tbuckley/cabot-search huggingface dataset:
Abstract 6297: Mapping cellular interactions from spatially resolved transcriptomics data
Authors: James Zhu, Yunguan Wang, Woo Yong Chang, Alicia Malewska, Fabiana Napolitano, Jeffrey C. Gahan, Nisha Unni, Min Zhao, Rongqing Yuan, Fangjiang Wu, Lauren Yue, Lei Guo, Zhuo Zhao, Danny Chen, Raquibul Hannan, Siyuan Zhang, Guanghua Xiao, Ping Mu, Ariella B. Hanker, Douglas Strand, Carlos L. Arteaga, Neil Desai, Xinlei Wang, Yang Xie, Tao Wang
Journal: Cancer Research (2025)

Abstract Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia’s power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications. Citation Format: James Zhu, Yunguan Wang, Woo Yong Chang, Alicia Malewska, Fabiana Napolitano, Jeffrey C. Gahan, Nisha Unni, Min Zhao, Rongqing Yuan, Fangjiang Wu, Lauren Yue, Lei Guo, Zhuo Zhao, Danny Chen, Raquibul Hannan, Siyuan Zhang, Guanghua Xiao, Ping Mu, Ariella B. Hanker, Douglas Strand, Carlos L. Arteaga, Neil Desai, Xinlei Wang, Yang Xie, Tao Wang. Mapping cellular interactions from spatially resolved transcriptomics 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 6297.
Abstract 6307: ELViS : An R package for estimating copy number levels of viral genomic segments at base-resolution
Authors: Jin-Young Lee, Hyo Young Choi, Jeremiah R. Holt, Xiaobei Zhao, Katherine A. Hoadley, D. Neil Hayes
Journal: Cancer Research (2025)

Abstract Tumor virus is one of the major causes of human cancers, accounting for about 10-20% of their global incidence. Viral oncogenes in the tumor viruses promote tumorigenesis by deregulating host cancer genes. For instance, E6 and E7 proteins of human papillomavirus 16 (HPV16) induce the degradation of the protein products of tumor suppressor genes (TSGs) including p53 and pRb, to promote head and neck squamous cell carcinoma (HNSCC). Therefore, it is imperative to understand how differences in the dosage of viral genes affect cancer biology. However, how the relative and absolute abundance of each viral gene affects tumorigenesis and tumor biology has remained elusive, due to the difficulties in the analysis. Unlike human genomes that exist in two copies per cell, viral genome copies can take dynamic ranges, making copy number analyses challenging. Thus, we developed an R package ELViS that analyzes viral copy number change events from targeted DNA sequencing of whole viral genomes. In the first step, it takes aligned reads in the BAM file to generate base-resolution read depth and finds baseline regions based on the shape of the data. Next, to overcome the uneven base-resolution depths of targeted sequencing data and find copy number events, our framework performs joint segmentation in two-dimensional data space using both normalized read depths and Z-scores. In the simulation dataset, ELViS could detect deletions as small as 50bp and duplications as small as 500bp with F1 score &amp;gt 0.75 at 50X coverage depths. ELViS provides base-resolution copy number change calls, along with publication-ready read depth line plots and cohort-level heatmap visualization of multiple data tracks with integrative clustering results. Citation Format: Jin-Young Lee, Hyo Young Choi, Jeremiah R. Holt, Xiaobei Zhao, Katherine A. Hoadley, D. Neil Hayes. ELViS : An R package for estimating copy number levels of viral genomic segments at base-resolution [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 6307.
Abstract 6409: Epigenetic regulation of drug and disease related genes impact outcomes in pediatric AML
Authors: Naifah Alshameri, Francisco Marchi, Xueyuan Cao, Stanley Pounds, Jatinder Lamba
Journal: Cancer Research (2025)

Abstract DNA methylation is dysregulated in Acute Myeloid Leukemia (AML), which can have significant impact on regulation and expression levels of genes involved in Pharmacokinetic/Pharmacodynamics of AML chemotherapy as well as genes involved in AML pathogenesis. Thus, in this study we evaluated methylation levels of these genes for association with outcome, in pediatric AML patients. DNA methylation data available from GEO and GDC datasets and gene expression data available from TARGET for the discovery cohort of patient treated on Children’s Oncology Group clinical trials (n=924) were evaluated by Cox proportional hazard analysis for association with event-free survival (EFS) and overall survival (OS) and Minimal Residual Disease after induction I (MRD1). The results were validated in an independent cohort of patients treated on AML02 trial (n=159). We identified 23 CpGs in drug pathway genes, and 43 CpGs within AML relevant genes, that were associated with at least one endpoint at p&amp;lt; 2.17×10-5. These included, ABCA3, a drug efflux transporter, with higher methylation associated with poor OS (HR= 1.23, p &amp;lt;10−12), EFS (HR= 1.14, p &amp;lt;10−10), and greater MRD1 positivity (p &amp;lt;10−5). KIT hypermethylation was associated with unfavorable outcomes (OS, HR= 1.22, p &amp;lt;10−9; EFS, HR= 1.15, p &amp;lt;10−7). Among other genes DNMT3B hypermethylation was also predictive of detrimental survival (OS, HR= 1.143, p= 0.04). These results were replicated in the validation cohort. Our study shows significant age-adjusted correlations between DNA methylation of some CpGs sites and gene expression level. In conclusion, this focused approach demonstrated impact of variation in DNA methylation of key genes of relevance to AML drugs or pathogenesis on clinical outcome in AML opening up opportunities to predict outcome and strategically utilize hypomethylating agents to improve outcome. Citation Format: Naifah Alshameri, Francisco Marchi, Xueyuan Cao, Stanley Pounds, Jatinder Lamba. Epigenetic regulation of drug and disease related genes impact outcomes in pediatric AML [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 6409.
Abstract 6474: Comprehensive immunophenotypic profiling in anti-PD-1-responsive and -resistant mouse models reveals perforin-expressing CD4+ T cells as key effectors of cytolytic activity
Authors: Hiroyuki Inoue, Takayuki Hamasaki, Kazuhiko Inoue, Akira Nakao, Noriyuki Ebi, Hirofumi Minomo, Ichiro Nagata, Naoto Horai, Masaki Fujita
Journal: Cancer Research (2025)

Abstract Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis have dramatically advanced cancer therapy, but resistance mechanisms remain a significant hurdle. This study explores immune changes in syngeneic mouse models sensitive (MC-38) or resistant (LLC1) to anti-PD-1 monoclonal antibody (mAb) treatment. Multicolor flow cytometry was employed to assess immune cell profiles in the tumor microenvironment (TME) and tumor-draining lymph nodes (TdLNs). In the MC-38 model, anti-PD-1 mAb therapy significantly altered immune landscapes, including enhanced dendritic cell (DC) and macrophage populations and decreased myeloid-derived suppressor cells (MDSCs). Upregulated antigen presentation molecules (MHC I/II) and functional markers (CD80, CD86, CCR7, PD-L1) were observed in tumor-associated DCs and macrophages. Notably, there was an increase in tumor-infiltrating CD4+ T cells, CD8+ T cells, including central memory (TCM) and effector memory (TEM) subsets, regulatory T cells (Tregs), natural killer (NK) cells, and natural killer T (NKT) cells. A key finding was the enhanced cytotoxic potential across various lymphocyte subsets, with perforin identified as a pivotal marker among 3 different cytolytic markers, correlating with therapeutic efficacy. Tumor volume inversely correlated with levels of perforin-expressing CD4+ T cells and NKT cells, highlighting their critical roles in antitumor responses. Conversely, the LLC1 model displayed limited immune alterations following anti-PD-1 mAb treatment, emphasizing distinct resistance mechanisms. This study delineates the dynamic immunophenotypic changes induced by anti-PD-1 therapy and identifies perforin, particularly in CD4+ T cells and NKT cells, as a promising biomarker for treatment response. Furthermore, these findings suggest potential combinatorial strategies targeting DC/MDSC ratios and perforin pathways to overcome resistance in refractory tumors. Citation Format: Hiroyuki Inoue, Takayuki Hamasaki, Kazuhiko Inoue, Akira Nakao, Noriyuki Ebi, Hirofumi Minomo, Ichiro Nagata, Naoto Horai, Masaki Fujita. Comprehensive immunophenotypic profiling in anti-PD-1-responsive and -resistant mouse models reveals perforin-expressing CD4+ T cells as key effectors of cytolytic activity [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 6474.
Abstract 5679: A novel approach for antibody affinity maturation using AI-driven mutation prediction
Authors: Yiran Li, Hao Peng, Hui Zhao, Xiaoyi Han, Panpan Zhang, Jinying Ning, Feng Hao
Journal: Cancer Research (2025)

Abstract Background: Therapeutic antibodies, characterized by their exceptional specificity and efficacy, have been a cornerstone of biotherapeutics development since their discovery. However, achieving high-affinity functional antibodies remains a significant challenge. Traditional affinity maturation methods, such as saturation mutagenesis combined with phage display screening, require extensive library construction and screening, which are time-consuming, costly, and present a high barrier to implementation. Here, we introduce a novel affinity screening approach leveraging AI-based deep learning networks to predict potential mutation sites that enhance affinity. Coupled with high-throughput expression systems, this approach effectively reduces screening scale and rapidly generates higher-affinity antibodies. Methods: We developed and trained a novel AI model based on deep learning networks to facilitate antibody affinity optimization. The model simulates antigen-antibody docking and predicts affinity improvements by mutating the parent antibody's CDRs, identifying hotspot residues. We constructed the hotspot mutations and high-throughput expression in 96-well deep plates. The expressed antibody in supernatants were captured by protein A, and screened using sandwich ELISA. Mutations showing at least a 1.2-fold increase in OD450 are selected in preliminarily screening for AI to design combination mutations. Results: For IgG antibodies, computational simulations reduced the initial single-point mutation library size by an order of magnitude, from 103 to 102 mutations, significantly decreasing library construction and screening costs. This approach enabled the completion of the initial screening within three weeks, with 10% of positive mutations exhibiting an OD450 increase of at least 1.2-fold. For VHH antibodies, optimization resulted in blocking activity increase up to a twofold reduction in IC50 and an 18.89-fold increase in affinity as measured by SPR. Similarly, IgG antibodies exhibited a 6.01-fold improvement in affinity (SPR). Conclusions: We developed an AI model to simulate antigen-antibody binding and identify mutations for affinity enhancement. These predicted mutations were subsequently validated through experimental screening, confirming improved affinity. This approach enables the rapid and cost-effective generation of high-affinity antibodies. Citation Format: Yiran Li, Hao Peng, Hui Zhao, Xiaoyi Han, Panpan Zhang, Jinying Ning, Feng Hao. A novel approach for antibody affinity maturation using AI-driven mutation prediction [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 5679.
Abstract 6599: A comprehensive benchmark study of multiple library amplification solutions for NGS
Authors: Giulia Corbet, Caitlyn Mendik, Lee French, Leo Karamanof, Meghan Oddy, Andrew Sutherland, Philip Benson, Josh Haimes, Ross Wadsworth, Travis Sanders, Craig Marshall, Kristina Giorda, Thomas Harrison, Eduard Casas, Brian Kudlow
Journal: Cancer Research (2025)

Abstract The emerging field of precision oncology utilizes next-generation sequencing (NGS) to guide decisions based on individual genetic signatures. As demand for these applications grows, minimizing errors and biases introduced during library preparation and sequencing is critical. Advances in sequencing chemistries have improved base quality scores, shifting the focus to library preparation to improve data quality. PCR amplification is a key potential source of error during NGS library preparation; single-base substitutions, insertions, and deletions (indels) introduced by PCR in addition to PCR-induced biases in DNA length and GC content can distort data leading to decreased assay sensitivity and specificity. Improved library amplification, combined with recent innovations in sequencer technology, will increase the reliability of NGS assays and lead to better assay performance. Herein, we benchmark several commercial PCR amplification systems, including two solutions from Watchmaker Genomics: Equinox, and a re-engineered library amplification mix in development. We employed a UMI-powered NGS assay to quantify GC-uniformity, chimeric reads, base incorporation fidelity, and indel rates in microsatellite sequences. Libraries were sequenced on multiple platforms to leverage the ultra-high base quality scores of recent sequencing chemistries, resulting in low-noise measurements of polymerase fidelity. We also interrogate UMI family depth uniformity, a powerful proxy for template sequence bias and an informative indicator for error-correction efficacy. Furthermore, we assess amplification size bias by comparing the ability to amplify long templates in the presence of short templates. Compatibility with both SPRI and streptavidin beads was assessed. Finally, we test the ability to generate high terminal library yield, which is important for hybridization capture workflows. These results indicate that while some products may excel in one or two metrics, they dramatically underperform in others. Equinox, in particular the newly engineered variant, demonstrates robust performance across all relevant metrics, making it a leading solution for precise and unbiased library amplification. Citation Format: Giulia Corbet, Caitlyn Mendik, Lee French, Leo Karamanof, Meghan Oddy, Andrew Sutherland, Philip Benson, Josh Haimes, Ross Wadsworth, Travis Sanders, Craig Marshall, Kristina Giorda, Thomas Harrison, Eduard Casas, Brian Kudlow. A comprehensive benchmark study of multiple library amplification solutions for NGS [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 6599.
Abstract 3050: Targeting SREBP-driven transcriptional programs in metastatic prostate cancer
Authors: Prashanth Parupathi, Lakshmi Sirisha Devarakonda, Enkiel Francois, Bhaskar C Das, Avinash Kumar
Journal: Cancer Research (2025)

Prostate cancer (PCa) is the most commonly diagnosed cancer and the second leading cause of cancer-related deaths among men in the United States. Metastasis is the major cause of mortality associated with prostate cancer, and despite the availability of targeted therapeutic agents, metastatic prostate cancer remains a lethal disease. Therefore, there is a significant unmet need to identify new drug targets and develop novel targeted therapeutic agents. Sterol regulatory element binding proteins (SREBPs) are a family of transcription factors, comprising SREBP-1 and SREBP-2, that govern lipid homeostasis by regulating the expression of numerous enzymes involved in fatty acid and cholesterol synthesis (lipogenesis). The role of SREBPs in regulating non-lipogenic transcriptional programs is beginning to be elucidated. Published findings associate perturbation of SREBP-dependent transcriptional programs in the development, progression, and metastasis of various cancers, including PCa. Our analysis of publicly available patient datasets revealed increased gene expression for SREBPs in prostate tumors compared to normal prostate. Further, we discovered significantly higher gene expression for SREBPs in metastatic samples compared to primary prostate tumors. Moreover, we found significantly elevated levels of SREBPs in more invasive PC3M and C4-2B compared to the less invasive PC3 and LNCaP prostate cancer cell lines. Hence, we hypothesized that pharmacological inhibition of SREBPs might be an effective therapeutic strategy against metastatic prostate cancer. We have developed a novel boron-containing compound, BF175, which inhibits the expression and transcriptional activity of SREBPs, but the information on the anticancer action of BF175 in various cancers, including prostate cancer, is very limited. In this study, we investigated the SREBP-mediated anticancer activity of BF175 in prostate cancer. We found that BF175 inhibited the viability, proliferation, and metastatic potential as well as induced apoptosis in all prostate cancer cell lines tested. We also confirmed that BF175 inhibited the expression of SREBPs in the tested prostate cancer cell lines. Of note, BF175 exhibited significantly higher potency as an anticancer agent in more invasive prostate cancer cell lines compared to their less invasive counterparts. Taken together, our results demonstrate that BF175 shows promise as a novel anticancer agent that may improve outcomes in lethal metastatic prostate cancer. Citation Format: Prashanth Parupathi, Lakshmi Sirisha Devarakonda, Enkiel Francois, Bhaskar C Das, Avinash Kumar. Targeting SREBP-driven transcriptional programs in metastatic prostate 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 3050.
T-ICAHT: Grading and Prognostic Impact of Thrombocytopenia After CAR T-cell Therapy
Authors: Kai Rejeski, Jaime Sanz, Teng Fei, Monica S Nair, Hamza Hashmi, Abraham Avigdor, Ofrat Beyar-Katz, Veit L. Bücklein, Kevin J. Curran, Sigrun Einarsdottir, Jonathan H Esensten, Netta Glaubach, Noa Golan-Accav, Marina Gomez-Llobell, Iris Halamis, Orit Itzhaki, Frederick L. Locke, Sham Mailankody, Ronit Marcus, Marcela V. Maus, M. Lia Palomba, Jae H Park, Marcelo C. Pasquini, Sandeep S Raj, Sridevi Rajeeve, Gilles A. Salles, Michael Scordo, Gunjan L Shah, Avichai Shimoni, Marion Subklewe, Tobias Tix, Saad Z Usmani, Ori Ben Valid, Yannis K. Valtis, Tsila Zuckerman, Nirali N Shah, Miguel-Angel Perales, Roni Shouval
Journal: Blood (2025)
Abstract 6303: DrBioRight: an AI-driven chatbot for cancer data analysis
Authors: Han Liang, Jun Li, Wei Liu
Journal: Cancer Research (2025)

We present DrBioRight, a cutting-edge chatbot designed to streamline omics data analysis by employing large language models (LLMs). Modern omics technologies generate extensive cancer omics datasets, but conventional software often requires significant technical expertise. DrBioRight addresses this challenge by enabling researchers to interact with the platform in a conversational manner, simplifying complex analytical workflows. DrBioRight's chatbot-driven interface allows users to search datasets, perform data analysis, access insights, and generate visualizations by posing natural language questions or commands. This intuitive approach eliminates the need for programming knowledge, making omics research more accessible. Key features include LLM-powered conversational capabilities, real-time task prediction and guidance, collaborative functionalities for research teams, and integration of new tools and datasets via community contributions. By transforming the interaction model from technical to conversational, DrBioRight serves as an intelligent research assistant that democratizes omics data analysis, enhances collaboration, and accelerates scientific discoveries in cancer research. Citation Format: Han Liang, Jun Li, Wei Liu. DrBioRight: an AI-driven chatbot for cancer data 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 6303.