tbuckley/cabot-search huggingface dataset:
Abstract 2335: MultiNMF: multiview factorization for joint modeling of spatial multi-omics and histology images
Authors: William Bowie, Stacy Wang, Benjamin Strope, Qian Zhu
Journal: Cancer Research (2024)

Abstract An increasing number of cancer research studies employ spatially resolved transcriptomics (SRT) to investigate the composition of tumor microenvironment in a cancer type of interest. These studies have defined tumor microenvironment (TME) states and spatial domains based on clustering spatial gene expression patterns in SRT in an unbiased manner, yet a more thorough delineation of TME states requires the incorporation of the tumor’s histology image. Here, we develop MultiNMF, a multiview factorization approach that is suitable for cancer research studies where joint profiles of spatial multi-omics and tumor histology images are available. We apply MultiNMF to analyze a set of TNBC SRT primary tumor samples and reveal TME states in the stromal, epithelial, and immune enriched compartments, defined by distinct histomorphological features. We further illustrate the ability of the approach to extend to paired spatial ATAC-seq and histology dataset that is recently published on HER2 breast cancer. MultiNMF thus permits an automated and data-driven decomposition of SRT and spatial ATAC data supported by histomorphological evidence. Context In SRT by 10X Visium, the hematoxylin eosin (H&amp;E) staining image is automatically aligned to the slide where the tissue section is mounted. Image patches can be easily extracted from areas under the SRT barcoded spots. For these images, I will apply a pre-trained convolutional neural network (CNN) model to extract high dimensional features from spot images. The image features can be added as a second view after gene expression view for joint analysis under MultiNMF. Results MultiNMF can factorize the SRT data into components well-supported by histological evidence. It has identified T-cell infiltrating regions, and EMT-enriched regions with distinct immune and stroma morphological characteristics. The T-cell infiltration neighbors a region that has an appearance of necrosis according to the pathologist evaluation. These domains further demonstrate enrichments of modules with motif enrichment (known as regulons) according to SCENIC analysis. Analysis of spatial ATAC illustrates not only HER2 but also its amplicon amplification. From MultiNMF components of spatial ATAC, we derive regulatory regions for T-cell/B-cell marker genes. Overall, MultiNMF is a novel model that has not been applied to SRT field. It provides an multiomic extension to the traditional NMF. Citation Format: William Bowie, Stacy Wang, Benjamin Strope, Qian Zhu. MultiNMF: multiview factorization for joint modeling of spatial multi-omics and histology images [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 2335.
Abstract 954: An evaluation of the combination of metformin and Y15 for the treatment of platinum-resistant ovarian cancer
Authors: Manuel A. Duarte, Kristal Garcia, Marco A. Arriaga, Sue Anne Chew, Arkene S. Levy, Isabela Mendez
Journal: Cancer Research (2024)

Abstract Ovarian cancer is the fifth leading cause of cancer mortality among women. This high mortality rate is linked to the development of resistance to first-line chemotherapy with platinum compounds which has been attributed in part to increased activity of focal adhesion kinase (FAK). The anti-diabetic drug Metformin was previously shown to inhibit the proliferation and migration of ovarian cancer cells and thus, the combination of a FAK inhibitor, Y15, and Metformin may be a promising treatment for platinum-resistant ovarian cancer (PROC). The objective of this study was to evaluate the combination of Y15 and Metformin on PROC cell viability and the mechanism of cell death. An MTT assay was used to analyze cell viability in PROC OVCAR3 cells after 48 h of treatment by measuring the absorbance at 570 nm with a microplate reader. Western blot was used to determine the protein levels of the apoptosis marker cleaved PARP and the autophagy marker LC3B-II. The exposure of OVCAR3 platinum-resistant ovarian cancer cells to Metformin (4.5 mM) + Y15 (5.5 µM) resulted in a significantly enhanced cytotoxicity (32.6 ± 1.8%) compared to single drug treatment with either Metformin (4.5 mM) (65.0 ± 4.2%) or Y15 (5.5 µM) (66.0 ± 4.8%). A combination of Metformin (7.8 mM) + Y15 (5.5 µM) also resulted in a significantly enhanced cytotoxicity (22.8 ± 1.5%) compared to single drug treatment with either Metformin (7.8 mM) (50.7 ± 3.7%) or Y15 (5.5 µM) (66.0 ± 4.8%). Cells treated with the combination of Metformin and Y15 for 24, 48, and 72-hours showed an increase in cleaved PARP compared to the control, Metformin alone and Y15 alone. For Metformin alone, there was an increase in cleaved PARP compared to the control at all timepoints and increased from 24 hours to 72 hours. For Y15 alone, the amount of cleaved PARP was also higher compared to the control at all time points, however, decreased over time from 24 hours to 72 hours. Cells treated with Metformin, Y15, and a combination of Metformin and Y15 showed no conversion of LC3B-I to LC3B-II, which occurs during autophagy. Thus, it is concluded that the mechanism of cell death for both Metformin and Y15 is through apoptosis and not autophagy and apoptosis is enhanced with the combination of the drugs. The delivery of Metformin and Y15 can result in an additive effect on cell viability through apoptosis and can be further explored as a promising approach for the treatment of PROC. Citation Format: Manuel A. Duarte, Kristal Garcia, Marco A. Arriaga, Sue Anne Chew, Arkene S. Levy, Isabela Mendez. An evaluation of the combination of metformin and Y15 for the treatment of platinum-resistant 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 954.
Abstract 7112: Preclinical profile of novel and potent small molecule inhibitors of PARG
Authors: Sukanya Patra, Manoj Pothuganti, Githavani Kummari, Venu Sankeshi, Jyothipriya Kapaka, Navnath Karche, Ramamohan Mekala, Srikant Viswanadha
Journal: Cancer Research (2024)

Abstract Poly(ADP-ribose) glycohydrolase (PARG) is primary enzyme involved in the dePARylation process of DNA repair, specifically in the single strand break repair (SSBRs). PARG is a macrodomain protein with exo- and endo-glycohydrolase activity that liberates free ADP-ribose and PAR chains, respectively. It interacts with various Poly(ADP-ribose) polymerase (PARP) proteins to facilitate appropriate and timely DNA repair. The balance between PARP and PARG activity is essential for efficient DDR. Inhibition of PARG lead to altered DNA repair in cancer cells. We have identified highly potent PARG inhibitors with picomolar IC50 in. PARG is the primary enzyme for dePARylation and accounts for 90% of dePARylation activity. Treatment of HCC-1806 cells with PARG inhibitors showed dose dependent increase of PARylation signature, with sub micromolar IC50. Compounds also demonstrate significant functional, anti-proliferative activity in ovarian and breast cancer cell lines besides a favourable ADME profile. Further pharmacokinetic and pharmacodynamic characterization of the identified hits is currently in progress. Citation Format: Sukanya Patra, Manoj Pothuganti, Githavani Kummari, Venu Sankeshi, Jyothipriya Kapaka, Navnath Karche, Ramamohan Mekala, Srikant Viswanadha. Preclinical profile of novel and potent small molecule inhibitors of PARG [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 7112.
Abstract 7184: Simulating extended dosing intervals for atezolizumab: A comparative analysis between NONMEM and Pumas
Authors: Francis Bateman, Oluwatobi Arisa, Natalia Corvalan Cabanas, Yi Zeng, Cody Peer, Keith Schmidt, William Douglas Figg
Journal: Cancer Research (2024)

Abstract The initially approved dosage regimens of 840 mg every 2 weeks, 1200 mg every 3 weeks, and 1680 mg every 4 weeks are considered excessive concerning the specified minimum effective concentration of 6 ug/mL and the significant exposure-response relationship with Adverse Events of Special Interest (AESI). Our previous research, demonstrated through simulation, indicated that an extended-interval dosing regimen (840 mg every 6 weeks) after two standard loading doses maintained efficacy with a nonsignificant exposure-response relationship with AESI.In alignment with the FDA's initiative "Project Optimus," which aims to optimize drug dosing for improved therapeutic outcomes, we explore alternative dosage regimens for atezolizumab. Through a comparison of the traditional 30-year-old industry standard NONMEM software and the recently developed PumasAI software, we simulate these regimens, utilizing our previously published article as a benchmark. Given the rapid evolution of artificial intelligence (AI) and machine learning, it is crucial for pharmacokinetic modeling to adapt and leverage these advancements. We anticipate that this paper will contribute to the integration of AI and machine learning software in pharmacokinetic analysis, aligning with the goals of "Project Optimus" to ensure that drug concentrations are optimized for efficacy and safety. Table 1: Comparative Summary of Simulated Pharmacokinetic Parameters: Pumas vs. NONMEM Pumas NONMEM Regimen Cycle Week Cmax (ug/mL) Cmax (ug/mL) Cmax (ug/mL) Cmax (ug/mL) Cmin (ug/mL) Weekly AUC (ug*day/mL) 1200 mg q3w 1st 3 384(278-533) 64.1(31.4-112) 919(640-1290) 394 (278-559) 75.6 (44.5-121) 1003 (755-1342) 7th 21 541(371-795) 146(53.8-349) 1750(977-3210) 583 (414-847) 180 (75.9-396) 2014 (1169-3535) 840 mg q2w x2, 840 mg q6w 1st 2 270(195-379) 61.6(35.5-100) 775(570-1085) 275 (184-397) 71.2 (46.4-108) 834 (620-1131) 7th 38 314(221-448) 33.6(8.8-127) 650(357-1375) 336 (235-487) 46.9 (13.5-150) 774 (441-1562) 1200 mg q3w x2, 840 mg q6w 1st 3 384(278-533) 64.1(31.4-112) 919(640-1290) 394 (278-559) 75.6 (44.5-121) 1003(755-1342) 7th 39 316(224-459) 36.0(8.4-129) 670(340-1406) 327 (217-492) 46.6 (13.4-157) 763 (411-1594) 1680 mg q4w x2, 840 mg q6w 1st 4 545(380-779) 63.1(26.9-122) 1096(735-1555) 552 (375-793) 78.1 (40.7-135) 1203(869-1651) 7th 40 318(222-469) 33.9(7.7-126) 657(343-1385) 332 (227-491) 46.5 (13.6-140) 771 (436-1464) Citation Format: Francis Bateman, Oluwatobi Arisa, Natalia Corvalan Cabanas, Yi Zeng, Cody Peer, Keith Schmidt, William Douglas Figg. Simulating extended dosing intervals for atezolizumab: A comparative analysis between NONMEM and Pumas [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 7184.
Abstract 5162: Tumor transcriptional identity as a key predictor of clinical outcome in EGFR-Mutant non-small cell lung cancer
Authors: Simon Heeke, Monique Nilsson, Don Gibbons, Jianjun Zhang, Ignatio I. Wistuba, Keunchil Park, John V. Heymach, Xiuning Le
Journal: Cancer Research (2024)

Abstract Background: EGFR tyrosine-kinase inhibitors (TKIs) produce significant clinical benefit in patients (pts) harboring EGFR-activating mutations, however, patient outcomes remain heterogeneous, highlighting the need to identify novel biomarkers for treatment response prediction. We aim to investigate the association of tumor transcriptional identity with clinical outcomes in EGFR-mutant NSCLC. Methods: We used consensus classification to derive unsupervised transcriptionally distinct clusters in EGFR-mutant NSCLC from the TCGA (N= 44). Then, we built a predictive model using extreme-gradient boosting to (1) classify an additional 88 surgically resected tumors from the LCCS study and analyzed for overall survival (OS) prediction; (2) classify metastatic EGFR-mutant NSCLC from the RELAY trial with available RNAseq data (N = 106) and analyzed for progression-free survival (PFS) with erlotinib treatment. Results: Consensus clustering revealed two distinct clusters, one associated with more lineage-preserving features and one with more lineage-independent features that could transcriptionally distinguish EGFR-mutant lung cancer. No differences in primary EGFR mutations were detected across the two clusters. Ingenuity Pathway Analysis revealed TGFB1 as the most significantly enriched pathway in the lineage-independent cluster which was also significantly more mesenchymal (p = 0.0028). For surgically resectioned EGFR-mutant NSCLC, OS was consistently shorter in the lineage-independent cluster for TCGA (23.6 vs 58.3 mo; p = 0.049; HR = 0.4) and the LCCS cohort (55.7 vs not-reached; p = 0.0073; HR = 0.27). In the metastatic setting with erlotinib treatment, there was a trend for PFS to be shorter in the lineage-independent cluster (15.2 vs 19.4 mo; p = 0.12; HR = 0.61). Conclusions: Transcriptional identity in EGFR-mutant lung cancer are prognostic of clinical outcome. The lineage-independent tumors have inferior benefit to EGFR TKI as well as inferior OS, indicating that transcriptional biomarkers can be independent of oncogene biomarkers. These transcriptional biomarkers may help identify pts with poor outcome for future intensification of therapies. Citation Format: Simon Heeke, Monique Nilsson, Don Gibbons, Jianjun Zhang, Ignatio I. Wistuba, Keunchil Park, John V. Heymach, Xiuning Le. Tumor transcriptional identity as a key predictor of clinical outcome in EGFR-Mutant non-small cell lung 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 5162.
Abstract 6881: Investigating the role of post-transplant bone marrow immune dysregulation in multiple myeloma
Authors: Julia T. Wang, Mark Fiala, Kazuhito Sato, Julie Fortier, Ravi Vij, Li Ding
Journal: Cancer Research (2024)

Abstract Multiple myeloma (MM) is a cancer of bone marrow plasma cells (BMPCs), which to date remains incurable. Standard of care for qualifying patients involves high-dose chemotherapy followed by autologous stem cell transplant (ASCT), after which minimal residual disease (MRD) testing is used to assess the degree of tumor clearance. Though strongly associated with better survival, MRD-negativity is an imperfect predictor for patient outcomes and fails to account for the pro-tumorigenic BM dysregulation often found in MM patients. As the post-transplant BM milieu in MM is not well understood, we conducted 3' single-cell RNA sequencing (scRNAseq) of 49 newly diagnosed (NDMM) and 79 day-100 post-ASCT whole-BM aspirates to investigate how immunosuppressive microenvironmental signatures may persist or develop following ASCT to influence patient outcomes. 68 of 79 post-ASCT samples come from uniformly-treated clinical trial enrollees with progression-free survival (PFS) ranging from 10 months to over 7 years, with both MRD- and MRD+ cases at either PFS extreme. We observe that although post-ASCT samples have uniformly lower PC count than NDMM samples, only erythroblasts and B cells appear relatively depleted in NDMM samples to compensate for higher PC proportions. T/NK cells and myeloid lineages, though similar in percentage across samples, exhibit clear subtype bias between the two timepoints. Post-ASCT samples are significantly enriched for GZMH+ CD8+ T and NK cells, HIF1A+ hypoxia-responsive monocytes, and depleted for S100A8/9-high CD14+ monocytes. NDMM samples are also enriched for SPINK2+, CDK6+, and AVP+ neutrophils, which are rare in post-ASCT samples. Notably, higher HIF1A expression in myeloid cells post-ASCT is further associated with longer PFS. Regardless of MRD status, NFKBIA, FOS, and GNLY upregulation and GZMK downregulation in post-ASCT T cells are associated with worse PFS. Conversely, enrichment of CD16+ IFN-responsive monocytes and depletion of ARG1+/FUT4+/CEACAM8+ myeloid-derived suppressor cells (MDSCs) are associated with longer PFS. Beyond such general trends, we observe MRD-dependent PFS-association of several CD8+ T cell subsets. Overall, this study enhances our understanding of how post-transplant BM repopulation and immune interaction may impact treatment response in MM. Citation Format: Julia T. Wang, Mark Fiala, Kazuhito Sato, Julie Fortier, Ravi Vij, Li Ding. Investigating the role of post-transplant bone marrow immune dysregulation 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 6881.
Abstract 7428: S2Map: An online interactive analytical platform for quantitative signal measurement of cell Secretion Signal Map
Authors: Zongliang Yue, Lang Zhou, Fengyuan Huang, Pengyu Chen
Journal: Cancer Research (2024)

Abstract Cell communication is predominantly governed by secreted proteins, whose diverse secretion patterns often signify underlying physiological irregularities. Unraveling the intricacies of these secreted signals at the single-cell level is crucial for a comprehensive understanding of regulatory mechanisms involving various molecular agents. To elucidate the array of cell secretion signals, encompassing both anisotropic and isotropic biomolecular cues from individual immune cells or disparate cell types, we introduce the Secretion Signal Map (S2Map). S2Map is an innovative online interactive analytical platform that incorporates qualitative metrics, the signal inequality index (SII) and the signal coverage index (SCI), which are exquisitely sensitive in facilitating the measurement of dissymmetry and diffusion of cumulative signals and signal dynamics in temporal analysis. S2Map's innovation lies in its depiction of signals through real-time secretion hotspots, and cumulative secretion contour lines, complemented by the visualization of potential cell secretion signal velocities via stream plots generated by regression models. We evaluated the SII and SCI performance in distinguishing the simulated signal diffusion model. Presently, S2Map hosts a repository of 12 datasets that map single-cell and two-cell secretion dynamics. These datasets serve as a resource to explore secretion signal types. We anticipate that S2Map will be a powerful approach to delve into the complexities of physiological systems, providing insights into the regulation of protein production, such as cytokines at the remarkable resolution of single cells. The S2Map server is publicly accessible via https://au-s2map.streamlit.app/. Citation Format: Zongliang Yue, Lang Zhou, Fengyuan Huang, Pengyu Chen. S2Map: An online interactive analytical platform for quantitative signal measurement of cell Secretion Signal Map [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 7428.
Abstract 5169: Circulating levels of ANGPTL3, PCSK9, apoCIII and lipoprotein (a) in high-grade serous ovarian cancer
Authors: Emilie Wong Chong, France-Hélène Joncas, Pierre Douville, Dimcho Bachvarov, Frédéric Calon, Nabil Seidah, Caroline Diorio, Anne Gangloff
Journal: Cancer Research (2024)

Abstract Background: Ovarian cancers require abundant amounts of lipids for their growth. Recent years have highlighted the role of ANGPTL3, PCSK9, ApoCIII and Lp(a) as key players in lipid metabolism. But circulating levels of these factors in ovarian cancers have not been thoroughly investigated to date. Objective: We assessed the profiles of plasma ANGPTL3, PCSK9, ApoCIII and Lp(a) in ovarian high-grade serous carcinoma (HGSC). Methods: Plasma samples collected from women diagnosed with an HGSC (n=31) or a benign ovarian lesion (BOL, n=40) were analyzed for plasma lipid profile (ApoB, total cholesterol, HDL, triglycerides, Lp(a) levels) on a clinical modular platform (Roche). ApoCIII, ANGPTL3 and PCSK9 levels were measured with commercially-available ELISA kits. Differences between HGSC and BOL were assessed by two-group comparisons. Pairwise correlation strength between variables was evaluated with Spearman’s rank order tests. Logistic regression modelling was used to further examine the associations between selected variables and HGSC diagnoses. Results: ANGPTL3 levels increased in HGSC (84.1 ng/ml, SD=29.2ng/ml, n=31) compared to BOL (66.9ng/ml, SD=30.7 ng/ml, n=40; HGSC vs BOL p=0.02). Receiver operating characteristic (ROC) curve suggested that plasma ANGPTL3 levels were moderately associated with HGSC (AUC=67.7%, p=0.005 1) and did not increase predictive performance of ovarian tumor biomarkers CA125 and HE4 in multivariable model. Associations between ANGPTL3 levels and cholesterol (HDL, non-HDL, LDL and total) could be observed in the control group but not in the HGSC group. The same dissociation was observed for the inverse correlation between HDL and triglycerides in HGSC. Furthermore, moderate associations were observed between PCSK9 &amp; CA19-9 in the entire cohort (rho=0.34, n=53, p=0.013) and Lp(a) &amp; CA125 in HGSC cases (rho=0.51, p= 0.017). Conclusion: In this cohort of 71 women, we report increased levels of ANGPTL3 in HGSC with an effect size (Cohen’s D) of 0.57. Confirmation of this result in larger cohort studies (minimal n&amp;gt;50 in each group) would warrant further investigation into the roles of circulating ANGPTL3. Its inhibition may allow to tackle deregulated lipid metabolic pathways contributing to ovarian cancer progression. Citation Format: Emilie Wong Chong, France-Hélène Joncas, Pierre Douville, Dimcho Bachvarov, Frédéric Calon, Nabil Seidah, Caroline Diorio, Anne Gangloff. Circulating levels of ANGPTL3, PCSK9, apoCIII and lipoprotein (a) 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 5169.