Novel digital tissue phenotypic signatures of distant metastasis in colorectal cancer
This work addresses the problem of early risk stratification for distant metastasis in colorectal cancer patients, which is incremental as it builds on existing recognition of the tumor microenvironment's role.
The paper tackled predicting distant metastasis in colorectal cancer by developing an automated method to analyze digitized tissue images, focusing on the tumor microenvironment's cellular composition to estimate metastasis-free survival and support clinical decisions.
Distant metastasis is the major cause of death in colorectal cancer (CRC). Patients at high risk of developing distant metastasis could benefit from appropriate adjuvant and follow-up treatments if stratified accurately at an early stage of the disease. Studies have increasingly recognized the role of diverse cellular components within the tumor microenvironment in the development and progression of CRC tumors. In this paper, we show that a new method of automated analysis of digitized images from colorectal cancer tissue slides can provide important estimates of distant metastasis-free survival (DMFS, the time before metastasis is first observed) on the basis of details of the microenvironment. Specifically, we determine what cell types are found in the vicinity of other cell types, and in what numbers, rather than concentrating exclusively on the cancerous cells. We then extract novel tissue phenotypic signatures using statistical measurements about tissue composition. Such signatures can underpin clinical decisions about the advisability of various types of adjuvant therapy.