Subclonal reconstruction of tumors by using machine learning and population genetics.
Giulio CaravagnaTimon HeideMarc J WilliamsLuis ZapataDaniel NicholKetevan ChkhaidzeWilliam CrossGeorge D CresswellBenjamin WernerAhmet AcarLouis CheslerChristopher P BarnesGuido SanguinettiTrevor A GrahamAndrea SottorivaPublished in: Nature genetics (2020)
Most cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction methods based on machine learning aim to separate those subpopulations in a sample and infer their evolutionary history. However, current approaches are entirely data driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in the analysis if evolution is not accounted for, and this is exacerbated with multi-sampling of the same tumor. We present a novel approach for model-based tumor subclonal reconstruction, called MOBSTER, which combines machine learning with theoretical population genetics. Using public whole-genome sequencing data from 2,606 samples from different cohorts, new data and synthetic validation, we show that this method is more robust and accurate than current techniques in single-sample, multiregion and longitudinal data. This approach minimizes the confounding factors of nonevolutionary methods, thus leading to more accurate recovery of the evolutionary history of human cancers.
Keyphrases
- machine learning
- big data
- electronic health record
- papillary thyroid
- healthcare
- genome wide
- artificial intelligence
- induced apoptosis
- squamous cell
- high resolution
- data analysis
- emergency department
- squamous cell carcinoma
- dna methylation
- cross sectional
- signaling pathway
- cell cycle arrest
- endoplasmic reticulum stress
- lymph node metastasis