Biology subjects

Wang, X. W.

Publications and source records attributed to Wang, X. W..

7 recordsLinked to original sources

bioRxiv2026

Cell type-centric interaction networks define spatial architecture of intrahepatic cholangiocarcinoma

Tumor spatial organization critically shapes disease progression and therapeutic response, yet remains poorly defined. Intrahepatic cholangiocarcinoma (iCCA), a rare and aggressive liver malignancy with extensive stromal and immune remodeling, provides a compelling model to study tumor architecture. We generated a single-cell spatial atlas of 1 million cells from 131 iCCA patients using 53-plex spatial proteomics. To systemically characterize tumor spatial organization, we developed a graph-based deep learning framework to define cell type-centric interaction networks, identifying 41 distinct multicellular spatial patterns. Integration of these networks revealed higher-order tumor- and immune-enriched microenvironments associated with patient outcomes. Notably, neutrophil-associated tumor-enriched and tumor-desert microenvironments delineated patient groups with opposing clinical outcomes and distinct neutrophil states. These findings were validated by single-cell spatial transcriptomic profiling of 6 million cells from 162 iCCA patients. Together, this study defines the spatial architecture of iCCA and provides a comprehensive resource for exploring tumor spatial organization.

cancer biology↗

bioRxiv2026

Differential serum binding patterns predicting healthy subjects and cancer patients

A viral exposure signature (VES) has been previously described predicting the development of Hepatocellular carcinoma (HCC) in at-risk patients. This has been achieved by a serological profiling of the viral infection history using a synthetic human virome including >100k epitopes (VirScan). In the present study we applied the same VirScan strategy to identify a differential serum binding pattern (DSBP) for classifying patients of different cancer types from healthy individuals. In particular, the healthy group included both age-matched (ADULTS) as well as elderly (ELDERS) individuals, the latter counting also nonagenarians and centenarians. The class comparison performed with serological data show DSBPs supporting class predictions, as confirmed by the receiver operating characteristic (ROC) curve analysis. Antibody responses supporting the class predictions are specific to peptides from persistent herpesviruses, acute-infecting viruses and, consistently in all comparisons, human respiratory syncytial virus (HRSV). Strikingly, the DSB of the ELDERS vs. CANCER comparison is characterized by higher titers in the healthy subjects; on the contrary, the DSB of the ADULTS vs. CANCER comparison is characterized by lower titers in the healthy subjects. Overall, the results show a differential serological binding pattern predicting healthy individuals (ADULTS or ELDERS) from patients with different types of cancer. Such results provide the first evidence suggesting a close link between anti-microbial immunity and cancer development. They may be of the highest relevance in terms of predictive, diagnostic and/or prognostic impact in oncology.

immunology↗

bioRxiv2026

Enteroviral epitope mimicry enables NK cell-mediated targeting of ASPH in hepatocellular carcinoma

Cancer development is shaped by host-microbe interactions, including viral infections. While several viruses are established oncogenic drivers, their potential protective roles in cancer remain unclear. Here we identify a dominant antibody response to CE1, a consensus epitope of enterovirus and rhinovirus, that is associated with reduced hepatocellular carcinoma (HCC) incidence and mortality. Anti-CE1 antibodies selectively recognize HCC cells and mediate anti-tumor activity through NK cell-mediated antibody-dependent cellular cytotoxicity (ADCC). Mechanistically, anti-CE1 antibodies cross-react with aspartate {beta}-hydroxylase (ASPH), with CE1-ASPH sequence homology underpinning tumor recognition and cytotoxicity. Clinically, ASPH is aberrantly upregulated in HCC and correlates with inferred NK cell-associated ADCC activity and improved survival in CE1-seropositive patients. Collectively, these findings reveal a mechanism by which antiviral humoral immunity confers cancer protection through molecular mimicry and highlight anti-CE1 immunity as a potential therapeutic strategy in HCC.

cancer biology↗

bioRxiv2025

Multimodal Approach for Identification and Validation of Hepatocellular Carcinoma Targets for Radiotheranostics

Identifying tumor selective targets is critical for the development of precision diagnostic and therapeutic agents in oncology. Despite advances in precision oncology elsewhere, there are no FDA-approved hepatocellular carcinoma (HCC)-selective treatments. HCC is the most common type of liver cancer and accounts for significant morbidity and mortality worldwide. Here, we sought to integrate bulk (371 cases) and single cell RNA sequencing (scRNAseq, n=2 datasets, 34 cases, 102,956 cells) of patient samples to enrich for molecules that are overexpressed in HCC, which could serve as HCC-selective targets. To guide definitions of tumor and normal cell clusters with higher fidelity, we also imported a normal liver scRNAseq dataset. Using this integrated approach, we identified several HCC-selective plasma membrane molecules. To validate these targets, we performed immunohistochemical staining of HCC and normal tissue microarrays and confirmed HCC-selective staining of identified targets. Next, we verified the presence of these targets in several commercially available HCC cell lines by flow cytometry and western blot. Finally, we designed, engineered, and tested novel antibody-based positron emission tomography (immunoPET) agents to these targets in various murine models of liver cancer. Our findings confirm that we can leverage this multimodal approach to identify and validate of HCC-selective targets, which can be used to develop tumor-selective diagnostic and therapeutic radiopharmaceuticals, or radiotheranostics, and other precision oncology agents. One Sentence SummaryA multimodal pipeline defines and validates tumor-selective surface targets for radiotheranostic use in hepatocellular carcinoma.

cancer biology↗

bioRxiv2020

Single-cell atlas of tumor clonal evolution in liver cancer

Tumor evolution is a key feature of tumorigenesis and plays a pivotal role in driving intratumor heterogeneity, treatment failure and patients prognosis. Here we performed single-cell transcriptome profiling of 46 primary liver cancers from 37 patients enrolled for interventional studies. We surveyed the landscape of ~57,000 malignant and non-malignant cells and determined tumor cell clonality by developing a machine learning-based consensus clustering method. We found evidence of tumor cell branching evolution using hierarchical clustering, RNA velocity as well as reverse graph embedding methods. Interestingly, an increasing tumor cell clonality was tightly linked to patients prognosis, accompanied by a polarized immune cell landscape. We identified osteopontin as a key player for tumor cell evolution and microenvironmental reprogramming. Our study offers insight into the collective behavior of tumor cell communities in liver cancer as well as potential drivers for tumor evolution in response to therapy.

cancer biology↗

bioRxiv2019

Functional Genomic Complexity Defines Intratumor Heterogeneity and Tumor Aggressiveness in Liver Cancer

BackgroundChronic inflammation and chromosome aneuploidy are major traits of primary liver cancer (PLC), which represent the second most common cause of cancer related death worldwide. Increased cancer fitness and aggressiveness of PLC may be achieved by enhancing tumoral genomic complexity that alters tumor biology.\n\nMethodHere, we developed a scoring method, namely functional genomic complexity (FGC), to determine the degree of molecular heterogeneity among 580 liver tumors with diverse ethnicities and etiologies by assessing integrated genomic and transcriptomic data.\n\nResultsWe found that tumors with higher FGC scores are associated with chromosome instability and TP53 mutations, and a worse prognosis, while tumors with lower FGC scores have elevated infiltrating lymphocytes and a better prognosis. These results indicate that FGC scores may serve as a surrogate to define genomic heterogeneity of PLC linked to chromosomal instability and evasion of immune surveillance.\n\nConclusionOur findings demonstrate an ability to define genomic heterogeneity and corresponding tumor biology of liver cancer based only on bulk genomic and transcriptomic data. Our data also provide a rationale for applying this approach to survey liver tumor immunity and to stratify patients for immune-based therapy.\n\nSTATEMENT OF SIGNIFICANCEGenomic heterogeneity contributes to therapeutic failure and poor outcome in patients with liver cancer and poses a challenge in defining targeted therapy. Our findings demonstrate an ability to define genomic heterogeneity and corresponding tumor biology of liver cancer based only on bulk genomic and transcriptomic data. Our data also provide a rationale for applying this approach to survey liver tumor immunity and to stratify patients for immune-based therapy. \\body

genomics↗

bioRxiv2019

MS.liverK: an R package for transcriptome-based computation of molecular subtypes and functional signatures in liver cancer

SummaryLiver cancer is a highly heterogeneous disease in terms of etiology, tissue and cellular morphology, tumor molecular characteristics, microenvironment composition and prognosis. Several studies, based on tumor gene-expression profiling (GEP) data, have dissected the molecular heterogeneity of liver cancer. They resulted in various tools, either delineating homogeneous tumor subtypes or calculating molecular scores of prognostic or biological functions. Here, we present MS.liverK, an easy-to-use R package providing a comprehensive implementation of these tools, for research use. Availability and implementationThe MS.liverK R package is available from GitHub (https://github.com/cit-bioinfo/MS.liverK).

bioinformatics↗