Cancer Genomics and Diagnostics

CNApp: quantification of genomic copy number alterations in cancer and integrative analysis to unravel clinical implications

CNApp quantifies genomic copy number alterations in cancer and integrates them with clinical and molecular data to identify clinical implications.

Improved detection of gene fusions by applying statistical methods reveals new oncogenic RNA cancer drivers

DEEPEST improves gene fusion detection in cancer by reducing false positives and identifying new oncogenic RNA drivers using statistical methods.

Identifying Combinations of Cancer Drivers in Individual Patients

This paper introduces CRSO to identify cancer driver combinations in individual patients, showing that rule-based analysis improves outcome prediction compared to single-alteration analysis.

Bioinformatics workflows for genomic analysis of tumors from Patient Derived Xenografts (PDX): challenges and guidelines

The paper develops bioinformatics workflows to analyze genomic data from patient-derived xenografts, addressing challenges like separating mouse and human reads and identifying somatic mutations without paired normal DNA.

Modelling cancer progression using Mutual Hazard Networks

This paper introduces Mutual Hazard Networks to model cancer progression from cross-sectional data, finding that IDH1 mutations promote TP53 mutations in glioblastoma.

Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data

This paper develops a computational method to infer tumour evolution trees from combined single-cell and bulk sequencing data, showing improved accuracy in subclone identification and mutation history reconstruction.

OncodriveCLUSTL: a sequence-based clustering method to identify cancerdrivers

OncodriveCLUSTL identifies cancer drivers by detecting mutation clustering signals in genomic data, outperforming existing methods across tumor whole-exomes.

CHASMplus reveals the scope of somatic missense mutations driving human cancers

CHASMplus identifies cancer-specific driver missense mutations across 32 cancer types using TCGA data, revealing over 4,000 unique mutations in 240 genes.

Robust Cancer Mutation Detection with Deep Learning Models Derived from Tumor-Normal Sequencing Data

This study develops and validates deep learning strategies for robust cancer mutation detection using tumor-normal sequencing data across multiple technologies and scenarios.

A comprehensive analysis of RNA sequences reveals macroscopic somatic clonal expansion across normal tissues

This study analyzes RNA-seq data from 6,700 normal tissues to identify somatic mutations and their association with age and tissue proliferation, finding tissue-specific cancer mutation hotspots.

Inferring tumor evolution from longitudinal samples

This paper introduces CALDER, an algorithm that infers tumor phylogenies from longitudinal bulk DNA sequencing data, showing improved accuracy over existing methods.

Accuracy of somatic variant detection in multiregional tumor sequencing data

This study evaluates somatic variant calling methods on multiregional tumor data, finding that multisample approaches do not always outperform single-sample methods, with Mutect2 in multisample mode performing best under challenging conditions.

Ribosomal DNA methylation as stable biomarkers for detection of cancer in plasma

This study identifies ribosomal DNA methylation as a stable biomarker for detecting cancer in plasma with high accuracy across multiple cancer types.

Impact of cancer mutational signatures on transcription factor motifs in the human genome

The paper links cancer mutational signatures to changes in transcription factor binding site motifs, showing correlations between motif alteration and signature information content.

Intra-individual changes in the frequency of mosaic loss of chromosome Y over time estimated with a new method

The paper introduces a method to estimate intra-individual changes in mosaic loss of chromosome Y (LOY) over time using standardized SNP-array data, showing higher correlation with other methods and significant individual variation.

Tumor Evolution Decoder (TED): Unveiling Tumor Evolution Based on Mutation Profiles of Subclones or Single Cells

The paper introduces TED, an algorithm for constructing tumor phylogenetic trees from mutation profiles, and applies it to cancer datasets to identify early mutation events in tumor evolution.

Inference of clonal selection in cancer populations using single-cell sequencing data

This paper introduces SCIFIL, a tool to infer fitness landscapes of cancer clones from single-cell sequencing data, demonstrating its accuracy on simulated and experimental data.

A Bayesian Nonparametric Approach to Discover Clinico-Genetic Associations across Cancer Types

This paper introduces a Bayesian nonparametric method to find genetic-clinical associations across cancer types, showing it recovers known associations and outperforms classical methods.

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

TRaIT infers mutational graphs from single-cell and multi-region sequencing data to model tumour evolution, improving accuracy and robustness.

In silico learning of tumor evolution through mutational time series

This paper uses LSTMs to model mutational time series in cancer, predicting mutation occurrence and identifying conditional drivers and their interactions.

Coupling multiplex pre-amplification and droplet digital PCR for longitudinal monitoring of ESR1 and PIK3CA mutations from plasma cell-free DNA

This paper develops a method combining multiplex pre-amplification and ddPCR to detect ESR1 and PIK3CA mutations in plasma cfDNA from MBC patients, achieving high sensitivity and reproducibility.

Experimental analysis of exome-scale mutational signature of glycidamide, the reactive metabolite of acrylamide

The paper identifies a unique mutational signature of glycidamide, a metabolite of acrylamide, through experimental and computational analysis, linking it to cancer genome patterns.

iEDGE: integration of Epi-DNA and Gene Expression and applications to the discovery of somatic copy number-associated drivers in cancer

iEDGE integrates epi-DNA and gene expression data to identify cis and trans driver genes in cancer, showing enrichment for known oncogenes and tumor suppressors.

The Tangent copy-number inference pipeline for cancer genome analyses

The paper introduces Tangent and Pseudo-Tangent pipelines for copy-number inference from cancer genome data, improving noise reduction and accuracy in SNP array and WES analyses.

Deep convolutional neural networks for accurate somatic mutation detection

NeuSomatic is a convolutional neural network for somatic mutation detection that outperforms existing methods across various sequencing platforms and tumor purities.

CaDrA: A computational framework for performing candidate driver analyses using binary genomic features

CaDrA identifies functionally relevant subsets of binary genomic features associated with specific outcomes, showing sensitivity and specificity in cancer datasets.

Comprehensive analysis of tumour initiation, spatial and temporal progression under multiple lines of treatment

PhylogicNDT statistically reconstructs cancer driver timing, subclonal phylogenies, and population dynamics from single- or multi-timepoint sequencing, revealing subtype-specific trajectories and therapy resistance evolution across 498 lung adenocarcinomas.

WITER: A powerful method for the estimation of cancer-driver genes using a weighted iterative regression accurately modelling background mutation rate

WITER improves cancer-driver gene detection by accurately modeling background mutations, identifying 178 driver genes across 26 cancers with high validation rates.

A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations

The paper identifies putative cancer driver genes and mutations using a CATH domain functional family-based approach, finding 151 candidates including previously unlisted genes.

Ordino: a visual cancer analysis tool for ranking and exploring genes, cell lines, and tissue samples

Ordino is a web-based tool for cancer genomics that allows users to rank, filter, and explore genes, cell lines, and tissue samples using pre-loaded datasets.