Biology subjects

Kim, T.-M.

Publications and source records attributed to Kim, T.-M..

4 recordsLinked to original sources

bioRxiv2026

Developmental plasticity of cancer-associated fibroblasts contributes to the malignant progression of gastric cancers

Cancer-associated fibroblasts are transcriptionally heterogeneous, yet how their reprogramming shapes gastric cancer progression remains unclear. Integrating single-cell and spatial transcriptomics, patient-derived fibroblast co-culture, and clinical cohorts, we identified a spatially organized continuum of extracellular matrix-producing fibroblasts. An inferred trajectory connected homeostatic ECMh through inflammatory-chemoattractive ECMi to tissue-remodeling ECMr, with increasing GLIS2 regulon activity and ECMr enrichment near the tumor-stroma interface. ECMr co-culture induced a non-EMT epithelial program characterized by coordinated laminin-332 subunit expression, hemidesmosome-associated genes, a matrix-remodeling protease profile, and reduced proliferative activity. Longitudinal imaging showed progressive cancer-cell elongation and positional displacement over the fibroblast layer. ECMr-associated GLIS2 regulon activity in resected primary tumors was associated with shorter peritoneal metastasis-free survival across two gastric cancer cohorts, with a similar association in colorectal cancer. A peritoneal metastasis recapitulated the corresponding stromal and epithelial organization. Together, these findings define an ECMr-centered stromal-epithelial wound-repair circuit linked to subsequent peritoneal dissemination.

cancer biology↗

bioRxiv2024

Evaluation of false positive and false negative errors in targeted next generation sequencing

BackgroundAlthough next generation sequencing (NGS) has been adopted as an essential diagnostic tool in various diseases, NGS errors have been the most serious problem in clinical implementation. Especially in cancers, low level mutations have not been easy to analyze, due to the contaminating normal cells and tumor heterozygosity. ResultsIn targeted NGS (T-NGS) analyses for reference-standard samples containing mixtures of homozygote H. mole DNA with blood genomic DNA at various ratios from four certified NGS service providers, large differences in the lower detection limit of variants (16.3 times, 1.51[~]24.66%) and the false positive (FP) error rate (4280 times, 5.814 x 10-4 [~]1.359 x 10-7) were found. Employment of the commercially available Dragen system for bioinformatic analyses reduced FP errors in the results from companies BB and CC, but the errors originating from the NGS raw data persisted. Bioinformatic conditional adjustment to increase sensitivity (less than 2 times) led to a much higher FP error rate (610[~]8200 times). In addition, problems such as biased preferential reference base calls during bioinformatic analysis and high-rate FN errors in HLA regions were found in the NGS analysis. ConclusionT-NGS results from certified NGS service providers can be quite various in their sensitivity and FP error rate, suggesting the necessity of further quality controls for clinical implementation of T-NGS. The present study also suggests that mixtures of homozygote and heterozygote DNAs can be easily employed as excellent reference-standard materials for quality control of T-NGS.

genomics↗

bioRxiv2020

Ultra-fast Prediction of Somatic Structural Variations by Reduced Read Mapping via Pan-Genome k-mer Sets

Genome rearrangements often result in copy number alterations of cancer-related genes and cause the formation of cancer-related fusion genes. Current structural variation (SV) callers, however, still produce massive numbers of false positives (FPs) and require high computational costs. Here, we introduce an ultra-fast and high-performing somatic SV detector, called ETCHING, that significantly reduces the mapping cost by filtering reads matched to pan-genome and normal k-mer sets. To reduce the number of FPs, ETCHING takes advantage of a Random Forest classifier that utilizes six breakend-related features. We systematically benchmarked ETCHING with other SV callers on reference SV materials, validated SV biomarkers, tumor and matched-normal whole genomes, and tumor-only targeted sequencing datasets. For all datasets, our SV caller was much faster ([≥]15X) than other tools without compromising performance or memory use. Our approach would provide not only the fastest method for largescale genome projects but also an accurate clinically practical means for real-time precision medicine.

bioinformatics↗

bioRxiv2020

Correlation-based and feature-driven mutation signature analyses to identify genetic features associated with DNA mutagenic processes in cancer genomes

Mutation signatures represent unique sequence footprints of somatic mutations resulting from specific DNA mutagenic and repair processes; however, their causal associations and potential utility for genome research remain largely unknown. In this study, we performed PanCancer-scale correlative analyses to identify the genomic features associated with tumor mutation burdens (TMB) and individual mutation signatures. We observed that TMB was correlated with tumor purity, ploidy, and the level of aneuploidy, as well as with the expression of cell proliferation-related genes representing genomic covariates in evaluating TMB. Correlative analyses of mutation signature levels with genes belonging to DNA damage-repair processes revealed that deficiencies of NHEJ1 and ALKBH3 may elevate TMB levels in cancer genomes accompanying APOBEC overactivity and DNA mismatch repair deficiency, respectively. We further employed a strategy to identify feature-driven, de novo mutation signatures and demonstrated they can be reconstructed using known causal features such as APOBEC overexpression, MLH1 underexpression, POLE mutations, and the level of homologous recombination deficiency. We further demonstrated, that tumor hypoxia-related mutation signatures are similar to those associated with APOBEC suggesting that APOBEC-related mutagenic activity mediates hypoxia-related mutational consequences in cancer genomes, and also, that mutation signatures can be further used to predict hypoxic tumors. Taken together, our study advances mutation signature-level mechanistic insights in cancer genomes, extending categories of cancer-relevant mutation signatures and their potential biological implications. Author summaryMutation signature analysis is powerful in deciphering the causative mutagenic events and their contributions in individual cancer genomes, but the causal relationship of individual mutation signatures are still largely unknown. PanCancer-scaled correlative analysis revealed mutation resource candidates in cancer genomes such as NHEJ1 and ALKBH3 deficiencies that may facilitate the accumulation of mutations in the setting of APOBEC overactivity and DNA mismatch repair deficiency, respectively. A feature-driven mutation discovery approach was employed to identify the mutation signatures representing homologous recombination deficiency and tumor hypoxia, the extent of which may serve as mutation-based phenotypic measures, previously estimated by DNA copy number alterations and mRNA expression signatures, respectively. Our study advances our understanding into the mechanistic insights of mutation signatures and proposes a method to utilize somatic mutations as a molecular proxy in terms of mutation signatures.

genomics↗