bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 2204: Influence of chronic comorbidities on colorectal cancer screening adherence in a population-based Canadian cohort
Authors: Bhatia Dominika , Sutradhar Rinku , Tinmouth Jill , Singh Simron , Lipscombe Lorraine 
Published: August 2018

Abstract
Background: Regular screening for colorectal cancer (CRC) is associated with a lower stage at diagnosis and improved survival. National guidelines recommend periodic screening for adults aged 50-74 years. Chronic diseases may distract from regular screening and increase CRC burden. We sought to determine whether having certain chronic conditions is associated with a lower rate of CRC screening adherence.
Methods: Using linked healthcare databases from Ontario, Canada, we assembled a population-based cohort of adults aged 50-74 years, who were eligible and overdue for routine CRC screening (fecal occult blood testing &gt; 2 years, sigmoidoscopy &gt; 5 years, or colonoscopy &gt; 10 years) at any point between April 1, 2004 and March 31, 2016. We implemented Andersen-Gill extended Cox models for recurrent events using an age time scale to examine the impact of chronic conditions on the relative rate (RR, 95% confidence interval, CI) of becoming adherent with periodic CRC tests, adjusted for the relevant time-varying covariates.
Results: During the 12-year observation window, included individuals contributed a total of 19,818,050 person-years spent overdue for CRC tests. Over 70% of all CRC tests performed during the study period were fecal occult blood tests. At baseline, of the 4,642,422 individuals that met the inclusion criteria, 31% had hypertension, 12% had diabetes, 9% had chronic obstructive pulmonary disease (COPD), 7% had mental illness, 5% had cardiovascular disease (CVD), 1.6% had congestive heart failure (CHF), and 0.1% had end-stage renal disease (ESRD). After adjustment for sociodemographic and health system factors, the highest rates of nonadherence were observed for ESRD on dialysis (RR 0.66, CI 0.63-0.68), CHF (RR 0.75, CI 0.75-0.76), COPD (RR 0.84, CI 0.83-0.84), CVD (RR 0.85, CI 0.84-0.85), diabetes (RR 0.86, CI 0.86-0.87), and mental illness (RR 0.88, CI 0.87-0.88). Hypertension (RR 0.98, CI 0.97-0.98) and ESRD with kidney transplant (RR 1.00, CI 0.84-1.19) were associated with modest to no risk.
Conclusion: The presence of a major chronic condition is associated with lower adherence to CRC screening. Future studies should explore reasons for lower CRC screening adherence in individuals with chronic conditions and the appropriateness of secondary cancer prevention and early diagnosis in this population.
Citation Format: Dominika Bhatia, Rinku Sutradhar, Jill Tinmouth, Simron Singh, Lorraine Lipscombe. Influence of chronic comorbidities on colorectal cancer screening adherence in a population-based Canadian cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2204.
Abstract 2205: Prediction model of CA125 among premenopausal women
Authors: Sasamoto Naoko , Babic Ana , Rosner Bernard A. , Vitonis Allison F. , Cramer Daniel W. , Tworoger Shelley S. , Terry Kathryn L. 
Published: August 2018

Abstract
Background: Cancer antigen 125 (CA125) is a membrane-bound glycosylated mucin that has been reported to be the most promising biomarker for ovarian cancer screening, although results from two large randomized trials comparing screening using CA125 and transvaginal ultrasound to usual care have shown no clinically significant difference in ovarian cancer mortality. A major limitation of CA125 as an ovarian cancer screening biomarker has been low specificity and variation between individuals by personal characteristics. Identifying personal characteristics that influence CA125 levels could be used to create personalized thresholds for CA125 for each individual, thereby improving its performance as an ovarian cancer screening biomarker. However, reports on factors that influence CA125 among premenopausal women are limited.
Methods: We evaluated the association between reproductive and lifestyle factors and CA125 among 828 premenopausal population-based controls enrolled in the New England Case-Control study between 1992 and 2008. CA125 was measured using the CA125II assay at the CERLab at Boston Children's Hospital. We developed a prediction model of log-transformed CA125 using stepwise linear regression with &lt;0.15 as significance level for entry and stay considering factors associated with CA125 in postmenopausal women and ovarian cancer risk factors, such as age, body mass index (BMI), race, smoking status, age at menarche, oral contraceptive use and its duration, parity, endometriosis, tubal ligation, unilateral oophorectomy, hysterectomy, family history of ovarian cancer, previous history of cancer, genital powder use, and caffeine intake as candidate predictors.
Results: In our univariate analyses, older age and endometriosis were associated with significantly higher CA125 while tubal ligation, hysterectomy, and time since last menstrual period were associated with significantly lower CA125 (p&lt;0.05). Age, endometriosis, tubal ligation, hysterectomy, smoking status, caffeine intake, and interval days since last menstrual period were selected in our final prediction model, which explained 6% of the total variability of log-transformed CA125 levels.
Conclusion: Efforts to validate this CA125 prediction model in an independent cohort of premenopausal women and evaluate whether using the personalized CA125 cutoff based on this prediction model discriminates cases and controls better than the single 35 cutoff are ongoing.
Citation Format: Naoko Sasamoto, Ana Babic, Bernard A. Rosner, Allison F. Vitonis, Daniel W. Cramer, Shelley S. Tworoger, Kathryn L. Terry. Prediction model of CA125 among premenopausal women [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2205.
Abstract 2216: A novel prognostic model based on the tumor and its microenvironment in tongue squamous cell carcinoma
Authors: Yu Pei , Wang Weiwang , Zhuang Zehang , Xu Jieyun , Liu Xiqiang 
Published: August 2018

Abstract
Objective: Pathological factors of tumor including tumor budding, invasive type and invasive depth has been reported to predict survival in tongue squamous cell carcinoma (TSCC). However, their prognostic value remains inconclusive and the role of tumor microenvironment is unknown. In the current study, both parameters of tumor and its microenvironment were examined and a new grading system associated with high impact on patient outcome has been proposed. Methods: A total of 246 patients with TSCC were included in this retrospective study. Using H&E stained sections, factors including pathological differentiation, T classification, lymph node metastasis (LNM), intensity of tumor budding, invasive pattern, depth, inflammation response (KM grade), tumor stroma percentage (TSP) were evaluated; the prognostic models including BD model (combination of tumor budding and invasive depth) and GMS grade (combination of KM grade and TSP) were also scored. The associations between variables and the survival was examined. Results: High intensity of tumor budding was associated with increasing T classification and positive LNM. Deep invasive depth was correlated with poorly differentiation, high degree of T classification, positive LNM, late clinical stage, and recurrence. BD score had strong correlation with T classification, LNM and clinical stage. Low KM grade showed significant correlation with late clinical stage and high tumor budding. TSP is positively related to tumor budding. GMS grade had significant association with clinical stage. Cox's regression models indicated that both tumor budding and KM grade were independent variables for predicting overall survival (OS) and disease-free survival (DFS). BD score and GMS grade had significant prognostic impact on survival but showed inferior separation between score 0 and 1, score 1 and 2, respectively. The novel designed model based on the combination of the inflammatory infiltration (KM grade) and BD score, iBD model, was proved to be strongly correlated with T classification, LNM, clinical stage, and recurrence. Patients with high iBD score showed reduced OS and DFS with excellent separation of score 0, 1 and 2. Conclusion: iBD scoring model was a promising predictor of survival and strongly associated with lymph node metastasis and recurrence in TSCC.
Citation Format: Pei Yu, Weiwang Wang, Zehang Zhuang, Jieyun Xu, Xiqiang Liu. A novel prognostic model based on the tumor and its microenvironment in tongue squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2216.
Abstract 2231: Insulin and multi-system alterations in non-diabetic American adults: NHANES 1999-2016
Authors: Caporaso Neil E. , Jones Rena R. , Kahle Lisa L. , Graubard Barry I. 
Published: August 2018

Abstract
Epidemiological evidence for a relationship between insulin resistance and cancer is emerging, and insulins' complex actions suggest that its dysregulation results in multisystem alterations. Using cross-sectional demographic and laboratory data from the continuous NHANES conducted from 1999-2016. we surveyed the range of multisystem alterations observed in normal (not diabetic or prediabetic) adult subjects in relation to variation in insulin and glucose levels. The homeostatic model of insulin resistance (HOMA-IR) was used as the key outcome measure of insulin resistance. We examined cardiovascular (BP), respiratory (FEV1, FVC), renal (BUN, creatinine), inflammatory (C-reactive protein, fibrinogen), lipid (triglycerides, LDL, HLD), hematologic (WBC, hemoglobin, cell counts), and nutritional (vitamin levels) factors in relation to HOMA-IR, C-peptide, Hgb1Ac and fasting glucose levels after adjustment for demographic (age, gender, smoking, alcohol, race, SES) and anthropometric (BMI) factors. Every system exhibited highly significant associations with insulin/glucose measures. Strong correlations (p &lt; 0.0001, Pearson, accounting for sample weights) of HOMA-IR were observed with waist size, systolic and diastolic blood pressure, triglycerides, HDL (inverse), LDL and inflammatory markers: fibrinogen, C-reactive protein, ferritin. Measured serum levels of vitamin C, vitamin D, and folate (RBC), were inversely associated with HOMA-IR. The relationship for selected variables, to HOMA-IR quartiles is shown below.
In a broad, representative sample of up to 9000 non-diabetic US subjects, we relate insulin resistance to adverse changes in anthropometric, cardiovascular, renal, metabolic, inflammatory and nutritional markers. Analyses accounting for NHANES sample design, confounding and potential interactions will be reported. Our preliminary results suggest that insulin resistance is linked to perturbations in normal function across numerous systems.
HOMA-IR Quartiles, adjusted (95% CI)1st2nd3rd4thFerritin79.1(71-87)75.9 (70-82)82.3 (76-89)96.2 (82-110)BP (d)69.7 (69-71)71.8 (70-73)71.4 (70-73)73.2 (72-75)HDL60.8 (60-62)56.3 (56-57)54.1 (53-55)51.3 (50-52)WBC6.37 (6.3-6.5)6.44 (6.3-6.6)6.68 (6.5-6.8)6.96 (6.8-7.1)
Citation Format: Neil E. Caporaso, Rena R. Jones, Lisa L. Kahle, Barry I. Graubard. Insulin and multi-system alterations in non-diabetic American adults: NHANES 1999-2016 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2231.
Abstract 2232: A high-sucrose diet does not enhance spontaneous metastases of Lewis lung carcinoma in mice
Authors: Yan Lin , Sundaram Sneha 
Published: August 2018

Abstract
Obesity is a risk factor for cancer, and high caloric intake contributes to obesity. We previously reported that a high-fat diet enhances metastasis in mice (Clin Exp Metastasis 2010). The purpose of this study was to determine the effects of a high-sucrose diet in comparison to a high-fat diet, on an isocaloric basis, on the spontaneous metastasis of Lewis lung carcinoma (LLC) in C57BL/6 mice. Mice (male) were fed a low-fat (AIN93G-based), a high-fat, or a high-sucrose diet for eight weeks before receiving a subcutaneous injection of 2.5x105 viable LLC cells. These diets contained 16% of energy from corn oil, 45% of energy from corn oil, or sucrose. The resulting primary tumor was resected 10 days later. The experiment was terminated 10 days after resection. There were no differences in caloric intake among the groups. The body fat mass of the high-sucrose group was lower than the high-fat group but higher than the low-fat group. The number and size of lung metastases were significantly higher in the high-fat group than in the low-fat group; however, they were similar between the high-sucrose and the low-fat groups. Compared to the low-fat diet, the high-fat diet, but not the high-sucrose diet, significantly increased plasma concentrations of insulin, inflammatory cytokines (leptin, plasminogen activator inhibitor-1, and monocyte chemotactic protein-1) and angiogenic factors (vascular endothelial growth factor and tissue inhibitor of metalloproteinase-1). These findings showed that the high-sucrose diet does not enhance metastasis in this LLC model. This null effect suggests that the high-sucrose diet, compared to the high-fat diet, is less capable of promoting adipogenesis and production of related inflammatory cytokines and angiogenic factors.
Citation Format: Lin Yan, Sneha Sundaram. A high-sucrose diet does not enhance spontaneous metastases of Lewis lung carcinoma in mice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2232.
Abstract 2244: Differential DNA methylation analysis of TCGA cholangiocarcinoma data
Authors: Mishra Nitish K. , Meng Niu , Guda Chittibabu 
Published: August 2018

Abstract
Cholangiocarcinoma (CCA), also known as bile duct cancer, is a rare malignancy that develops in bile duct, arising from the epithelial cells (cholangiocytes) lining the bile duct. CCA is the second most common type of primary liver cancer, and its incidence has been rising in the Unites States. Most of the patients with CCA are diagnosed at an advanced stage with a median survival of less than 1 year despite administering chemotherapy. In the current study, we analyze the global patterns of DNA methylation in CCA data from TCGA. Pathway and gene ontology (GO) enrichment analysis of differentially methylated genes (DMGs) enabled us to understand how changes in methylation and gene expression affect the biologic pathways involved in the progression of CCA. TCGA level-3 DNA methylation data were downloaded for 485,578 CpG sites with annotations. Further, we normalized β value by using Beta Mixed Integer-quantile normalization (BMIQ) to adjust type I and type II probes. For a CpG site to be considered differentially methylated, the difference in the median β value in primary tumor and normal samples should be at least 0.2 and the BH adjusted p-value should be less than 0.005 and AUC ≥ 0.7. A total of 12,259 differentially methylated (Δβ &gt;) CpG were observed; out of these 9,534 are hypermethylated and 2,725 are hypomethylated. About 4,714 differentially methylated CpGs (dm-CpGs) were observed within 1.5Kb from the TSS of corresponding genes. Chromosome 1 has maximum number of dm-CpGs, while chromosome 18 has lowest number of dm-CpGs. Differential DNA methylation frequency per Mb was also calculated and it was observed that chromosome 17 has the highest differential methylation frequency (9.61 dm-CpGs/Mb) while chromosome 18 has the lowest (1.47 dm-CpG/Mb). KEGG pathway analysis of genes with differentially methylated CpGs (Δβ ≥ 0.2) observed the enrichment of cancer, cell division and differentiation, amino acid metabolism, xenobiotics degradation, and immune response pathways. Hippo signaling pathway also got affected by DNA methylation (FDR 3.70e-18), which also got affected by differential gene expression. Change in DNA methylation and gene expression level of hippo signaling pathway genes plays a vital role in CCA.
Citation Format: Nitish K. Mishra, Niu Meng, Chittibabu Guda. Differential DNA methylation analysis of TCGA cholangiocarcinoma data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2244.
Abstract 2252: Biomarkers for staging melanoma, a search at transcriptome level
Authors: Ortega-Bernal Daniel , Rangel-Escareño Claudia , Arechaga-Ocampo Elena , Rosa Claudia H. Gonzalez-De la 
Published: August 2018

Abstract
Background: Melanoma represents one of the most aggressive malignancies and has a high tendency to metastasize. Metastatic potential may not be predicted from the morphologic features alone, and the use of routine histologic prognostic factors is insufficient for melanoma staging. Because metastatic melanoma remains extremely difficult to cure, there has been an urgent need to find new markers to identify tumors capable of metastasizing. Here, we used a computational strategy based on transcriptome meta-analysis for melanoma staging. We aimed to identify biomarkers that differentiate between non-metastatic and metastatic melanomas.
Methods: Affymetrix HU 133A Plus 2.0 microarray data were obtained from the GEO database for the meta-analysis, representing benign nevi (N), stages I, II and III melanoma (EI-III), stage IV melanoma from primary site (EIV) and stage IV melanoma from metastatic site (Mm). Differential expression was analyzed using linear models (118 files) and time course (64 files). Transcripts with a log fold change of &lt;-1/&gt;1 and B-statistic &gt;3 using linear models and evaluate by MB-statistic for Time Course were included in the study. The analysis focused on genes that encode secreted proteins, whose expression was upregulated throughout the progression of melanomas, starting at N and increasing gradually from EI-III to EIV and finally to Mm.
Results: We obtained 6137 differentially expressed genes with linear models (between EI-III vs. N, EIV vs. EI-III, Mm vs. EIV, EIV vs. N and Mm vs. N contrasts) and took the top 6137 genes ranked with Time Course. Our results show 17 possible biomarkers that grouped into three types: 1) genes capable of differentiating between EIV/Mm and EI-III/N (SPP1); 2) genes capable of differentiating between Mm and EIV/EI-III/N (CXCR4, FCGR1B, NGRN, PHTF1, RAB8B, ARHGEF2, ARMC9, BCL2A1, CALU, DLGAP5, DMXL2, FKBP11 and STARD3NL); and 3) genes capable of differentiating between Mm/EIV/EI-III and N (ADAM10, CLIC4 and STK36).
Conclusions: Our analyses, in agreement with previously reported results, identify SPP1 and CXCR4 as biomarkers of metastatic disease and poor clinical outcome, supporting the quality of our analysis. We also identified new biomarkers that may be used to predict undetectable metastasis. In order to provide a more precise evaluation, experimental validation or functional exploration of the reported gene expression differences is warranted.
Citation Format: Daniel Ortega-Bernal, Claudia Rangel-Escareño, Elena Arechaga-Ocampo, Claudia H. Gonzalez-De la Rosa. Biomarkers for staging melanoma, a search at transcriptome level [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2252.
Abstract 2256: Efficient large-scale cell classification and analysis for MultiOmyxTMassays: A deep learning approach
Authors: Nagy Mate L. , Hanifi Arezoo , Tirupsur Ahalya , Wong Geoffrey , Fang Jun , Hoe Nicholas , Au Qingyan , Padmanabhan Raghav K. 
Published: August 2018

Abstract
Traditional immunohistochemistry (IHC) techniques utilize one slide per biomarker. When clinical samples are precious and the number of serial sections is limited, comprehensive biomarker profiling becomes difficult with IHC. MultiOmyx is a proprietary, multiplexing methodology capable of staining up to 60 biomarkers on the same slide. The output of the assay enables quantitative profiling of tissues at a single cell level. The assay generates data for millions of cells with billions of queryable data points. To detect and classify cells efficiently at this large scale, we developed an image analysis framework using Deep Learning. Our framework consists of seven major steps: (1) manual annotation of a small subset of the nuclear staining channel (DAPI); (2) training of a fully convolutional neural network [1] on this annotation-set to generate a feature map identifying cell centers; (3) application of the trained network in (2) on the nuclear stain (DAPI) of the entire dataset to delineate individual cells; (4) manual annotation of a small subset of each of the other biomarker channels; (5) training of a convolutional neural network on these annotation-sets for binary classification of each of the biomarkers; (6) application of these classifiers to the entire dataset; (7) combination of the binary classification results to identify phenotypes of interest. For our output, we provide both visual label maps and classification summary tables for individual and co-localized biomarkers at the region of interest level and the entire slide level. In addition, combining the phenotype and location information allows us to visualize complex spatial relationships in the tissue.
The benefits of using this Deep Learning framework are greatly felt through increased time efficiency without a loss in accuracy, when compared to more traditional computer vision methods requiring high levels of parameter fine-tuning. For future work, we plan on fully automating the approach as more manual annotation-sets are generated.
Citation Format: Mate L. Nagy, Arezoo Hanifi, Ahalya Tirupsur, Geoffrey Wong, Jun Fang, Nicholas Hoe, Qingyan Au, Raghav K. Padmanabhan. Efficient large-scale cell classification and analysis for MultiOmyxTMassays: A deep learning approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2256.