tbuckley/cabot-search huggingface dataset:
Abstract 5672: Transcriptomic profiling of lung adenocarcinoma from never-smokers reveals molecular subtypes with clinical implications
Authors: Wei Zhao, Tongwu Zhang, Phuc H. Hoang, Jian Sang, Maria Teresa Landi
Journal: Cancer Research (2024)

Abstract Lung cancer is the leading cause of cancer mortality worldwide and about 10-25% of lung cancers are from never-smokers (LCINS). Previous genomic studies of LCINS identified multiple genomic subtypes with different genetic drivers and evolutionary processes. To dissect the profile of LCINS cell states and tumor microenvironment, and their relationship with genomic lesions, we sequenced and assembled a large RNA-seq data set of 685 samples of lung adenocarcinoma from never-smokers and integrated them with whole genome sequencing data, pathological features and clinical outcomes from the same subjects. We identified three transcriptomic subtypes defined by distinct gene expression patterns. The three subtypes were associated with different pathway activities and showed remarkable heterogeneity in cell composition, lineage fidelity and pathological features. The genomic driver events and mutational signatures were significantly enriched in specific subtypes. Clinical outcomes also differed across the subtypes. For example, one subtype had prolonged overall survival and was associated with predicted response to immune checkpoint blockade. This study emphasizes the importance of transcriptome-based classification of LCINS, which has profound clinical implications beyond those provided by genomic and pathological assessment. Citation Format: Wei Zhao, Tongwu Zhang, Phuc H. Hoang, Jian Sang, Maria Teresa Landi. Transcriptomic profiling of lung adenocarcinoma from never-smokers reveals molecular subtypes with clinical implications [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 5672.
Abstract 4481: Enhancing AOH1996 through structure activity relationship exploration for caPCNA inhibition
Authors: Pouya Haratipour, Maryam Zangi, Mahshid Yaghoubi, Long Gu, Jennifer Jossart, John J. Perry, Linda H. Malkas, Robert J. Hickey
Journal: Cancer Research (2024)

Abstract Recently, we have developed AOH1996, a small molecule PCNA ligand currently in phase I clinical trial, designed to selectively inhibit the functions of cancer-associated PCNA (caPCNA). Seeking to enhance the potency and selectivity of AOH1996, we conducted an exhaustive Structure-Activity-Relationship (SAR) investigation. Over 100 analogues of AOH1996 were synthesized, systematically modifying its three main components: the naphthyl group, glycine linker, and diphenyl ether. The glycine linker underwent substitution with various natural and unnatural amino acids, while the 1-naphthyl group saw replacements with diverse monocyclic and bicyclic aromatic groups, including quinoline and isoquinoline derivatives. The diphenyl ether moiety was extensively explored by substituting the bridging oxygen with different functional groups (sulfonyl, imido, sulfur, amide), substituting both phenyl rings with various functional groups (fluorine, chlorine, hydroxy, methoxy, methyl, trifluoromethyl), and adopting a nitrogen-walk approach on the terminal phenyl ring to evaluate critical ligand/caPCNA-binding positions. This meticulous effort resulted in the identification of several analogues, such as AOH1160S (featuring a diphenyl thioether group), AOH1996S-4CH3 (with a methyl group at the para-position of the terminal thiophenyl ring), and AOH1996-3CH3 (incorporating an m-methoxy function in the terminal phenyl ring and bridging oxygen), exhibiting similar or superior potency compared to AOH1996. The target binding of these analogues was verified by capturing the binary complex of the more polar version of these analogues in the crystal structure. The synthesis, characterization, and detailed biological activities of these promising AOH analogues will be thoroughly described. Citation Format: Pouya Haratipour, Maryam Zangi, Mahshid Yaghoubi, Long Gu, Jennifer Jossart, John J. Perry, Linda H. Malkas, Robert J. Hickey. Enhancing AOH1996 through structure activity relationship exploration for caPCNA inhibition [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 4481.
Abstract 4013: Increasing mitochondrial respiration by silencing MCJ/DnaJC15 enhances CD8 CAR-T cell therapy efficacy
Authors: Meng-Han Wu, Emily L. Giddings, Felipe Valenca-Pereira, Catherine Pham-Danis, Francesca Cendali, Michael C. Yarnell, Amanda J. Novak, Jorge Henao-Mejia, Richard A. Flavell, Angelo D'Alessandro, Mark Eric Kohler, Mercedes Rincon
Journal: Cancer Research (2024)

Abstract Chimeric antigen receptor (CAR)-T cell therapy is a promising treatment against leukemia and lymphoma. However, poor initial treatment response and post-therapy relapse have found to be common and remained to be resolved. Modulating T cell immunometabolism is considered a novel therapeutic approach to enhance CAR-T cell therapy. We have identified MCJ (DnaJC15) as an endogenous negative regulator of Complex I of the electron transport chain in mitochondria. MCJ is abundantly expressed in mouse CD8 cells relative to other immune cells. We propose that MCJ can be a therapeutic target to enhance mitochondrial respiration in CD8 CAR-T cells. We will show that loss of MCJ in mouse CD8 cells leads to superior anti-tumor response against melanoma. In addition, we will show that MCJ-deficient murine CD8 CAR-T cells have enhanced killing activity against leukemia both in vitro and in vivo. We have recently revealed that MCJ is also expressed in human CD8 cells and the levels of MCJ are heterogenous among human population. Similar to murine CD8 cells, MCJ knockdown in human CD8 cells enhances mitochondrial membrane potential and effector functions. We have designed a new CAR construct to silence MCJ in human CD8 CAR-T cells. MCJ-deficient human CD8 cells show superior cytotoxicity against leukemia with increased mitochondrial metabolism. Together, these data demonstrate that MCJ is a promising target to improve CAR-T cell therapy through modulating the T cell metabolism. We are currently investigating the effect of MCJ-deficient CD8 CAR-T cells against solid malignancy. Citation Format: Meng-Han Wu, Emily L. Giddings, Felipe Valenca-Pereira, Catherine Pham-Danis, Francesca Cendali, Michael C. Yarnell, Amanda J. Novak, Jorge Henao-Mejia, Richard A. Flavell, Angelo D'Alessandro, Mark Eric Kohler, Mercedes Rincon. Increasing mitochondrial respiration by silencing MCJ/DnaJC15 enhances CD8 CAR-T cell therapy 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 4013.
Abstract 4008: DASH CAR-T: accelerating CD276-targeted immunotherapy for pancreatic cancer
Authors: Haiying Wang, Tian Deng, Hao Li, Yue Yao, Yuan Tian, Hongwei Wang, Pengfei Jiang
Journal: Cancer Research (2024)

Abstract B7 homolog 3 protein (B7-H3), recognized as CD276, is a pivotal immune checkpoint molecule extensively expressed in cancer cells, presenting a limited presence in normal tissues. Immunohistochemical analysis of 150 pancreatic cancer patients revealed positive CD276 staining in 66% of cases, with elevated expression correlating to diminished disease-free survival. Additional findings from an independent study on 59 patients highlighted significantly elevated CD276 levels, particularly in lymph node metastasis and advanced pathology. This study introduces an innovative Chimeric Antigen Receptor T (CAR-T) therapy targeting CD276, leveraging our DASH CAR-T manufacturing platform. Significantly, our approach reduces the ex vivo cell expansion time from 10 days to 1-2 days. T cells were enriched, activated, and transduced with a CD276 CAR-encoding retroviral vector, achieving the final product in 2-3 days. In preclinical models, our shorten-time CAR-T against CD276 exhibited superior expansion and heightened tumor-killing efficacy. Remarkably, DASH CAR-T demonstrated superior tumor control compared to conventional CAR-T, requiring only one-tenth of the dosage. This research advances our understanding of CD276 in pancreatic cancer and presents a transformative approach in CAR-T therapy manufacturing. The shortened production timeline and enhanced therapeutic outcomes underscore the potential clinical impact of our novel CAR-T strategy, offering a more efficient and potent treatment option for pancreatic cancer. Citation Format: Haiying Wang, Tian Deng, Hao Li, Yue Yao, Yuan Tian, Hongwei Wang, Pengfei Jiang. DASH CAR-T: accelerating CD276-targeted immunotherapy for pancreatic 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 4008.
Abstract 3504: ARS2 and paraspeckle interactions: A nexus for lncRNA mediated drug resistance and cancer progression in hepatocellular carcinoma
Authors: Gobi Thillainadesan, Hon Sing Leong, Omar Alawamry
Journal: Cancer Research (2024)

Abstract Paraspeckles, elaborate sub-nuclear assemblies composed of the lncRNA NEAT1-2 and a cohort of RNA-binding proteins, have a noted association with cancer progression and drug resistance. The specific mechanisms, however, have remained obscure. Our investigation into Hepatocellular Carcinoma (HCC) reveals a critical interaction: the sequestration of ARS2—an RNA-binding protein vital for the suppression of mRNA transcripts and the maturation of miRNAs—within paraspeckles. This sequestration leads to increased levels of NEAT1-2, derails miRNA processing, and diminishes the gene-silencing function of ARS2, thereby enhancing the expression of oncogenic genes. This discovery positions the ARS2-paraspeckle axis as a pivotal factor in cancer biology and a promising target for future treatments. Citation Format: Gobi Thillainadesan, Hon Sing Leong, Omar Alawamry. ARS2 and paraspeckle interactions: A nexus for lncRNA mediated drug resistance and cancer progression in hepatocellular 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 3504.
Abstract 3517: A deep learning-based virtual staining system with multimodal-attention for precision medicine
Authors: Zixia Zhou, Yuming Jiang, Ruijiang Li, Lei Xing
Journal: Cancer Research (2024)

Abstract Purpose: This study introduces a novel multimodal-attention-based virtual mIF staining (MAS) system designed for efficient, reliable virtual mIF staining from label-free fluorescence images. Our goal was to overcome the performance bottleneck and time-cost limitations associated with traditional mIF techniques and thereby enhance their clinical utility. Method and Materials: Our approach involved developing a cutting-edge MAS model. This model is built upon a sophisticated end-to-end generative convolutional neural network (CNN) architecture. It ingeniously leverages autofluorescence and 4',6-diamidino-2-phenylindole (DAPI) slides as inputs to generate mIF images. To achieve this, we equipped the model with feature extractors enhanced by pretrained masked auto-encoders (MAEs) and a self-attention combination strategy. These components worked harmoniously to extract antigen-label-related features and precisely locate specific cells within the images. Results: In our comprehensive study, we engaged 94 gastric cancer patients, utilizing the MAS system for automated virtual mIF staining of seven biomarkers, namely CD3, CD20, FOXP3, PD1, CD8, CD163, and PDL1, in both cancerous and non-cancerous tissues. Importantly, the MAS-produced virtual mIF stains matched the quality of traditional manual stains. Furthermore, we validated the prognostic accuracy for gastric cancer using these virtual mIF images, demonstrating their ability to provide clinical information that is as reliable and valuable as that obtained from manually stained mIF images. Conclusions: Our study identifies the MAS system as an important advancement in mIF staining, enhancing personalized medicine with its efficiency and quality. This tool holds significant potential to enable personalized medicine efficiently and cost-effectively, streamlining the process of diagnosing diseases, making prognoses, and developing treatment plans. Quantitative Comparison of Ablation Experiments for MAS system in predicting multiple biomarkers Biomarkers Index U-Net (Avg±Std) ReU-Net (Avg±Std) Att-ReU-Net (Avg±Std) MAS (Avg±Std) CD3 PSNR 27.053±4.10 27.419±4.15 28.073±4.00 28.546±4.40 CD3 SSIM 0.733±0.07 0.724±0.07 0.728±0.05 0.742±0.07 CD20 PSNR 28.835±4.66 29.244±4.76 28.672±4.52 29.243±4.99 CD20 SSIM 0.921±0.058 0.921±0.057 0.915±0.051 0.923±0.051 FOXP3 PSNR 31.769±3.29 31.838±3.10 31.098±3.20 31.862±3.11 FOXP3 SSIM 0.613±0.11 0.618±0.11 0.617±0.112 0.617±0.12 PD1 PSNR 27.908±4.47 27.950±4.43 30.283±4.37 31.717±4.17 PD1 SSIM 0.654±0.13 0.659±0.13 0.694±0.12 0.726±0.11 Citation Format: Zixia Zhou, Yuming Jiang, Ruijiang Li, Lei Xing. A deep learning-based virtual staining system with multimodal-attention for precision medicine [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 3517.
Abstract 3537: Assessing machine learning models of drug response predictions using orthotopic patient-derived xenograft
Authors: Yuan-Hung Chien, Raffaella Pippa, Warren Andrews, Javier Rodriguez, Kristen Buck, Derrick Gorospe, Elizabeth Valencia, Bianca Carapia, Emily Eastwood, Jantzen Sperry, Jonathan Nakashima, Long Do
Journal: Cancer Research (2024)

Abstract Accurate prediction of drug response using machine learning (ML) remains a significant challenge in drug development as well as in personalized cancer therapy. Furthermore, there is a scarcity of rigorous external validation studies for evaluating drug response prediction models. Even when available, such validations are often confined to in-vitro studies. Orthotopic patient-derived xenograft (O-PDX) mice serve as essential preclinical models that closely replicate the human tumor microenvironment and assess the therapeutic response to cancer treatments. We present CertisAITM, a novel ensemble of machine learning models that have been trained on a vast array of experimental high-throughput screenings of both monotherapies and combination therapies, incorporating over 4500 investigational and FDA-approved drugs across 10 major cancer indications. The results of our ML drug response prediction models show an average R2 of 0.75 and RMSE of 0.62 (across internal 5-fold cross-validation) for all 10 cancer prediction models. Our evaluation of CertisAI's drug response predictions within O-PDX pharmacology studies has shown a notable overall correlation between actual observed tumor growth inhibition (TGI) and predicted TGI, with an r value of 0.45 across seven indications. The most accurate prediction model achieved an r value of 0.7 for colorectal cancer, encompassing 37 treatments in 6 O-PDX studies. Altogether, these results highlight CertisAI’s potential in enhancing pre-clinical model selection as well as personalized treatment strategies in oncology. Citation Format: Yuan-Hung Chien, Raffaella Pippa, Warren Andrews, Javier Rodriguez, Kristen Buck, Derrick Gorospe, Elizabeth Valencia, Bianca Carapia, Emily Eastwood, Jantzen Sperry, Jonathan Nakashima, Long Do. Assessing machine learning models of drug response predictions using orthotopic patient-derived xenograft [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 3537.
Abstract 3512: Joint probabilistic modeling of multimodal metabolic data with UnitedMet
Authors: Amy X. Xie, Casey Bradshaw, Christopher Tosh, Wesley Tansey, Ed Reznik
Journal: Cancer Research (2024)

Abstract Metabolites play a crucial role in the functioning of biological systems. Measurements of metabolite abundances by metabolomics and metabolic pathway activity by isotope-tracing are powerful approaches to study metabolic phenotypes. However, large-scale measurements of metabolite levels and pathway activity remain extremely scarce due to the technical challenges. Motivated by the strength of RNA-metabolite covariation, we present UnitedMet, a Bayesian probabilistic method for end-to-end joint modeling of RNA-sequencing and metabolic datasets that is capable of dimensionality reduction, data integration and metabolic modality prediction. UnitedMet demonstrates high accuracy predicting metabolite levels and isotopologue distributions from gene expression data in human tumor samples. To evaluate UnitedMet’s efficacy in clinical applications, we inferred metabolomic profiles and isotope labeling patterns from the RNA sequencing data of clear cell renal cell carcinoma (ccRCC) patients enrolled in TCGA and clinical trials. Correlation analysis between genetic mutations and predicted metabolic phenotypes revealed that BAP1 mutations were associated with increased contribution of glucose to TCA cycle in ccRCC. Increased TCA cycle activity was found to be associated with disease progression and poorer clinical outcome. UnitedMet, therefore, provides a solution that could be broadly applicable to enhance multimodal metabolomic datasets and facilitates comprehensive study of interplays between metabolic profiles, molecular alterations and human phenotypes. Citation Format: Amy X. Xie, Casey Bradshaw, Christopher Tosh, Wesley Tansey, Ed Reznik. Joint probabilistic modeling of multimodal metabolic data with UnitedMet [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 3512.
Abstract 3520: A scalable single cell RNA-seq pipeline leveraging machine learning and high-quality references for cell-type prediction
Authors: Abhijit Dasgupta, Grant Duclos, Ricardo Miragaia, Brychan Manry, Etai Jacob, Natasha Markuzon, Asaf Rotem
Journal: Cancer Research (2024)

Abstract Single cell RNA-seq (scRNA-seq) technology transformed our understanding of biology at the cell level. Inferring cell types provide insights into the relative abundance of, and genomic differences between, different cell types. Current methods leverage known cell type markers and genomic similarity measures to attribute cell types to groups of cells. We present a scalable machine learning-based pipeline that can leverage high quality reference annotation data to infer cell types quickly for multiple scRNA-seq experimental samples. This pipeline leverages two machine learning technologies: variational autoencoders (VAE) to create a low-dimensional representation of the reference transcriptome; and, supervised learning methods that use this representation to learn cell types from the reference data. This pipeline can quickly score new experimental data using GPU-enabled computing platforms and provide probabilities of a cell being each cell type present in the reference data. This novel pipeline is evaluated using 5 public reference data sets. We evaluate whether VAE-based representations benefit supervised learning, compared to PCA and t-SNE. We next evaluate different supervised learning methods for predictive performance. Finally, we compare the pipeline’s performance with commonly-used cell type prediction algorithms (Seurat, scANVI). We find that using a VAE is generally better than both PCA and t-SNE for feature generation to predict cell type. Using the VAE representation, there is no significant difference in accuracy between logistic regression and other supervised learning algorithms. Finally, using logistic regression as the learning machine, this pipeline is as accurate as Seurat and better than scANVI, while running about 2-5x faster than Seurat. Table 1 Balanced accuracy (%) from leave-one-subject-out cross-validation across 5 public data sets. Intervals are the range of the metric across the cross-validation folds. Algorithm PBMC (10X) HLCA Eraslan snRNA Blueprint Breast Blueprint Lung Our pipeline 92 (87-95) 92 (88-93) 93 (90-97) 89 (80-96) 97 (95-99) Seurat 96 (95-97) 90 (94-98) 94 (93-97) 96 (87-98) 98 (94-99) scANVI 91 (84-95) 72 (78-86) 92 (88-96) 94 (80-97) 88 (86-100) Citation Format: Abhijit Dasgupta, Grant Duclos, Ricardo Miragaia, Brychan Manry, Etai Jacob, Natasha Markuzon, Asaf Rotem. A scalable single cell RNA-seq pipeline leveraging machine learning and high-quality references for cell-type prediction [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 3520.
Abstract 3501: Concurrent amplification of TBXT and highly activated enhancers in extrachromosomal DNA (ecDNA) drives chordoma tumorigenesis
Authors: Bo Zhao, Martin Barrio Ines, Lingqun Ye, Xizi Wu, Alice Soragni, Andrew Futreal, Kadir Akdemir
Journal: Cancer Research (2024)

Abstract Chordoma, a rare bone cancer characterized by limited actionable clinical targets, exhibits a pivotal hallmark in the activation of the developmental transcription factor gene TBXT (Brachyury) through its association with highly active enhancers. TBXT amplification emerges as a major causative factor in familial chordomas, yet the amplification mechanism and its consequential impact on genomic instability remain inadequately elucidated. In this study, we conducted a comprehensive analysis of multiple genomic and epigenomic sequencing datasets, including WGS-seq, H3K27ac ChIP-seq, and ATAC-seq in six chordoma cell lines, and HiC data in two of them, to unravel the mechanisms of structural variations and their biological implications. Notably, our focus extends to JHC7 cells, which exhibit extrachromosomal amplification of a 2.4M region encompassing TBXT and the associated enhancers. The existence of such extrachromosomal DNA (ecDNA) in JHC7 was further validated through DNA FISH and Sanger sequencing. Comparing JHC7 with CH22, a chordoma cell line lacking TBXT amplification, revealed heightened chromatin relaxation and increased DNA rearrangements in JHC7. Interestingly, FISH results showed significant genomic ploidy heterogeneity within JHC7 cells, a finding demonstrated by nucleus FACS sorting. Single-cell multiomic analysis, combining snRNA-seq and snATAC-seq, demonstrated heterogeneous gene expression and distinct copy number variations among JHC7 cells. However, the amplification of the TBXT locus was observed in nearly all cells examined, indicating the rapid genomic evolution in the cultured JHC7 cells. These results collectively show that TBXT is extensively amplified through the formation of ecDNA, providing a fitness advantage to chordoma cells and concurrently exacerbating genomic instability. The findings in this study provide an insight for understanding the pathogenesis of chordoma and may inform the development of targeted therapeutic strategies. Citation Format: Bo Zhao, Martin Barrio Ines, Lingqun Ye, Xizi Wu, Alice Soragni, Andrew Futreal, Kadir Akdemir. Concurrent amplification of TBXT and highly activated enhancers in extrachromosomal DNA (ecDNA) drives chordoma tumorigenesis [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 3501.