tbuckley/cabot-search huggingface dataset:
Abstract 2706: Engineered tumor-targeting bacteria expressing cancer-killing protein for breast cancer
Authors: Duy Binh Tran, Konstantin Christov, Balaji Ganesh, Jing Wang, Albert Green, Tohru Yamada
Journal: Cancer Research (2025)

Abstract Despite significant advancements in the treatment of advanced breast cancer, therapies using chemotherapeutic agents still face considerable challenges. One key issue is the hypoxic tumor microenvironment (TME), characterized by insufficient blood flow and oxygen supply within tumors. This hypoxia reduces the sensitivity of breast tumors to treatment, diminishes therapeutic efficacy, and promotes resistance and metastasis. Approximately 25-40% of invasive breast cancers exhibit hypoxic tumors, which are strongly associated with increased risks of metastasis and chemotherapy resistance. Therefore, there is a pressing clinical need for novel strategies to specifically target these hypoxic tumor regions. To address this limitation, our objective is to develop an innovative approach using tumor-targeting bacteria carrying cancer-killing protein, controlled by an engineered hypoxia-inducible expression system. In this study, four Escherichia coli strains from the probiotic Symbioflor® 2 were isolated and characterized based on colony morphology and PCR results. These strains were transformed with luciferase gene, and luciferase activity was monitored to determine biodistributions of each E. coli after intravenous injection into mice. Our data showed that G5 strain preferentially colonized in xenografted triple-negative breast tumors. To specifically express cancer-killing azurin gene within the hypoxic tumor TME, we optimized the hypoxia-inducible promoter in E. coli G5 cells. To test its anti-tumor efficacy, TNBC cells were orthotopically injected into the mammary fat pads of immunodeficient mice. The engineered E. coli G5Hyp*-azurin strain was administered at varying concentrations (1×105, 1×106, and 1×107 CFU/mouse) through weekly tail vein injections over three weeks. Our results demonstrated that E. coli G5Hyp*-azurin, at doses of 1×107 and 1×106 CFU, effectively inhibited tumor progression. In contrast, lower dose (1×105 CFU) showed no significant effect. Additionally, no noticeable side effects were observed, as there was no evidence of weight loss. Overall, our results highlight the potential of our newly designed bacterial approach to develop an innovative treatment strategy. Citation Format: Duy Binh Tran, Konstantin Christov, Balaji Ganesh, Jing Wang, Albert Green, Tohru Yamada. Engineered tumor-targeting bacteria expressing cancer-killing protein for 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 2706.
Abstract 7449: Evaluating the effectiveness of machine learning models using polygenic risk scores and clinical factors for breast cancer risk stratification
Authors: Lannawill Caruth, Roukiatou Sore, Sarah Ehsan, Anne-Marie McCarthy, Shefali Setia-Verma
Journal: Cancer Research (2025)

Abstract Breast cancer is the most common cancer among women in the United States, aside from skin cancer. Early detection and treatment significantly reduce mortality rates, highlighting the importance of effective risk prediction methods. This study evaluates the predictive performance of multiple machine learning models that include polygenic risk scores (PRS), clinical and demographic factors. We compared three predictive models using 313 SNPs model from PGS catalog and incorporated age, body mass index (BMI), five genetic principal components (PC1-5), and breast density as predictor variables. Logistic Regression (LR), Multilayer Perceptron (MLP), and Probabilistic Graphical Model (PGM) were implemented to assess breast cancer risk. MLPs are a type of artificial neural network that consists of layers of interconnected "neurons" that work together to process and learn from data. Each layer processes the information and passes it to the next layer, allowing the model to learn complex patterns and detect non-linear relationships in the data, which makes it useful for predictions. The selected PGM implementation uses Bayesian statistics to determine feature importance and correlation. The model then uses this information to predict the probability an instance belongs to a group (e.g. case or control). Among 3, 000 African ancestry women from the Penn Medicine BioBank, model performance varied. Logistic Regression demonstrated the highest predictive accuracy at 0.69, followed by Probabilistic Graphical Model at 0.64 and Multilayer Perceptron at 0.63. Our study suggests limited interaction between PRS and clinical factors in breast cancer prediction. These findings aligns with prior research demonstrating the largely independent nature of polygenic risk scores from traditional clinical risk factors. These findings underscore the challenges in developing more sophisticated risk prediction models and highlight the need for alternative approaches to capture the complex genetic architecture of breast cancer risk Subsequent research will incorporate moderate and high penetrance genes to enhance the predictive power of our risk assessment models, potentially improving precision in breast cancer risk stratification. Citation Format: Lannawill Caruth, Roukiatou Sore, Sarah Ehsan, Anne-Marie McCarthy, Shefali Setia-Verma. Evaluating the effectiveness of machine learning models using polygenic risk scores and clinical factors for breast cancer 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 7449.
Abstract 5617: Development of an initial triple combination therapy targeting EGFR, AXL, and FGFR for EGFR-mutated non-small cell lung cancer
Authors: Ryota Nakamura, Tadaaki Yamada, Tomoko Yasuhiro, Ryohei Kozaki, Koichi Takayama
Journal: Cancer Research (2025)

Abstract Background: Based on our prior preclinical research, a clinical trial is currently underway to assess the safety and efficacy of combining the third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) osimertinib with the AXL inhibitor ONO-7475 in treatment-naive patients with advanced EGFR-mutated non-small cell lung cancer (NSCLC) in Japan (jRCT2051210045). However, even with this combined treatment, the development of acquired resistance is expected in clinical settings. Therefore, we aimed to establish a multidrug combination therapy from the first-line setting to eliminate such resistance. Methods: We investigated adaptive resistance mechanisms to the combination of osimertinib and ONO-7475 and developed a novel therapeutic strategy to overcome them using in vitro and in vivo experiments. Results: In an siRNA screening assay, knockdown of fibroblast growth factor receptor 1 (FGFR1) significantly inhibited cell viability in the presence of osimertinib plus ONO-7475. The combination of osimertinib and ONO-7475 activated FGFR1 through the upregulation of FGF2 via the c-Myc pathway. In cell-based assays, triple combination therapy with the pan-FGFR inhibitor BGJ398, along with osimertinib and ONO-7475, significantly enhanced cell apoptosis by increasing the proapoptotic factor Bim and inhibited cell viability compared to the combination of osimertinib and ONO-7475 alone. In cell line-derived xenograft (CDX) models, the combination of osimertinib, ONO-7475, and BGJ398 substantially suppressed tumor regrowth. No apparent adverse effects were observed during this treatment. Conclusions: Our findings demonstrate that the FGF2-FGFR1 signaling pathway contributes to adaptive resistance to the combination of osimertinib and ONO-7475 in EGFR-mutated NSCLC with high AXL expression. The triple combination therapy of osimertinib, ONO-7475, and BGJ398 showed safety and prevented tumor regrowth in CDX models, suggesting that this regimen has the potential to improve outcomes for patients with EGFR-mutated NSCLC. Citation Format: Ryota Nakamura, Tadaaki Yamada, Tomoko Yasuhiro, Ryohei Kozaki, Koichi Takayama. Development of an initial triple combination therapy targeting EGFR, AXL, and FGFR for EGFR-mutated non-small cell lung 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 5617.
Abstract 7450: Leveraging large language models' own knowledge for obtaining and exploring gene embeddings
Authors: Dailin Gan, Siyuan Zhang, Xin Lu, Jun Li
Journal: Cancer Research (2025)

Abstract Transfer learning is increasingly prevalent in gene expression analysis. Large transformer-based models generate embeddings from genes, which are used to construct machine-learning models for various downstream tasks. Given the high cost of training these large models, recent work has utilized ChatGPT-4, a pre-trained language model, to produce embeddings from manually curated NCBI gene descriptions. However, this method does not leverage the vast pre-trained knowledge of ChatGPT-4. In this work, we propose directly querying ChatGPT-4 about genes and converting its responses into embeddings rather than using static NCBI descriptions. For each gene, we independently asked ChatGPT-4 four specific questions designed to elicit ChatGPT’s knowledge about each gene’s general characteristics, regulatory functions, gene structure, and implications in cancer research. We evaluate our approach by comparing the embeddings it produces with those derived from NCBI descriptions. The comparison suggests, notably, that ChatGPT-4’s knowledge closely aligns with that of NCBI despite differences in presentation. Furthermore, ChatGPT-4 reveals more detailed and comprehensive information about many genes. We also used the ChatGPT-answer-derived gene embeddings to distinguish long and short-range transcription factors, dosage-sensitive and insensitive genes, and bivalent, Lys4-methylated, and non-methylated genes. Results indicate a preference for ChatGPT-answer-derived gene embeddings in downstream applications by machine learning models. Overall, our findings suggest that direct querying of ChatGPT-4 offers clear advantages for generating gene embeddings. Citation Format: Dailin Gan, Siyuan Zhang, Xin Lu, Jun Li. Leveraging large language models' own knowledge for obtaining and exploring gene embeddings [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 7450.
Heterogeneity of high-potency multilineage hematopoietic stem cells and identification of 'Super' transplantability
Authors: Fang Dong, Sen Zhang, Caiying Zhu, Zining Yang, Lisha Wang, Yao Ma, Jiayi Lu, Xialin Li, Xiaofang Wang, Nini Wang, Shanshan Zhang, Miner Xie, Jinhong Wang, Xiaobing Zhang, Yawei Zheng, Shihui Ma, Hideo Ema, Hui Cheng, Sha Hao, Toshio Suda, Yu Lan, Bing Liu, Ping Zhu, Junren Chen, Tao Cheng
Journal: Blood (2025)

Hematopoietic stem cells (HSCs) are heterogeneous, and the quality of HSCs - that is, 'transplantability' - is a key determinant for posttransplant hematopoietic reconstitution. However, molecular modalities of high-potency HSCs with superior transplantability still remain poorly understood. Here, we conducted large-scale single-clone serial-transplant experiments and tracked descendant cells of 288 HSC clones to quantify their intrinsic capability for hematopoietic reconstitution. Using integrated single-cell transcriptional, immunophenotypical, and Bayesian dynamic analyses, we uncovered three classes of HSC clones - 'Super', 'Flash', and 'Trickle' - that had higher output in the 1st generation but exhibited markedly different behavior in later generations. The 'Super'-class HSC clones comprised 4% of the HSCs and manifested persistent superior transplantability and balanced myeloid/lymphoid lineage outputs across generations in serial transplants. The 'Super'-class HSCs had a unique molecular signature, including low expression of CD27, that was distinct from previously known 'Classical HSC' signatures. Validation experiments indicated that CD27- HSCs had superior transplantability compared to CD27+ HSCs. Our study asserted an operational definition for 'Super' transplantability of HSCs, defined its molecular program, and suggested new directions for enriching high-potency HSCs in grafts.
Abstract 1405: NAT10-mediated RNA acetylation as a critical dependency in pancreatic ductal adenocarcinoma
Authors: Maria Farooq, Ayush Raman, Hamid Beiki, Shalini Oberdoerffer
Journal: Cancer Research (2025)

Abstract Pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related death worldwide, underscoring the urgent need to uncover its molecular underpinnings and develop novel therapeutic targets. Over 170 RNA modifications constitute the epitranscriptome. Notably, the chemical modification N4-acetylcytidine (ac4C) and its writer enzyme, N-acetyltransferase 10 (NAT10), are overexpressed in most solid tumors, including pancreatic cancer, with high NAT10 expression correlating with worse disease-free survival. Acetylation of mRNA has been shown to enhance translation efficiency and RNA stability, while acetylation of ribosomal RNA is critical for ribosomal biogenesis, thereby influencing protein production. Furthermore, NAT10 has emerged as a critical dependency in most pancreatic cancer cell lines. This study aims to elucidate the role of NAT10 and RNA acetylation in PDAC. By knocking down NAT10 in isogenic pancreatic cell lines, we observed significant reductions in cell proliferation, colony formation, and epithelial-to-mesenchymal transition. Additionally, NAT10-depleted cell lines exhibited increased sensitivity to inhibitors targeting KRAS G12D. RNA sequencing revealed that NAT10 downregulation is associated with upregulated TGF-β signaling, altered lipid metabolism and mitochondrial function. We further mapped ac4C using two orthogonal approaches: ac4C-RNA immunoprecipitation followed by sequencing and a chemical-based method (RetraC:T) that identifies ac4C at base resolution through C:T mismatches. These approaches identified acetylation of oncogenic transcripts implicated in tumorigenesis. Our findings suggest that NAT10 represents a promising therapeutic target in pancreatic ductal adenocarcinoma, offering potential avenues for the development of targeted treatments. Citation Format: Maria Farooq, Ayush Raman, Hamid Beiki, Shalini Oberdoerffer. NAT10-mediated RNA acetylation as a critical dependency in pancreatic ductal adenocarcinoma [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 1405.
Abstract 7451: Efficacy of chemoradiotherapy in older patients with advanced stage head and neck cancer: A propensity and machine learning analysis
Authors: Rasheed Omobolaji Alabi, Alhadi Almangush, Antti Mäkitie
Journal: Cancer Research (2025)

Abstract Background: The rising incidence of head and neck squamous cell carcinoma (HNSCC) is linked with both human papillomavirus and aging population. As a result, there is an emerging need for developing management strategies for older patients with advanced-stage HNSCC. This study aims to use machine learning (ML) techniques to examine the efficacy of definitive, concurrent chemoradiotherapy (CRT) for older patients (≥65 years) with stage III-IV HNSCC. We used permutation feature importance (PFI) to compare the efficacy of CRT with single-modality radiotherapy (RT) for the overall survival (OS) of this HNSCC subgroup. Finally, we compared the results obtained using ML analysis with propensity-score matched (PSM) analysis to clarify the efficacy of CRT in this patient population. Methods: A total of 3570 patients with HNSCC were obtained from the Surveillance, Epidemiology, and End Results (SEER) Program to build the ML model. We compared voting ensemble and extreme gradient boosting algorithms for stratifying older (≥65 years) HNSCC patients into OS risk groups. Furthermore, the PFI analysis was based on stacked ML models to evaluate the efficacy of CRT regarding OS of older (≥65 years) HNSCC patients. Then, we performed a 1:1 propensity-score matched (PSM) analysis to reduce treatment selection bias and analyze the prognostic role of CRT. Results: Our ML model showed a performance accuracy of 85.2% in predicting OS in this subgroup of HNSCC. The aggregate feature importance showed that CRT is the most important feature for OS in this group of HNSCC patients. In addition, patients with stage IV disease showed better survival than those with stage III. The PSM confirmed the efficacy of CRT compared to RT alone. Conclusions: This study showed that CRT may be more feasible and effective than RT alone for older patients with stage III-IV HNSCC. This further emphasized that advanced-stage treatment decisions should rather be made according to other patient-related factors than chronologic (calendar) age alone. Therefore, validating these models with multi-institutional datasets and testing them in the context of clinical trials is warranted to confirm the efficacy of CRT over RT alone for older patients (≥65 years) with stage III-IV HNSCC. Citation Format: Rasheed Omobolaji Alabi, Alhadi Almangush, Antti Mäkitie. Efficacy of chemoradiotherapy in older patients with advanced stage head and neck cancer: A propensity and machine learning analysis [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 7451.
Abstract 6889: A gold-based antibody-drug conjugate with theranostic potential for synovial sarcoma
Authors: Jeffrey Okojie, Bridger Kearns, Melanie l'anson-Holton, Andralyn McKell, Jared James Barrott
Journal: Cancer Research (2025)

Abstract Synovial sarcoma is a rare form of cancer that occurs in the soft tissue adjacent to bones in adolescents and young adults. Although rare, it is ranked as the second most common malignant soft-tissue tumor in children. This cancer has a high metastatic rate with 50% of all cases exhibiting metastasis at the time of diagnosis. The first line of therapy for this cancer is surgery which is effective for the primary tumor, but in patients with metastatic disease, chemotherapy and radiation are the main therapeutic options. These therapies tend to have negative side effects due to their lack of specificity. Antibody-drug conjugates are a promising class of cancer therapeutics due to their specificity and reduced side effects. Of particular interest are gold-based antibody-drug conjugates due to their laser-induced temporal control and theranostic (diagnostic and therapeutic) capabilities. This work sought to develop a novel gold-based antibody-drug conjugate for synovial sarcoma with limited side effects. To accomplish this goal, a potential target receptor, Oncostatin M Receptor (OSMR), was identified. This receptor has been shown to be involved in cancer proliferation and migration when activated by its ligand, Oncostatin M(OSM). OSMR is highly expressed in various cancers including breast cancer. The mRNA and protein expression of OSMR in synovial sarcoma tumors was compared to normal tissues. An anti-OSMR antibody was conjugated to a fluorophore and the conjugate was injected into synovial sarcoma-bearing mice to assess the specificity and targeting ability of the antibody in vivo. Gold nanorods were then synthesized, characterized, and conjugated site-specifically to the anti-OSMR antibody. The over-expression of OSMR in synovial sarcoma tumors as compared to normal tissues was validated. An anti-OSMR antibody conjugated to a fluorophore targeted OSMR-expressing tumors and metastatic cells and discriminated between tumor and normal tissue in vivo. Gold nanorods were successfully synthesized and conjugated site-specifically to an anti-OSMR antibody. These findings suggest OSMR as a viable candidate for a targeted treatment of synovial sarcoma. They also suggest that an anti-OSMR conjugate can target both primary and metastatic tumors. The successful conjugation of gold nanorods to the anti-OSMR antibody opens the possibility of a theranostic approach to synovial sarcoma. Citation Format: Jeffrey Okojie, Bridger Kearns, Melanie l'anson-Holton, Andralyn McKell, Jared James Barrott. A gold-based antibody-drug conjugate with theranostic potential for synovial sarcoma [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 6889.
Abstract 5607: Discovery and preclinical evaluation of a potent, orally bioavailable, highly selective, small molecule PTPN2/1 inhibitor
Authors: Alexei Pushechnikov, Vladislav Parchinsky, Aleksei Riakhovskii, Stepan Mochalov, Ruben Karapetian, Amy Burd, Nikolay Savchuk, Iain Dukes, Ruben Abagyan
Journal: Cancer Research (2025)

Abstract Protein tyrosine phosphatase non-receptor type 2 (PTPN2) is ubiquitously expressed, primarily in hematopoietic and placental cells. Critical negative regulator of the JAK-STAT pathway, PTPN2 functions to directly regulate signaling through cytokine receptors, including IFNγ. Thus, enhancing IFNγ sensing and signaling through the inhibition of PTPN2 is a potential therapeutic strategy to improve the efficacy of cancer immunotherapy regimens. Historically, phosphatases are difficult drug targets; due to an intrinsic physicochemical profile of phosphatase inhibitors, finding bioavailable molecules is a great challenge. ZE00-0388 demonstrates sub-nanomolar PTPN2/N1 inhibitory activity and outstanding over 1000-fold selectivity against all other phosphatases. ZE00-0388 demonstrated complete tumor remission in combination with the anti-mPD-1 recombinant mAb therapy in the murine colon adenocarcinoma MC38 in vivo model. ZE00-0388 demonstrates good safety and tolerability; its ADME properties confirm that ZE00-0388 is suitable for further development for combination therapies. Citation Format: Alexei Pushechnikov, Vladislav Parchinsky, Aleksei Riakhovskii, Stepan Mochalov, Ruben Karapetian, Amy Burd, Nikolay Savchuk, Iain Dukes, Ruben Abagyan. Discovery and preclinical evaluation of a potent, orally bioavailable, highly selective, small molecule PTPN2/1 inhibitor [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 5607.