70 Beyond PD-L1: novel PD-1 biomarkers identified by driving T cell dysfunction in vitro

• Published in: Journal for immunotherapy of cancer 2020-11, Vol.8 (Suppl 3), p.A43-A43
• Main Authors: Pabla, Simarjot, Khendu, Tenzing, Chand, Dhan, Aksoy, Bulent, Duckless, Benjamin, Basinski, Andrew, Joyce, Cailin, Horn, Thomas, Swiech, Lukasz, Waight, Jeremy, Savitsky, David, Buell, Jennifer
• Format: Article
• Language: English
• Subjects: Biomarkers; Clinical outcomes; Gene expression; Immunotherapy; Lymphocytes
• ISSN: 2051-1426
• DOI: 10.1136/jitc-2020-SITC2020.0070

BackgroundAnti-PD-1 therapies have achieved durable clinical responses in a wide range of malignancies, but responses are limited to a small subset of patients. Expression of PD-L1 on tumor cells by immunohistochemistry (IHC) has been applied as a companion diagnostic for anti-PD-1 therapy. However, recent studies have called in to question the reliability of this method to predict response.MethodsHere we developed a novel platform that integrates in vitro pharmacogenomic and functional data with clinical pharmacodynamic responses to immunotherapy using proprietary in silico approaches. The data originate from a long-term co-culture of primary antigen-specific T cells and cancer cells which drives T cells to a terminally dysfunctional, PD-1 refractory state. T cell effector functions and gene expression changes were monitored in the presence or absence of anti-PD-1 antibody or genetic knockouts. RNA expression signatures were refined with a randomized sliding window approach to generate a deep learning neural network for PD-1 response prediction.ResultsWe defined five T cell states associated with distinct phenotypic and molecular features - naïve, active, effector, transition and dysfunction. Among the genes that were selectively expressed in the dysfunction state, we identified a 96-gene signature that is closely associated with clinical outcomes to anti-PD-1 therapy. In PD-1 treated patients across multiple solid tumor indications, this signature correlates with objective response rate and outperforms traditional metrics such as tumor mutation burden or PD-L1 IHC signal. Moreover, this signature combines with tumor sequencing data to generate a powerful machine-learning model that predicts anti-PD-1 responses in metastatic melanoma patients with significantly higher accuracy than PD-L1 IHC. Having established that the T cell states in our co-culture relate to clinical outcomes, we leveraged the system to investigate the molecular basis for PD-1 responses. Single cell mapping of transition state T cells in the presence of anti-PD-1 revealed an expanded population of T cells that co-expresses PD-1, TIGIT and activation markers. Likewise, PD-L1 knockout on cancer cells identified the TIGIT ligand, CD155, as a potential tumor escape mechanism to anti-PD-1 therapy. Consistent with this, the combination of PD-1 and TIGIT blockade enhanced T cell cytotoxicity of tumor cells relative to monotherapies.ConclusionsAgenus’ T cell dysfunction platform combines deep