Cancer Genomics and Diagnostics

Breaking Bad: Exploring & Complementing the Effects of the DNASE1L3 p.Arg206Cys Variant on Cell-Free DNA from an Isogenic Cell Line Model

The paper develops an isogenic cell line model to study the effects of the DNASE1L3 p.Arg206Cys variant on cell-free DNA fragmentation and shows how wildtype DNASE1L3 can enrich for mononucleosomal fragments.

LeukGenePipeline: Modular Workflow for Genomic Datasets

LeukGenePipeline automates genomic analyses for AML research using COSMIC data, identifying frequently mutated genes and enriched transcription factors.

Oncodrive3D: Fast and accurate detection of structural clusters of somatic mutations under positive selection

Oncodrive3D detects structural clusters of somatic mutations under positive selection in the human proteome using AlphaFold 2 models, showing high sensitivity and computational efficiency.

Nanopore-based cell-free DNA fragmentation and methylation profiles from the cerebral spinal fluid of patients with lung cancer brain metastases

This study analyzes DNA fragmentation and methylation in CSF-derived cfDNA from NSCLC-BMET patients and finds distinct patterns compared to controls and plasma-derived cfDNA.

Distinct aneuploid karyotypes are universally selected for across cancers

The paper identifies binary karyotypes as universally selected in cancers and yeast, showing lower TP53 mutation rates and increased fitness compared to other aneuploid karyotypes.

Correcting errors in PCR-derived libraries for rare allele detection by reconstructing parental and daughter strand information

This paper introduces SPIDER-seq, a method to detect rare alleles in PCR-derived libraries by tracking molecular identity through overwritten barcodes, achieving sensitivity down to 0.125% mutation frequency.

A reference-free strategy for circulating tumor DNA detection from whole-genome sequencing data

The paper introduces ctDNAmer, a reference-free tool for detecting ctDNA from WGS data by analyzing k-mers, showing improved tumor fraction estimation and detection compared to traditional methods.

Analyzing and Visualizing Distance Measures for Clonal Trees

This work defines four classes of clonal tree differences, analyzes them with three distance measures, and introduces a visualization tool to better understand tree comparisons.

Enumerating and Exploring the Space of Clonal Trees

The paper enumerates and analyzes clonal trees under different evolutionary models to improve simulated data for clonal tree inference methods.

Mutational and Expression Profile of ZNF217, ZNF750, ZNF703 Zinc Finger Genes in Kenyan Women Diagnosed with Breast Cancer

This study analyzes mutational and expression patterns of ZNF217, ZNF703, and ZNF750 in Kenyan breast cancer patients, finding distinct mutation burdens and expression upregulation in tumors.

Development and extensive sequencing of a broadly-consented Genome in a Bottle matched tumor-normal pair

The paper describes a comprehensive genomic dataset from a tumor-normal pair with broad consent, generated using multiple sequencing technologies for benchmarking somatic variant detection.

Ongoing genome doubling promotes evolvability and immune dysregulation in ovarian cancer

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

Robust and scalable inference of cancer progression pathways using Conjunctive Bayesian Networks

This study introduces R-CBN, a robust Bayesian network model for inferring cancer progression pathways, showing improved reliability and scalability over existing methods.

Dissecting cell-free DNA fragmentation variation in tumors using cell line-derived xenograft mouse

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.

Improved Mutation Detection in Duplex Sequencing Data with Sample-Specific Error Profiles

DupCaller improves mutation detection in duplex sequencing by using sample-specific error profiles and a strand-aware model, increasing sensitivity across various samples and conditions.

Cell-free DNA fragmentomic characteristics in transposon elements inform molecular regulators and enhance cancer diagnosis

This study identifies cfDNA fragmentomic features in transposon elements as biomarkers for cancer diagnosis, showing improved performance over genome-wide measurements.

Mutational landscape of pure ductal carcinoma in situ and associations with disease prognosis and response to radiotherapy

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

Unsupervised Detection of Rare Events in Liquid Biopsy Assays

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.

From Mutation to Prognosis: AI-HOPE-PI3K Enables Artificial Intelligence-Agent Driven Integration of PI3K Pathway Data in Colorectal Cancer Precision Medicine

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: Somatic copy number profiling using long-range sequence models

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

RNA liquid biopsy via nanopore sequencing for novel biomarker discovery and cancer early detection

This study uses long-read nanopore sequencing of cell-free RNA to detect early-stage cancer and discover novel biomarkers, identifying 270,679 intergenic RNAs and potential therapeutic targets.

Fast tumor phylogeny regression via tree-structured dual dynamic programming

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

High-resolution detection of copy number alterations in single cells with HiScanner

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

MultiAlloDriver: a multi-model method to predict and identify cancer driver mutations

MultiAlloDriver predicts and identifies cancer driver mutations by integrating protein sequence, structure, and surface information, achieving over 93% accuracy.

Mapping cancer gene dynamics through state-specific interactions

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.

Evaluating discordant somatic calls across mutation discovery approaches to minimize false negative drug-resistant findings

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

PhyClone: Accurate Bayesian reconstruction of cancer phylogenies from bulk sequencing

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

cfDNAanalyzer: a comprehensive toolkit for analyzing cell-free DNA genomic sequencing data in liquid biopsy

cfDNAanalyzer provides a toolkit for analyzing cfDNA data to enable disease detection through machine learning models.

Functional impact of the hyperduplication genomophenotype in high copy number endometrial cancer

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.

Forecasting oncogene amplification and tumour suppressor deletion

This paper develops a framework to predict future oncogene amplification or tumour suppressor deletion in cancer using genomic data, achieving an average AUC of 0.87.