tbuckley/cabot-search huggingface dataset:
Abstract 4880: Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics
Authors: Chanchal Bareja, Kountay Dwivedi, Apoorva Uboveja, Ankit Mathur, Naveen Kumar, Daman Saluja
Journal: Cancer Research (2024)

Abstract This work aims to decipher p73-regulated biomarkers for a prompt diagnosis of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling technologies. Transcriptome profile of HCT116 cell line p53−/− p73+/+ and p53−/− p73 knockdown was performed to identify differentially expressed genes (DEGs) followed by cross-checking with three CRC tissue expression datasets available in Gene Expression Omnibus. KEGG and Gene ontology were performed on differentially expressed transcripts obtained via the transcriptome profile and intersected genes. The PPI network was constructed via Cytoscape to extract hub genes. KM plots assisted in investigating the prognostic significance of the hub genes. The clinicopathological relevance was explored using the GEPIA and UALCAN databases. Finally, machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1,289 upregulated and 1,897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. The analysis of gene ontology and KEGG showed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan Meier, GEPIA, and UALCAN analyses uncovered the prognostic and diagnostic relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. This is a novel study utilizing transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-relevant genes. These genes are found relevant regarding the patients’ prognosis and diagnosis. The unveiled biomarkers are also found robust in TNM-stage prediction. This is the first study where transcriptomics, publicly available tissue datasets, and machine learning are altogether employed to reveal key genes in CRC pathogenesis. Our research highlights the robust analysis of four independent data sets to find the key hub genes, further predicting the performance of the key genes in stage-wise classification. Citation Format: Chanchal Bareja, Kountay Dwivedi, Apoorva Uboveja, Ankit Mathur, Naveen Kumar, Daman Saluja. Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4880.
Abstract 4477: The development of a small molecule dual inhibitor of CXCR4 and Mcl-1 to target bortezomib resistant and stem-like cells in multiple myeloma
Authors: Weam Othman Elbezanti, Omar S. Al-Odat, Subash C. Jonnalagadda, Tulin Budak-Alpdogan, Manoj K. Pandey
Journal: Cancer Research (2024)

Abstract Multiple myeloma (MM) is a malignant plasma-cell illness defined by clonal growth of malignant plasma cells within the bone marrow (BM) microenvironment. Despite the availability of many therapeutic alternatives, MM remains consistently lethal, demanding the development of novel techniques. The intimate relationship between myeloma cells and the BM microenvironment is a distinguishing hallmark of MM. The chemokine stromal cell-derived factor-1 (SDF-1) and its receptor CXCR4 enhance the homing of MM cells to the BM, which has important functional implications. The BM microenvironment secretes a variety of hormones and cytokines that activate several signaling pathways, resulting in enhanced production of the anti-apoptotic Mcl-1 protein, boosting the proliferation, survival, and migration of MM cells and conferring resistance to standard chemotherapy. As a result, we created GA-A, a hybrid molecule in which we coupled an aralkyl carboxylic acid moiety, known to preferentially inhibit Mcl-1, to gambogic acid, which has previously been demonstrated to inhibit CXCR4 in MM. Our western blot has shown that this unique hybrid compound successfully suppressed CXCR4 and Mcl-1. Interestingly, using qPCR we found that CXCR4 was observed to be higher expressed in CD138- versus CD138+ cells, while GA-A inhibited CXCR4 in both subpopulations. In addition, using annexin-V flow cytometry staining we found that GA-A increased the apoptotic effect of bortezomib in MM stem-like cells while inhibiting the viability of bortezomib resistant cells. Using human cytokine array we found that GA-A activates some of proinflammatory cytokines that can activate the immune system such as GM-CSF, IFN gamma, IL-2 and IL-12. In summary, our findings indicate the successful creation of a novel dual CXCR4-Mcl1 inhibitor that demonstrates efficacy against the stem cell-like population and bortezomib resistant cells and enhances the effectiveness of bortezomib. Citation Format: Weam Othman Elbezanti, Omar S. Al-Odat, Subash C. Jonnalagadda, Tulin Budak-Alpdogan, Manoj K. Pandey. The development of a small molecule dual inhibitor of CXCR4 and Mcl-1 to target bortezomib resistant and stem-like cells in multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4477.
Abstract 6672: Unraveling the microbiome: antibiotic impact on CAR T therapy and the synergistic potential of vancomycin in enhancing antitumor immunity
Authors: Andrea Facciabene, Mireia Uribe-Herranz, Vijay Bhoj
Journal: Cancer Research (2024)

Abstract The landscape of treating hematologic malignancies has undergone a revolutionary transformation with the advent of Anti-CD19 chimeric antigen receptor T cell therapy (CART-19). Despite its success, persistent challenges like disease relapse and CAR-mediated toxicities remain. A comprehensive multicenter study involving 228 individuals revealed a significant association between antibiotic administration, particularly piperacillin/tazobactam, meropenem, and imipenem/cilastatin (P-I-M), targeting anaerobes within four weeks preceding CART-19 therapy, and adverse outcomes, including worsened survival and increased neurotoxicity. Intriguingly, another antibiotic, Cefepime, also used for neutropenic fever, displayed a positive trend when compared with P-I-M. This highlights the intricate relationship between the modulation of the intestinal microbiome and outcomes in CART-19 therapy for B cell malignancies. Building on these critical observations, our study delved into the impact of vancomycin-induced gut microbiota dysbiosis on chimeric antigen receptor (CAR) T immunotherapy. Utilizing preclinical models of CD19+ A20 lymphoma and CD19+ B16 melanoma, we discovered that mice receiving vancomycin alongside CART-19 therapy exhibited enhanced tumor control and increased cross-presentation of tumor-associated antigens (TAAs) compared to those receiving CART-19 alone. This effect was further validated through fecal microbiota transplants from healthy human donors to pre-conditioned mice, replicating the favorable findings. Importantly, our study extends to clinical correlates, as B cell acute lymphoblastic leukemia patients treated with CART-19 and oral vancomycin displayed elevated CART-19 peak expansion compared to unexposed patients. These compelling results underscore the profound influence of the gut microbiota on CAR T cell therapy and suggest that vancomycin-induced gut microbiota modulation may emerge as a potent strategy to enhance therapeutic outcomes across various tumor types. Citation Format: Andrea Facciabene, Mireia Uribe-Herranz, Vijay Bhoj. Unraveling the microbiome: antibiotic impact on CAR T therapy and the synergistic potential of vancomycin in enhancing antitumor immunity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6672.
Abstract 4878: Enhancing non-invasive detection of urothelial carcinoma through combined cytology and methylation profiling of urinary cell-free DNA
Authors: Huan Zhao, Hang Dong, Haoran Tang, Zhihui Zhang, Na Wei, Jingjing Xu, Pan Du, Shidong Jia, Ting Xiao, Huiqin Guo
Journal: Cancer Research (2024)

Abstract Detection of urothelial carcinoma (UC) faces a significant challenge in sensitivity due to the limitations of existing methodologies such as cytology. Identifying tumors through epigenetics pattern presents a promising advancement in diagnostic precision. This study aims to explore a non-invasive and more sensitive strategy by analyzing urinary cell-free DNA (ucfDNA) methylation profiles in conjunction with traditional urine cytology. The cohort for this study were collected from 20 patients with UC and 17 patients with benign or non-tumor lesions which histopathologic and cytopathologic results were reviewed by pathologist. Additional 16 cancer samples and 40 healthy donor samples were collected from independent research centers. We employed PredicineEPIC, a comprehensive whole-genome methylation assay, to investigate the DNA methylation characteristics in urine samples. The dataset was splited into 1:2 proportions for modeling and validation. The methylation signals were fed to three machine learning algorithms for determining the most effective classification model. Differentiating cancer and benign samples through urinary cytology has high specificity (1.0) but low sensitivity (0.3). Then we examined differentially methylated regions and implemented four most abnormally methylated regions as classify features. We found that the Gradient Boosting Machine (GBM) model achieved optimal performance in the validation set, with an AUC of 0.93, sensitivity of 0.81 (21/26), and specificity of 1.0 (36/36). The combination of methylation- and urine cytology-based models raised 2% prediction accuracy without reducing precision, suggesting that this assay could be applied to augment the cytology testing for UC detection. This study proposes a new and promising approach, leveraging cytology combined with epigenetic profiles of urinary cell-free DNA, for the precise diagnosis of urothelial carcinoma. Citation Format: Huan Zhao, Hang Dong, Haoran Tang, Zhihui Zhang, Na Wei, Jingjing Xu, Pan Du, Shidong Jia, Ting Xiao, Huiqin Guo. Enhancing non-invasive detection of urothelial carcinoma through combined cytology and methylation profiling of urinary cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4878.
Abstract 4473: Targeting matrix metalloproteinase-3 (MMP-3) in high-grade serous ovarian cancer
Authors: Víctor G. Reyes-Burgos, Eliud Hernández-O'Farrill, Luis E. Vazquez Quiñones, Marienid Flores-Colón, Fatima Valiyeva, Pablo E. Vivas-Mejía
Journal: Cancer Research (2024)

Abstract High-Grade Serous Ovarian Cancer (HGSOC) is the most aggressive and lethal gynecological malignancy. Surgery and chemotherapy (with platinum compounds) are the main treatments. Many patients relapse and develop chemotherapy resistance, limiting the therapeutic utility of these therapies. This persistent resistance makes HGSOC treatment difficult. Cisplatin-resistant ovarian cancer cells upregulate Matrix Metalloproteinase-3. MMP3 is an enzyme involved in active tissue remodeling and extracellular matrix protein breakdown during dynamic physiological processes. The structure of MMP3 includes the prodomain, the catalytic domain and the hemopexin domain. We showed that MMP3 protein levels are higher in cisplatin resistant as compared with cisplatin sensitive HGSOC cells. Our research team showed that small interference RNA (siRNA)-mediated MMP3 knockdown reduced cisplatin-resistant ovarian cancer cell growth and invasion. This data indicates that MMP3 may be a promising HGSOC treatment target. Targeting MMP3 with small chemical inhibitors (SCI) could represent a better option than siRNA because SCI are more stable and easier to deliver. The purpose of this project is to design SCI of MMP3 in the propeptide, catalytic, and hemopexin domains of MMP3. We used in silico methods to identify MMP3 inhibitors in these domains. The ZINC20 database of commercially compounds was used for virtual screening using Python Prescription (PyRx) software. We identified 25 ligands out of 370,000 with the greatest affinity for each MMP3 functional domains. SwissADME was then used to evaluate in silico absorption, distribution, metabolism, pharmacokinetics, drug-likeliness, and medicinal chemistry. This research lays the groundwork for HGSOC therapeutic strategies to improve treatment efficacy, especially for chemotherapy-resistant patients. Citation Format: Víctor G. Reyes-Burgos, Eliud Hernández-O'Farrill, Luis E. Vazquez Quiñones, Marienid Flores-Colón, Fatima Valiyeva, Pablo E. Vivas-Mejía. Targeting matrix metalloproteinase-3 (MMP-3) in high-grade serous ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4473.
Abstract 4476: Novel auristatins with improved tolerability and unique bystander activity profile
Authors: Philip N. Moquist, Nicole M. Eng-Duncan, Tim Bovee, Katie Snead, Jenn Wright, Kaleb Smith, Teri Blevins, Brittney Blackburn, Michelle Ulrich, Forgivemore Magunda, Jessica Simmons, Peter D. Senter, Svetlana O. Doronina
Journal: Cancer Research (2024)

Abstract Auristatins are a class of clinically validated payloads used extensively in antibody-drug conjugate (ADC) technology. There are currently 4 FDA approved ADCs utilizing the vedotin drug linker to treat an array of solid tumors and lymphomas and several more in clinical trials. Auristatin payloads have several desirable properties, including strong anticancer cytotoxicity, bystander activity, and induction of immunologic cell death. While auristatin-based ADCs have been remarkably successful, there remain opportunities to further understand and improve payload properties. Bystander activity is one such property that can greatly impact the balance between efficacy and tolerability. Herein, we present a medicinal chemistry investigation into a series of auristatins with similar tubulin binding and biochemical potency that differ in lipophilicity and ability to cross the cell membrane. This design enabled us to focus on and characterize the impact of bystander activity while maintaining all other parameters. Ultimately, this effort resulted in ADCs with similar on-target in vitro cytotoxicity but differed in the extent of off-target bystander activity. Evaluation of in vivo efficacy, in vivo bystander activity, and rodent tolerability afforded a lead drug linker that was well-tolerated as an ADC and exhibited strong antitumor growth delay. Citation Format: Philip N. Moquist, Nicole M. Eng-Duncan, Tim Bovee, Katie Snead, Jenn Wright, Kaleb Smith, Teri Blevins, Brittney Blackburn, Michelle Ulrich, Forgivemore Magunda, Jessica Simmons, Peter D. Senter, Svetlana O. Doronina. Novel auristatins with improved tolerability and unique bystander activity profile [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4476.
Abstract 3509: Using generative AI for filtering and comprehension of drug target discovery screen results
Authors: Andrew Rech, Yury Goltsev, Nikolay Samusik, Anna Kaznadzey
Journal: Cancer Research (2024)

Abstract Recent advancements in generative AI, including models such as GPT-4 and LLAMA2, have been rapidly integrated into cancer research. Cellformatica, Inc. has developed a pipeline designed to enhance the accuracy of functional and genomic drug target discovery screens by reducing the incidence of false positives and refining the interpretation process. This pipeline is capable of conducting comprehensive investigations, including genome-wide analyses, and is accessible via web and API interfaces. At the core of the pipeline is the use of the extensive global corpus of scientific literature to assess the relevance of each gene within the context defined by the experimental design of the screen. To address the issue of AI-generated inaccuracies, often referred to as 'hallucinations,' the pipeline incorporates a custom-tuned language model (LLM) component within the generative AI framework. This fine-tuning process is specifically tailored to the gene set under investigation. The pipeline's multi-agent architecture is a distinctive feature. It scores candidates based on three orthogonally-tuned criteria: effectiveness, confidence, and novelty. In benchmarking tests, this multi-agent scoring system has outperformed GPT-4 and has proven particularly adept at identifying candidate targets that may have been overlooked in previous research. These candidates are recognized for their significant therapeutic potential and are corroborated by existing scientific findings. The versatility of the Cellformatica pipeline is demonstrated through its application in three distinct areas of cancer research. First, it has been employed to optimize chimeric antigen receptor (CAR) T-cell therapies, a form of immunotherapy for cancer treatment. Second, the pipeline has been used to elucidate the network of chemotactic molecules that orchestrate immune cell infiltration and tissue remodeling in the lungs of patients with COVID-19. Third, it has been used to spatially define signaling pathways in humanized xenograft murine tumor experiments, for which existing cross-species database knowledge is limited. These applications underscore the pipeline's potential to contribute to the understanding and treatment of complex diseases. Citation Format: Andrew Rech, Yury Goltsev, Nikolay Samusik, Anna Kaznadzey. Using generative AI for filtering and comprehension of drug target discovery screen results [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3509.
Abstract 4475: Discovery of an oral tricyclic STING agonist with superior pharmacokinetic properties and potent in vivo efficacy
Authors: Hong-Yi Zhao, Zhongwei Liu, Shuai Mao, Miao He, Meilin Wang, Bo Wen, Wei Gao, Duxin Sun
Journal: Cancer Research (2024)

Abstract Stimulator of interferon genes (STING) plays a pivotal role in regulating immune microenvironment for cancer immunotherapy. However, the use of most STING agonists has been hindered by the necessity for intra-tumoral or intravenous administration, underscoring the challenge in developing an orally bioavailable STING agonist with favorable pharmacokinetic (PK) properties. In this study, we discovered oral STING agonists featuring a novel tricyclic benzo[4,5]thieno[2,3-c]pyrrole-1,3-dione scaffold, thus achieving both superior PK properties and potent in vivo efficacy. Our lead compound, ZSA-51, exhibited nanomolar cellular STING activation activity (EC50 = 100 nM) in THP1 cells, surpassing the reported oral STING agonist MSA-2 by 32-fold (EC50 = 3200 nM). Furthermore, ZSA-51 demonstrated superior PK properties with an oral bioavailability of 49%, and robust in vivo antitumor activity both in colon and pancreatic cancers model upon oral administration. Notably, ZSA-51 displayed preferential distribution to the lymph nodes and spleen, enhancing its systemic immune-stimulating effects. The specificity of ZSA-51 was further confirmed by a comparison with a negative control compound (ZSA-52NC2) that was unable to stimulate in vitro and in vivo STING activity. Molecular docking and MD simulation confirmed that the active form of ZSA-51 stably bound to STING dimer with validated hydrogen bond network. Taken together, our findings suggest ZSA-51 as a promising oral STING agonist characterized by superior PK properties and potent in vivo efficacy, holding potential for future development for cancer immunotherapy. Citation Format: Hong-Yi Zhao, Zhongwei Liu, Shuai Mao, Miao He, Meilin Wang, Bo Wen, Wei Gao, Duxin Sun. Discovery of an oral tricyclic STING agonist with superior pharmacokinetic properties and potent in vivo efficacy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4475.
Abstract 573: SLC22A17 as a promising target for triple-negative breast cancer
Authors: Ajaikumar B. Kunnumakkara, Krishan Kumar Thakur
Journal: Cancer Research (2024)

Abstract Introduction: Triple-negative breast cancer (TNBC) represents a highly aggressive subtype of breast cancer, accounting for approximately 15% of all cases of invasive breast cancer globally. The absence of a well-defined cell membrane receptor has significantly limited the therapeutic options available for treating this malignancy. Consequently, there is a compelling need to explore novel receptors that could potentially revolutionize the treatment strategies employed for TNBC. Recent scientific research has unveiled SLC22A17, a cell surface receptor known for its involvement in iron transport, as a substantial contributor to the development of various cancer types. Thus, the primary objective of this study is to comprehensively evaluate the significance of SLC22A17 and its potential as a therapeutic target in TNBC. Experimental Procedures: In this study, we meticulously examined the expression patterns of SLC22A17 across distinct subtypes of breast cancer utilizing immunohistochemistry. Computational tools were employed to delve into the role of SLC22A17 within TNBC datasets. To assess the impact of SLC22A17 on TNBC, we employed various methods, including siRNA-mediated silencing, to investigate its role in cell proliferation, apoptosis, epithelial-mesenchymal transition (EMT), and migration. Additionally, we conducted western blot analyses to assess the expression levels of SLC22A17 and cellular signaling molecules, such as Akt/mTOR and JAK/STAT, within the context of TNBC cells. Results: Our analysis revealed a significant upregulation of SLC22A17 in both TNBC tissues and cell lines, establishing a direct correlation with patient survival outcomes. Silencing the expression of SLC22A17 resulted in reduced cell proliferation, migration, EMT, increased apoptosis, and enhanced autophagy. These effects were accompanied by the inhibition of Akt/mTOR and STAT3 signaling pathways. Moreover, we observed the involvement of the inflammatory cytokine TNF-α, which induced the expression of SLC22A17 in TNBC cell lines and governed various hallmark features of TNBC. Notably, the suppression of SLC22A17 mitigated these processes. Conclusion: In conclusion, this investigation suggests the pivotal role played by SLC22A17 in TNBC and posits its potential as a novel and promising therapeutic target for the management of this formidable disease. Citation Format: Ajaikumar B. Kunnumakkara, Krishan Kumar Thakur. SLC22A17 as a promising target for triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 573.