Cancer Genomics and Diagnostics

iEDGE integrates epi-DNA and gene expression data to identify cis and trans driver genes in cancer, showing enrichment for known oncogenes and tumor suppressors.

The paper introduces Tangent and Pseudo-Tangent pipelines for copy-number inference from cancer genome data, improving noise reduction and accuracy in SNP array and WES analyses.

PhylogicNDT statistically reconstructs cancer driver timing, subclonal phylogenies, and population dynamics from single- or multi-timepoint sequencing, revealing subtype-specific trajectories and therapy resistance evolution across 498 lung adenocarcinomas.

WITER improves cancer-driver gene detection by accurately modeling background mutations, identifying 178 driver genes across 26 cancers with high validation rates.

The paper predicts cancer cell-of-origin from DNA sequences by matching mutational landscapes to chromatin modification patterns in normal tissues.

The paper identifies copy number aberrations that alter kinase signaling and highlights potential therapeutic targets by integrating genomic and proteomic data across cancer samples.

The paper identifies genetic determinants of mosaic Y chromosome loss in blood and links it to genomic instability and non-haematological cancer risk.

The paper investigates how whole genome doubling (WGD) in cancer may mitigate Muller’s Ratchet by reducing negative selection on mutations in essential genes after duplication.

The paper uses machine learning to predict drug response in tumour models by reducing molecular profile dimensionality, finding multi-gene predictors outperform single-gene markers in several cancer types.

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

The paper quantifies somatic mutation accumulation in fetal liver and intestinal stem cells, finding higher mutation rates and distinct mutation spectra in fetal liver compared to fetal intestine and adult tissues.

AmpliconArchitect reconstructs focal amplifications in cancer genomes, revealing novel insights into extrachromosomal mechanisms and oncoviral pathogenesis.

The paper examines how somatic mutation distributions vary across chromatin folding domains in cancer genomes, revealing distinct patterns associated with different mutational processes.

SCDT detects sub-chromosome CNVs in cf-DNA with high sensitivity and specificity using GC correction and segmentation algorithms.

The paper identifies methylation patterns specific to intestinal epithelial cells to detect cell-free DNA in plasma, showing increased levels in advanced CRC compared to IBD and PDAC.

The paper identifies somatically mutated genes under positive and negative selection and links them to oncogenic processes using transcriptome sequence analysis.

The paper develops eTumorRisk, a network-based algorithm that predicts cancer risk using germline genome data, achieving 74.1-91.7% accuracy for six cancers with low false-negative rates.

S22S is a reference-free method for somatic variant calling that uses genomic and population data to accurately identify SNVs without matched normal samples.

Helmsman generates mutation spectra matrices efficiently, enabling mutation signature analysis on large datasets with high speed and low memory usage.

LumosVar 2.0 improves somatic variant calling by jointly analyzing tumor and adjacent normal samples without a germline reference.

This study shows that inherent mutation patterns in human genes distinguish cancer genes from non-cancer genes and shape their mutation profiles.

This study identifies 10 consensus driver genes in HCC across six cohorts and shows their associations with mRNA transcriptomes, patient survival, and demographic factors.

This paper introduces qCNV and sCNA-seq tools to profile somatic copy number alterations in cell-free DNA without a reference panel, showing effectiveness in detecting clinically relevant mutations from plasma samples.

The paper analyzes 2500 cancer genomes to assess the molecular functional impact of mutations, finding that some putative passenger variants may influence gene activity and contribute to cancer phenotypes.

The paper analyzes RNA alterations across 27 cancer types using whole-genome sequencing data, identifying gene expression associations with DNA variants and structural fusions.

This paper introduces a Bayesian method to identify causative genome alterations in individual tumors by inferring their impact on transcriptomic changes.

A haplotype-based somatic variant caller paired with a machine learning filter, reported as more accurate than other callers on in-silico mixtures of GIAB reference samples.

The paper introduces a machine learning framework to identify cancer driver genes by analyzing the functional impact of somatic mutations, finding 593 genes with significant harmful mutation bias.

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.