bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 2353: A comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer
Authors: Waldron Levi , Riester Markus , Birrer Michael , Parmigiani Giovanni 
Published: May 2015

Abstract
Numerous gene signatures of patient prognosis for late-stage, high-grade serous ovarian cancer (HSOC) have been proposed, but diverse data and methods make these difficult to compare or prioritize for clinical application. However, the corresponding volume of publicly available expression data creates an opportunity to validate previous findings and to develop more robust signatures. To establish the current state of HSOC prognostic gene signatures, we undertook a systematic review and meta-analysis of the publicly available microarray data and published prognostic gene signatures. This effort generated a curated database of 1,319 clinically annotated HSOC microarray assays from 10 studies (Ganzfried et al., DATABASE 2013) and implementations of 14 published prognostic signatures for evaluation in independently available data. For each published prognostic signature we evaluated 1) its reproducibility, 2) its prognostic accuracy for overall survival in independent datasets, and 3) the prognostic value of the genes used in each signature relative to random gene sets. Twelve published models performed better than 97.5% of randomized risk scores, and six out-performed 97.5% of random signatures of the same size trained on the same data. The four top-ranked models achieved overall validation C-Indices of 0.56 to 0.60, and shared anti-correlation with expression of immune response pathways. Most models demonstrated lower accuracy in new datasets than in validation sets presented in their publication. This analysis provides definitive support for several prognostic models, but confirms that these require improvement to be of clinical value. This work addresses outstanding controversies in the ovarian cancer literature, including whether the prognostic signature proposed by The Cancer Genome Atlas Consortium (Nature 2011) improved on existing signatures, the impact of batch effects on a frequently re-used, high-profile dataset (Dressman et al., J Clin Oncol 2007), and the prognostic value of a recently proposed DNA damage repair signature (Kang et al., J Natl Cancer Inst 2012). Finally, this work provides a reproducible framework for meta-analytic evaluation of gene signatures.
Citation Format: Levi Waldron, Markus Riester, Michael Birrer, Giovanni Parmigiani. A comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2353. doi:10.1158/1538-7445.AM2014-2353
Abstract 2357: Gene expression profiling robustly predicts the outcome of patients diagnosed with early stage lung adenocarcinoma
Authors: Gaston-Mathé Yann , FERTE CHARLES , gauthier benoit , bateson mathilde , planchard david , besse benjamin , Armand Jean-Pierre , soria jean-charles 
Published: May 2015

Abstract
Background: Although the management of metastatic lung adenocarcinoma has been profoundly modified by the identification of actionnable molecular traits, decision making for early-stage lung adenocarcinoma still relies on the tumor stage (TNM) only. Most of these patients recur within 5 years after tumor resection and are given platinum based adjuvant chemotherapy. The recent availability of multiple gene expression data and the concomitant developments of novel algorithms - such as the Hypercube algorithm - could dramatically improve such predictive challenges.
Methods: Datasets were selected from public repositories (Director's challenge (DC) n=274, Rousseaux n=86, Hou n=35, JBR.21 n=31) upon the following criteria: lung adenocarcinoma, stage IA-3A, no adjuvant therapy, R0 tumor resection. To generate robust predictors of 3-year overall survival, an iterative feature selection framework (Hypercube algorithm) was applied successively on DC and Hou. Only highly predictive rules in both datasets (AUC &gt;.7) were then included in a subsequent logistic regression model and finally assessed for external validation. The predictive performance of the models was evaluated by ROC-AUC and by Kaplan Meier curves.
Results: Our best model combining gene expression (10 genes) and clinical data (TNM) was robust across three lung adenocarcinoma datasets (AUC= .85, .80 and .59). Particularly, it was able to better predict the 3 year of survival compared to the TNM-based clinical model only (100% vs. 77%). Kaplan Meier estimates of overall survival in the validation datasets were dramatically discriminated according to the high- and low-risk groups predicted by our model (all P values =.01). Interestingly, this predictive classifier was specific to lung adenocarcinoma and has no predictive value in lung squamous datasets.
Conclusion: We developed a robust and highly performing gene expression predictive signature of 3-year overall survival, specific to the lung adenocarcinoma setting. These promising results have the potential to change the decision making at bedside for early stage lung adenocarcinoma patients.
Citation Format: Yann Gaston-Mathé, CHARLES FERTE, benoit gauthier, mathilde bateson, david planchard, benjamin besse, Jean-Pierre Armand, jean-charles soria. Gene expression profiling robustly predicts the outcome of patients diagnosed with early stage lung adenocarcinoma. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2357. doi:10.1158/1538-7445.AM2014-2357
Abstract 2358: Dissecting the cellular response to cisplatin from RNA transcription to translation
Authors: Mackintosh Carlos , Konovalov Sergiy , Garcia-Bassets Ivan 
Published: May 2015

Abstract
Platinum-based antineoplastic drugs, such as cisplatin, are DNA damaging agents typically utilized in cancer treatment. In ovarian cancer, however, these drugs have a relatively low therapeutic index (which is a measure of clinical benefit over toxicity of these drugs), thus even a small change (reduction) in the cellular sensitivity to these chemotherapeutic agents leads to clinical treatment failure, also known as ‘drug resistance’. To increase the clinical efficacy of platinum-based antineoplastic drugs, we need to develop therapies that are less likely to result in drug resistance, or we need to modify the current therapies to prevent or reduce drug resistance.
To gain mechanistic insights into the process of drug resistance that may help us to develop new or modified therapies that are less prone to clinical drug resistance, we have focused our studies on the comprehensive analysis of two still poorly studied cisplatin-associated processes at genome-wide scale: the processes of RNA nascent transcription and RNA translation. In particular, we investigate by global run-on combined with next-generation sequencing (GRO-seq) how cisplatin-sensitive A2780 ovarian cancer cells and its cisplatin-resistance derivative, A2780cis cells, respond to acute cisplatin treatment at the level of the nascent transcriptome. We also investigate, in this case by ribosome profiling, how the same cells respond to acute cisplatin treatment at the level of the ribosome-associated transcriptome (also known as translatome). These two massive analyses, complemented with conventional profiling of steady-state RNA (profiled by RNA-seq) provide the first multidimensional analysis of the actions induced by any chemotherapeutic agent at the level of the three basic states of the transcriptome (nascent RNA, steady-state RNA, and translated RNA). Interestingly, this analysis reveals an unexpected large fraction of uncoupled events between these three transcriptomic states, which challenges the conventional one-dimensional approach based exclusively on RNA-seq for the study of drug resistance.
Note: This abstract was not presented at the meeting.
Citation Format: Carlos Mackintosh, Sergiy Konovalov, Ivan Garcia-Bassets. Dissecting the cellular response to cisplatin from RNA transcription to translation. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2358. doi:10.1158/1538-7445.AM2014-2358
Abstract 2365: Analysis of angiogenesis pathway-related gene expression impacted by MDM2 using PCR array
Authors: Venkatesan Thiagarajan , Stinson Corine , Dhandayuthapani Sivanesan , Rathinavelu Appu 
Published: May 2015

Abstract
The Murine Double Minute 2 (MDM2) gene encodes an oncogenic MDM2 protein, which is a negative regulator of the p53 tumor suppressor. MDM2 amplification or over expression is found in many tumors that eventually lead to the inactivation of the cell cycle control and loss of pro-apoptotic functions that are typically exerted by wild type p53. Many of the oncogenic effects of MDM2 are resulting from the inactivation of p53 through direct binding or degradation of p53 following E3 ligase mediated ubiquitination. However, our recent findings have indicated that MDM2 over expression can also promote tumor angiogenesis through p53 independent pathways and thereby enhance the metastatic potential of cancer cells. In this respect increasing the levels of pro-angiogenic factors such as VEGF has been confirmed as one of the mechanisms regulated by MDM2. However, the actual cluster of genes or pathways regulated by MDM2 leading to such increase in the metastatic potentials of cancer cells have not been clearly identified. Therefore, the main purpose of our study was to understand the impact of MDM2 over expression on the levels of angiogenesis related genes that can also increase growth rate and metastatic potential. Hence, for our study we used the Human Angiogenesis PCR Array to compare the gene expression profile of LNCaP (prostate cancer cells) and LNCaP-MST (MDM2 transfected prostate cancer) cells. The MST cells expressed at least 10 fold increase in the levels of MDM2 after transfection. As a result of the elevated levels of MDM2 in LNCaP-MST cells the expression of genes such as THBS1 (125 fold), TNF-α (14 fold), MMP-9 (1.4 fold) were found to be significantly up-regulated compared to the MDM2 non-transfected cells. Cluster analysis further confirmed that these gene expression profiles are positively correlated with the pro-angiogenic potential of the LNCaP-MST cells compared to the non-transfected cells. In addition, the levels of EREG, TIMP1 and CXCL3 were down regulated significantly that favors the suppression of anti-angiogenic mechanisms. Our results offer a conclusive evidence that MDM2 can impact the cancer growth and metastatic potential of cancer cells by shifting the balance towards pro-angiogenic mechanisms (The financial support from the Royal Dames of Cancer Research Inc., Ft. Lauderdale, Florida is gratefully acknowledged).
Citation Format: Thiagarajan Venkatesan, Corine Stinson, Sivanesan Dhandayuthapani, Appu Rathinavelu. Analysis of angiogenesis pathway-related gene expression impacted by MDM2 using PCR array. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2365. doi:10.1158/1538-7445.AM2014-2365
Abstract 2367: High expression of GAPDH is relevant to lung adenocarcinoma with low expression of the tumor suppressor gene Deleted in Liver Cancer-1 (DLC1)
Authors: Wang Dunrui , Zhang Guangxian 
Published: May 2015

Abstract
Although glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is often considered to be a stable housekeeping marker gene, we recently determined that high levels of GAPDH mRNA expression in solid tumors was highly correlated with the expression of a group of genes, most of which are classified as cell cycle-dependent (Wang et al. PLoS One, 2013). In the current project, we have further evaluated the role of GAPDH in lung adenocarcinoma. Using data from an integrated microarray gene expression dataset and The Cancer Genome Atlas (TCGA) Project, we have now found a correlation between high mRNA expression of GAPDH and low expression of the tumor suppressor gene Deleted in Liver Cancer-1 (DLC1). DLC1 encodes a protein with strong RhoGAP (GTPase activating protein) activity and is inactivated in various human malignancies. In lung adenocarcinoma, low DLC1 expression occurred frequently together with up-regulation of GAPDH. The correlation coefficient of GAPDH and DLC1 expression in the integrated gene expression dataset was -0.6. In addition, high GAPDH and low DLC1 expression in lung cancer was associated with unfavorable prognosis. The combination of DLC1 expression level and GAPDH level improved the predictive prognostic power compared with that of the individual gene level alone. Pathway analysis indicated that low DLC1 in lung cancer was accompanied by increased expression of genes implicated in glycolysis and reduced expression of fructose-1,6-bisphosphatase (FBP1) in gluconeogenesis. Our findings suggest that GAPDH may contribute to the regulation of cell cycle genes and of tumor suppressor genes in cancer.
Citation Format: Dunrui Wang, Guangxian Zhang. High expression of GAPDH is relevant to lung adenocarcinoma with low expression of the tumor suppressor gene Deleted in Liver Cancer-1 (DLC1). [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2367. doi:10.1158/1538-7445.AM2014-2367
Abstract 2373: Deep computational analysis of human and mouse specific next-generation sequencing data generated from PDX specimen
Authors: Menon Roopika , Schneider Petra , Peifer Martin , Leenders Frauke , Heuckmann Johannes M. 
Published: May 2015

Abstract
Patient derived xenograft (PDX) samples are powerful tools to analyze tumor biology and drug sensitivity of primary tumors in animal models. However, sequencing studies on those samples face the challenge of mouse stromal contamination, which varies in each of the explanted tumor specimen. Thus, without applying mouse and human specific filters on sequencing data, species specific genomic alterations and differences in expression are impossible to detect. To allow mouse and human specific transcriptome/genome analyses, we adapted our NGS data analysis pipelines to filter for mouse and human specific reads. By applying the analysis algorithms on hybrid-capture and transcriptome sequencing data from 60 lung cancer PDX samples, we were able to generate a comprehensive overview on tumor specific alterations, including point mutations, deletions and insertions, copy number alterations, gene fusions and isoform specific expression levels, as well as species specific genomic alterations and transcriptome wide expression levels. From 5781 nonsynonymous substitutions which were detected by hybrid-capture sequencing of 333 cancer relevant genes (CAGE-Rx Scanner), 863 human specific nonsynonymous substitutions were detected after filtering for mouse specific variations. The mean sequencing coverage of the CAGE analysis across all targeted bases in all 60 samples was 662x. An integrated analysis of this data set and corresponding transcriptome sequencing data revealed TP53 and KRAS as the most frequently mutated genes which were expressed. As expected, due to the relative high tumor content in all samples (ranging from 30 to 90% with a median of 90% as determined by pathological review), 80% of all mutations called from transcriptome sequencing in an unfiltered fashion were human specific and called after species specific filtering. In addition, the detection of mouse specific transcriptome sequencing reads identified several growth factors expressed by tumor surrounding tissue, which would have been discarded as unmapped reads in the normal analysis setting. Thus, our analysis alorithms not only enable to remove mouse specific false positive single nucleotide variants (SNVs), but also a detailed analysis of mouse specific expression patterns which might strongly influence tumor growth via the tumor microenvironment.
Citation Format: Roopika Menon, Petra Schneider, Martin Peifer, Frauke Leenders, Johannes M. Heuckmann. Deep computational analysis of human and mouse specific next-generation sequencing data generated from PDX specimen. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2373. doi:10.1158/1538-7445.AM2014-2373
Abstract 2376: MutationValidator: A computational method for variant cross-validation in next-generation sequencing data
Authors: Rosenberg Mara , Getz Gad , Kiezun Adam , Sivachenko Andrey 
Published: May 2015

Abstract
Background:
Massively parallel next-generation sequencing (NGS) technologies already enable comprehensive genome-wide detection of germline and somatic variants in thousands of individuals, and the pace of data production keeps accelerating. However, with increased sequencing techniques and variant calling algorithms, proper validation and determination of ground truth is essential for benchmarking, comparisons, and confirmation of novel discoveries. Datasets generated on different sequencing platforms, derived from distinct biological samples and/or using different library construction protocols can provide at least partially independent sources for crosschecking variants. While methods for validation exist to date, they fail to account for the inherent variability in the datasets, and thereby propose biased estimates for the validation rates.
Results:
Here we present a method for variant cross-validation and present software implementation (written in Matlab) that generates validation metrics and diagnostics to characterize a mutation as true somatic, germline, or artifactual. Our method adjusts for the power to observe variant-supporting evidence in the validation dataset, which allows for accurate assessment of validation rates across wide ranges of sequencing coverages and variant allelic fractions. When this method is applied to a test cohort of 172 patients from The Cancer Genome Atlas Lung Adenocarcenoma dataset, we observe a SNP and Indel validation rate of 95% and 96% respectively with a 99% power for detection. A previously implemented naïve method of &gt;8% supporting alleles in the tumor and &lt;3% alternate allele in the normal for a validated event would have yielded rates of 72% and 95% for SNPs and Indels, values we claim to be underestimations of the true rates as they misclassify underpowered sites as a false positives.
Conclusions:
Our proposed method, MutationValidator, enables confident classification of mutations using orthogonal next-generation sequencing technologies for validation as it takes into consideration the variability in and across the datasets.
Citation Format: Mara Rosenberg, Gad Getz, Adam Kiezun, Andrey Sivachenko. MutationValidator: A computational method for variant cross-validation in next-generation sequencing data. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2376. doi:10.1158/1538-7445.AM2014-2376
Abstract 2383: Translesion DNA synthesis by DNA polymerase kappa somatic variants found in prostate cancer
Authors: Yadav Santosh , Mukhopadhyay Sudurika , Makridakis Nick 
Published: May 2015

Abstract
Prostate cancer is one of the most prevalent malignancies affecting men worldwide, and is the most frequent cancer among men in the USA, with an estimated 238,590 new cases and 29720 deaths in 2013 (NCI). DNA damage found in prostate cancer promotes tumor progression. Repair of DNA lesions that occur endogenously or in response to diverse genotoxic stresses is indispensable for genome integrity. DNA lesions activate checkpoint pathways that regulate specific DNA-repair mechanisms. During replication, DNA polymerases frequently encounter unrepaired DNA lesions and posses 3′→5′ exonuclease proofreading activities. Such polymerases tend to stall at sites of damage, which can lead to replication fork collapse and which, unless rescued, can eventually cause large-scale genomic rearrangements and cell death. As a preventative measure, most organisms have alternative polymerases that are capable of ‘damage tolerance’ and can synthesize past DNA lesions by a mechanism called translesion synthesis (TLS). Polymerase kappa (Polκ) a Y-family polymerase participates in the process of TLS. Current study was designed to investigate the TLS of somatic missense mutations of Polκ such as p.F155S, p.L442F, p.G154E, p.E29K, p.E449K and p.E430G found after sequence analysis of prostate tumor DNA from 30 prostate cancer patients. We studied these variants to identify the nucleotide incorporated opposite to the apurinic (AP) site template, in vitro using site-specific tetrahydrofuran (AP site analog). We report that missense somatic mutations of Polκ at p.F155S, p.L442F, p.G154E, p.E29K, p.E449K mutants significantly decreases catalytic efficiency (k(cat)/Km). The biological consequence of an unrepaired DNA lesion is determined by whether it is bypassed or not, bypassed error prone. Our results provide evidence that mutations in the polymerase kappa gene can causes interference in Translesion DNA synthesis, this interference in pol
κ function may play a role in prostate tumor progression.
We acknowledge to Prof. Dr. Guengerich, for providing Polκ construct.
This work was supported by DOD grant PC094628, and NIH grant 8 P20 GM103518.
Citation Format: Santosh Yadav, Sudurika Mukhopadhyay, Nick Makridakis. Translesion DNA synthesis by DNA polymerase kappa somatic variants found in prostate cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2383. doi:10.1158/1538-7445.AM2014-2383