Geoff Macintyre

This paper identifies a chromosomal instability (CIN) biomarker that predicts response to PORCN inhibitors in cancers, validated through in vitro models, cell lines, and patient data.

This paper develops a computational framework using single-cell whole-genome sequencing to identify ongoing chromosomal instability, showing its utility in cancer models and clinical samples.

This study presents a comprehensive catalog of human double-strand break repair genes using genome-wide knockout data, revealing novel mechanisms and factors involved in DSB repair.

This paper develops a framework to predict future oncogene amplification or tumour suppressor deletion in cancer using genomic data, achieving an average AUC of 0.87.

The paper identifies pan-cancer evolutionary signatures linked to clonal expansion and immune microenvironment changes using TCGA data.

scAbsolute measures single-cell ploidy and replication status using single-cell DNA sequencing without prior knowledge of cell cycle phase.

CNpare matches cancer cell lines based on high-resolution DNA copy number profiles, accurately identifying matched lines in all tested cases.

The paper evaluates tools for estimating absolute copy numbers from shallow whole genome sequencing data and introduces an improved tool called Rascal.

FrenchFISH improves DNA copy number quantification from FISH images by using Poisson models, outperforming manual and automated methods in ovarian cancer tissue sections.

The paper analyzes whole-genome sequencing data from cancer samples to identify genomic footprints of telomere maintenance mechanisms, finding specific telomere variant repeat patterns associated with ATRX/DAXX mutations.

The paper examines how somatic structural variations affect TAD boundaries and gene regulation in human cancers using whole genome sequencing data.

This study analyzes chromothripsis in 2,658 cancers using whole-genome sequencing, finding it prevalent across multiple cancer types with complex structural alterations.

Ccube estimates cancer cell fractions and subclonal composition from whole-genome sequencing data using a probabilistic framework and variational inference.