LINE-1 retrotransposons are overexpressed in more than half of human cancers. We identified a colorectal cancer wherein a fast-growing tumor subclone downregulated LINE-1, prompting us to examine how LINE-1 expression affects cell growth. We find that non-transformed cells undergo a TP53 -dependent growth arrest and activate interferon signaling in response to LINE-1. TP53 inhibition allows LINE-1(+) cells to grow, and genome wide knockout screens show that these cells require replication-coupled DNA repair pathways, replication stress signaling, and replication fork restart factors. Our findings demonstrate that LINE-1 expression creates specific molecular vulnerabilities and reveal a retrotransposition-replication conflict that may be an important determinant of cancer growth.

Advances in digital pathology, specifically imaging instrumentation and data management, have allowed for the development of computational pathology tools with the potential for better, faster, and cheaper diagnosis, prognosis, and prediction of disease. Images of tissue sections frequently vary in color appearance across research laboratories and medical facilities due to differences in tissue fixation, staining protocols, and imaging instrumentation, leading to difficulty in the development of robust computational tools. To address this challenge, we propose a novel non-linear tissue-component discrimination (NLTD) method to automatically register the color space of histopathology images and visualize individual tissue components, independent of color differences between images. Our results show that the NLTD method could effectively discriminate different tissue components from different types of tissues prepared at different institutions. Further, we demonstrate that NLTD can improve the accuracy of nuclear detection and segmentation algorithms, compared to using conventional color deconvolution methods, and can quantitatively analyze immunohistochemistry images. Together, the NLTD method is objective, robust, and effective, and can be easily implemented in the emerging field of computational pathology.

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