Biology subjects

Rui, H.

Publications and source records attributed to Rui, H..

5 recordsLinked to original sources

bioRxiv2026

SPTBN2 promotes an immunosuppressive tumor microenvironment and cross-resistance to anti-cancer therapies

Immunosuppressive tumor microenvironment (TME) inactivates CD8+ cytotoxic lymphocytes (CTLs). Here, we identify SPTBN2 spectrin as a key immunosuppressive regulator induced in CTLs in response to nutritional deficit. In human pancreatic and colorectal cancers, SPTBN2 expression negatively correlated with CTL infiltration and patients survival. In TME of mouse pancreatic and colorectal adenocarcinomas, SPTBN2 inactivated intratumoral CTLs, stimulated tumor growth and conferred cross-resistance to anti-cancer therapies. SPTBN2 knockout protected CAR T-cells from trogocytosis and increased their memory state. SPTBN2 maintained levels of cell surface proteins such as BTLA that undermine CAR T-cell cytotoxicity and promote exhaustion. Re-expression of BTLA largely reversed phenotypes in SPTBN2-deficient CAR T-cells. In manufactured CAR T cells, SPTBN2 was associated with their clinical failure in pediatric patients with leukemia. Accordingly, ablation of SPTBN2 in CAR T-cells increased their cytotoxicity, in vivo persistence and therapeutic effects indicating that SPTBN2 can be targeted to increase the efficacy of anti-cancer therapies.

cancer biology↗

bioRxiv2022

Machine Learning Reveals Genetic Modifiers of the Immune Microenvironment of Cancer

Heritability in the immune tumor microenvironment (iTME) has been widely observed, yet remains largely uncharacterized and systematic approaches to discover germline genetic modifiers of the iTME still being established. Here, we developed the first machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWAS) for breast cancer (BrCa) incidence and outcome. A random forest model was trained on a positive set of immune-oncology (I-O) targets using BrCa and immune phenotypes from genetic perturbation studies, comparative genomics, Mendelian genetics, and colocalization with autoimmunity and inflammatory disease risk loci. Compared with random negative sets, an I-O target probability score was assigned to the 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Pathway analysis of the most probable I-O targets revealed significant enrichment in drivers of BrCa and immune biology, including the LSP1 locus associated with BrCa incidence and outcome. Quantitative cell type-specific immunofluorescent imaging of 1,109 BrCa patient biopsies revealed that LSP1 expression is restricted to tumor infiltrating leukocytes and correlated with BrCa patient outcome (HR = 1.73, p < 0.001). The human BrCa patient-based genomic and proteomic evidence, combined with phenotypic evidence that LSP1 is a negative regulator of leukocyte trafficking, prioritized LSP1 as a novel I-O target. Finally, a novel comparative mapping strategy using mouse genetic linkage revealed TLR1 as a plausible therapeutic candidate with strong genomic and phenotypic evidence. Collectively, these data demonstrate a robust and flexible analytical framework for functionally fine-mapping GWAS risk loci to identify the most translatable therapeutic targets for the associated disease.

cancer biology↗

bioRxiv2020

RNA-binding protein FXR1 drives cMYC translation by mRNA circularization through eIF4F recruitment in ovarian cancer

BackgroundThe RNA-binding protein FXR1 (fragile X-related protein 1) has been implicated as an important regulator of post-transcriptional changes of mRNAs. However, its role in mRNA circularization and recruitment of eukaryotic translation initiation factors for protein translation remains obscure. Here, we aimed to investigate the molecular mechanisms and potential clinical applications of FXR1 in ovarian cancer growth and progression. MethodsFXR1 copy number variation, mRNA expression, protein levels, and their association with prognosis were determined in clinical datasets. An orthotopic ovarian cancer model and bioluminescence imaging were used for preclinical evaluation of FXR1 in vivo. Reverse phase protein arrays (RPPA) and qPCR arrays were performed to identify FXR1s key targets and downstream effects. SUnSET and polysome profiling were used to determine the translational effects of FXR1. Immunoprecipitation and immunofluorescence were performed to identify the interaction between FXR1 and cMYC mRNA and eIF4F complex. RNA-immunoprecipitation (RIP), RNA electrophoretic mobility shift assays (REMSA), proximity ligation assays (PLA), and biochemical assays were used to identify the specific site on cMYC mRNA to which FXR1 binds to promote mRNA circularization and translation. ResultsWe found that amplification and copy-gain of FXR1 increased the expression of FXR1 mRNA and FXR1 protein in ovarian cancer patients, and these events associated with poor prognosis. We demonstrated that FXR1 binds to AU-rich elements (ARE) within the 3 untranslated region (3UTR) of cMYC. As a consequence, FXR1 binding to cMYC 3UTR leads to the circularization of mRNA and facilitated the recruitment of eukaryotic translation initiation factors (eIFs) to translation start site for improving protein synthesis. ConclusionWe found that FXR1 upregulates a known oncogene, cMYC, by binding to AU-rich elements within the 3UTR, leading to the recruitment of the eIF4F complex for cMYC translation. Our findings uncover a novel mechanism of action of FXR1 in tumorigenesis and provides opportunities to use FXR1 and its downstream effectors as biomarkers or therapeutic targets in ovarian and other cancers.

cancer biology↗

bioRxiv2019

Individualized multi-omic pathway deviation scores using multiple factor analysis

SO_SCPLOWUMMARYC_SCPLOWMalignant progression of normal tissue is typically driven by complex networks of somatic changes, including genetic mutations, copy number aberrations, epigenetic changes, and transcriptional reprogramming. To delineate aberrant multi-omic tumor features that correlate with clinical outcomes, we present a novel pathway-centric tool based on the multiple factor analysis framework called padma. Using a multi-omic consensus representation, padma quantifies and characterizes individualized pathway-specific multi-omic deviations and their underlying drivers, with respect to the sampled population. We demonstrate the utility of padma to correlate patient outcomes with complex genetic, epigenetic, and transcriptomic perturbations in clinically actionable pathways in breast and lung cancer.

bioinformatics↗

bioRxiv2017

Exploring Drivers of Gene Expression in The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA) has greatly advanced cancer research by generating, curating, and publicly releasing deeply measured molecular data from thousands of tumor samples. In particular, gene expression measures, both within and across cancer types, have been used to determine the genes and proteins that are active in tumor cells. To more thoroughly investigate the behavior of gene expression in TCGA tumor samples, we introduce a statistical framework for partitioning the variation in gene expression due to a variety of molecular variables including somatic mutations, transcription factors (TFs), microRNAs, copy number alternations, methylation, and germ-line genetic variation. As proof-of-principle, we identify and validate specific TFs that influence the expression of PTPN14 in breast cancer cells. We provide a freely available, user-friendly, browseable interactive web-based application for exploring the results of our transcriptome-wide analyses across 17 different cancers in TCGA at http://ls-shiny-prod.uwm.edu/edge_in_tcga.

cancer biology↗