Cancer Genomics and Diagnostics

SCOPE explores the space of tumor phylogenies consistent with scWGS data, showing multiple phylogenies are possible and highlighting how data quality and evolutionary constraints affect uncertainty.

SigDyn analyzes single-cell mutational signature dynamics to uncover tumor evolution and heterogeneity, revealing therapeutic insights in invasive ductal carcinoma and laryngeal squamous cell carcinoma.

fastMHN enables pan-cancer tumor evolution analysis by approximating complex progression models, identifying a progression group linked to STK11 mutations across multiple cancer types.

SigFormer improves single-sample mutational signature decomposition by using a transformer framework, outperforming existing methods in high-noise and overcomplete settings.

This paper identifies stable cancer attractor states by integrating embryonic origin with hallmark-related mutations, revealing organ-specific metastatic patterns and prognostic inversions.

SNAP improves classification performance for cell-free chromatin data while maintaining data retention through optimized quality filters and modular design.

This study identifies somatic and germline mutational signatures across 661 species, revealing greater diversity than in human samples, likely due to environmental or biological factors.

The paper develops a Transformer-based model called cfAI to profile and detect cancer-derived cfDNA by integrating methylation, fragmentomics, and histone modifications, achieving high sensitivity and specificity in multi-cancer detection.

This study identifies three distinct evolutionary trajectories in lung adenocarcinomas using whole-genome sequencing, revealing that some never-smoking tumors follow smoking-like paths driven by KRAS mutations.

This study uses single-cell RNA-sequencing of cerebrospinal fluid to identify circulating tumour cells in children with brain cancer, revealing their dynamics and potential as biomarkers.

This paper introduces SSED, a metric using ESM-3 to quantify evolutionary divergence, showing cancer driver genes are structurally constrained and that oncogenic mutations follow similar principles, predicting immune response.

This study benchmarks six duplex sequencing technologies to evaluate their performance in detecting somatic mutations across different genomic contexts and tissue types.

The paper identifies epistatic interactions between cancer driver mutations and allele-specific copy number variations in tumors, showing their impact on patient survival.

This study evaluates how variant calling strategies affect mutational signature inference, finding that consensus calling reduces artifacts while preserving true signals.

This study identifies POLE2 as a contributor to genome stability, a cancer biomarker, and therapeutic target through pan-cancer multi-omics analysis.

This paper presents a workflow using XGBoost, LightGBM, and TabNet to classify variants in tumour-only data, showing improved accuracy across ancestries and tumour types.

This study identifies tissue architecture and immune niches as key factors influencing ctDNA release in colorectal cancer by integrating spatial transcriptomics and in situ mutation detection.

This study analyzes ctDNA shedding in pancreatic cancer using paired tissue and plasma whole genomes, finding that subclones contribute significantly to early dissemination and ctDNA levels.

DTCFinder detects and characterizes disseminated and circulating tumor cells in liquid biopsies using single-cell RNA sequencing and machine learning without relying on labels or markers.

This study benchmarks DNA methylation deconvolution methods for tumoral fraction estimation in cfDNA, finding CelFiE most accurate and introducing DecoNFlow as a comprehensive pipeline.

ATHENA predicts functional genomic mutations and synergistic vulnerabilities in prostate cancer using deep learning, identifying driver events and therapeutic targets through integration with variant discovery pipelines.

This paper develops a computational framework using single-cell whole-genome sequencing to identify ongoing chromosomal instability, showing its utility in cancer models and clinical samples.

The paper uses single-cell whole-genome sequencing and Bayesian phylodynamic modeling to show colorectal tumors often originate decades before diagnosis and expand rapidly in the last two years.

The paper estimates the number of mutations needed to reconstruct clonal lineage trees from large datasets, showing that modest mutation counts suffice for accurate reconstruction.

LongPhase-S improves somatic variant accuracy by jointly estimating tumor purity and recalibrating variants using somatic haplotyping in long-read sequencing.

This study shows that mtDNA copy number increases with mutational load in cancer and influences disease progression and therapy response across multiple cancer types.

This study tracks genomic changes in TNBC during neoadjuvant chemotherapy, finding that whole-genome duplication events drive structural alterations and contribute to tumor evolution and response to treatment.

This paper develops a computational model to simulate clonal evolution in pediatric leukemia using a hybrid OU-Branching framework and applies it to clinical data to study relapse patterns and therapeutic response.

This study introduces a genome editing method to rapidly generate and test driver mutations in target genes, identifying novel resistance mutations in lung cancer.

This paper uses agent-based simulations to test models of lung tumour growth, finding that stringent cell competition after tumour size stabilization produces realistic clonal expansions and better estimates of driver mutation effects.