Tumor antigens are recognized as nonself or foreign by the host immune system.1 They can initiate the adaptive immune response, a process known as immunologic priming.1,2 Several I-O biomarkers related to tumor antigens are currently under investigation:
Neoantigens are newly formed antigens that have not been previously recognized by the immune system. They can arise from altered peptides formed as a result of tumor mutations or viral proteins.1,2
Neoantigens can be recognized by the immune system as nonself, and as such, can elicit an immune response.2,3 Neoantigen-specific T cells have been identified in several human cancers.4 High tumor mutation burden (TMB) and/or microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR) status may be associated with increased neoantigen production.3,5-7
Tumors with a high burden of neoantigens have been shown to be more sensitive to immunotherapy, indicating that neoantigens may be a potential I-O biomarker.8 As immunogenic neoantigens can be challenging to identify directly, TMB may potentially be used as a surrogate to indirectly assess neoantigen load.9,10
Tumor mutational burden (TMB)
Tumor mutational burden (TMB) is defined as the number of somatic (acquired) mutations in the tumor genome.1,2 The number of mutations can vary across different tumor types.3,4
High mutational burden in tumors is correlated with an increased number of predicted neoantigens.5 The increased presence of tumor-specific neoantigens makes the tumor more immunogenic, leading to an increased number of tumor-infiltrating immune cells.5,6 High TMB has been shown to be associated with infiltration of cytotoxic T cells into the tumor microenvironment, supporting its use as a neoantigen surrogate.7-9
Distinct mechanisms of DNA mutation, such as dMMR and exposure to environmental mutagens (eg, tobacco smoke and UV light), can lead to a high TMB.3,10
TMB is assessed using next-generation sequencing (NGS). Next-generation sequencing (NGS) is a laboratory method in which tumor DNA can be read and analyzed for mutations against a reference genome.11,12 TMB can be determined using 3 methods: assessing the whole genome, whole exome, or a targeted gene panel.1,13
The more genes assessed in a gene panel, the greater the sensitivity in quantifying TMB for the whole tumor.1,14-16 As tumors with high TMB are more likely to be recognized and targeted by the immune system, testing for TMB may provide information about the likelihood of an antitumor immune response.7-9 The threshold for defining a high level of TMB is currently under investigation, as levels of TMB can vary across tumors.1,3
TMB is an emerging biomarker that may predict the likelihood of an immune response against cancer cells, which could help inform individualized treatment across tumor types.5,17
Research to investigate the potential use of TMB as an I-O biomarker is ongoing.
Microsatellite instability-high/mismatch repair
Microsatellite instability-high/mismatch repair (MSI-H/dMMR) are indicators of genomic instability:
MSI-H: Microsatellite instability (MSI) is a change in the number of nucleotide repeats in DNA sequences, resulting in a different number of repeats than when the DNA was inherited.1 An MSI-H tumor has at least 2 unstable markers among 5 microsatellite markers analyzed (or ≥30% of the markers if a larger panel is used).2
dMMR: Mismatch repair (MMR) is a key DNA repair pathway facilitated by the MMR protein complex. dMMR represents a loss of function in the MMR pathway.3
In MSI-H/dMMR tumors, more neoantigens may be produced.3,4 Neoantigens have been associated with increased T-cell activation and immune-cell infiltration of the tumor microenvironment.5,6 Though tumors with high TMB are also more likely to possess more neoantigens, MSI status does not serve as a sufficient surrogate for TMB, as not all MSI-H tumors have high TMB.7
MSI status is most commonly detected using polymerase chain reaction (PCR), while dMMR status is commonly detected immunohistochemistry (IHC) for loss of expression of proteins in the DNA MMR complex.3,8
Research to better understand the role of MSI-H/dMMR as I-O biomarkers is ongoing.
REFERENCES: Tumor antigens
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1. Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. Science. 2015;348(6230):69-74. 2. Lu Y-C, Robbins PF. Cancer immunotherapy targeting neoantigens. Semin Immunol. 2016;28(1):22-27. 3. Hause RJ, Pritchard CC, Shendure J, Salipante SJ. Classification and characterization of microsatellite instability across 18 cancer types. Nat Med. 2016;22(11):1342-1350. 4. Bobisse S, Foukas PG, Coukos G, Harari A. Neoantigen-based cancer immunotherapy. Ann Transl Med. 2016;4(14):262. 5. Bogaert J, Prenen H. Molecular genetics of colorectal cancer. Ann Gastroenterol. 2014;27(1):9-14. 6. Chalmers ZR, Connelly CF, Fabrizio D, et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 2017;9(1):34. doi:10.1186/s13073-017-0424-2. 7. Kim JM, Chen DS. Immune escape to PD-L1/PD-1 blockade: seven steps to success (or failure). Ann Oncol. 2016;27(8):1492-1504. 8. Efremova M, Finotello F, Rieder D, Trajanoski Z. Neoantigens generated by individual mutations and their role in cancer immunity and immunotherapy. Front Immunol. 2017;8:1679. doi:10.3389/fimmu.2017.01679. 9. Chabanon RM, Pedrero M, Lefebvre C, Marabelle A, Soria JC, Postel-Vinay S. Mutational landscape and sensitivity to immune checkpoint blockers. Clin Cancer Res. 2016;22(17):4309-4321.
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REFERENCES: Tumor mutational burden
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11. Frampton GM, Fichtenholtz A, Otto GA, et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol. 2013;31(11):1023-1031. 12. Meyerson M, Gabriel S, Getz G. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet. 2010;11(10):685-696. 13. Ng SB, Turner EH, Robertson PD, et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature. 2009;461(7261):272-276. 14. Drilon A, Wang L, Arcila ME, et al. Broad, hybrid capture-based next-generation sequencing identifies actionable genomic alterations in lung adenocarcinomas otherwise negative for such alterations by other genomic testing approaches. Clin Cancer Res. 2015;21(16):3631-3639.
15. Garofalo A, Sholl L, Reardon B, et al. The impact of tumor profiling approaches and genomic data strategies for cancer precision medicine. Genome Med. 2016;8:79. doi:10.1186/s13073-016-0333-9. 16. 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 Med. 2016;14(1):168. doi:10.1186/s12916-016-0705-4. 17. Yuan J, Hegde PS, Clynes R, et al. Novel technologies and emerging biomarkers for personalized cancer immunotherapy. J Immunother Cancer. 2016;4:3. doi:10.1186/s40425-016-0107-3.
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