Biology subjects

Ha, P.

Publications and source records attributed to Ha, P..

2 recordsLinked to original sources

bioRxiv2018

Subjugation of TGFβ Signaling by Human Papilloma Virus in Head and Neck Squamous Cell Carcinoma Shifts DNA Repair from Homologous Recombination to Alternative End-Joining

Purpose: Following cytotoxic therapy, 70% of patients with human papillomavirus (HPV) positive oropharyngeal head and neck squamous cell carcinoma (HNSCC) are alive at 5 years compared to 30% of those with similar HPV-negative cancer, which is thought to be due to dysregulation of DNA repair. Loss of transforming growth factor {beta} (TGF{beta}) signaling is a poorly studied consequence of HPV that could contribute to this phenotype.\n\nExperimental Design: Human HNSCC cell lines (n=9), patient-derived xenografts (n=9), tissue microarray (n=194), TCGA expression data and primary tumor specimens (n=10) were used to define the relationship between TGF{beta} competency, response to DNA damage, and type of DNA repair.\n\nResults: Analysis of HNSCC specimens in situ and in vitro showed that HPV associates with loss of TGF{beta} signaling that increases the response to radiation or cisplatin. TGF{beta} suppressed miR-182 that inhibited both BRCA1, necessary for homologous recombination repair, and FOXO3, which is required for ATM kinase activity. TGF{beta} signaling blockade by either HPV or inhibitors released this control, compromised HRR and increased response to PARP inhibition. Antagonizing miR-182 rescued the homologous recombination deficit in HPV+ cells. Loss of TGF{beta} signaling unexpectedly increased error-prone, alternative end-joining repair.\n\nConclusions: HPV-positive HNSCC cells are unresponsive to TGF{beta}. Abrogated TGF{beta} signaling compromises homologous recombination and shifts reliance on alt-EJ repair that provides a mechanistic basis for sensitivity to PARP inhibitors. The effect of HPV in HNSCC provides critical validation of TGF{beta}s role in DNA repair proficiency and further raises the translational potential of TGF{beta} inhibitors in cancer therapy.

cancer biology↗

bioRxiv2016

Splice Expression Variation Analysis (SEVA) for Differential Gene Isoform Usage in Cancer

MotivationCurrent bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches.\n\nResultsWe introduce Splice Expression Variability Analysis (SEVA) to model increased heterogeneity of splice variant usage between conditions (e.g., tumor and normal samples). SEVA uses a rank-based multivariate statistic that compares the variability of junction expression profiles within one condition to the variability within another. Simulated data show that SEVA is unique in modeling heterogeneity of gene isoform usage, and benchmark SEVAs performance against EBSeq, DiffSplice, and rMATS that model differential isoform usage instead of heterogeneity. We confirm the accuracy of SEVAin identifying known splice variants in head and neck cancer and perform cross-study validation of novel splice variants. A novel comparison of splice variant heterogeneity between subtypes of head and neck cancer demonstrated unanticipated similarity between the heterogeneity of gene isoform usage in HPV-positive and HPV-negative subtypes and anticipated increased heterogeneity among HPV-negative samples with mutations in genes that regulate the splice variant machinery.\n\nConclusionThese results show that SEVA accurately models differential heterogeneity of gene isoform usage from RNA-seq data.\n\nAvailabilitySEVA is implemented in the R/Bioconductor package GSReg.\n\nContactbahman@jhu.edu, favorov@sensi.org, ejfertig@jhmi.edu

genomics↗