Abstract
Dissecting tumor heterogeneity is a key to understanding the complex mechanisms underlying drug resistance in cancers. The rich literature of pioneering studies on tumor heterogeneity analysis spurred a recent community-wide benchmark study that compares diverse modeling algorithms. Here we present FastClone, a top-performing algorithm in accuracy in this benchmark. FastClone improves over existing methods by allowing the deconvolution of subclones that have independent copy number variation events within the same chromosome regions. We characterize the behavior of FastClone in identifying subclones using stage III colon cancer primary tumor samples as well as simulated data. It achieves approximately 100-fold acceleration in computation for both simulated and patient data. The efficacy of FastClone will allow its application to large-scale data and clinical data, and facilitate personalized medicine in cancers.
Highlights
Dissecting tumor heterogeneity is a key to understanding the complex mechanisms underlying drug resistance in cancers
One strategy to deconvolute spatial and temporal tumor heterogeneity is to perform multiregional and longitudinal sampling2. This strategy may not be suitable under all circumstances because of the ethics of carrying out unnecessary invasive procedures as well as the practicality of longitudinal sampling for solid tumors, since the majority of cancer samples are obtained from surgical procedures, and if a tumor has been removed at the one-time point, it will not be available for sampling at a later time point3
FastClone starts by inferring the prevalence of cells that contain a certain single-nucleotide variations (SNVs) in the tumor sample (ρ)
Summary
Dissecting tumor heterogeneity is a key to understanding the complex mechanisms underlying drug resistance in cancers. While applauding for these pioneer works including but not limited to PyClone, PhyloWGS, and SciClone, the field needed a standard for evaluating these algorithms28 This need was addressed by the DREAM Somatic Mutation CallingHeterogeneity Challenge (DREAM SMC-Het Challenge), which evaluated the models on three aspects28: [1] evaluating each model’s ability in predicting global traits, including tumor purity, the number of subclones, and the proportion of each subclone; [2] assessing each model’s ability to assign SNVs to each subclone; [3] assessing each model’s accuracy in inferring phylogenetic relationships. The efficiency and accuracy of the algorithm will allow FastClone to be applied in clinical research and very large-scale datasets
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