bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 2239: Is folic acid safe for non-muscle-invasive bladder cancer patients? An evidence-based cohort study
Authors: Tu Huakang , Dinney Colin P. , Ye Yuanqing , Grossman Barton H. , Lerner Seth P. , Wu Xifeng 
Published: August 2018

Abstract
Background: Cancer patients are highly concerned about food choices and dietary supplements that may affect their treatment outcomes. Excess folic acid (synthetic folate) from supplements or fortification can lead to accumulation of unmetabolized folic acid in the systemic circulation and urine, and may promote cancer growth, especially among those with neoplastic alterations.
Objective: We investigated the prospective association between synthetic versus natural folate intake and clinical outcomes in non-muscle-invasive bladder cancer (NMIBC), which is a highly recurrent disease.
Design: In a cohort of 619 NMIBC patients, folate intake at diagnosis was assessed with a previously validated food frequency questionnaire and categorized according to tertiles. After a median follow-up of 5.2 years, 303 tumor recurrence and 108 progression events were documented from medical record review. Multivariable Cox proportional hazards and logistic models were used to estimate adjusted hazard ratios (HR) and odds ratios (OR) with 95% confidence intervals (95% CI).
Results: Synthetic folic acid intake was positively associated with risk of recurrence among NMIBC patients (medium vs. low intake: HR=1.72; 95% CI=1.20, 2.48; P=0.003; high vs. low intake: HR=1.80; 95% CI=1.14, 2.84; P=0.01). Patients with higher folic acid intake were more likely to have multifocal tumors at diagnosis (medium/high vs. low: OR=2.08; 95% CI=1.08, 4.02; P=0.03). In contrast, natural folate intake tended to be inversely associated with risk of progression (medium/high vs. low: HR=0.68; 95% CI=0.44, 1.04; P=0.08).
Conclusions: High intake of synthetic folic acid, in contrast to the natural forms, is associated with increased risk of recurrence in NMIBC and multifocal tumors at diagnosis, suggesting that folic acid may be unsafe for NMIBC patients. These findings provide some evidence for nutritional consultation regarding folate intake among NMIBC patients.
Citation Format: Huakang Tu, Colin P. Dinney, Yuanqing Ye, Barton H. Grossman, Seth P. Lerner, Xifeng Wu. Is folic acid safe for non-muscle-invasive bladder cancer patients? An evidence-based cohort study [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 2239.
Abstract 2258: Chromothripsis and survival in human malignancies
Authors: Mansfield Aaron S. , Garcia-Rivera Enrique 
Published: August 2018

Abstract
Chromothripsis is a complex genomic event characterized by a high number of chromosomal rearrangements and copy number changes that typically localize to a specific region on a single chromosome. Next generation sequencing has led to the initial discovery and description of these events which have since been observed in multiple human cancers. Whereas most mutagenic processes occur gradually, the multiple structural variations of each chromothriptic event likely occur at once. The clinical significance of chromothripsis remains uncertain, so we sought to characterize the relationship between chromothripsis and survival across human malignancies.
We analyzed 13,318 tumors of 36 types using publicly available data from The Cancer Genome Atlas with CTLPScanner, which uses a sliding window scan statistic to identify events consistent with a chromothripsis-like pattern (CTLP). Our cutoffs for CTLP of copy number variation ≥ 20 and a log10 likelihood ratio ≥ 8 were derived from a clinical training set previously described by CTLPScanner.
We detected one or more events consistent with a CTLP in 8,276 tumors (62.1%) for a total 24,879 events. There were no events in 5,042 cases (37.86%), a significant decrease from established rates of CTLPs. CTLPs were more common in ovarian serous cystadenocarcinomas (89.8%), esophageal carcinomas (86.5%) and sarcomas (81.2%) than in thymomas (26.6%) and thyroid carcinomas (15.2%). Chromosomes 8, 1 and 6 were most frequently involved with CTLPs. CTLPs in chromosome 6 were associated with uveal melanoma, chromophobic renal cell carcinoma and cholangiocarcinomas whereas CTLPs involving chromosome 12 were associated with testicular germ cell tumors. Multiple CTLPs were found in 5,262 tumors (39.5%, range of events 2-18+) and were most common in ovarian serous cystadenocarcinomas and sarcomas. The presence of CTLPs was associated with diminished overall survival across all tumor types (HR 1.5, p&lt;2e-16). Given the potential for multiple CTLPs to have a greater effect on outcomes, we compared the survival of patients whose tumors contained 3 or more CTLPs (n=3,255, 24.4%) to those with 0-1 or 2 CTLPs. The presence of 3 or more CTLPs was associated with the worst survival amongst these groups (HR 1.67, p&lt;2e-16).
We identified that CTLPs are common in human cancers despite their prior descriptions as rare events. This discrepancy is likely due to our detection parameters. In addition, we identified that having multiple co-occurring CTLPs is associated with a worse prognosis, suggesting this type of event is more clinically relevant than a single CTLP and can be used as a negative prognostic marker in human malignancies.
Citation Format: Aaron S. Mansfield, Enrique Garcia-Rivera. Chromothripsis and survival in human malignancies [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 2258.
Abstract 2262: Inference of kinase activity for cancer phosphoproteomics using substrate prediction scores
Authors: Liao Peter , Yori Jennifer , Keri Ruth , Koyuturk Mehmet , Barnholtz-Sloan Jill 
Published: August 2018

Abstract
Background: Quantification of global protein phosphorylation abundance levels with residue-level resolution is feasible with mass spectrometry and phosphopeptide enrichment methods. Researchers have inferred kinase activity from phosphoproteomics data using a variety of methods to extract interpretable and biologically relevant information, but analysis of phosphoproteomics data is limited by the fact that detected phosphorylation sites often have unknown function and relevance.
Methods: We developed a novel method for aggregating and interpreting phosphoproteomics data that combines kinase prediction scores from NetworKIN with experimental summary statistics to estimate abundance changes in predicted kinase substrates. By weighting student t-statistic scores by kinase prediction scores and summing cumulative scores across all phosphopeptides detected in a given screen, we generated kinase activity scores that reflect the overall direction, magnitude and significance of observed changes in predicted kinase substrates. These kinase scores were assessed using permutation testing to determine their significance versus changes that would be expected from random chance. To evaluate our method, we applied it to a shotgun phosphoproteomic dataset generated to study phosphorylation responses to rapamycin, dasatinib and combination treatment in MDA-MB-231 breast cancer cells.
Results: We were able to confirm phosphoproteomic perturbations in expected “hallmark” kinase activities using prediction-based kinase activity inference scores, including decreased phosphorylation of Src and Abl substrates in response to dasatinib treatment, as well as decreases in p70S6K substrate phosphorylation in response to rapamycin. Compared to KSEA, we identified changes in substrate abundance for a variety of potential downstream kinases including DNAPK, PAK2, CDK3, and multiple members of the MAPK family that were observed to be downregulated in single-agent and in combination treatment.
Conclusions: Our novel method provides a representation of phosphoproteomic changes based on abundance of predicted kinase substrates. Using predicted substrates allows for a global view of phosphoproteomic changes that extends beyond the relatively small number of known kinase-substrate relationships. Using a drug combination with potential application in breast cancer as an example, we demonstrate that this method can be used to identify phosphoproteomic changes and can potentially be used to rationally design and investigate novel drug combinations.
Citation Format: Peter Liao, Jennifer Yori, Ruth Keri, Mehmet Koyuturk, Jill Barnholtz-Sloan. Inference of kinase activity for cancer phosphoproteomics using substrate prediction scores [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 2262.
Abstract 2265: viSNE in Cytobank enables rapid exploratory data analysis for RNA-seq biomarker discovery
Authors: Sethi Ashu , Polikowsky Hannah , Drake Katherine A. 
Published: August 2018

Abstract
Biomarkers provide great potential for understanding and predicting patient outcomes. However, the large numbers of putative biomarkers assayed by many discovery technologies can be difficult to parse quickly to understand their potential as predictors of an outcome of interest. viSNE is a non-linear dimensionality reduction algorithm available on the Cytobank informatics platform that can rapidly assess whether a set of biomarkers can meaningfully group samples. We establish a method for applying viSNE to RNA-seq data. We demonstrate this method using data from The Cancer Genome Atlas for 1,011 samples from 5 different types of kidney cancer and matched normal tissue samples for 139 of the subjects. The tumors ranged from stage I to stage IV, and subjects varied by age, gender, and whether they were living when the sample was taken. Using FPKM data from the 806 most variable transcripts in these samples, viSNE was able to group the samples into six distinct “islands” that separated cancer samples from each of the five disease types and the matched normal tissue. We demonstrate that viSNE is robust to noise and is able to group the samples even when variables that do not contribute to the signal are present in the data, and illustrate how this can be leveraged as part of a larger biomarker discovery workflow with RNA-seq and other data types. viSNE allows rapid exploratory data analysis that helps to build understanding of correlations between biomarkers and patient outcomes and improve overall time-to-results in biomarker discovery pipelines.
Citation Format: Ashu Sethi, Hannah Polikowsky, Katherine A. Drake. viSNE in Cytobank enables rapid exploratory data analysis for RNA-seq biomarker discovery [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 2265.
Abstract 2268: Differential splicing analysis of doxorubicin treatment response in cancer cells
Authors: Simpson Edward , Liu Yunlong 
Published: August 2018

Abstract
Background:Splicing of transcripts is an important epigenetic control mechanism that significantly influences expression of protein isoforms in a cell. Alternative splicing is known to contribute to development and progression of cancer, however the impact of cellular splicing profiles on cancer drug response has been largely unexplored. In this study, we integrated two large public data sets and report findings on the basal splicing profiles between doxorubicin treated sensitive and resistant cancer cell lines. We introduced a new method to analyze of splicing data using the quasi-binomial distribution that is incredibly simple, powerful and well suited for comparison of large groups of samples. We also identified exons whose inclusion or exclusion level is likely to be damaging to intended gene products. Finally, we performed RNA-binding protein motif enrichment at these identified exons to characterize proteins that may play a role in their regulation.
Methods:Drug response data from the Cancer Therapeutic Response Portal (CTRP) was integrated with RNAseq data from the Cancer Cell Line Encyclopedia (CCLE). AUC values from drug-response time curves in CTRP were used to classify cells as either doxorubicin sensitive, intermediate or resistant. The upper and lower tertiles provided a total of 253 sensitive and 258 resistant cell lines respectively. Splice junction read counts for skipped-exon events were calculated using the matched cell line data from CCLE. Response groups were then used to train a generalized linear model with quasi-binomial family. Results were filtered by group weight significance at an FDR of &lt;=0.01. The remaining events were again filtered for &gt;=10% difference in mean exon inclusion ratio.
Results:In total we identified 277 differentially spliced events between doxorubicin sensitive and resistant groups. When performing GO term over-representation our results demonstrated significantly enriched biological processes supporting the role of alternative splicing in EMT and cellular differentiation. This further confirms previous studies linking alternative splicing to these cancer hallmarks, suggesting that splicing may facilitate acquired drug resistance through survival mechanisms similar to those seen in cancer stem cells.
Citation Format: Edward Simpson, Yunlong Liu. Differential splicing analysis of doxorubicin treatment response in cancer cells [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 2268.
Abstract 2269: Transcription factor relationships associated with androgen-deprivation therapy response and metastatic progression in prostate cancer
Authors: Sharma Nitya V. , Pellegrini Kathryn L. , Ouellet Veronique , Giuste Felipe O. , Ramalingam Selvi , Watanabe Kenneth , Adam-Granger Eloise , Fossouo Lucresse , You Sungyong , Freeman Michael R. , Vertino Paula , Conneely Karen , Osunkoya Adeboye O. , Trudel Dominique , Mes-Masson Anne-Marie , Petros John A. , Saad Fred , Moreno Carlos S. 
Published: August 2018

Abstract
Patients with recurrent, aggressive prostate cancer typically undergo androgen-deprivation therapy (ADT), but the benefits are often short-lived, and responses are variable. Failure to respond to ADT invariably leads to metastatic disease, and ultimately death. To investigate differential responses to ADT, we performed whole-transcriptome analysis of 20 patient-matched pre-ADT biopsies and post-ADT prostatectomy specimens, and observed that all patients lost transcriptional signatures indicative of the androgen receptor (AR)-dependent subtype, after treatment. We also identified two subgroups of patients with either a strong or weak transcriptional response to ADT. The strong responders maintained the more aggressive subtype signal, while the weak responders lost expression of these genes and more resembled an AR-suppressed, basal subtype. We generated a strong responder transcriptional network using the PANDA program and integrated expression data from our cohort, protein-protein interaction, and DNA binding motif data. We also leveraged the expression data from a large public dataset of over 800 metastatic and primary samples to construct a metastatic lesion transcriptional network. We identified 20 common transcription factor coordinated groups (TFCGs) associated with both the strong responders and metastatic lesions, including GLI3/GLI2, SOX4/FOXA2/GATA4, ERF/ETV5/ETV3/ELF4, and a TFCG containing JUN, JUNB, JUND, FOS, FOSB, and FOSL1. Many TFCGs in the metastatic network were subsets of larger groups in the strong responders network, implicating these transcription factor associations as potentially critical for both the differential ADT response and metastatic disease progression.
Citation Format: Nitya V. Sharma, Kathryn L. Pellegrini, Veronique Ouellet, Felipe O. Giuste, Selvi Ramalingam, Kenneth Watanabe, Eloise Adam-Granger, Lucresse Fossouo, Sungyong You, Michael R. Freeman, Paula Vertino, Karen Conneely, Adeboye O. Osunkoya, Dominique Trudel, Anne-Marie Mes-Masson, John A. Petros, Fred Saad, Carlos S. Moreno. Transcription factor relationships associated with androgen-deprivation therapy response and metastatic progression in prostate cancer [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 2269.
Abstract 2279: The GenePattern Notebook environment for reproducible cancer research
Authors: Reich Michael M. , Tabor Thorin T. , Liefeld Ted , Hill Barbara , Thorvaldsdottir Helga , Mesirov Jill P. 
Published: August 2018

Abstract
As the availability of genetic and genomic data and analysis tools from large-scale cancer initiatives continues to increase, the need has become more urgent for a software environment that supports the entire “idea to dissemination” cycle of an integrative cancer genomics analysis. Such a system would need to provide access to a large number of analysis tools without the need for programming, be sufficiently flexible to accommodate the practices of non-programming biologists as well as experienced bioinformaticians, and would provide a way for researchers to encapsulate their work into a single “executable document” including not only the analytical workflow but also the associated descriptive text, graphics, and supporting research. To address these needs, we have developed GenePattern Notebook, based on the GenePattern environment for integrative genomics and the Jupyter Notebook system. GenePattern Notebook unites the phases of in silico research - experiment design, analysis, and publication - into a single interface.
GenePattern Notebook presents a familiar lab notebook format that allows researchers to build a record of their work by creating “cells” containing text, graphics, and executable analyses. Researchers add, delete, and modify cells as the research evolves, supporting the initial research phases of prototyping and collaborative analysis. When an analysis is ready for publication, the same document that was used in the design and analysis phases becomes a research narrative that interleaves text, graphics, data, and executable analyses. The online notebook format allows researchers to explain the analytical and scientific considerations of each step in any level of detail, promoting reproducibility and adoption. Notebooks can also be shared between researchers for collaborative development.
GenePattern Notebook features are designed to help nonprogramming users create and adapt notebooks. We have developed additional cell types allowing users to choose analyses, specify inputs, navigate results, send result files to new analyses, and create richly formatted text, all without the need for programming.
A free online GenePattern Notebook workspace is available at http://www.genepattern-notebook.org, where researchers can develop, share, and publish notebook documents. We have provided a collection of template notebooks that walk users through various genomic and machine learning analyses, and are collaborating with cancer research laboratories to create integrative cancer genomics notebooks.
Citation Format: Michael M. Reich, Thorin T. Tabor, Ted Liefeld, Barbara Hill, Helga Thorvaldsdottir, Jill P. Mesirov. The GenePattern Notebook environment for reproducible cancer research [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 2279.
Abstract 2289: Precision-weighted RNA-seq analyses of molecular abundance (RoMA) for detecting differential gene expression
Authors: Cai Guoshuai , Franks Jennifer M. , Whitfield Michael L. 
Published: August 2018

Abstract
In this study, we propose a novel method, RoMA, to accurately detect differential expression and unlock the integration with numerous upstream and downstream analyses on mRNA abundance in RNA-seq studies. Various methods have been proposed, each with its own limitations. Some naive normal-based tests have low testing power with the invalid normal distribution assumptions for RNA-seq read counts, whereas count-based methods lack a biologically meaningful interpretation and have limited capability for integration with other analysis packages for mRNA abundance. RoMA incorporates information from both mRNA abundance and raw counts by modeling RPKM (reads per kilobase per million), which represents the relative abundance of mRNA transcripts, and borrowing mean-variance dependency from CPM (counts per million) as a precision weight accounting for the variability in sequencing depth. Studies on simulated data and two real datasets showed that RoMA provides an accurate quantification of mRNA abundance and a value adjustment-tolerant DE analysis with high AUC, low FDR and a desirable type I error rate. This study provides a valid strategy for mRNA abundance modeling and data analysis integration for RNA-seq studies, which will greatly facilitate the identification and interpretation of DE genes. The methods is implemented in a user-friendly R package (RoMA).
Citation Format: Guoshuai Cai, Jennifer M. Franks, Michael L. Whitfield. Precision-weighted RNA-seq analyses of molecular abundance (RoMA) for detecting differential gene expression [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 2289.
Abstract 2293: Outstanding chromosomal expression patterning in human cancers
Authors: Barnes Spencer , Song Mingzhou 
Published: August 2018

Abstract
Cancer transcriptome is an effect of abnormal genetic and/or epigenetic regulation. In the past, transcriptome was studied at the level of individual transcripts for their differential expression. Spatial gene expression patterns along chromosomes were not yet characterized for cancer. We hypothesized that gene expression patterning is not randomly distributed along the chromosomes in cancer versus normal tissues. We analyzed cancer transcriptome from the National Cancer Institute Genomic Data Commons, and identified genomic regions with significant shifts in expression from normal tissues to tumors. We used a novel highly efficient method in the Ckmeans.1d.dp package for clustering genes by chromosomal location and expression level. We identified two chromosomal zones of note that exhibit similar expression patterns across several cancer types. In breast, lung, liver, and esophageal cancers, a zone near 6p22.2 contains genes that are similarly highly upregulated in tumor tissues compared to matched normal tissues. This zone contains a group of histone cluster 1 genes, among which are 14 known cancer genes. In colorectal, lung, liver, and esophageal cancers, a zone on 14q32.33 exhibits either heavy up- or downregulation, dependent on the specific cancer. This zone is rich in immunoglobulin heavy variable genes. Table 1 lists genomic zones of note specific to six cancer types. Our study provides an atlas of outstanding chromosomal expression patterning that can be used as markers for cancer in general and also for specific cancer types.
Table 1. Outstanding genomic zones specific to six human cancer types.Cancer type Chr:start-end (Mb) Cytogenetic band # Matched tumor/normal pairsBreastchr2:88-92p11.2112Colorectalchr7:141-146q3541Esophagealchr11:7-12p15.48Kidneychr12:85-91q21.3323Liverchr8:15-22p2250Lungchr8:121-126q24.1357
Citation Format: Spencer Barnes, Mingzhou Song. Outstanding chromosomal expression patterning in human cancers [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 2293.