Cancer Epidemiology Biomarkers Prevention: Multiancestry Transcriptome-Wide Association Study Identifies Candidate Genes Associated with Hepatoblastoma (Tomlinson)

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  • Tiankai Xie
  • Josey C. Sorenson
  • Logan G. Spector
    Nathan Pankratz
  • R. Stephanie Huang
  • Eiso Hiyama
  • Jenny N. Poynter
  • Gail E. Tomlinson
    Carolina Armengol
  • Roland Kappler
  • Michael E. Scheurer
  • Eve Roman
  • Aurora Castellano
  • Michael A. Grotzer
  • David S. Ziegler
  • Saonli Basu
  • Erin L. Marcotte
  • Tianzhong Yang

Background:

Hepatoblastoma (HB) is a rare embryonal liver tumor, with an increasing global incidence that underscores the need to understand its genetic etiology.

Methods:

Utilizing the ancestry-matched expression quantitative loci data, we performed a HB transcriptome-wide association study (TWAS) on 4,539 Europeans, 1,047 Latinos, and 378 African Americans (∼1:10 case–control ratio). We conducted a meta-analysis of multiancestry transcriptome-wide analysis (METRO), followed by METRO-Egger sensitivity analysis and ancestry-specific gene set enrichment analyses. We further explored genes with additional evidence gathered from independent cohorts and databases.

Results:

Across the three ancestries, the discovered genes shared the same effect direction across ancestries. A meta-analysis of the three ancestries identified 28 genes significantly associated with HB risk, and 15 were nominally significant for at least two ancestries. Our post-TWAS analyses highlighted 8 genes among these 28, including OXER1 (meta-analysis P value = 7.34 × 10−6), FADS1 (P value = 4.01 × 10−6), and UGDH (P value = 5.29 × 10−8), which were expressed in fetal liver hepatoblast cells and were differentially expressed in tumor and normal tissues in an independent Japanese HB study (P values = 2.61 × 10−13, 3.62 × 10−3, and 1.95 × 10−9, respectively).

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Journal of Clinical Investigation: Efficacy and safety of a therapeutic humanized FSH–blocking antibody in obesity and Alzheimer’s disease models (Gupta)

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Abstract

There is growing evidence for direct actions of follicle–stimulating hormone (FSH) on tissues other than the ovaries and testes. Blocking FSH action, either genetically or pharmacologically, protects against bone loss, fat gain, and memory loss in mice. We thus developed a humanized FSH–blocking antibody––MS-Hu6––as a lead therapeutic for three diseases of public health magnitude––osteoporosis, obesity and Alzheimer’s disease (AD) that track together in post–menopausal women. Here, we report the crystal structure of MS-Hu6 and its interaction with FSH in atomistic detail. Using our Good–Laboratory–Practice–Compliant platform (21CFR58), we formulated MS-Hu6 and the murine equivalent Hf2 at an ultra–high concentration; both formulated antibodies displayed enhanced thermal and colloidal stability. A single injection of 89Zr–labelled MS-Hu6 revealed a beta–phase t½ of 89 and 131 hours for female and male mice, respectively, with retention in regions of interest. Female mice injected subcutaneously with Hf2 displayed a dose–dependent reduction in body weight and body fat. Hf2 also rescued recognition memory and spatial learning loss in a context– and time–dependent manner in AD–prone 3xTg and APP/PS1 mice. MS-Hu6 injected into African green monkeys (8 mg/kg) intravenously, and then subcutaneously at monthly intervals, was safe, and without effects on vitals, blood chemistries or blood counts. There was a notable ~4% weight loss in all four monkeys after the first injection, which continued in two of four monkeys. We thus provide IND–enabling data towards an upcoming first–in–human study.

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Computational & Structural Biotechnology: Cell type prediction with neighborhood-enhanced cellular embedding using deep learning on hematoxylin and eosin-stained images (Chen Lab)

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Purpose

This study aimed to predict the cell types that infiltrate the tumor microenvironment using hematoxylin and eosin-stained images from colon cancer and breast cancer samples.
Methods

Two datasets, one focused on colon cancer and the other on breast cancer, were used to develop deep learning models. Cell segmentation was performed using Stardist, followed by the K-Nearest Neighbor method to construct a neighborhood-enhanced cellular extraction matrix for model training. Transductive semi-supervised learning was applied to the breast cancer dataset, where the Base-4 model was trained on S1 and S2 samples and subsequently used to generate assigned labels for the S3, S4, and S5 sets, on which the Base-4+ model was trained.
Results

The Base-7 model trained on colon cancer cell images achieved an accuracy of 0.85 on the hold-out test set and 0.74 on the independent test set, with six neighboring cells identified as the optimal condition for prediction. In addition, the Base-4 model achieved a prediction accuracy of 0.69 with four neighboring cells as the optimal condition in the breast cancer dataset. In contrast, the Base-4+ model reached an accuracy of up to 0.93 on the validation set. The model also captured invasive and ductal carcinoma cells with overall agreement relative to spot-based cell types (0.63).
Conclusions

Deep learning models accurately predicted cell types in breast and colon cancer datasets using only cell morphology and neighborhood embedding.

Cell Reports: IGSF10 is a RET antagonist regulating Ewing sarcoma growth and GnRH neuron migration (Chen, Houghton, & Shiio Lab)

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Highlights

  • Ewing sarcoma depends on IGSF10, which inhibits the RET receptor tyrosine kinase
  • IGSF10 assembles an inhibitory RET-GAS1 complex and suppresses cdc42
  • The IGSF10-RET-GAS1-cdc42 pathway regulates the migration of GnRH neurons
  • IGSF10 mutants linked to delayed puberty are defective in RET-cdc42 regulation

Summary

RET is a receptor tyrosine kinase that plays important roles in development, cancers, and Parkinson’s disease. Here, we identify immunoglobulin superfamily member 10 (IGSF10) as a RET antagonist. We show that Ewing sarcoma depends on IGSF10 and that IGSF10 prevents RET-mediated activation of cdc42, a Rho family G protein and a key regulator of Ewing sarcoma growth as well as cell migration. We demonstrate that IGSF10 binds to RET and GAS1, a cell surface RET inhibitor, and assembles an inhibitory RET-GAS1 complex, thereby preventing the formation of a stimulatory RET-GFRA complex. IGSF10 mutations are associated with delayed puberty, and IGSF10 is shown to be necessary for the proper migration of gonadotropin-releasing hormone (GnRH) neurons. We show that the IGSF10-RET-GAS1-cdc42 pathway regulates migration of GnRH neurons and that IGSF10 mutants linked to delayed puberty are defective in RET-cdc42 regulation. These results reveal a critical role of IGSF10 as a RET antagonist in Ewing sarcoma and GnRH neurons.
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UT Health San Antonio scientists uncover how some cancers outsmart immune system (Rao, Sung, Zheng, & Chen Labs)

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July 17, 2025

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FOXM1 protein identified as key driver in immunotherapy resistance, unlocking new therapeutic avenues  

For years, scientists have found the overexpression of a specific protein called FOXM1 in a wide range of cancers, including ovarian, breast, and pediatric cancers. A recent study by scientists at The University of Texas Health Science Center at San Antonio (UT Health San Antonio) has now led to understanding its exact role in cancer development and is the first study to define the cascade of events that FOXM1 initiates to help cancer cells escape immune detection. The study, published in April 2025 in Nature Communications, examines how this key protein allows several types of cancer to evade the immune system. The insight opens the door to more effective immunotherapies, personalized treatments, and even therapies to prevent cancer recurrence.

“In many cancers, FOXM1 expression is high, but it was unclear how and whether it contributed to immune evasion,” said Manjeet Rao, PhD, professor in the Department of Cell Systems and Anatomy, deputy director of the Greehey Children’s Cancer Research Institute, and Greehey Distinguished Chair in pediatric heme-malignancies at UT Health San Antonio. “We discovered that FOXM1 has an amazing ability to support cancer cell survival and progression by dampening the anti-tumor immune response.”

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Biochemistry: The Spliceosomal Peptidyl Prolyl Isomerase Like 1 Interacts with the Low-Complexity Domain of the RNA Binding Protein EWS Modulating Its Phase Separation Behavior (Libich Lab)

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Abstract

RNA binding protein EWS, a member of the FET (FUS, EWS, TAF15) family, contributes to mRNA biogenesis through roles in transcription, splicing, and RNA transport. Despite evidence linking EWS to spliceosomal complexes, its interactions with spliceosome-associated cyclophilins remain unclear. Here, we describe the first structural and biochemical characterization of the EWS low-complexity domain (EWSLCD) interaction with the spliceosomal cyclophilin PPIL1. Nuclear magnetic resonance (NMR) titration experiments reveal that the proline-rich PxxP motifs of EWSLCD engage the catalytic face of PPIL1, forming low-affinity “fuzzy” complexes. Notably, this interaction is undetected in an EWS construct containing the RNA recognition motif (RRM) and RGG2 domain, suggesting that PxxP accessibility or local context is critical for PPIL1 binding. Phase separation assays demonstrate that PPIL1 is recruited into EWSLCD condensates under physiological salt conditions, but its recruitment is altered at lower salt concentrations. These findings support a model in which EWS is recruited to spliceosomal cyclophilins, potentially influencing splicing and the processing of nascent mRNA. This study underscores the functional importance of proline-rich motifs within EWS and highlights the potential of spliceosomal cyclophilins as both catalytic and structural partners. Our work provides a foundation for exploring the mechanism by which cyclophilins modulate EWS biology and for developing novel therapeutic strategies targeting EWS-cyclophilin interactions in cancer.

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Zhao Lai, PhD to Receive $365K NIH Grant to Expand Integrated High-Performance Imaging-Based in-situ Spatial Profiling

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The 10x Genomics Xenium Analyzer is a cutting-edge, fully integrated platform for high-performance, imaging-based in situ spatial transcriptomics. It enables researchers to perform highly sensitive, high-throughput targeted gene expression profiling at subcellular resolution, providing a powerful tool for uncovering the molecular and spatial complexity of tissue architecture.

Xenium leverages targeted, high-plex in situ analysis to simultaneously detect and map hundreds to thousands of RNA transcripts alongside multiplexed protein markers within the same tissue section. This allows for detailed characterization of cellular phenotypes and their spatial relationships. Compatible with both fresh frozen (FF) and formalin-fixed, paraffin-embedded (FFPE) tissues, Xenium also enables the use of precious archived samples, including clinical biopsies from pediatric cancer patients.

For childhood cancer research, the Xenium Analyzer offers a transformative advantage. Pediatric tumors often exhibit distinct biological features compared to adult cancers, such as low mutational burden, unique cellular origins, and highly dynamic tumor microenvironments. Understanding these complexities requires tools that can capture not only what genes are expressed but also where they are expressed in the context of surrounding cells and tissue structures. Xenium makes this possible.

Using Xenium, researchers can:

  • Precisely map tumor heterogeneity, identifying different cell populations—including cancer stem-like cells, immune cells, and stromal components—within the tumor microenvironment.

  • Track rare cell infiltrates or metastatic cells that may contribute to relapse or treatment resistance.

  • Characterize tumor-immune interactions, which is crucial for understanding the mechanisms of immune evasion in pediatric cancers and informing immunotherapy strategies.

  • Identify biomarkers linked to prognosis, therapeutic response, or resistance by directly observing expression patterns within their native histological context.

  • Utilize archival tissue samples from pediatric patients to build spatial expression atlases that can guide new hypotheses and validate findings from other platforms.

By enabling researchers to visualize and quantify gene and protein expression at single-cell resolution within intact tissues, Xenium empowers studies that surpass the limitations of bulk or single-cell dissociation methods. This spatial context is crucial in pediatric oncology, where tissue architecture, developmental context, and cell signaling pathways play pivotal roles in determining tumor behavior and therapeutic response.

In summary, the Xenium Analyzer will accelerate discoveries in childhood cancer research by providing deep, spatially resolved insights into tumor biology—helping researchers develop more targeted, effective, and less toxic therapies for children with cancer.

 

Learn more about the Greehey CCRI Genome Sequencing Facility (GSF)

Molecular Cancer Therapeutics: Targeting DDR2 for Treating Pancreatic Cancer (Rao Lab & Chen)

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Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer with limited effective treatments, partly due to its complex tumor microenvironment. Herein, we report Discoidin Domain Receptor 2 (DDR2), a receptor tyrosine kinase, as a critical protein that promotes PDAC growth and survival. Our results reveal that DDR2 is highly expressed, and its expression correlates with the worst survival outcome of PDAC patients. Using an unbiased high-throughput screen of small-molecule inhibitor libraries, we identified CIDD-8633, a novel inhibitor targeting DDR2. Our study suggests that CIDD-8633 interacts with DDR2 and inhibits DDR2-associated signaling. Importantly, in vivo studies demonstrate that CIDD-8633 effectively blocks PDAC tumor growth in preclinical mouse models.Additionally, combining CIDD-8633 with gemcitabine enhanced its efficacy in a synergistic manner. Mechanistically, CIDD-8633 treatment induces pro-apoptotic genes in PDAC cells. These findings position DDR2 as a promising therapeutic target and CIDD-8633 as a potential DDR2 inhibitor, providing new avenues for treating PDAC.

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BioRxiv: Robust statistical assessment of Oncogenotype to Organotropism translation in xenografted zebrafish (Kurmasheva, et al)

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Abstract

Organotropism results from the functional versatility of metastatic cancer cells to survive and proliferate in diverse microenvironments. This adaptivity can originate in clonal variation of the spreading tumor and is often empowered by epigenetic and molecular reprogramming of cell regulatory circuits. Related to organotropic colonization of metastatic sites are environmentally-sensitive, differential responses of cancer cells to therapeutic attack. Accordingly, understanding the organotropic profile of a cancer and probing the underlying driver mechanisms are of high clinical importance. However, determining systematically the organotropism of one cancer versus the organotropism of another cancer, potentially with the granularity of comparing the same cancer type between patients or tracking the evolution of a cancer in a single patient for the purpose of personalized treatment, has remained very challenging. It requires a host organism that allows observation of the spreading pattern over relatively short experimental times. Moreover, organotropic patterns often tend to be statistically weak and superimposed by experimental variation. Thus, an assay for organotropism must give access to statistical powers that can separate ‘meaningful heterogeneity’, i.e., heterogeneity that determines organotropism, from ‘meaningless heterogeneity’, i.e., heterogeneity that causes experimental noise. Here we describe an experimental workflow that leverages the physiological properties of zebrafish larvae for an imaging-based assessment of organotropic patterns over a time-frame of 3 days. The workflow incorporates computer vision pipelines to automatically integrate the stochastic spreading behavior of a particular cancer xenograft in tens to hundreds of larvae allowing subtle trends in the colonization of particular organs to emerge above random cell depositions throughout the host organism. We validate our approach with positive control experiments comparing the spreading patterns of a metastatic sarcoma against non-transformed fibroblasts and the spreading patterns of two melanoma cell lines with previously established differences in metastatic propensity. We then show that integration of the spreading pattern of xenografts in 40 – 50 larvae is necessary and sufficient to generate a Fish Metastatic Atlas page that is representative of the organotropism of a particular oncogenotype and experimental condition. Finally, we apply the power of this assay to determine the function of the EWSR1::FLI1 fusion oncogene and its transcriptional target SOX6 as plasticity factors that enhance the adaptive capacity of metastatic Ewing sarcoma.

 

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Daohong Zhou, MD, to Receive $2.4M CPRIT Award to Better Identify Therapeutic Targets and Develop Technologies to Target Hard-to-Treat Cancers.

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Develop transformative technologies that better target drug-resistant cancers

Daohong Zhou, MD, tenured professor in the Department of Biochemistry and Structural Biology in the Joe R. and Teresa Lozano Long School of Medicine, received CPRIT’s academic research award of over $2.4 million to expand UT Health San Antonio’s core facilities laboratories to increase researchers’ capabilities to better identify therapeutic targets and develop technologies to target hard-to-treat cancers.

Daohong Zhou, MD

Zhou, who serves as the director of the Target Discovery Core at the Greehey Children’s Cancer Research Institute at UT Health San Antonio, associate director for drug development at the Mays Cancer Center at UT Health San Antonio and director of the institution’s Center for Innovative Drug Discovery, shared that while there has been significant progress in treating cancers, we are still facing big challenges, especially with certain tough-to-treat pediatric and adult cancers like brain tumors, soft tissue sarcomas and those that do not respond well to current therapies.

“However, the absence of core facilities for target identification and validation (TIV) in Texas has limited the success in discovering and developing new cancer therapeutics because TIV is essential for drug discovery and development,” Zhou said. “In addition, the CPRIT award will allow us to acquire and develop new TIV technologies, including the state-of-the-art arrayed CRISPR knockout screening for TIV and groundbreaking small molecule degrader (SMD) discovery platform for screening SMDs to target undruggable proteins, which are not available in any other core facilities in Texas.”

 

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