Gavin Ha

This study identifies molecular and architectural changes in normal-adjacent field tissue in HNSCC that disrupt epithelial differentiation and may promote cancer development.

This paper develops a deep learning framework to infer gene expression from cell-free DNA, improving reliability and enabling functional genomics in precision oncology.

cfClone, a Bayesian model, deconvolves cfDNA whole-genome sequencing using scWGS-derived haplotype-specific clone copy-number profiles to estimate tumour fraction, clonal prevalence, and flag missed clones.

This study identifies subtype-specific molecular and immune features associated with recurrence risk in breast cancer using multi-omic analysis of 340 tumors and validates predictive models in an external cohort.

This study identifies RCC1 depletion as a cause of protein transport defects and micronuclei rupture, linking it to nuclear lamina defects and genome instability.

This paper develops a machine learning model to identify molecularly distinct DDR subgroups in prostate cancer, revealing concurrent repair deficiencies and improving therapeutic stratification.

This study shows that acute CDK12 loss in prostate cancer causes HR deficiency via polyadenylation, but chronic loss leads to adaptation, making PARP inhibitors ineffective and suggesting CDK13 targeting as a new therapeutic approach.

FinaleMe predicts DNA methylation and tissues-of-origin from plasma whole-genome sequencing by using a non-homogeneous Hidden Markov Model.

The paper compares somatic mutations and aneuploidy in lung adenocarcinomas from never-smokers and smokers, finding that mutations, not copy number changes, distinguish the two groups.

This study identifies distinct structural variation patterns in prostate cancer across disease states, defining clinically actionable subgroups based on whole genome profiling.

The paper develops DNA- and RNA-based models combining immune and tumor features to predict outcomes of checkpoint inhibition in melanoma.