Cancer Genomics and Diagnostics

A Divide-and-Conquer Approach to Large-Scale Evolutionary Analysis of Single-Cell DNA Data

The paper introduces a divide-and-conquer method for large-scale evolutionary analysis of single-cell DNA data, demonstrating its effectiveness through experiments on clustering and dimensionality reduction methods.

A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer

DIALECT improves the identification of mutual exclusivity and co-occurrence between driver mutations by distinguishing them from passenger mutations in cancer genomics data.

Accelerated somatic mutation calling for whole-genome and whole-exome sequencing data from heterogenous tumor samples

The paper introduces MuSE2.0, a tool that accelerates somatic mutation calling for whole-genome and whole-exome sequencing data from heterogeneous tumor samples, achieving significant speed improvements over existing methods.

Evidence for negative selection against somatic mutations in fibroblasts with high N-ethyl-N-nitrosourea-induced mutation load

The paper shows that human fibroblasts undergo negative selection against harmful mutations, maintaining growth despite high mutation loads.

Alleviating cell-free DNA sequencing biases with optimal transport

This paper introduces an optimal transport-based domain adaptation method to correct technical biases in cfDNA sequencing data, improving cancer detection and enabling cohort integration.

APOBEC shapes tumor evolution and age at onset of lung cancer in smokers

The paper identifies two lung cancer subtypes defined by APOBEC mutagenesis levels and shows their association with different genetic mutations, aging, and immune responses.

Machine learning predictions improve identification of real-world cancer driver mutations

The paper uses machine learning to identify cancer driver mutations, finding that methods incorporating protein structure or functional genomic data outperform those using only evolutionary data, and validates their predictions using real-world patient cohorts.

FinaleMe: Predicting DNA methylation by the fragmentation patterns of plasma cell-free DNA

FinaleMe predicts DNA methylation and tissues-of-origin from plasma whole-genome sequencing by using a non-homogeneous Hidden Markov Model.

Evolution of chromosome arm aberrations in breast cancer through genetic network rewiring

The paper investigates the evolution of chromosome 4p loss in basal breast cancer, identifying C4orf19 as a novel gene that suppresses proliferation and is part of a kinase module.

Protein structural context of cancer mutations reveals molecular mechanisms and identifies novel candidate driver genes

The paper analyzes cancer mutations in protein structures to identify novel driver genes by distinguishing mutational patterns in tumour suppressors and oncogenes.

Evolving copy number gains promote tumor expansion and bolster mutational diversification

The paper estimates the arrival time of clonal copy number gains in tumors and shows they promote mutational diversification and malignant growth.

Detecting Somatic Mutations Without Matched Normal Samples Using Long Reads

This paper develops a method to detect somatic mutations in tumor samples without matched normal samples using long-read sequencing and demonstrates its application on cancer cell lines.

Machine learning identifies cell-free DNA 5-hydroxymethylation biomarkers that detect occult colorectal cancer in PLCO Screening Trial subjects

This study uses machine learning to identify 5-hydroxymethylation biomarkers in cell-free DNA that can detect occult colorectal cancer up to 36 months before diagnosis.

A comprehensive personal omics clinical interpreter based on genomic and transcriptomic profiles

The paper introduces POI, a tool that integrates genomic and transcriptomic data to suggest personalized therapies by matching patients to treatments using PreMedKB and predictive algorithms.

Accurate and sensitive mutational signature analysis with MuSiCal

MuSiCal improves mutational signature analysis by outperforming existing methods in discovery and assignment, revealing new signatures and resolving ambiguities in cancer genomes.

Liquid footprinting: A novel approach for DNA footprinting using short double-stranded cell-free DNA from plasma

This paper introduces a method to enrich short double-stranded cfDNA from plasma for genome-wide DNA footprinting, identifying disease-specific biomarkers in cancer and sepsis.

Analytical validation and clinical utilization of K-4CARETM: a comprehensive genomic profiling assay with personalized MRD detection

This paper validates a comprehensive genomic profiling assay, K-4CARE, for detecting genetic variants and ctDNA, showing high sensitivity and specificity across multiple cancer types.

Quantifying the seed sensitivity of cancer subclonal reconstruction algorithms

This study evaluates how different random seeds affect subclonal reconstruction algorithms in cancer genomics, finding significant variability in subclone estimates across seeds.

BayVarC: an ultra-sensitive ctDNA variant caller using Bayesian approach

BayVarC is a Bayesian variant caller for liquid biopsy that detects low-frequency variants with high sensitivity and low false positive rate.

Modeling metastatic progression from cross-sectional cancer genomics data

This study introduces metMHN, a model to infer metastatic progression from cross-sectional cancer genomics data, identifying TP53 and EGFR as key mediators and highlighting copy number alterations in post-seeding adaptation.

Frequently Together, Among Model Systems Molecular Alterations: Cancer Maintenance Markers with Translational Study Design

The paper introduces methods to identify molecular alterations that frequently co-occur across cancer models, aiming to discover cancer maintenance markers and inform translational study design.

Optimizing Accuracy and Efficiency in Analyzing Non-UMI Liquid Biopsy Datasets Using the Sentieon ctDNA Pipeline

This study introduces a consensus-based ctDNA analysis pipeline that improves accuracy and efficiency in detecting low-frequency somatic variants from non-UMI liquid biopsy data.

Clinical application of tumour-in-normal contamination assessment from whole genome sequencing

The paper develops a tool to assess tumour contamination in normal samples using whole-genome sequencing data, finding contamination in various cancer types and highlighting risks to variant calling.

TCGAnalyzeR: a web application for integrative visualization of molecular and clinical data of cancer patients for cohort and associated gene discovery

TCGAnalyzeR provides an integrative web tool for visualizing TCGA data to identify patient cohorts and gene sets for cancer research and clinical decision-making.

Cell cycle alterations associate with a redistribution of mutation rates across chromosomal domains in human cancers

The paper identifies two global RMD signatures of mutation rate redistribution in tumors associated with RB1 and TP53 pathway alterations and cell cycle changes.

CRUX, a platform for visualising, exploring and analysing cancer genome cohort data

CRUX is a platform for analyzing cancer genome data, enabling cohort comparisons, biomarker discovery, and visualization with a user-friendly interface and local analysis options.

cancercelllines.org - a Novel Resource for Genomic Variants in Cancer Cell Lines

The paper introduces cancercelllines.org, a resource curating genomic variants in cancer cell lines, providing copy number and SNV data for over 16,000 cell lines.

cfDNA UniFlow: A unified preprocessing pipeline for cell-free DNA data from liquid biopsies

The paper introduces cfDNA UniFlow, a unified preprocessing pipeline for cell-free DNA data, enabling standardized processing and analysis across different settings.

ZygosityPredictor

ZygosityPredictor evaluates the number of gene copies affected by mutations in sequencing data, providing confidence measures for precision oncology applications.

Optimizing Design of Genomics Studies for Clonal Evolution Analysis

This paper develops a simulation-based optimization framework for designing genomic studies focused on clonal evolution, particularly in cancer, to optimize resource allocation and study design.