Projects per year
Abstract / Description of output
Recurrent successions of genomic changes, both within and between patients, reflect repeated evolutionary processes that are valuable for the anticipation of cancer progression. Multi-region sequencing allows the temporal order of some genomic changes in a tumor to be inferred, but the robust identification of repeated evolution across patients remains a challenge. We developed a machine-learning method based on transfer learning that allowed us to overcome the stochastic effects of cancer evolution and noise in data and identified hidden evolutionary patterns in cancer cohorts. When applied to multi-region sequencing datasets from lung, breast, renal, and colorectal cancer (768 samples from 178 patients), our method detected repeated evolutionary trajectories in subgroups of patients, which were reproduced in single-sample cohorts (n = 2,935). Our method provides a means of classifying patients on the basis of how their tumor evolved, with implications for the anticipation of disease progression.
Original language | English |
---|---|
Pages (from-to) | 707-714 |
Number of pages | 8 |
Journal | Nature Methods |
Volume | 15 |
Issue number | 9 |
Early online date | 31 Aug 2018 |
DOIs | |
Publication status | Published - 1 Sept 2018 |
Fingerprint
Dive into the research topics of 'Detecting repeated cancer evolution from multi-region tumor sequencing data'. Together they form a unique fingerprint.Projects
- 1 Finished
-
MLCS - Machine learning for computational science statistical and formal modeling of biological systems
Sanguinetti, G.
1/10/12 → 30/09/17
Project: Research