tbuckley/cabot-search huggingface dataset:
Abstract 2442: STHD: Probabilistic cell typing of single spots in whole transcriptome spatial data with high definition
Authors: Chuhanwen Sun, Yi Zhang
Journal: Cancer Research (2025)

Abstract Understanding of the cellular heterogeneity of tumor has been essential for uncovering cancer progression mechanisms and developing effective cancer immunotherapies. Emerging spatial transcriptomics (ST) technologies that enable single- and sub-cellular resolution profiling of gene expression across the whole transcriptome have recently become compatible with cancer samples. However, the transition to high-definition ST significantly increased data sparsity and dimensionality, posing significant computational challenges. For example, identifying cell types, deciphering neighborhood structure, and detecting differential expression are all crucial steps to interpret cancer ST samples. Here we present STHD, a novel machine learning method for probabilistic cell typing of single spots in whole-transcriptome, high-resolution ST data. Unlike the current binning-aggregation-deconvolution strategy, STHD directly models gene expression at single-spot level to infer cell type identities without cell segmentation or spot aggregation. STHD addresses sparsity by modeling count statistics, incorporating neighbor similarities, and leveraging reference single-cell RNA-seq data. We show in VisiumHD data that STHD accurately predicts cell type identities at single-spot level, which achieves precise segmentation of both global tissue architecture and local multicellular neighborhoods. The high-resolution labels facilitate various downstream analyses, including cell type-stratified bin aggregation, spatial compositional comparisons, and cell type-specific differential expression analyses. We applied STHD to analyze the newest colon cancer VisiumHD data, which identified patterns like spatial regions of cancer cell-specific stemness, spatially heterogenous cytokine communication activities, confined hypoxia macrophages, and distinct T cell-myeloid cell interactions and cytotoxicity activities located in different immune hubs. Moreover, STHD labels further reveal frontlines of inter-cell type interactions at immune hubs. STHD is scalable and generalizable across diverse samples, tissues, diseases, and cancer types, facilitating genome-wide analyses in various spatial organization contexts. Overall, STHD maps cell type of cancer ST samples with ultra-resolution and facilitates discovery of novel spatial biomarkers and molecular mechanism of cancer progression and treatment responses. The STHD software is publicly available at https://github.com/yi-zhang-compbio-lab/STHD. Citation Format: Chuhanwen Sun, Yi Zhang. STHD: Probabilistic cell typing of single spots in whole transcriptome spatial data with high definition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2442.
Abstract 5587: <i>In silico</i> and <i>in vitro</i> analysis of imiquimod as a potential tankyrase 2 inhibitor in wnt-active cancers
Authors: Philemon N. Ubanako, Ramesh Pandian, Adedapo Adeyinka, Clement Penny
Journal: Cancer Research (2025)

Abstract Dysregulation of the Wnt signaling pathway is a prevalent molecular pathology in cancer biology. Tankyrase (TNKS) is a positive regulator of the Wnt signaling pathway, which is overexpressed in several types of cancer: including lung, breast, colorectal, pancreatic, hepatocellular, and gastric cancers. TNKS inhibitors have shown potent activity against cancer. Using molecular docking and dynamic simulations, we identified TNKS2 as a potential binding target for imiquimod, a Toll-like receptor 7 (TLR7) agonist. Imiquimod, was predicted to bind to the PARP catalytic domain of TNKS2 using the HT Docking program. The GLIDE program on the Schrodinger software was used to dock imiquimod on the TNKS2 catalytic site. Induced-fit docking scores of imiquimod at the B site of TNKS2 was -925.73, which was slightly higher when compared to -924.68 for the co-crystallized ligand, M3F. Cavity-guided blind docking using the CB Dock 2 program showed that imiquimod shared up to 16 amino acid residues in the binding pocket of TNKS2 when compared with the binding of a commercial TNKS inhibitor, XAV939. Molecular dynamic simulations were run to assess RMSD values using the Desmond package in Schrodinger. RMSD values over 100 ns showed a relatively stable interaction of imiquimod in the catalytic site of TNKS2 which was comparable to that of the co-crystallized ligand, M3F. These data suggest that imiquimod might mediate its anticancer therapeutic activities through TNKS2 inhibition as a novel molecular mechanism. In vitro studies using Wnt-active cancer cell lines are currently underway to probe the predicted in silico results. This is the first study that proposes a novel potential anticancer mechanism of Imiquimod through Wnt pathway inhibition. Citation Format: Philemon N. Ubanako, Ramesh Pandian, Adedapo Adeyinka, Clement Penny. In silico and in vitro analysis of imiquimod as a potential tankyrase 2 inhibitor in wnt-active cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5587.
Abstract 6445: Atypical teratoid rhabdoid tumor development mirrors developmental programs
Authors: Fupan Yao, Dean Popovski, Andrew Bondoc, James Rutka, Trevor Pugh, Annie Huang
Journal: Cancer Research (2025)

Abstract Atypical Teratoid Rhabdoid Tumors (ATRTs) are pediatric malignancies that arise primarily in young children under the age of 3. While ATRTs are known to segregate into three epigenetically defined subgroups, termed SHH, TYR, and MYC, little is known about the cellular contexts of how these deadly tumors arise and gain tumorigenic properties. We hypothesized that this early onset of disease reflects tumorigenic events in utero. Indeed, previous research has demonstrated maintenance of embryogenesis programs in other pediatric brain tumors. To characterize the cellular contexts that give rise to ATRTs, we curated 12 single cell and single nuclei RNAseq datasets of human and mouse embryogenesis and developed an atlas and well as a pipeline to match tumors to their most similar embryogenic landmark and timepoint, based on the published cell type identification tool SingleR. We applied this pipeline to two ATRTs single cell datasets generated on the 10X genomics platform: one from primary human samples (N = 33), and one from a tamoxifen inducible ATRT mouse model (N = 8). To validate the accuracy of our pipeline, we first examined the signatures from our ATRT mouse model. Our pipeline accurately predicted cell types and timepoint within the induction window of E6.5-8.5, suggesting that indeed tumors transcriptionally resemble their cellular contexts. Results on both human and mouse ATRTs reveals that ATRTs resemble two distinct developmental trajectories: one resembling a gradient of neuronal differentiation from neuroepithelial (NES, SOX2, OTX2) to radial glial cell (TTYH1, ASCL1, FAPB7) types, and a second trajectory characterized by distinctly mesenchymal signatures (COL1A2, PDGFRB). Single cell GSEA showed enrichment of pathways involved in radial glial and mesenchymal signalling, such as WNT, SHH, and PDGFRB signalling. Findings were validated through marker gene immunohistochemistry and spatial transcriptomics. Taken together, our results demonstrate that ATRTs transcriptionally resemble the developmental contexts of which they arise from. By identifying cellular origins of tumorigenesis, we can exploit this novel information to discover potential therapeutic avenues. Citation Format: Fupan Yao, Dean Popovski, Andrew Bondoc, James Rutka, Trevor Pugh, Annie Huang. Atypical teratoid rhabdoid tumor development mirrors developmental programs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6445.
Abstract 6354: ATTUNE: optimizing the treatment journey for lung cancer patients by predicting EGFR mutation from routinely stained histology images
Authors: Talha Qaiser, Harish RaviPrakash, José Domingo Salazar, Marco Rosati, Alma Andoni, Guillem Portella, Adriana Hepner
Journal: Cancer Research (2025)

Abstract Background: Identifying targetable mutations through tissue-based molecular testing is part of the standard of care management of patients with NSCLC. Nevertheless, globally up to 50% of lung cancer patients are currently not being tested. Liquid biopsy can provide valuable material for molecular diagnosis; however, low sensitivity, especially in early-disease stages, may prevent its broader applicability in the clinic. Predicting EGFR status from digitalized hematoxylin and eosin (H&amp;E) images using machine learning (ML) could be key to addressing such unmet needs. Methods: We propose an automated deep learning algorithm that retrospectively analyzes H&amp;E images for EGFR status in a pivotal clinical trial (ADAURA), in early stage resectable EGFRm NSCLC and in a publicly available data from TCGA; totaling more than 1, 000 patients. The proposed algorithm includes automated tumor segmentation (trained using all TCGA data) and prediction of EGFR mutation from tumor regions with weakly supervised learning (trained using 80% of ADAURA). The segmentation was validated on ADAURA and the proposed EGFR mutation prediction task was validated on held-out 20% of ADAURA and all of TCGA data. Results: We outperformed existing published solutions by a margin of 10% in terms of the AUC metric. The model achieved the AUROC of 0.81 ± 0.04 and 0.75 ± 0.01 on over 100 phase III clinical trial patients and over 500 patients in TCGA, respectively. The model can be calibrated to achieve either &amp;gt; 90% sensitivity or specificity, depending on the clinical use case. Conclusion: The proposed approach does not require any regions marked by pathologists to predict EGFR status and our results reinforce using H&amp;E digital pathology slides with ML to predict molecular status, thereby optimizing the treatment journey for millions of lung cancer patients. Citation Format: Talha Qaiser, Harish RaviPrakash, José Domingo Salazar, Marco Rosati, Alma Andoni, Guillem Portella, Adriana Hepner. ATTUNE: optimizing the treatment journey for lung cancer patients by predicting EGFR mutation from routinely stained histology images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6354.
Abstract 635: Unraveling the potential of neogenin 1 in non-small cell lung cancer: A path toward innovative combination therapies
Authors: Asmita Pandey, Shaoqiu Chen, Li Ma, Mayumi Jijiwa, Masaki Nasu, Vedbar Khadka, Yu Chen, Hua Yang, Sudhir Rai, Youping Deng
Journal: Cancer Research (2025)

Non-small cell lung cancer (NSCLC) has a poor survival rate due to delayed diagnosis and treatment resistance. Currently, immunotherapy has advanced the NSCLC treatment; however, only a small fraction of patients respond to the therapy, demanding the identification of novel biomarkers associated with resistance to develop new therapeutic strategies. Herein, we investigated the role of neogenin 1, a transmembrane protein that belongs to the immunoglobulin superfamily, in seven different human NSCLC cell lines and compared the expression with Jurkat T cell line and human peripheral blood mononuclear cell (PBMC)-derived CD3/CD28-activated CD8+ T cells using qPCR and western blot. We also performed the cell proliferation assay in adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) human cell lines after knockdown and overexpression of neogenin 1. We found that neogenin 1 was highly expressed in primary CD8+ T cells compared to the cancer cell lines, implying that genetic alteration of neogenin 1 might interfere more with CD8+ T cell effector function. We also discovered that both adenocarcinoma and squamous cell carcinoma cell proliferation ability were influenced by genetic alteration of neogenin 1. In addition, gene ontology enrichment analysis and KEGG pathway analysis indicated that neogenin 1 was involved in the TGF-beta signaling pathway, possibly suggesting that neogenin 1 might play a role in therapeutic resistance, including immunotherapy, in NSCLC. Correlation analysis in LUAD and LUSC in the TCGA dataset also revealed that neogenin 1 was positively correlated with a set of genes, such as IGF1R and USP8, associated with immunotherapy resistance. Further investigation of this potential gene in vivo will unfold its role in immunotherapy resistance in NSCLC, paving the way for developing novel and effective combinatorial therapeutic strategies targeting neogenin 1. Citation Format: Asmita Pandey, Shaoqiu Chen, Li Ma, Mayumi Jijiwa, Masaki Nasu, Vedbar Khadka, Yu Chen, Hua Yang, Sudhir Rai, Youping Deng. Unraveling the potential of neogenin 1 in non-small cell lung cancer: A path toward innovative combination therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 635.
Abstract 4939: Social determinants disadvantage score and risk of liver cancer in the All of Us Research Program
Authors: Xinyuan (Cindy) Zhang, Longgang Zhao, David Vlahov, Yun Chen, Tamar Taddei, Xuehong Zhang
Journal: Cancer Research (2025)

Abstract Background: Social determinants of health (SDOH) are crucial in shaping health outcomes, yet comprehensive studies that span all key SDOH domains (economic stability, education, healthcare access and quality, neighborhood and built environment, and social and community context) to examine their associations with liver cancer risk are sparse. We developed and validated a Social Determinants Disadvantage Score (SDDS) to address this knowledge gap. Methods: We conducted a cross-sectional analysis of 117, 783 participants from the All of Us Research Program. The SDDS was systematically constructed using validated questionnaires covering five domains recognized by the World Health Organization and U.S. Department of Health and Human Services. Each domain was dichotomized into advantage (0) or disadvantage (1), with the SDDS ranging from 0 to 5. Prevalent liver cancer, especially hepatocellular carcinoma (HCC), was identified by the Electronic Health Record. We used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the associations of SDDS with HCC, adjusting for age, sex, race, ethnicity, birthplace, marital status, smoking status, and alcohol intake. Results: We identified 110 HCC cases in the participants who completed the SDOH questionnaires. Disadvantage in SDOH, as assessed by higher SDDS, was associated with a higher risk of HCC. Per unit increase in SDDS, the adjusted OR (95% CI) was 1.21 (1.02, 1.43) for HCC. The OR was 5.62 (1.95, 16.22) comparing the most disadvantaged to the most advantaged SDDS group. Strongest associations were observed for disadvantage in economic stability (OR = 2.37; 95% CI = 1.22, 4.63) and education (OR = 1.87; 95% CI = 1.08, 3.24). Conclusion: The SDDS provides a comprehensive, validated tool for assessing SDOH. Our findings highlight significant associations between social disadvantage and HCC prevalence, emphasizing the need for future longitudinal studies to inform targeted interventions. Citation Format: Xinyuan (Cindy) Zhang, Longgang Zhao, David Vlahov, Yun Chen, Tamar Taddei, Xuehong Zhang. Social determinants disadvantage score and risk of liver cancer in the All of Us Research Program [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4939.
Abstract 4592: TERT expression predicts progression-free survival in meningiomas
Authors: Chloe Gui, Justin Z. Wang, Vikas Patil, Andrew Ajisebutu, Jeff Liu, Zeel Patel, Rebeca Yakubov, Ramneet Kaloti, Yosef Ellenbogen, Christopher Wilson, Aaron Cohen-Gadol, Ghazaleh Tabatabai, Marcos Tatagiba, Felix Behling, Eric C. Holland, Jill S. Barnholtz-Sloan, Andrew E. Sloan, Craig Horbinski, Silky Chotai, Lola B. Chambless, Andrew Gao, Serge Makarenko, Stephen Yip, Kenneth Aldape, Farshad Nassiri, Gelareh Zadeh
Journal: Cancer Research (2025)

Abstract Background: TERT promoter mutation (TPM) is a rare but established biomarker in meningiomas associated with aberrant TERT expression, reduced progression-free survival (PFS), and reduced overall survival. While TERT is highly expressed in tumors with promotor mutations, its expression has also been observed even in tumors with wildtype TERT promoters (TP-WT). This study aimed to assess the prevalence of TERT expression and its association with clinical outcome in meningiomas. Methods: Bulk RNA sequencing (n=604), Sanger sequencing of the TERT promoter (n=1095), and methylation profiling (n=1218) of a multi-institutional cohort of meningiomas (total n=1241) were performed to determine TERT expression, TERT promoter mutation status, and TERT promoter methylation. A cohort of 380 meningiomas from Toronto was used for discovery, and 861 meningioma samples from external institutions were compiled as a validation cohort. Results: TERT expression was significantly higher in meningiomas with TPMs compared to TP-WT. However, TERT was still expressed in 30.4% of meningiomas that lacked TPM. TERT expression increased with higher WHO grades and was associated with shorter progression-free survival, even among TP-WT tumors. WHO grade 1 tumors that expressed TERT had PFS similar to those of WHO grade 2, while WHO grade 2 meningiomas expressing TERT had a PFS similar to those of WHO grade 3 meningiomas. Among grade 3 meningiomas, tumors expressing TERT had PFS similar to those with TPMs. Conclusions: Our findings highlight the prognostic significance of TERT expression in meningiomas, even in the absence of TPMs. Its presence may identify patients at greater risk of rapid progression. These results support the inclusion of TERT expression in risk stratification models and management strategies, including future WHO classification and cIMPACT-NOW molecular testing criteria. Citation Format: Chloe Gui, Justin Z. Wang, Vikas Patil, Andrew Ajisebutu, Jeff Liu, Zeel Patel, Rebeca Yakubov, Ramneet Kaloti, Yosef Ellenbogen, Christopher Wilson, Aaron Cohen-Gadol, Ghazaleh Tabatabai, Marcos Tatagiba, Felix Behling, Eric C. Holland, Jill S. Barnholtz-Sloan, Andrew E. Sloan, Craig Horbinski, Silky Chotai, Lola B. Chambless, Andrew Gao, Serge Makarenko, Stephen Yip, Kenneth Aldape, Farshad Nassiri, Gelareh Zadeh. TERT expression predicts progression-free survival in meningiomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4592.
Abstract 6393: Patient selection, target engagement and pharmacodynamic markers of WRN inhibitor GSK4418959 (IDE275)
Authors: Yanhua Rao, Gabriele Picco, Diana M. Munoz, Susan Merrihew, Yang Lee, Anjana Srivatsan, Marya Liimatta, Yang Peng, Thomas E. Angel, Robert Silvers, Joshua E. Cottom, Matthew G. Rees, Melissa M. Ronan, Jennifer A. Roth, Raynold Shenje, Brian T. Jones, Edward J. Brnardic, Michael P. DeMartino, Geeta Sharma, Mathew J. Garnett, Joshua P. Taygerly, Michael A. White, Benjamin Schwartz
Journal: Cancer Research (2025)

Abstract GSK4418959 (IDE275) is a clinical-stage WRN helicase inhibitor designed to treat MSI-H cancer. This compound phenocopies WRN genetic inactivation, with exquisite MSI-H selectivity in large panel of cell lines. Results from extensive cell line profiling indicate a strong correlation between MSI score and sensitivity to the compound. Interestingly, sensitivity to the compound also correlates with the TA-repeat prevalence of individual cell lines. There appears to be no bias toward specific genetic mutations in the dMMR panel, and the compound's sensitivity is not affected by the driver mutation status of the tested cell lines or models. These findings support the use of dMMR and MSI-H as patient-selection biomarkers for GSK4418959 (IDE275). A mass spectrometry-based target occupancy assay was developed to measure WRN target engagement by GSK4418959 (IDE275), using a covalent WRN inhibitor as a probe. GSK4418959 (IDE275) demonstrated dose-dependent target occupancy in cells, showing that over 90% target occupancy is required to inhibit growth. This assay was successfully applied to xenograft tumors, demonstrating dose-dependent WRN occupancy by GSK4418959 (IDE275) in vivo. GSK4418959 (IDE275) induced significant DNA damage selectively in MSI-H cells, but not in MSS cells. DNA damage markers such as gH2AX and pKAP1 were strongly induced both in vitro and in vivo. Immunohistochemistry (IHC) results suggested dose- and time-dependent induction of these markers in MSI-H xenograft models by GSK4418959 (IDE275), providing evidence to support their use in monitoring drug response in clinical settings. Citation Format: Yanhua Rao, Gabriele Picco, Diana M. Munoz, Susan Merrihew, Yang Lee, Anjana Srivatsan, Marya Liimatta, Yang Peng, Thomas E. Angel, Robert Silvers, Joshua E. Cottom, Matthew G. Rees, Melissa M. Ronan, Jennifer A. Roth, Raynold Shenje, Brian T. Jones, Edward J. Brnardic, Michael P. DeMartino, Geeta Sharma, Mathew J. Garnett, Joshua P. Taygerly, Michael A. White, Benjamin Schwartz. Patient selection, target engagement and pharmacodynamic markers of WRN inhibitor GSK4418959 (IDE275) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6393.