Gavin Ha

Assistant Professor at Fred Hutch
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Dr. Gavin Ha was co-supervised by Dr. Aparicio and Dr. Shah, graduating in 2014 with a PhD in Bioinformatics with his thesis on Probabilistic approaches for profiling copy number aberrations and loss of heterozygosity landscapes in cancer genomes. He continued his career in career doing postdoctoral research fellowships at the Broad Institute of MIT and Harvard and then Dana-Farber Cancer Institute, focusing on liquid biopsies for cancer diagnosis.

Since 2018, Dr. Ha has been leading his own research group at the Fred Hutchinson Cancer Research Centre studying the role of genomic alterations in cancer. He and his research team focus on developing and applying computational methods to profile cancer genomes from patient tumors and blood. His laboratory develops novel approaches to study cell-free DNA released from tumor cells into the blood (also known as circulating tumor DNA). The use of this approach, called “liquid biopsies,” combined with insights from tumor genome analysis, will be critical to uncover causes of treatment resistance, discover novel genetic biomarkers from the blood, and develop non-invasive applications for cancer precision medicine.


Genomic consequences of aberrant DNA repair mechanisms stratify ovarian cancer histotypes.

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.

TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data.

PyClone: statistical inference of clonal population structure in cancer.

DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.

Integrative analysis of genome-wide loss of heterozygosity and monoallelic expression at nucleotide resolution reveals disrupted pathways in triple-negative breast 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.

JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data.

Recurrent somatic DICER1 mutations in nonepithelial ovarian cancers.

ARID1A mutations in endometriosis-associated ovarian carcinomas.