Cancer Genomics and Diagnostics

ctDNAtools is an R package for analyzing ctDNA sequencing data, offering MRD testing and cfDNA fragmentation analysis.

This study uses 5-hydroxymethyl cytosine (5hmC) profiles in plasma cell-free DNA to detect multiple cancers with high accuracy using machine learning.

This paper introduces CelFiE, a method to estimate cell type contributions to cfDNA, showing it can detect rare cell types and find increased skeletal muscle cfDNA in ALS patients.

This study examines intratumor heterogeneity and evolution patterns in colorectal cancer, finding a Darwinian pattern with differences between right-sided and left-sided tumors.

This study assesses geographic information in cancer genome profiling publications to identify gaps in representation and highlight inequalities in cancer research geographies.

The paper develops and validates a targeted RNAseq panel (Fusion-STAMP) for detecting gene fusions in solid tumors, showing high sensitivity and specificity.

dryclean improves somatic copy number alteration detection in cancer genomes by reducing read depth waviness through signal processing.

The paper identifies three novel complex structural variation patterns in cancer genomes using genome graph analysis across 2,833 tumor WGS.

ClinCNV detects allele-specific somatic copy-number alterations in NGS data using a statistical approach, showing performance on WES and TPS cohorts.

The paper identifies a bias in VAF estimates from the Illumina Isaac aligner and introduces FixVAF to correct it, improving cancer cell fraction analysis.

The paper introduces a benchmark dataset of validated somatic mutations derived from cell lineage, providing a large collection of true somatic variation for use in mutation discovery pipelines.

The paper identifies RNA-specific gene fusions in tumors and normal tissues, showing they are linked to spliceosomal gene dysregulation and may contribute to cancer development.

Deep Purity (DePuty) uses convolutional neural networks to predict tumor purity from SNP-based copy number and minor allele frequency data without matched normals, outperforming existing methods with fewer samples.

MAGOS improves subclone detection in tumors sequenced at standard depths by correcting sequencing errors and outperforms existing methods in accuracy and efficiency.

TrackSig estimates mutation evolutionary trajectories from bulk tumor sequencing data, showing improved accuracy in detecting subclonal populations and signature activity changes compared to baseline methods.

The paper identifies copy number motifs in breast cancer genomes that predict disease outcome and driver events, outperforming existing clinical and genomic markers.

The paper detects circulating tumor DNA in plasma and urine of glioma patients using sequencing guided by tumor mutations.

The paper uses single-cell sequencing of genomic DNA to resolve sub-clonal heterogeneity in the COLO829 cell line, identifying four major sub-clones and explaining conflicting bulk sequencing results.

The paper introduces PertInInt, a method that identifies cancer genes by analyzing somatic mutations in interaction sites across 10,037 tumor samples, revealing disrupted interactions as a common cancer mechanism.

The paper identifies 301 non-coding somatic mutations affecting gene expression in 12 cancer types using TCGA data and shows they have features of noncoding drivers.

The paper introduces two Cytoscape-based tools, cyTRON and cyTRON/JS, for inferring cancer evolution models from genomic and proteomics data.

CNApp quantifies genomic copy number alterations in cancer and integrates them with clinical and molecular data to identify clinical implications.

This paper introduces CRSO to identify cancer driver combinations in individual patients, showing that rule-based analysis improves outcome prediction compared to single-alteration analysis.

This study develops and validates deep learning strategies for robust cancer mutation detection using tumor-normal sequencing data across multiple technologies and scenarios.

This study evaluates somatic variant calling methods on multiregional tumor data, finding that multisample approaches do not always outperform single-sample methods, with Mutect2 in multisample mode performing best under challenging conditions.

This study identifies ribosomal DNA methylation as a stable biomarker for detecting cancer in plasma with high accuracy across multiple cancer types.

The paper introduces a method to estimate intra-individual changes in mosaic loss of chromosome Y (LOY) over time using standardized SNP-array data, showing higher correlation with other methods and significant individual variation.

The paper introduces TED, an algorithm for constructing tumor phylogenetic trees from mutation profiles, and applies it to cancer datasets to identify early mutation events in tumor evolution.

This paper introduces a Bayesian nonparametric method to find genetic-clinical associations across cancer types, showing it recovers known associations and outperforms classical methods.

This paper develops a method combining multiplex pre-amplification and ddPCR to detect ESR1 and PIK3CA mutations in plasma cfDNA from MBC patients, achieving high sensitivity and reproducibility.