tbuckley/cabot-search huggingface dataset:
Abstract 3659: Biology-driven, machine learning-based development of a biomarker to predict response to WEE1 inhibitor Debio 0123
Authors: Jeannette Fuchs, Luke Piggott, Kristian Urh, Matjaž Žganec, Eva Lavrenčič, Mark Uhlik, Carolina Haefliger
Journal: Cancer Research (2025)

Abstract Introduction: The WEE1 kinase is a central regulator of G2/M and S phase cell cycle checkpoints which is potently activated in response to DNA damage. To date, a number of biomarker candidates have been proposed for WEE1 monotherapy sensitivity, including TP53 alterations, however, there is currently limited clinical evidence that biomarker-positive patient populations are significantly enriched in response to WEE1 inhibitors. Debio 0123 is a WEE1 inhibitor that has previously shown significant preclinical efficacy as a single agent and is currently being investigated in phase 1 clinical studies either as a monotherapy or in combination with different therapies. Herein we present a first-generation digital biomarker utilizing the ResponderID™ framework and the GenialisTM Supermodel to configure a model capable of predicting responders from non-responders to the Debio 0123 WEE1 inhibitor. Methods: The initial model is based on an extra-trees regression algorithm spanning five biological pathways related to DNA damage response as features. It was primarily trained on public and proprietary datasets to build a foundation DDR biology model, prior to further training on Debio 0123 patient-derived organoid RNA-seq datasets. The model was then tested on Debio 0123 datasets to measure model performance utilizing leave-one-out cross-validation. Results: The first-generation DDR biomarker showed potential to predict Debio 0123 IC50 values for viability (prediction accuracy of 0.7). Further refinement of optimal activity thresholds, tuning of features by tissue transferability, and establishment of a classifier-based model yielded an improved prediction accuracy (0.8 vs. null model accuracy of 0.5). Furthermore, this optimized model accurately predicted independent PDX model responses. Specifically, the model accurately predicted a non-responder (tumor growth inhibition &amp;lt;5%) in vivo and strikingly, an in vivo responder that was considered a non-responder by ex vivo organoid IC50. Conclusion: This biology-driven, machine learning-based classifier predicts response to Debio 0123 in patient derived organoid and xenograft models at an accuracy significantly greater than that of a dummy model and serves as a basis for further optimization and retrospective analyses of Debio 0123 clinical studies. Citation Format: Jeannette Fuchs, Luke Piggott, Kristian Urh, Matjaž Žganec, Eva Lavrenčič, Mark Uhlik, Carolina Haefliger. Biology-driven, machine learning-based development of a biomarker to predict response to WEE1 inhibitor Debio 0123 [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 3659.
Abstract 4087: Panel-adjustable and user-friendly hybridization-capture technology delivers high performance demonstrated by different exome probe panels
Authors: Alexandra Wang, Daniel Saatman, Ekaterina Star, Jinglie Zhou
Journal: Cancer Research (2025)

Targeted sequencing by hybridization-capture is a versatile and powerful way to interrogate selected regions of interest in large and complex genomes. However, the performance of a given hybridization-capture workflow is often optimized only towards a specific approach to probe panel design. Here we demonstrate a hybridization-capture enrichment system that delivers high performance on three commercially available Exome panels with short and overnight hybridizations. This is achieved using our new xGen Hybridization and Wash v3 (xGen v3), which features a simple user-friendly workflow without any pre-heating, hot-liquid handling, or time-and-temperature sensitive steps. Human genomic reference libraries were constructed with xGen™ cfDNA &amp; FFPE DNA Library Preparation Kit using Coriell NA12878 gDNA. These equivalent libraries are hybridized to xGen™ Exome Hyb Panel v2, xGen™ Exome Hyb Panel v1, or a double-stranded-probe Exome panel. Hybridization incubations from 1-hour to overnight were compared, spanning total inputs from 100ng to microgram quantities per hybridization-capture. xGen v3 delivered consistent performance for all three Exome panels, without the increase in GC-bias that typically occurs for short hybridizations. Benchmarking using a currently available commercial kit is presented for performance comparison. Non-biased sampling reveals properties of the library as directed by the panel design. The demonstration here displays a panel-directed enrichment workflow that is reliable for wide range of total capture inputs and hybridization times for each given Exome panel, spanning different panel design approaches as well as single- and double-stranded probes. This behavior can be extrapolated to applications that requires multiplexing, low-pass or deep-sequencing needs, and custom panels. The panel-directed consistency over a large range of inputs and hybridization times makes xGen v3 adaptable to a large variety of applications, from quick surveys to high-throughput and/or in-depth interrogations as directed by the panel design. Citation Format: Alexandra Wang, Daniel Saatman, Ekaterina Star, Jinglie Zhou. Panel-adjustable and user-friendly hybridization-capture technology delivers high performance demonstrated by different exome probe panels [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 4087.
Abstract 1356: The lactylation of SLC26A3 in acidic micro-environment contributes to colorectal carcinoma malignant progression
Authors: Chong Chen, Peishan Hu, Yufeng Chen
Journal: Cancer Research (2025)

A recently discovered post-translational modification, lactylation, can regulate the structure and function of proteins, influencing cellular functions and disease progression. However, its role in tumor progression remains unclear.This study explored the contribution of lactylation modification of solute carrier family 26 member 3 (SLC26A3) in acidic microenvironments to the malignant progression of colorectal carcinoma (CRC). The findings revealed that the acidic tumor microenvironment reduced the stability and expression of SLC26A3 by promoting its lactylation. co-IP experiment results showed SLC26A3 interacted with RNA-binding proteins HuR and CUGBP1, and when the expression of SLC26A3 was decreased, its binding capacity to HuR/CUGBP1 was weakened. This led to an increased interaction of HuR and CUGBP1 with a subset of oncogenic mRNAs, regulating their stability and expression, ultimately promoting the progression of malignant tumors. The study also indicated that the expression of SLC26A3 was negatively correlated with the expression of tumor stem cell markers, and low expression of SLC26A3 promoted the stemness of CRC cells. Furthermore, the expression of SLC26A3 affected the resistance to oxaliplatin (OXA), invasiveness, and migratory ability of CRC cells. In animal models, normalizing the tumor acidic microenvironment and inducing the expression of SLC26A3 inhibited the development of CRC.These findings highlight the role of SLC26A3 as a potential suppressor of CRC recurrence, drug resistance, and metastasis, providing new insights for improving the clinical treatment and prognosis of CRC. Citation Format: Chong Chen, Peishan Hu, Yufeng Chen. The lactylation of SLC26A3 in acidic micro-environment contributes to colorectal carcinoma malignant progression [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 1356.
Abstract 5001: Multimodal topic modeling for single-cell multiomics
Authors: Cigdem Ak, Nicole Szczepanski, Aaron Doe, Gurkan Yardimci
Journal: Cancer Research (2025)

Abstract Single-cell (sc) multiomics assays simultaneously measure transcriptomic, epigenomic, and proteomic modalities in single cells. These multimodal assays enable deeper characterization of cells compared to single-omic assays by offering a more comprehensive measurement of the cell state. Additionally, they can identify cross modality associations, such as promoter-enhancer linkages. Recently, numerous computational methods for sc-multiomics data have been released; however, there is a dearth of interpretable methods While matrix factorization and deep learning approaches, such as MOFA+ and scVI, are effective at cell type clustering; however, these methods are difficult to interpret, masking the underlying biology that defines cell states. Our solution, EpiConfig, is a multimodal topic model that can accurately cluster cells based on cell types and yields biological insights by jointly modeling transcriptomic and epigenetic states combinations. Unlike other sc-multiomics topic models, EpiConfig explicitly models cross modality associations. Furthermore, we describe important cell filtering and subsetting approaches to speed up and improve the convergence of topic model. Benchmarking EpiConfig on publicly available scRNA+ATAC datasets to healthy and cancerous cell populations, we show that EpiConfig outperforms competing methods, such as Cobolt and MOFA. Furthermore, EpiConfig cross modality associations cell type capture 3D genome interactions. Modeling sc-multiomics data from lymphoma, EpiConfig identifies topics for cancerous cells. Coupled with our model interpretation R Shiny app, EpiConfig allows for the interpretation and identification of associations between transcriptomic and epigenomic features that define cell types and states. Citation Format: Cigdem Ak, Nicole Szczepanski, Aaron Doe, Gurkan Yardimci. Multimodal topic modeling for single-cell multiomics [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 5001.
Abstract 2431: Enhanced cancer detection using TransUnet for low-resolution histopathology images across multiple cancer types
Authors: Muhammad Shaiq Paracha, Faisal F. Khan, Arsalan Riaz, Madina Shirdel
Journal: Cancer Research (2025)

Abstract Digital histopathology has become an indispensable tool for cancer diagnosis and prognosis, enabling pathologists to analyze tumor morphology using Whole Slide Images (WSI). However, the high cost and limited availability of high-resolution imaging equipment in underdeveloped regions pose significant challenges. This study investigates the use of Low-Cost Low-Resolution (LCLR) histopathology images for detecting four cancer sites: Oral, Gastrointestinal, Colorectal and Breast cancers. We compared the performance of four models: AlexNet, EfficientNet, Vision Transformer and TransUnet. These models were trained on two datasets: the Tabassum et al. (2020) OSCC dataset (1, 223 images, 10X and 40X magnification) used for selecting the right model and HistoVault v1 (17, 500 images, 10X and 40X magnification) for training the selected model for all cancer sites. TransUnet, which integrates U-Net's encoder-decoder architecture with Transformers, was fine-tuned with hyperparameters including a learning rate of 1e-5, batch size of 32 and a dropout rate of 20%. The evaluation metrics included accuracy, sparse categorical cross-entropy loss and confusion matrices to quantify the models' robustness. TransUnet achieved the highest accuracy of 96.8% with a loss of 3% over other models on Tabassum et al. (2020) OSCC dataset. Individual accuracies for Oral, Gastrointestinal, Colorectal and Breast cancers were 98.7%, 95.9%, 96.1% and 97.2% on Histovault v1 dataset. Its coarse-to-fine attention refinement enabled precise segmentation in noisy, low-resolution images, outperforming other models. Notably, the model exhibited robust generalization for multi-class classification, achieving a test accuracy of 90.5% in differentiating eight classes (Non Cancerous and Cancerous with each cancer site). TransUnet possesses significant promises for LCLR image-based cancer detection, especially in environments with limited resources. Its hybrid design improves accuracy and segmentation precision in challenging datasets by combining the advantages of transformers and convolutional neural networks. With the objective to improve precision diagnosis, future research will investigate cancer grading and use explainable AI algorithms. Citation Format: Muhammad Shaiq Paracha, Faisal F. Khan, Arsalan Riaz, Madina Shirdel. Enhanced cancer detection using TransUnet for low-resolution histopathology images across multiple cancer types [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 2431.
Abstract 4956: Exploration of sub-Saharan African immigrants’ reactions to prostate cancer diagnosis
Authors: Kaitlin Van Voorhis, Ernie Kaninjing
Journal: Cancer Research (2025)

Abstract Background: About 300, 000 men will hear “you have Prostate Cancer” annually in the United States. Black men are 1.7 times more likely to be diagnosed than White men, and sub-Saharan African Immigrants (SSAIs) are 1.5 times more likely than Black men. These 4 words spark various reactions as the journey towards treatment and survivorship begins. Emotions influence the decision-making process, mental health, adherence to treatment, and treatment outcomes. An exploration of the initial reactions to diagnosis displayed by SSAI will better inform future interventions aimed at improving health outcomes. Methods: In depth interviews were conducted with 22 SSAI Prostate Cancer (CaP) survivors, guided by Grounded Theory. Purposeful sampling was employed to ensure the study population reflected the cultural diversity among SSAIs. Interviews were transcribed, verified, and analyzed in NVivo 13. Results: Participants expressed a range of reactions, unique to their individual experiences. These reactions were categorized into 3 over-arching categories: positive (showing signs of agreement or acceptance with the diagnosis), negative (showing signs of resistance to the diagnosis), or neutral (showing no strong pull to positive or negative, often revolving around action-based desires). Participants often experienced multiple reactions across this spectrum, but analysis showed potential correlation among various demographic variables such as age and time passed from diagnosis resulting in more neutral recollections of reactions. Conclusions: The wide range of initial reactions to CaP diagnosis are reflective of the individual and their unique experiences as an SSAI. These common reactions and mindsets immediately following diagnosis are insightful to a patient’s current headspace, focus, personal outlook, and overall mental health. All these emotions impact decision making with regards to treatment, treatment adherence, and even treatment regret as the survivorship journey continues. The National Institute of Health’s Cancer Control Continuum includes focus areas for etiology, prevention, detection, diagnosis, treatment, and survivorship. However, there remains a lack of focus on the initial reaction to diagnosis. Future studies should focus on further illuminating the relationship between a patient’s initial reaction to diagnosis and their treatment decisions, adherence, and overall survivorship. Citation Format: Kaitlin Van Voorhis, Ernie Kaninjing. Exploration of sub-Saharan African immigrants’ reactions to prostate cancer diagnosis [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 4956.
Abstract 3570: Impact of private equity ownership on quality of cancer care in U.S. hospitals
Authors: Megan Worthington, Ruth Carlos
Journal: Cancer Research (2025)

Introduction: This study examined the potential impact of private equity (PE) acquisitions on patient satisfaction in U.S. hospitals with cancer care centers. Given the growing trend of PE involvement in healthcare, we hypothesized that ownership changes could correlate with measurable declines in perceived quality of care. Methods: From a national dataset of 5,395 Medicare-participating hospitals, we filtered to 2,405 facilities that met criteria for dedicated oncology services and comprehensive cancer care, including defined treatment facilities and national program affiliations. Using Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey data (2016-2023), which provides patient satisfaction scores on a 1-to-5-star scale, we analyzed satisfaction ratings for 118 hospitals acquired by PE to identify changes pre- and post-acquisition. Pitchbook provided insights into deal type, deal frequency, and financing status. Julius AI was utilized for analysis, with particular attention to deal structures and rating trends. Results: Over half of the hospitals studied (51.7%) experienced declines in satisfaction ratings post-acquisition, with an average decrease of 0.061 stars. Bankruptcy and debt-related deals corresponded with more substantial declines (-0.318 and -0.095 stars, respectively), suggesting that financial pressures may directly or indirectly impact patient experiences. Comparatively, single-deal hospitals showed a mean decrease of 0.061 stars, with 56.7% reporting declines, versus 44.9% for multiple-deal hospitals, which showed a more stable rating profile. Although a weak negative correlation (-0.101) existed between multiple deals and rating stability, hospitals with three or more deals showed minimal fluctuation, possibly reflecting stronger organizational adaptation over time. Conclusion: This analysis reveals a consistent trend: patient satisfaction ratings tend to decrease in a considerable portion of PE-owned hospitals, with variations based on deal type and structure. The findings underscore the need for ongoing evaluation of PE ownership models in healthcare, particularly as financial motivations intersect with patient-centered care priorities. These preliminary trends merit deeper investigation into the mechanisms through which PE acquisitions may influence hospital service quality, especially in oncology-focused settings where patient care and continuity are critical. This abstract was prepared with the assistance of generative AI (Julius AI) for data analysis. Citation Format: Megan Worthington, Ruth Carlos. Impact of private equity ownership on quality of cancer care in U.S. hospitals [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 3570.
Abstract 1752: SY-13380, a novel menin-MLL inhibitor, exhibits efficacy against resistant mutations and anti-tumor activity for MLL-rearranged and NPM1-mutant acute myeloid leukemia in pre-clinical models
Authors: Xiaopeng Li, Tiantian Hao, Hongjuan Li, Chang Lu, Shikang Cheng, Xinhe Xu, Huiyan Luo, Xijie Liu, Hong Luo, Yinghui Sun
Journal: Cancer Research (2025)

Abstract The menin-KMT2A interaction is a critical epigenetic regulator for leukemogenesis in many acute leukemias, including KMT2A-rearranged (KMT2Ar) acute leukemia and NPM1-mutant (NPM1m) acute myeloid leukemia (AML). Patients with these alterations always have dismal response rates and poor prognosis. Some selective inhibitors of the menin-KMT2A complex have demonstrated encouraging activity in patients with R/R AML. However, early trials with revumenib have reported the rapid acquisition of resistance somatic mutations of MEN1 gene in about 40% patients which can impair treatment success. Herein, we introduce SY-13380, a novel menin-MLL inhibitor, exhibits potent binding affinity against wild type and resistant mutations and displayed excellent anti-tumor activity in pre-clinical models. Biochemical assay revealed that SY-13380 potently blocked menin-MLL binding with an IC50 under 10.0 nM, and it also displayed very strong potency on series of resistant mutants (M327I, M327V, G331R and T349M) similar to that on WT proteins. In cellular assays, SY-13380 also strongly inhibited cell proliferation in multiple cancer cell lines carrying KMT2Ar or NPM1m, especially in M327I mutant MOLM13 cells which was insensitive to approved Menin inhibitor SNDX-5613. In cell functional studies, SY-13380 dose-dependently down-regulated menin-MLL target gene HOXA9 and MEIS1, and induced the expression of differentiation marker CD11b. In vivo studies showed that treatment of SY-13380 achieved excellent tumor growth inhibition in various xenograft tumor models including MV-4-11, RS4;11, OCI AML3 and MOLM13 harboring M327I mutant menin. Notably, tumor regression was found in MV-4-11 xenograft tumor models after treatment with SY-13380 at dosage of 10mpk. In addition, SY-13380 displayed good PK parameters and tolerability in vitro and in vivo models. These data indicate that SY-13380 is a novel and potent menin-MLL inhibitor with robust anti-tumor activities, favorable PK and safety profiles making it promising for further clinical development and efficacy against resistant mutations warranting it long-term response for treatment of MLL-r and NPM1m acute leukemias. Citation Format: Xiaopeng Li, Tiantian Hao, Hongjuan Li, Chang Lu, Shikang Cheng, Xinhe Xu, Huiyan Luo, Xijie Liu, Hong Luo, Yinghui Sun. SY-13380, a novel menin-MLL inhibitor, exhibits efficacy against resistant mutations and anti-tumor activity for MLL-rearranged and NPM1-mutant acute myeloid leukemia in pre-clinical models [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 1752.