Self-supervised learning in non-small cell lung cancer discovers novel morphological clusters linked to patient outcome and molecular phenotypes

Histopathological images provide the definitive source of cancer diagnosis, containing information used by pathologists to identify and subclassify malignant disease, and to guide therapeutic choices. These images contain vast amounts of information, much of which is currently unavailable to human interpretation. Supervised deep learning approaches have been powerful for classification tasks, but they are inherently limited by the cost and quality of annotations. Therefore, we developed Histomorphological Phenotype Learning, an unsupervised methodology, which requires no annotations and operates via the self-discovery of discriminatory image features in small image tiles. Tiles are grouped into morphologically similar clusters which appear to represent recurrent modes of tumor growth emerging under natural selection. These clusters have distinct features which can be identified using orthogonal methods. Applied to lung cancer tissues, we show that they align closely with patient outcomes, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype

Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides

Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study

STEM-09. DEFINING THE ROLE OF CD97 IN GLIOBLASTOMA STEM CELL SELF-RENEWAL

Abstract Glioblastoma (GBM) is the most common and aggressive primary brain malignancy. Despite multimodal therapy, resistant GBM stem-like cells (GSCs) inevitably mediate disease recurrence. To identify novel vulnerabilities of GSCs, we performed an arrayed CRISPR/Cas9 screen against select adhesion G protein-coupled receptors (aGPCRs), many of which we found to be de novo expressed in GBM. Knockout of CD97 (ADGRE5), previously implicated in GBM cell migration, produced the most striking proliferative disadvantage in patient-derived GBM cultures (PDGC) among aGPCRs tested. We found high CD97 surface expression in all our PDGCs, while levels remained nearly undetectable in non-neoplastic brain cells, confirming that CD97 is de novo expressed in GBM. Upon shRNA-mediated knockdown of CD97 in PDGCs from all three TCGA transcriptional subtypes, we observed significantly reduced proliferation, as measured by Ki67 and Hoechst cell cycle analysis, and significantly diminished surface expression of CD133, a GSC marker. Notably, CD97 knockdown also significantly reduced tumorsphere initiation capacity in six PDGCs, as measured by extreme limiting dilution assays. These findings suggest that CD97 regulates GSC self-renewal in vitro. RNA-sequencing and GSEA pathway analysis from PDGCs following CD97 knockdown indicate an enrichment of aerobic respiratory gene sets, suggesting one of the major regulatory roles of CD97 is metabolic regulation. Indeed, metabolic assays show that CD97 knockdown alters oxygen consumption and glycolysis rates in PDGCs. Lastly, we have developed human synthetic antibodies to target CD97 in order to investigate its therapeutic potential. We have observed internalization of some of these antibodies, thus identifying candidates for the development of antibody-drug conjugates. In addition, other clones reduced GBM cell proliferation and elicited expression of various differentiation markers. Overall; our studies identify novel roles of CD97 in regulating the cellular hierarchy in GBM and tumor cell metabolism, and provide a strong scientific rationale for developing biologics to target CD97 in GBM.</jats:p

Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer

Abstract Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole slide images (WSIs). We train an SSL Barlow Twins encoder on 435 colon adenocarcinoma WSIs from The Cancer Genome Atlas to extract features from small image patches (tiles). Leiden community detection groups tiles into histomorphological phenotype clusters (HPCs). HPC reproducibility and predictive ability for overall survival are confirmed in an independent clinical trial (N = 1213 WSIs). This unbiased atlas results in 47 HPCs displaying unique and shared clinically significant histomorphological traits, highlighting tissue type, quantity, and architecture, especially in the context of tumor stroma. Through in-depth analyses of these HPCs, including immune landscape and gene set enrichment analyses, and associations to clinical outcomes, we shine light on the factors influencing survival and responses to treatments of standard adjuvant chemotherapy and experimental therapies. Further exploration of HPCs may unveil additional insights and aid decision-making and personalized treatments for colon cancer patients