Cancer Genomics and Diagnostics

This study shows that plasma DNA fragmentation analysis from dried blood spots can detect cancer with machine learning, offering a cost-effective method for early cancer detection in resource-limited settings.

This paper uses supervised non-negative matrix factorization on cfDNA fragmentomic features to improve early cancer detection, achieving high AUC scores in both pan-cancer and HCC-specific classifications.

cancerSimCraft simulates cancer genome data at clonal and single-cell resolution, enabling benchmarking of computational methods under varied conditions.

The paper presents a machine learning framework to classify variants in cell-free DNA as clonal hematopoiesis or tumor origin, achieving better classification rates than existing methods.

This study monitors ctDNA in metastatic cancer patients receiving an individualized vaccine, showing the assay's sensitivity and insights into treatment response.

The paper analyzes genome-wide coverage differences between nuclear DNA and cell-free DNA in healthy and cancer samples, identifying factors influencing coverage and developing a new cancer screening method.

The paper introduces SNMF, a supervised method to learn mutational signatures and predict DNA repair deficiencies, achieving high accuracy in cell lines and predicting DDR status in tumors.

This paper quantifies how tobacco smoke alters somatic selection and epistasis in lung adenocarcinoma, showing distinct mutation selection pressures in smokers and non-smokers.

Dyscovr identifies driver-associated transcriptional changes and prioritizes genes for therapeutic targeting in cancer.

This study identifies nucleosome breakpoints in cfDNA from cancer patients and shows how chromatin structure affects mutation detection sensitivity.

This paper shows that using pangenome references improves biomarker estimation in tumor sequencing, reducing bias in TMB calculations for non-European populations.

This study identifies signaling pathway changes during cancer metastasis using two models, revealing common and specific factors across 17 cancer types.

The paper introduces a high-accuracy sequencing method to detect somatic mutations in tissues, revealing extensive selection and driver mutations in oral epithelium and blood samples.

The paper develops a Bayesian framework to model mutual exclusivity in cancer mutations, finding potential mutual exclusivity between EGFR and IDH1 in glioblastoma multiforme.

This study shows APOBEC3A, not APOBEC3B, causes significant mutagenesis in human gastric epithelium through whole-genome sequencing of organoids with inducible APOBEC3A or APOBEC3B.

The paper improves somatic mutation detection in tumor samples without matched normal samples by estimating tumor purity and variant allele frequency, reducing false positive and negative rates.

The paper updates the COSMIC database and identifies SBS39 as a new homologous recombination deficiency signature.

The paper identifies expression-driven genetic dependency targets for precision oncology using a Bayesian approach, revealing new druggable genes and validating their therapeutic potential.

This study identifies MET and MERTK as frequently mutated protein kinases in cancer, with associations to survival and therapeutic response.

The paper identifies pathogenic post-zygotic variants in normal breast tissue of poor-prognosis cancer patients, linking them to reduced survival and disease recurrence.

The paper identifies differences in mtDNA mutation accumulation and selective pressures in pediatric leukemias using bulk and single-cell sequencing, linking these to functional heterogeneity among leukemic cells.

The paper shows that prior NOTCH1 mutation in aging esophageal epithelium alters the selection of subsequent somatic mutations, increasing NOTCH2 mutants and decreasing TP53 mutants.

This study examines the evolutionary dynamics of peritoneal metastases in colorectal cancer, finding they are more heterogeneous than liver metastases but less diverse than locoregional metastases.

The paper analyzes pan-cancer profiles of 17 mitotic intra-S DNA damage checkpoint genes across 9000+ samples to identify potential diagnostic, prognostic, and therapeutic biomarkers.

This study analyzes mitochondrial DNA repair genes across cancers, finding TP53 mutations and CNV impacts on survival, and links these genes to cell cycle activation and drug response.

The paper reports that sun exposure induces pervasive mutations at UV-sensitive RNA-processing gene promoters, with clonal expansion observed in both tumor suppressor gene hotspots and CPD hyperhotspots.

This study examines somatic evolution of cancer genes on sex chromosomes, finding sex-specific differences in mutation rates and inactivation patterns of tumor suppressor and oncogenes.

The paper introduces REMIND-Cancer Pipeline and pSNV Hunter to identify and prioritize functional promoter SNVs, including lowly recurrent and singleton mutations, using multi-omics data from the PCAWG dataset.

The paper identifies copy number-independent allelic imbalance in mRNA as a selected feature in cancer with prognostic significance.