The evolving landscape of biomarkers for target therapies and checkpoint inhibitors

Gabriel Macedo, PhD

In the last years, DNA sequencing costs have dropped 1000-fold, and the number of genetic tests has risen in a very significant way (Muir et al., 2016). Oncology has faced a tremendous impact of the Next-Generation Sequencing (NGS) implementation for mutation drivers discovery and biomarkers validation, launching the era of precision medicine in which the identification of targetable somatic alterations may predict response to specific molecularly targeted agents. Improved selection of patients based on biomarkers has led to a significant impact on response rates, overall survival, and progression-free survival for different neoplasms (Gagan & Van Allen, 2015). 

Two well-known examples of somatic genetic alterations that are currently used in the clinic to select patients for molecularly targeted therapy are:

  • HER2 amplification in patients with breast cancer, which determines their eligibility for anti-HER2 therapies (Romond et al., 2005);
  • Sensitizing EGFR mutations in patients with lung adenocarcinoma that predict response to EGFR inhibitors (Lynch et al., 2004).

In both cases, a greater clinical benefit is obtained in patients who harbor these drivers in comparison to those that do not. In contrast to somatic alterations, which have been widely employed as predictive biomarkers, germline mutations in tumor suppressor genes have been mainly informative of cancer risk(Huang et al., 2018). This scenario has changed with the approval of PARP inhibitors for ovarian tumors in patients with BRCA1 and BRCA2 germline mutations (Ledermann et al., 2014). The roles of these genes in DNA repair by homologous recombination and the discovery of synthetic lethal interaction between PARP inhibition and BRCA1 or BRCA2 deficiency allowed advancing, for the first time, in the therapeutic field in hereditary tumors. Some PARP inhibitors (i.g Olaparib and Niraparib) were already approved and others are in different phases of clinical studies. PARP inhibitors are the first drugs designed specifically to treat cancers in people with certain inherited mutations(Macedo, Alemar, & Ashton-Prolla, 2019).

More recently, some evidence has suggested that pediatric ultra-hypermutant cancers are strongly associated with germline mutations in mismatch repair (MMR) genes, which have been shown to result in a clinically significant response to immune checkpoint inhibitors (Campbell et al., 2017). 

Currently, most of the drugs that have been approved in oncology are based on a biomarker found in a specific tumor or histology. However, more recently the tumor-agnostic treatment concept has provided a way to treat a tumor based on its specific biomarker rather than just its location in the body (Yan & Zhang, 2018). The two examples of agnostic drugs are pembrolizumab (Keytruda), approved in May 2017 by U.S. Food and Drug Administration (FDA) and larotrectinib (Vitrakvi), which received FDA approval in November 2018. Pembrolizumab is an immunotherapy indicated to treat adults and children with metastatic solid tumors that have a molecular alteration called microsatellite instability or MMR deficiency, both predictors of greater number of somatic mutations within the tumor (Le et al., 2017).

In contrast, larotrectinib is a targeted therapy used to treat adults and children with solid tumors that harbor a gene fusion involving the neurotrophic receptor tyrosine kinase (NTRK). NTRK fusions have been found in a small percentage of multiple tumor types, but are highly enriched in others. For instance, the ETV6–NTRK3 fusion is found in more than 90% of the secretory breast carcinoma, congenital mesoblastic nephroma and infantile fibrosarcomas, being considered practically pathognomonic in these rare tumors (Drilon et al., 2018). 

Immune checkpoint inhibitors therapies, such as antibodies that target CTLA-4 or PD-1/PD-L1, have revolutionized the treatment of some specific advanced-stage tumors. A large number of clinical studies have shown a correlation between PD-L1 expression and the efficacy of these agents (Bellmunt et al., 2017; Reck et al., 2016). However, some phase 3 trials have found negative results in different patient selection criteria, highlighting the importance of a more accurate predictive biomarker (Sharma et al., 2016). In this context, some studies have shown progress towards developing novel biomarkers of immune checkpoint inhibitors. High tumor mutation burden (TMB) seems to increase immunogenicity of the tumor and lead to a more suitable benefit from immunotherapy (Chan et al., 2019). In fact, TMB biomarker can better distinguish the benefit group to immunotherapy when compared to PD-L1 expression (Tong et al., 2018). More recently, it has been shown that TMB cutpoints associated with improved survival varied markedly between cancer types, demonstrating that there may not be one universal definition of high TMB (Samstein et al., 2019).

Finally, although tumor profiling is the best way to identify targetable somatic mutations, it also results in the identification of inherited (germline, constitutional) variants. In some laboratories in which somatic testing is performed, germline DNA from normal tissue is also tested to aid in filtering tumor-specific events by subtraction of germline variants (Stjepanovic et al., 2018). In others, somatic testing has been also employed in order to suspect of potentially hereditary alterations, since guidelines for cancer genetic testing based on family history may miss clinically actionable genetic changes. Some data have found that around 17.5% of the patients showing advanced disease had clinically actionable mutations conferring cancer susceptibility, most of them with moderate- to high-penetrance mutations. More important, a significant proportion would not have had these mutations detected using clinical guidelines (Mandelker et al., 2017).

About the author:

Gabriel de Souza Macedo, has a degree in Biological Sciences, a master’s and a doctorate from the Graduate Program in Genetics and Molecular Biology at the Federal University of Rio Grande do Sul. He is currently the coordinator of the Personalized Medicine Program at Hospital das Clínicas de Porto Alegre and coordinator of the Molecular Biology Laboratory of Hospital Moinhos de Vento.


[1] Bellmunt, J., de Wit, R., Vaughn, D. J., Fradet, Y., Lee, J. L., Fong, L., . . . Investigators, KEYNOTE-045. (2017). Pembrolizumab as Second-Line Therapy for Advanced Urothelial Carcinoma. N Engl J Med, 376(11), 1015-1026. doi:10.1056/NEJMoa1613683

[2] Campbell, B. B., Light, N., Fabrizio, D., Zatzman, M., Fuligni, F., de Borja, R., . . . Shlien, A. (2017). Comprehensive Analysis of Hypermutation in Human Cancer. Cell, 171(5), 1042-1056.e1010. doi:10.1016/j.cell.2017.09.048

[3] Chan, T. A., Yarchoan, M., Jaffee, E., Swanton, C., Quezada, S. A., Stenzinger, A., & Peters, S. (2019). Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol, 30(1), 44-56. doi:10.1093/annonc/mdy495

[4] Drilon, A., Laetsch, T. W., Kummar, S., DuBois, S. G., Lassen, U. N., Demetri, G. D., . . . Hyman, D. M. (2018). Efficacy of Larotrectinib in TRK Fusion-Positive Cancers in Adults and Children. N Engl J Med, 378(8), 731-739. doi:10.1056/NEJMoa1714448

[5] Gagan, J., & Van Allen, E. M. (2015). Next-generation sequencing to guide cancer therapy. Genome Med, 7(1), 80. doi:10.1186/s13073-015-0203-x

[6] Huang, K. L., Mashl, R. J., Wu, Y., Ritter, D. I., Wang, J., Oh, C., . . . Network, Cancer Genome Atlas Research. (2018). Pathogenic Germline Variants in 10,389 Adult Cancers. Cell, 173(2), 355-370.e314. doi:10.1016/j.cell.2018.03.039

[7] Le, D. T., Durham, J. N., Smith, K. N., Wang, H., Bartlett, B. R., Aulakh, L. K., . . . Diaz, L. A. (2017). Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science, 357(6349), 409-413. doi:10.1126/science.aan6733

[8] Ledermann, J., Harter, P., Gourley, C., Friedlander, M., Vergote, I., Rustin, G., . . . Matulonis, U. (2014). Olaparib maintenance therapy in patients with platinum-sensitive relapsed serous ovarian cancer: a preplanned retrospective analysis of outcomes by BRCA status in a randomised phase 2 trial. Lancet Oncol, 15(8), 852-861. doi:10.1016/S1470-2045(14)70228-1

[9] Lynch, T. J., Bell, D. W., Sordella, R., Gurubhagavatula, S., Okimoto, R. A., Brannigan, B. W., . . . Haber, D. A. (2004). Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med, 350(21), 2129-2139. doi:10.1056/NEJMoa040938

[10] Macedo, G. S., Alemar, B., & Ashton-Prolla, P. (2019). Reviewing the characteristics of BRCA and PALB2-related cancers in the precision medicine era. Genet Mol Biol. doi:10.1590/1678-4685-GMB-2018-0104

[11] Mandelker, D., Zhang, L., Kemel, Y., Stadler, Z. K., Joseph, V., Zehir, A., . . . Offit, K. (2017). Mutation Detection in Patients With Advanced Cancer by Universal Sequencing of Cancer-Related Genes in Tumor and Normal DNA vs Guideline-Based Germline Testing. JAMA, 318(9), 825-835. doi:10.1001/jama.2017.11137

[12] Muir, P., Li, S., Lou, S., Wang, D., Spakowicz, D. J., Salichos, L., . . . Gerstein, M. (2016). The real cost of sequencing: scaling computation to keep pace with data generation. Genome Biol, 17, 53. doi:10.1186/s13059-016-0917-0

[13] Reck, M., Rodríguez-Abreu, D., Robinson, A. G., Hui, R., Csőszi, T., Fülöp, A., . . . Investigators, KEYNOTE-024. (2016). Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. N Engl J Med, 375(19), 1823-1833. doi:10.1056/NEJMoa1606774

[14] Romond, E. H., Perez, E. A., Bryant, J., Suman, V. J., Geyer, C. E., Davidson, N. E., . . . Wolmark, N. (2005). Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med, 353(16), 1673-1684. doi:10.1056/NEJMoa052122

[15] Samstein, R. M., Lee, C. H., Shoushtari, A. N., Hellmann, M. D., Shen, R., Janjigian, Y. Y., . . . Morris, L. G. T. (2019). Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet, 51(2), 202-206. doi:10.1038/s41588-018-0312-8

[16] Sharma, P., Callahan, M. K., Bono, P., Kim, J., Spiliopoulou, P., Calvo, E., . . . Rosenberg, J. E. (2016). Nivolumab monotherapy in recurrent metastatic urothelial carcinoma (CheckMate 032): a multicentre, open-label, two-stage, multi-arm, phase 1/2 trial. Lancet Oncol, 17(11), 1590-1598. doi:10.1016/S1470-2045(16)30496-X

[17] Stjepanovic, N., Stockley, T. L., Bedard, P. L., McCuaig, J. M., Aronson, M., Holter, S., . . . Kim, R. H. (2018). Additional germline findings from a tumor profiling program. BMC Med Genomics, 11(1), 65. doi:10.1186/s12920-018-0383-5

[18] Tong, M., Wang, J., He, W., Wang, Y., Pan, H., Li, D., & Zhang, H. (2018). Predictive biomarkers for tumor immune checkpoint blockade. Cancer Manag Res, 10, 4501-4507. doi:10.2147/CMAR.S179680

[19] Yan, L., & Zhang, W. (2018). Precision medicine becomes reality-tumor type-agnostic therapy. Cancer Commun (Lond), 38(1), 6. doi:10.1186/s40880-018-0274-3

Leave a message