Cancer Genomics and Diagnostics

PhISCS - A Combinatorial Approach for Sub-perfect Tumor Phylogeny Reconstruction via Integrative use of Single Cell and Bulk Sequencing Data

PhISCS integrates single-cell and bulk sequencing data to reconstruct sub-perfect tumor phylogenies by minimizing false negatives, false positives, and ISA violations.

Genomic profiling of childhood tumor patient-derived xenograft models to enable rational clinical trial design

This paper genomically characterizes 261 pediatric cancer patient-derived xenograft models to guide rational clinical trial design and drug development.

Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing

The paper presents a machine learning classifier to identify true somatic variants from next-generation sequencing data, achieving high specificity and sensitivity with minimal uncertainty.

Somatic Truth Data from Cell Lineage

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.

PRODIGY: personalized prioritization of driver genes

PRODIGY identifies patient-specific driver genes by analyzing mutation and expression data with pathway and interaction information, outperforming existing methods in multiple cancer cohorts.

Found in Transcription: Gene fusions arise through defects in RNA processing in the absence of chromosomal rearrangements.

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.

Optimized data representation and convolutional neural network model for predicting tumor purity

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: Discovering Subclones in Tumors Sequenced at Standard Depths

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

RNAIndel: a machine-learning framework for discovery of somatic coding indels using tumor RNA-Seq data

RNAIndel identifies somatic coding indels in tumor RNA-Seq data with high sensitivity and low false positives, outperforming existing methods.

LOTUS: a Single- and Multitask Machine Learning Algorithm for the Prediction of Cancer Driver Genes

LOTUS predicts cancer driver genes by integrating mutation and interaction data, outperforming existing methods across multiple cancer types.

TrackSig: reconstructing evolutionary trajectories of mutations in cancer

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.

Intragenomic variability and extended sequence patterns in the mutational signature of ultraviolet light

The paper analyzes UV-induced mutational signatures, showing variability across chromatin states and improved modeling with extended sequence patterns.

Copy number motifs expose genome instability type and predict driver events and disease outcome in breast cancer

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

Computational tools to detect signatures of mutational processes in DNA from tumours: a review and empirical comparison of performance

This study evaluates and compares computational tools for detecting mutational signatures in cancer genomes using real and simulated data.

Integrated clonal analysis reveals circulating tumor DNA in urine and plasma of glioma patients

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

Low abundance of circulating tumor DNA in localized prostate cancer

The paper shows that ctDNA is undetectable in localized prostate cancer but present in metastatic cases, suggesting lower dissemination and detectability in early stages.

Resolving sub-clonal heterogeneity within cell-line growths by single cell sequencing genomic DNA

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.

Passenger Hotspot Mutations in Cancer

This paper shows that many cancer hotspots are passengers, not drivers, and introduces a new model to better identify true driver mutations.

SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events

SigProfilerMatrixGenerator classifies and visualizes small mutational events in cancer genomes, offering faster and more comprehensive analysis than existing tools.

HiLDA: a statistical approach to investigate differences in mutational signatures

HiLDA identifies mutational patterns and their frequencies in cancer data, showing significant associations with mutation timing in colon cancer.

Leveraging protein dynamics to identify cancer mutational hotspots in 3D-structures

This paper identifies cancer driver genes by analyzing mutational hotspots in 3D protein structures, incorporating protein dynamics to improve sensitivity.

An integrative approach uncovers genes with perturbed interactions in cancers

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.

Detection of residual disease after neoadjuvant therapy in breast cancer using personalized circulating tumor DNA analysis

A personalized ctDNA test achieves high accuracy for residual disease detection after neoadjuvant therapy in breast cancer.

Every which way? On predicting tumor evolution using cancer progression models

This study evaluates four cancer progression models to predict tumor progression paths and estimates evolutionary unpredictability, finding poor performance under multi-peaked fitness landscapes and unreliable predictions in most cancer datasets.

Model-based analysis of positive selection significantly expands the list of cancer driver genes, including RNA methyltransferases

driverMAPS improves cancer driver gene detection by modeling positive selection and mutation patterns, identifying 159 new potential driver genes including RNA methyltransferases.

Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data

This paper shows that spatial tumour structure affects mutation patterns in sequencing data and introduces a framework to account for these effects in cancer genomic analysis.

A catalog of cis-regulatory mutations in 12 major cancer types

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.

FERMI: A novel method for sensitive detection of rare mutations in somatic tissue

FERMI improves detection of rare somatic mutations by reducing sequencing and library preparation errors, enabling tracking of clonal evolution in leukemia.

Hidden Markov Models Lead to Higher Resolution Maps of Mutation Signature Activity in Cancer

This paper introduces sequence-dependent models for mutation signatures, improving the resolution of mutation signature activity in cancer by accounting for dependencies between mutations.

cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models

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