Cancer Genomics and Diagnostics

The paper analyzes 100 whole genome sequences of DIPG tumors to uncover evolutionary patterns, revealing early WGD acquisition, TP53 loss, complex SVs, and a unique late-stage mutation signature.

This study reclassifies tumors based on drug response and genomic alterations, revealing molecular concordance in therapy across histologically different cancers.

bMSI-CAST improves cfDNA MSI detection by enhancing locus selection, duplicate removal, and calling algorithm, achieving high accuracy in gastrointestinal cancer samples.

This study evaluates somatic CNV callers using NGS data from 91 samples with varying factors, finding that caller choice and tumor purity significantly impact call quality.

This paper introduces CNAqc, a toolset for validating clonal and subclonal copy number alterations in bulk tumor sequencing by addressing tumor purity, ploidy, and heterogeneity using simulations and single-cell data.

The paper develops a method to estimate cell turnover and mutation rates from bulk sequencing data, finding both are elevated in a hepatocellular carcinoma sample.

This study uses whole genome sequencing of placental biopsies to show that human placentas exhibit clonal expansions, copy number changes, and developmental bottlenecks similar to childhood cancer.

MAESTRO enables efficient and accurate tracking of thousands of low-frequency mutations with reduced sequencing, improving mutation testing in cancer and liquid biopsies.

This study shows that transcriptomic features, not genomic characteristics, drive the histologic phenotype of lung cancers.

The paper analyzes clonal evolution in liver cancer at single-cell and single-variant resolution, revealing genetic and phenotypic heterogeneity and their evolutionary relationships.

The paper introduces a method to detect DNA contamination in tumor samples using microhaplotypes, achieving sensitivity for 1% contamination without a normal sample.

The paper reconstructs phylogenies of human development using whole-genome sequencing to show variable embryonic patterns and lineage asymmetries across individuals and tissues.

cancerAlign improves tumor stratification by aligning tumors across cancer types using adversarial learning and consensus clustering, showing better results than methods that cluster within single cancer types.

The paper uses Perturb-seq to assess the phenotypic impact of somatic variants in cancer, revealing a continuum of gain-of-function KRAS variants.

The paper describes a targeted sequencing assay and bioinformatics workflow for comprehensive genomic profiling of cancer, achieving high accuracy in variant detection and biomarker assessment.

mutSigMapper provides a method to map mutational spectra to signatures using shot-noise modeling, offering a non-parametric statistical assessment of significance.

This study examines how cancer polygenic risk scores correlate with somatic alterations in tumors from The Cancer Genome Atlas, revealing associations with specific molecular subtypes and driver events.

STAMPede is a web-based portal for visualizing somatic tumor sequencing data from a solid tumor oncology panel, enabling query and display of gene-, variant-, and cancer-level summary statistics.

The paper identifies frequent RRM2B amplification in multiple tumors, links it to DNA repair defects and poor outcomes, and suggests therapeutic opportunities.

This study evaluates targeted RNA sequencing for detecting fusion transcripts, mutations, and expression in hematological malignancies, finding it effective for diagnosing leukemia subtypes and identifying most DNA mutations at the mRNA level.

This study analyzes mutational landscapes in normal breast tissue across age and pregnancy to understand cancer risk differences.

The paper develops a method to identify cell-free DNA fragmentation hotspots in plasma whole-genome sequencing and shows their utility in detecting early-stage cancers with high specificity and sensitivity.

The paper identifies genetic polymorphisms preferentially selected during cancer development by analyzing allele retention patterns in TCGA whole exome sequencing data.

The paper describes the development and clinical application of MSK-ACCESS, a liquid biopsy assay that improves the specificity of somatic variant detection in cell-free DNA by using paired normal sequencing.

The paper combines single cell DNA-Seq and RNA-Seq in gastric cancer to reveal subclonal genomic instability and gene expression patterns, showing evidence of tumor evolution.

The paper constructs a pan-cancer atlas showing how DNA methylation and copy number aberrations influence gene expression across cancers, finding that copy number changes drive heterogeneity and expression-methylation links are often due to cell-of-origin and normal cell mix.

This paper introduces mutation profiles from healthy genomes to classify cancer types using machine learning, achieving 93% accuracy in distinguishing pancreatic and brain cancer patients.

This study uses deep learning to analyze exome sequencing data and detect cancer-specific SNVs, addressing the challenge of mutation exclusivity in cancer.

The paper identifies a common postzygotic mutational signature across healthy tissues linked to embryonic hypoxia, observed in both normal and some tumor samples.

This study evaluates how variant calling decisions affect downstream cancer genomics analyses and finds that different callers and strategies yield significant differences in driver gene identification, mutational signatures, and actionable variant detection.