Sohrab Shah received a PhD in computer science from UBC in 2008 and did a Postdoctoral Fellowship under Dr. Aparicio and Dr. Huntsman at BC Cancer. He was soon appointed Principal Investigator in 2010 and was Senior Scientist and Associated Professor at BC Cancer and the University of British Columvia, respectively. As well, Dr. Shah was appointed to MSK in April 2018 as the inaugural Chief of the Computational Oncology Service and is the incumbent of the Nicholls-Biondi Chair. He held the Canada Research Chair in Computational Cancer Genomics, and was the recipient of both a Michael Smith Foundation for Health Research Career Investigator Award and a Terry Fox Research Institute New Investigator Award. His research focuses on understanding how tumours evolve over time through integrative approaches involving genomics and computational modeling. Dr. Shah has pioneered computational methods and software for inference of mutations in cancer genomes as well as deciphering patterns of cancer evolution which have been widely disseminated internationally. He has a track record of developing novel, innovative Bayesian statistical models, algorithms, and computational approaches to analyze large, high dimensional genomics and transcriptomic data sets, from both patient tumours and model systems. This includes advancing molecular profiling of cancer cells at single cell resolution. Dr. Shah has been at the forefront of studying tumor evolution in breast, ovary and lymphoid malignancies. His work has been published in Nature, Nature Genetics, Nature Methods, NEJM, Genome Research, Genome Biology, amongst others. Dr. Shah oversees an annual budget of >$1M in competitively awarded funding from philanthropic, government and international bodies.
Single-cell mtDNA dynamics in tumors is driven by coregulation of nuclear and mitochondrial genomes.
Single-cell DNA replication dynamics in genomically unstable cancers.
Single-cell genomic variation induced by mutational processes in cancer.
Accurate determination of CRISPR-mediated gene fitness in transplantable tumours.
Clonal fitness inferred from time-series modelling of single-cell cancer genomes.
Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data.
Eleven grand challenges in single-cell data science.
Clonal Decomposition and DNA Replication States Defined by Scaled Single-Cell Genome Sequencing.
Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling.
Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer.
Genomic consequences of aberrant DNA repair mechanisms stratify ovarian cancer histotypes.
CDK12 regulates alternative last exon mRNA splicing and promotes breast cancer cell invasion.
CX-5461 is a DNA G-quadruplex stabilizer with selective lethality in BRCA1/2 deficient tumours.
Scalable whole-genome single-cell library preparation without preamplification.
Clonal genotype and population structure inference from single-cell tumor sequencing.
Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer.
Systematic analysis of somatic mutations impacting gene expression in 12 tumour types.
Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution.
PyClone: statistical inference of clonal population structure in cancer.
DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.
The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups.
The clonal and mutational evolution spectrum of primary triple-negative breast cancers.
Recurrent somatic DICER1 mutations in nonepithelial ovarian cancers.
Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data.
ARID1A mutations in endometriosis-associated ovarian carcinomas.
Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution.