Biology subjects

Kim, Y.-N.

Publications and source records attributed to Kim, Y.-N..

4 recordsLinked to original sources

bioRxiv2026

Longitudinal transcriptomic analysis reveals adaptive transcript usage remodeling associated with PARP inhibitor resistance

Poly (ADP-ribose) polymerase inhibitors (PARPi) have revolutionized the treatment of ovarian and breast cancers with homologous recombination deficiency (HRD). Despite substantial initial efficacy, the frequent development of acquired resistance poses a critical clinical challenge, necessitating a deeper understanding of the underlying molecular mechanisms. While genomic profiling provides information on baseline HRD status, it is insufficient to capture the dynamic and functional adaptations accompanying resistance during treatment. In this study, we investigated the role of alternative splicing in PARPi resistance using longitudinal pre- and post-treatment RNA-sequencing data from an ovarian cancer patient cohort (n=40). We demonstrated that profiling alternative splicing dynamics and differentially used transcripts (DUTs) provides transcriptomic insights distinct from conventional differential gene expression analysis. Notably, we observed significant differences between acquired resistance (AR) and innate resistance (IR) groups. Specifically, longitudinal analysis revealed that AR tumors exhibit a distinct transcript usage pattern, characterized by an increase in canonical protein-coding transcripts involved in cell-cycle processes and a decrease in non-canonical non-coding transcripts within the homologous recombination pathway following treatment. Extending this comparison across AR and IR tumors at both pre- and post-treatment stages, these transcriptomic features were most pronounced in the AR post-treatment group. Further analysis of subsequent treatment outcomes showed that, among AR patients, greater resistance-associated transcriptomic remodeling was associated with poorer response and shorter progression-free survival. The resistance-associated transcriptomic signatures identified through longitudinal profiling were further recapitulated in an independent pre-treatment cohort (n=71; 43 AR and 28 IR), with more pronounced differences among BRCA wild-type patients. Taken together, our findings identify differential transcript usage as dynamic transcriptomic features associated with acquired PARPi resistance and establish a framework for utilizing longitudinal transcriptome dynamics to investigate the molecular basis of clinical drug resistance.

cancer biology↗

bioRxiv2025

A chromatin fiber model explains cell-free DNA fragmentation signatures of active regulatory elements

Circulating cell-free DNA (cfDNA) assays are being widely adopted in oncology and maternal-fetal medicine. Patterns of cfDNA fragmentation can provide useful information about gene regulation and expression in human disease from a blood draw. Here, we demonstrate that enhancer RNA expression - a marker of enhancer activity - can be inferred from local patterns of cfDNA fragmentation. We define a transcriptional activation score (TAS) that predicts expression of enhancers and genes based on cfDNA fragment sizes and positions near transcriptional start sites (TSSs). The TAS identifies activity of cancer-associated enhancers in patients with cancer, distinguishes clinically relevant cancer subtypes, and identifies activation of enhancers associated with treatment resistance and therapy response. We propose a simple model to account for our findings based on chromatin fiber structure and the depletion of H1 histone proteins near active TSSs. Our model provides a unified framework that reconciles seemingly conflicting observations from prior fragmentomics studies. Broadly, this work enables blood-based assessments of gene regulation in cancer and non-oncologic diseases to inform pathobiology, diagnosis, and treatment selection.

genomics↗

bioRxiv2025

Tissue specificity and chromosomal alterations shape divergent immune programs in HRD tumors

Homologous recombination deficiency (HRD) activates pro-inflammatory cGAS/STING signaling, positioning it as a biomarker for combining immune checkpoint blockade (ICB) and PARP inhibition (PARPi). However, the consequences of HRD on the immune landscape across cancers remain unclear. Here, we applied a pan-cancer HRD classifier to >10,000 tumors from The Cancer Genome Atlas and uncovered striking heterogeneity in immune activity. Compared to HR-proficient tumors, HRD tumors showed elevated inflammation in breast, ovarian, and endometrial cancers. These tumors exhibited robust activation of innate and adaptive immune pathways (IFN, NF-{kappa}B) and transcriptional hallmarks of senescence, angiogenesis, and adenosine signaling. In contrast, lung, head and neck, and melanoma HRD tumors displayed suppressed inflammation and evidence of immune escape through large-scale loss-of-heterozygosity (LOH) at IFNA/B, STING, and other loci. These tumors also frequently presented HLA LOH and oncogene amplifications, suggesting selection under immune pressure and replication stress. Together, our study resolves HRD tumors into two immune archetypes, immune-inflamed and immune-evasive, linked to chromosomal instability and lineage, informing biomarker-driven evaluation of immune checkpoint blockade/PARPi combinatorial therapies.

genomics↗

bioRxiv2023

Genomic hypomethylation in cell-free DNA predicts responses to checkpoint blockade in lung and breast cancer

Genomic hypomethylation has recently been identified as a determinant of therapeutic responses to immune checkpoint blockade (ICB). However, tumor tissue is often unattainable, and tissue-based methylation profiling suffers from low tumor purity. In this study, we developed an assay named iMethyl to estimate the genomic hypomethylation status from cell-free DNA (cfDNA) as well as tissue by deep targeted sequencing of young LINE-1 elements with > 400,000 reads per sample. iMethyl was applied to a total of 653 ICB samples encompassing lung cancer (cfDNA n=167; tissue n=137; cfDNA early during treatment n=40), breast cancer (cfDNA n=91; tissue n=50; PBMC n=50; cfDNA at progression n=44), and ovarian cancer (tissue n=74). iMethyl-tissue had better predictive power than tumor mutation burden and PD-L1 expression. Furthermore, iMethyl-liquid predicted ICB responses accurately regardless of the tumor purity of tissue samples. iMethyl-liquid was also able to monitor therapeutic responses early during treatment (3 or 6 weeks after initiation of ICB) and detect progressive hypomethylation accompanying tumor progression. In conclusion, our method allows for reliable noninvasive prediction, early evaluation, and monitoring of clinical responses to ICB therapy.

cancer biology↗