bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 6488: Correlation of availability of primary care physicians and colorectal cancer-related mortality in the United States: Do racial disparities exist
Authors: Grewal Udhayvir S. , Gaddam Shiva J. , Aggarwal Manik , Garikipati Subhash C. , Kumar Prabhat , Fei Naomi 
Published: April 2023

Abstract
Introduction: Colorectal cancer (CRC) is the second leading cause of cancer death in the United States (US). Primary care physicians (PCP) are critical towards a successful CRC screening program. However, the impact of availability of PCP on CRC-related mortality is not well studied.
Materials and Methods: We extracted data on crude incidence and mortality rates of CRC among all 50 states and District of Columbia (D.C.) from the Center for Disease Control (CDC) WONDER database for the year 2019. Data on number of actively practicing PCPs in all 50 states and D.C. for the year 2020 were obtained from Association of American Medical Colleges (AAMC) State Physician Workforce Data Report. The correlation between the ratio of number of PCPs per CRC case with age-adjusted mortality rate (AAMR) for CRC was studied by state and within White and African American (AA) populations from 2017-2019. Pearson’s co-efficient was used to study correlations and T-test was used for comparing independent samples. Statistical analysis was performed using VassarStats.
Results: The median AAMR for CRC from 2017-2019 in the US was 13.2 (IQR 11,15.4) per 100,000 population. AAMR for CRC was significantly higher among AA versus White populations (t=5.79, p&lt;0.001). The national median PCP per CRC case ratio was 2.14 (IQR 1.46, 2.82). Higher state-wide PCP per CRC case ratio correlated with lower state CRC-related mortality (r=-0.36, p=0.011). PCP per CRC case ratio was significantly lower among AA as compared to White populations (t=-15.95, p&lt;0.0001). Higher PCP per CRC case ratio correlated with lower CRC-related mortality among both White (r=-0.64, p&lt;0.0001) and AA (r=-0.57, p=0.0002) populations. Using Fisher r-to-z transformation, we found no significant difference between the correlation of PCP per CRC case ratio with CRC-related mortality in White versus AA populations (z=-0.51, p=0.61).
Conclusion: Results of this study suggest that greater availability of primary care physicians correlates with lower CRC-related mortality. AA have a significantly higher AAMR for CRC with a lower PCP per CRC case ratio. This may indicate a lower access to primary care among AA populations with CRC. Our results underscore the need to expand access to primary care among AA populations with the goal of minimizing disparities in CRC-related mortality.
Citation Format: Udhayvir S. Grewal, Shiva J. Gaddam, Manik Aggarwal, Subhash C. Garikipati, Prabhat Kumar, Naomi Fei. Correlation of availability of primary care physicians and colorectal cancer-related mortality in the United States: Do racial disparities exist. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6488.
Abstract 6515: Pancreatic ductal adenocarcinoma (PDAC) early detection
Authors: Hinestrosa Juan P. , Dhani Harmeet , Schroeder Gregor , Lewis Jean M. , Balcer Heath I. , Kurzrock Razelle , Keith Dove , Sears Rosalie , Billings Paul 
Published: April 2023

Abstract
Introduction Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer and is one of the deadliest cancer in the U.S., with a 5-year survival rate of just 11%. It is expected that ~62,000 new cases will be diagnosed in the US in 2023 and &gt;70% of those will be at the regional or distant state, when curative resection is no longer an option. It is imperative that tools enabling early disease detection (stages I and II) are developed to improve patient survival outcomes. Here, we report the results of a study on the utility of extracellular vesicles (EVs) for early PDAC detection.
Methods Our case-control training set was comprised of 75 pathologically confirmed PDAC cases (Stage I = 31, Stage II = 44) and 640 controls (including 11 subjects with pancreatitis) for EV biomarker and algorithm selection via machine learning. EVs from plasma were isolated using a proprietary technology, then EV-bound proteins of interest were analyzed via an immunoassay. A second independently collected cohort of 20 confirmed PDAC cases (Stage I = 10, Stage II = 10) and 27 controls (including 9 patients newly diagnosed with diabetes) was used for validation.
Results The machine learning analysis using EV-isolated proteins generated a signature that utilized 8 different biomarkers, with an AUC of 0.989 (95% CI: 0.980 - 0.998) in the training set and an AUC of 0.987 (CI: 0.964 - 1.000) in the validation set. For the validation set, the sensitivity was 95% (CI: 76.4% - 99.1%) and the specificity was 92.6% (CI: 76.6% - 97.9%), with 10 out of 10 stage I cases and 9 out of 10 stage II cases correctly classified.
Conclusions The ability to alter the future outcomes of PDAC patients will be predicated on development of a new generation of early-detection biomarkers and technologies. The use of EV protein signatures, a new class of cell-free biomarker, permits detection at high sensitivity and specificity, as demonstrated for the independently collected validation cohort. Studies aimed to assess this test for high-risk patients (family history of PDAC, known germline mutations, precursor lesions, hereditary pancreatitis, new onset diabetes will continue towards the establishment of real-world evidence.
Citation Format: Juan P. Hinestrosa, Harmeet Dhani, Gregor Schroeder, Jean M. Lewis, Heath I. Balcer, Razelle Kurzrock, Dove Keith, Rosalie Sears, Paul Billings. Pancreatic ductal adenocarcinoma (PDAC) early detection. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6515.
Abstract 6525: Colon cancer screening and treatment with engineered probiotics
Authors: Gurbatri Candice R. , Radford Georgette , Im Jongwon , Coker Courtney , Arpaia Nicholas , Woods Susan L. , Worthley Daniel L. , Danino Tal 
Published: April 2023

Abstract
Bioengineered probiotics enable new opportunities to improve colorectal cancer (CRC) screening, prevention and treatment strategies. Here, we demonstrate selective colonization of CRC tumors with the orally-delivered probiotic E. coli Nissle 1917 (EcN) in genetically-engineered and orthotopic murine models of CRC, and human CRC patients. We subsequently engineer EcN to produce screening and therapeutic molecules, resulting in non-invasive detection of precursor CRC lesions and reduction of tumor burden through local induction of robust anti-tumor immunity.
Citation Format: Candice R. Gurbatri, Georgette Radford, Jongwon Im, Courtney Coker, Nicholas Arpaia, Susan L. Woods, Daniel L. Worthley, Tal Danino. Colon cancer screening and treatment with engineered probiotics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6525.
Abstract 6557: A 6-microbial signature risk score for overall survival prediction in hepatocellular carcinoma: a machine learning approach
Authors: Al-Bzour Nour N. , Al-Bzour Ayah N. 
Published: April 2023

Abstract
Introduction: Hepatocellular carcinoma (HCC) is a severe type of cancer associated with high malignancy and poor prognosis. To reach a better prognosis, several factors affecting HCC patients must be considered. In this study, we aim to investigate the relationship between gut microbiota with the survival of HCC patients, and to build a machine learning (ML) prognostic model based on microbiome signatures.
Methods: Microbial and survival data for HCC patients from The Cancer Genome Atlas Program (TCGA) were obtained using the cBioportal database. The microbiome signatures were screened for prognostic effect using the univariate cox regression. The significant microbial signatures were fitted into the Least Absolute Selection and Shrinkage Operator - Cox (LASSO-Cox) model to reduce multicollinearity. Signatures with non-zero LASSO coefficients were screened for independent effect in the multivariate cox regression analysis.
Results: A total of 1407 microbial signatures were downloaded from TCGA dataset. 63 prognostic microbial signatures were identified using the univariate cox. A total of 30 prognostic microbial signatures were screened out using LASSO and further evaluated using independent effect to obtain microbiome risk score. Six microbial signatures were identified as independent prognostic markers including: Marichromatium (HR: 1.26; 95%CI: [0.01-1.56]), p=0.038), Paraclostridium (HR: 1.45; 95%CI:[1.15-1.82], p=0.002), Pseudorhodoferax (HR: 1.20; 95%CI:[1.01-1.43], p=0.037), Chitinimonas (HR: 1.47 95%CI: [1.10-1.97], p=0.009), Marinobacter (HR: 0.79; 95%CI: [0.65-0.97], p=0.022), Desulfovermiculus (HR: 1.44; 95%CI: [1.16-1.78, p=0.001]). Kaplan-Meier survival plot showed better overall survival (OS) in the low-risk group compared to the high-risk group (median OS: 83.6 vs. 41.8; log-rank p-value &lt;0.0001).
Conclusion: Our results show that the gut microbiota is significantly associated with HCC prognosis, in which 5 of them were associated with poorer prognosis and one was associated with better survival. These microbial signatures provide a potential prognostic marker for HCC survival.
Citation Format: Nour N. Al-Bzour, Ayah N. Al-Bzour. A 6-microbial signature risk score for overall survival prediction in hepatocellular carcinoma: a machine learning approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6557.
Abstract 6563: Glioma stem cell index recapitulates grade, IDH mutation status and correlates with survival of glioma patients
Authors: Simões Renan L. , Marção Maycon , Malta Tathiane M. 
Published: April 2023

Abstract
Adult diffuse gliomas are heterogeneous and the most common primary brain tumors. Gliomas have high tissue invasion, proliferation and therapeutic resistance potential. Although much knowledge has been recently gained regarding glioma biology and evolution, many questions are still open, such as which is the cell of origin, what are its characteristics and how the tumor propagation occurs. The cancer stem cell (CSC) model proposes a population of cells with high self-renew capacity and capable of propagating the tumor with more differentiated cells, generating intratumoral heterogeneity. Based on the CSC model, our work aims to integrate the most advanced techniques available such as scRNAseq and machine learning models to estimate the enrichment of glioma stem cells (GSCs) in tumors, by defining a GSC-Stemness Index (GSCsi). To build the prediction model, we used public scRNA-seq data from glioblastoma (GBM)-enriched GSCs. We used the standard Seurat pipeline for quality control, normalization and downstream analysis. The GSC single cell data was used to train a prediction model using the One Class Logistic Regression (OCLR) algorithm. Several models were tested, including overdispersed genes, differentially expressed genes between GSCs and the whole tumor, GSCs from each patient individually and a model with all GSCs. The prediction models were applied to gene expression data from TCGA, GLASS, and publicly available scRNA-seq data from different glioma subtypes. The model built with all the GSCs and all genes showed the best performance, being able to identify with higher indices grade 4 gliomas and IDHwt in the TCGA and GLASS data. Survival analysis resulted in a hazard ratio greater than 20, indicating a high correlation between increased GSCsi and poor prognosis. By applying the model to scRNA-seq data from gliomas, clusters of high GSCsi were identified. We can partially conclude that the GSCsi obtained with the model is capable of identifying grade 4 gliomas and IDHwt and we are performing analyzes with the genes with the highest correlation (positive and negative) with the GSCsi in the TCGA and GLASS data. The analysis of these genes together with genes differentially expressed in the high GSCsi clusters in scRNA-seq data can elucidate pathways responsible for the therapeutic resistance and propagation of gliomas, in addition to proposing theories about the cell of origin and potential therapeutic targets to improve the diagnosis and treatment of these patients.
Citation Format: Renan L. Simões, Maycon Marção, Tathiane M. Malta. Glioma stem cell index recapitulates grade, IDH mutation status and correlates with survival of glioma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6563.
Abstract 6573: Drug Combo: A unique resource for efficient phase 1 trial design with manually curated MTD and DLT
Authors: Wang Lei , Zhang Shijun , Wei Lai , Zhu Yuxi , Owen Dwight , Li Lang 
Published: April 2023

Abstract
Background: With prior preclinical and clinical knowledge, an effective and efficient cancer drug combinatory trial design could be expected, which will significantly accelerate the translation. However, our internal survey results suggested that some knowledge and data sources were underused or underestimated by researchers. One primary reason is that knowledge of drug combinations is scattered in many distinct databases. Moreover, some essential information, such as MTD and DLT was missed in current databases. Drug Combo (https://drugcombo.shinyapps.io/DrugCombo/) is the first knowledge base that integrates drug toxicity and PK data that may help design an efficient Phase I trial. It is a unique informatics resource that manually curated MTD and DLT are well organized in the knowledgebase.
Methods: Drug Combo integrates various publicly available PK and toxicity knowledge for individual drugs and drug combinations (Table 1). More importantly, it is the unique knowledgebase with MTD and DLT. An expert team manually curated MTD and DLT data from publicly available publications. Some standard healthcare terminologies, e.g., Rx-Norm and MedDRA were applied for concept normalization at the back end. We implemented a User-Centered Design process in the user interface (UI).The UI layout will be simple, consistent, purposeful, comprehensive, easy to navigate and informative with readable or scannable typography. A summary view was provided to inform users of some essential information, e.g., MTD, and Overlapping toxicities. The user can find the details in the “Detail Tab”.
Results: Currently, Drug Combo integrates pharmacological, pharmacokinetics, and toxicity data. Especially, MTD and DLT data from more than 1,600 published Phase I trials results are exclusive in Drug Combo.
Conclusions: Drug Combo is the first knowledge base that provides expert-curated MTD and DLT information. It could help design an effective and efficient drug combination phase 1 trial.
Current knowledge type in Drug Combo Type Source Drug Names DrugBank, RxNorm Target DrugBank, Manual curation Associated Condition RxNorm, Drug Labels PK profiles DrugBank Adverse Drug Event Drug Label, FAERS MTD Manual curation DLT Manual curation DDI DrugBank, Manual curation
Citation Format: Lei Wang, Shijun Zhang, Lai Wei, Yuxi Zhu, Dwight Owen, Lang Li. Drug Combo: A unique resource for efficient phase 1 trial design with manually curated MTD and DLT [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6573.
Abstract 6587: Method to increase performance of genome-wide association study using imputation for Asian ancestry
Authors: Ryu GangPyo , Koh Youngil , Yoon Sung-soo 
Published: April 2023

Abstract
The emergence of the large-scale public genomic database facilitates genome-wide association study. An unprecedented number of samples emerged, which enable elucidate valid genetic inferences and contribute to finding novel associations for various phenotypes including cancer. However, most of the genome data is from European-based ancestry, which leads to the disparity in study for relatively minor ethnic groups including Asian. Meanwhile, among public genomic database, TOPMed consortium consists of ~180k participants, over 60% of the 180k samples are of predominantly non-European ancestry, and Asian ancestry consisted of 8 % in total. In addition, 1000 Genomes Project(1KG) contains 2,504 individuals from 26 populations and has sequenced 698 samples in addition. These projects serve as a reference to impute missing variants to improve association power for Genome-Wide Association Study (GWAS) subsequently. We compared the imputation results using those reference panels. Korean Genome Project (Korea1K), including 1094 whole genomes are public datasets consisting of only Korean (East Asian) origin. On GRCh38, Korea1K variants consisting of 59,463,566 variants in total, 99.6% with MAF &lt;1% rare variant, only 35.7% of those rare variants passed from GATK Best Practice VQSR filter. With Korea1K data, we have downsampled the result into the exonic level, imputed with reference panels, and seen the consequences for which reference panel shows the best performances. Using 1KG as a reference, we could identify novel rare variants only in 0.1 to 0.38 fold of filtered rare variant in 1KG phase3 and 1KG phase3 30X respectively. On the other hand, TOPMed imputation resulted in rare variants over 1.65 fold increase. With well-imputed variants R2 over 0.8, all three reference database showed poor conservation which only 0.009 % of rare variant are rescued. The reference panel we commonly use are large but required to have more diversity in a Asian rare variant perspective. Despite increase of the Asian fraction in the public genomic database, our result suggests that the number of reference Asian samples are still low to perform association study. In conclusion, our finding highlights both the utility and limitation of the imputation reference panel for Asian ancestry. Novel variants filled by imputation are promising for adding power to various association studies.
Citation Format: GangPyo Ryu, Youngil Koh, Sung-soo Yoon. Method to increase performance of genome-wide association study using imputation for Asian ancestry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6587.
Abstract 6590: Undergraduate e-mentoring in cancer research: The ACE scholars program
Authors: Baciu Cristina , Zachary Compton , Maley Carlo 
Published: April 2023

Abstract
Only 1 in 4 undergraduate college students report having had access to mentoring while in college. To create community and training opportunities for a diverse body of undergraduate students (incl. online students) interested in cancer research, we developed a well-rounded e-mentoring program that incorporates alternated sessions of scientific mentoring (i.e., design a study, create a poster) and professional development (i.e., graduate school strategy and applications, networking). The theoretical background of our program derives from project and team-based learning, which state that students tend to learn better and be more engaged when working hands-on on a given project, in teams. Our teams are led by experienced undergraduate researchers, which enables them to gain leadership experience before applying to graduate school.
We implemented this e-mentoring program in a research center at a large southwestern public university, where 43 undergraduate research assistants were mentored via Zoom during the Spring of 2022. At the end of the semester, we administered a survey to assess program effectiveness and collect student feedback. 31 students took the survey, which revealed that during Spring 2022, 25% of students were responsible for the care of children or other family member(s), 47% worked part-time and 22% worked full-time, and 56% of students encountered personal and/or professional challenges. Furthermore, we measured program effectiveness using the College Student Mentoring Scale, a comprehensive tool that measures students’ perceived psychological and emotional support, degree and career support, academic knowledge support, and role modeling they received during their time in the program. Findings indicate that 82% of students felt they received psychological and emotional support and degree and career support while in the program. Moreover, 96.3% of students indicated that they felt they received academic knowledge support and benefited from role modeling while in the ACE Scholars Program. Additionally, 85% of students agreed or strongly agreed that the ACE Scholars Program, with its research experience and career and professional development components, has helped them be prepared for their future endeavors. Overall, 86% of the students indicated that they were extremely satisfied with the ACE Scholars Program. We will discuss the implications of this program and its findings for the practice of e-mentoring undergraduate students who are interested in cancer research.
Citation Format: Cristina Baciu, Compton Zachary, Carlo Maley. Undergraduate e-mentoring in cancer research: The ACE scholars program [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6590.