Triage of High-Risk Cancer Patients Through Imaging, Genetic, and Integrative Approaches

Abstract

Metastasis, the spread of cancer cells from their original site to other body parts, is responsible for 90% of cancer mortality. This work applies machine learning and bioinformatic approaches on histopathological images and transcriptomic data of primary tumors to identify cancer early-stage melanoma and prostate cancer patients at high-risk for metastasis. In melanoma, we analyze digitized histopathological images of tumor biopsies to predict metastasis risk and survival. This is a common task in computational pathology, but many methods rely on “black box” approaches, such as deep learning, which are not directly interpretable. This is a barrier to adoption to pathologists, who need to understand how a tumors specific morphology is associated with prognosis. To overcome this, we develop human-interpretable features that measure the shape and arrangement of cells and nuclei, tissue texture. Our models provide prognostic power, recapitulate existing knowledge, and provide new insights into understanding metastatic events in early-stage primary tumors. For prostate cancer, we use our deep transfer learning framework, DEGAS, which combines single cell, spatial and bulk tissue transcriptomic data to identify regions of tissue in spatial transcriptomics that are highly associated with prostate cancer spread. DEGAS repeatedly identifies glands that appear histologically normal but share gene expression patterns with high-grade cancers. These results highlight the “Field Effect”, which suggest environmental and genetic factors can cause widespread genetic and epigenetic changes in tissue, a known phenomenon in pathology, but identified in high resolution transcriptomics for the first time in this work. Taken together, the work in melanoma and prostate cancer bridges the gap between traditional pathology and modern disease prognosis models. By constructing the tools to identify high risk patients and tissue, we aim to enhance metastasis research and improve clinical care for at-risk patients.