tbuckley/cabot-search huggingface dataset:
Abstract 5893: Crosstalk between sensory innervation and pancreatic ductal adenocarcinoma
Authors: Kaiyuan Wang, Bo Ni, Xiuchao Wang, Jihui Hao
Journal: Cancer Research (2024)

Abstract In recent years, a growing body of research has illuminated the integral role of the nervous system in the initiation and progression of cancer, giving rise to the establishment of an emergent field known as cancer neuroscience. Pancreatic ductal adenocarcinoma (PDAC), constituting 90% of pancreatic cancers, manifests as a highly malignant solid tumor characterized by insidious onset, strong invasiveness, and a notable propensity for recurrence or metastasis. Consequently, the 5-year survival rate for PDAC remained dismally low at approximately 10% in 2021. Notably, PDAC is distinguished by perineural invasion (PNI), a phenomenon linked to postoperative recurrence and metastasis. A thorough exploration of the interplay between peripheral innervation and PDAC holds promise for identifying novel targets and therapies to enhance outcomes in PDAC. This study unveils a correlation between PDAC patients exhibiting high calcitonin gene-related peptide-positive (CGRP+) sensory innervation in the tumor microenvironment (TME) and diminished overall survival (OS) and disease-free survival (DFS) compared to those with lower CGRP+ nociceptive innervation. Concurrently, heightened CGRP+ sensory innervation corresponds to increased cancer pain, as assessed by Visual Analog Scale (VAS) scores. Further analysis reveals a negative association between CGRP+ nociceptive nerves and lymphatic metastasis, as well as tumor size in PDAC patients. In a murine PDAC model, the genetic or chemical ablation of CGRP+ sensory neurons significantly impedes disease progression. Additionally, sensory neuron ablation attenuates cancer-induced vesical pain and enhances the locomotor function of mice, suggesting an improvement in overall quality of life. Mechanically, within the TME of PDAC, the secretion of neuropeptides substance P (SP) and CGRP from sensory neurons is elevated, with CGRP exhibiting a more pronounced increase. Furthermore, the expression of the CGRP receptor—receptor activity modifying protein 1 (RAMP1)—surpasses that of the SP receptor in the TME. Subsequent blockade of the CGRP pathway using the RAMP1 antagonist Rimegepant significantly suppresses PDAC progression and alleviates cancer pain, accompanied by improved locomotor function. In summary, sensory innervation actively participates in the progression of PDAC through the secretion of CGRP. Consequently, the inhibition of the CGRP pathway emerges as a potential therapeutic strategy for both PDAC and cancer-induced pain. Citation Format: Kaiyuan Wang, Bo Ni, Xiuchao Wang, Jihui Hao. Crosstalk between sensory innervation and pancreatic ductal adenocarcinoma [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 5893.
Abstract 6191: Simultaneous prediction of tumor microenvironment biomarkers from pathology slides using multi-task deep regression
Authors: Omar S. El Nahhas, Marta Ligero, Jakob N. Kather
Journal: Cancer Research (2024)

Abstract Understanding the Tumor Microenvironment (TME) is crucial for tailoring personalized cancer treatments and improving patient outcomes. Using deep learning, we can predict properties of the TME from standard Hematoxylin and Eosin (H&amp;E) stained slides, including specific gene expression signatures. While past models were limited to single signature predictions, we explore the potential of multi-task deep learning to identify multiple biomarkers at once, allowing more complex relationships and interactions to be modeled simultaneously. Our study focuses on the prediction of expression patterns of tumor, stromal, and immune cells in breast cancer (BC). We developed a multi-task transformer-based deep regression model, trained in a weakly-supervised setting, to predict biomarkers in the TME from routine H&amp;E-stained pathology slides of a BC cohort (n=876). The performance was evaluated using Pearson’s correlation coefficient (r) and spatial heatmaps generated via gradient-weighted class activation mapping. Our model achieved a significant correlation coefficient above 0.40 (p&amp;lt;0.0001) for all predicted TME biomarkers in the holdout test set (n=176). This includes the biomarkers lymphocyte infiltrating signature score (r=0.46, p&amp;lt;0.0001), tumor infiltrating lymphocyte regional fraction (r=0.48, p&amp;lt;0.0001), leukocyte fraction (r=0.43, p&amp;lt;0.0001), stromal fraction (r=0.42, p&amp;lt;0.0001), and tumor cell proliferation (r=0.47, p&amp;lt;0.0001). Notably, the model's heatmaps highlighted the pertinent cells and histological characteristics for its predictions. These data show that our deep learning model accurately predicts multiple TME biomarkers concurrently from routine pathology slides. This approach offers a promising avenue for cost-effective and efficient biomarker quantification in the TME, with potential applicability across various cancer types. Citation Format: Omar S. El Nahhas, Marta Ligero, Jakob N. Kather. Simultaneous prediction of tumor microenvironment biomarkers from pathology slides using multi-task deep regression [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 6191.
Abstract 112: The NSG-Flt3KO; Tg(hFLT3LG); hIL15 mouse strain improves engraftment of innate immune cells post-CD34+ humanization
Authors: Srinivas Reddy Boreddy, Shuang Hu, Brian Soper
Journal: Cancer Research (2024)

Abstract Human immune system mouse models play critical roles in numerous preclinical drug development pipelines, including immunology, infectious diseases, and immuno-oncology. These models are constantly evolving, with addition of human cytokines to sustain engraftment of human immune cells. To date, most humanized models have primarily focused on T lymphocyte cell biology, leaving room for improvement in developing models that effectively represent NK cells and the myeloid lineage. Previously, we demonstrated that the introduction of human IL-15 (NSG-IL-15) or FLT3L (NSG-FLT3L) to NOD-scid IL2rγnull (NSG) mice enhanced engraftment of innate immune cells, including human CD56+ NK cells, CD141+ and CD1c+ DC subsets, CD123+ pDCs, and CD14+ monocytes. In this study, we describe a new NSG mouse variant (NSG-Flt3KO; Tg(hFLT3LG); hIL15) that expresses physiological levels of both human IL-15 and FLT3L and detail the immune cell diversity following HSC engraftment. NSG-Flt3KO; Tg(hFLT3LG); hIL15 mice, along with control strains NSG, NSG-IL15, and NSG-FLT3L, were engrafted with human umbilical cord blood derived CD34+ hematopoietic stem cells from three different donors, and blood samples were collected until Week 20 post-transplantation. Blood samples were analyzed via flow cytometry to assess the engraftment of innate and adaptive immune cells. Spleen and bone marrow samples were collected at Week 12 post-transplantation and analyzed for lymphoid and myeloid immune cell populations via flow cytometry. To compare the differences among the strains in terms of immune cell sub-populations across weeks, a Two-Way ANOVA (mixed-model) was conducted, with p&amp;lt;0.05 considered statistically significant. Overall, the results demonstrate that NSG-Flt3KO; Tg(hFLT3LG); hIL15 mice have enhanced CD56+/dim CD16+ NK cells compared to NSG-FTL3L strain (20.5±12.8% vs 7.0±3.5% at Week 12), but comparable to NSG-IL15. Furthermore, myeloid sub-lineage cells in NSG-IL15-FLT3L mice, such as monocytes, macrophages (M1), granulocytes, dendritic cells, and neutrophils, were greater than in NSG or NSG-IL15 strains, but comparable to NSG-FLT3L mice. Overall levels of hCD45+ and T cell sub-populations (CD3+ or CD4+ or CD8+) were comparable across all strains tested. In sum, our NSG-Flt3KO; Tg(hFLT3LG); hIL15 engraftment data suggest an improved model that supports drug development targeting human myeloid and NK cell populations. Citation Format: Srinivas Reddy Boreddy, Shuang Hu, Brian Soper. The NSG-Flt3KO; Tg(hFLT3LG); hIL15 mouse strain improves engraftment of innate immune cells post-CD34+ humanization [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 112.
Abstract 1846: Integrative histopathologic, proteomic, phosphoproteomic, genomic, and transcriptomic characterization of renal cell carcinoma
Authors: Woo Sun Kwon, Chang Gon Kim, Harim Koo, Woo Kyun Bae, Eu Chang Hwang, Won Sik Ham, Nam Hoon Cho, Jason K. Sa, Jong Bae Park, Sun Young Rha
Journal: Cancer Research (2024)

Abstract Although histologically defined subtypes exist, renal cell carcinoma (RCC) is a heterogenous disease, resulting in various treatment outcomes and prognosis. Here, we conduct comprehensive profiling of RCC with histopathologic, proteomic, phosphoproteomic, genomic, and transcriptomic analyses on tumor and paired adjacent normal tissues from 113 patients. RCC tumors are distinctly separated from corresponding normal tissues through multi-omics clustering, a distinction further supported by pathway analysis. In addition, various hallmarks of cancer were differentially observed at the individual patient level, highlighting the intertumoral heterogeneity of RCC. Proteomic subtyping classifies RCC tumors into four subtypes (C1-4) with distinct phosphoproteomic, genomic, and transcriptomic characteristics with prognostic and predictive implications. Among these subtypes, C1-3 were almost composed of clear cell RCC (ccRCC; 96.8%), whereas C4 was composed of non-clear cell RCC (nccRCC) or other types of cancer (100.0%). Among each subtype, C1 (N=37) exhibits the strongest adaptive immune response and angiogenesis with favorable prognosis after surgical resection. C2 (N=39) was characterized with enrichment in DNA repair pathways and alteration in VHL (100.0%) along with BAP1 (24.0%) genes. C3 (N=19) was associated with enrichment in inflammatory response with dismal prognosis and therapeutic resistance to tyrosine kinase inhibitors targeting vascular endothelial growth factor receptors. C4 (N=18) was exclusively enriched with nccRCC with active mitochondrial and oxidation phosphorylation. Transglutaminase 2 (TGM2), a crosslinking enzyme, is identified as a potential marker with therapeutic implication for ccRCC. Collectively, this study reports a large-scale multi-omics-based approach of RCC, providing insights into biologic underpinning and evidence for rational treatment selection as well as linking the multi-omics-derived phenotypes to clinical outcomes of ccRCC. Citation Format: Woo Sun Kwon, Chang Gon Kim, Harim Koo, Woo Kyun Bae, Eu Chang Hwang, Won Sik Ham, Nam Hoon Cho, Jason K. Sa, Jong Bae Park, Sun Young Rha. Integrative histopathologic, proteomic, phosphoproteomic, genomic, and transcriptomic characterization of renal cell carcinoma [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 1846.
Abstract 788: Leveraging multi omics approach to examine the potential role of causative viruses in glioblastoma
Authors: Bavani Gunasegaran, Aziz Abdullah A Alnakli, Gilles J. Guillemin4, Seong Beom Ahn, Benjamin Heng
Journal: Cancer Research (2024)

Abstract Glioblastoma (GB), a grade IV brain cancer, has the lowest survival rate. Despite aggressive and targeted therapies, patients remain to have the worst prognosis. More than 16% of cancer incidence worldwide has been attributed to infectious agents, raising uncertainties about the role of infections in the development of brain cancer. While numerous studies have suggested potential viral involvement in GB pathogenesis, a common limitation is the reliance on a singular technology, such as DNA/RNA sequencing or immunohistochemistry, within each study. This approach may restrict the comprehensive exploration of viral contributions, as different technologies offer distinct insights into the complex mechanisms underlying GB development. Utilizing a combination of techniques could provide a more holistic understanding, enhancing the robustness of findings and potentially uncovering nuanced aspects of viral influence that a single technology might overlook. To clarify the role of viruses in GB pathology, our study employs a comprehensive approach integrating three distinct research methods. We screened 178 publicly accessible raw mass spectrometry (MS) data of GB tumors from three independent publications (PMID: 31331834, 31154438, 36720864) by employing our internally developed data identification pipeline. The pipeline transforms raw MS data for downstream analysis, conducts database searches against the latest viral protein databases and undergoes peptide validation for enhanced robustness. Subsequent analysis filters duplicate hits and consolidates nested peptides. The final protein list includes only those identified with two or more peptides of over eight amino acids. The pipeline concludes by inferring and listing the corresponding viruses, ensuring a comprehensive and refined approach to the identification and analysis of viral proteins in the context of MS data. To enhance this dataset, we incorporated whole-genome metagenomics and MS-based proteomics, examining a preliminary cohort of15 GB tumor tissues. This approach explored a wide range of viral encoded proteins. Our comprehensive analysis, integrating bioinformatics, genomics, and proteomics, identified multiple herpesvirus species across our cohorts. This potential identification of viruses within GB has the capacity to redefine the stratification of tumor types in GB patients, signaling a significant development in our understanding of the disease. Citation Format: Bavani Gunasegaran, Aziz Abdullah A Alnakli, Gilles J. Guillemin4, Seong Beom Ahn, Benjamin Heng. Leveraging multi omics approach to examine the potential role of causative viruses in glioblastoma [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 788.
Abstract 1663: Single-cell signaling analysis reveals that major vault protein facilitates RasG12C inhibitor resistance
Authors: Jason Z. Zhang, Shao-En Ong, David Baker, Dustin J. Maly
Journal: Cancer Research (2024)

Abstract Covalent inhibitors of RasG12C are the first clinically-approved drugs for targeting mutant Ras-driven cancers. However, the rapid development of clinical resistance to current Ras G12C inhibitors is common. It has been demonstrated that a subpopulation of cancer cells facilitate drug resistance but the mechanistic basis of this heterogenous response is not well understood. Here, we utilized recently developed Ras sensors to profile the environment of active Ras and to measure the activity of endogenous Ras to pair structure (Ras signalosome) to function (Ras activity), respectively, at a single-cell level. With this approach, we found that a subpopulation of KRasG12C cells treated with RasG12C-GDP inhibitors underwent oncogenic signaling and metabolic changes driven by WT Ras at the golgi and mutant Ras at mitochondria, respectively. Our Ras sensors identified Major Vault Protein (MVP) as a mediator of Ras activation at both subcellular locations by scaffolding Ras signaling pathway components and metabolite channels. We found that recently developed RasG12C-GTP inhibitors also led to MVP-mediated WT Ras signaling at the golgi, demonstrating that this a general mechanism of RasG12C inhibitor resistance. Overall, single-cell analyses enabled the discovery of a RasG12C inhibitor-resistant subpopulation driven by MVP, providing insight into the complex and heterogenous rewiring that confers drug resistance in cancer. Citation Format: Jason Z. Zhang, Shao-En Ong, David Baker, Dustin J. Maly. Single-cell signaling analysis reveals that major vault protein facilitates RasG12C inhibitor resistance [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 1663.
Abstract 6237: Large-scale imputation models for multi-ancestry proteome-wide association analysis
Authors: Chong Wu, Zichen Zhang, Xiaochen Yang, Bingxin Zhao
Journal: Cancer Research (2024)

Abstract Proteome-wide association studies (PWAS) decode the intricate proteomic landscape of biological mechanisms for complex diseases. Traditional PWAS model training relies heavily on individual-level reference proteomes, thereby restricting its capacity to harness the emerging summary-level protein quantitative trait loci (pQTL) data in the public domain. Here we introduced a novel framework to train PWAS models directly from pQTL summary statistics. By leveraging extensive pQTL data from the UK Biobank, deCODE, and ARIC studies, we applied our approach to train large-scale European PWAS models (total n = 88,838 subjects). Furthermore, we developed PWAS models tailored for Asian and African ancestries by integrating multi-ancestry summary and individual-level data resources (total n = 914 for Asian and 3,042 for African ancestries). We validated the performance of our PWAS models through a systematic multi-ancestry analysis of over 700 phenotypes across five major genetic data resources. Specifically, we identified several novel and well-known genes for various cancers; we highlighted eight genes associated with six cancer traits across all three PWAS model cohorts, including CTSF, RSPO3, CRTAM, LAYN, CHRDL2, DPEP1, NFASC, and NAAA. Our results bridge the gap between genomics and proteomics for drug discovery, highlighting novel protein-phenotype links and their transferability across diverse ancestries. The developed PWAS models and data resources are freely available at www.gcbhub.org. Citation Format: Chong Wu, Zichen Zhang, Xiaochen Yang, Bingxin Zhao. Large-scale imputation models for multi-ancestry proteome-wide association analysis [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 6237.
Abstract 6150: <i>MMP14</i> and <i>MKLN1</i> as colorectal cancer susceptibility genes and a drug repurposing candidate from genome-wide association study in South Korea
Authors: Dabin Yun, Jung-Ho Yang, Sun-Seong Kweon, Jin-ah Sim, Minjung Kim, Ji Won Park, Seung Yong Jeong, Aesun Shin, Nan Song
Journal: Cancer Research (2024)

Abstract Background: One of the major topics about colorectal cancer (CRC) is to find susceptible genes. Furthermore, functional interpretation of post-GWAS analysis gains attention and various approaches to find drug-repurposing candidates are used in cancer research. Our study aims to identify susceptible genes for CRC and provide drug-repurposing candidates. Methods: CRC patients in Seoul National University Hospital with prospective follow-up were recruited. From blood-derived DNA, genome-wide SNPs were genotyped using the Korea Biobank Array and 409,063 SNPs were extracted. SNP-based logistic regression model was fit for the event of CRC estimating beta values. Furthermore, post-GWAS analysis was conducted using regulatory function, multiple annotation and gene expression database. Drug-repurposing candidate was identified through the pre-trained deep neural network with susceptible genes. Results: We used 500 participants of CRC cases and 1,500 participants of healthy controls. For GWAS, statistically significant associations were mapped to MKLN1, MMP14, PTPN14 and MKL2 genes. For post-GWAS analysis, strong associations were found for MKLN1 (rs75170436, 7q32.3, Beta=-0.90, P-value=5.90×10-13) and MMP14 (rs3751489, 14q11.2, Beta=-1.91, P-value=2.31×10-12). From the drug-repurposing analysis, PPARδ/β agonist (GW0742; Binding score=3.79(MKLN1), 12.06(MMP14)) was identified for both genes with the highest score. Conclusions: We revealed MKLN1 and MMP14 genes as susceptible genes associated with CRC development. Additionally, we identified PPARδ/β agonist (GW0742) as a drug-repurposing candidate for the treatment of CRC. These findings can promote the understanding of CRC development and provide a novel therapeutic candidate for CRC patients in the future. Citation Format: Dabin Yun, Jung-Ho Yang, Sun-Seong Kweon, Jin-ah Sim, Minjung Kim, Ji Won Park, Seung Yong Jeong, Aesun Shin, Nan Song. MMP14 and MKLN1 as colorectal cancer susceptibility genes and a drug repurposing candidate from genome-wide association study in South Korea [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 6150.
Abstract 5075: Transcriptomic landscape of tumor microenvironment in gastric cancer in treatment with different types of cancer drugs
Authors: Hiroki Yamashita, Jason Guoqiang Zhang, Chiaki Mashima, Ryo Kamata, Tomoko Yamamori, Izuma Nakayama, Akihito Kawazoe, Kohei Shitara, Tomonori Yano, Qunli Xu, Akihiro Ohashi, Takeshi Saito
Journal: Cancer Research (2024)

Abstract Gastric cancer (GC) in the fifth-leading types of cancer and the third-leading cause of death from cancer. Characterization of tumor microenvironment (TME) in GC is important not only to comprehensively understand tumor biology in GC but also to develop a novel cancer therapeutics targeting TME. In this study, we conducted multi-omics analyses of single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing (bulk RNA-seq), and whole exome sequencing (WES) using biopsied tumor samples through endoscopy from 23 GC patients: 3 treatment-naïve patients, 7 patients in treatment with chemotherapy (Chemo), 5 patients in combination treatment with chemotherapy and immune checkpoint blockade (Chemo+CPI), 3 patients in combination treatment with chemotherapy and Her2-antibody targeted therapy (Chemo+Her2i), and 5 patients in third-line or later treatment. We initially classified the GC samples into 4 molecular subtypes (CIN, GS, MSI, and EBV) using datasets of WES, bulk RNA-seq and scRNA-seq, showing that our samples were mainly composed of CIN (n=7) and GS (n=16) types. In scRNA-seq, we revealed that drug treatments profoundly modulated not only proportions of TME components but also their molecular characteristics. For instance, Chemo+CPI treated patients showed higher proportion of CD8+ cytotoxic T cells with activation and/or exhaustion markers. Combination treatments with Chemo+Her2i, in addition, resulted in higher Trail and Estrogen signaling pathways in epithelial cells of TME components. These findings suggest that interventions with certain cancer therapeutic drugs could substantially convert transcriptome landscape and reconstitute GC TME components. High-resolution of GC TME analyses should provide a clue for developing a next generation of cancer therapeutic drugs. Citation Format: Hiroki Yamashita, Jason Guoqiang Zhang, Chiaki Mashima, Ryo Kamata, Tomoko Yamamori, Izuma Nakayama, Akihito Kawazoe, Kohei Shitara, Tomonori Yano, Qunli Xu, Akihiro Ohashi, Takeshi Saito. Transcriptomic landscape of tumor microenvironment in gastric cancer in treatment with different types of cancer drugs [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 5075.