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150 Circulatory plasma proteomic biomarkers predict response to immunotherapy in melanoma patients and reveal biological insights into the tumor microenvironment
  1. Arnav Mehta1,
  2. Marijana Rucevic2,
  3. Emmett Sprecher2,
  4. Nir Hacohen3,
  5. Genevieve Boland4,
  6. Milan Parikh3,
  7. Jiajia Chen1,
  8. Dennie Frederick4,
  9. Elliot Woods4,
  10. Lynn Bi3,
  11. David Lieb3,
  12. Lina Hultin-Rosenberg2,
  13. Jamey Guess2,
  14. Ryan Park3,
  15. Alexis Schneider3,
  16. William Michaud4,
  17. Benchun Miao4,
  18. Gyulnara Kasumova4,
  19. Michelle Kim4,
  20. Xue Bai4,
  21. Russell Jenkins4,
  22. Samuel Klempner4,
  23. Anna Gonye4,
  24. Keren Yizhak5,
  25. Moshe Sade-Feldman3,
  26. David Liu1,
  27. Ryan Sullivan4 and
  28. Keith Flaherty4
  1. 1Dana-Farber Cancer Institute, Boston, MA, USA
  2. 2Olink Proteomics, Boston, USA
  3. 3Broad Institute of MIT and Harvard, Boston, USA
  4. 4Massachusetts General Hospital, Boston, MA, USA
  5. 5Technion Israel Institute of Technology, Haifa, Israel


Background The majority of patients treated with immunotherapy do not have durable treatment responses. Therefore, there is an urgent need to identify early non-invasive biomarkers for treatment response.

Methods In this study, we performed plasma proteomic analysis of >700 proteins at three timepoints on 174 metastatic melanoma patients treated with immune checkpoint blockade (ICB). We then expanded our analyses to >3000 proteins performed on a larger cohort of 250 patients for deeper exploration of baseline and early on-treatment predictive biomarkers for response to ICB treatment. As a result, we built a predictor of immunotherapy response that outperforms several tissue-based approaches.

Results From the differentially expressed proteins between ICB responders (R) and non-responders (NR), we identified a co-regulated module of proteins associated with treatment resistance comprising IL-6, IL-8, MIA, LIF and GDF-15 enriched in certain NR patients. By analyzing single-cell RNA-sequencing data of tumor biopsies from 32 patients and bulk RNA-sequencing data from 70 patients, we determined the relative contribution of cells in the tumor to proteins in circulation, and associated plasma protein levels with tumor immune microenvironment (TME) phenotypes. The major TME subsets driving the expression of the non-response module proteins were tumor and myeloid cells. Amongst myeloid cells, a subset of tumor-associated macrophages (TAMs) with a suppressive phenotype were identified as potential key drivers of non-response, having the highest expression of all the proteins in the co-regulated NR module.

Conclusions In summary, an integrated longitudinal analyses of circulatory plasma proteins, combined with TME transcriptomics, provides deeper insight into the biology of immunotherapy resistance, and demonstrates prognostic significance and utility of plasma proteomics in biomarker discovery for cancer immunotherapy.

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