Computerized histologic image predictor of cancer outcome

摘要：越来越需要预测和预后分析来区分更多和更少

SUMMARY: There is an increased need for predictive and prognostic assays to distinguish more and less aggressive phenotypes of cancer due to A) dramatic increase in cancer incidence and; B) improvements in early diagnosis. Predictive assays in particular will allow for patients with less aggressive disease to be spared more aggressive treatment. Most prognostic tests in the US and Europe are based on gene expression assays (e.g. Oncotype DX (ODx)). Recent studies have shown extensive genetic heterogeneity among cancer cells between tumors and even within the same tumor, suggesting that approaches for recommending therapy for a patient based on the “average” molecular signal of many cells are overly simplistic. Interestingly, for a number of cancers, tumor grade (morphologic appearance on tissue as assessed qualitatively or semi-quantitatively by a pathologist) has been found to be highly correlated with disease outcome. However pathologic grade tends to suffer from significant inter-observer variability. Digitzation of histological samples, or whole slide imaging, facilitates a quantitative approach towards evaluating disease progression and predicting outcome, while also facilitating the adoption of telepathology. Recently, research groups (including our own) have begun to show that computer extracted measurements of tumor morphology (e.g. capturing nuclear orientation, texture, shape, architecture) from routine H&E stained cancer tissue images can predict disease aggressiveness and treatment outcome. By computationally interrogating the entire tumor landscape and its most invasive elements from a standard H&E slide, these approaches can allow for more accurate capture of tumor heterogeneity, disease risk and hence the most appropriate treatment strategy. The goal of this academic-industrial partnership is to develop and validate a computerized histologic image-based predictor (CHIP) to identify which early-stage, estrogen receptor positive (ER+) breast cancer patients are candidates for hormonal therapy alone and which women are candidates for adjuvant chemotherapy based off analysis of the pathology slides derived from biopsy and surgical specimens. Inspirata Inc., a cancer diagnostics company which has recently licensed a number of histomorphometry based technologies from the Madabhushi group, will bring quality management systems and production software standards to help create a pre-commercial companion diagnostic test of the CHIP assay. Additionally Inspirata Inc. will build a complete regulatory pathway for successful translation of the assay in the US and abroad. Finally, the pre-commercial prototype of the CHIP assay will be independently validated using the same strategy and data cohorts as ODx. Our approach has several advantages over molecular assays such as ODx in that it (1) can interrogate the entire expanse of the pathology image enabling a more accurate capture of tumor heterogeneity and hence disease risk, (2) is non-disruptive of pathology workflow, (3) non-destructive of tissue and would be substantially (4) cheaper (critical in low to middle income countries) and (5) faster.