BIOINFORMATIC IMMUNOGENOMIC PROFILING AND TRANSCRIPTOMIC ANALYSIS OF MELANOMA BIOPSIES TO STUDY MECHANISMS OF RESPONSE TO TREATMENT

YAMIL DAMIÁN MAHMOUD; FLORENCIA VEIGAS; FEDERICO PRADA; KARINA V. MARIÑO; GABRIEL A. RABINOVICH

Chicago

Simposio; ISCB Student Council Symposium 2018; 2018

ISCB Student Council

Background: Melanoma is the deadliest form of skin cancer. Targeted therapies and immunotherapies have revolutionized melanoma treatment showing unprecedented survival benefits. However, responses are not universal and patients who initially respond to therapy develop resistance. Critically, patients who relapse to targeted therapies do not respond to immunotherapy. Therefore, establishing which patients would derive clinical benefit from immunotherapy is a compelling clinical question. We aimed to determine predictive molecular biomarkers of response to immunotherapy and to quantify the cellular composition of the immune infiltrate of tumor biopsies to investigate their relationship with response to treatment. Description: We analyzed the gene expression profiles of melanoma samples from public datasets from 269 biopsies from 5 datasets from Gene Expression Omnibus. We implemented an established computational approach (CIBERSORT) to infer the proportion of 22 immune cells from gene expression profiles. Bioinformatics and statistical analysis were performed with the R programming language and public web tools. We found that patients who relapse to targeted therapy present an immunosuppressive microenvironment characterized by increased M2-macrophages and diminished activated NK-cells, this could impact in the response to immunotherapy as second line therapy. Moreover, we found that Galectin-1, an immunosuppressive lectin that binds to glycan structures on the surface of immune cells, is overexpressed in resistant tumours. Additionally, patients? biopsies that do not respond to anti-PD1 immunotherapy are infiltrated by an immunosuppressive cell population characterized by increased eosinophils and monocytes, and decrease of naïve B-cells. Oppositely, patients who respond to anti-PD1 treatment have tumour biopsies with increased total immune infiltrate and effector immune populations such as CD8 T-cells. This group is also characterized by a increased M1-macrophages and CD4 memory activated T-cells and decreased monocytes. Also, patients who respond to anti-PD1 treatment show increased sialyltransferases ST6GAL1, ST3GAL3 and ST3GAL6 expression which could be linked to decreased binding-affinity for Galectin-1. Conclusions: Our preliminary findings show a differential immune infiltrate in melanoma tumors sensitive and resistant to targeted therapies and to anti-PD1 immunotherapy. This could impact patient response to treatment. Lectin/glycan axis molecules could be potential biomarkers of response as well as new actionable targets for the development of combination therapies.