Biology subjects

Chu, P.-Y.

Publications and source records attributed to Chu, P.-Y..

5 recordsLinked to original sources

bioRxiv2026

Histopathology-inferred spatial transcriptomics characterizes the tumor microenvironment in 1,500 head and neck tumors and predicts clinical outcomes

Head and neck squamous cell carcinoma (HNSC) is a prevalent malignancy associated with poor prognosis despite recent therapeutic advances. We hypothesized that a comprehensive understanding of the spatial heterogeneity and organization of the tumor microenvironment (TME) can substantially improve risk stratification and prediction of treatment response in HNSC. As spatial transcriptomics (ST) remains labor-intensive and costly, we developed HEiST (H&E-Inferred Spatial Transcriptomics), a deep learning framework that predicts spatially resolved gene expression profiles directly from routine hematoxylin and eosin (H&E)-stained histology slides. After rigorous validation across two independent external ST cohorts, we applied HEiST to infer spatial transcriptomes across 1,500 HNSC patient tumors spanning two publicly available datasets and two newly generated cohorts, one treated with concurrent chemoradiotherapy (CCRT) and one with immunotherapy. This large-scale analysis uncovered reproducible spatial clusters characterizing the HNSC TME, defining two distinct prognostic Spatiotypes, Immune-Exhausted and Immune-Activated, with significantly distinct survival outcomes. Critically, spatial cluster composition accurately predicts HPV status and yields treatment response predictors for both CCRT/radiotherapy and immunotherapy that outperform costly gene-expression and direct image-based approaches. Notably, the ST cluster-based predictor of immunotherapy response markedly surpasses the performance of commonly used FDA-approved biomarkers, including CPS, TPS, and their combination. To the best of our knowledge, this represents the first virtual spatial profiling effort and the most comprehensive large-scale spatial TME analysis in HNSC to date. HEiST thus introduces a scalable, low-cost, and spatially grounded biomarker discovery for precision oncology in HNSC.

cancer biology↗

bioRxiv2025

Pan-cancer prediction of tumor immune activation and response to immune checkpoint blockade from tumor transcriptomics and histopathology

Accurately predicting which patients will respond to immune checkpoint blockade (ICB) remains a major challenge. Here, we present TIME_ACT, an unsupervised 66-gene transcriptomic signature of tumor immune activation derived from TCGA (The Cancer Genome Atlas) melanoma data. First, we demonstrate that TIME_ACT scores accurately identify tumors with activated immune microenvironments across different cancer types. Further, analysis of spatial features reveals that tumor microenvironment regions with dense lymphocyte infiltration near tumor cells have high TIME_ACT scores, successfully marking localized immune activation. Second, across 25 transcriptomic ICB cohorts encompassing nine cancer types, TIME_ACT achieves a mean AUC of 0.76 and a mean odds ratio of 5.77, significantly outperforming 30 established transcriptomic signatures and prediction methods for ICB response, including a recently developed foundation model for immunotherapy response prediction. Third, we show that TIME_ACT scores can be accurately inferred from routine tumor histopathology slides and that slide-inferred TIME_ACT scores predict ICB response across nine new independent patient cohorts spanning eight cancer types, achieving a mean AUC of 0.72 and a mean odds ratio of 4.99. These findings establish TIME_ACT as a robust, pan-cancer biomarker that enables accurate, low-cost, and clinically scalable prediction of ICB response from routine histopathology.

cancer biology↗

bioRxiv2024

Spatial Transcriptomic Profiling Of Advanced Ovarian Clear Cell Carcinoma Reveals Intra-Tumor Heterogeneity In Epithelial-Mesenchymal Gradient

Intratumoral heterogeneity is intrinsically comprised of molecular alterations of tumor cells and extrinsically from interconnections with microenvironments. This study explores the spatial heterogeneity of ovarian clear cell carcinoma (OCCC), a rare cancer with significance to East Asian women. We profile 21 primary-metastatic tumor pairs in a discovery cohort and 16 tumors in two validation cohorts using spatial transcriptomic (ST) platforms. Our integrative analysis revealed an inverse relationship between OXPHOS and inflammation along the EMT gradient. OCCC cells undergoing partial EMT have metabolic shifts and lose LCN2 expression, possibly via concomitant down-regulation of SOX9. Conversely, LCN2 expression correlated with OXPHOS-enriched tumor signature, low EMT, and better outcomes in OCCC. Single-cell ST profiling using CosMx further identifies nine spatially distinct cancer cell populations including the LCN2-high cancer subclone with a high epithelial score. SOX9 induction could partially restore epithelial-ness in LCN2-low cells suggesting that plasticity in OCCC is achieved via transcriptional reprogramming. Our findings provide further insights into epithelial-mesenchymal plasticity and the adaptive interactions between cancer cells and their microenvironments in OCCC.

cancer biology↗

bioRxiv2020

EGF-Activated Grb7 Confers to STAT3-Mediated EPHA4 Gene Expression in Regulating Lung Cancer Progression

Growth factor receptor bound protein-7 (Grb7) is a multi-domain signaling adaptor protein that regulates various cellular functions acting as an adaptor protein in relaying signal transduction. Although several studies indicated that Grb7 amplifies EGFR-mediated signaling in cancers, the detailed regulatory mechanism of whether and how Grb7 is involved in EGFR-mediated lung cancer progression remains unclear. Here, we demonstrate that EGF-regulated Grb7 phosphorylation promotes lung cancer progression through phosphorylation of STAT3. Intrinsically, EGF/EGFR signal is required for the formation of Grb7/STAT3 complex as well as its nuclear accumulation. Once in the nucleus, STAT3 interacts with EPHA4 promoter, which in turn affects the gene expression level of EPHA4 through transcriptional regulation. Functionally, EphA4 together with EGFR promotes cancer migration, proliferation, and anchorage-independent growth. Our study reveals a novel mechanism in which Grb7 contribute to lung cancer malignancies through its interaction with STAT3 that leads to sequential regulation of EPHA4 gene expression in an EGF/EGFR signal-dependent manner.

cancer biology↗

bioRxiv2020

Decoding the regulatory architecture of the maize leaf

The transcription regulatory network underlying essential and complex functionalities inside a eukaryotic cell is defined by the combinatorial actions of transcription factors (TFs). However, TF binding studies in plants are too few in number to produce a general picture of this complex regulatory netowrk. Here, we used ChIP-seq to determine the binding profiles of 104 TF expressed in the maize leaf. With this large dataset, we could reconstruct a transcription regulatory network that covers over 77% of the expressed genes, and reveal its scale-free topology and functional modularity like a real-world network. We found that TF binding occurs in clusters covering [~]2% of the genome, and shows enrichment for sequence variations associated with eQTLs and GWAS hits of complex agronomic traits. Machine-learning analyses were used to identify TF sequence preferences, and showed that co-binding is key for TF specificity. The trained models were used to predict and compare the regulatory networks in other species and showed that the core network is evolutionarily conserved. This study provided an extensive description of the architecture, organizing principle and evolution of the transcription regulatory network inside the plant leaf.

molecular biology↗