2019 ASCO Annual Meeting!
Session: Developmental Immunotherapy and Tumor Immunobiology
Type: Poster Session
Time: Saturday June 1, 8:00 AM to 11:00 AM
Location: Hall A
CD8+ T cells in tumor parenchyma and stroma by image analysis (IA) and gene expression profiling (GEP): Potential biomarkers for immuno-oncology (I-O) therapy.
Developmental Immunotherapy and Tumor Immunobiology
2019 ASCO Annual Meeting
Poster Board Number:
Poster Session (Board #238)
J Clin Oncol 37, 2019 (suppl; abstr 2594)
Author(s): Peter M Szabo, George Lee, Scott Ely, Vipul Baxi, Harsha Pokkalla, Hunter Elliott, Dayong Wang, Benjamin Glass, Jennifer K Kerner, Ilan Wapinski, Cyrus Hedvat, Darren Locke, Dimple Pandya, Neeraj Adya, Zhenhao Qi, Alex Greenfield, Robin Edwards, Michael Montalto; Bristol-Myers Squibb, Princeton, NJ; PathAI, Boston, MA; PathAI, Inc, Boston, MA
Background: Distribution patterns of CD8+ T cells within the tumor microenvironment (TME) can be assessed by IA, which may reflect underlying tumor biology and serve as a potential biomarker to assess the utility of I-O therapy. These patterns are variable and may be classified as immune desert (minimal infiltrate), excluded (T cells confined to tumor stroma or to the invasive margin), or inflamed (T cells diffusely infiltrating tumor parenchyma and stroma). We hypothesized that association of a GEP signature with abundance of parenchymal and stromal T-cell infiltrates may identify biomarkers of response or resistance to I-O therapy. To test this, we applied an AI-powered IA platform to quantify CD8+ T cells by geographical location and used GEP to define both CD8 abundance and associated geographic localization to tumor parenchyma and stroma. Methods: We performed an analysis using a tumor inflammatory GEP assay and CD8 immunohistochemistry on procured specimens (335 melanoma, 391 SCCHN). Digitized slides were used to train a convolutional neural network to quantify the number of CD8+ T cells in stroma, tumor parenchyma, parenchyma-stromal interface, and invasive margin. Generalized constrained regression models were used to predict GEP signatures specifically for stromal and parenchymal CD8+ T cells. Results: Parenchymal and stromal GEP scores were highly concordant with CD8+ infiltrate geography (adj-r2: 0.67, 0.65, respectively; P≤ 0.01). Little overlap existed between gene sets associated with parenchymal and stromal CD8 T-cell geographies. CSF1R and NECTIN2 gene expression was observed to correlate inversely with parenchymal localization and directly with stromal CD8+ T-cell abundance. Conclusions: GEP signatures can be identified that are concordant with various CD8+ T-cell localization patterns in melanoma and SCCHN, demonstrating that GEP-IA can be developed to identify the immune status of interest in the TME. The specific genes identified have potential to elucidate mechanisms of resistance and/or inform I-O targets that can be further evaluated in relation to clinical significance in future studies.