Tumour Mutational Load: ESMO Biomarker Factsheet

Tumour Mutational Load or Tumour Mutation Burden

Defining biomarkers that predict therapeutic response to immune checkpoint blockade is an important goal, given the profound impact these novel immunotherapies have had in the treatment of several common tumour types, including lung and bladder cancer but perhaps most especially in metastatic melanoma [1]. When successful, treatment can achieve remission or even a cure, and approximately 25% to 50% of patients with metastatic melanoma treated with ipilimumab, which blocks cytotoxic T-lymphocyte–associated protein 4 (CTLA-4), or anti–PD-1 (programmed death 1) monoclonal antibody pembrolizumab, achieve prolonged progression-free survival [1,2]. Expression of the PD-1 ligand, (PD-L1), has been associated with favourable response rates and survival in patients treated with PD-1/PD-L1 immune checkpoint inhibitors, but is confounded by a number of methodological issues and robust responses are also observed in some patients with low levels of expression [3,4]. Given the large number of patients who do not respond to immunotherapy and the risk of serious immune-related side effects, the hunt for further biomarkers of response is pressing.

One biomarker, which has been associated with response to immunotherapy in multiple cancer types, is tumour mutational load or tumour mutational burden. An association between mutational load and response to immune checkpoint inhibitors was first observed in studies of ipilimumab in advanced melanoma [5,6], raising the possibility that the genetic landscape of a tumour may affect the clinical benefit provided by immunotherapies, in this case of CTLA-4 blocking agents [6].  

Tumour mutational load or mutation burden is a measure of the number of mutations within a tumour genome, defined as the total number of mutations per coding area of a tumour genome. There is large variability in mutation burden within tumour types, ranging from just a few to 1000s of mutations [7-9]. Lower grade and paediatric malignancies tend to have the lowest mutation burden, while epithelial cancers, associated with environmental DNA damage, are most highly mutated. For example, in patients with non-small cell lung cancer (NSCLC), tumours of never-smokers have few somatic mutations compared with tumours of smokers which may have 10-fold more [10,11]. Melanomas also have very high mutational burdens (0.5 to >100 mutations per megabase [Mb]) as compared with other solid tumours [7] and important studies have shown that these somatic mutations can give rise to neoepitopes and that these may serve as neoantigens [12-14]. Higher tumour mutational load has been shown to correlate with higher levels of neoantigens [15,16]. Neoantigens are a type of cancer-specific antigen that may allow for a more robust immune response and therefore more durable response to immunotherapy. Measuring mutational load can act as a proxy for determining the number of neoantigens per tumour, and may offer a potential addition to the current targeted therapy landscape that can be combined with protein and gene expression markers to help inform physicians to maximise the benefits of immunotherapy.

Tumour Mutational Load as a Predictive Biomarker

Tumour mutational load is an emerging sensitive, quantitative clinical marker that can help predict responses to certain cancer immunotherapies. Studies have shown that high tumour mutational load is associated with better response rates to immunotherapies in melanoma, NSCLC and urothelial cancer [6,10,17-20].

In patients with NSCLC treated with the PD-1 inhibitor, pembrolizumab, high tumour mutational load correlated with clinical efficacy. Higher levels of non-synonymous mutations were significantly associated with durable clinical response, improved objective response, and longer progression-free survival [13].

A study that compared the efficacy and safety of the PD-1 inhibitor, nivolumab, with chemotherapy in patients with PD-L1-positive advanced NSCLC, demonstrated higher response rates and longer progression-free survival with nivolumab in a subset of patients with high mutational load [18]. In the total trial population, first-line nivolumab did not result in longer progression-free survival compared to platinum-based chemotherapy. However, in a subpopulation of patients with a high tumour mutational load, nivolumab was associated with a higher response rate (47% vs. 28%) and longer median progression-free survival (9.7 vs. 5.8 months, hazard ratio 0.62, 95% CI 0.38–1.00) [17].

In an analysis of mutational load in 150 patients with metastatic urothelial carcinoma, high tumour mutational load was independently predictive of better responses to inhibition of the programmed death-ligand 1 (PD-L1) with atezolizumab, with median mutation load significantly increased in responders (12.4 per Mb) compared with non-responders (6.4 per Mb, p<0.0001) [18].

In patients with melanoma treated with CTLA-4 inhibitors, high mutational load was associated with a sustained clinical benefit from CTLA-4 blockade, but alone was not sufficient to predict benefit [6]. A more recent study, in patients with melanoma treated with the PD-1 inhibitors, nivolumab and pembrolizumab and the PD-L1 inhibitor, atezolizumab, reported significant correlations between increasing tumour mutational load and treatment response, progression-free survival and overall survival [19].

Cases of high tumour mutational load have been identified in most cancer types, which may contribute to an expansion of the population that benefit from immunotherapy, and may allow more informed design of clinical trials of immunotherapy agents in untested cancer types [7,8,21,22]. A recent analysis of tumour mutational load in 100,000 cancer genomes identified a novel mutation hotspot in the promoter of DNA mismatch repair (MMR) gene, PMS2, that was significantly associated with high tumour mutational load [21]. A high tumour mutation load is also associated with genetic alterations leading to dysregulation of the mechanisms of DNA repair, such as microsatellite instability (MSI) or mutations of the polymerase ε gene (POLE) [23]. Studies demonstrate that the number of mutations present in MMR-deficient tumours is a positive predictive factor for response to immune-checkpoint inhibitors, providing proof-of-concept for the relationship between tumour mutational load and treatment response [24].

Tumour Mutational Load as a Prognostic Biomarker

Evidence suggests that tumour mutational load has a positive prognostic influence, which may be driven through increased immunogenic and cytolytic activity [15,25,26]. In ovarian cancers carrying mutations in BRCA1 or BRCA2, high tumour mutational load has been associated with favourable outcome [27]. BRCA-1/2-mutated high grade serous ovarian cancers with high mutational load have been shown to harbour increased numbers of neoantigens compared with tumours proficient in homologous recombination, and is associated with improved overall survival [26].

Quantification of Tumour Mutational Burden

Tumour mutational burden can be determined by whole-exome sequencing but this method is unrealistic on a large scale in a clinical setting due to high cost and resource use. Studies show that mutational burden of the whole genome can be inferred from sequencing a much smaller panel of just a few hundred genes [21,28-30]. A validated, next-generation sequencing assay that targets approximately 1.1 megabase of coding genome has been developed that correlates with whole-exome sequencing to accurately assesses tumour mutational burden [18,19,21,28]. The targeted sequencing platform characterises base substitutions, short insertions and deletions, copy number alterations and selected fusions in more than 300 cancer-related genes. Reliable mutation thresholds are identified to distinguish high-mutation from low-mutation samples to enable prediction of response to immunotherapies [18,19]. The test is currently under evaluation by the FDA Expedited Access Pathway.

The quantitative nature of measuring tumour mutational burden makes it more reliable than the qualitative measurement of expression of individual dynamic protein biomarkers by immunohistochemistry (IHC), which is associated with inherent variability and is not always an accurate predictor of response [18,31].

Interestingly, PD-L1 IHC staining and tumour mutation load only partially overlap [17]. The use of both markers could increase the number of patients selected for treatment with checkpoint inhibitors. Preliminary data suggest that the combination of the two tests might better identify patients who greater benefit from immunotherapy.

The development of predictive biomarkers is needed to optimise patient benefit and minimise risk of toxicities from cancer immunotherapy. Tumour mutational burden, measured by comprehensive genomic profiling, is an important emerging biomarker that shows promise in its ability to predict response to immune checkpoint inhibitors. Further studies are required to fully understand how this novel biomarker can complement the current targeted immunotherapy landscape and its use across multiple tumour types. A multicomponent predictive biomarker system that combines tumour mutational load with other parameters, such as gene and protein expression, neoantigens, microsatellite instability status, and immune targets, is likely required to enable physicians to more accurately select patients who will benefit from these therapies.

References

1. Byrne EH and Fisher DE. Immune and Molecular Correlates in Melanoma Treated With Immune Checkpoint Blockade. Cancer 2017;123(S11):2143-2153.
2. Robert C, Schachter J, Long GV, et al. Pembrolizumab versus ipilimumab in advanced melanoma. N Engl J Med 2015;372:2521-2532.
3. Patel SP and Kurzrock R. PD-L1 Expression as a Predictive Biomarker in Cancer Immunotherapy. Mol Cancer Ther 2015;14(4):847-856.
4. Reck M, Rodríguez-Abreu D, Robinson AG, et al. Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. N Engl J Med 2016;375(19):1823-1833.
5. Van allen EM, Miao D, Schilling B, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 2015;350(6257):207–211.
6. Snyder A, Makarov V, Merghoub T, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 2014;371:2189-2199.
7. Alexandrov LB, Nik-Zainal S, Wedge DC, et al. Signatures of mutational processes in human cancer. Nature 2013;500(7463):415-421.
8. Lawrence MS, Stojanov P, Polak P, et al. Mutational heterogeneity in cancer and the search for new cancer genes. Nature 2013;499:214–218.
9. Vogelstein B, Papadopoulos N, Velculescu VE, et al. Cancer genome landscapes. Science 2013;339:1546–1558.
10. Rizvi NA, Hellmann MD, Snyder A, et al. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 2015;348(6230):124-128.
11. Govindan R, Ding L, Griffith M, et al. Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 2012;150(6):1121–1134.
12. Matsushita H, Vesely MD, Koboldt DC, et al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature 2012;482(7385):400-404.
13. van Rooij N, van Buuren MM, Philips D, et al. Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J Clin Oncol 2013;31(32):e439-e442.
14. Tran E, Turcotte S, Gros A, et al. Cancer immunotherapy based on mutation-specific CD4+ T cells in a patient with epithelial cancer. Science 2014;344(6184):641-645.
15. Brown SD, Warren RL, Gibb EA, et al. Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival. Genome Research 2014;24(5):743-750.
16. Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. Science 2015;348(6230):69-74.
17. Carbone DP, Reck M, Paz-Arez L, et al. First-Line Nivolumab in Stage IV or Recurrent Non–Small-Cell Lung Cancer. N Engl J Med 2017; 376:2415-2426.
18. Rosenberg JE, Hoffman-Censits J, Powles T, et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicenter, phase 2 trial. Lancet 2016;387(10031):1909-1920.
19. Johnson DB, Frampton GM, Rioth MJ, et al. Targeted next generation sequencing identifies markers of response to PD-1 blockade. Cancer Immunol Res 2016;4:959–967.
20. Champiat S, Ferté C, Lebel-Binay S, et al. Exomics and immunogenics. OncoImmunology 2014;3(1):e27817.
21. Chalmers ZR, Connelly CF, Fabrizio D, et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Medicine 2017;9(1):34.
22. Weinstein JN, Colisson EA, Mills GB, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 2013;45:1113–1120.
23. Comprehensive molecular characterization of human colon and rectal cancer. The Cancer Genome Atlas Network. Nature 2012;487(7407):330-337.
24. Le DT, Durham JN, Smith KN, et al. Mismatch-repair deficiency predicts response of solid tumors to PD-1 blockade. Science 2017; 357(6349):409-413.
25. Bibisse S, Foukas PG, Coukps G, et al. Neoantigen-based cancer immunotherapy. Ann Transl Med 2016;4(14):262.
26. Strickland KC, Howitt BE, Shukla SA, et al. Association and prognostic significance of BRCA1/2-mutation status with neoantigen load, number of tumor-infiltrating lymphocytes and expression of PD-1/PD-L1 in high grade serous ovarian cancer. Oncotarget 2016;7(12):13587-13598.
27. Birkbak NJ, Kochupurakkal B, Izarzugaza JMG, et al. Tumor Mutation Burden Forecasts Outcome in Ovarian Cancer with BRCA1 or BRCA2 Mutations. Plos One 2013;8(11):e80023.
28. Frampton GM, Fichtenholtz A, Otto GA, et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nature Biotechnology 2013;31:1023–1031.
29. Roszik J, Haydu LE, Hess KR, et al. Novel algorithmic approach predicts tumor mutation load and correlates with immunotherapy clinical outcomes using a defined gene mutation set. BMC Medicine 2016;14:168.
30. Campesato LF, Barroso-Sousa R, Jimenez L, et al. Comprehensive cancer-gene panels can be used to estimate mutational load and predict clinical benefit to PD-1 blockade in clinical practice. Oncotarget 2015;6(33):34221-34227.
31. Topalian SL, Taube JM, Anders RA, et al. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat Rev Cancer 2016;16(5):275–287.