Skip to main content
Download PDF

The role of tumour heterogeneity and clonal cooperativity in metastasis, immune evasion and clinical outcome

BMC Medicine volume 15, Article number: 133 (2017) Cite this article

Abstract

Background

The advent of rapid and inexpensive sequencing technology allows scientists to decipher heterogeneity within primary tumours, between primary and metastatic sites, and between metastases. Charting the evolutionary history of individual tumours has revealed drivers of tumour heterogeneity and highlighted its impact on therapeutic outcomes.

Discussion

Scientists are using improved sequencing technologies to characterise and address the challenge of tumour heterogeneity, which is a major cause of resistance to therapy and relapse. Heterogeneity may fuel metastasis through the selection of rare, aggressive, somatically altered cells. However, extreme levels of chromosomal instability, which contribute to intratumour heterogeneity, are associated with improved patient outcomes, suggesting a delicate balance between high and low levels of genome instability.

Conclusions

We review evidence that intratumour heterogeneity influences tumour evolution, including metastasis, drug resistance, and the immune response. We discuss the prevalence of tumour heterogeneity, and how it can be initiated and sustained by external and internal forces. Understanding tumour evolution and metastasis could yield novel therapies that leverage the immune system to control emerging tumour neo-antigens.

Peer Review reports

Background

In his 1958 essay, Foulds [1] explains that linear tumour progression, the orthodox view of tumour evolution at the time, is the theory that neoplasia advances through an orderly sequence from local invasion, to progressive lymph node invasion, to metastasis [1]. Foulds goes on to discuss tumour progression and metastasis in several cancer types and concludes that neoplasia is not linear, but a complex, ever-diverging route throughout tumour development [1]. This was one of the first articulate explanations of tumourigenesis as a multistep pathway that can progress, persist, or regress [1]. Nowell’s 1976 [2] cancer evolution model proposed that genomic instability drives branched evolutionary pathways from a clone of origin. Heppner [3] reviewed literature on the emergence of heterogeneity in tumours, and the challenges surrounding its study, emphasising that tumour cells exist in a society, and that the interactions between heterogeneous populations are as important as the subclones themselves [3].

Recent technological advances have allowed scientists to study tumour complexity in more detail. Following exome sequencing on multiple spatially separated samples obtained from primary carcinomas and metastatic sites, intratumour heterogeneity and parallel evolution of subclonal driver events was uncovered [4,5,6,7]. Multi-region sequencing has now been performed in many cancer types including breast, lung, colorectal, renal, oesophageal cancer and glioma, uncovering intratumour heterogeneity in all cancer types studied [4,5,6,7,8,9,10,11,12,13,14,15].

Drivers of heterogeneity

Intratumoral heterogeneity exists in many forms, from somatic coding and non-coding alterations to epigenetic, transcriptomic and post-translational modifications [16, 17]. Intratumoral copy number heterogeneity also exists (see [16]). Many endogenous triggers of cancer genome instability contribute to intercellular heterogeneity (see [18]), some of which may be therapeutically exploitable. Defective DNA mismatch repair results in hypermutation and microsatellite instability, and mutations inhibiting the proofreading ability of DNA polymerases δ and ε increase base mismatches [19]. Evidence also exists that tumour cell dormancy contributes to tumour heterogeneity (see [20]). Recently, it has been uncovered that APOBEC (apolipoprotein B mRNA editing enzyme catalytic polypeptide) family members are endogenous drivers of tumour diversity in many tumour types [21]. These enzymes initiate DNA cytosine deamination [21, 22], and are a major source of subclonal cancer gene mutations in bladder, breast, head and neck squamous cancers, lung adenocarcinomas and lung squamous cell carcinomas [21, 23,24,25,26]. External factors such as cytotoxic therapy [27, 28], and patient factors such as genetic background [29, 30], can also influence tumour heterogeneity.

Mechanisms mediating levels of genomic instability and tumour heterogeneity

Cahill and Vogelstein [31] discussed the conflict between disadvantages and advantages of genomic instability in tumour evolution. They questioned how cancer cells are able to select for alterations driving genomic instability, which is, usually disadvantageous to the cell, can sometimes lead to cell death, and has no direct growth advantage [31]. Looking to basic studies of mutation rate and cellular fitness in bacteria, the authors reasoned that in stressful environments bacteria with higher overall levels of genomic instability eventually dominate the population because they can adapt [31]. This model can be applied to tumour populations, where genomic instability may be critical for tumour progression [31].

Most normal diploid cells negatively select against chromosomal instability (CIN). This is partially mediated by p53, which inhibits cell propagation after genome instability [32, 33]. CIN mouse models support the concept that low or moderate CfIN levels promote tumour formation, but excessive CIN suppresses tumour formation [34]. This is analogous to mutational meltdown and error-prone catastrophe in bacterial and viral genetics [34,35,36,37]. Aneuploid, specifically trisomic cell lines, grow poorly in vitro and as xenografts compared to genetically matched euploid cells [38]. Yet, following prolonged growth, aneuploid cells adapt by acquiring additional alterations correlating with improved fitness [38]. While aneuploidy was detrimental initially, over time it became more advantageous.

In tumours, selection might favour the mitigation of excessive CIN to prevent cell autonomous lethality. Partial dysfunction of anaphase-promoting complex/cyclosome (APC/C) lengthens mitosis, allowing more time for correction of impending chromosome segregation errors. This permits tumour cells to fine-tune CIN during tumour evolution, navigating the delicate equilibrium between cell death as a result of too much or too little genomic instability [39]. In colorectal cancer, alterations in BCL9L promote tolerance of chromosome missegregation events, propagation of aneuploidy and genetic heterogeneity [40]. This tolerance is induced because BCL9L dysfunction leads to lower levels of caspase-2, impairing MDM2 cleavage, p53 stabilisation, and generation of the pro-apoptotic protein tBID, upon chromosome missegregation [40].

Once cells have undergone genome doubling (GD), propagation of aneuploidy follows as the tolerance of GD allows cells to endure continuing CIN [41]. One mechanism of GD tolerance involves cyclin D1, which overrides a p53/p21-dependent checkpoint in G1, allowing tetraploid cells to proliferate [42, 43]. Additional mechanisms mediating levels of instability include buffering protein changes caused by aneuploidy, activating autophagy, and enhancing proteasomal degradation [44, 45]. Mechanisms that both buffer and allow cells to tolerate instability contribute to heterogeneity in tumours, as cells that propagate genomic alterations and aneuploidy survive. Understanding the processes that balance tumour heterogeneity and promote aneuploidy tolerance may contribute to novel therapeutics to prevent tumour heterogeneity and drug resistance.

Tumour heterogeneity and metastasis

The study of intratumour heterogeneity has yielded novel findings about the timing and evolutionary drivers of metastasis. Metastasis is a multistep process consisting of local invasion, intravasation, survival in the circulation, extravasation and distant colonisation [46, 47].

Two prominent models of metastasis are the linear and parallel progression models [46,47,48]. Both are founded on the clonal relationships between a primary tumour and its metastases [47]. In the linear progression model, metastasis is seeded at a late stage of tumour progression, resulting in minimal genetic divergence between the primary tumour and its metastases [47]. Conversely, in the parallel progression model, metastases are seeded early in tumour progression, so high levels of genetic divergence are expected between the primary tumour and its metastases [47, 49, 50] (Fig. 1). To reconcile either model requires accounting for the subclonal complexity of the primary tumour and its relationship to subclones present at the metastatic sites (see [47]).

Fig. 1
figure 1

The linear progression model versus the parallel progression model of tumour evolution. In the linear progression model (upper panel), late stage tumour cells disseminate and form metastases. In contrast, in the parallel progression model (lower panel), early tumour cells disseminate and form metastases alongside the primary tumour, and both primary tumour and metastases progress in parallel gaining multiple subclonal populations

Full size image

In colorectal, breast, pancreatic, renal, and prostate cancer, multiregional sequencing supports both metastatic models [47]. Both models were observed in one colorectal cancer case [51]. One large metastasis had genetically diverged from the primary tumour suggesting early formation, while the other metastases were similar to the primary tumour, so most likely disseminated at a late stage of tumour progression [51]. This implies that heterogeneous subclones can form multiple distinct metastases. In breast and pancreatic cancer mouse models, metastases aligned with the parallel progression model [50, 52,53,54], whereas the linear progression model was supported in a lung adenocarcinoma mouse model [48].

These data suggest metastasis is complex and diverse, and that tumour heterogeneity is critical in the metastatic process. Since more work is required to understand tumour heterogeneity in late stage tumours and metastases, in depth efforts are now examining post-mortem tumour heterogeneity. The UK national PEACE autopsy (posthumous evaluation of advanced cancer environment) program, may help to decipher the complexity cancer evolutionary processes at death.

Subclonal interactions in heterogeneous tumours

Background and Drosophila melanogaster models of subclonal interaction

It was recognised in the 1970s that heterogeneous subclones within tumours contain differing capacities for growth and metastatic ability [55,56,57]. Early work also recognised that interactions between subpopulations resulted in differences in drug sensitivity when injected into opposite flanks of mice or co-cultured in collagen [58, 59]. The importance of these studies was not fully recognised until over two decades later [60].

Growing interest in tumour heterogeneity and subclonal interactions led to the development of a useful model for studying subclonal competition and cooperation in Drosophila melanogaster [60]. Eichenlaub et al. [61] used a D. melanogaster model of epithelial tumour formation to show that overexpression of both epidermal growth factor receptor (EGFR) and miR-8 in wing imaginal disc cells results in supercompetitive cells that engulf those surrounding them. These supercompetitive cells drive tumourigenesis and metastasis, whereas cells overexpressing either EGFR or miR-8 alone do not. Competition between normal and oncogenic tissues was also uncovered: in D. melanogaster, imaginal epithelial cells activate nonapoptotic JNK signalling in response to oncogenic mutant cells [62]. This leads to the activation of the ELMO/Mbc-mediated phagocytic pathway, which eliminates oncogenic cells [62]. Growing evidence suggests that subclones within tumours compete, but also cooperate. Several D. melanogaster studies demonstrated cooperation between cells with oncogenic Ras V12 mutations and cells lacking the scribbled gene (scrib-/-) to promote growth and invasion of the Ras V12 mutant cells [60, 63].

Xenotransplant models of subclonal interaction

Xenotransplant models have been used to understand interactions between human tumour subclones. In a glioblastoma multiforme mouse xenotransplant model, a minor mutant EGFR subpopulation enhanced tumourigenicity of the entire tumour. This occurred via a paracrine mechanism that promoted growth in wtEGFR cells within the tumour, illustrating the advantages of heterogeneity [64]. In a zebrafish-melanoma xenograft model, inherently invasive (MITFhigh) melanoma cells cooperated with poorly invasive (MITFlow) cells to invade away from the primary site through solid tissue [65]. The protease activity and extracellular matrix deposition of MITFhigh cells around the primary tumour was critical for this co-invasion of MITFhigh and MITFlow cells [65]. Heterogeneity may be critical in tumour progression and metastasis.

Polyak and colleagues [66] used a mouse xenograft model to investigate the effect of a polyclonal tumour population on tumour growth and metastasis. A minor IL11-overexpressing subclone changed the tumour microenvironment by increasing intratumoural vascularisation and reorganising extracellular matrix, in turn increasing tumour growth [66]. Importantly, this subclone was also outcompeted by a faster proliferating competitor, leading to tumour collapse [66].

These xenograft studies provide insights into subclonal interaction dynamics, demonstrating their complexity, and that they are involved in many aspects of tumour evolution including tumorigenesis, vascularisation, invasion and metastasis.

Mouse models of subclonal interaction

Advances in molecular biology have allowed a deeper study of subclonal cooperation in cancer mouse models. Autochthonous models permit long-term study of clonal dynamics in a specific genetic context [67].

In a mouse model of small cell lung cancer (SCLC), tumours were composed of distinct cell populations with either neuroendocrine or mesenchymal markers [68]. Both cell types shared specific genomic aberrations derived from a common ancestor [68]. Transition between the neuroendocrine and mesenchymal phenotype was achieved through ectopic expression of RasV12, and the mixed population endowed mesenchymal cells with metastatic ability [68].

Cleary and colleagues [69] used a mouse model of breast cancer to show that interclonal cooperation can be critical for tumour propagation. A portion of tumours harboured distinct Hras mut Wnt low and luminal Hras wt Wnt1 high subclones, which cooperated to maintain tumour propagation [69]. Although Wnt activation is rare in human breast cancer, this remains a valuable model for studying paracrine mechanisms of subclonal communication.

In a p53-null mouse model of basal-like breast cancer, two tumour cell populations were identified, one expressing mesenchymal markers (“mesenchymal-like” cells), and another defined as tumour-initiating cells (TICs) [70]. When both populations were co-transplanted using limited dilutions, mesenchymal-like cells promoted self-renewal and tumour initiation capacity of the TICs [67]. Surprisingly, the mesenchymal population was maintained as a minor subpopulation [70].

Mouse models have revealed how subpopulations within tumours cooperate to promote tumour growth, maintenance and metastasis. Although not fully representative of the clinical presentation of disease in patients, these autochthonous models show the diverse ways in which subclones can interact and affect tumour progression. These studies suggest that to more effectively treat heterogeneous tumours, we must first understand the dynamics between different populations within a tumour, and how targeted treatment changes these interactions.

Therapy and subclonal resistance

Therapeutic resistance often develops in the advanced setting following therapeutic targeting of clonal alterations, as subclones containing somatic events driving resistance can pre-exist or be generated de novo.

In non-small cell lung cancer (NSCLC) a low frequency subclone with MET amplification was selected for during treatment with EGFR tyrosine kinase inhibitors (TKI), allowing for development of a resistant MET- amplified tumour [71]. The EGFR T790M mutation, which drives EGFR TKI resistance, was also present prior to treatment in NSCLC [72]. In colorectal cancer, EGFR antibody-resistant subclones with pre-existing KRAS mutations emerged in 38% of patients following 5–6 months of treatment with panitumumab [73]. In patients with NSCLC, a subset of EGFR TKI resistant cancers acquire alterations, such as RB and p53 loss contributing to small cell carcinoma transformation [74].

In patients with HER2-positive breast cancer, Janiszewska et al. [75] discovered a dramatic increase in a minor resistant population of PIK3CA mutant cells, and a slight decrease in the dominant population of HER2-amplified cells post neoadjuvant therapy with trastuzamab. Alterations in the phosphoinositide 3-kinase (PI3K)-AKT pathway commonly drive resistance in patients treated with the HER2-targeting antibody trastuzamab [75].

In patients with colorectal cancer, mutant KRAS clones emerging during treatment with EGFR-specific antibodies declined during treatment breaks [76]. Engelman and colleagues [77] demonstrated that resistance to EGFR TKIs can develop through pre-existing clones and newly developed drug-tolerant clones simultaneously, though through separate mechanisms. When treating the NSCLC cell line PC-9 with increasing concentrations of gefitinib, one resistant population derived from EGFR T790M pre-existing clones emerged early, while another with characteristics of a drug-tolerant state and the EGFR T790M mutation, emerged late. This late emergent EGFR T790M subpopulation arose from drug-tolerant cells that were, initially, partially resistant to gefitinib, and then gained the EGFR T790 mutation to become fully resistant [77].

These studies demonstrate that heterogeneous tumours develop resistance when specific clones are targeted, allowing pre-existing or newly evolved subclones to emerge.

Competition between subclones is frequently revealed during and following therapy exposure. Keats and collaborators [78] illustrated that, in a patient with multiple myeloma, clonal dominance alternated between two main subclones. Modelling these subclones in a mouse, treatment with bortezomib exerted selective pressure on all subclones, but one clone eventually emerged and outcompeted the others [78]. Conversely, minor therapy-resistant subpopulations can also support the survival of therapy-sensitive populations, preserving tumour heterogeneity. In patients with colorectal cancer, EGFR therapy-resistant KRAS mutant subclones support non-mutant therapy-sensitive cells by secreting increased levels of TGFalpha and amphiregulin [79], in turn sustaining EGFR/ERK signalling in sensitive cells [79]. These data demonstrate how resistance to targeted therapy can develop with both preservation and loss of heterogeneity.

Heterogeneity and patient outcome

Is intratumour heterogeneity associated with worse patient outcomes? In premalignant Barrett’s oesophagus, clonal diversity is an important predictor of tumour progression [80]. By adapting diversity measures from ecology to measure both the number and abundance of clones relative to others in the population, the upper quartile of the number of clones and the genetic divergence based on loss of heterozygosity (LOH) were strongly predictive of increased progression from Barrett’s oesophagus to oesophageal adenocarcinoma [80].

In breast, ovarian, gastric and NSCLC, there is a paradoxical relationship between CIN and prognosis. The worst outcomes are in tumours with intermediate CIN, while those with extreme CIN scores had an improved outcome [81, 82]. The tumour cell of origin and the order of somatic events may also influence tumour heterogeneity and patient outcome [83].

In a recent study of 12 different cancer types [84], mortality risk increased when more than two clones coexisted in the same tumour sample, but decreased with the coexistence of more than four clones, emphasising that heterogeneity levels within tumours can strongly influence outcome [84]. Tumours with high levels of somatic copy number alterations (SCNAs) have increased proliferation markers and a decreased immune signature [85]. Interestingly, both phenotypes are controlled by different types of aneuploidy, and may be important patient outcome markers, as patients with higher SCNA levels tend to have worse responses to immunotherapy [85]. Recently, our group demonstrated in the TRACERx prospective study of 100 patients with NSCLC, that heterogeneity of DNA copy number events rather than point mutations, was associated with poor outcome [86].

In breast cancer, enriched areas of tandem duplications previously thought to be unimportant passengers, were shown to be potentially important for tumourigenesis [87]. These tandem duplications were characterised into two different rearrangement signatures [87]: one enriched in areas that disrupted tumour suppressor genes such as PTEN and RB1, and the other enriched in oncogenes, including MYC, and in putative regulatory elements of genes such as ESR1, with direct effects on transcription [87]. These enrichments demonstrate that mutational processes not only stochastically mutate the genome and induce heterogeneity but are also likely to be critical in cancer evolution and patient outcome. Further work to understand how established clinical parameters of outcome such as tumour size, grade and stage reflect intratumour heterogeneity is required.

Heterogeneity and immune responses in cancer

Emerging evidence suggests that intratumour heterogeneity may also influence the anti-tumour immune response. Rapid advances in cancer genome sequencing have enabled scientists to decipher the impact of somatic coding alterations upon immune recognition and surveillance. Non-silent mutations generate neoantigens that can be recognised by the immune system [88, 89]. Both CD4+ and CD8+ T lymphocytes were shown to recognise tumour neoantigens [89,90,91,92,93]. Using whole genome sequencing, scientists have established that mutation burden is highly variable among different tumour types [94]. Mutation levels in melanomas and lung cancers are among the highest because of exposure to ultraviolet light and tobacco carcinogens respectively [94, 95]. This makes these cancer types ideal candidates for immunotherapy; however, others such as clear cell renal cancer also respond to checkpoint therapy despite a much lower mutational burden [96].

Mutation burden and immunotherapy

In patients with melanoma, whole exomes from pre-treatment melanoma tumour biopsies and matching germline tissue samples were examined to determine if neoantigen burden affects responses to antibodies directed against cytotoxic T lymphocyte-associated antigen-4 (CTLA4) [97]. Overall mutational load and expression of cytolytic markers in the immune microenvironment were significantly associated with an anti-CTLA4 response and clinical benefit [97].

Rizvi and colleagues [98] explored how mutational burden affects sensitivity to PD-1 blockade in NSCLC. Expressed by activated T cells, PD-1 is a key immune checkpoint receptor that mediates immunosuppression [88]. They concluded that a higher nonsynonymous mutation burden was associated with improved objective response, durable clinical benefit and progression-free survival [98]. However, some patients with both high and low mutational burdens failed to respond to therapy [98], suggesting that other factors are involved in immunotherapy responses.

We investigated whether melanoma and NSCLC sensitivity to PD-1 and CTLA-4 treatment was enhanced in tumours where tumours had a high clonal neoantigen burden. Considering a combination of neoantigen clonality and neoantigen burden allowed us to better discriminate responder from non-responder patients than either metric alone [99]. These findings suggest that the degree of intratumour heterogeneity may be associated with a differential response to checkpoint blockade, and that multiple factors are involved in tumour immune responses. These studies raise the possibility that technologies now exist to exploit clonal neoantigens, present in every tumour cell for therapeutic benefit, either through vaccination or cell therapy approaches.

Resistance and immune evasion

Multiple laboratories are now focusing on a deeper understanding of immune evasion and resistance to immunotherapy. One study [100] uncovered two novel mechanisms of evasion: first, the elimination of neoantigen-containing tumour cells within a subset of the tumour population, and second, the acquisition of one or more genetic events that resulted in neoantigen loss. Both clonal and subclonal alterations were lost following immunotherapy, but all clonal neoantigens were eliminated by chromosomal deletions and LOH, whereas subclonal neoantigens were lost through LOH and elimination of tumour cells [100]. We uncovered that CIN likely contributes to mutational heterogeneity within tumours through SCNAs [86]. More than 14% of subclonal mutations (range: 0–56%) are subclonal because of SCNA loss events of clonal alterations [86]. This suggests that CIN contributes to neoantigen heterogeneity, and may play a role in immune evasion and immunotherapy resistance. These findings raise questions about whether immunotherapy will be effective as tumours evolve and become heterogeneous, and to what extent underlying CIN, resulting in rapid karyotype evolution, drives resistance to such approaches. Understanding neoantigen heterogeneity within tumours will aid the development of more effective, patient-specific immunotherapies, which may not prevent eventual relapse but could significantly extend patient’s lives.

T cell exhaustion is another mechanism of immune evasion. This state of T cell dysfunction arises during chronic antigen exposure, which is defined by poor effector function, sustained expression of inhibitory receptors, and an abnormal transcriptional state [101,102,103]. In this state, PD-1 is one of many cell surface inhibitory receptors to co-regulate T cell exhaustion [103]. Anti-PD-1 immunotherapy partially restores T cell function from an exhausted state [102, 104, 105]. It is also clear that the relative abundance of partially exhausted tumour-infiltrating CD8+ T cells predicts response to anti-PD-1 therapy [106]. However, resistance to PD-1 therapy develops in many patients.

Alterations in the genes encoding interferon receptor-associated Janus kinase 1 (JAK1) or 2 (JAK2), along with deletion of the wild-type allele, lead to defects in pathways involved in interferon receptor signalling and in antigen presentation. This results in resistance to PD-1 blockade in melanoma [107]. Loss of PTEN also inhibits T cell-mediated tumour-killing by decreasing T cell infiltration in tumours, and is associated with worse outcomes with anti PD-1 therapy [108]. These studies suggest that tumour heterogeneity is beneficial to immune evasion and the development of immunotherapy resistance. Approaches that attenuate tumour heterogeneity by inhibiting the molecular mechanisms driving cell-to-cell variation, or that target multiple truncal mutations occurring consistently in all regions of the tumour while stimulating the immune response using checkpoint blockade, might help limit diversity and resistance acquisition [99].

Conclusions

We have begun to witness how tumour heterogeneity affects tumour progression and metastasis, and the tumour immune response. A deeper understanding of how the immune system can be leveraged to tackle clonal alterations within tumour cells is required, to identify high-risk tumour subclones that might be extinguished prior to, or at the time of, metastatic seeding. Longitudinal characterisation of tumour evolution using advanced sequencing technology is important, as will be characterisation of the distinct mechanisms of tumour heterogeneity from tumour diagnosis through to progression and death. These studies may open avenues to attenuate tumour heterogeneity in minimal residual disease when disease burden is low, or to target multiple clonal mutations present in every cell simultaneously to limit the acquisition of therapy resistance.

As tumour heterogeneity is arguably the major force behind tumour progression, evolution and metastasis, insight into the clonal complexity of individual tumours and its contribution to progression, immune evasion and exhaustion may be critical to the development of more effective cancer therapeutics.

References

  1. Foulds L. The natural history of cancer. J Chronic Dis. 1958;8:2–37.

    Article  CAS  PubMed  Google Scholar 

  2. Nowell PC. The clonal evolution of tumor cell populations. Science. 1976;194:23–8.

    Article  CAS  PubMed  Google Scholar 

  3. Heppner GH. Tumor heterogeneity. Cancer Res. 1984;44:2259–65.

    CAS  PubMed  Google Scholar 

  4. Kovac M, Navas C, Horswell S, Salm M, Bardella C, Rowan A, et al. Recurrent chromosomal gains and heterogeneous driver mutations characterise papillary renal cancer evolution. Nat Comms. 2015;6:1–11.

    Google Scholar 

  5. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Grönroos E, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Campbell PJ, Yachida S, Mudie LJ, Stephens PJ, Pleasance ED, Stebbings ED, et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature. 2010;467:1109–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Shah SP, Morin RD, Khattra J, Prentice L, Pugh T, Burleigh A, et al. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature. 2009;461:809–13.

    Article  CAS  PubMed  Google Scholar 

  8. Jones S, Zhang X, Parsons DW, Lin JC-H, Leary RJ, Angenendt P, et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science. 2008;321:1801–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Yates LR, Gerstung M, Knappskog S, Desmedt C, Gundem G, Van Loo P, et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat Med. 2015;21:751–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sjöblom T, Jones S, Wood LD, Parsons DW, Lin J, Barber TD, et al. The consensus coding sequences of human breast and colorectal cancers. Science. 2006;314:268–74.

    Article  PubMed  CAS  Google Scholar 

  11. de Bruin EC, McGranahan N, Mitter R, Salm M, Wedge DC, Yates L, et al. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science. 2014;346:251–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Lu Y-W, Zhang H-F, Liang R, Xie Z-R, Luo H-Y, Zeng Y-J, et al. Colorectal cancer genetic heterogeneity delineated by multi-region sequencing. PLoS One. 2016;11:e0152673.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Cao W, Wu W, Yan M, Tian F, Ma C, Zhang Q, et al. Multiple region whole-exome sequencing reveals dramatically evolving intratumor genomic heterogeneity in esophageal squamous cell carcinoma. Oncogenesis. 2015;4:e175.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Nikbakht H, Panditharatna E, Mikael LG, Li R, Gayden T, Osmond M, et al. Spatial and temporal homogeneity of driver mutations in diffuse intrinsic pontine glioma. Nat Comms. 2016;7:11185.

    Article  CAS  Google Scholar 

  15. Zhang J, Fujimoto J, Zhang J, Wedge DC, Song X, Zhang J, et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science. 2014;346:256–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. McGranahan N, Swanton C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell. 2017;168:613–28.

    Article  CAS  PubMed  Google Scholar 

  17. Gasgoigne KE, Taylor SS. Cancer cells display profound intra- and interline variation following prolonged exposure to antimitotic drugs. Cancer Cell. 2008;14:111–22.

    Article  CAS  Google Scholar 

  18. Le DT, Uram JN, Wang H, Bartlett BR. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372:2509–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Burrell RA, McGranahan N, Bartek J, Swanton C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature. 2013;501:338–45.

    Article  CAS  PubMed  Google Scholar 

  20. Sosa MS, Bragado P, Aguirre-Ghiso JA. Mechanisms of disseminated cancer cell dormancy: an awakening field. Nat Rev Cancer. 2014;14:611–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Swanton C, McGranahan N, Starrett GJ, Harris RS. APOBEC enzymes: mutagenic fuel for cancer evolution and heterogeneity. Cancer Discov. 2015;5:704–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kanu N, Cerone MA, Goh G, Zalmas L-P, Bartkova J, Dietzen M, et al. DNA replication stress mediates APOBEC3 family mutagenesis in breast cancer. Genome Biol. 2016;17:185.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Burns MB, Temiz NA, Harris RS. Evidence for APOBEC3B mutagenesis in multiple human cancers. Nat Genet. 2013;45:977–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Roberts SA, Lawrence MS, Klimczak LJ, Grimm SA, Fargo D, Stojanov P, et al. An APOBEC cytidine deaminase mutagenesis pattern is widespread in human cancers. Nat Genet. 2013;45:970–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Starrett GJ, Luengas EM, McCann JL. The DNA cytosine deaminase APOBEC3H haplotype I likely contributes to breast and lung cancer mutagenesis. Nature Commun. 2016;7:12918.

    Article  CAS  Google Scholar 

  26. Law EK, Sieuwerts AM, LaPara K, Leonard B, Starrett GJ, Molan AM, et al. The DNA cytosine deaminase APOBEC3B promotes tamoxifen resistance in ER-positive breast cancer. Sci Adv. 2016;2:e1601737.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Murugaesu N, Wilson GA, Birkbak NJ, Watkins TBK, McGranahan N, Kumar S, et al. Tracking the genomic evolution of esophageal adenocarcinoma through neoadjuvant chemotherapy. Cancer Discov. 2015;5:821–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Johnson BE, Mazor T, Hong C, Barnes M, Aihara K, McLean CY, et al. Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science. 2014;343:189–93.

    Article  CAS  PubMed  Google Scholar 

  29. Fei SS, Mitchell AD, Heskett MB, Vocke CD, Ricketts CJ, Peto M, et al. Patient-specific factors influence somatic variation patterns in von Hippel-Lindau disease renal tumours. Nat Commun. 2016;7:11588.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Haddad AQ, Margulis V. Tumour and patient factors in renal cell carcinoma-towards personalized therapy. Nat Rev Urol. 2015;12:253–62.

    Article  CAS  PubMed  Google Scholar 

  31. Cahill DP, Kinzler KW, Vogelstein B, Lengauer C. Genetic instability and darwinian selection in tumours. Trends Cell Biol. 1999;9:M57–60.

    Article  CAS  PubMed  Google Scholar 

  32. Thompson SL, Compton DA. Proliferation of aneuploid human cells is limited by a p53-dependent mechanism. J Cell Biol. 2010;188:369–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Fujiwara T, Bandi M, Nitta M, Ivanova EV, Bronson RT. Cytokinesis failure generating tetraploids promotes tumorigenesis in p53-null cells. Nature. 2005;437:1043–7.

    Article  CAS  PubMed  Google Scholar 

  34. Weaver BA, Silk AD, Montagna C, Verdier-Pinard P, Cleveland DW. Aneuploidy acts both oncogenically and as a tumor suppressor. Cancer Cell. 2007;11:25–36.

    Article  CAS  PubMed  Google Scholar 

  35. Iwanaga Y, Chi YH, Miyazato A, Sheleg S, Haller K, Peloponese JM, et al. Heterozygous deletion of mitotic arrest-deficient protein 1 (MAD1) increases the incidence of tumors in mice. Cancer Res. 2007;67:160–6.

    Article  CAS  PubMed  Google Scholar 

  36. Silk AD, Zasadil LM, Holland AJ, Vitre B, Cleveland DW, Weaver BA. Chromosome missegregation rate predicts whether aneuploidy will promote or suppress tumors. Proc Natl Acad Sci U S A. 2013;110:E4134–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Duijf PHG, Benezra R. The cancer biology of whole-chromosome instability. Oncogene. 2013;32:4727–36.

    Article  CAS  PubMed  Google Scholar 

  38. Sheltzer JM, Ko JH, Replogle JM, Burgos NCH, Chung ES, Meehl CM, et al. Single-chromosome gains commonly function as tumor suppressors. Cancer Cell. 2016;31:240–55.

    Article  CAS  Google Scholar 

  39. Sansregret L, Patterson JO, Dewhurst SM, Lopez-Garcia C, Koch A, McGranahan N, et al. APC/C dysfuntion limits excessive cancer chromosomal instability. Cancer Discov. 2017;7:218–33.

    Article  CAS  PubMed  Google Scholar 

  40. Lopez-Garcia C, Sansregret L, Domingo E, McGranahan N, Hobor S, Birkbak NJ, et al. BCL9L dysfunction permits caspase-2 dependent aneuploidy tolerance in colorectal cancer. Cancer Cell. 2017;31:79–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Dewhurst SM, McGranahan N, Burrell RA, Rowan AJ, Grönroos E, Endesfelder D, et al. Tolerance of whole-genome doubling propagates chromosomal instability and accelerates cancer genome evolution. Cancer Discov. 2014;4:175–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Crockford A, Zalmas LP, Gronroos E, Dewhurst SM, Mcgranahan N, Cuomo ME, et al. Cyclin D mediates tolerance of genome-doubling in cancers with functional p53. Ann Oncol. 2016. doi:10.1093/annonc/mdw612.

  43. Potapova TA, Seidel CW, Box AC, Rancati G, Li R. Transcriptome analysis of tetraploid cells identifies cyclin D2 as a facilitator of adaptation to genome doubling in the presence of p53. Mol Biol Cell. 2016;27:3065–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Stingele S, Stoehr G, Storchova Z. Activation of autophagy in cells with abnormal karyotype. Autophagy. 2013;9:246–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Torres EM, Dephoure N, Panneerselvam A, Tucker CM. Identification of aneuploidy-tolerating mutations. Cell. 2010;143:81–3.

    Article  CAS  Google Scholar 

  46. Nguyen DX, Bos PD, Massagué J. Metastasis: from dissemination to organ-specific colonization. Nat Rev Cancer. 2009;9:274–84.

    Article  CAS  PubMed  Google Scholar 

  47. Turajlic S, Swanton C. Metastasis as an evolutionary process. Science. 2016;352:169–75.

    Article  CAS  PubMed  Google Scholar 

  48. Caswell DR, Chuang C-H, Yang D, Chiou S-H, Cheemalavagu S, Kim-Kiselak C, et al. Obligate progression precedes lung adenocarcinoma dissemination. Cancer Discov. 2014;4:781–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Stoecklein NH, Klein CA. Genetic disparity between primary tumours, disseminated tumour cells, and manifest metastasis. Int J Cancer. 2010;126:589–98.

    Article  CAS  PubMed  Google Scholar 

  50. Klein CA. Parallel progression of primary tumours and metastases. Nat Rev Cancer. 2009;9:302–12.

    Article  CAS  PubMed  Google Scholar 

  51. Naxerova K, Brachtel E, Salk JJ, Seese AM, Power K, Abbasi B, et al. Hypermutable DNA chronicles the evolution of human colon cancer. Proc Natl Acad Sci U S A. 2014;111:E1889–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Rhim AD, Mirek ET, Aiello NM, Maitra A, Bailey JM, McAllister F, et al. EMT and dissemination precede pancreatic tumor formation. Cell. 2012;148:349–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hüsemann Y, Geigl JB, Schubert F, Musiani P, Meyer M, Burghart E, et al. Systemic spread Is an early step in breast cancer. Cancer Cell. 2008;13:58–68.

    Article  PubMed  CAS  Google Scholar 

  54. Podsypanina K, Du Y-CN, Jechlinger M, Beverly LJ, Hambardzumyan D, Varmus H. Seeding and propagation of untransformed mouse mammary cells in the lung. Science. 2008;321:1841–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Fidler IJ, Kripke ML. Metastasis results from preexisting variant cells within a malignant tumor. Science. 1977;197:893–5.

    Article  CAS  PubMed  Google Scholar 

  56. Fidler IJ. Tumor heterogeneity and the biology of cancer invasion and metastasis. Cancer Res. 1978;38:2651–60.

    CAS  PubMed  Google Scholar 

  57. Dexter DL, Kowalski HM, Blazar BA, Fligiel Z, Vogel R, Heppner GH. Heterogeneity of tumor cells from a single mouse mammary tumor. Cancer Res. 1978;38:3174–81.

    CAS  PubMed  Google Scholar 

  58. Miller BE, Miller FR, Heppner GH. Interactions between tumor subpopulations affecting their sensitivity to the antineoplastic agents cyclophosphamide and methotrexate. Cancer Res. 1981;41:4378–81.

    CAS  PubMed  Google Scholar 

  59. Miller BE, Machemer T, Lehotan M, Heppner GH. Tumor subpopulation interactions affecting melphalan sensitivity in palpable mouse mammary tumors. Cancer Res. 1991;51:4378–87.

    CAS  PubMed  Google Scholar 

  60. Tabassum DP, Polyak K. Tumorigenesis: it takes a village. Nat Rev Cancer. 2015;15:473–83.

    Article  CAS  PubMed  Google Scholar 

  61. Eichenlaub T, Cohen SM, Herranz H. Cell competition drives the formation of metastatic tumors in a Drosophila model of epithelial tumor formation. Curr Biol. 2016;26:419–27.

    Article  CAS  PubMed  Google Scholar 

  62. Ohsawa S, Sugimura K, Takino K, Xu T, Miyawaki A, Igaki T. Elimination of oncogenic neighbors by JNK-mediated engulfment in Drosophila. Dev Cell. 2011;20:315–28.

    Article  CAS  PubMed  Google Scholar 

  63. Wu M, Pastor-Pareja JC, Xu T. Interaction between RasV12 and scribbled clones induces tumour growth and invasion. Nature. 2010;463:545–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Inda M-D-M, Bonavia R, Mukasa A, Narita Y, Sah DWY, Vandenberg S, et al. Tumor heterogeneity is an active process maintained by a mutant EGFR-induced cytokine circuit in glioblastoma. Genes Dev. 2010;24:1731–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Chapman A. Fernandez del Ama L, Ferguson J, Kamarashev J, Wellbrock C, Hurlstone A. Heterogeneous tumor subpopulations cooperate to drive invasion. Cell Rep. 2014;8:688–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Marusyk A, Tabassum DP, Altrock PM, Almendro V, Michor F, Polyak K. Non-cell-autonomous driving of tumour growth supports sub-clonal heterogeneity. Nature. 2014;514:54–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Le Magnen C, Dutta A, Abate-Shen C. Optimizing mouse models for precision cancer prevention. Nat Rev Cancer. 2016;16:187–96.

    Article  PubMed  CAS  Google Scholar 

  68. Calbo J, van Montfort E, Proost N, van Drunen E, Beverloo HB, Meuwissen R, et al. A functional role for tumor cell heterogeneity in a mouse model of small cell lung cancer. Cancer Cell. 2011;19:244–56.

    Article  CAS  PubMed  Google Scholar 

  69. Cleary AS, Leonard TL, Gestl SA, Gunther EJ. Tumour cell heterogeneity maintained by cooperating subclones in Wnt-driven mammary cancers. Nature. 2014;508:113–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Zhang M, Tsimelzon A, Chang C-H, Fan C, Wolff A, Perou CM, et al. Intratumoral heterogeneity in a Trp53-null mouse model of human breast cancer. Cancer Discov. 2015;5:520–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Turke AB, Zejnullahu K, Wu Y-L, Song Y, Dias-Santagata D, Lifshits E, et al. Preexistence and clonal selection of MET amplification in EGFR mutant NSCLC. Cancer Cell. 2010;17:77–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Su K-Y, Chen H-Y, Li K-C, Kuo M-L, Yang JC-H, Chan W-K, et al. Pretreatment epidermal growth factor receptor (EGFR) T790M mutation predicts shorter EGFR tyrosine kinase inhibitor response duration in patients with non-small-cell lung cancer. J Clin Oncol. 2012;30:433–40.

    Article  CAS  PubMed  Google Scholar 

  73. Diaz Jr LA, Williams RT, Wu J, Kinde I, Hecht JR, Berlin J, et al. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature. 2012;26:1626.

    Google Scholar 

  74. Niederst MJ, Sequist LV, Poirier JT, Mermel CH, Lockerman EL, Garcia AR, et al. RB loss in resistant EGFR mutant lung adenocarcinomas that transform to small-cell lung cancer. Nat Commun. 2015;6:6377.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Janiszewska M, Liu L, Almendro V, Kuang Y, Paweletz C, Sakr RA, et al. In situ single-cell analysis identifies heterogeneity for PIK3CA mutation and HER2 amplification in HER2-positive breast cancer. Nat Genet. 2015;47:1212–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Siravegna G, Mussolin B, Buscarino M, Corti G, Cassingena A, Crisafulli G, et al. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nat Med. 2015;21:795–801.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Hata AN, Niederst MJ, Archibald HL, Gomez-Caraballo M, Siddiqui FM, Mulvey HE, et al. Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition. Nat Med. 2016;22:262–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Keats JJ, Chesi M, Egan JB, Garbitt VM, Palmer SE, Braggio E, et al. Clonal competition with alternating dominance in multiple myeloma. Blood. 2012;120:1067–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Hobor S, Van Emburgh BO, Crowley E, Misale S, Di Nicolantonio F, Bardelli A. TGFα and amphiregulin paracrine network promotes resistance to EGFR blockade in colorectal cancer cells. Clin Cancer Res. 2014;20:6429–38.

    Article  CAS  PubMed  Google Scholar 

  80. Maley CC, Galipeau PC, Finley JC, Wongsurawat VJ, Li X, Sanchez CA, et al. Genetic clonal diversity predicts progression to esophageal adenocarcinoma. Nat Genet. 2006;38:468–73.

    Article  CAS  PubMed  Google Scholar 

  81. Birkbak NJ, Eklund AC, Li Q, McClelland SE, Endesfelder D, Tan P, et al. Paradoxical relationship between chromosomal instability and survival outcome in cancer. Cancer Res. 2011;71:3447–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Roylance R, Endesfelder D, Gorman P, Burrell RA, Sander J, Tomlinson I, et al. Relationship of extreme chromosomal instability with long-term survival in a retrospective analysis of primary breast cancer. Cancer Epidemiol Biomarkers Prev. 2011;20:2183–94.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Ortmann CA, Kent DG, Nangalia J, Silber Y, Wedge DC, Grinfeld J, et al. Effect of mutation order on myeloproliferative neoplasms. N Engl J Med. 2015;372:601–12.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Andor N, Graham TA, Jansen M, Xia LC, Aktipis CA, Petritsch C, et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat Med. 2015;22:105–13.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Davoli T, Uno H, Wooten EC, Elledge SJ. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science. 2017;355. doi: 10.1126/science.aaf8399.

  86. Jamal-Hanjani M, Wilson GA, McGranahan N, Birkbak NJ, Watkins TBK, Veeriah S, et al. Tracking the evolution of non-small-cell lung cancer. N Engl J Med. 2017;376:2109–21.

    Article  PubMed  Google Scholar 

  87. Glodzik D, Morganella S, Davies H, Simpson PT, Li Y, Zou X, et al. A somatic-mutational process recurrently duplicates germline susceptibility loci and tissue-specific super-enhancers in breast cancers. Nat Genet. 2017;49:341–8.

    Article  CAS  PubMed  Google Scholar 

  88. Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med. 2012;366:2443–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. van Rooij N, van Buuren MM, Philips D, Velds A, Toebes M, Heemskerk B, et al. Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J Clin Oncol. 2013;31:e439–42.

    Article  PubMed  Google Scholar 

  90. Monach PA, Meredith SC, Siegel CT, Schreiber H. A unique tumor antigen produced by a single amino acid substitution. Immunity. 1995;2:45–59.

    Article  CAS  PubMed  Google Scholar 

  91. Coulie PG, Lehmann F, Lethé B, Herman J, Lurquin C, Andrawiss M, et al. A mutated intron sequence codes for an antigenic peptide recognized by cytolytic T lymphocytes on a human melanoma. Proc Natl Acad Sci U S A. 1995;92:7976–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Lennerz V, Fatho M, Gentilini C, Frye RA, Lifke A, Ferel D, et al. The response of autologous T cells to a human melanoma is dominated by mutated neoantigens. Proc Natl Acad Sci U S A. 2005;102:16013–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Lu YC, Yao X, Li YF, El-Gamil M, Dudley ME, Yang JC, et al. Mutated PPP1R3B is recognized by T cells used to treat a melanoma patient who experienced a durable complete tumor regression. J Immunol. 2013;190:6034–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Boussiotis VA. Somatic mutations and immunotherapy outcome with CTLA-4 blockade in melanoma. N Engl J Med. 2014;371:2230–2.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  95. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SAJR, Behjati S, Biankin AV, et al. Signatures of mutational processes in human cancer. Nature. 2013;500:415–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Sharma P, Allison JP. The future of immune checkpoint therapy. Science. 2015;348:56–61.

    Article  CAS  PubMed  Google Scholar 

  97. Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. 2015;350:207–11.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  98. Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348:124–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. McGranahan N, Furness AJS, Rosenthal R, Ramskov S, Lyngaa R, Saini SK, et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science. 2016;351:1463–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Anagnostou V, Smith KN, Forde PM, Niknafs N, Bhattacharya R, White J, et al. Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer. Cancer Discov. 2017;7:264–76.

    Article  CAS  PubMed  Google Scholar 

  101. Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012;12:252–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Barber DL, Wherry EJ, Masopust D, Zhu B, Allison JP, Sharpe AH, et al. Restoring function in exhausted CD8 T cells during chronic viral infection. Nature. 2005;439:682–7.

    Article  PubMed  CAS  Google Scholar 

  103. Wherry EJ, Kurachi M. Molecular and cellular insights intoT cell exhaustion. Nat Rev Immunol. 2015;15:486–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Okazaki T, Honjo T. PD-1 and PD-1 ligands: from discovery to clinical application. Int Immunol. 2007;19:813–24.

    Article  CAS  PubMed  Google Scholar 

  105. Sakuishi K, Apetoh L, Sullivan JM, Blazar BR, Kuchroo VK, Anderson AC. Targeting Tim-3 and PD-1 pathways to reverse T cell exhaustion and restore anti-tumor immunity. J Exp Med. 2010;207:2187–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Daud AI, Loo K, Pauli ML, Sanchez-Rodriguez R, Sandoval PM, Taravati K, et al. Tumor immune profiling predicts response to anti–PD-1 therapy in human melanoma. J Clin Invest. 2016;126:3447–52.

    Article  PubMed  PubMed Central  Google Scholar 

  107. Zaretsky JM, Garcia-Diaz A, Shin DS, Escuin-Ordinas H, Hugo W, Hu-Lieskovan S, et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N Engl J Med. 2016;375:819–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Peng W, Chen JQ, Liu C, Malu S, Creasy C, Tetzlaff MT, et al. Loss of PTEN promotes resistance to T cell-mediated immunotherapy. Cancer Discov. 2016;6:202–16.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

CS is a Royal Society Napier Research Professor. This work was supported by the Francis Crick Institute which receives its core funding from Cancer Research UK (grant number FC001169), the UK Medical Research Council (MR/FC001169/1), and The Wellcome Trust (FC001169). CS is funded by Cancer Research UK (TRACERx), the CRUK Lung Cancer Centre of Excellence, Stand Up 2 Cancer (SU2C), the Rosetrees Trust, NovoNordisk Foundation (ID 16584), the Prostate Cancer Foundation, the Breast Cancer Research Foundation (BCRF), the European Research Council (THESEUS) and Marie Curie Network PloidyNet. Support was provided to CS by the National Institute for Health Research, the University College London Hospitals Biomedical Research Centre, and the Cancer Research UK University College London Experimental Cancer Medicine Centre.

Availability of data and materials

Not applicable.

Author information

Authors and Affiliations

  1. Translational Cancer Therapeutics Laboratory, The Francis Crick Institute, 1 Midland Rd, London, NW1 1AT, UK

    Deborah R. Caswell & Charles Swanton

  2. Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, Paul O’Gorman Building, 72 Huntley Street, London, WC1E 6BT, UK

    Charles Swanton

Authors
  1. Deborah R. Caswell
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Charles Swanton
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

DC conceived the manuscript. DC and CS drafted the manuscript. DC and CS read and approved the final version of the manuscript.

Corresponding author

Correspondence to Deborah R. Caswell.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors are both employed at the Francis Crick Institute. Charles Swanton is also a founder of Achilles Therapeutics. Neither author has conflicts of interest related to this article.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Caswell, D.R., Swanton, C. The role of tumour heterogeneity and clonal cooperativity in metastasis, immune evasion and clinical outcome. BMC Med 15, 133 (2017). https://doi.org/10.1186/s12916-017-0900-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12916-017-0900-y

Keywords

BMC Medicine

ISSN: 1741-7015

Contact us