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Mingxiang Teng

Publications and source records attributed to Mingxiang Teng.

2 recordsLinked to original sources

Integrated Genomic and Epigenomic Analysis Reveals Epigenetic Plasticity in Disease Progression and Multidrug Resistance in Multiple Myeloma.

UNLABELLED: Multiple myeloma is marked by recurrent cytogenetic abnormalities and mutations that accumulate as the disease progresses. In this study, we sought to elucidate the transitions driving tumorigenesis and therapy resistance in multiple myeloma using a unique cohort of nearly 900 patients spanning premalignant to late-stage refractory multiple myeloma, comprehensively characterized at molecular and clinical levels. Waves of epigenetic dysregulation drove these critical transitions. In this paradigm, genomic and cytogenetic events unlocked epigenetic plasticity, reshaping multiple myeloma cell biology to evade tumor microenvironment constraints and therapeutic pressures. Functional perturbation studies in an isogenic proteasome inhibitor-resistant cell line model demonstrated enhanced reliance on transcriptional cofactors, supporting a mechanistic link between chromatin plasticity and therapy adaptation. Collectively, these findings support a unifying framework in which genomic heterogeneity unlocks gene regulatory plasticity, enabling plasma cells (PC) to evade microenvironmental constraints and therapeutic pressure. These results provide a mechanistic explanation for sequential relapse without new genomic alterations and nominate epigenetic plasticity-mediated PC adaptation as a therapeutic vulnerability in the heterogeneous genetic background of multiple myeloma. SIGNIFICANCE: Assembly and analysis of a multiple myeloma cohort spanning the continuum from premalignant to late relapse that integrates bulk transcriptomics with single-cell multiomic data provides insights into disease progression and epigenetic plasticity.

Multiple Myeloma

qcCHIP: an R package to identify clonal hematopoiesis variants using cohort-specific data characteristics.

SUMMARY: Clonal hematopoiesis (CH) is a molecular biomarker associated with various adverse outcomes in both healthy individuals and those with underlying conditions, including cancer. Detecting CH usually involves genomic sequencing of individual blood samples followed by robust bioinformatics data filtering. We report an R package, qcCHIP, a bioinformatics pipeline that implements permutation-based parameter optimization to guide quality control filtering and cohort-specific CH identification. We benchmark qcCHIP under various data settings, including different sequencing depths, ranges of cohort sizes, with and without normal-tumor paired samples, and across different cancer types. We show that qcCHIP allows users to customize analysis needs to generate CH calls based on cohort-specific data characteristics. AVAILABILITY AND IMPLEMENTATION: qcCHIP R package is freely accessible at GitHub https://github.com/tenglab/qcCHIP and DOI: 10.5281/zenodo.16421861.

Humans