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Poster Discussion 1 – Translational research

2267 - Targeting Molecular Mediators of T cell Exclusion for Effective Immunotherapy in Ovarian Cancer

Date

29 Sep 2019

Session

Poster Discussion 1 – Translational research

Presenters

Yulei Wang

Citation

Annals of Oncology (2019) 30 (suppl_5): v760-v796. 10.1093/annonc/mdz268

Authors

Y. Wang1, M. Desbois2, A. Udyavar3, L. Ryner1, C. Kozlowski1, Y. Guan1, M. Dürrbaum3, S. Lu1, J. Fortin3, H. Koeppen4, J. Ziai4, C. Chang5, A. Lo4, S. Keerthivasan6, M. Plante7, C. Bais1, P. Hegde1, A. Daemen3, S. Turley6

Author affiliations

  • 1 Oncology Biomarker, Genentech Inc. - Roche - USA, 94404 - Foster City/US
  • 2 Oncology Biomarker, Genentech Inc. - Roche - USA, South San Francisco/US
  • 3 Bioinformatics, Genentech Inc. - Roche - USA, 94404 - Foster City/US
  • 4 Pathology, Genentech Inc. - Roche - USA, 94404 - Foster City/US
  • 5 Biostatistics, Genentech Inc. - Roche - USA, 94404 - Foster City/US
  • 6 Research, Genentech Inc. - Roche - USA, 94404 - Foster City/US
  • 7 Cancer Research Center, Laval University, 94404 - Quebec/CA

Resources

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Abstract 2267

Background

Close proximity between cytotoxic T lymphocytes and tumour cells is required for effective immunotherapy. However, what determines the spatial distribution of T cells in the tumour microenvironment is not well understood. Coupling digital pathology and transcriptome analysis on large ovarian tumour cohorts, here we report classification and functionally dissection of tumour-immune contexture in human ovarian cancer.

Methods

CD8 IHC and RNAseq analysis were performed on 370 ovarian tumours from the ICON7 phase III clinical trial. Coupling digital pathology with transcriptome analysis, a random forest machine learning algorithm was developed and independently validated for classifying tumour-immune phenotypes in ovarian cancer. Anti-tumour activity of TGFβ blockade in combination with anti-PD-L1 was evaluated in an ovarian cancer mouse model.

Results

We show the identified tumour-immune phenotypes are of biological and clinical importance with interconnection to molecular subtypes and association with clinical outcome in ovarian cancer. Two important hallmarks of T cell exclusion were identified: 1) loss of antigen presentation on tumour cells and 2) upregulation of TGFβ and activated stroma. We identified TGFβ as a key mediator of T cell exclusion. TGFβ reduced MHC class I expression in ovarian cancer cells and induced extracellular matrix (ECM) production and immunosuppressive molecules in human primary fibroblasts. Finally, we demonstrated that combination of anti-TGFβ and anti-PD-L1 in a mouse ovarian cancer model significantly improved the anti-tumour efficacy and survival.

Conclusions

This study provided the first systematic and in-depth characterization of the molecular features and mechanisms underlying the tumour-immune phenotypes in ovarian cancer. We illuminated a multi-faceted role of TGFβ in mediating consequential crosstalk between tumour cells and cancer associated fibroblasts to shape the tumour-immune contexture. Our findings support that targeting the TGFβ pathway represents a promising therapeutic strategy to overcome T cell exclusion and optimize response to cancer immunotherapy.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Genentech/Roche.

Disclosure

All authors have declared no conflicts of interest.

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