Deterministic evolutionary trajectories influence primary tumor growth: TRACERx RenalMore about Open Access at the Crick
Authors listSamra Turajlic Hang Xu Kevin Litchfield Andrew Rowan Stuart Horswell Tim Chambers Tim O’Brien Jose I Lopez Tom Watkins David Nicol Mark Stares Ben Challacombe Steve Hazell Ashish Chandra Thomas J Mitchell Lewis Au Claudia Eichler-Jonsson Faiz Jabbar Aspasia Soultati Simon Chowdhury Sarah Rudman Joanna Lynch Archana Fernando Gordon Stamp Emma Nye Aengus Stewart Wei Xing Jonathan C Smith Mickael Escudero Adam Huffman Nik Matthews Greg Elgar Ben Phillimore Marta Costa Sharmin Begum Sophia Ward Max Salm Stefan Boeing Rosalie Fisher Lavina Spain Carolina Navas Eva Gronroos Sebastijan Hobor Sarkhara Sharma Ismaeel Aurangzeb Sharanpreet Lall Alexander Polson Mary Varia Catherine Horsfield Nicos Fotiadis Lisa Pickering Roland F Schwarz Bruno Silva Javier Herrero Nicholas Luscombe Mariam Jamal-Hanjani Rachel Rosenthal Nicolai Birkbak Gareth Wilson Orsolya Pipek Dezso Ribli Marcin Krzystanek Istvan Csabai Zoltan Szallasi Martin Gore Nicholas McGranahan Peter Van Loo Peter Campbell James Larkin Charles Swanton TRACERx Renal Consortium
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The evolutionary features of clear-cell renal cell carcinoma (ccRCC) have not been systematically studied to date. We analyzed 1,206 primary tumor regions from 101 patients recruited into the multi-center prospective study, TRACERx Renal. We observe up to 30 driver events per tumor and show that subclonal diversification is associated with known prognostic parameters. By resolving the patterns of driver event ordering, co-occurrence, and mutual exclusivity at clone level, we show the deterministic nature of clonal evolution. ccRCC can be grouped into seven evolutionary subtypes, ranging from tumors characterized by early fixation of multiple mutational and copy number drivers and rapid metastases to highly branched tumors with >10 subclonal drivers and extensive parallel evolution associated with attenuated progression. We identify genetic diversity and chromosomal complexity as determinants of patient outcome. Our insights reconcile the variable clinical behavior of ccRCC and suggest evolutionary potential as a biomarker for both intervention and surveillance.
Issue number 3