Cancer Genomics and Diagnostics

Differential Allele-Specific Expression Uncovers Breast Cancer Genes Dysregulated By Cis Noncoding Mutations

Somatic mutation landscape reveals differential variability of cell-of-origin for primary liver cancer

The paper uses whole-genome sequencing to predict cell-of-origin for primary liver cancers, finding heterogeneity in origin predictions across cancer types.

Discovery of cancer driver genes based on nucleotide context

This study identifies novel cancer driver genes by analyzing mutation nucleotide contexts in whole-exome sequencing data from 12,004 tumors.

The consequences of variant calling decisions in secondary analyses of cancer sequencing data

This study evaluates how variant calling decisions affect downstream cancer genomics analyses and finds that different callers and strategies yield significant differences in driver gene identification, mutational signatures, and actionable variant detection.

CANCERSIGN: a user-friendly and robust tool for identification and classification of mutational signatures and patterns in cancer genomes

CANCERSIGN identifies and classifies mutational signatures in cancer genomes using raw mutation data, enabling clustering and analysis of ICGC datasets to discover novel signatures.

ctDNAtools: An R package to work with sequencing data of circulating tumor DNA

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

Pilot study demonstrating changes in DNA hydroxymethylation enable detection of multiple cancers in plasma cell-free DNA

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

Estimating the rate of cell type degeneration from epigenetic sequencing of cell-free DNA

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.

Intratumor Heterogeneity and Evolution of Colorectal Cancer

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

A protein-centric approach for exome variant aggregation enables sensitive association analysis with clinical outcomes

The paper introduces a protein-centric method to aggregate exome variants for association analysis with clinical outcomes, identifying sequence units predictive of survival that conventional gene-level analysis misses.

Geographic assessment of cancer genome profiling studies

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

Genomic evidence supports a clonal diaspora model for metastases of esophageal adenocarcinoma

The paper uses whole genome sequencing and phylogenetic analysis to show that metastases in esophageal adenocarcinoma spread rapidly through a clonal diaspora model.

VarStack: a Web Tool for Data Retrieval to Interpret Somatic Variants in Cancer

VarStack retrieves somatic variant data from multiple databases to aid in cancer variant interpretation.

Modeling and analysis of site-specific mutations in cancer identifies known plus putative novel hotspots and bias due to contextual sequences

The paper presents an algorithm and statistical model to detect cancer hotspots in TCGA data, identifying known and novel hotspots and analyzing sequence context biases.

Creating Standards for Evaluating Tumour Subclonal Reconstruction

This study develops quantitative metrics and simulated datasets to evaluate subclonal reconstruction methods in tumours.

JuLI: accurate detection of DNA fusions in clinical sequencing for precision oncology

JuLI improves DNA fusion detection in clinical sequencing by reducing false positives and increasing sensitivity, outperforming existing methods in high-depth sequencing.

Deep sequencing of 3 cancer cell lines on 2 sequencing platforms

The paper benchmarks a new sequencing platform and develops a somatic calling pipeline using deep sequencing of cancer cell lines on two platforms.

Development and clinical validation of a targeted RNAseq panel (Fusion-STAMP) for diagnostic and predictive gene fusion detection in solid tumors

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

Evaluation of colorectal cancer subtypes and cell lines using deep learning

This paper develops a deep learning method to classify colorectal cancer subtypes and match them to cell lines using multi-omics data.

The somatic mutation landscape of the human body

The paper catalogs somatic mutations across 36 tissues from 7,500 samples, revealing tissue-specific mutation profiles and selection pressures.

Using Transcriptional Signatures to Find Cancer Drivers with LURE

The paper introduces LURE, a semi-supervised method to predict cancer drivers using gene expression signatures, and applies it to TCGA data to find new associations with known mutation events.

GPCRs show widespread differential mRNA expression and frequent mutation and copy number variation in solid tumors

This study examines GPCR expression, mutations, and copy number variation in solid tumors, finding widespread differential expression and frequent mutations, suggesting potential roles as biomarkers and targets.

Ontology-based prediction of cancer driver genes

This paper introduces a method to predict cancer driver genes using ontology-based features and validates it in nasopharyngeal and colorectal cancers.

Robust foreground detection in somatic copy number data

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

Identification of 12 cancer types through genome deep learning

This paper introduces GDL, a deep learning method for cancer identification using genomic data, achieving high accuracy in distinguishing 12 cancer types and healthy tissues.

Molecular Profiles of Matched Primary and Metastatic Tumor Samples Support a Linear Evolutionary Model of Breast Cancer

The paper develops a computational approach to model cancer progression using static tumor samples and applies it to breast cancer data, revealing a linear, branching evolutionary model with subtype shifts during progression.

All-FIT: Allele-Frequency-based Imputation of Tumor Purity from High-Depth Sequencing Data

All-FIT estimates tumor purity from high-depth sequencing data by analyzing allele frequencies, outperforming existing methods in simulated and clinical data.

Distance Measures for Tumor Evolutionary Trees

This paper introduces two new distance measures for tumor evolutionary trees, CASet and DISC, which better capture mutation inheritance patterns and show improved performance on simulated and breast cancer datasets.

The mutational footprints of cancer therapies

This study identifies mutational footprints of six cancer therapies using whole-genome data from metastatic tumors to assess their contribution to mutation burden and risk of driver mutations.

Functional Genomic Complexity Defines Intratumor Heterogeneity and Tumor Aggressiveness in Liver Cancer

The paper introduces a functional genomic complexity (FGC) score to assess genomic heterogeneity in liver cancer, finding higher FGC correlates with worse prognosis and immune evasion.