Shashwat Depali Nagar

Abstract Cancer metastasis, a process in which cancer cells migrate to secondary sites, accounts for 90% of cancer deaths. While many machine learning models have been developed and applied to predict metastasis, they tend to be restricted to tumor types or classification tasks. Here, we apply a pan-cancer machine learning model that is easily interpretable and directly links the transcriptomic and proteomic profiles of hundreds of cancer cell lines with metastasis in a tissue-agnostic manner.

Abstract Cancer metastasis, a process in which cancer cells migrate to secondary sites, accounts for 90% of cancer deaths. While machine learning has been used to predict metastasis in a variety of ways, they tend to be specific to tumor types or classification tasks. Here, we provide a pan-cancer machine learning model that is easily interpretable and directly links the transcriptomic and proteomic profiles of hundreds of cancer cell lines from DepMap with metastasis in a tissue-agnostic manner.