Dataset Information

Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer.

ABSTRACT: 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 automated analysis of digitized images from locally advanced colorectal cancer tissue slides can provide estimate of risk of distant metastasis on the basis of novel tissue phenotypic signatures of the tumor 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.

SUBMITTER: Sirinukunwattana K

PROVIDER: S-EPMC6135776 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer.

Sirinukunwattana Korsuk K Sirinukunwattana Korsuk K Snead David D Epstein David D Aftab Zia Z Mujeeb Imaad I Tsang Yee Wah YW Cree Ian I Rajpoot Nasir N

Scientific reports 20180912 1

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 automated analysis of digitized images from locally advan ...[more]

PMID: 30209315

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Project description:BackgroundTo establish a distant metastasis (DM) cells must disseminate from the primary tumor and overcome a series of obstacles, the metastatic cascade. In this study we develop a mathematical model for this cascade to estimate the tumor size and the circulating tumor cell (CTC) load before the first metastasis has formed from a primary breast cancer tumor.MethodsThe metastatic cascade is described in discrete steps: 1. local tumor growth; 2. dissemination into circulation; 3. survival in circulation; 4. extravasation into tissue; and 5. growth into a metastasis. The model was built using data and relationships described in the literature to predict the relationship between tumor size and probability of distant metastasis for 38715 patients with surgically removed TXNXM0 primary breast cancer from the Netherlands Cancer Registry. The model was calibrated using primary tumor size, probability of distant metastasis and time to distant metastasis for 1489 patients with stage T1BNXM0 (25% of total patients with T1BNXM0). Validation of the model was done with data for all patients.ResultsFrom the time to distant metastasis of these 38715 breast cancer patients, we determined a tumor doubling time of 1.7?±?0.9 months. Fitting the data for 25% of T1B patients estimates a metastatic efficiency of 1 metastasis formed per 60 million disseminated tumor cells. Validation of the model to data of patients in all T-stages shows good agreement between model and epidemiological data. To reduce the 5-year risk of distant metastasis for TXNXM0 from 9.2% to 1.0%, the primary tumor needs to be detected and removed before it reaches a diameter of 2.7?±?1.6 mm. At this size, the model predicts that there will be 9?±?6 CTC/L blood.ConclusionsTo reduce the rate of distant metastasis in surgically treated TXNXM0 breast cancer to 1%, imaging technology will need to be able to detect lesions of 2.7 mm in diameter or smaller. Before CTC detection can be applied in the early disease setting, sensitivity will need to be improved by at least 15-fold and combined with technology that minimizes false positives.

Dataset Information

Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer.

Publications

Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer.

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