Imaging-based histological features are predictive of MET alterations in Non-Small Cell Lung Cancer

• URL: http://arxiv.org/abs/2203.10062v1
• Date: Fri, 18 Mar 2022 17:11:05 GMT
• Title: Imaging-based histological features are predictive of MET alterations in Non-Small Cell Lung Cancer
• Authors: Rohan P. Joshi, Bo Osinski, Niha Beig, Lingdao Sha, Kshitij Ingale, Martin C. Stumpe
• Abstract summary: MET is a proto-oncogene whose activation in non-small cell lung cancer leads to increased cell growth and tumor progression. We investigated the association of cell-morphological features with MET amplifications and MET exon 14 deletions. A predictive model could distinguish MET wild-type from MET amplification or MET exon 14 deletion.
• Score: 1.2885809002769635
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: MET is a proto-oncogene whose somatic activation in non-small cell lung cancer leads to increased cell growth and tumor progression. The two major classes of MET alterations are gene amplification and exon 14 deletion, both of which are therapeutic targets and detectable using existing molecular assays. However, existing tests are limited by their consumption of valuable tissue, cost and complexity that prevent widespread use. MET alterations could have an effect on cell morphology, and quantifying these associations could open new avenues for research and development of morphology-based screening tools. Using H&E-stained whole slide images (WSIs), we investigated the association of distinct cell-morphological features with MET amplifications and MET exon 14 deletions. We found that cell shape, color, grayscale intensity and texture-based features from both tumor infiltrating lymphocytes and tumor cells distinguished MET wild-type from MET amplified or MET exon 14 deletion cases. The association of individual cell features with MET alterations suggested a predictive model could distinguish MET wild-type from MET amplification or MET exon 14 deletion. We therefore developed an L1-penalized logistic regression model, achieving a mean Area Under the Receiver Operating Characteristic Curve (ROC-AUC) of 0.77 +/- 0.05sd in cross-validation and 0.77 on an independent holdout test set. A sparse set of 43 features differentiated these classes, which included features similar to what was found in the univariate analysis as well as the percent of tumor cells in the tissue. Our study demonstrates that MET alterations result in a detectable morphological signal in tumor cells and lymphocytes. These results suggest that development of low-cost predictive models based on H&E-stained WSIs may improve screening for MET altered tumors.

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