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75 Generalizability of potential biomarkers of response to CTLA-4 and PD-1 blockade therapy in cancer
  1. Dante Bortone1,
  2. Steven Vensko1,
  3. Sarah Entwistle1,
  4. Alexandria Cogdill2,
  5. Anne Monette3,
  6. Yana Najjar4,
  7. Randy Sweis5,
  8. Nicholas Tschernia1,
  9. Erik Wennerberg6,
  10. Praveen Bommareddy7,
  11. Cara Haymaker2,
  12. Uqba Khan8,
  13. Heather McGee9,
  14. Wungki Park8,
  15. Houssein Sater10,
  16. Christine Spencer11,
  17. Maria Ascierto12,
  18. Valentin Barsan13,
  19. Vinita Popat14,
  20. Sara Valpione15,
  21. Danny Wells16,
  22. Vésteinn Thorsson17,
  23. Roberta Zappasodi18,
  24. Nils Rudqvist2 and
  25. Benjamin Vincent1
  1. 1University of North Carolina – Chapel Hill, Chapel Hill, NC, USA
  2. 2The University of Texas MD Anderson Cancer Center, HOUSTON, TX, USA
  3. 3Jewish General Hospital and McGill University, Montreal, Canada
  4. 4UPMC Hillman Cancer Center, Pittsburgh, PA, USA
  5. 5University of Chicago, Chicago, IL, USA
  6. 6The Institute of Cancer Research, London, New York, UK
  7. 7Replimune, Woburn, MA, USA
  8. 8Memorial Sloan Kettering Cancer Center, Sterling Heights, MI, USA
  9. 9Salk Institute for Biological Sciences, New York, NY, USA
  10. 10National Cancer Institute, Bethesda, USA
  11. 11Parker Institute for Cancer Immunotherapy, San Fransisco, USA
  12. 12AstraZeneca, Gaithersburg, MD, USA
  13. 13Stanford University School of Medicine, La Jolla, CA, USA
  14. 14UT Southwestern Medical Center, Dallas, TX, USA
  15. 15The University of Manchester, Manchester, UK
  16. 16Parker Institute of Cancer Immunotherapy, San Francisco, CA, USA
  17. 17Institute of Systems Biology, Seattle, Washington, USA
  18. 18Weill Cornell Medicine, New York, USA


Background Multiple genomics-based biomarkers of response to immune checkpoint inhibition have been reported or proposed, including tumor mutation/neoantigen frequency, PD-L1 expression, T cell receptor repertoire clonality, interferon gene signature expression, HLA expression, and others.1 Although genomics associations of response have been reported, the primary studies have used a variety of data generation and processing techniques. There is a need for data harmonization and assessment of generalizability of potential biomarkers across multiple datasets.

Methods We acquired patient-level RNA sequencing FASTQ data files from 10 data sets reported in seven pan-cancer PD-1 and CTLA-4 immune checkpoint inhibition trials with matched clinical annotations.2–7 We applied a common bioinformatics workflow for quality control, mapping to reference (STAR), generating gene expression matrices (SALMON), T cell receptor repertoire inference (MiXCR), extraction of immune gene signatures and immune subtypes,8 and differential gene expression analysis (DESeq2). We analyzed i) immunogenomics features proposed as biomarkers, and ii) gene expression signatures built from each trial for association with overall survival across the set of trials using univariable Cox proportional hazards regression. In all, we assessed 9 total immunogenomics features/signatures. P-values were adjusted for multiple testing using the Benjamini-Hochberg method.

Results Of the 9 immunogenomics features assessed, cytolytic activity score and expression of the Follicular Dendritic Cell Secreted Protein gene (FDCSP) were associated with survival in two of seven studies, respectively (adjusted p < 0.05) (figure 1). No proposed biomarkers were significantly associated with survival in more than two studies. The sets of genes significantly associated with clinical benefit across the studies were highly disjoint, with only three genes significant in three studies and thirteen genes significant in two studies (figure 2). No genes were significantly associated with clinical benefit in more than three of seven studies.

Abstract 75 Figure 1

Association of immunogenomics features and proposed biomarkers with survival in 10 publicly available datasets from 7 clinical trials with immune checkpoint blockade. Nine immunogenomics features were tested in 10 publicly available RNAseq data sets from 7 published clinical trials with immune checkpoint blockade for their correlation with outcome. SKCM, skin cutaneous melanoma; BLCA, bladder cancer; Kidney, kidney cancer; Ureter, ureteral cancer; GBM, glioblastoma

Abstract 75 Figure 2

Association of gene expression of single genes with survival in 10 publicly available datasets from 7 clinical trials with immune checkpoint inhibitors

Conclusions No proposed biomarkers were highly generalizable across studies. We expect that integrated modeling incorporating multiple immunogenomics features will be required to build a robust and generalizable biomarker for ICI response. Further work is needed to analyze determinants of response and clinical benefit.

Acknowledgements We would like to thank SITC for funding for this work as part of the Sparkathon TimIOS collaborative project.


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