Cancer Genomics and Diagnostics

Discovering fragment length signatures of circulating tumor DNA using Non-negative Matrix Factorization

This paper uses non-negative matrix factorization to analyze cfDNA fragment length distributions, showing it can infer tumor load, detect cancer, and identify open chromatin-associated signatures.

cloneMap: a R package to visualise clonal heterogeneity

cloneMap visualizes clonal heterogeneity in genomic data by representing clone size and phylogenetic relationships, applied to cancer datasets.

Circulating extracellular vesicles in lung cancer patients are not enriched in tumor-derived DNA fragments as revealed by whole genome sequencing.

The paper shows that EV-DNA in lung cancer patients is not enriched in tumor signal compared to plasma cfDNA, with similar fragmentation and tumor content.

Detecting cell-of-origin and cancer-specific methylation features of cell-free DNA from Nanopore sequencing

This study demonstrates the ability to detect cell-of-origin and cancer-specific methylation features from cell-free DNA using Oxford Nanopore sequencing.

Frameshifts may carry oncogenic potential beyond loss of function and categorize genes role in tumor development

The paper shows frameshift mutations have oncogenic potential in multiple tumors and suggests a new gene categorization beyond tumor suppressor and oncogene.

Pan-cancer detection and typing by mining patterns in large genome-wide cell-free DNA sequencing datasets

The paper develops a method to detect and type cancer using cfDNA sequencing data, achieving 81.62% accuracy in distinguishing malignant from control samples.

High prevalence of somatic PIK3CA and TP53 pathogenic variants in the normal mammary gland tissue of sporadic breast cancer patients revealed by duplex sequencing.

Immune evasion impacts the selective landscape of driver genes during tumorigenesis

The paper identifies differences in driver gene selection and mutational landscapes between immune-escaped and non-escaped tumors using TCGA data.

Pan-cancer whole genome comparison of primary and metastatic solid tumors

This study compares whole-genome sequences of primary and metastatic tumors across 7,152 samples, revealing cancer-type-specific genomic changes and treatment-induced evolutionary bottlenecks.

Crowd-sourced benchmarking of single-sample tumour subclonal reconstruction

The paper benchmarks 31 subclonal reconstruction algorithms on simulated tumours, finding that algorithm choice significantly impacts performance and no single algorithm excels across all tasks.

A Conservative Approach for Describing Cancer Progression

This paper introduces a cancer progression model based on Single Cell DNA Sequencing data that avoids the infinite site assumption and provides an open-source implementation.

CONET: Copy number event tree model of evolutionary tumor history for single-cell data

CONET infers tumor evolutionary trees and copy number calls from single-cell data using a probabilistic model with MCMC and regularization.

Processing UMI Datasets at High Accuracy and Efficiency with the Sentieon ctDNA Analysis Pipeline

The paper introduces the Sentieon ctDNA pipeline for accurate and efficient small somatic variant calling from ctDNA sequencing data, showing improved accuracy over existing methods.

Estimating tumor mutational burden from RNA-sequencing without a matched-normal sample

This paper develops a method to estimate tumor mutational burden from RNA-sequencing data without matched-normal samples, achieving high precision and recall in melanoma samples and showing association with patient survival.

Estimation of cancer cell fractions and clone trees from multi-region sequencing of tumors

This paper introduces PICTograph, a Bayesian model for estimating cancer cell fractions and clone trees from multi-region sequencing data, showing improved accuracy in subclone reconstruction and evolutionary inference.

Genomic profiling defines variable clonal relatedness between invasive breast cancer and primary ductal carcinoma in situ

The paper uses genomic profiling to show that some invasive breast cancers are clonally unrelated to their preceding DCIS, suggesting DCIS may act as both a precursor and independent risk factor.

Mutational signatures are markers of drug sensitivity of cancer cells

The paper identifies mutational signatures as markers of drug sensitivity in cancer cells by analyzing drug and genetic screening data.

Examining clustered somatic mutations with SigProfilerClusters

SigProfilerClusters identifies all types of clustered mutations by calculating sample-dependent thresholds and subclassifies them into established categories.

Individualized discovery of rare cancer drivers in global network context

The paper introduces NEAdriver, a method that combines network analysis and mutation co-occurrence to identify rare cancer drivers in individual tumor genomes, revealing previously undetected driver events.

PhiDsc: Protein functional mutation Identification by 3D Structure Comparison

PhiDsc identifies functionally relevant mutations in protein families by comparing 3D structures and mutation recurrence data, revealing known and novel hotspots in RAS and RHO families.

PersonaDrive: A Method for the Identification and Prioritization of Personalized Cancer Drivers

PersonaDrive identifies and prioritizes patient-specific cancer drivers by integrating genomic data, pathways, and protein connectivity, outperforming existing methods on TCGA and cell-line data.

RNA-sequencing as an expression-informed alternative for somatic variant detection and cancer driver identification

This study uses RNA-seq data to detect somatic variants and identify cancer drivers in 1,359 samples, showing RNA-seq can effectively replace WES/WGS for this task.

Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

This study introduces PIVOT, a multi-omic machine learning model that classifies genes as tumor suppressors, oncogenes, or neutral, improving prediction accuracy for BRCA, LUAD, and COAD datasets.

A Simulator for Somatic Evolution Study Design

The paper introduces a simulator for generating synthetic data to optimize study designs for analyzing somatic evolution in cancer and other diseases.

Unifying comprehensive genomics and transcriptomics in individual cells to illuminate oncogenic and drug resistance mechanisms

This study uses ResolveOME to analyze genomic and transcriptomic data in individual cells, revealing mutations and gene expression changes linked to drug resistance and oncogenic mechanisms.

Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning

This paper introduces TumE, a synthetic supervised learning method that improves accuracy and speed in inferring cancer evolution from single tumour biopsies.

All2: A tool for selecting mosaic mutations from comprehensive multi-cell comparisons

All2 filters mosaic mutations from multi-cell comparisons by classifying variants as germline, mosaic, or false positive, improving sensitivity and reducing false positives in whole genome and exome analysis.

Neural network approach to somatic SNP calling in WGS samples without a matched control.

This paper introduces a neural network method for somatic SNP calling in WGS tumor samples without a matched normal, showing effectiveness across 5 cancer types.

Interplay between mosaic chromosomal alterations and polygenic risk score increases risk of non-small cell lung cancer

The paper examines the association between mosaic chromosomal alterations and polygenic risk scores in NSCLC risk, finding that mosaic loss increases risk, especially in high genetic risk individuals.

Panel Informativity Optimizer (PIO): an R package to improve cancer NGS panel informativity

PIO optimizes cancer NGS panel informativity by selecting genomic intervals to maximize patient coverage while minimizing panel size.