Contributions of Tumour Heterogeneity to Metastasis

Describe how tumour heterogeneity is thought to contribute to metastasis and how this may be overcome for the treatment of cancer

Introduction

 The evolution of cancer has been described as the repetitive process of genetic diversification, clonal expansion and the outgrowth of clones which have been selected due to phenotypic advantage within tumour-microenvironment (1). In 1976, Nowell was the first to propose this revolutionary perspective of cancer evolution which is parallel to Darwinian natural selection. Natural selection of tumour clones takes place through com­petition for resources and space (1,2) (figure 1). Moreover, tumour heterogeneity has been established in a variety of cancer types and is defined as the ability of different tumour types to possess different phenotypic and morphological properties. This is classified into intra-tumour heterogeneity (ITH) (within a single tumour) and inter-tumour heterogeneity (between different tumours) (1,3). Tumour heterogeneity is believed to contribute to metastasis as well as to provide the fuel for resistance to treatment (1,4); therefore, a better understanding is essential for effective cancer therapies.

ITH evolution

 ITH arises through various mechanisms and the two main models described are the clonal evolution and cancer stem cell models. In the latter, a small set of cells known as cancer stem cells sustain tumour growth through self-renewal and differentiation. In the clonal evolution model, genetic driver events offer advantageous proliferative capacity to some cells, and this leads to the formation of genetically discrete sub-clones. The clonal evolution model can be further categorised into (1) the linear evolutionary model, which describes the progression of a single progenitor cell to aggressive phenotype through genetic variability; (2) the branched evolutionary model, which describes the independent development of several tumour foci with various tumour subclones present from the beginning; (3) the braided river model where cancer progression is comparable to a braided river with capabilities to both diverge and converge; (4) the neutral evolution model, which eliminates fitness changes or selection. Advancements in next-generation sequencing studies have demonstrated that catastrophic genetic abnormalities such as chromotrhipsis, chromoplexy and genomic re-arrangements can drive tumour progression through bursts of evolutionary benefit, leading to the proposal of a punctuated evolution model (figure 1) (5,6). Branched evolu­tion has now been recognized in a range of tumour types, including breast cancer (7) and pancreatic cancer (8).

ITH in primary tumours and metastasis

 Advancements in sequencing techniques has improved our understanding of ITH and its significance in metastasis, treatment failure and subsequently clinical outcome (9). Using exome sequencing on samples obtained from distinct areas in primary and metastatic tumour sites, ITH was revealed in multiple tumour types (10) including breast (7,11), lung (12,13), oesophageal (14), colorectal (15) and renal (16). For example, in renal cell carcinoma, Gerlinger et al. performed sequencing of multiple biopsies from the same primary tumour and demonstrated spatially distinct subclones, accumulating heterogeneous copy-number events and somatic mutations (16). Moreover, Campbell et al. used a massively parallel paired-end sequencing approach to uncover founder mutations vital for the formation of primary pancreatic tumour as well as further mutations which are likely required in metastasis. They have also suggested that genomic instability and re-arrangements are happening in the early stages of pancreatic cancer development and continue to arise at metastatic sites (17). In addition, with the use of multi-region sampling Sottoriva et al. demonstrated heterogeneous copy number events between different areas of the same tumour in glioblastoma (18). Equivalent evidence supporting clonal diversity between metastatic and primary sited has also been established in medulloblastoma (19) and breast cancer (20).

 Increased ITH was also linked to worse clinical outcome, reduced overall survival and poorer response to treatment (21). In a study including twelve different cancer types, the co-existence of more than 2 clones in the same tumour sample was linked to an increased in mortality risk, however the risk decreased when more than 4 clones co-existed, highlighting that ITH levels have a significant impact on clinical outcome (22). Moreover, in the TRACERx prospective study of patients with non-small cell lung cancer, heterogeneity of DNA copy number events was linked to poor clinical outcome (23).

 Metastasis is a complex process consisting of multiple steps including detachment of the primary tumour, local invasion, intravasation, adhesion and survival in the circulation, extravasation and proliferation at the secondary site (24). Current evidence has identified two prominent metastatic models, the parallel and linear progression models. In the former, metastases are seeded early in the stages of tumour development and progression, therefore there are high levels of genetic variability between the primary tumour and its metastases, while in the latter, metastases are seeded at a late stage in tumour progression, as evident by the limited genetic variability between the primary tumour and its metastases (10) (figure 2). Multiregional sequencing supports the presence of both metastatic models in breast, colorectal, renal, pancreatic and prostate cancer (25). Interestingly, both metastatic models were demonstrated in a case of colorectal cancer. Some metastases had a similar genetic profile with the primary tumour, suggesting that they disseminated late in the stage of tumour progression, while one large metastasis had a diverge genetic profile signifying an early dissemination and formation (26). All this evidence emphasizes that ITH plays a critical role in the metastatic process, which is diverse and complex (10).

Resistance to treatment

 The resistance to treatment of metastatic cancer is a result of its complexity and the unique ability to adapt in the changing ecosystem, supported by its genetic diversity. One of the most significant mechanisms leading to failure or partial response to treatment is the presence of resistant subclones (1, 27). Selection pressures imposed by the therapy can cause the outgrowth of small subclones, already present in previously insignificant or non-detectable levels, which have resistance mechanisms. Cancer therapies including chemotherapy and radiotherapy can drastically altered the cancer-tissues environment. During treatment, majority of cancer cells may respond and die, however the altered micro-environment may offer the opportunity to pre-existing resistant subclones to survive. Otherwise, new mutations can cause the formation of further cancer subclones, which can be resistant to treatment (1, 4, 28) (figure 2).

 Dynamic therapy strategies have been developed to help clinicians target the evolutionary resistance to treatment driven by ITH and the presence of resistant sub-clones. One example of this, is the adaptive therapy which allow the clinician to maintain a stable population of cells sensitive to the treatment, with the aim to prolong the use of that treatment. An alternative is to force the tumour to follow a specific evolutionary route which can result in the attained sensitivity to a different drug, as demonstrated by the use sequential therapy (29).

How to overcome ITH: Adaptive therapy

 Understanding the importance of ITH in cancer evolution has led to the development of innovative therapeutic strategies. For example, it was pro­posed that the therapeutic resilience could be avoided if the treatment is given in a way that prevents the outgrowth of resistant subclones by over competing the therapy-sensitive cancer cells. This means that we should turn cancer into a chronic disease rather than aiming to eradicate the disease. Cytotoxic drugs are thought to offer an advantageous environment for resistant cells by removing all their competitors. On the other hand, cytostatic drugs prevent that by enabling sensitive cells to remain in the area, therefore occupying space and consuming resources, that would otherwise fuel resistant sub-clones (1,4,29). Gatenby and colleagues have demonstrated that by aiming to maintain a stable size of an aggressive ovarian tumour, rather than to eliminate completely, the overall survival of the mice was improved (30).

 The use of adaptive therapy was tested in a recent small-scale clinical trial, including 11 patients with metastatic castrate-resistant prostate cancer. Results shown the use of adaptive therapy maintained stable tumour in 10 patients and reduced use of cumulative drug, equivalent to <50% of the standard dose of abiraterone. This clinical trial emphasises the importance of evolution of both sensitive and resistant cancer cells on and off treatment (31) (figure 3). Larger clinical trials are required to test the application of this strategy to metastatic cancer.

Conclusion

 ITH, which is often seen as a result of a series of genetic driver events, is an extremely significant phenomenon for tumour progression, metastasis and clinical outcome as highlighted by several studies in various cancer types (10). With the advancements in sequencing techniques and given its clinical significance, measurement of ITH should be included into our standard practise. There is a crucial need to understand the mechanisms driving ITH so that therapeutic approaches to limit cancer diversity, adaptation and drug resistance can be developed.

References

1. M. Greaves, C.C. Maley, Clonal evolution in cancer, Nature 481 (2012) 306–313.
2. Nowell, P.C. (1976). The clonal evolution of tumor cell populations. Science 194, 23–28.
3. Burrell, R.A., McGranahan, N., Bartek, J., and Swanton, C. (2013). The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501, 338–345.
4. Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol 2018; 15(2): 81–94.
5. A. Davis, R. Gao, N. Navin, Tumor evolution: Linear, branching, neutral or punctuated? Biochim. Biophys. Acta 1867, 151–161 (2017).
6. Yadav SS, Stockert JA, Hackert V, Yadav KK and Tewari AK. Intratumor heterogeneity in prostate cancer. Urol Oncol 2018 36: 349- 360.
7. Shah, S. P. et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486, 395–399 (2012).
8. Campbell, P. J. et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 467, 1109–1113 (2010).
9. Gerlinger M, Swanton C. How Darwinian models inform therapeutic failure initiated by clonal heterogeneity in cancer medicine. Br J Cancer 2010; 103:1139-43.
10. Caswell DR, Swanton C. The role of tumour heterogeneity and clonal cooperativity in metastasis, immune evasion and clinical outcome. BMC Med. 2017; 15:133.
11. 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.
12. 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.
13. 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.
14. 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.
15. 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.
16. 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.
17. 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.
18. Sottoriva, A. et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc. Natl Acad. Sci. USA 110, 4009–4014 (2013).
19. Wu, X. et al. Clonal selection drives genetic divergence of metastatic medulloblastoma. Nature 482, 529–533 (2012).
20. Shah, S. P. et al. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature 461, 809–813 (2009).
21. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
22. 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.
23. Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. http://dx.doi.org/10.1056/NEJMoa1616288 (2017).
24. Valastyan, S. & Weinberg, R. A. Tumor metastasis: molecular insights and evolving paradigms. Cell 147, 275–292 (2011)
25. Turajlic S, Swanton C. Metastasis as an evolutionary process. Science. 2016; 352:169–75.
26. 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.
27. Greaves M. Evolutionary determinants of cancer. Cancer Discov 2015;5: 806-20
28. G. Stanta, S.W. Jahn, S. Bonin, G. Hoefler, Tumour heterogeneity: principles and practical consequences, Virchows Arch. (2015), doi: http://dx.doi.org/ 10.1007/s00428-016-1987-9.
29. Venkatesan, S., and Swanton, C. (2016). Tumor evolutionary principles: how intratumor heterogeneity influences cancer treatment and outcome. Am. Soc. Clin. Oncol. Educ. Book 35, e141–e149.
30. Gatenby RA, Silva AS, Gillies RJ, et al. Adaptive therapy. Cancer Res.2009; 69:4894-4903.
31. Zhang J, Cunningham JJ, Brown JS, Gatenby RA. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat Commun. 2017;8(1):1816. doi:10.1038/s41467-017-01968-5