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CHENG, S.

Publications and source records attributed to CHENG, S..

4 recordsLinked to original sources

INSM1 Regulates Neuroendocrine Plasticity and Tumor Progression in Prostate Cancer

AbstractNeuroendocrine prostate cancer (NEPC) is a highly aggressive and therapy-resistant subtype that arises from adenocarcinoma through lineage plasticity; however, the molecular mechanisms driving this transition remain incompletely defined. Insulinoma-associated protein 1 (INSM1), a zinc-finger transcription factor and established neuroendocrine lineage marker, has been implicated in a variety of neuroendocrine malignancies, yet its functional contribution to NEPC progression is not well understood. In this study, we demonstrate that INSM1 is consistently upregulated across NEPC patient tumors and experimental models, including both ASCL1 and NEUROD1 molecular subtypes, as revealed by integrated bulk and single-cell transcriptomic analyses. Functional studies revealed that INSM1 is sufficient to induce and necessary to maintain neuroendocrine lineage programs in prostate cancer, as overexpression promoted and depletion suppressed neuroendocrine-associated transcriptional networks. Mechanistically, pro-neural transcription factors, including ASCL1, NEUROD1, NEUROG3, and MYCN, directly or indirectly activate INSM1 expression, positioning it as a critical downstream effector of neuroendocrine lineage specification. Therapeutically, we identify homo-harringtonine (HHT), an FDA-approved protein synthesis inhibitor, as a potent suppressor of INSM1. HHT selectively reduces viability of INSM1-high NEPC cells at nanomolar concentrations, promotes ubiquitin-mediated degradation of INSM1, and significantly inhibits tumor growth in vivo. Notably, INSM1 depletion further enhances cellular sensitivity to HHT treatment. Collectively, our findings establish INSM1 as a key regulator of neuroendocrine plasticity and a promising therapeutic vulnerability in NEPC, providing a rationale for targeting INSM1 to suppress tumor progression.

cancer biology↗

ATHENA: A deep learning-based AI for functional prediction of genomic mutations and synergistic vulnerabilities in prostate cancer

Identifying functional mutations that drive therapy resistance remains a major challenge in prostate cancer. Large-scale sequencing often produces extensive lists of mutations but provides limited insight into which alterations are functionally relevant. To overcome this gap, we developed ATHENA (Attention-based Therapeutic Network Analyzer), a deep learning-based AI framework that predicts the functional impact of genomic mutations and reveals their synergistic vulnerabilities. Integrated with our RNA/DNA-informed variant discovery pipeline OncoVar, ATHENA models nonlinear dependencies among mutations to distinguish driver events from passenger variants. Trained on large multi-cohort datasets and interpreted using SHAP analysis, ATHENA not only stratifies patients by clinical outcomes but also predicts which specific mutations alter tumor behavior and therapy response, enabling direct validation through base editing experiments. Applied to prostate cancer progression models, the OncoVar-ATHENA framework identified stage-specific driver signatures across castration-resistant, AR-variant-driven, and metastatic disease, and uncovered cooperative interactions such as SYVN1-STC2 that promote tumor proliferation. By moving beyond simple mutation identification, ATHENA enables functional prediction of genomic interactions. This approach accelerates the discovery of actionable targets and provides a foundation for rational design of next-generation combination therapies in advanced prostate cancer.

cancer biology↗

SCAPE: An AI-Driven Platform for Comprehensive Single-Cell Data Analysis

Single-cell RNA sequencing (scRNA-seq) has transformed the study of cellular heterogeneity, but downstream analysis remains fragmented and technically demanding. Current pipelines often lack analytical diversity, require programming expertise, and offer limited integration of advanced methods. To address these challenges, we developed SCAPE, an AI-driven automated and interactive platform that unifies multi-omics, multi-resource, and multi-modal single-cell data analysis. SCAPE provides platform-independent installation, customizable workflows, and integration of R- and Python-based tools. Beyond Seurat and Scanpy, it incorporates modules for transcription factor and pathway inference, pseudotime trajectory reconstruction, spatial transcriptomics deconvolution, and cell-cell communication analysis. To demonstrate SCAPE, we curated a unified atlas of lung cancer progression in human patients and mouse models, spanning primary tumors and metastatic sites. The platform enabled harmonized integration, regulatory program inference, and spatial mapping, revealing conserved epithelial programs that promote metastatic seeding and organ-specific adaptations driven by microenvironments. Collectively, SCAPE offers an accessible and comprehensive framework for single-cell analysis, providing new insights into cancer progression and broad utility across biological systems.

cancer biology↗

HuPSA and MoPSA Atlases Revealed Novel Cell Populations and Lineage Plasticity at Single-Cell Resolution in Prostate Cancer

Recent advancements in single-cell RNA sequencing (scRNAseq) have facilitated the discovery of previously unrecognized subtypes within prostate cancer (PCa), offering new insights into disease heterogeneity and progression. In this study, we integrated scRNAseq data from multiple studies, comprising both publicly available cohorts and data generated by our research team, and established the HuPSA (Human Prostate Single cell Atlas) and the MoPSA (Mouse Prostate Single cell Atlas) datasets. Through comprehensive analysis, we identified two novel double-negative PCa populations: KRT7 cells characterized by elevated KRT7 expression, and progenitor-like cells marked by SOX2 and FOXA2 expression, distinct from NEPCa, and displaying stem/progenitor features. Furthermore, HuPSA-based deconvolution allowed for the re-classification of human PCa specimens, validating the presence of these novel subtypes. Leveraging these findings, we developed a user-friendly web application, "HuPSA-MoPSA" (https://pcatools.shinyapps.io/HuPSA-MoPSA/), for visualizing gene expression across all newly-established datasets. Our study provides comprehensive tools for PCa research and uncovers novel cancer subtypes that can inform clinical diagnosis and treatment strategies. Graph abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=161 SRC="FIGDIR/small/553009v3_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@efa7c3org.highwire.dtl.DTLVardef@1ef0c4eorg.highwire.dtl.DTLVardef@110f8c0org.highwire.dtl.DTLVardef@13b2ed4_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗