Biology subjects

Yang, M.-H.

Publications and source records attributed to Yang, M.-H..

6 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↗

bioRxiv2026

TIGIT-NECTIN2/3 signaling preserves ignorant CD8⁺ T cells for favorable immune checkpoint outcomes in HBV-related hepatocellular carcinoma

BackgroundImmune checkpoint inhibitors (ICIs) have revolutionized cancer therapy by restoring anti-tumor immunity. However, persistent antigen exposure drives T cell exhaustion, limiting the effectiveness of ICIs. Ignorant T cells are antigen-specific T cells that maintain a naive state by regaining stem-like properties, allowing them to remain fully responsive to subsequent immunization. Virus-related hepatocellular carcinoma (HCC) demonstrates superior responses to ICIs compared to non-viral HCC, prompting us to investigate whether immunologically ignorant T cells exist in HBV-associated HCC and represent a promising target for improving immunotherapy outcomes. MethodsSingle-cell RNA sequencing (scRNA-seq) was performed on tumor tissues from patients with HBV-associated HCC. For validation, immunostaining was conducted on the discovery cohort and an independent cohort of 16 non-B non-C HCC and 22 HBV HCC. The enrichment of TIGIT and NECTIN3 in the proposed ignorant T cell was further validated using the TCGA database. ResultsscRNA-seq identified distinct HBV-infected HCC populations and revealed NECTIN3 upregulation in HBV-enriched subsets. CellChat analysis uncovered a novel NECTIN3-TIGIT tumor-immune interaction in HBV-enriched subsets, which shifted toward TIGIT-NECTIN2 as viral transcription declines. Trajectory analysis revealed the emergence of ignorant CD8 T cells following T cell exhaustion. TIGIT-NECTIN2/3 interactions deliver a weak exhaustion signal. This allows T cells to survive and regain naive-like properties as ignorant cells. Integration of bulk RNA-seq data identified CD24, STMN1, and EZH2 as potential biomarkers of ignorant CD8 T cells. ConclusionsTIGIT-NECTIN2/3 interactions present a promising axis for preserving immunologically ignorant T cells and sustaining ICI responsiveness in HBV-associated HCC.

immunology↗

bioRxiv2025

Cooperativity between Ras pathway mutations in colonic tumorigenesis

The Kirsten rat sarcoma (KRAS) gene is the most frequently mutated oncogene in colorectal cancer (CRC). We previously characterized two activating alleles of KRAS, A59T and A59E, that show impaired BRAF dimerization. These alleles of KRAS are enriched in CRC tumors with genetic alterations in genes that regulate MAPK signaling (e.g. EGFR, NF1). Using a new conditional mouse model of K-Ras (LSL-K-Ras A59E) we show that, despite its inability to universally activate RAF kinases, K-Ras A59E disturbs colon epithelium and decreases mouse survival in a tumor model. By combining LSL-K-Ras A59E mice with a conditional knockout of Nf1, we demonstrate cooperation between these alleles at multiple levels. Consistent with cooperation and clinical observations, we show that K-Ras A59E neither promotes EGF independence of organoid growth, nor confers intrinsic resistance to EGFR inhibition. Thus, our data provide a deeper understanding of the role of RAF isoforms in mutant KRAS driven CRC and argue for the use of anti-EGFR therapies against some Ala59 mutant alleles of KRAS. Statement of significanceOur studies on oncogenic K-RAS mutants demonstrate that the biochemical properties of a mutant oncoprotein are reflected in the somatic genetics of cancer, in particular in the cooperating mutations that occur. These mutant-specific genetic interactions influence therapeutic responses. As such, KRAS mutation may not be a univariate predictor of response to EGFR inhibition.

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↗

bioRxiv2020

A Statistical Framework for QTL Hotspot Detection

Quantitative trait loci (QTL) hotspots (genomic locations enriched in QTL) are a common and notable feature when collecting many QTL for various traits in many areas of biological studies. The QTL hotspots are important and attractive since they are highly informative and may harbor genes for the quantitative traits. So far, the current statistical methods for QTL hotspot detection use either the individual-level data from the genetical genomics experiments or the summarized data from public QTL databases to proceed with the detection analysis. These detection methods attempt to address some of the concerns, including the correlation structure among traits, the magnitude of LOD scores within a hotspot and computational cost, that arise during the process of QTL hotspot detection. In this article, we describe a statistical framework that can handle both types of data as well as address all the concerns at a time for QTL hotspot detection. Our statistical framework directly operates on the QTL matrix and hence has a very cheap computation cost, and is deployed to take advantage of the QTL mapping results for assisting the detection analysis. Two special devices, trait grouping and top {gamma}n, profile, are introduced into the framework. The trait grouping attempts to group the closely linked or pleiotropic traits together to take care of the true linkages and cope with the underestimation of hotspot thresholds due to non-genetic correlations (arising from ignoring the correlation structure among traits), so as to have the ability to obtain much stricter thresholds and dismiss spurious hotspots. The top {gamma}n, profile is designed to outline the LOD-score pattern of a hotspot across the different hotspot architectures, so that it can serve to identify and characterize the types of QTL hotspots with varying sizes and LOD score distributions. Real examples, numerical analysis and simulation study are performed to validate our statistical framework, investigate the detection properties, and also compare with the current methods in QTL hotspot detection. The results demonstrate that the proposed statistical framework can effectively accommodate the correlation structure among traits, identify the types of hotspots and still keep the notable features of easy implementation and fast computation for practical QTL hotspot detection.

genetics↗

bioRxiv2018

A Statistical Procedure for Genome-wide Detection of QTL Hotspots Using Public Databases with Application to Rice

Genome-wide detection of quantitative trait loci (QTL) hotspots underlying variation in many molecular and phenotypic traits has been a key step in various biological studies since the QTL hotspots are highly informative and can be linked to the genes for the quantitative traits. Several statistical methods have been proposed to detect QTL hotspots. These hotspot detection methods rely heavily on permutation tests performed on summarized QTL data or individual-level data (with genotypes and phenotypes) from the genetical genomics experiments. In this article, we propose a statistical procedure for QTL hotspot detection by using the summarized QTL (interval) data collected in public web-accessible databases. First, a simple statistical method based on the uniform distribution is derived to convert the QTL interval data into the expected QTL frequency (EQF) matrix. And then, to account for the correlation structure among traits, the QTLs for correlated traits are grouped together into the same categories to form a reduced EQF matrix. Furthermore, a permutation algorithm on the EQF elements or on the QTL intervals is developed to compute a sliding scale of EQF thresholds, ranging from strict to liberal, for assessing the significance of QTL hotspots. With grouping, much stricter thresholds can be obtained to avoid the detection of spurious hotspots. Real example analysis and simulation study are carried out to illustrate our procedure, evaluate the performances and compare with other methods. It shows that our procedure can control the genome-wide error rates at the target levels, provide appropriate thresholds for correlated data and is comparable to the methods using individual-level data in hotspot detection. Depending on the thresholds used, more than 100 hotspots are detected in GRAMENE rice database. We also perform a genome-wide comparative analysis of the detected hotspots and the known genes collected in the Rice Q-TARO database. The comparative analysis reveals that the hotspots and genes are conformable in the sense that they co-localize closely and are functionally related to relevant traits. Our statistical procedure can provide a framework for exploring the networks among QTL hotspots, genes and quantitative traits in biological studies. The R codes that produce both numerical and graphical outputs of QTL hotspot detection in the genome are available on the worldwide web http://www.stat.sinica.edu.tw/~chkao/.

genetics↗