Cancer Genomics and Diagnostics

Ennet identifies cancer-driving networks using somatic enhancer mutations, revealing known and novel networks across multiple cancer types.

DriverPower identifies cancer driver mutations by combining mutational burden and functional impact scores, outperforming existing methods in coding and non-coding driver discovery.

ACEseq estimates allele-specific copy numbers, tumor purity, ploidy, and homologous recombination deficiency scores from tumor whole-genome sequencing by combining GC-bias correction, replication-timing correction, and structural variant breakpoints.

MatchMiner automates matching cancer patients to precision medicine trials using genomic and clinical criteria, enabling efficient and accurate trial enrollment.

The paper identifies cancer gene biomarkers associated with drug sensitivity and provides open-source tools for analysis.

MuClone improves somatic mutation detection and classification by integrating clonal structure information across multiple tumor samples.

This study uses whole-exome sequencing of plasma cell-free DNA to analyze somatic mutations in metastatic colorectal cancer and identifies KDR/VEGFR2 mutations affecting anti-angiogenic therapy response.

The paper identifies driver genes in prostate cancer by analyzing convergence of non-coding mutations in regulatory regions using 3D chromatin interactions and genomic data.

The paper introduces SASE-mapper to detect accelerated somatic evolution in cancer genomes and finds recurrent SASE in regulatory regions across multiple cohorts.

This paper introduces a method for allele-specific copy number segmentation across multiple samples, enabling the identification of shared and private segment boundaries for phylogenetic analysis.

The paper refutes claims of MET gene over-expression in tumor-educated platelets by showing the analysis used non-specific intronic sequences, not specific exonic ones.

The paper describes the PCAWG project's large-scale analysis of cancer whole genomes across multiple computing environments, producing validated consensus variants for downstream research.

The paper systematically characterizes gene fusions in cancer using RNA and whole genome DNA sequencing data from 1188 donors across 27 cancer types, identifying 3297 high-confidence fusions and revealing novel mechanisms.

The paper shows that selecting short DNA fragments in plasma improves the detection of circulating tumour DNA by enriching mutated DNA, enabling identification of copy number alterations previously undetected.

This paper introduces a latent variable model to improve sensitivity and control false discovery rate in somatic indel discovery, showing significant improvements in recall for deletions.

This study analyzes simple somatic mutations across multiple cancer types, identifying distinct mutational patterns in different cancers.

The paper evaluates adaptive somatic mutation models using a real dataset and finds that semi-simulation improves model performance on real data.

This study profiles somatic mutations in cfDNA and white blood cells from 1134 healthy individuals to understand mutation patterns and origins in cfDNA.

The paper develops a method to detect co-mutated pathways in cancer samples and identifies enriched pathway pairs across six cancer types, suggesting potential mechanisms for immune evasion.

AGFusion reconstructs and visualizes gene fusion proteins from genomic breakpoints to aid in assessing their functional impact.