Cancer Genomics and Diagnostics

Pan-Cancer PDOs Preserve Tumor Heterogeneity and Uncover Therapeutic Vulnerabilities

This study develops a pan-cancer PDO platform to preserve tumor heterogeneity and identify therapeutic vulnerabilities, finding that 58% of PDOs respond to Talazoparib and combination screens reveal resistance-overcoming drugs.

A Proposed Statistical Approach for Conducting Longitudinal Assessment of Circulating Tumor DNA

This paper proposes a statistical framework using hierarchical mixed-effects models to analyze ctDNA dynamics in liquid biopsy data, finding that a hierarchical linear spline model best captures patient-level ctDNA evolution.

CINner: modeling and simulation of chromosomal instability in cancer at single-cell resolution

CINner models chromosomal instability in cancer, showing how selection parameters and WGD influence tumor progression and heterogeneity.

Nanopore-based consensus sequencing enables accurate multimodal tumor cell-free DNA profiling

This paper introduces NanoRCS, a method using nanopore sequencing and consensus calling to accurately estimate tumor fractions from cfDNA by analyzing SNVs, CNAs, and fragmentomics.

Model-based tumour subclonal deconvolution accounting for spatio-temporal sampling biases

This paper introduces MOBSTERm, a Bayesian framework for subclonal deconvolution that accounts for spatio-temporal sampling biases in cancer sequencing data, improving the analysis of tumour evolution.

Timing and clustering co-occurring genome amplifications incancers

This paper introduces TickTack, a Bayesian model to time and cluster co-occurring genome amplifications in cancer, revealing insights into tumor evolution and genomic instability.

Protocol for Extracting Circulating Cell-Free DNA from Murine Saliva: Insights into Oral and Systemic Disease Research

This paper describes a protocol for isolating circulating cell-free DNA from murine saliva and validates its use in oral and systemic disease research.

SLAYER: A Computational Framework for Identifying Synthetic Lethal Interactions through Integrated Analysis of Cancer Dependencies

SLAYER identifies synthetic lethal interactions by integrating cancer genomic data and CRISPR screens, finding 142 high-confidence candidates with 15-fold enrichment over random pairs.

Marker selection strategies for circulating tumor DNA guided by phylogenetic inference

The paper develops methods to improve marker selection for ctDNA analysis by refining phylogenetic models and quantifying clonal frequencies through optimization.

The Indian Cancer Genome Atlas:A Multi-Omics Resource for Advancing Cancer Research

The paper presents a multi-omic resource for Indian breast cancer patients, identifying genetic and proteomic alterations and immune-related gene expression patterns.

Comparative analysis of RNA expression in a single institution cohort of pediatric cancer patients

The paper evaluates the CARE pipeline for identifying druggable targets in pediatric cancers using RNA-Seq data from 33 patients, showing 94% of cases had clinically significant findings.

The somatic mutation landscape of normal gastric epithelium

This study uses whole-genome sequencing to analyze somatic mutations in normal gastric epithelium, revealing mutation rates, clonal dynamics, and associations with cancer risk factors.

Reconstructing tumor evolution without chromosomal instability reveals in vivo determinants of therapeutic response

This study develops an in vivo platform called SETs to reconstruct tumor evolution without chromosomal instability, identifying therapeutic response determinants in prostate cancer.

Deciphering lung adenocarcinoma evolution and the role of LINE-1 retrotransposition

This study analyzes 1024 lung adenocarcinomas to identify evolutionary patterns and mutational mechanisms, revealing distinct trajectories linked to smoking, ancestry, and retrotransposition.

SVelfie: A method for discovering cancer drivers based on enrichment of likely functional structural variants

SVelfie identifies cancer driver genes by analyzing enrichment of functional structural variants in cancer genomes.

Standardized droplet preamplification method for downstream circulating cell-free DNA analysis

This paper introduces a standardized droplet preamplification method to improve recovery efficiency of ccfDNA/ctDNA from low-input samples, achieving a 41% average improvement.

Early ctDNA kinetics as a dynamic biomarker of cancer treatment response

This study develops a framework to design ctDNA biomarkers for cancer treatment response by modeling ctDNA kinetics and simulating virtual patient cohorts.

Intra-clustering analysis reveals tissue-specific mutational patterns

The paper identifies tissue-specific mutational patterns in cancer by analyzing clustering of 8303 patients across 21 cancer types using network graphs and statistical methods.

Genomic alterations and transcriptional phenotypes in circulating tumor DNA and matched metastatic tumor

This study compares genomic alterations and transcriptional phenotypes in circulating tumor DNA and matched metastatic tumors in SCLC patients, finding cfDNA reflects tumor mutations, identifies resistance mechanisms, and tracks tumor evolution.

Inferring active mutational processes in cancer using single cell sequencing and evolutionary constraints

This paper introduces ArtiCull, a method to analyze single-cell whole-genome sequencing data to distinguish active from historical mutational processes in cancer, revealing temporal and spatial patterns in mutational signatures.

SAEG: A Novel Deep Learning Architecture for Somatic Alterations

SAEG is a deep learning model that predicts gene knockout susceptibility by integrating mutations, copy number alterations, and protein interactions, outperforming existing methods.

Unveiling Candidate Drivers in Cancer Progression Using Somatic-IV Analysis

This paper introduces Somatic-IV, a method to identify candidate driver genes in cancer by integrating genetic, transcriptomic, and clinical data, and finds ZBED2 and CSNK2A2 as potential drivers in pancreatic and colorectal cancers.

Somatic repetitive element insertions Define New Biomarkers in Pan-cancer genome

This study identifies somatic repetitive element insertions as new biomarkers in pan-cancer by analyzing 325 cancer genomes using long-read sequencing.

Comparing the predictors of mutability among healthy human tissues inferred from mutations in single cell genome data

The paper analyzes mutation rates in healthy human tissues using single-cell data to identify genetic and epigenetic factors influencing mutability across different tissues.

Intraline genomic heterogeneity of the triple-negative breast cancer MDA-MB-231 cell line

The paper examines genomic diversity within the MDA-MB-231 breast cancer cell line across sublines, identifying intraline heterogeneity driven by single nucleotide variants and copy number alterations.

Pan-Cancer Biomarker Analysis from the Cancer Dependency Map: A Blueprint for Precision Oncology

This study analyzes cancer dependency maps across 1,150 molecular profiles to identify biomarkers for patient stratification and therapeutic response prediction.

Longitudinal structural variant phylogenies define tumor evolution under therapeutic selection pressure in metastatic prostate cancer

SVCFit estimates SV cellular fractions from bulk sequencing to reconstruct clonal evolution in mCRPC under therapy, showing treatment-driven clonal reconfiguration.

Hyper-mutational processes provide a head-start for weak cancer drivers: explaining atypical KRAS variants

The paper shows that hypermutational processes can explain the prevalence of weak driver mutations like atypical KRAS variants in cancers without requiring differential selection.

Bayesian inference of fitness landscapes via tree-structured branching processes

FiTree infers fitness landscapes from single-cell tumor mutation trees using Bayesian methods, outperforming existing models and identifying epistatic effects in leukemia data.

Unbiased co-occurring mutational landscapes in tumorigenesis

The paper identifies co-occurring mutations in cancer genomes using FP-Growth, revealing 100,933 mutation pairs across 22 cancer types and highlighting potential therapeutic targets.