
V
,
Wadsworth Ii
MH
,
Hughes
TK
,
Griffin
GK
,
Battaglia
S
, et al
.
Single-Cell RNA-seq reveals AML hierarchies relevant to disease progression and immunity
.
Cell
2019
;
176
:
1265
81
.
85.
Rodriguez-Meira
A
,
Buck
G
,
Clark
S-A
,
Povinelli
BJ
,
Alcolea
V
,
Louka
E
, et al
.
Unravelling intratumoral heterogeneity through high-sensitivity single-cell mutational analysis and parallel RNA sequencing
.
Mol Cell
2019
;
73
:
1292
305
.
86.
Gaiti
F
,
Chaligne
R
,
Gu
H
,
Brand
RM
,
Kothen-Hill
S
,
Schulman
RC
, et al
.
Epigenetic evolution and lineage histories of chronic lymphocytic leukaemia
.
Nature
2019
;
569
:
576
80
.
87.
Nam
AS
,
Chaligne
R
,
Landau
DA
.
Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics
.
Nat Rev Genet
2021
;
22
:
3
18
.
88.
Tedesco
M
,
Giannese
F
,
Lazarević
D
,
Giansanti
V
,
Rosano
D
,
Monzani
S
, et al
.
Chromatin velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin
.
Nat Biotechnol
2022
;
40
:
235
44
.
89.
Chan
JM
,
Zaidi
S
,
Love
JR
,
Zhao
JL
,
Setty
M
,
Wadosky
KM
, et al
.
Lineage plasticity in prostate cancer depends on JAK/STAT inflammatory signaling
.
Science
2022
;
377
:
1180
91
.
90.
Kamimoto
K
,
Stringa
B
,
Hoffmann
CM
,
Jindal
K
,
Solnica-Krezel
L
,
Morris
SA
.
Dissecting cell identity via network inference and in silico gene perturbation
.
Nature
2023
;
614
:
742
51
.
91.
Nam
AS
,
Kim
K-T
,
Chaligne
R
,
Izzo
F
,
Ang
C
,
Taylor
J
, et al
.
Somatic mutations and cell identity linked by genotyping of transcriptomes
.
Nature
2019
;
571
:
355
60
.
92.
Nam
AS
,
Dusaj
N
,
Izzo
F
,
Murali
R
,
Myers
RM
,
Mouhieddine
TH
, et al
.
Single-cell multi-omics of human clonal hematopoiesis reveals that DNMT3A R882 mutations perturb early progenitor states through selective hypomethylation
.
Nat Genet
2022
;
54
:
1514
26
.
93.
Izzo
F
,
Lee
SC
,
Poran
A
,
Chaligne
R
,
Gaiti
F
,
Gross
B
, et al
.
DNA methylation disruption reshapes the hematopoietic differentiation landscape
.
Nat Genet
2020
;
52
:
378
87
.
94.
Chaligne
R
,
Gaiti
F
,
Silverbush
D
,
Schiffman
JS
,
Weisman
HR
,
Kluegel
L
, et al
.
Epigenetic encoding, heritability and plasticity of glioma transcriptional cell states
.
Nat Genet
2021
;
53
:
1469
79
.
95.
Hanahan
D
.
Heritable formation of pancreatic beta-cell tumours in transgenic mice expressing recombinant insulin/simian virus 40 oncogenes
.
Nature
1985
;
315
:
115
22
.
96.
Olson
P
,
Lu
J
,
Zhang
H
,
Shai
A
,
Chun
MG
,
Wang
Y
, et al
.
MicroRNA dynamics in the stages of tumorigenesis correlate with hallmark capabilities of cancer
.
Genes Dev
2009
;
23
:
2152
65
.
97.
Michael
IP
,
Saghafinia
S
,
Hanahan
D
.
A set of microRNAs coordinately controls tumorigenesis, invasion, and metastasis
.
Proc Natl Acad Sci U S A
2019
;
116
:
24184
95
.
98.
Michael
IP
,
Saghafinia
S
,
Tichet
M
,
Zangger
N
,
Marinoni
I
,
Perren
A
, et al
.
ALK7 signaling manifests a homeostatic tissue barrier that is abrogated during tumorigenesis and metastasis
.
Dev Cell
2019
;
49
:
409
24
.
99.
Saghafinia
S
,
Homicsko
K
,
Di Domenico
A
,
Wullschleger
S
,
Perren
A
,
Marinoni
I
, et al
.
Cancer cells retrace a stepwise differentiation program during malignant progression
.
Cancer Discov
2021
;
11
:
2638
57
.
100.
Travis
WD
,
Brambilla
E
,
Nicholson
AG
,
Yatabe
Y
,
Austin
JHM
,
Beasley
MB
, et al
.
The 2015 World Health Organization classification of lung tumors: impact of genetic, clinical and radiologic advances since the 2004 classification
.
J Thorac Oncol
2015
;
10
:
1243
60
.
101.
Tavernari
D
,
Battistello
E
,
Dheilly
E
,
Petruzzella
AS
,
Mina
M
,
Sordet-Dessimoz
J
, et al
.
Nongenetic evolution drives lung adenocarcinoma spatial heterogeneity and progression
.
Cancer Discov
2021
;
11
:
1490
507
.
102.
Hong
SP
,
Chan
TE
,
Lombardo
Y
,
Corleone
G
,
Rotmensz
N
,
Bravaccini
S
, et al
.
Single-cell transcriptomics reveals multi-step adaptations to endocrine therapy
.
Nat Commun
2019
;
10
:
3840
.
103.
Rambow
F
,
Rogiers
A
,
Marin-Bejar
O
,
Aibar
S
,
Femel
J
,
Dewaele
M
, et al
.
Toward minimal residual disease-directed therapy in melanoma
.
Cell
2018
;
174
:
843
55
.
104.
Razavi
P
,
Chang
MT
,
Xu
G
,
Bandlamudi
C
,
Ross
DS
,
Vasan
N
, et al
.
The genomic landscape of endocrine-resistant advanced breast cancers
.
Cancer Cell
2018
;
34
:
427
38
.
105.
Patten
DK
,
Corleone
G
,
Győrffy
B
,
Perone
Y
,
Slaven
N
,
Barozzi
I
, et al
.
Enhancer mapping uncovers phenotypic heterogeneity and evolution in patients with luminal breast cancer
.
Nat Med
2018
;
24
:
1469
80
.
106.
Marin-Bejar
O
,
Rogiers
A
,
Dewaele
M
,
Femel
J
,
Karras
P
,
Pozniak
J
, et al
.
Evolutionary predictability of genetic versus nongenetic resistance to anticancer drugs in melanoma
.
Cancer Cell
2021
;
39
:
1135
49
.
107.
Viswanathan
VS
,
Ryan
MJ
,
Dhruv
HD
,
Gill
S
,
Eichhoff
OM
,
Seashore-Ludlow
B
, et al
.
Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway
.
Nature
2017
;
547
:
453
7
.
108.
Raghavan
S
,
Winter
PS
,
Navia
AW
,
Williams
HL
,
DenAdel
A
,
Lowder
KE
, et al
.
Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer
.
Cell
2021
;
184
:
6119
37
.
109.
Marx
V
.
Method of the year: spatially resolved transcriptomics
.
Nat Methods
2021
;
18
:
9
14
.
110.
Moffitt
JR
,
Lundberg
E
,
Heyn
H
.
The emerging landscape of spatial profiling technologies
.
Nat Rev Genet
2022
;
23
:
741
59
.
111.
Karras
P
,
Bordeu
I
,
Pozniak
J
,
Nowosad
A
,
Pazzi
C
,
Van Raemdonck
N
, et al
.
A cellular hierarchy in melanoma uncouples growth and metastasis
.
Nature
2022
;
610
:
190
8
.
112.
Polyak
K
,
Haviv
I
,
Campbell
IG
.
Co-evolution of tumor cells and their microenvironment
.
Trends Genet
2009
;
25
:
30
8
.
113.
Hirata
E
,
Sahai
E
.
Tumor microenvironment and differential responses to therapy
.
Cold Spring Harb Perspect Med
2017
;
7
:
a026781
.
114.
Binnewies
M
,
Roberts
EW
,
Kersten
K
,
Chan
V
,
Fearon
DF
,
Merad
M
, et al
.
Understanding the tumor immune microenvironment (TIME) for effective therapy
.
Nat Med
2018
;
24
:
541
50
.
115.
Haas
L
,
Elewaut
A
,
Gerard
CL
,
Umkehrer
C
,
Leiendecker
L
,
Pedersen
M
, et al
.
Acquired resistance to anti-MAPK targeted therapy confers an immune-evasive tumor microenvironment and cross-resistance to immunotherapy in melanoma
.
Nat Cancer
2021
;
2
:
693
708
.
116.
Kielbassa
K
,
Vegna
S
,
Ramirez
C
,
Akkari
L
.
Understanding the origin and diversity of macrophages to tailor their targeting in solid cancers
.
Front Immunol
2019
;
10
:
2215
.
117.
Pittet
MJ
,
Michielin
O
,
Migliorini
D
.
Clinical relevance of tumour-associated macrophages
.
Nat Rev Clin Oncol
2022
;
19
:
402
21
.
118.
Erbani
J
,
Boon
M
,
Akkari
L
.
Therapy-induced shaping of the glioblastoma microenvironment: macrophages at play
.
Semin Cancer Biol
2022
;
86
:
41
56
.
119.
Borst
K
,
Dumas
AA
,
Prinz
MM
.
Immune and non-immune functions
.
Immunity
2021
;
54
:
2194
208
.
120.
Wu
L
,
Wu
W
,
Zhang
J
,
Zhao
Z
,
Li
L
,
Zhu
M
, et al
.
Natural coevolution of tumor and immunoenvironment in glioblastoma
.
Cancer Discov
2022
;
12
:
2820
37
.
121.
Kumar
V
,
Ramnarayanan
K
,
Sundar
R
,
Padmanabhan
N
,
Srivastava
S
,
Koiwa
M
, et al
.
Single-cell atlas of lineage states, tumor microenvironment, and subtype-specific expression programs in gastric cancer
.
Cancer Discov
2022
;
12
:
670
91
.
122.
Wu
SZ
,
Al-Eryani
G
,
Roden
DL
,
Junankar
S
,
Harvey
K
,
Andersson
A
, et al
.
A single-cell and spatially resolved atlas of human breast cancers
.
Nat Genet
2021
;
53
:
1334
47
.
123.
Jackson
HW
,
Fischer
JR
,
Zanotelli
VRT
,
Ali
HR
,
Mechera
R
,
Soysal
SD
, et al
.
The single-cell pathology landscape of breast cancer
.
Nature
2020
;
578
:
615
20
.
124.
Karimi
E
,
Yu
MW
,
Maritan
SM
,
Perus
LJM
,
Rezanejad
M
,
Sorin
M
, et al
.
Single-cell spatial immune landscapes of primary and metastatic brain tumours
.
Nature
2023
;
614
:
555
63
.
125.
Sorin
M
,
Rezanejad
M
,
Karimi
E
,
Fiset
B
,
Desharnais
L
,
Perus
LJM
, et al
.
Single-cell spatial landscapes of the lung tumour immune microenvironment
.
Nature
2023
;
614
:
548
54
.
126.
Danenberg
E
,
Bardwell
H
,
Zanotelli
VRT
,
Provenzano
E
,
Chin
S-F
,
Rueda
OM
, et al
.
Breast tumor microenvironment structures are associated with genomic features and clinical outcome
.
Nat Genet
2022
;
54
:
660
9
.
127.
Stubbington
MJT
,
Rozenblatt-Rosen
O
,
Regev
A
,
Teichmann
SA
.
Single-cell transcriptomics to explore the immune system in health and disease
.
Science
2017
;
358
:
58
63
.
128.
Andreatta
M
,
Corria-Osorio
J
,
Müller
S
,
Cubas
R
,
Coukos
G
,
Carmona
SJ
.
Interpretation of T cell states from single-cell transcriptomics data using reference atlases
.
Nat Commun
2021
;
12
:
2965
.
129.
Öhlund
D
,
Handly-Santana
A
,
Biffi
G
,
Elyada
E
,
Almeida
AS
,
Ponz-Sarvise
M
, et al
.
Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer
.
J Exp Med
2017
;
214
:
579
96
.
130.
Wu
SZ
,
Roden
DL
,
Wang
C
,
Holliday
H
,
Harvey
K
,
Cazet
AS
, et al
.
Stromal cell diversity associated with immune evasion in human triple-negative breast cancer
.
EMBO J
2020
;
39
:
e104063
.
131.
Wu
SZ
,
Swarbrick
A
.
Single-cell advances in stromal-leukocyte interactions in cancer
.
Immunol Rev
2021
;
302
:
286
98
.
132.
Grout
JA
,
Sirven
P
,
Leader
AM
,
Maskey
S
,
Hector
E
,
Puisieux
I
, et al
.
Spatial positioning and matrix programs of cancer-associated fibroblasts promote t-cell exclusion in human lung tumors
.
Cancer Discov
2022
;
12
:
2606
25
.
133.
Li
L
,
Hanahan
D
.
Hijacking the neuronal NMDAR signaling circuit to promote tumor growth and invasion
.
Cell
2013
;
153
:
86
100
.
134.
Li
L
,
Zeng
Q
,
Bhutkar
A
,
Galván
JA
,
Karamitopoulou
E
,
Noordermeer
D
, et al
.
GKAP acts as a genetic modulator of NMDAR signaling to govern invasive tumor growth
.
Cancer Cell
2018
;
33
:
736
51
.
135.
Bejarano
L
,
Jordāo
MJC
,
Joyce
JA
.
Therapeutic targeting of the tumor microenvironment
.
Cancer Discov
2021
;
11
:
933
59
.
136.
Vendramin
R
,
Litchfield
K
,
Swanton
C
.
Cancer evolution: darwin and beyond
.
EMBO J
2021
;
40
:
e108389
.
137.
Black
JRM
,
McGranahan
N
.
Genetic and non-genetic clonal diversity in cancer evolution
.
Nat Rev Cancer
2021
;
21
:
379
92
.
138.
Quintanal-Villalonga
A
,
Taniguchi
H
,
Zhan
YA
,
Hasan
MM
,
Chavan
SS
,
Meng
F
, et al
.
Multiomic analysis of lung tumors defines pathways activated in neuroendocrine transformation
.
Cancer Discov
2021
;
11
:
3028
47
.
139.
Pavlova
NN
,
Thompson
CB
.
The emerging hallmarks of cancer meta­bolism
.
Cell Metab
2016
;
23
:
27
47
.
140.
Fendt
S-M
,
Frezza
C
,
Erez
A
.
Targeting metabolic plasticity and flexibility dynamics for cancer therapy
.
Cancer Discov
2020
;
10
:
1797
807
.
141.
Martínez-Reyes
I
,
Chandel
NS
.
Cancer metabolism: looking forward
.
Nat Rev Cancer
2021
;
21
:
669
80
.
142.
Helmink
BA
,
Khan
MAW
,
Hermann
A
,
Gopalakrishnan
V
,
Wargo
JA
.
The microbiome, cancer, and cancer therapy
.
Nat Med
2019
;
25
:
377
88
.
143.
Sepich-Poore
GD
,
Zitvogel
L
,
Straussman
R
,
Hasty
J
,
Wargo
JA
,
Knight
R
.
The microbiome and human cancer
.
Science
2021
;
371
:
eabc4552
.
144.
Hill
W
,
Lim
EL
,
Weeden
CE
,
Lee
C
,
Augustine
M
,
Chen
K
, et al
.
Lung adenocarcinoma promotion by air pollutants
.
Nature
2023
;
616
:
159
67
.
145.
Jarosz
DF
,
Lindquist
S
.
Hsp90 and environmental stress transform the adaptive value of natural genetic variation
.
Science
2010
;
330
:
1820
4
.
146.
Rohner
N
,
Jarosz
DF
,
Kowalko
JE
,
Yoshizawa
M
,
Jeffery
WR
,
Borowsky
RL
, et al
.
Cryptic variation in morphological evolution: HSP90 as a capacitor for loss of eyes in cavefish
.
Science
2013
;
342
:
1372
5
.
147.
Lipinski
KA
,
Barber
LJ
,
Davies
MN
,
Ashenden
M
,
Sottoriva
A
,
Gerlinger
M
.
Cancer evolution and the limits of predictability in precision cancer medicine
.
Trends Cancer
2016
;
2
:
49
63
.
148.
Fountzilas
E
,
Tsimberidou
AM
,
Vo
HH
,
Kurzrock
R
.
Clinical trial design in the era of precision medicine
.
Genome Med
2022
;
14
:
101
.
149.
Bailey
C
,
Black
JRM
,
Reading
JL
,
Litchfield
K
,
Turajlic
S
,
McGranahan
N
, et al
.
Tracking cancer evolution through the disease course
.
Cancer Discov
2021
;
11
:
916
32
.
150.
Turajlic
S
,
Xu
H
,
Litchfield
K
,
Rowan
A
,
Horswell
S
,
Chambers
T
, et al
.
Deterministic evolutionary trajectories influence primary tumor growth: TRACERx renal
.
Cell
2018
;
173
:
595
610
.
151.
Turajlic
S
,
Xu
H
,
Litchfield
K
,
Rowan
A
,
Chambers
T
,
Lopez
JI
, et al
.
Tracking cancer evolution reveals constrained routes to metastases: TRACERx Renal
.
Cell
2018
;
173
:
581
94
.
152.
Al Bakir
M
,
Huebner
A
,
Martínez-Ruiz
C
,
Grigoriadis
K
,
Watkins
TBK
,
Pich
O
, et al
.
The evolution of non-small cell lung cancer metastases in TRACERx
.
Nature
2023
;
616
:
534
42
.
153.
Martínez-Ruiz
C
,
Black
JRM
,
Puttick
C
,
Hill
MS
,
Demeulemeester
J
,
Larose Cadieux
E
, et al
.
Genomic–transcriptomic evolution in lung cancer and metastasis
.
Nature
2023
;
616
:
543
52
.
154.
Zhang
J
,
Cunningham
JJ
,
Brown
JS
,
Gatenby
RA
.
Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer
.
Nat Commun
2017
;
8
:
1816
.
155.
Acar
A
,
Nichol
D
,
Fernandez-Mateos
J
,
Cresswell
GD
,
Barozzi
I
,
Hong
SP
, et al
.
Exploiting evolutionary steering to induce collateral drug sensitivity in cancer
.
Nat Commun
2020
;
11
:
1923
.
156.
Wölfl
B
,
te Rietmole
H
,
Salvioli
M
,
Kaznatcheev
A
,
Thuijsman
F
,
Brown
JS
, et al
.
The contribution of evolutionary game theory to understanding and treating cancer
.
Dyn Games Appl
2022
;
12
:
313
42
.
157.
Bhinder
B
,
Gilvary
C
,
Madhukar
NS
,
Elemento
O
.
Artificial intelligence in cancer research and precision medicine
.
Cancer Discov
2021
;
11
:
900
15
.
This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.