Copy-number analysis and inference of subclonal populations in cancer genomes using Sclust

This protocol describes how to use Sclust, a method for copy-number analysis and mutational clustering, to identify subclonal populations in tumor samples. The genomes of cancer cells constantly change during pathogenesis. This evolutionary process can lead to the emergence of drug-resistant mutations in subclonal populations, which can hinder therapeutic intervention in patients. Data derived from massively parallel sequencing can be used to infer these subclonal populations using tumor-specific point mutations. The accurate determination of copy-number changes and tumor impurity is necessary to reliably infer subclonal populations by mutational clustering. This protocol describes how to use Sclust, a copy-number analysis method with a recently developed mutational clustering approach. In a series of simulations and comparisons with alternative methods, we have previously shown that Sclust accurately determines copy-number states and subclonal populations. Performance tests show that the method is computationally efficient, with copy-number analysis and mutational clustering taking <10 min. Sclust is designed such that even non-experts in computational biology or bioinformatics with basic knowledge of the Linux/Unix command-line syntax should be able to carry out analyses of subclonal populations.