Tumor antigens are recognized as nonself or foreign by the host immune
system.1 They can initiate the adaptive immune response by
priming the immune system.1,2 Several
Neoantigens are newly formed antigens that arise from mutated tumor proteins. They can elicit an immune response and may help predict sensitivity to I-O therapy.
- Neoantigens are newly formed antigens that have not been previously recognized by the immune system1,2
- Neoantigens can arise from altered peptides formed as a result of tumor mutations or viral proteins1,2
- High tumor mutational burden (TMB), microsatellite instability-high (MSI-H), mismatch repair deficient (dMMR) status, or MGMT methylation may be associated with increased neoantigen production3-7
- Neoantigens can be recognized by the immune system as nonself and, as such, can elicit an immune response2,3
- Neoantigen-specific T cells have been identified in several human cancers8
- Tumors with a high neoantigen burden may be more sensitive to immunotherapy, indicating that neoantigens may be a potential I-O biomarker9
Tumor Mutational Burden (TMB)
TMB is the amount of acquired mutations in the tumor genome, and may be a surrogate biomarker for neoantigens. It can vary across tumor types and may predict the likelihood of an immune response, and can be measured using next-generation sequencing.
- TMB is defined as the number of somatic (acquired) mutations in the tumor genome1,2
- High mutational burden in tumors is correlated with an increased number of predicted neoantigens3
- High TMB has been shown to be associated with infiltration of cytotoxic T cells in the tumor microenvironment, supporting its use as a neoantigen surrogate4-6
- The increased presence of tumor-specific neoantigens makes the tumor more immunogenic, leading to an increased number of tumor-infiltrating immune cells3,7
- Distinct mechanisms of DNA mutation, such as dMMR and exposure to environmental mutagens (eg, tobacco smoke and UV light), can lead to high TMB8,9
Research to investigate the potential use of TMB as a predictive I-O biomarker is ongoing.
- The number of mutations can vary within tumors, across tumor types and can also change over time (eg, over course of disease)8-11
- 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 response4-6
- Thus, TMB is an emerging biomarker that may predict the likelihood of an immune response against tumor cells, which could help inform individualized treatment across tumor types3,12
- TMB is assessed using NGS, a laboratory method in which tumor DNA can be read and analyzed for mutations against a reference genome13,14
- TMB can be determined using three methods: assessing the whole genome, assessing the whole exome, or by using targeted gene panels1,15
- Targeted gene panels can simultaneously examine mutations and other genetic alterations in hundreds of pre-specified genes and provide information on TMB and other important clinical markers, including traditional genetic driver mutations13,16
- With a targeted gene panel, the assessment of a larger number of genes correlates with greater sensitivity in quantifying TMB for the whole tumor1,17-19
- The threshold for defining a high level of TMB is currently under investigation, as levels of TMB can vary across tumors1,9
Microsatellite instability (MSI), caused by deficiencies in mismatch repair (dMMR), can be an indicator of genomic instability. MSI-H/dMMR tumors correlate with increases in neonantigen production.
- MSI: 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-High (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: 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 produced3,4
- Neoantigens have been associated with increased T-cell activation and immune-cell infiltration of the tumor microenvironment5,6
- 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 high-TMB tumors are MSI-H7
- MSI status is most commonly detected using polymerase chain reaction (PCR), while dMMR status is commonly detected by immunohistochemistry (IHC) for loss of expression of proteins in the DNA MMR complex3,15
1. Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. Science. 2015;348(6230):69-74. 2. Eggermont LJ, Paulis LE, Tel J, Figdor CG. Towards efficient cancer immunotherapy: advances in developing artificial antigen-presenting cells. Trends Biotechnol. 2014; 32(9):456-465.
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. doi:10.1038/nm.4191. 4. Bogaert J, Prenen H. Molecular genetics of colorectal cancer. Ann Gastroenterol. 2014;27(1):9-14. 5. 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. 6. 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. 7. Germano G, Lamba S, Rospo G, Barault L, Magrì A, Maione F, et al. Inactivation of DNA repair triggers neoantigen generation and impairs tumour growth. Nature. 2017;552:116-20. 8. Bobisse S, Foukas PG, Coukos G, Harari A. Neoantigen-based cancer immunotherapy. Ann Transl Med. 2016;4(14):262. 9. 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. 10. 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. 11. 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.
References–Tumor mutational burden
1. 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. 2. Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009;458(7239):719-724. 3. Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell. 2015;160(1-2):48-61. 4. 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. 5. 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. 6. Giannakis M, Mu XJ, Shukla SA, et al. Genomic correlates of immune-cell infiltrates in colorectal carcinoma. Cell Rep. 2016;15(4):857-865. 7. Brown SD, Warren RL, Gibb EA, et al. Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival. Genome Res. 2014;24(5):743-750. 8. Alexandrov LB, Nik-Zainal S, Wedge DC, et al. Signatures of mutational processes in human cancer. Nature. 2013;500:415-421. 9. 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. 10. Johnson BE, Mazor T, Hong C, Barnes M, et al. Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science. 2014;343(6167):189-93. 11. Wang J, Cazzato E, Ladewig E, et al. Clonal evolution of glioblastoma under therapy. Nat Genet. 2016;48(7):768. 12. 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. 13. 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. 14. Meyerson M, Gabriel S, Getz G. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet. 2010;11(10):685-696. 15. Ng SB, Turner EH, Robertson PD, et al. Targeted capture and massively parallel sequencing of twelve human exomes. Nature. 2009;461(7261):272-276. 16. Behjati S, Tarpey PS. What is next generation sequencing? Arch Dis Child Educ Pract Ed. 2013;98(6):236-238. 17. 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. 18. 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. 19. 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.
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