The DNA damage response in advanced ovarian cancer: functional analysis combined with machine learning identifies signatures that correlate with chemotherapy sensitivity and patient outcome

Autor: Thomas D. J. Walker, Zahra F. Faraahi, Marcus J. Price, Amy Hawarden, Caitlin A. Waddell, Bryn Russell, Dominique M. Jones, Aiste McCormick, N. Gavrielides, S. Tyagi, Laura C. Woodhouse, Bethany Whalley, Connor Roberts, Emma J. Crosbie, Richard J. Edmondson

Publikováno v: Walker, T D J, Faraahi, Z F, Price, M J, Hawarden, A, Waddell, C A, Russell, B, Jones, D M, McCormick, A, Gavrielides, N, Tyagi, S, Woodhouse, L C, Whalley, B, Roberts, C, Crosbie, E J & Edmondson, R J 2023, ' The DNA damage response in advanced ovarian cancer: functional analysis combined with machine learning identifies signatures that correlate with chemotherapy sensitivity and patient outcome ', British Journal of Cancer . https://doi.org/10.1038/s41416-023-02168-3

Background Ovarian cancers are hallmarked by chromosomal instability. New therapies deliver improved patient outcomes in relevant phenotypes, however therapy resistance and poor long-term survival signal requirements for better patient preselection.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1280700fee921d034c284eae9e468e52
https://doi.org/10.1038/s41416-023-02168-3