I-O Biomarkers: Discovering the Possibilities

Research in the field of Immuno-Oncology (I-O) biomarkers seeks to characterize the relationship between the immune system, the tumor and its microenvironment, and the host. Unique interactions of these factors, as well as I-O biomarker presence and prevalence, contributes to the balance of activation versus suppression of the antitumor immune response.^1-3

Tumors can be characterized based on their degree of immune-cell infiltration, ranging from noninflamed to inflamed.⁴ I-O biomarkers that can identify inflamed tumors may help predict a pre-existing antitumor immune response.^3,5 Therefore, the goal of I-O biomarker testing is to help enable a more personalized approach to treatment by identifying patients who are likely to respond to specific immunotherapies.^3,5,6

I-O biomarkers can be classified into the following categories: tumor antigens, inflamed tumor markers, immune suppression markers, and host environment factors.^5,7,8

Bristol-Myers Squibb is committed to the exploration of biomarkers in I-O research.

Biomarkers in Immuno-Oncology Research

Biomarkers can help guide clinical decisions

Biomarkers are biologic molecules, cells, or processes found in tissues or body fluids (such as blood) that are a sign of a normal or abnormal process or disease.^1,2 The goal of biomarker testing is to individualize cancer treatment by targeting the right patients with the right therapy at the right time. Three common types of biomarkers include:

Prognostic biomarkers may identify the likelihood of a clinical event, such as disease progression, disease recurrence, or death, independent of the therapy received.^3,4 For example, the expression level of a protein on tumor cells may be associated with poor disease outcome independent of treatment
Predictive biomarkers are present at diagnosis and may identify whether individuals are more likely to experience a favorable or unfavorable response to treatment.^3,4 For example, the presence or upregulation of a protein on tumor cells may correlate with a favorable outcome in response to a certain treatment
Pharmacodynamic biomarkers may show that a biologic response has occurred in an individual who has received treatment.^4,5 For example, comparing tissue- or blood-based assay results before, during, and afte treatment may reveal changes in the level of a protein, indicating that the therapy has had a biologic effect

I-O biomarkers can be indicators of antitumor immune activity

I-O biomarkers are a type of biomarker that can help evaluate an active antitumor immune response within the body.⁶

While the immune system seeks to detect and destroy tumor cells, the tumor attempts to evade or suppress immune activity. The balance between antitumor immune activation and suppression results from complex interactions among several factors, including:^7-9

The interplay between tumor and immune cells within the tumor microenvironment¹⁰
- Tumors can be characterized based on their degree of immune-cell infiltration, ranging from noninflamed to inflamed¹¹
The host environment, which can modulate antitumor immune activity¹²

The field of I-O biomarkers aims to characterize these ongoing interactions.¹³

I-O biomarkers are a dynamic and diverse subset of biomakers

Traditional Genetic Driver Mutations vs I-O Biomarkers

Descriptions of I-O biomarkers and traditional genetic driver mutations represent common features of each group, but are not exhaustive

I-O biomarkers are distinct from traditional genetic driver mutation biomarkers. While I-O biomarkers measure dynamic immune activity within the tumor microenvironment and host environment, genetic driver biomarkers measure stable DNA alterations within a tumor.⁹ For example, traditional genetic driver mutations such as EGFR and BRAF tend to have binary expression patterns that are either present or absent.^9,14-17 They are typically homogenously expressed within a tumor.¹⁸

In contrast, I-O biomarkers are not binary and have a range of expression or magnitude.^19-21 Heterogeneous within the tumor microenvironment, they are dynamic in nature and can be inducible.^12,22-24 I-O biomarkers can also vary based on the stage of disease and location.^22,23,25,26

Bristol-Myers Squibb is committed to researching I-O biomarkers in the following categories:

BMS is committed to researching I-O biomarkers in the
following categories: tumor antigens, immune suppression,
inflamed tumors, and host environment

Multiple I-O biomarkers may be needed to provide a more precise representation of the tumor microenvironment

As components and regulators of the immune response, I-O biomarkers are dynamic and complex.^8,9 The immune response is regulated by an intricate network of activating and inhibitory signaling pathways.^27,28 Therefore, the presence or absence of any single I-O biomarker may not provide a complete understanding of the diverse interactions occurring within the tumor microenvironment.^9,29 Evaluating multiple I-O biomarkers in combination may provide a more accurate and comprehensive assessment of immune status.⁹

Learn more about categories of I-O biomarkers

REFERENCES: Discovering the Possibilities of I-O Biomarkers

1. Gkretsi V, Stylianou A, Papageorgis P, Polydorou C, Stylianopoulos T. Remodeling components of the tumor microenvironment to enhance cancer therapy. Front Oncol. 2015;5:214. 2. Nelson D, Fisher S, Robinson B. The ‘‘trojan horse’’ approach to tumor immunotherapy: targeting the tumor microenvironment. J Immunol Res. 2014. doi:10.1155/2014/789069. 3. Sharma P, Allison JP. The future of immune checkpoint therapy. Science. 2015;348(6230):56-61. 4. Hegde PS, Karanikas V, Evers S, et al. The where, the when, and the how of immune monitoring for cancer immunotherapies in the era of checkpoint inhibition. Clin Cancer Res. 2016;22(8):1865-1874. 5. Yuan J, Hegde PS, Clynes R, et al. Novel technologies and emerging biomarkers for personalized cancer immunotherapy. J Immunother Cancer. 2016;4:3. 6. Gibney GT, Weiner LM, Atkins MB. Predictive biomarkers for checkpoint inhibitor-based immunotherapy. Lancet Oncol. 2016;17(12):e542-e551. 7. Blank CU, Haanen JB, Ribas A, Schumacher TN. The "cancer immunogram." Science. 2016;352(6286):658-660. 8. Chen DS, Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature. 2017;541(7637):321-330.

REFERENCES: Biomarkers in Immuno-Oncology Research

1. Henry NL, Hayes DF. Cancer biomarkers. Mol Oncol. 2012;6(2):140-146. 2. Strimbu K, Tavel JA. What are biomarkers? Curr Opin HIV AIDS. 2010;5(6):463-466. 3. Ballman KV. Biomarker: predictive or prognostic? J Clin Oncol. 2015;33(33):3968-3971. 4. US Food and Drug Administration. About biomarkers. www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugDevelopmentToolsQualificationProgram/BiomarkerQualificationProgram/ucm535922.htm. Accessed August 1, 2017. 5. Gainor JF, Longo DL, Chabner BA. Pharmacodynamic biomarkers: falling short of the mark? Clin Cancer Res. 2014;20(10):2587-2594. 6. Whiteside TL. Immune responses to cancer: are they potential biomarkers of prognosis? Front Oncol. 2013;3:107. 7. Gkretsi V, Stylianou A, Papageorgis P, Polydorou C, Stylianopoulos T. Remodeling components of the tumor microenvironment to enhance cancer therapy. Front Oncol. 2015;5:214. 8. Nelson D, Fisher S, Robinson B. The ‘‘trojan horse’’ approach to tumor immunotherapy: targeting the tumor microenvironment. J Immunol Res. 2014. doi:10.1155/2014/789069. 9. Sharma P, Allison JP. The future of immune checkpoint therapy. Science. 2015;348(6230):56-61. 10. Balkwill FR, Capasso M, Hagemann T. The tumor microenvironment at a glance. J Cell Sci. 2012;125(Pt 23):5591-5596. 11. Hegde PS, Karanikas V, Evers S, et al. The where, the when, and the how of immune monitoring for cancer immunotherapies in the era of checkpoint inhibition. Clin Cancer Res. 2016;22(8):1865-1874. 12. Chen DS, Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature. 2017;541(7637):321-330. 13. Yuan J, Hegde PS, Clynes R, et al. Novel technologies and emerging biomarkers for personalized cancer immunotherapy. J Immunother Cancer. 2016;4:3. 14. Davies H, Bignell GR, Cox C, et al. Mutations of the BRAF gene in human cancer. Nature. 2002;417(6892):949-954. 15. Lawrence RT, Perez EM, Hernández D, et al. The proteomic landscape of triple-negative breast cancer. Cell Rep. 2015;11(4):630-644. 16. Mok TS. Personalized medicine in lung cancer: what we need to know. Nat Rev Clin Oncol. 2011;8(11):661-668. 17. Van Allen EM, Wagle N, Levy MA. Clinical analysis and interpretation of cancer genome data. J Clin Oncol. 2013;31(15):1825-1833. 18. Yap TA, Gerlinger M, Futreal PA, Pusztai L, Swanton C. Intratumor heterogeneity: seeing the wood for the trees. Sci Transl Med. 2012;4(127):127ps10. 19. Kerr KM, Tsao MS, Nicholson AG, Yatabe Y, Wistuba II, Hirsch FR. Programmed death-ligand 1 immunohistochemistry in lung cancer: in what state is this art? J Thorac Oncol. 2015;10(7):985-989. 20. 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. 21. Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. Science. 2015;348(6230):69-74. 22. Wang X, Teng F, Kong L, Yu J. PD-L1 expression in human cancers and its association with clinical outcomes. Onco Targets Ther. 2016;9:5023-5039. 23. Ohaegbulam KC, Assal A, Lazar-Molnar E, Yao Y, Zang X. Human cancer immunotherapy with antibodies to the PD-1 and PD-L1 pathway. Trends Mol Med. 2015;21(1):24-33. 24. Patel SP, Kurzrock R. PD-L1 expression as a predictive biomarker in cancer immunotherapy. Mol Cancer Ther. 2015;14(4):847-856. 25. Jakobsen JN, Santoni-Rugiu E, Ravn J, Sørensen JB. Intratumour variation of biomarker expression by immunohistochemistry in resectable non-small cell lung cancer. Eur J Cancer. 2013;49(11):2494-2503. 26. Taube JM, Klein A, Brahmer JR, et al. Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti–PD-1 therapy. Clin Cancer Res. 2014;20(19):5064-5074. 27. Long EO, Kim HS, Liu D, Peterson ME, Rajagopalan S. Controlling natural killer cell responses: integration of signals for activation and inhibition. Annu Rev Immunol. 2013;31:227-258. 28. Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012;12(4):252-264. 29. Blank CU, Haanen JB, Ribas A, Schumacher TN. The "cancer immunogram." Science. 2016;352(6286):658-660.