Cancer Genomics and Diagnostics

This study shows that ongoing whole-genome doubling in ovarian cancer promotes cell diversity and immune dysregulation through chromosomal instability and quiescent states.

The paper investigates cfDNA fragmentation patterns in tumor models to distinguish between non-tumor cfDNA and ctDNA, finding differences in fragment length and site-specific variation.

This study identifies genetic alterations associated with recurrence and radiotherapy response in ductal carcinoma in situ using whole-exome sequencing and copy number analysis.

This paper introduces an automated method to detect rare events in liquid biopsies without prior knowledge of their signatures, demonstrating its effectiveness on immunofluorescence microscopy data.

AI-HOPE-PI3K enables real-time, population-aware analysis of PI3K pathway alterations in colorectal cancer using natural language queries, revealing survival differences and ancestry-linked biomarkers.

araCNA is a deep learning model that accurately predicts somatic copy number alterations in cancer genomes using simulated data and long-range sequence models.

This study examines the long-term effects of chemotherapy on normal blood cells by analyzing mutation burdens and cell population structure in treated individuals.

The paper introduces fastppm, a fast tool for solving the regression problem in tumor phylogeny reconstruction, achieving significant speedups and accuracy improvements.

HiScanner detects high-resolution copy number alterations in single cells, outperforming existing methods and revealing distinct CNA patterns in neurons and oligodendrocytes.

The paper analyzes mutations and copy-number alterations in 25,000 tumor samples to identify state-specific interactions in cancer genes, revealing dynamic progression mechanisms.

The paper evaluates somatic mutation detection methods across different sequencing and analysis pipelines to identify false negatives in drug-resistant mutations.

PhyClone is a Bayesian method that reconstructs cancer phylogenies from bulk sequencing data, showing improved accuracy and scalability over existing methods.

The paper investigates the functional impact of the HyperDuplication GenomoPhenotype in high copy number endometrial cancer, showing it enhances tumor fitness and therapy resistance through coordinated duplication of oncogenes.

BiGPICC identifies carcinogenic gene combinations from mutation data using a graph-based heuristic approach, achieving high sensitivity and specificity comparable to exhaustive search.

This paper develops Ultra-Rapid droplet digital PCR (UR-ddPCR) for rapid intraoperative tumor mutation detection and quantification, demonstrating its feasibility in 13 glioma cases.

This study uses stochastic models to analyze how genetic intra-tumour heterogeneity changes during and after treatment, finding distinct patterns in mutation frequencies and resistant clone sizes.

SigProfilerTopography evaluates how chromatin organization and other genomic features influence mutational signatures in cancer genomes.

This study identifies APOBEC3 mutagenesis as a common driver of therapy resistance in breast cancer, associated with poorer clinical outcomes.

This study introduces tumor break load (TBL) as a measure of genomic instability across cancers, showing its association with gene expression changes and clinical outcomes.

DiffInvex identifies chemotherapy resistance genes by analyzing 10,000 cancer genomes, finding known and novel genes under conditional selection for specific therapies.

The paper develops a pharmacokinetic model for ctDNA shedding under targeted therapy and evaluates a biomarker using early ctDNA samples, achieving high sensitivity and specificity.

The paper shows non-hypermutator cancers adapt by reversing germline mutation bias, enabling access to driver mutations through altered mutation spectra.

DirectHRD is a whole-genome sequencing-based classifier that improves HRD detection sensitivity in low tumor fraction samples.

This paper introduces a Bayesian multi-study NMF method to identify common and condition-specific mutational signatures across multiple datasets.

This paper evaluates models for predicting tissue of origin from circulating DNA by comparing features of fragmentation around TSSs and the tissue specificity of lncRNAs versus coding genes.

This study compares oncogenic pathway mutations in hepatocellular carcinoma between Hispanic/Latino and Non-Hispanic White patients, identifying ethnicity-specific alterations in key pathways.

DeepCopy improves haplotype-specific copy number calling in single-cell DNA sequencing by incorporating evolutionary constraints, leading to more accurate and biologically plausible CNA phylogenies.

CINner models chromosomal instability in cancer, showing how selection parameters and WGD influence tumor progression and heterogeneity.

This paper introduces NanoRCS, a method using nanopore sequencing and consensus calling to accurately estimate tumor fractions from cfDNA by analyzing SNVs, CNAs, and fragmentomics.

SLAYER identifies synthetic lethal interactions by integrating cancer genomic data and CRISPR screens, finding 142 high-confidence candidates with 15-fold enrichment over random pairs.