Cancer Genomics and Diagnostics

LiquidCNA tracks subclonal evolution from liquid biopsies using somatic copy number alterations, revealing new subpopulations in metastatic lung cancer.

The paper analyzes somatic substitutions in non-unique regions of the human genome across 2,658 cancers, revealing hidden mutations in coding sequences and regulatory elements.

APSiC identifies novel cancer genes and non-genetic drivers from perturbation screens, revealing context-dependent oncogenic mechanisms and functional roles in specific cancers.

This paper develops a semi-supervised algorithm to deconvolve clonal variants in tumors, accounting for tumor cell fraction and copy number changes across samples.

This study shows that blood-based liquid biopsy using next-generation sequencing can detect genomic alterations in dogs with cancer, matching tumor tissue findings and identifying alterations from spatially separated tumor sites.

Aperture detects structural variations and viral integrations in ctDNA with high sensitivity and specificity using an alignment-free algorithm.

This paper introduces cfSNV, a somatic SNV caller for cfDNA that outperforms existing tools in detecting low-frequency mutations and applies it to immunotherapy prognosis in non-small-cell lung cancer.

The paper develops computational methods combining sequencing and miFISH data to improve accurate inference of clonal copy number evolution in tumors.

SparseSignatures is a framework for discovering mutational signatures in tumor genomes, outperforming existing methods and identifying signatures linked to known mutagenic mechanisms in pancreatic and breast tumors.

The paper analyzes mobile element insertions and structural variants in longitudinal breast cancer samples to assess their timing and impact on tumor progression.

This paper introduces a pooled sequencing strategy to address intratumoral genetic heterogeneity in cancer, showing it improves mutation detection and risk stratification while reducing costs.

DISMIR detects cancer using cfDNA methylation and sequence data with high accuracy at low sequencing depth.

The paper introduces Bourque distances to compare mutation trees in cancer, addressing limitations of existing metrics like Robinson-Foulds distance.

This paper introduces a Bayesian network model for oncogenetic network estimation, showing its ability to infer progression networks from synthetic and real data.

This study disentangles genetic, epigenetic, and stochastic factors contributing to drug-response variability in an in vitro tumor heterogeneity model using PC9 cell lines.

driveR prioritizes cancer driver genes using somatic genomic data through a multi-task learning model, outperforming existing methods across 28 datasets.

This paper develops and validates a new bioinformatic algorithm to accurately identify somatic SNVs from small DNA amounts in FFPE breast cancer samples, showing improved performance over existing methods.

Alleloscope analyzes allele-specific copy number and chromatin accessibility in single-cell cancer data, revealing complex copy number events and their role in intratumor heterogeneity.

SHARC rapidly identifies somatic genomic structural variations from nanopore sequencing for blood-based cancer monitoring, detecting ten SVs per sample and enabling treatment response tracking in metastatic prostate cancer.

This study develops NBDriver, a tool that uses sequence neighborhoods to predict pathogenic mutations in cancer genomes, outperforming existing methods in identifying driver mutations.

This study develops a bioinformatics pipeline to generate CNA signatures for 31 cancer subtypes and constructs a classifier to identify cancer type and origin from copy number data.

The paper systematically examines mutation groupings in cancer genes to identify downstream expression programs and reveals functional divergence among mutations within the same gene.

This study validates whole genome sequencing as a reliable single assay for detecting various cancer mutations with high sensitivity and precision.

This study identifies five copy number signatures in prostate cancer using sigminer, revealing distinct etiologies and improved clinical outcome associations compared to single base substitution signatures.

The paper characterizes mutation rate variability in regulatory elements across cancer genomes and identifies mutational signatures and drivers associated with specific genomic features.

The paper identifies short single-stranded DNA fragments in plasma that form non-canonical structures and are enriched in open chromatin and transcription factor-binding sites.

The paper analyzes 3000 whole genome sequencing datasets to uncover links between bacteria, viruses, and cancer, identifying novel associations and phenotypic differences.

pDriver identifies personalized coding and miRNA cancer drivers for individual patients using gene networks and outperforms existing methods on TCGA datasets.

The paper introduces HotPho, a tool that identifies 3D co-clustering of phosphosites and cancer mutations on protein structures, revealing 474 hybrid clusters with potential functional significance.

This paper evaluates three cfDNA extraction kits and a PEG-based cleanup method, finding that DNApure improves yield and purity of cfDNA from urine and plasma.