Abstract 5408: AI powered-platform to predict gene modifications from prostate and breast cancer whole slide images

Abstract

Abstract There is little published evidence of predicting cancer genotypes directly from tissue histology, especially for breast and prostate cancers. Artificial intelligence (AI) enables discriminating and extracting morphological features reflective of the underlying genomic alterations at visual and subvisual levels. We have built a morphology-based and AI-powered platform for cancer genotyping, risk stratification and outcome prediction that addresses the needs for treatment decision-making in a cost effective and timely fashion. A cohort of 390 prostate and 742 invasive breast cancer patients with known molecular status of key genes, such as TP53, PIK3CA, MYC, ERBB2, TMPRSS2-ERG and PTEN from The Cancer Genome Atlas were included in this study. Hematoxylin and eosin (H&E) stained whole slide images (WSI) of the cancer tissue sections were available at 20x or 40x. The WSI from the two cancer cohorts were split 2:1 into a training and test dataset, respectively. Our platform involved two different deep Convolutional Neural Network (DCNN) architectures. The platform first divided each WSI into multiple tiles. Each tile was then analyzed using a DCNN that graded the tile and generated a high dimensional vector to provide a mathematical representation of the morphology. The combination of high dimensional vectors across the WSI was then fed into a second DCNN that generated a morphological score, which predicted whether the gene under consideration was wild type or modified. Our platform has achieved 70 - 80% accuracy as defined by the Area under the Curve for the receiver operating characteristics curves for the genetic markers on the test cohorts (Table 1). Our platform can predict genotypes/molecular alterations directly from H&E stained WSI with high accuracy. This technology presents a novel, practical and cost-effective approach for cancer molecular classification and risk stratification, enabling timely and optimal treatment decision-making for positive clinical outcomes. Genotype Prediction for Breast Cancer (BrCa) and Prostate Cancer (PCa) from WSI Cohort Molecular Biomarker Training Dataset (n) Test Dataset (n) Test ROC AUC Score (%) Gene Modification/Loss Intact/Wild type Gene Modification/Loss Intact/Wild type BrCa TP53 196 302 96 149 80 PIK3CA 153 344 75 170 74 MYC 68 414 33 204 77 ERBB2 66 416 32 205 78 PCa TMPRSS2-ERG 104 157 51 78 70 PTEN 57 201 28 99 73 Citation Format: Wei Huang, Parag Jain, Chensu Xie, Hassan Muhammad, Hirak Basu, George Wilding, Rajat Roy. AI powered-platform to predict gene modifications from prostate and breast cancer whole slide images. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5408.