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Discovering the Possibilities of I-O Biomarkers

With a focus on precision medicine, our research and development program aims to rapidly translate research into novel regimens to accelerate delivery of the right treatment, for the right patient, at the right time.

To accelerate our ability to identify precision medicine solutions for individual patients, BMS is pursuing a unique multifaceted approach to translational medicine. Based upon our comprehensive analysis of the tumor microenvironment, including tumor-intrinsic signaling and immune biology, BMS aims to identify clinical characteristics and Immuno-Oncology (I-O) biomarkers to determine the patient populations most likely to benefit from I-O therapy.

Possibilities of I-O biomarkers

I-O biomarkers may be used to determine the immune potential of the tumor microenvironment

Research in the field of I-O biomarkers seeks to characterize the relationship between the immune system, the tumor and its microenvironment, and the host
Unique interactions among these factors contribute to the balance between activation and suppression of the antitumor immune response^1-3
Tumors can be characterized based on their degree of immune-cell infiltration, ranging from noninflamed to inflamed⁴

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 four key areas:

I-O biomarkers may be used to advance precision medicine, enabling tailored therapeutic solutions for individualized patients

For each patient, the interaction of the immune system, cancer, and therapy is complex and unique
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, as a composite biomarker approach may provide a more accurate and comprehensive assessment of the tumor and tumor microenvironment.

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I-O Biomarkers: Under investigation for their role in immuno-therapy

See how several biomarkers are under investigation for their potential to predict response to immunotherapy

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I-O biomarkers & antitumor immune activity

I-O biomarkers, a subset of biomarkers, can be indicators of antitumor immune activity

While the immune system seeks to detect and destroy tumor cells, the tumor attempts to evade or suppress immune activity^1-3
The balance between antitumor immune activation and suppression results from complex interactions among several factors, including^1-3:

The interplay between tumor and immune cells within the tumor microenvironment⁹

Tumors can be characterized based on their degree of immune-cell infiltration, ranging from noninflamed to inflamed⁴

The host environment, which can modulate antitumor immune activity⁷

I-O biomarkers are a class of biomarker that can help evaluate an active antitumor immune response within the body¹⁰

I-O biomarkers can be prognostic, predictive, pharmacodynamic, or a combination^11-14

I-O biomarker research aims to characterize the ongoing interactions between tumor cells and the immune system using improved high-throughput technologies⁵

Diagnostic testing for the presence or prevalence of I-O biomarkers can help identify immune activity within tumors and possibly aid in predicting response to immunotherapy^5,15,16

As we continue to learn more about cancer biology—and with advancements in high-throughput 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^5,17

I-O biomarkers are dynamic and diverse*

Types of Biomarkers

Traditional genetic-driver mutations and corresponding 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^3,18

I-O biomarkers measure dynamic immune activity and immune responsiveness within the tumor microenvironment and host environment

I-O biomarkers are not typically binary and have a range of expression or magnitude.^19-21 They are dynamic in nature and can be induced^7,22-24

Genetic-driver biomarkers measure specific pathways and measure DNA alterations within a tumor

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^3,30-32

The expression and/or relevance of I-O biomarkers can also vary based on tumor type, stage of disease, and location^22,23,28
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:

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Biomarkers in I-O research

Biomarkers can help guide clinical decisions

Diagram of how biomarkers can individualize cancer treatment by identifying the right patients for the right therapy

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.^29,30 Three common types of biomarkers include prognostic, predictive, and pharmacodynamic biomarkers.

Prognostic biomarkers identify the likelihood of a clinical event, such as disease progression, disease recurrence, or death, independent of the therapy received.^11,12 For example, the expression level of a protein on tumor cells may be associated with poor disease outcome independent of treatment

Negative prognostic biomarkers, such as increased serum lactate dehydrogenase (LDH) levels correlate with poor patient outcomes^31,32

Predictive biomarkers may identify whether individuals are more likely to experience a favorable or unfavorable response to treatment.^11,12 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 a specific 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 therapy²⁵
Negative predictive biomarkers, such as a mutation in the KRAS gene in colorectal cancer, can identify patients unlikely to respond to targeted therapy^33,34

Pharmacodynamic biomarkers may show that a biologic response has occurred in an individual who has received treatment.^12,13 For example, the presence of a measured protein before, during, and after treatment may indicate that the therapy has had a biologic effect

Biomarkers such as BCR-ABL transcript levels may be used to determine whether a patient is responding to a treatment^35-39
Studies indicate that the fusion protein BCR-ABL is essential for initiation, maintenance, and progression of CML. BCR-ABL transcript levels may be reduced during the course of treatment, which may indicate treatment success^40,41

A composite I-O biomarker approach 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.^2,3 The immune response is regulated by an intricate network of activating and inhibitory signaling pathways.^42,43 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.^3,6 A composite I-O biomarker evaluation may provide a more comprehensive assessment of immune status.³

The goal of biomarker testing is to individualize cancer treatment by identifying the right patients for the right therapy at the right time

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REFERENCES–Discovering the possibilities of I-O biomarkers

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PD-L1 expression in human cancers and its association with clinical outcomes. Onco Targets Ther. 2016;9:5023-5039. 24. Patel SP, Kurzrock R. PD-L1 expression as a predictive biomarker in cancer immunotherapy. Mol Cancer Ther. 2015;14(4):847-856. 25. Van Allen EM, Wagle N, Levy MA. Clinical analysis and interpretation of cancer genome data. J Clin Oncol. 2013;31(15):1825-1833. 26. Davies H, Bignell GR, Cox C, et al. Mutations of the BRAF gene in human cancer. Nature. 2002;417(6892):949-954. 27. Mok TS. Personalized medicine in lung cancer: what we need to know. Nat Rev Clin Oncol. 2011;8(11):661-668. 28. 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. 29. Henry NL, Hayes DF. Cancer biomarkers. Mol Oncol. 2012;6(2):140-146. 30. Strimbu K, Tavel JA. What are biomarkers? Curr Opin HIV AIDS. 2010;5(6):463-466. 31. Yu SL, Xu LT, Qi Q, et al. Serum lactate dehydrogenase predicts prognosis and correlates with systemic inflammatory response in patients with advanced pancreatic cancer after gemcitabine-based chemotherapy. Sci Rep. 2017;7:45194. 32. Weide B, Elsässer M, Büttner P, et al. Serum markers lactate dehydrogenase and S100B predict independently disease outcome in melanoma patients with distant metastasis. Br J Cancer. 2012;107(3):422-428. 33. 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. 34. 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. 35. Cramer SD, Chang BL, Rao A, et al. Association between genetic polymorphisms in the prostate-specific antigen gene promoter and serum prostate-specific antigen levels. J Natl Cancer Inst. 2003;95(14):1044-1053. 36. Lilja H, Ulmert D, Vickers AJ. Prostate-specific antigen and prostate cancer: prediction, detection and monitoring. Nat Rev Cancer. 2008;8(4):268-278. 37. Gaudreau PO, Stagg J, Soulières D, Saad F. The present and future of biomarkers in prostate cancer: proteomics, genomics, and immunology advancements: supplementary issue: biomarkers and their essential role in the development of personalised therapies (A). Biomarkers in Cancer. 2016;8(S2):15-33. doi:10.4137/BIC.S31802. 38. Radich JP, Gooley T, Bryant E, et al. The significance of bcr-abl molecular detection in chronic myeloid leukemia patients “late,” 18 months or more after transplantation. Blood. 2001;98(6):1701-1707. 39. Yuda J, Miyamoto T, Odawara J, et al. Persistent detection of alternatively spliced BCR-ABL variant results in a failure to achieve deep molecular response. Cancer Sci. 2017;108(11):2204-2212. 40. Ren R. Mechanisms of BCR-ABL in the pathogenesis of chronic myelogenous leukaemia. Nat Rev Cancer. 2005;5(3):172-183. 41. An X, Tiwari AK, Sun Y, Ding PR, Ashby CR, Chen ZS. BCR-ABL tyrosine kinase inhibitors in the treatment of Philadelphia chromosome positive chronic myeloid leukemia: a review. Leuk Res. 2010;34(10):1255-1268. 42. 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. 43. Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. 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