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Discovering the Possibilities of I-O Biomarkers

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

To identify I-O biomarkers that clarify this unique interplay between the immune system and the tumor, BMS biomarker research is focused on 4 key areas: tumor antigens, inflamed tumor markers, immune suppression markers, and host environment factors.5,7

For each patients, the interaction of the immune system, cancer, and therapy is complex and unique.3 Therefore, the goal of I-O biomarker development is to enable a more personalized approach to treatment by identifying patients who are likely to respond to specific immunotherapies.3,5,8

BMS is committed to the exploration of biomarkers in I-O research. This includes the evaluation of multiple biomarkers that may provide a more accurate and comprehensive assessment of the tumor and tumor microenvironment.


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 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 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
    • Positive predictive biomarkers, such as mutation in the EGFR gene in lung cancer or a mutation in the BRAF gene in melanoma, can identify patients likely to respond to targeted therapy5
    • Negative predictive biomarkers, such as a mutation in the KRAS gene in colorectal cancer, can identify patients unlikely to respond to targeted therapy6,7
  • Pharmacodynamic biomarkers may show that a biologic response has occurred in an individual who has received treatment.4,8 For example, the presence of a measured protein before, during, and after treatment may indicate that the therapy has had a biologic effect

The goal of biomarker testing is to individualize cancer treatment by targeting the right patients with the right therapy at the right time.

I-O biomarkers can be indicators of antitumor immune activity

I-O biomarkers are a class of biomarker that can help evaluate an active antitumor immune response within the body.9 I-O biomarkers can be prognostic, predictive, or pharmacodynamic.3,4,8

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:10-12

  • The interplay between tumor and immune cells within the tumor microenvironment13
    • Tumors can be characterized based on their degree of immune-cell infiltration, ranging from noninflamed to inflamed14
  • The host environment, which can modulate antitumor immune activity15

I-O biomarker research aims to characterize these ongoing interactions.16 Diagnostic testing for the presence or prevalence of I-O biomarkers can help identify immune activity within tumors.17,18 As we continue to learn more about cancer biology and with advancements in diagnostic technologies, the goal of I-O biomarker testing will be to provide actionable information toward developing personalized I-O therapy, including combinations with other treatment modalities.16,19

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

Traditional Genetic Driver Mutations vs I-O BiomarkersTraditional 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.12 For example, traditional genetic driver mutations, such as a mutation in the EGFR or BRAF genes, tend to have binary expression patterns that are either present or absent.5,12,20-22

In contrast, I-O biomarkers are not typically binary and have a range of expression or magnitude.23-25 They are dynamic in nature and can be induced.15,26-28 The expression and/or relevance of I-O biomarkers can also vary based on the stage of disease and location.26,27,29,30

I-O biomarker research at BMS aims to further characterize the unique interplay between the immune system and tumor cells. BMS 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.11,12 The immune response is regulated by an intricate network of activating and inhibitory signaling pathways.31,32 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.12,33 Evaluating multiple I-O biomarkers in combination may provide a more accurate and comprehensive assessment of immune status.12

BMS is committed to taking a comprehensive approach to emerging I-O biomarker research that may help to optimize personalized medicine and improve patient outcomes.


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. Blank CU, Haanen JB, Ribas A, Schumacher TN. The "cancer immunogram." Science. 2016;352(6286):658-660. 7. Chen DS, Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature. 2017;541(7637):321-330. 8. Gibney GT, Weiner LM, Atkins MB. Predictive biomarkers for checkpoint inhibitor-based immunotherapy. Lancet Oncol. 2016;17(12):e542-e551.

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. Van Allen EM, Wagle N, Levy MA. Clinical analysis and interpretation of cancer genome data. J Clin Oncol. 2013;31(15):1825-1833. 6. Allegra CJ, Jessup JM, Somerfield MR, et al. American Society of Clinical Oncology provisional clinical opinion: testing for KRAS gene mutations in patients with metastatic colorectal carcinoma to predict response to anti-epidermal growth factor receptor monoclonal antibody therapy. J Clin Oncol. 2009;27(12):2091-2096. 7. Sepulveda AR, Hamilton SR, Allegra CJ, et al. Molecular biomarkers for the evaluation of colorectal cancer: guideline from the American Society for Clinical Pathology, College of American Pathologists, Association for Molecular Pathology, and the American Society of Clinical Oncology. J Clin Oncol. 2017;35(13):1453-1486. 8. Gainor JF, Longo DL, Chabner BA. Pharmacodynamic biomarkers: falling short of the mark? Clin Cancer Res. 2014;20(10):2587-2594. 9. Whiteside TL. Immune responses to cancer: are they potential biomarkers of prognosis? Front Oncol. 2013;3:107. 10. 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. 11. 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. 12. Sharma P, Allison JP. The future of immune checkpoint therapy. Science. 2015;348(6230):56-61. 13. Balkwill FR, Capasso M, Hagemann T. The tumor microenvironment at a glance. J Cell Sci. 2012;125(Pt 23):5591-5596. 14. 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. 15. Chen DS, Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature. 2017;541(7637):321-330. 16. Yuan J, Hegde PS, Clynes R, et al. Novel technologies and emerging biomarkers for personalized cancer immunotherapy. J Immunother Cancer. 2016;4:3. 17. Hendry S, Salgado R, Gevaert T, et al. Assessing tumor-infiltrating lymphocytes in solid tumors: a practical review for pathologists and proposal for a standardized method from the international immunooncology biomarkers working group: Part 1: assessing the host immune reponse, TILs in invasive breast carcinoma and ductal carcinoma in situ, metastatic tumor deposits and areas for further research. Adv Anat Pathol. 2017;24(5):235-251. 18. Topalian SL, Taube JM, Anders RA, Pardoll DM. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat Rev Cancer. 2016;16(5):275-287. 19. Qiao M, Jiang T, Ren S, Zhou C. Combination strategies on the basis of immune checkpoint inhibitors in non-small-cell lung cancer: where do we stand? Clin Lung Cancer. 2018;19(1):1-11. 20. Davies H, Bignell GR, Cox C, et al. Mutations of the BRAF gene in human cancer. Nature. 2002;417(6892):949-954. 21. Lawrence RT, Perez EM, Hernández D, et al. The proteomic landscape of triple-negative breast cancer. Cell Rep. 2015;11(4):630-644. 22. Mok TS. Personalized medicine in lung cancer: what we need to know. Nat Rev Clin Oncol. 2011;8(11):661-668. 23. 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. 24. 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. 25. Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. Science. 2015;348(6230):69-74. 26. 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. 27. 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. 28. Patel SP, Kurzrock R. PD-L1 expression as a predictive biomarker in cancer immunotherapy. Mol Cancer Ther. 2015;14(4):847-856. 29. 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. 30. 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. 31. 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. 32. Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012;12(4):252-264. 33. Blank CU, Haanen JB, Ribas A, Schumacher TN. The "cancer immunogram." Science. 2016;352(6286):658-660.

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