Several studies have shown that the location and expression of infiltrating immune cells in patient tumors can better identify which patients are more likely to respond to anti- PD-1/PD-L1 therapy. In particular, immunohistochemistry-based studies have shown that the spatial location of PD-L1 expression has particular biological relevance, as PD-L1 expression in the tumor cells or immune cells in the tumor syncytium, tumor microenvionment (TME), or tumor-stroma boundary all describe differential PD-L1 biology.

Here, we use Flagship’s digital pathology platform (cTA®) to investigate IHC based PD-L1 and CD8 staining patterns in Non-Small Cell Lung (NSCLC) and Urothelial Carcinoma (UC) tissue biopsies. The cTA platform creates thousands of per-cell Biofeatures™ derived from the digital pathology images of the IHC stained tissue, and applies Artificial Intelligence (AI) to the data to summary score endpoints for patient and cohort classification. In this approach, each tissue’s IO landscape is represented using an “IO Scorecard“, which summarizes the IHC biomarker data in a summary score which captures a comprehensive analysis of the tissue sample. The AI-determined scorecard models can be used to monitor changes before and after drug treatment and/or create predictive models for patient response outcomes.

In this study, NSCLC and Urothelial Carcinoma samples were sectioned and stained using either the FDA-approved Dako 22C3 or SP263 PD-L1 IHC assays. Serial sections of each tissue specimen were also stained for CD8 expression. The cTA process detected all cells, assigned them to the tumor or TME compartments, and recorded the Biofeatures™ data which characterized PD-L1 or CD8 staining in the Tumor, Tumor/TME margin, or TME compartments. The method was validated by its ability to reproduce pathologist scoring for PD-L1 and CD8. Using the accepted data, we used the AI system to create novel Scorecard based patient classifications which describe differential PD-L1 status, CD8 status, and PD-L1/CD8 status combined. The AI Scorecard approach demonstrated that certain PD-L1 staining Biofeatures™ may also predict the CD8 status of a tumor, suggesting that additional CD8 staining may not be necessary to understand important expression patterns pertaining to cytotoxic T-cells.

In summary, we demonstrated how the “IO Scorecards” are able to classify patients into differential immune status cohorts using a novel AI based scoring system, which relies only on PD-L1 IHC staining, by creating a comprehensive, contextual profile of PD-L1 staining that does not require additional CD8 IHC staining to characterize the impact of cytotoxic T-cells in a tissue sample.

Citation Format: Charles Caldwell, Will Paces, Jeni Caldara, Bharathi Vennapusa, Joseph S. Krueger. Using digital pathology based “IO Scorecards” to describe relationships between PD-L1 expression and CD8 positive immune cell infiltration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3130.