tbuckley/cabot-search huggingface dataset:
Abstract 3639: <i>ARID1</i> mutations may serve as a predictor of MSI and TMB levels in gastric cancer
Authors: Chunsheng Xu, Bo Lian, Yujie Dai, Weiming Duan, Feilong Zhao, Mengbin Li
Journal: Cancer Research (2024)

Abstract Background: ARID1A is a gene that produces a protein called BAF250a, which is a component of the chromatin remodeling complex. This complex plays a critical role in regulating gene expression by altering the structure of chromatin. ARID1A mutations and MSI-H are mutually exclusive in endometrial cancer, but not in ovarian cancer. ARID1A mutations are associated with high TMB in certain types of cancer, such as endometrial and gastric cancer. The relationship between ARID1B mutations and MSI-H/TMB is complex, and this study aims to clarify it in gastric cancer. Methods: The formalin-fixed paraffin-embedded (FFPE) tissues 337 patients with gastric cancer who have underwent next-generation sequencing (NGS) include 733 genes from Nov, 2019 to Mar, 2023 in 3DMed Clinical Laboratory Inc. were investigated in this study. These patients are still being followed up. Result: In this study, 337 patients with gastric cancer were analyzed for ARID1A and ARID1B mutations, MSI-H status, and TMB levels. The results showed that 17.8% of the patients had ARID1A mutations, 8.9% had ARID1B mutations, and 3.6% had both mutations. Additionally, 7.7% of the all patients had MSI-H. The study found that patients with ARID1 mutations had significantly higher rates of MSI-H compared to unselected patients. Specifically, 35% of patients with ARID1A mutations, 30% with ARID1B mutations, and 75% with both mutations had MSI-H, compared to 7.7% in unselected patients. Furthermore, TMB levels were higher in patients with ARID1A or ARID1B mutations compared to wild-type and unselected patients (p&amp;lt;0.0001). There was no significant difference observed between patients with ARID1A and ARID1B mutation subgroups in terms of TMB levels (p = 0.3515). However, TMB levels were lower in patients with ARID1A or ARID1B mutations compared to patients with both mutations (p = 0.0036, p = 0.0104). These findings suggest that ARID1 mutations may cause genomic and chromosomal instability in gastric cancer, leading to higher rates of MSI-H and TMB. Conclusion: The study highlights the potential clinical significance of ARID1 mutations as a biomarker for predicting MSI-H and TMB levels in gastric cancer, which could help guide personalized treatment strategies for patients with this disease. However, further research is needed to confirm these findings and to determine the clinical implications of ARID1 mutations in gastric cancer. Citation Format: Chunsheng Xu, Bo Lian, Yujie Dai, Weiming Duan, Feilong Zhao, Mengbin Li. ARID1 mutations may serve as a predictor of MSI and TMB levels in gastric 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 3639.
Abstract 4943: Benchmarking unpaired single-cell RNA and single-cell ATAC integration
Authors: Jiani Chen, Wanzi Xiao, Eric Zhang, Xiang Chen
Journal: Cancer Research (2024)

Abstract The integration of single-cell RNA-sequencing (scRNA-seq) and single-cell ATAC-sequencing (scATAC-seq) data offers a unique opportunity to gain a comprehensive view of cellular identity with defining features and to infer gene-regulatory relationships. Despite the emergence of technologies that simultaneously capture both the gene expression and chromatin accessibility of individual cells (paired data), the practical challenges of these approaches (e.g., the unavailability in previous samples and prohibitive cost) have led researchers to turn to the existing trove of single-modality data generated from independent biological samples (unpaired data). Various computational tools have been developed to integrate these unpaired single modality datasets. However, the comparative performance of these tools has not been comprehensively evaluated, and a standard benchmark pipeline is still lacking. To address these challenges, we used pseudo-unpaired scRNA-seq and scATAC-seq data derived from publicly available paired single-cell multi-omics datasets to benchmark 14 publicly available integration methods. The primary goal of unpaired single-cell multi-omics integration is to narrow the omics gap while preserving cell type diversity. We therefore focused on pair-wise cell distance and cluster performance in the joint latent space constructed by various integration tools in our benchmarking pipeline. To ensure the robustness of these computational approaches, we examined their stability across a variety of scenarios, including variations in cell number, cell types, and biological and technical batch effects. A number of the integration methods tested produced promising results. While the widely used Seurat package was recently reported to have the best performance (Lee et al., Genome Biology 2023), other computational tools such as scVI, Cobolt, scJoint, scglue and scBridge performed equally well or better in reducing omics differences and facilitating the identification of cell clusters. Notably, scglue and Cobolt demonstrated strong performance in aligning the same cell from different modalities, and discrete clusters emerged in the joint latent space using scJoint and scBridge. These findings suggest that it may not be strictly necessary to use paired multi-omics data to guide integration to achieve favorable results. Our freely available benchmarking pipeline will empower researchers to identify the optimal data integration methods for their specific data, facilitate the benchmarking of new methods, and contribute to future method development in the field. Citation Format: Jiani Chen, Wanzi Xiao, Eric Zhang, Xiang Chen. Benchmarking unpaired single-cell RNA and single-cell ATAC integration [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 4943.
Abstract 779: Functional role of TGF-beta receptor type-2 in breast cancer progression and survival
Authors: Meera Srivastava, Alakesh Bera, Harvey B. Pollard, Hai Hu, Craig D. Shriver
Journal: Cancer Research (2024)

Abstract Background and Rational: Transforming growth factor β (TGFβ) signaling is pivotal in cellular proliferation and can exhibit either tumor-suppressive or promoting effects depending on cellular context and genetic alterations. TGFβ receptor type-2 (TGFβR2), the ligand-binding receptor, is implicated in various cancers. This study explores the functional role of TGFβR2 in breast cancer (BrCa) progression. Materials and Method: Genomic alterations in TGFβR2 were assessed in TCGA samples. TGFβR2 mRNA and protein expression were examined in a panel of BrCa cell lines, including MCF7, MCF12A, MDA-MB-231, and MDA-MB-468. Additionally, serum samples from BrCa patients, meticulously categorized into subtypes such as triple-negative (TN), human epidermal growth factor receptor 2-positive (Her2+), Luminal A (LA), Luminal B1 (LB1), and Luminal B2 (LB2), were analyzed. BrCa cell lines were treated with TGFβ ligand to investigate receptor expression modulation. Results: Analysis of genomic alterations in TGFβR2 from The Cancer Genome Atlas (TCGA, n=1084, BrCa samples) revealed TGFβ copy number amplification in 25% of samples, while less than 1% showed TGFβR2 amplification. Elevated TGFβR2 levels were identified in aggressive triple-negative MDA-MB-231 cells compared to other tested cell lines. Serum samples from BrCa patients (n=240) demonstrated higher TGFβR2 levels, particularly in Luminal B1 subtype and African American patients. Treatment of BrCa cell lines with TGFβ ligand induced TGFβR2 expression, suggesting ligand-induced receptor upregulation. Discussion: The findings suggest TGFβR2 as a potential biomarker for BrCa, with higher levels associated with aggressiveness and racial disparities. Ligand-induced receptor upregulation implies a potential oncogenic role. These results contribute to understanding TGFβR2's functional significance and its implications in breast cancer progression, offering insights into potential biomarkers and therapeutic targets. Citation Format: Meera Srivastava, Alakesh Bera, Harvey B. Pollard, Hai Hu, Craig D. Shriver. Functional role of TGF-beta receptor type-2 in breast cancer progression and survival [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 779.
Abstract 769: Incorporating a cross-ancestry polygenic risk score into a clinical model improved breast cancer risk prediction in women with pathogenic variants
Authors: Placede Tshiaba, Monika Sun, Dariusz Ratman, Jeffrey N. Weitzel, Premal Shah, Matthew Rabinowitz, Akash Kumar, Kate Im
Journal: Cancer Research (2024)

Abstract Introduction. We previously developed and validated a cross-ancestry polygenic risk score (caPRS) to predict risk of developing breast cancer (BC) in women who do not carry pathogenic variants (PVs) in BC susceptibility genes. Here we aimed to expand upon that study by integrating the caPRS with the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model to combine the effects of genetic and clinical factors in carriers of PVs in BRCA1, BRCA2, PALB2, CHEK2 and ATM. Methods. We explored the association of caPRS among 12,525 women from the UK Biobank and the Consortium of Investigators of Modifiers of BRCA1/2. The effect size of the caPRS was evaluated separately for individuals carrying PVs in BRCA1, BRCA2, CHEK2, ATM and PALB2. We used a logistic regression model adjusted for age, first-degree BC family history (FHx) and cohort for BRCA1 and BRCA2 to examine the association of the caPRS with BC. The effect sizes were expressed as standardized odds ratios (ORs) with 95% confidence intervals (CIs). The estimated absolute risks to age 80 of developing BC were calculated for unaffected women by combining the caPRS-based risk with gene-specific clinical risk estimates from the BOADICEA model based on U.S. incidences. Results. The caPRS was significantly associated with BC risk across all PV carrier groups. Using BOADICEA alone, the estimated absolute BC risk by age 80 for an average unaffected 20-year-old female in the U.S. with an unknown FHx ranged from 23.8% for CHEK2 carriers to 79.4% for BRCA2 carriers. Integration of caPRS into BOADICEA, yielded the distribution of risk for each gene. (Table) Gene N OR per SD (95% CI) Absolute risk by BOADICEA Median absolute risk by BOADICEA + caPRS (Range) ATM 2,037 1.46 (1.25 - 1.71) 24.9% 27.1% (0.1 - 70.5) BRCA1 5,794 1.24 (1.18 - 1.31) 76.1% 75.6% (55.6 - 87.7) BRCA2 4,018 1.40 (1.31 - 1.49) 79.4% 78.7% (45.2 - 92.1) CHEK2 372 1.88 (1.41 - 2.55) 23.8% 28.2% (0.0 - 78.4) PALB2 304 1.81 (1.37 - 2.43) 53.9% 56.3% (2.9 - 90.9) Conclusions. The caPRS significantly modified BC risk for carriers of PVs and could help tailor guidelines for women with PVs, particularly in moderate penetrance genes. More data is needed to increase the precision and reach of risk assessment in diverse populations. Citation Format: Placede Tshiaba, Monika Sun, Dariusz Ratman, Jeffrey N. Weitzel, Premal Shah, Matthew Rabinowitz, Akash Kumar, Kate Im. Incorporating a cross-ancestry polygenic risk score into a clinical model improved breast cancer risk prediction in women with pathogenic variants [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 769.
Abstract 6512: BPI-501836: A highly potent small molecule inhibitor targeting KRASG12D mutation
Authors: Xiang Yang, Yuan Lu, Renqi Xu, Xiaoqiang Kong, Yongqing Sun, Xingjie Zhu, Xiaoyun Liu, Zhengyao Zou, Haibo Chen, Yanju Liu, Jialing Sun, Han Han, Cheng Yang, Jing Guo, Hong Lan, Quan Zhou, Lieming Ding, Hao Wu, Jiabing Wang
Journal: Cancer Research (2024)

Abstract As one of the most notorious un-druggable oncogenes, mutant KRAS impairs the transition of RAS from its active GTP-binding form to the inactive GDP-binding form, leading to sustained activation of downstream MAPK pathway and ultimately tumorigenesis. The recent approved KRASG12C inhibitors have demonstrated clinical benefits in KARSG12C mutant patients. However, the other prevalent KRAS mutations, such as KRASG12D, remain a huge unmet medical challenge. There’s no approved KRASG12D specific inhibitors yet. Here we present BPI-501836, a highly potent and selective small molecular inhibitor for KRASG12D. In vitro, BPI-501836 exhibited very high potency by disrupting the KRASG12D and cRAF interaction in a cell-free assay. In cell-based assays, BPI-501836 inhibited proliferation of KRASG12D mutant cell lines with sub-nanomolar IC50 values, without affecting wide type or non-G12D KRAS mutant cell lines. Moreover, it also attenuated ERK phosphorylation in KRASG12D mutant cells. In vivo, tumor regression was observed following the administration of 6 mg/kg (iv, QW) BPI-501836 in KRASG12D human pancreatic Panc04.03 xenograft mouse models, or in the KRASG12D human pancreatic PK-59 xenograft mouse models with an intraperitoneal injection of 20 mg/kg per week. Noteworthy was the prolonged half-life of BPI-501836 observed across various species, coupled with its high distribution in tumor tissues. Moreover, sustained suppression of ERK phosphorylation in tumor tissues persisted for up to 144 hours after the last dose of BPI-501836 in both xenograft mouse models. This extended pharmacodynamic effect supports the feasibility of a once-weekly dosing schedule in clinical applications. Safety assessments conducted through a 4-week repeated infusion (intravenous, twice weekly) of BPI-501836 in beagle dog demonstrated favorable safety profiles. In conclusion, BPI-501836 emerges as a highly potent and selective KRASG12D inhibitor, exhibiting promising pharmacokinetics, pharmacodynamics, and safety profiles in pre-clinicalstudies. BPI-501836 is currently in IND-enabling stage and holds potential for further development as a therapeutic agent. Citation Format: Xiang Yang, Yuan Lu, Renqi Xu, Xiaoqiang Kong, Yongqing Sun, Xingjie Zhu, Xiaoyun Liu, Zhengyao Zou, Haibo Chen, Yanju Liu, Jialing Sun, Han Han, Cheng Yang, Jing Guo, Hong Lan, Quan Zhou, Lieming Ding, Hao Wu, Jiabing Wang. BPI-501836: A highly potent small molecule inhibitor targeting KRASG12D mutation [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 6512.
Abstract 6518: BPM 31510: Targeting the tumor microenvironment (TME) via mitochondrial-mediated ROS production
Authors: Stephane Gesta, Maria D. Nastke, Seema Nagpal, Lawrence Recht, Michael A. Kiebish, Niven R. Narain, Vijay R. Modur
Journal: Cancer Research (2024)

Abstract The tumor microenvironment (TME) consists of an enriched population of immune cells and milieu of factors that modulate key cellular processes such as angiogenesis, metabolism, apoptosis, migration, and proliferation. Previous studies have suggested that BPM 31510 induces ROS through high intra-cellular levels of ubidecarenone in aggressive cancer phenotypes both in vitro and in vivo through induction of mitochondrial ROS. Furthermore, we have previously observed efficacy of BPM 31510 in a highly aggressive in vivo orthotopic rat C6 glioma model where BPM 31510 was administered in combination with radiotherapy. In a pancreatic xenograft mouse model, we previously reported that BPM 31510 impacted the immune cells in the TME by increasing absolute number of infiltrated CD8+ T-cells and decreasing absolute number of infiltrated CD4+ T-cells. Moreover, an ex vivo study of PBMC’s from healthy volunteers suggests that BPM 31510 increases T-cell viability and function while also decreasing T-cell exhaustion. Currently, an ongoing first-line phase 2b study of BPM 31510 in glioblastoma in combination with radiotherapy and temozolomide will assess the clinical efficacy and potential for mechanistic synergy of multiple ROS-inducing mitochondrial mediated regimens on this aggressive cancer phenotype. BPM 31510 will be assessed in this study to provide further evidence for the impact of ROS on immunomodulation. Taken together, BPM 31510 increases mitochondrial ROS/OXPHOS in cancer cells while impacting TME-related immune cell modulation. Citation Format: Stephane Gesta, Maria D. Nastke, Seema Nagpal, Lawrence Recht, Michael A. Kiebish, Niven R. Narain, Vijay R. Modur. BPM 31510: Targeting the tumor microenvironment (TME) via mitochondrial-mediated ROS production [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 6518.
Abstract 7641: Prognostic impact of functional germline mutations in muscle-invasive bladder cancer identified by neural network analysis
Authors: Guru P. Sonpavde, Amin H. Nassar, Arvind Ravi, Layne Sadler
Journal: Cancer Research (2024)

Abstract Background: An accurate prognostic algorithm based on germline whole exome sequencing (WES) would complement tumor omics as a non-invasive source of biomarkers for guiding cancer patient therapy. In a previous study, we used WES and RNA-seq data from muscle-invasive bladder cancer (MIBC) and normal host cells to train a survival model, wherein we discovered that mutated germline genes accounted for 90% of feature importance. We have since defined a larger cohort to identify key germline biomarkers of overall survival (OS) that may be scalable for application in the clinic. Methods: 169 patients from The Cancer Genome Atlas (TCGA) with MIBC stages T2-T4 that underwent radical cystectomy (RC) w/o neoadjuvant treatment were eligible. OS at 4 years was the primary clinical endpoint as it is associated with death due to metastatic tumor recurrence. Patients were dichotomized to control survivors who were alive at 4+ years, and cases who died within 4 years but no less than 4 months after RC. 49 qualifying survivors were propensity matched against 49 dead patients by stage, adjuvant treatment, gender, race, and age. The remaining 71 dead patients served as a holdout. Only high (e.g. frameshift) and moderate (e.g. indel) impact germline variants were included. Mutations were weighted for allele frequency (penalize common) and genotype (reward homozygous) before grouping at the gene level. A panel of the 21 most differential genes in stratified patients were selected for neural network training using AIQC. The model forced protective/pathogenic genes to interact and produced individual survival probabilities. Gene importance was determined via permutation. Results: OS was accurately predicted for 98.2% of patients: 1 FN, 2 FP, 37 TN, 119 TP. The top 3 permuted genes (and related pathways) were: SORL1 (Aβ), KIF27 (hedgehog), CUL7 (p53). The most differential gene was CUL7: 31% of survivors mutated vs 2% of dead. Conclusion: Neural network analysis identified the promise of germline genetics as an accurate predictor of survival and tumor recurrence in MIBC patients. Upon validation in an external cohort, these biomarkers could be used to inform adjuvant therapy and complement molecular residual disease alongside circulating tumor (ct)-DNA. Citation Format: Guru P. Sonpavde, Amin H. Nassar, Arvind Ravi, Layne Sadler. Prognostic impact of functional germline mutations in muscle-invasive bladder cancer identified by neural network 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 7641.
Abstract 651: 3D-bioprinted cancer models for target discovery, drug development, and personalized therapy
Authors: Ronit Satchi-Fainaro, Anne Krinsky, Anshika Katyal, Opal Avramoff, Eilam Yeini, Lena Neufeld, Sharon Halparin, Ronnie Frommer-Shapira
Journal: Cancer Research (2024)

Abstract Many drugs show promising results in laboratory research, but eventually fail in clinical trials. We hypothesize that one main reason for this translational gap is that the cancer models used are inadequate. Most models lack the complex tumor-stromal cell interactions with their microenvironment which are required for tumor progression. Conventional 2D cultures, where cells grow on rigid plastic plates mainly as mono-cultures of a single type of cells, are not able to recapitulate the complex settings of such interactions. Therefore, there is a need to develop a 3D model that better mimics the tumor microenvironment. Hence, we developed a vascularized, hydrogel-based 3D-bioprinted tumor model consisting of patient-derived tumor and microenvironmental cells. Our 3D-bioprinted models are based on a library of hydrogels that we developed as scaffolds for different tumor types, designed according to the mechanical properties of the tissue/organ of origin. The patient-derived models consist of cells from a biopsy, constructed based on CT/MRI scans, and include functional vessels that allow serum and peripheral blood mononuclear cells (PBMC) to flow when connected to a pump. Using this unique model platform, we identified P-selectin as a novel immune checkpoint in the brain regulating cancer-microglia-tumor-associated macrophages (TAMs) interactions. Moreover, we are currently validating our 3D platform for its ability to mimic patient-specific tumors and their microenvironment in order to predict patient response to different treatments (such as chemotherapy, immunotherapy, and targeted therapies). Hence, we are currently conducting an 80-patient “basket” clinical trial testing different treatments on patient samples to validate the ability of our model to predict patient outcomes covering 7 different cancer types. These unique 3D-bioprinted models have the potential to facilitate target discovery and drug development, as well as to serve as a reliable system for precision medicine. Citation Format: Ronit Satchi-Fainaro, Anne Krinsky, Anshika Katyal, Opal Avramoff, Eilam Yeini, Lena Neufeld, Sharon Halparin, Ronnie Frommer-Shapira. 3D-bioprinted cancer models for target discovery, drug development, and personalized therapy [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 651.