tbuckley/cabot-search huggingface dataset:
Abstract 851: AdAPT-001 demonstrates potent oncolytic activity, high levels of TGFβ inhibition, and encouraging clinical activity in refractory sarcoma: update of the BETA-PRIME trial and preclinical correlates
Authors: Chris Larson, Ana Sanchez, Farah Hedjran, Garett Soapes, Bryan Oronsky, Anthony P. Conley, Meaghan Stirn, Tony Reid
Journal: Cancer Research (2025)

Soft tissue sarcomas are an exceedingly rare and aggressive malignancy, occurring in about 5000 patients per year with an estimated median overall survival of around 12-18 months. Standard chemotherapy includes doxorubicin alone or with ifosfamide with high toxicity and low response rates. Other approved therapies in sarcoma are trabectedin, eribulin, and pazopanib, but their response rates are less than 10%. Immunotherapy trials have been largely disappointing. In the SARC028 phase 2 clinical trial, the PD-1 inhibitor, pembrolizumab, had limited activity except in undifferentiated pleomorphic sarcoma and liposarcoma. In the Phase 2 Alliance A091401 trial, the response to nivolumab was very low at only 5%, which led the investigators to arrive at a conclusion of medical futility, stating that, “Nivolumab alone does not warrant further study in an unselected sarcoma population given the limited efficacy”. AdAPT-001 is currently under investigation in a Phase 1/2 clinical trial for the treatment of advanced malignant solid tumor(s) for patients who have received all conventional therapies considered appropriate by Investigator. To date, of the 25 patients who received multiple doses of AdAPT-001 plus a checkpoint inhibitor, 14 of them were diagnosed with soft tissue sarcoma. Of these 14 patients, 69.2% (9/14 patients) reached a 3-month PFS and 35.7% (5/14) had an objective response. Most of these sarcomas had previously progressed on a PD-1 or PD-L1 inhibitor or were considered nonimmunogenic and, therefore, unsuitable for checkpoint inhibitor monotherapy. To date, the median PFS is 8.5 months. To further understand the activity of AdAPT-001 in sarcoma, we analyzed the lytic activity, TGFβ trap expression, and checkpoint expression patterns of sarcoma cell lines after infection. We demonstrate robust infection, replication, lysis and high-levels of TGFβ trap expression in sarcoma cell lines and objective responses, including pathologic complete responses, among patients who have progressed on chemotherapy and immunotherapy. Citation Format: Chris Larson, Ana Sanchez, Farah Hedjran, Garett Soapes, Bryan Oronsky, Anthony P. Conley, Meaghan Stirn, Tony Reid. AdAPT-001 demonstrates potent oncolytic activity, high levels of TGFβ inhibition, and encouraging clinical activity in refractory sarcoma: update of the BETA-PRIME trial and preclinical correlates [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 851.
Abstract 1037: Deep learning-derived dietary patterns and gastric cancer risk in women: A large-scale prospective cohort study
Authors: Hyobin Lee, Dongseok Heo, Sukhong Min, Sinyoung Cho, Bongwon Suh, Daehee Kang
Journal: Cancer Research (2025)

Abstract Background &amp; Aims: Machine learning can address limitations of principal components analysis by capturing nonlinear relationships, enhancing dietary pattern identification. This study aimed to identify dietary patterns using deep learning and cluster analysis and to assess their association with gastric cancer incidence in a prospective cohort study. Methods: Data were obtained from participants of the large-scale prospective cohort study, Health Examinees-Gem (HEXA-G) study, with baseline surveys conducted between 2004 and 2013. Cancer diagnosis information was retrieved from the Korea Central Cancer Registry up to December 31, 2018. The final analysis included 84, 725 women. Dietary intake was assessed using a validated semiquantitative food frequency questionnaire at baseline. Dimensionality reduction was performed using an autoencoder, followed by k-means clustering. The optimal cluster number was determined using the silhouette coefficient. Multivariable Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for gastric cancer risk associated with the dietary patterns. Results: Ten dietary patterns were identified: ‘Common’, ‘Low calorie’, ‘Rice’, ‘High calorie’, ‘Traditional’, ‘Dairy’, ‘Meat’, ‘Processed’, 'Noodles' and ‘Salty’ patterns. A total of 558 cases of gastric cancer were identified during median 9.4 years of follow-up. Compared to the ‘High calorie’ pattern (reference), the ‘Noodles’ (HR 1.97, 95% CI 1.05-3.70), and ‘Low calorie’ (HR 1.91, 95% CI 1.23-2.97) were associated with higher incidence of gastric cancer after adjusting for potential confounders. When stratified by age, ‘Salty’ pattern was associated with 4.34 times higher risk of gastric cancer (HR 4.34, 95% CI 1.49-12.60) among participants aged 40-49. Conclusion: Our results suggest that relative to the ‘High calorie’ pattern, the ‘Noodles’, ‘Low calorie’, and ‘Salty’ patterns are associated with increased risk of gastric cancer among Korean women. While Helicobacter pylori infection—a major risk factor for gastric cancer—was not accounted for in this study, our findings highlight the importance of dietary interventions to reduce high-risk patterns, such as salty or noodle-based diets, to lower the risk of gastric cancer. Citation Format: Hyobin Lee, Dongseok Heo, Sukhong Min, Sinyoung Cho, Bongwon Suh, Daehee Kang. Deep learning-derived dietary patterns and gastric cancer risk in women: A large-scale prospective cohort study [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 1037.
Abstract 1055: Inclusion of large genomics databases in disease modeling improves outcomes across ancestral populations
Authors: Leslie A. Smith, Franco Krepel, James A. Cahill, Ji-Hyun Lee, Kiley Graim
Journal: Cancer Research (2025)

Abstract Rapid advancements in genomic sequencing technologies have revolutionized the study and treatment of human diseases. Even with the wealth of genomic information available, successful application of developed treatments is often limited to the human populations represented in the original study, suggesting that these studies are overfitting to their data and thus limiting scientific understanding of complex traits and diseases. Human populations have developed population-specific variants in response to evolutionary changes as a result of human migrations and other stressors which can impact disease occurrence and outcome. Lack of sufficient sampling for equal representation of this variation in disease studies has led to disparate outcomes and treatments. While obtaining a representative study population for a disease is often unrealistic, equitable machine learning approaches can bridge the gap. Here, we demonstrate an equitable machine learning approach, PhyloFrame, showing that the inclusion of publicly available genomic resources in disease modeling methods can force machine learning and artificial intelligence models to be more robust to data distribution shifts caused by unequal sampling of human populations, thereby increasing model generalizability. We demonstrate PhyloFrame’s utility across several diseases at high risk of ancestral bias, which we identified via an analysis of heritable disease signatures. We first perform statistical enrichment of ancestry-specific alleles within existing disease signatures for Mendelian disorders by calculating variant frequency enrichment for each of the populations in gnomADv4.1 and the 1000 Genomes Project. After identifying the diseases at highest risk for ancestral bias, we apply our PhyloFrame method to RNA-seq datasets from those diseases and contrast PhyloFrame’s performance in each ancestry group to a comparable benchmark model that does not incorporate equitable machine learning techniques. Our results demonstrate that incorporating publicly available genomic data into disease modeling methods improves outcomes for all. Growing resources for publicly available large-scale genomic databases provide unprecedented opportunities to understand disease. Resulting analysis of variation across human populations is a powerful approach to supplement disease models. Here we demonstrate how to incorporate this information into disease modeling, and show it is a unique and feasible approach to advancing equity in precision medicine. Citation Format: Leslie A. Smith, Franco Krepel, James A. Cahill, Ji-Hyun Lee, Kiley Graim. Inclusion of large genomics databases in disease modeling improves outcomes across ancestral populations [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 1055.
Abstract 457: QLS1304, a highly potent and selective KAT6A/B inhibitor treating ER+HER2- breast cancer
Authors: Yuxing Zhang, Chundao Yang, Huijie Guo, Changliang He, Lan Zhang, Ling Li, Mengmeng Zhang, Xuejian Zheng, Ping Chen, Dong Yang, Dongdong Wu, Guqin Shi, Daqing Sun, Wenyuan Qian, Weimei Sun, Weikang Tao
Journal: Cancer Research (2025)

Abstract KAT6A and KAT6B, members of the MYST family of lysine acetyltransferases, are implicated in estrogen receptor-positive (ER+) breast cancer. KAT6A amplification occurs in about 10% of these patients, linking to overexpression and poorer outcomes. KAT6B is known to be functionally redundant with KAT6A in regulating levels of H3K23Ac, although its specific role in ER+HER2- breast cancer is not well understood. The simultaneous inhibition of KAT6A and KAT6B may lead to a more profound suppression of histone acetylation. Thus, the development of a dual inhibitor targeting KAT6A and KAT6B has been aggressively pursued, with the goal to offer an effective therapeutic solution for patients with ER+ breast cancer. QLS1304 is a highly potent and selective inhibitor of KAT6A/B (KAT6A Ki: 0.2 nM, KAT6B Ki: 0.4 nM; and &amp;gt;200-fold selectivity over KAT5/KAT7/KAT8). H3K23 acetylation was dose-dependently inhibited in ER+HER2- breast cancer cell lines, while ER expression was only suppressed in KAT6A-high cells. QLS1304 demonstrates strong in vitro and in vivo efficacy in multiple ER+HER2- breast cancer CDX models, either as a single agent or in various combinations with current standard therapies. Notably, QLS1304 has demonstrated efficacy in an ESR1 mutant ER+HER2- PDX model derived from a patient resistant to SoC. QLS1304 also exhibits desirable drug-like properties with an excellent pharmacokinetics profile across different animal species, and preclinical safety assessment also indicates a wide therapeutic window. Collectively, QLS1304 appears to be a promising candidate for the treatment of ER+HER2- breast cancer given its efficacy in both SoC-sensitive and SoC-resistant models. Moving it forward into clinical trials is warranted both as single agent and combining with other therapies. Citation Format: Yuxing Zhang, Chundao Yang, Huijie Guo, Changliang He, Lan Zhang, Ling Li, Mengmeng Zhang, Xuejian Zheng, Ping Chen, Dong Yang, Dongdong Wu, Guqin Shi, Daqing Sun, Wenyuan Qian, Weimei Sun, Weikang Tao. QLS1304, a highly potent and selective KAT6A/B inhibitor treating ER+HER2- breast cancer [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 457.
Abstract 1099: Leveraging RNA-seq for breast cancer survival analysis and risk stratification
Authors: Pooya Naseri-Nosar, Zahra Eftekhari, Chi Wah Wong
Journal: Cancer Research (2025)

Abstract Advancements in breast cancer treatment have significantly improved patient survival rates; however, the increasing incidence and stratified mortality risks underscore the critical need for Precision Medicine and Artificial Intelligence (AI) tools to provide clinicians with more accurate assessments of patient status. In this study, we analyzed solid tumor RNA-seq data from over 1, 800 breast cancer patients treated at the City of Hope National Medical Center. We employed both supervised and unsupervised machine learning methodologies to develop models aimed at improving the understanding and prediction of patient survival outcomes. Unsupervised clustering of RNA-seq data revealed that, despite conventional clinical groupings (HR+/ER+, HER2+, HR+/HER2+, and TNEG), breast cancer patients may segregate into distinct subgroups with statistically significant differences in survival rates. Notably, HR+/ER+ patients, typically associated with better survival outcomes, were observed to cluster alongside high-risk TNEG patients, challenging conventional survival expectations. Building on these insights, we developed two classes of predictive survival models: (i) binary classifiers to predict survival beyond specific time thresholds (e.g., 3- and 5-year survival) and (ii) probabilistic survival models that estimate survival probabilities for clinician-specified timeframes. Evaluation on a 10% hold-out test set demonstrated strong model performance. The top-performing 3-year survival model achieved AUC’s of 0.88 and 0.84 for the ROC and Precision-Recall curves, respectively, while the 5-year survival model reported elevated AUC’s exceeding 0.95 and 0.98. Furthermore, the probabilistic survival model achieved a robust concordance index (C-index) of 0.83. Our findings, in their current form, may equip clinicians with data-driven solutions to support more accurate diagnoses and better-informed personalized treatment planning. Further integration of these models with EMR data may provide deeper insights into the biological and clinical factors underlying the observed survival disparities among breast cancer patients. These insights could inform both improvements to current clinical practices and the development of novel therapeutic approaches. Citation Format: Pooya Naseri-Nosar, Zahra Eftekhari, Chi Wah Wong. Leveraging RNA-seq for breast cancer survival analysis and risk stratification [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 1099.
Abstract 454: Leveraging cell cycle to enhance differentiation therapy in acute myeloid leukemia
Authors: Riccardo Valzelli, Kourosh Hayatigolkhatmi, Amir Hosseini, Elena Ceccacci, Giorgia Parodi, Angela Bachi, Saverio Minucci
Journal: Cancer Research (2025)

Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy characterized by diverse genetic and epigenetic aberrations, making therapeutic interventions particularly challenging. We have identified a novel combination therapy that leverages differences in cell cycle dynamics to induce AML cell differentiation, and found AML cells which were highly sensitive to inhibitors of the histone demethylase LSD1/KDM1A depending on their cell cycle profile. Here, we aim at identifying other potential epigenetic vulnerabilities of AML cell lines exposed to the treatment with different CDK inhibitors. Given the heterogeneity of the epigenetic aberrations found in AML, targeting additional chromatin regulators could improve the clinical applicability of this combination therapy. Thus, we started an in vitro CRISPR-screening approach, targeting over 600 chromatin-related genes. Following optimization of Cas9-expressing clones and evaluation of knockout (KO) efficiency, we confirmed that the best-performing clone retained phenotypic consistency with parental cells under combination therapy. Preliminary data validated the effectiveness of our screening platform, as sgRNAs targeting LSD1 recapitulated the phenotype observed with CDK inhibitors treatment, thereby demonstrating the feasibility of the approach. In parallel, we investigated the molecular link between cell cycle manipulation and chromatin remodeling. We conducted a nuclear phospho-proteomics experiment on cells treated with CDK inhibitors. This analysis identified differential phosphorylation of several proteins, which are currently under investigation. Additionally, gene ontology analysis revealed enrichment in pathways regulating RNA metabolism, transcription, and DNA damage response. By integrating cell cycle modulation with epigenetic targeting, our work offers new insights into AML differentiation therapy, with the potential to improve therapeutic outcomes in diverse AML subtypes. Citation Format: Riccardo Valzelli, Kourosh Hayatigolkhatmi, Amir Hosseini, Elena Ceccacci, Giorgia Parodi, Angela Bachi, Saverio Minucci. Leveraging cell cycle to enhance differentiation therapy in acute myeloid leukemia [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 454.
Abstract 138: Lysyl oxidases suppress pancreatic cancer progression by inhibiting focal adhesion kinase signaling
Authors: Lijuan Sun, Jean Albrengues, John E. Wilkinson, Sarah L. Dallas, Valerie M. Weaver, Mikala Egeblad, Mario A. Shields
Journal: Cancer Research (2025)

Abstract Lysyl oxidases crosslink type I collagen to promote fibrosis and cancer progression in mouse mammary tumor models. Pancreatic ductal adenocarcinomas (PDACs) are highly fibrotic and contain abundant type I collagen with elevated expression of lysyl oxidases. Indeed, inhibition of lysyl oxidases constitute an attractive anti-tumor therapeutic strategy, with several reported preclinical studies demonstrating efficacy at reducing PDAC fibrosis and progression. Yet, lysyl oxidase was first described as an anti-oncogene through its effect of directly suppressing cell transformation by mutant Ras which is present in around 90% of human pancreatic tumors. These prior studies highlight the dual functions, anti-ras and pro-fibrotic, of lysyl oxidases in pancreatic cancer. As a result, clinical trials targeting lysyl oxidase in cancers have demonstrated limited efficacy. Here we examined the effects of perturbation of lysyl oxidase activity or expression using syngeneic orthotopic transplantation models expressing mutant Ras and intravital imaging. Unexpectedly, genetic or pharmacological inhibition of lysyl oxidases increased invasion along collagen fibers and distant metastasis. Furthermore, inhibition of lysyl oxidases promoted focal adhesion kinase (FAK) activity which was required for metastasis. We found that mutant Kras status dictated lysyl oxidase-mediated suppression on FAK signaling in both mouse and human pancreatic cancer cells. These results suggest that the effect of lysyl oxidase on metastasis are dependent on signaling from Ras and FAK. These results strongly caution against inhibiting lysyl oxidases for cancers driven by mutant Ras. Citation Format: Lijuan Sun, Jean Albrengues, John E. Wilkinson, Sarah L. Dallas, Valerie M. Weaver, Mikala Egeblad, Mario A. Shields. Lysyl oxidases suppress pancreatic cancer progression by inhibiting focal adhesion kinase signaling [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 138.
Abstract 1098: Combining random projection and stacking learning for NSCLC subtype classification based on transcriptomic data
Authors: Xinchao Wu, Jieqiong Wang, Shibiao Wan
Journal: Cancer Research (2025)

Abstract Lung cancer is the leading cause of cancer-related death and the second most diagnosed cancer worldwide. Non-small cell lung carcinoma (NSCLC), the predominant type of lung cancer, presents diverse pathological features and cellular heterogeneity. As the two major NSCLC subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) differ in their cells of origin and exhibit distinct disease progression, which, however, are difficult to distinguish solely based on conventional experimental methods like histological examination. Previous studies indicated that predicting subtypes based on transcriptomic profiles is a feasible and promising method. However, existing computational approaches may not fully capture information from transcriptomics data, and they are not effective in dealing with high dimensionality data. To address these concerns, we present a novel method called RPSLearner that combines Random Projection (RP) with Stacking Learning, to efficiently aggregate diverse information from base models for accurate identification of NSCLC subtypes. Specifically, we applied multiple RPs to reduce the dimensionality of transcriptomic profiles, which were then combined to capture diverse feature information. RP could efficiently reduce the dimensionality of the high-dimensional omics data while preserving distances between data points. Subsequently, the dimension-reduced feature vectors were classified by ten machine learning base models, which were further integrated by a stacking meta-learner. The stacking strategy combines the prediction results from various base models using a multi-layer perceptron (MLP) to boost the prediction performance. Experimental results based on 1,333 lung cancer patient samples from The Cancer Genome Atlas Program (TCGA) database suggested that RPSLearner significantly outperformed existing state-of-the-art methods. We further benchmarked RPSLearner against different cases including using only base classifiers, ensemble random projection with classifiers, and score ensemble methods, and demonstrated that RPSLearner achieved the best performance across multiple metrics including accuracy, AUC and F1-score. We believe that RPSLearner has direct positive impacts on downstream lung cancer risk stratification and tailored treatment design. We also expect that RPSLearner can be customized and extensible to identify subtypes of other types of cancer. Citation Format: Xinchao Wu, Jieqiong Wang, Shibiao Wan. Combining random projection and stacking learning for NSCLC subtype classification based on transcriptomic data [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 1098.