Cancer Genomics and Diagnostics

A framework for mutational signature analysis based on DNA shape parameters

This paper introduces a framework for mutational signature analysis using DNA shape parameters to better understand mutagenesis mechanisms and improve mutation rate baselines.

Transcription Factor-Centric Approach to Identify Non-Recurring Putative Regulatory Drivers in Cancer

The paper identifies non-recurring putative regulatory driver mutations in cancer by analyzing their effects on transcription factor-DNA binding rather than mutation recurrence.

Prediction of high-risk liver cancer patients from their mutation profile: Benchmarking of mutation calling techniques

The paper benchmarks mutation calling techniques for liver cancer prognosis using VCF and MAF files, finding MuTect2's VCF files best predict high-risk patients with high AUROC and HR.

Mutational Analysis and Deep Learning Classification of Uterine and Cervical Cancers

The paper develops a deep learning tool to classify uterine and cervical cancers based on somatic mutations with 94.3% training and 89.2% test accuracy.

Correlation-based and feature-driven mutation signature analyses to identify genetic features associated with DNA mutagenic processes in cancer genomes

Validation of a Pancreatic Cancer Detection Test in New-Onset Diabetes Using Cell-Free DNA 5-Hydroxymethylation Signatures

This paper validates a pancreatic cancer detection test using 5-hydroxymethylation signatures in cell-free DNA from patients with new-onset diabetes, showing high specificity and moderate sensitivity.

Influence of sequencing depth on the fidelity and sensitivity of 1%-5% low-frequency mutation detection and recommendation for standardization of sequencing depth

This paper investigates how sequencing depth affects low-frequency mutation detection, showing that increasing depth improves fidelity and sensitivity while reducing false positives.

Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?

This paper investigates short-term tumor evolution prediction using cancer progression models, finding that specific genotype and fitness landscape characteristics improve prediction accuracy.

Cancer type classification in liquid biopsies based on sparse mutational profiles enabled through data augmentation and integration

This paper develops a machine learning method to classify cancer cell of origin from sparse liquid biopsy data by using data augmentation and integration of multiple genomic features.

Longitudinal cancer evolution from single cells

This paper introduces LACE, a framework for analyzing longitudinal single-cell mutation data to model cancer evolution and therapy response.

mslp: a comprehensive pipeline in predicting cancer mutations specific synthetic lethal partners

The paper introduces mslp, a pipeline that identifies cancer mutation-specific synthetic lethal partners using genomic and transcriptomic data, showing improved consistency and impact on cell viability.

ConTreeDP: A consensus method of tumor trees based on maximum directed partition support problem

ConTreeDP computes clonal consensus trees from cancer genomic data by solving the Maximum Directed Partition Support Consensus Tree problem, showing efficiency and accuracy.

DGAT-onco: A powerful method to detect oncogenes by integrating differential mutational analysis and functional impacts of somatic mutations

DGAT-onco detects oncogenes by integrating differential mutational analysis and functional impacts of somatic mutations, identifying 22.8% of its results as verified oncogenes.

Attentive deep learning-based tumor-only somatic mutation classifier achieves high accuracy agnostic of tissue type and capture kit.

The paper develops a deep learning classifier to distinguish somatic and germline mutations in tumor-only samples, achieving high accuracy across tissue types and reducing racial bias in TMB estimates.

Cell free extrachromosomal circular DNA is common in human urine

This study identifies and characterizes over one million urinary cell free eccDNAs in healthy adults, revealing their genomic distribution, sequence features, and potential role of direct repeats in their biogenesis.

Discovery of Clinically Relevant Fusions in Pediatric Cancer

The paper develops an ensemble fusion-calling pipeline to improve the detection of clinically relevant gene fusions in pediatric cancer, identifying 67 fusions with a 29.3% diagnostic yield.

A Network-centric Framework for theEvaluation of Mutual Exclusivity Tests onCancer Drivers

The paper introduces a network-centric framework to evaluate mutual exclusivity tests for cancer drivers, addressing limitations in current evaluation methods by incorporating network knowledge and resolving confounding factors.

Whole-genome analysis of Nigerian patients with breast cancer reveals ethnic-driven somatic evolution and distinct genomic subtypes

The paper analyzes whole-genome data from Nigerian breast cancer patients, identifying ethnic-driven somatic evolution and distinct genomic subtypes associated with African ancestry.

GSA: An Independent Development Algorithm for Calling Copy Number and Detecting Homologous Recombination Deficiency (HRD) from Target Capture Sequencing

This paper introduces GSA, an algorithm for calculating tumor purity, ploidy, and HRD scores from target capture sequencing data, showing high accuracy and sensitivity in clinical samples.

A Common Methodological Phylogenomics Framework for intra-patient heteroplasmies to infer SARS-CoV-2 sublineages and tumor clones

The paper introduces a phylogenomics framework to infer SARS-CoV-2 sublineages and tumor clones from genomic data by analyzing variant frequencies and applying a computational method called Concerti.

The impact of rare germline variants on human somatic mutation processes

This study identifies 207 associations between rare germline variants and somatic mutational processes in cancer genomes, revealing novel genes influencing DNA repair and mutation signatures.

Whole Genome and Exome Sequencing Reference Datasets from A Multi-center and Cross-platform Benchmark Study

The paper presents whole-genome and whole-exome sequencing datasets from a multi-center, cross-platform study to benchmark NGS technologies and bioinformatics pipelines in cancer genomics.

Constructing germline research cohorts from the discarded reads of clinical tumor sequences

The paper demonstrates that discarded tumor sequencing reads can be used to impute germline genotypes, enabling the construction of large germline research cohorts from existing clinical data.

MSA: Reproducible mutational signature attribution with confidence based on simulations

MSA assigns mutational signatures to single samples using NNLS with uncertainty estimation, validated through simulations and reference signatures.

Identifying Lethal Dependencies with HUGE Predictive Power from Large-Scale Functional Genomic Screens

The paper improves prediction of lethal gene dependencies using the HUGE method, achieving 75x higher statistical power than existing methods in cancer genomics.

High complexity cellular barcoding and clonal tracing reveals stochastic and deterministic parameters of radiation resistance

This study uses cellular barcoding to analyze clonal dynamics in response to radiation, finding deterministic and stochastic selection processes contribute to radiation resistance in HNSCC cells.

Widespread redundancy in -omics profiles of cancer mutation states

The paper evaluates the effectiveness of various -omics data types in predicting cancer mutation states and finds that RNA sequencing is often the best, but other data types can be equally effective.

Cancer mutational processes vary in their association with replication timing and chromatin accessibility

The paper analyzes mutational signatures in tumors to determine their association with replication timing and chromatin accessibility, finding signature-specific associations and clinical relevance of SBS16 in liver cancer.

Identifying the oncogenic potential of gene fusions exploiting miRNAs

This paper introduces ChimerDriver, a neural network model that classifies gene fusions as oncogenic or not using transcriptional and post-transcriptional data, achieving high accuracy and recall.

High throughput and affordable genome-wide methylation profiling of circulating cell-free DNA by Methylated DNA sequencing (MeD-seq) of LpnPI digested fragments

This paper validates MeD-seq for cfDNA methylation profiling in cancer patients, showing distinct pre-surgical methylation patterns compared to healthy donors.