Abstract 1188: Detecting intratumor heterogeneity of PAM50 subtypes in H&amp;E-stained slides using deep learning
Authors: Mustafa Jaber, Bing Song, Clive R. Taylor, Charles J. Vaske, Christopher W. Szeto
Journal: Cancer Research (2018)

Abstract We present a methodology for identifying PAM50 intrinsic molecular breast cancer subtypes from only H&amp;E-stained tissue section whole-slide images (WSI) of breast biopsies without using RNA expression data. We then use this system to identify patients presenting multiple subtypes simultaneously (i.e. intra-tumor heterogeneity), and validate the clinical utility of identifying patients with heterogeneous tumors. Several methods have been proposed to stratify breast cancer subtypes including histological, immunohistochemical, and molecular. Intrinsic molecular subtypes such as PAM50 subgroups demonstrably outperform clinical factors and IHC in prognostic power. Yet molecular subtyping is fundamentally limited in two ways: 1) molecular characterization is relatively expensive and so not ubiquitously performed; and 2) non-single-cell molecular characterization assays the bulk tumor population, making studying intra-tumor heterogeneity difficult. The presented subtyping system uses routinely-gathered H&amp;E stained WSIs to mimic molecular subtyping. Three modules form the proposed WSI-based subtyping system: First, WSIs are broken into multi-scale 400px x 400px patches and converted to descriptive tensors using the Inception-v3 neural net architecture. Next, a subset of cancer-enriched patches is automatically selected to summarize WSI tumor content and used in further analysis. Finally, each patch is assigned a subtype in a 4-way classifier (Basal, HER2-enriched, Luminal A, and Luminal B). Optionally, patient-based subtype classifications can be made by employing a voting mechanism upon the patch-based results. We demonstrate this subtyping system using publicly available diagnostic WSIs from the TCGA BRCA cohort. We trained on 582 randomly selected patients, then tested subtyping accuracy on a held-out set of 223 patients. The subtype accuracy in held-out samples was 66% (compared to 34% from a random classifier, and 52% based on the majority-class classifier). We focused on 76 patients containing WSI patches with predictions for both Basal and Luminal A, and contrast them to 204 patients with majority Luminal A patches and 82 patients with majority Basal patches. We validated that this mixed-subtype population have outcomes and expression patterns that support a heterogeneous cellular population: The mixed subtype population have intermediate survival times between Luminal A and Basal in Kaplan-Meier analysis, varied hormone-receptor levels, and form a cluster equidistant between Luminal A and Basal in batch analysis. These results demonstrate using readily-available data to characterize tumor subtypes and sub-populations. Correctly identifying these sub-populations may provide crucial additional information that is lost to bulk-tumor assays. Citation Format: Mustafa Jaber, Bing Song, Clive R. Taylor, Charles J. Vaske, Christopher W. Szeto. Detecting intratumor heterogeneity of PAM50 subtypes in H&amp;E-stained slides using deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1188.