Cancer Genomics and Diagnostics

Shedding Light on the Dark Genome: Insights into the Genetic, CRISPR-based, and Pharmacological Dependencies of Human Cancers and Disease Aggressiveness

This study examines genetic, CRISPR-based, and pharmacological dependencies in human cancers by analyzing the dark genome, finding similar dependencies across light and dark genomes and linking dark gene mutations to worse patient outcomes.

Hotspot propensity across mutational processes

This paper identifies mutational processes with high hotspot propensity in tumors and normal tissues, showing that SBS1 and SBS17a/b have distinct genomic factors influencing their hotspot formation.

Analytical and clinical validation of a targeted-enhanced whole genome sequencing-based comprehensive genomic profiling test.

The paper validates a targeted-enhanced whole-genome sequencing assay for oncology genomic profiling, showing high sensitivity and specificity for SNVs and indels, and high concordance with reference methods.

Mutational signature decomposition with deep neural networks reveals origins of clock-like processes and hypoxia dependencies

The paper introduces SigNet, a deep learning method for mutational signature decomposition, showing improved accuracy and robustness in estimating signature contributions in cancer genomes.

Overcoming Observation Bias for Cancer Progression Modeling

This paper extends cancer progression models to account for observation bias by modeling how genetic events affect tumor detection, improving the accuracy of inferred causal interactions.

Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment

SigProfilerAssignment refits known substitution, doublet, indel and copy-number mutational signatures to single samples and probabilistically to individual mutations, outperforming four refitting tools on 2,700 synthetic genomes.

Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors

This study maps tumor evolution and microenvironment dynamics in colorectal cancer using spatial multi-omic data, identifying key genes associated with immune exclusion and prognosis.

ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model

ConDoR improves tumor phylogeny inference by integrating copy-number constraints with SNV data, outperforming existing methods on simulated data and producing more plausible phylogenies in real datasets.

Restriction site associated DNA sequencing for tumour mutation burden estimation and mutation signature analysis

This study shows that RADseq can be a cost-effective and accurate method for estimating tumour mutation burden and analyzing mutation signatures compared to existing methods.

oncotree2vec - A method for embedding and clustering of tumor phylogenetic trees

This paper introduces oncotree2vec, a method to embed and cluster tumor mutation trees, capturing genetic relationships for improved tumor subtyping and analysis.

Deciphering the Functional Roles of Individual Cancer Alleles Across Comprehensive Cancer Genomic Studies

This paper introduces REVEALER 2.0, an improved algorithm for identifying genomic alterations associated with functional profiles, showing increased speed and resolution for analyzing cancer genomic data.

scLongTree: an accurate computational tool to infer the longitudinal tree for scDNAseq data

scLongTree infers subclonal trees from longitudinal scDNA-seq data, outperforming existing tools in accuracy and scalability.

A zero-agnostic model for copy number evolution in cancer

The paper introduces a zero-agnostic copy number transformation model and develops an algorithm for inferring copy number phylogenies, showing improved performance on simulated and real data.

Tracing cancer evolution and heterogeneity using Hi-C

The paper develops HiDENSEC to analyze Hi-C data in heterogeneous cancer samples, identifying chromosomal rearrangements and their impact on melanoma progression.

Aggregation Tool for Genomic Concepts (ATGC): A deep learning framework for somatic mutations and other sparse genomic measures

The paper introduces a deep learning framework for genomic data that aggregates and predicts somatic mutations and other sparse genomic measures using attention-based multiple instance learning.

Cell-free multi-omics analysis reveals tumor status-informative signatures in gastrointestinal cancer patients' plasma

The paper analyzes cell-free multi-omics data from gastrointestinal cancer patients to identify tumor status-informative signatures in plasma.

SCsnvcna: Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data

SCsnvcna places SNVs on a CNA tree using independent cell sets, improving accuracy in predicting SNV placement and losses due to copy number changes.

A mouse-specific model to detect genes under selection in tumors

The paper adapts a human cancer driver gene detection model to mice using transfer learning, identifying 24 driver genes in mouse breast cancer exomes.

Refphase: Multi-sample reference phasing reveals haplotype-specific copy number heterogeneity

Refphase uses multi-sample reference phasing to infer haplotype-specific copy numbers and reveal intra-tumour heterogeneity in cancer samples.

iSCORED: nanopore-based random genomic sampling for intraoperative molecular diagnosis

iSCORED enables rapid, unbiased CNV detection and methylation classification in brain tumors using Nanopore sequencing, achieving 100% concordance with standard methods.

Detecting significantly recurrent genomic connections from simple and complex rearrangements in the cancer genome

This paper introduces two methods to detect recurrent genomic rearrangements in cancer genomes and identifies 80 simple and 29 complex rearrangements across 2,500 cancers.

Pan-cancer evolution signatures link clonal expansion to dynamic changes in the tumour immune microenvironment

The paper identifies pan-cancer evolutionary signatures linked to clonal expansion and immune microenvironment changes using TCGA data.

Phertilizer: Growing a Clonal Tree from Ultra-low Coverage Single-cell DNA Sequencing of Tumors

This paper introduces a method to infer clonal trees from ultra-low coverage single-cell DNA sequencing data of tumors, showing improved accuracy in uncovering clonal structure and genotypes.

Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network

This paper introduces Net-DMPred, a graph neural network-based method to predict cancer-specific driver missense mutations by incorporating molecular network structures.

Genomic landscape of endometrial, ovarian and cervical cancers in Japan from database in the Center for Cancer Genomics and Advanced Therapeutics.

This study analyzes genomic data from endometrial, ovarian, and cervical cancers in Japan to identify mutation patterns and associations with TMB-H and MSI-H.

Characterization of Cancer Evolution Landscape Based on Accurate Detection of Somatic Mutations in Single Tumor Cells

This study develops scNanoSeq to detect somatic mutations in single tumor cells, revealing subtype-specific evolutionary patterns and a reprogramming and critical mutation co-timing model in breast cancer.

Landscape of Large-Scale Somatic Genomic Insertions in Non-Small Cell Lung Carcinoma Revealed by Nanopore Sequencing

The paper uses nanopore sequencing to analyze large-scale somatic insertions in NSCLC, identifying three key insertion patterns and their regulatory impacts.

Predicting regional somatic mutation rates using DNA motifs

The paper predicts somatic mutation rates in 13 cancers using DNA motifs and identifies impactful motifs and cancer-specific regions for classification.

Multimodal cell-free DNA whole-genome analysis combined with TET-Assisted Pyridine Borane Sequencing is sensitive and reveals specific cancer signals

This paper develops a bioinformatics pipeline combining TAPS and whole-genome sequencing to detect ctDNA in cancer patients, achieving 85.2% sensitivity with 100% specificity.

Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients

This study identifies cancer type-specific evolutionary signatures using genomic data from over 35,000 patients, revealing patterns of driver mutation acquisition linked to prognosis.