bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 3284: Transcription factor YBX1 elevating the sorafenib IC50 in hepatocellular carcinoma cell lines
Authors: Kwabiah Dennis , AyalaPazzi Ana , Doxtater Kyle , Leslie Sophia , Tripathi Manish K. 
Published: March 2024

Abstract
Hepatocellular carcinoma (HCC) is the most common form of liver cancer which accounts for more than 90% of occurrences of liver cancer cases. Surgical therapies such as liver transplantation and hepatectomy may help cure hepatocellular carcinoma diagnosed in its early stages. Advanced stages of the disease have a poor prognosis and a high refractoriness because of drug resistance. Sorafenib is a tyrosine kinase inhibitor that has been approved for the systemic treatment of patients with advanced HCC. However, the efficacy of this medication has shown a limited prolongation of the overall survival in patients with advanced HCC. The mechanism of resistance to sorafenib in advanced HCC is poorly understood. The TCGA analysis of the HCC patient cohort shows an elevated expression of YBX1 in tumors as compared to normal. The expression is also correlated with progression, metastasis, and poor survival of HCC patients. Transcription factor YBX1 is involved in the regulation of drug resistance in different cancers, especially breast and colorectal cancers. Our lab is interested in understanding if YBX1 is involved in sorafenib resistance in HCC. Different HCC cell lines procured from ATCC SK-Hep1, Huh-7luc, and C3A, were analyzed for their IC50 values for sorafenib using MTT assays, at different time points. SK-Hep1 showed 8.780µM and 8.174µM, HepG2luc 4.659µM and 3.596µM at 48 and 72 hrs respectively. Overexpression of YBX1 in the SK-Hep1 cell line increased the cell survival and IC50 value of sorafenib at 48 and 72 hrs at 13.19µM and 10.64µM respectively. This supports the hypothesis which demonstrates the role of YBX1 in mediating resistance to sorafenib in advanced HCC. YBX1 could be a major therapeutic target and by inhibiting its action could improve the sensitivity of advanced HCC to sorafenib. We have developed YBX1 overexpressing and knockdown (GFP expressing) cell lines, FACS sorted and Puromycin selected for further mechanistic studies. We are also further developing the sorafenib-resistant HCC cell lines to understand the mechanism underlying sorafenib resistance in HCC. Protein and RNA analyses revealed a relative increase in YBX1 overexpression in sorafenib-resistant cell lines as compared to its parental cell lines. Understanding the mechanism of YBX1-induced drug resistance to sorafenib and its associated signaling pathways could help improve the sensitivity of advanced hepatocellular carcinoma to treatment by sorafenib and thus improve the livelihood of its patients.
Citation Format: Dennis Kwabiah, Ana AyalaPazzi, Kyle Doxtater, Sophia Leslie, Manish K. Tripathi. Transcription factor YBX1 elevating the sorafenib IC50 in hepatocellular carcinoma cell lines [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 3284.
Abstract 341: Co-detection of BCR::ABL1 common and atypical fusion and mutation using a novel NGS assay, Dup-Seq BCR-ABL1
Authors: Li Jin , Yan Zhenyu , Shi Lin , Li Wei , Liu Weihua , Spittle Cynthia , Galderisi Chad 
Published: March 2024

Abstract
The clinical management of CML and Ph+ ALL patients requires the identification of BCR::ABL1 transcript variants and drug resistance mutations. Currently these assessments need to be performed using separate assays resulting in higher sample requirements, longer turnaround times and higher costs. In this study a custom NGS assay (Dup-Seq BCR-ABL1) was developed and validated that enables co-detection and identification of transcript variants (common and atypical) and resistance mutations. The assay design covers BCR exon 1 to ABL1 exon 10 and employs duplicate PCR amplification of BCR-ABL1 from a single source of the 1st strand cDNA initiated by ABL1 specific priming. The custom data analysis pipeline enables overlapped mutation calling from duplicates as well as transcript determination. The assay minimizes the low-level artifacts that can be seen with other NGS-based BCR::ABL1 assays. This study demonstrates that the Dup-Seq BCR-ABL1 assay achieves high accuracy (PPA for fusion: 0.985; PPA and NPA for mutation at 0.978 and 0.9999, respectively) and sensitivity (LoD for mutation detection at 3% from 10,000 copies of BCR-ABL1 input). The use of the Dup-Seq BCR-ABL1 assay for both transcript variant identification and mutation detection provides comprehensive and accurate results for the clinical management of CML and Ph+ ALL patients while reducing sample requirements, testing costs and turnaround time.
Citation Format: Jin Li, Zhenyu Yan, Lin Shi, Wei Li, Weihua Liu, Cynthia Spittle, Chad Galderisi. Co-detection of BCR::ABL1 common and atypical fusion and mutation using a novel NGS assay, Dup-Seq BCR-ABL1 [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 341.
Abstract 3451: Association of multi-level factors with cervical cancer screening in healthcare settings
Authors: Rathod Rutu , Hu Ellen , Pruitt Sandi , Tiro Jasmin A. , Chubak Jessica , Haas Jennifer , Atlas Steven J. , Kruse Gina , Hughes Amy E. 
Published: March 2024

Abstract
Background- Few studies have examined the independent contribution of patient, clinician and system factors associated with completing cervical cancer screening.
Methods- Using a large retrospective cohort created from electronic health record data, we examined the association of patient- and provider-level characteristics with cervical cancer screening among unknown- and average-risk, screen eligible women who entered the cohort at a primary care visit in 1 of 3 U.S. healthcare systems (Kaiser Permanente Washington, Parkland Health, Mass General Brigham) between 2010-2019. The outcome was time, in days, from cohort entry to screening. We fit piecewise exponential (PWE) survival models, nesting patients hierarchically within provider seen and the facility at the time of their analytic cohort entry visit. We excluded patients whose visit was with providers at their non-principal facility (principal defined as the facility that they saw most patients in 2010). The analysis was stratified by healthcare setting.
Results- Median time from cohort entry until screening varied across settings (202-1063 days) and across providers and clinics (between cluster variance= 0.11-0.77, 0.06-1.18, respectively). Many patient-level characteristics (age, cohort entry year, race/ethnicity, number of comorbidities, smoking status, primary care visit within 12 months before cohort entry, and insurance), some of the provider-level characteristics (provider specialty and sex) and social determinants of health measures (Local Isolation Score Hispanic) were associated with time to screening. In the adjusted PWE models, for all three settings: a) Younger patients (21-49 years old) (vs older patients (50-65 years)); b) Hispanic and Non-Hispanic Asian/Pacific Islander (vs Non-Hispanic White); and c) patients seeing ob-gyn providers (vs family medicine providers) had shorter time to screening. Patients with a) higher (vs lower) comorbidity burden, and b) Medicare insurance (vs Commercial) were less likely to receive screened.
Conclusion- By identifying characteristics at multiple levels that are associated with the outcome, our results help us identify targets for interventions to improve and increase timely cervical cancer screening in healthcare settings.
Citation Format: Rutu Rathod, Ellen Hu, Sandi Pruitt, Jasmin A. Tiro, Jessica Chubak, Jennifer Haas, Steven J. Atlas, Gina Kruse, Amy E. Hughes. Association of multi-level factors with cervical cancer screening in healthcare settings [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 3451.
Abstract 3488: Decoding splicing isoforms and adaptive evolution in melanoma brain metastases through single cell long-read RNA sequencing
Authors: Lu Lina , Shiau Cheng-Kai , Fukumura Kazutaka , Lin Hsiaoyun , He Yueying , Pan Timothy , Norberg Lisa , Ritchie Jennifer , Huse Jason T. , Gao Ruli 
Published: March 2024

Abstract
Melanoma brain metastasis (MBM) is the third most common origin of metastasis to the brain, with a median overall survival of 8.9 months. Alternative splicing plays a critical role in cancer pathogenesis through altered protein coding complexity in tumor cells, which however has been poorly studied in MBM due to technical limitations. Long-read single cell RNA sequencing has the unique strength of measuring transcript isoforms and mutations simultaneously in individual cells due to its capacity of measuring full-length transcripts. In this study, we employed high throughout single cell long-read RNA sequencing (scNanoRNAseq) onto patient-matched MBM and extracranial metastasis (ECM) tumor pairs to evaluate the extent to which splicing isoforms impact adaptive clonal evolution in MBM. Our results showed that MBM tumors had distinct genetic alterations and ecological cellular compositions compared to matched ECM tumors, indicating that tumor clones were adaptively expanded in brain microenvironment. Comparative isoform analysis revealed that hundreds of genes expressed different combinations of isoforms (DCIs) involved in glucose and lipid metabolic pathways. Interestingly, our data demonstrated that ~10% of splicing related genes themselves were alternatively spliced including both spliceosome members and splicing activators. Furthermore, we calculated temporal changes of isoform usages along with tumor cell evolutionary lineages to quantify their impact on adaptive clonal expansion in MBM. Taken together, our work improves the understanding of transcriptional and splicing dynamics inherent in the adaptive evolution of MBM, providing evidence for developing new therapeutics targeting tumor cell-specific and MBM-specific splicing isoforms.
Citation Format: Lina Lu, Cheng-Kai Shiau, Kazutaka Fukumura, Hsiaoyun Lin, Yueying He, Timothy Pan, Lisa Norberg, Jennifer Ritchie, Jason T. Huse, Ruli Gao. Decoding splicing isoforms and adaptive evolution in melanoma brain metastases through single cell long-read RNA sequencing [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 3488.
Abstract 3494: KINOMO: A non-negative matrix factorization framework for recovering intra- and inter-tumoral heterogeneity from single-cell RNA-seq data
Authors: Tagore Somnath , Wang Yiping , Biermann Jana , D'Souza Edridge K. , Luthria Karan D. , Rabadan Raul , Azizi Elham , Izar Benjamin 
Published: March 2024

Abstract
Single-cell RNA-sequencing (scRNA-seq) is a powerful technology to uncover cellular heterogeneity in tumor ecosystems. Due to differences in underlying gene load, direct comparison between patient samples is challenging, and this is further complicated by the sparsity of data matrices in scRNA-seq. Here, we present a factorization method called KINOMO (Kernel dIfferentiability correlation-based NOn- negative Matrix factorization algorithm using Kullback-Leibler divergence loss Optimization). This tool uses quadratic approximation approach for error correction and an iterative multiplicative approach, which improves the quality assessment of NMF-identified factorization, while mitigating biases introduced by inter-patient genomic variability. We benchmarked this new approach against nine different methods across 150 scRNA-seq experiments and find that KINOMO outperforms prior methods when evaluated with an adjusted Rand index (ARI), ranging 0.82-0.91 compared to 0.68-0.77. Thus, KINOMO provides an improved approach for determining coherent transcriptional programs (and meta-programs) from scRNA- seq data of cancer tissues, enabling comparison of patients with variable genomic backgrounds.
Citation Format: Somnath Tagore, Yiping Wang, Jana Biermann, Edridge K. D'Souza, Karan D. Luthria, Raul Rabadan, Elham Azizi, Benjamin Izar. KINOMO: A non-negative matrix factorization framework for recovering intra- and inter-tumoral heterogeneity from single-cell RNA-seq data [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 3494.
Abstract 3520: A scalable single cell RNA-seq pipeline leveraging machine learning and high-quality references for cell-type prediction
Authors: Dasgupta Abhijit , Duclos Grant , Miragaia Ricardo , Manry Brychan , Jacob Etai , Markuzon Natasha , Rotem Asaf 
Published: March 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 3576: CRISPR Cas9 mediated MADD deletion can induce autophagy via downregulation of MAPK and PI3K/AKT/mTOR signaling in anaplastic thyroid cancer
Authors: Bakthavachalam Velavan , Madhayan Manikannan , Sanborn Mark , Rehman Jalees , Bellur Prabhakar 
Published: March 2024

Abstract
Thyroid cancer is a highly prevalent endocrine malignancy with one of the fastest growing incidences of all cancers in the United States. Anaplastic thyroid cancer (ATC) has an incidence of about 1.7% of all thyroid cancers, but it causes over 40% of thyroid cancer-related deaths with a median survival period of less than one year. This is why agents that inhibit the aberrant signaling pathways which drive ATC aggressiveness are of significant interest. MAPK-activating Death Domain-activating protein (MADD) plays a key role in cancer cell survival and proliferation. MADD is constitutively expressed at higher levels in most tumor tissues and cancer cells relative to normal tissues. Studies using shRNA suggest that MADD prevents apoptosis in ATC cells. For a more definitive and mechanistic analysis of MADD in ATC, we used conditional CRISPR-Cas9 gene editing to target MADD. We observed that MADD deletion in ATC cells induced spontaneous apoptosis and also activated the autophagy pathway in human ATC cells through downregulation of ERK and mTOR signals involving the MAPK and PI3K/Akt/mTOR signaling pathways. Transcriptomic analysis of publicly available TCGA data showed several PI3K/AKT/mTOR pathway genes were positively correlated with MADD levels. RNA sequencing data of ATC lines demonstrated that enhanced activity of the PI3K/AKT/mTOR signaling pathway was correlated with a worse prognosis in ATC. Targeted MADD deletion in ATC cells combined with treatment using ERK/PI3K/Akt/mTOR/autophagy inhibitors resulted in enhanced cell death compared with MADD deletion alone. Further, MADD deleted 8505C-ATC cells showed significantly delayed tumor formation in an orthotopic nude mouse model. These findings suggest that combining MADD deletion with inhibitors of PI3K/Akt/mTOR pathway could provide an attractive new therapeutic option for ATC.
Citation Format: Velavan Bakthavachalam, Manikannan Madhayan, Mark Sanborn, Jalees Rehman, Prabhakar Bellur. CRISPR Cas9 mediated MADD deletion can induce autophagy via downregulation of MAPK and PI3K/AKT/mTOR signaling in anaplastic thyroid 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 3576.
Abstract 3577: The role of PLXND1 in lineage plasticity and progression to neuroendocrine prostate cancer
Authors: Chen Bo , Xu Pengfei , Yang Joy C. , Ning Shu , Wang Leyi , Nip Christopher , Gao Allen C. , Liu Chengfei 
Published: March 2024

Abstract
Background: Neuroendocrine prostate cancer (NEPC) is a lethal subtype of prostate cancer and is commonly associated with therapy resistance and poor prognosis. Treatment-induced NEPC (t-NEPC) predominantly develops by lineage plasticity from adenocarcinoma in response to androgen receptor signaling inhibitors, such as enzalutamide. However, the mechanisms underlying the lineage plasticity and progression to NEPC are unclear. PLXND1 (PlexinD1) is a cellular receptor of the semaphorin family and has important functions in modulating the cytoskeleton and cell adhesion. However, the role of PLXND1 in lineage plasticity to NEPC development and progression is not well established.
Methods: We performed transcriptomic analysis on NEPC patients’ cohorts and the enzalutamide-resistant prostate cancer cell lines. Correlation of PLXND1 with neuroendocrine feature genes and AR-targeting genes was determined. Kaplan-Meier method and the Cox proportional hazards model was used to estimate the correlation between PLXND1 expression level and patients’ survival. Immunohistochemistry was used to analyze PLXND1 expression in prostate adenocarcinoma and NEPC. Cell viability was determined in C4-2B MDVR, CWR22Rv1, and H660 cells after knocking down PLXND1 using siRNA or knocking out PLXND1 using CRISPR/CAS9. Organoids viability of patient-derived xenografts was measured after knocking down or knocking out PLXND1.
Results: Our analysis on different patient cohorts suggests that PLXND1 is highly expressed in NEPC patients, and the expression of PLXND1 is positively correlated with neuroendocrine feature genes (CHGA, ENO2, SYP, and NCAM1). Kaplan-Meier survival analysis shows that high expression of PLXND1 is significantly associated with the poor prognosis of prostate cancer patients. PLXND1 is overexpressed and negatively regulated by androgen receptor signaling in enzalutamide-resistant cells and prostate cancer patients’ cohorts. Furthermore, downregulation of PLXND1 expression suppressed proliferation of the NEPC cells and viability of the PDX tumor organoids.
Conclusions: PLXND1 expression is elevated in NEPC and correlated with the poor survival of prostate cancer patients, suggesting it is a potential molecular indicator and therapy target for NEPC.
Citation Format: Bo Chen, Pengfei Xu, Joy C. Yang, Shu Ning, Leyi Wang, Christopher Nip, Allen C. Gao, Chengfei Liu. The role of PLXND1 in lineage plasticity and progression to neuroendocrine prostate 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 3577.
Abstract 3584: Context matters: Genomic profiling of PR isoform-specific actions in breast cancer stem cells reveals novel therapeutic insights for ER+ breast cancer
Authors: Gillis Noelle E. , Truong Thu H. , Finlay-Schultz Jessica , Sartorius Carol A. , Lange Carol A. 
Published: March 2024

Abstract
Exposure to progesterone is a recognized risk factor for breast cancer, and PGR polymorphisms are associated with various malignancies. The two progesterone receptor (PR) isoforms, full length PR-B and truncated PR-A, are expressed from the PGR gene in breast tissue and play crucial roles in normal physiology. An imbalance in the expression ratio of these isoforms, favoring increased levels of PR-A, is common in breast cancer and is associated with resistance to tamoxifen in Luminal A-type tumors. Notably, PRs have recently been implicated in promoting endocrine resistance and driving the expansion of cancer stem-like cell (CSC) populations implicated in early dissemination and maintenance of metastatic populations. Because prior gene expression studies were primarily conducted in 2D culture conditions, the isoform-specific molecular and epigenetic mechanisms underlying PR actions in advanced ER+ breast cancer remain elusive. Our goal was to define progesterone-driven gene expression in CSC cells by employing transcriptomic profiling of T47D cells cultivated in spheroid-culture conditions (3D) expressing exclusively PR-A or PR-B. Utilizing CUT&RUN, we have mapped the genomic binding sites unique to each PR isoform and identified shared sites. This genomic data has been integrated with phenotypic studies in cells expressing a single PR isoform. Our findings indicate that PR-A acts as a regulator of the cell cycle and senescence, while PR-B plays a pivotal role in cell metabolism and intracellular signaling. We find little overlap between PR-regulated gene sets derived from the same hormone-treated cells harvested from 2D relative to 3D conditions. Our genomic profiling of PRs in this 3D model system has unveiled novel strategies to combat CSC outgrowth and late recurrence in luminal breast cancer patients. This work has shifted our prior understanding of the role of PRs in gene regulation within the CSC population, and offers potential insights for therapeutic interventions in ER+ breast cancer.
Citation Format: Noelle E. Gillis, Thu H. Truong, Jessica Finlay-Schultz, Carol A. Sartorius, Carol A. Lange. Context matters: Genomic profiling of PR isoform-specific actions in breast cancer stem cells reveals novel therapeutic insights for ER+ breast 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 3584.