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P854 Construction of the immune landscape of durable response to checkpoint blockade therapy by integrating publicly available datasets
  1. Nils-Petter Rudqvist1,
  2. Roberta Zappasodi2,
  3. Daniel Wells3,
  4. Vésteinn Thorsson4,
  5. Alexandria Cogdill5,
  6. Anne Monette6,
  7. Yana Najjar7,
  8. Randy Sweis8,
  9. Erik Wennerberg9,
  10. Praveen Bommareddy10,
  11. Cara Haymaker5,
  12. Uqba Khan11,
  13. Heather McGee12,
  14. Wungki Park2,
  15. Houssein Abdul Sater13,
  16. Christine Spencer3,
  17. Nicholas Tschernia14,
  18. Maria Ascierto15,
  19. Valentin Barsan16,
  20. Vinita Popat17,
  21. Sara Valpione18 and
  22. Benjamin Vincent19
  1. 1Weill Cornell Medicine, New York, NY, USA; TimIOs Lead, New York, NY, USA
  2. 2Memorial Sloan Kettering Cancer Center, New York, NY, USA; Parker Institute of Cancer Immunotherapy, San Francisco, CA, USA
  3. 3Parker Institute of Cancer Immunotherapy, San Francisco, CA, USA
  4. 4Institute for Systems Biology, Seattle, WA, USA
  5. 5The University of Texas MD Anderson Cancer Center, Houston, TX, USA
  6. 6McGill University, Montréal, Québec, Canada
  7. 7University of Pittsburgh, Pittsburgh, PA, USA
  8. 8University of Chicago, Chicago, IL, USA
  9. 9Weill Cornell Medicine, New York, NY, USA
  10. 10Replimune, Woburn, MA, USA
  11. 11Weill Cornell Medicine, New York, NY, USA; Memorial Sloan Kettering Cancer Center, New York, NY, USA; New York Presbyterian Brooklyn Methodist Hospital, New York, NY, USA
  12. 12Icahn School of Medicine at Mount Sinai, New York, NY, USA
  13. 13National Cancer Institute, Bethesda, MD, USA
  14. 14University of North Carolina, Chapel Hill, NC, USA
  15. 15AstraZeneca, Gaithersburg, MD, USA
  16. 16Stanford University School of Medicine, Stanford, CA, USA
  17. 17University of Texas Southwestern Medical Center, Dallas, TX, USA
  18. 18Cancer Research UK Manchester Institute, Manchester, UK
  19. 19University of North Carolina, Chapel Hill, NC, USA


Background Immune checkpoint blockade (ICB) has revolutionized cancer treatment. However, long-term benefits are only achieved in a small fraction of patients. Understanding the mechanisms underlying ICB activity is key to improving the efficacy of immunotherapy. A major limitation to uncovering these mechanisms is the limited number of responders within each ICB trial. Integrating data from multiple studies of ICB would help overcome this issue and more reliably define the immune landscape of durable responses. Towards this goal, we formed the TimIOs consortium, comprising researchers from the Society for Immunotherapy of Cancer Sparkathon TimIOs Initiative, the Parker Institute of Cancer Immunotherapy, the University of North Carolina-Chapel Hill, and the Institute for Systems Biology. Together, we aim to improve the understanding of the molecular mechanisms associated with defined outcomes to ICB, by building on our joint and multifaceted expertise in the field of immuno-oncology. To determine the feasibility and relevance of our approach, we have assembled a compendium of publicly available gene expression datasets from clinical trials of ICB. We plan to analyze this data using a previously reported pipeline that successfully determined main cancer immune-subtypes associated with survival across multiple cancer types in TCGA.1

Methods RNA sequencing data from 1092 patients were uniformly reprocessed harmonized, and annotated with predefined clinical parameters. We defined a comprehensive set of immunogenomics features, including immune gene expression signatures associated with treatment outcome,1,2 estimates of immune cell proportions, metabolic profiles, and T and B cell receptor repertoire, and scored all compendium samples for these features. Elastic net regression models with parameter optimization done via Monte Carlo cross-validation and leave-one-out cross-validation were used to analyze the capacity of an integrated immunogenomics model to predict durable clinical benefit following ICB treatment.

Results Our preliminary analyses confirmed an association between the expression of an IFN-gamma signature in tumor (1) and better outcomes of ICB, highlighting the feasibility of our approach.

Conclusions In line with analysis of pan-cancer TCGA datasets using this strategy (1), we expect to identify analogous immune subtypes characterizing baseline tumors from patients responding to ICB. Furthermore, we expect to find that these immune subtypes will have different importance in the model predicting response and survival. Results of this study will be incorporated into the Cancer Research Institute iAtlas Portal, to facilitate interactive exploration and hypothesis testing.


  1. Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Yang T-H O, Porta-Pardo E. Gao GF, Plaisier CL, Eddy JA, et al. The Immune Landscape of Cancer. Immunity 2018; 48(4): 812–830.e14.

  2. Auslander N, Zhang G, Lee JS, Frederick DT, Miao B, Moll T, Tian T, Wei Z, Madan S, Sullivan RJ, et al. Robust Prediction of Response to Immune Checkpoint Blockade Therapy in Metastatic Melanoma. Nat. Med 2018; 24(10): 1545.

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