Abstract B092: Development of predictive models for expression of a tumor specific biomarker and CD3 on H&amp;E digital slides
Authors: Alan Jerusalmi, Krishna Bairavi, Ayushi Shah, Tom Chittenden, Mike Bonham, Chung-Wein Lee, Brandon Higgs, Anantharaman Muthuswamy
Journal: Cancer Research (2024)

Abstract Characterization of a patient’s tumor microenvironment is fundamental to advancing translational strategies in immuno-oncology. Histopathological evaluation of tissue slides from patients can provide this invaluable information, informing disease heterogeneity, tumor contexture, and target expression - all important to identifying patients more likely to benefit from therapy. However, availability of sufficient tissue is often a challenge to produce such a comprehensive tumor characterization. Measurement of target expression using an H&amp;E slide could greatly reduce the usage of tissue for IHC making sections available for other investigative purposes. Here, an AI-driven predictive model was developed on H&amp;E digital slides to simulate the expression of a tumor specific biomarker and CD3 across 4 different tumor types. Four epithelial tumor types (TNBC, NSCLC, ovarian, and cervical cancer) consisted of 400 H&amp;E slides, 400 CD3 slides and 400 tumor specific biomarker IHC slides (100 cases per tumor) were used for this study. The Bio-AI Predict-X platform was utilized for the optimization of separate tumor models using pathology annotations as basis for development. The network output included tumor tiles as well as the tiles from adjacent stromal/normal tissue. Models were optimized until an AUC &amp;gt; 0.8 was achieved and prediction results were approved by the combined pathology teams. Predict-X was used for IHC marker quantification on a tile basis for both CD3 and the tumor specific biomarker slides with significant correlation to manual counts (p&amp;lt;0.001, r&amp;gt;0.8). Images from each case were co-registered so that ground truth for each tile with the IHC count was used in the predictive model. Tiles from tumor and corresponding adjacent stroma/normal tissue from all cases were subdivided into 3 groups for predictive model development. A training, validation, and test set were used, the latter of which was not used for development, only as a final assessment of model performance. A pan-tumor predictive model was developed for CD3 IHC on TNBC, cervical and ovarian cancers. The test set AUC values on cases from all 4 tumors were &amp;gt;0.7. Since the tumor specific biomarker stain was specific to tumor morphology, 4 separate predictive models were developed - one for each of the indications used with a test AUC &amp;gt;0.86. These findings suggest that deep learning can be used as a complementary method to prescreen H&amp;E-stained images to enhance the detection rate of the tumor specific biomarker and CD3 positivity in patient tumors. Additional work will involve the optimization of these models for implementation in a clinical setting. Citation Format: Alan Jerusalmi, Krishna Bairavi, Ayushi Shah, Tom Chittenden, Mike Bonham, Chung-Wein Lee, Brandon Higgs, Anantharaman Muthuswamy. Development of predictive models for expression of a tumor specific biomarker and CD3 on H&amp;E digital slides [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B092.