Course Title

Computational methods for analyzing intra-tumor heterogeneity using next-generation sequencing

Course Abstract

Next-generation sequencing (NGS) technologies have enabled the sequencing of many cancer genomes. Recent studies of tumor samples have shown that most tumors exhibit extensive intra-tumor heterogeneity, with multiple subpopulations of tumor cells containing different somatic mutations. In this tutorial, we discuss several recent computational methods for the purpose of characterizing intra-tumor heterogeneity. In particular, we present novel algorithms for inferring clonal evolution and reconstructing tumor phylogenies from single or multiple NGS samples.


Reading List

1. A combinatorial approach for analyzing intra-tumor heterogeneity from high-throughput sequencing data
I Hajirasouliha, A Mahmoody, BJ Raphael
Bioinformatics 30 (12), i78-i86

2. Fast and scalable inference of multi-sample cancer lineages
V Popic, R Salari, I Hajirasouliha, D Kashef-Haghighi, RB West, S Batzoglou
Genome biology 16 (1), 91

3. Inferring clonal evolution of tumors from single nucleotide somatic mutations
W Jiao, S Vembu, AG Deshwar, L Stein, Q Morris
BMC bioinformatics 15 (1), 35

4. Clonality inference in multiple tumor samples using phylogeny
S Malikic, AW McPherson, N Donmez, CS Sahinalp
Bioinformatics 31 (9), 1349-1356

5. Cancer evolution: mathematical models and computational inference
N Beerenwinkel, RF Schwarz, M Gerstung, F Markowetz
Systematic biology 64 (1), e1-e25