bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 2410: Development of a custom genotyping platform and genetic prediction of prostate cancer risks in sub-Saharan Africa
Authors: Lachance Joseph , Harlemon Maxine , Kachambwa Paidamoyo , Ajayi Olabode , Kim Michelle , Adams Marcia , Pugh Elizabeth , Peterson Lindsay , Rebbeck Timothy 
Published: August 2019

Abstract
Prostate cancer is a highly heritable disease that disproportionally affects African and African-American men. With this in mind, the MADCaP (Men of African Descent Carcinoma of the Prostate) Network has developed a custom genotyping platform. This array is optimized for detection of novel genetic associations in sub-Saharan African populations. The MADCaP Array contains approximately 1.6 million markers, many of which overlap the H3Africa Consortium Array and the OncoArray. It includes an imputation backbone that successfully tags 94% of common (MAF &gt; 0.05) genetic variants in African populations (r2 threshold = 0.8). To aid in fine-mapping, the MADCaP Array has a high density of markers in genomic regions surrounding known cancer associations, including 8q24. Markers on the MADCaP Array also include over 27,000 prostate eQTLs. Using the MADCaP Array, we conducted a pilot study of 800 individuals with individual-level phenotype information. These samples include equal numbers of prostate cancer cases and controls, and they were collected from study sites in Ghana, Nigeria, Senegal, and South Africa. Here, we assess the extent to which polygenic risk scores are able to predict prostate cancer risks in African populations. We also identify non-African GWAS signals that replicate well in African populations. Additional analyses in this study include testing whether specific genetic ancestries are over-represented in cases, and quantifying the extent to which runs of homozygosity are found in the genomes of cases and controls.
Citation Format: Joseph Lachance, Maxine Harlemon, Paidamoyo Kachambwa, Olabode Ajayi, Michelle Kim, Marcia Adams, Elizabeth Pugh, Lindsay Peterson, Timothy Rebbeck. Development of a custom genotyping platform and genetic prediction of prostate cancer risks in sub-Saharan Africa [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2410.
Abstract 2414: Prognosis prediction of colorectal cancer using gene expression profiles
Authors: Pan Feixia , Chen Tianhui , Sun Xiaohui , Li Kuanrong , Jiang Xiyi , Försti Asta , Zhu Yimin , Lai Maode 
Published: August 2019

Abstract
Background: Investigation on prognostic markers for colorectal cancer (CRC) deserves efforts, but data from China are scarce. This study aimed to build a prognostic algorithm using differentially expressed gene (DEG) profiles and to compare it with the TNM staging system in their predictive accuracy for Chinese CRC prognosis.
Methods: DEGs in six-paired tumor and corresponding normal tissues were determined using RNA-Sequencing. Subsequently, matched tumor and normal tissues from 127 Chinese patients were assayed for further validation. Univariate and multivariate Cox regressions were used to identify informative DEGs. A predictive index (PI) was derived as a linear combination of the products of the DEGs and their Cox regression coefficients. The predictive performance of the PI was evaluated with receiver operating characteristics (ROC) curves and the area under curves (AUCs). A logistic regression model was built, including both the DEGs-based PI and tumors’ TNM stages.
Results: We identified significant associations of 13 DEGs (out of 75 candidate DEGs) with CRC survival. A PI based on these 13 DEGs (PI-13) predicted CRC survival probability more accurately than the TNM staging system [AUCs for 3-year survival probability 0.76 (95% confidence interval: 0.67, 0.83) vs. 0.63 (0.53, 0.71)] but comparable to a simplified PI (PI-5) using five DEGs (LOC646627, SCARA5, CDKN2A, ATP6V1A, and DNMT3B). The predictive accuracy was improved further by combining PI-5 and the TNM staging system [AUC for 3-year survival probability: 0.81 (0.73, 0.88)].
Conclusion: Prognosis prediction based on informative DEGs might yield a higher accuracy than the TNM staging system.
Citation Format: Feixia Pan, Tianhui Chen, Xiaohui Sun, Kuanrong Li, Xiyi Jiang, Asta Försti, Yimin Zhu, Maode Lai. Prognosis prediction of colorectal cancer using gene expression profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2414.
Abstract 2441: Association between NRF1 regulatory gene networks and cancer and aging
Authors: Bhawe Kaumudi , Felty Quentin , Yoo Changwon , Roy Deodutta 
Published: August 2019

Abstract
There is a growing convergence between molecular aspects of aging and cancer. Cellular senescence acts as a barrier through preventing the proliferation of cells under oncogenic stress. Therefore, we decided to examine the role of nuclear respiratory factor 1 (NRF1) transcription factor in human brain cancer and brain aging. In contrast to higher expression of NRF1 in human glioma tumors, lower NRF1 levels are observed in Alzheimer’s disease (AD) brain tissue. Using the glioma and AD transcriptomic data of NRF1 target genes, we conducted Transcription Factor Target Enrichment Analysis that detects transcription factor (TF) activity based on quantification of the collective transcriptional activation of their target genes. NRF1 TF activity was significantly lower in AD brain tissues from four independent cohorts compared to non-AD brain tissues. In contrast, we observed higher NRF1 transcription factor activity in glioma. NRF1 activity as well as expression of several cell cycle pathway genes including CDK1,2,6,9 and NRF1 target genes such as NR2E1, APOE and MAPT are significantly altered in these two population cohorts. We also discovered that expression of NRF1, RB1 and E2F influenced survival in glioma. Specifically, the mean overall survival for patients with a high NRF1, E2F1,2,6,7,8 and RB1 gene signature was 20.91 months (95% CI = 9.37-32.46), compared to 101.99 months (95% CI = 74.28-129.70), in high NRF1 expressing patients that lacked the specific gene signature. NRF1 based gene signatures combined with IDH1 mutation status are necessary and sufficient for explaining the observed differences in survival curves. We examined the association of NRF1, the E2Fs and RB1 through Bayesian graphical modeling and observed gender differences in gene networks between NRF1 and E2F family members in the glioma patient sub-populations as compared to normal aging human brain samples. Furthermore, the differentially expressed NRF1 regulated genes are part of the KEGG pathways associated with spliceosome, ribosome and lysosome function as well as a diverse array of other functions are enriched, indicating ways in which NRF1 may be involved in glioma biology. In summary these findings support our postulate that NRF1 is an important transcription factor in the human brain, where it plays an active role in both aging and cancer.
Citation Format: Kaumudi Bhawe, Quentin Felty, Changwon Yoo, Deodutta Roy. Association between NRF1 regulatory gene networks and cancer and aging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2441.
Abstract 2457: Prediction of selective genetic vulnerabilities from large-scale functional screens in cancer cells
Authors: Rauscher Benedikt 
Published: August 2019

Abstract
Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and create new vulnerabilities for potential therapeutic exploitation. To identify genotype-dependent vulnerabilities, a large number of forward genetic screens in different genetic backgrounds have been conducted. To aggregate and mine functional data from published screens in a standardized format, we recently developed GenomeCRISPR, a curated database for high-throughput CRISPR-Cas9 experiments. We then devised MINGLE, a computational framework that integrates genetic screens in large panels of cancer cell lines to predict genetic interactions between variant and knockout alleles. We showed that our method identifies specific known and previously unknown genetic relationships. For example, we identified a synthetic lethal dependency between RNF43 and members of the Glucosidase II complex. Additional experiments confirmed that the Glucosidase II complex acts as a regulator of Wnt/beta-catenin signalling. In our first study we analyzed a dataset consisting of screens performed in sixty cell lines. Since then the amount of data available for this analysis has increased several fold. We are continuously integrating the new data that become available as we work towards the next iteration of a genetic interaction map in cancer cells. We are specifically interested in using the increased resolution of this new map to understand how genetic networks differ between individual cancer types.
Citation Format: Benedikt Rauscher. Prediction of selective genetic vulnerabilities from large-scale functional screens in cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2457.
Abstract 2459: Identifying genetic drivers of uterine serous carcinoma patient prognosis with regulatory network analysis
Authors: Myers Emily , Tran Lynn K. , Tran Paul M. , Mysona David , Purohit Sharad , Wallbillich J. J. , She Jin-Xiong 
Published: August 2019

Abstract
Uterine serous carcinoma (USC) is a subtype of endometrial cancer (EC), which is the most common gynecological malignancy in the developed world. Though USC is a Type II EC, which are only responsible for 20% of EC cases, Type II EC causes 74% of EC deaths. Understanding the molecular mechanisms driving USC tumor biology could reveal novel therapies and improve patient outcomes. We developed and validated gene expression signatures capable of predicting USC patient’s prognosis. However, little was understood about why differential expression of these particular genes predicts patient prognosis. As such, we investigated the key transcription factors driving the phenotypic difference between the two groups by using the Passing Attributes between Networks for Data Assimilation (PANDA) algorithm. As described by Glass et al., PANDA uses message passing to integrate information from multiple data types to construct robust interaction networks between transcription factors and genes. Following regulatory network generation, we selected potential key regulators based on network analyses. First, we use linear regression analysis to select TFs involved in gene interactions with the greatest edge weight differential between the two groups. Second, we identify TFs involved in unique regulatory interactions to each group. Third, we analyze the TF-gene signature gene interaction networks for both patient populations to select hub transcription factors. TFs selected in all three analyses as potential key regulators are ranked using summed residual values to determine in which patient group the TFs were weighted more strongly. A literature review revealed that the TFs most heavily weighted in the poor survival group are implicated as suppressors of tumor growth. The TFs most heavily weighted in the good survival group are implicated in promoting tumor growth. This suggests that fast-growing tumors correlate with good survival and slow-growing tumors correlate with poor survival. We hypothesize that this effect is due to the impact of tumor growth rate on chemosensitivity, and we plan to test this hypothesis in future studies.
Citation Format: Emily Myers, Lynn K. Tran, Paul M. Tran, David Mysona, Sharad Purohit, J. J. Wallbillich, Jin-Xiong She. Identifying genetic drivers of uterine serous carcinoma patient prognosis with regulatory network analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2459.
Abstract 246: Glioblastoma cell-modified cell membrane lipid-extracted nanoparticles (CLENs) for imaging and treatment: Influence of magnetic nanoparticle on physiochemical properties of CLENs, cellular uptake, and cytotoxicity
Authors: Song Woongjae , Campbell Robert 
Published: August 2019

Abstract
Background: Glioblastoma multiforme (GBM), located in the cerebral hemispheres of the brain, is the most aggressive form of cancer. Surgery associated with chemotherapy with radiation treatment is the defined way to treat the disease. The delivery of chemotherapeutic agents frequently result in serious side effects. To address this issue we have taken the first steps in the development of CLENs (cell membrane lipid-extracted nanoliposomes) to enhance selective delivery of drugs to the brain.
Method: The CLENs used for the experiments were composed of various ratios of DOPC, DOTAP, cholesterol, and lipid extract (LE) material derived from the target cells (i.e., U87-MG), and DPPE-PEG5000 (PEG). Studies include the determination of drug (cisplatin) loading, magnetite (MAG-C) loading in CLENs, quantitative studies for particle size, zeta potential, SQUID (superconducting quantum interference device) measurements, and fluorescence detection to study cellular interactions.
Results: The average size for the CLENs fell within the range of 130 to 190 nm. The zeta potential reported for CLENs was negatively-charged. The inclusion of 15mol % LE resulted in the most significant cellular uptake by target cell U87-MG. Overall, the uptake by these cells was less compared to CLENS previously published for other organ tissue environments such as with breast tissue. The inclusion of cationic lipid DOTAP in CLENs did not alter the size distribution; however, the values for zeta potential increased from -8 ± 5 to 12 ± 6 mv. The additional inclusion of DOTAP in CLENs increased cellular uptake approximately 4-fold compared to CLENs without DOTAP (fluorescence intensity (A.U) from 145 ± 52 to 525 ± 49)). The inclusion of DOTAP increased the efficiency of loading Cisplatin in CLENs by approximately 30%. Studies involving the incorporation of MAG-C in U87-MG CLENs are currently underway, as well magnetization studies involving SQUID.
Conclusion: All CLENs demonstrated practical physicochemical properties similar to conventional liposomes. The cellular uptake results suggested that cellular uptake of CLENs in vitro, although relatively low overall, varied according to the total LE content. Furthermore, the inclusion of DOTAP in CLENs significantly increased uptake and the efficiency of drug loading.
Citation Format: Woongjae Song, Robert Campbell. Glioblastoma cell-modified cell membrane lipid-extracted nanoparticles (CLENs) for imaging and treatment: Influence of magnetic nanoparticle on physiochemical properties of CLENs, cellular uptake, and cytotoxicity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 246.
Abstract 2474: Automated somatic variant classifier to reduce false positives identified by tumor normal variant callers
Authors: Harley Alena S. , Lau Corine K. , Shinbrot Eve 
Published: August 2019

Abstract
Accurate identification of somatic mutations is key to targeted cancer therapies. However, due to the complexity of next-generation sequencing, there are many variables that give rise to sequencing and alignment artifacts. Most sequencing workflows using tumor normal paired samples rely on the intersection or union of mutations by more than one somatic variant caller such as Mutect2, Strelka, VarScan2, Muse, and SomaticSniper. Yet, they still contain a number of false positive and false negative calls. Therefore, there is a need for a manual review of variants using Integrative Genomics Viewer (IGV) to incorporate information that is difficult to take into account in automated workflows.
To alleviate the burden of manual inspection, we present an automated classification method to pre classify detected somatic variants into true or false classes for further manual review.
Methods: We created a false positive classifier that utilizes automated IGV visualization of somatic variants. Our somatic workflow involves tumor normal DNA sequencing, BWAMEM alignment, deduplication of reads via Picard, somatic calling using two somatic callers, followed by the union of the detected variants, SnpEff annotation, and extracting nonsilent variants with variant allele fractions greater than 5 percent. We used the data for 1,413 nonsilent variants from a set of 10 tumor normal pairs of various cancer types. The variants were scored by cancer analysts using IGV. Initial false positive rate was 28 percent. We fine tuned Residual Deep Neural Network which utilizes weights from the network with the same architecture trained on ImageNet. Discriminative fine tuning was performed in two stages: first unfreezing the last fully connected layer only, and then unfreezing all the layers and training with differential learning rates.
Results and Conclusion: Our method achieves 93 percent overall accuracy. The classifier greatly reduces the false positive rate for the set of variant calls from 28 percent down to 4 percent at the expense of calling 3 percent of true variants false. Previously, deep neural networks have been shown to yield the state of the art performance in multiple domains including image processing, speech, and text processing. In this work, we describe a way to fine tune Residual Neural Network trained on a large image data set to perform false positive variant detection in tumor-normal variant alignment snapshots from IGV. Our general approach that significantly reduces false positive rate of putative variants can be extended to any subset of somatic callers.
Citation Format: Alena S. Harley, Corine K. Lau, Eve Shinbrot. Automated somatic variant classifier to reduce false positives identified by tumor normal variant callers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2474.
Abstract 2485: Somatic analysis services with best practice workflows in a cloud-based platform
Authors: Tsuji Junko , Hollinger Andrew , MacBeth Alyssa , Grander Brian R. , Rickles-Young Micah , Bowers Tera , Cibulskis Carrie , Lennon Niall 
Published: August 2019

Abstract
With advances in high-throughput sequencing technologies and analytical tools, genomic analysis of tumors has led to the identification of various important somatic mutations that shed light on diagnosis, prevention, and treatment for cancer. However, detecting somatic variants is not a trivial task in terms of the technical aspects (e.g. filtering germline events and removing a variety of noises in tumor samples) and computational resources to handle large-scale cohort analysis. There is also a demand for maintaining stable software versions and the workflows for studies over extended periods of time, that need consistency and traceability, such as clinical trials. We introduce here, the Translational Analysis Group (TAG), a team which deploys, validates, and conducts scalable analytical workflows in a secure, cloud-based environment. We maintain 29 well-tested workflows with best practice methods and ample resources for both somatic and germline analysis. Our cloud platform, FireCloud, enables us to run workflows at any scale. Since May 2017, our team has performed nearly 10,000 analyses for mutation detection (SNV, InDel, CNV, and SV) and cohort analysis on tumor samples and cell-free DNA samples. TAG offers a range of options for somatic and germline variant detection, from legacy pipelines through recently validated contemporary pipelines to allow for continuity across long running projects.
Citation Format: Junko Tsuji, Andrew Hollinger, Alyssa MacBeth, Brian R. Grander, Micah Rickles-Young, Tera Bowers, Carrie Cibulskis, Niall Lennon. Somatic analysis services with best practice workflows in a cloud-based platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2485.
Abstract 2487: Application of RegTools to TCGA samples for the identification of tumor-specific splice variants
Authors: Cotto Kelsy C. , Feng Yang-Yang , Skidmore Zachary L. , Griffith Obi L. , Griffith Malachi 
Published: August 2019

Abstract
The interpretation of variants in cancer is frequently focused on direct protein coding alterations. However, this analysis strategy excludes somatic mutations in non-coding regions of the genome and even exonic mutations may have unidentified non-coding consequences. To address this issue, we created RegTools (Feng et al. 2018), a free, open-source software package that integrates analysis of somatic variant calls from genomic data with splice junctions extracted from transcriptomic data in order to efficiently identify variants that may cause aberrant splicing in tumors. Initially, this software was developed using 188 cancer samples from 4 cancer types and enabled us to identify important non-coding variants which would otherwise be undervalued or perhaps even completely ignored by traditional methods and annotators. Here, we present the application of RegTools to over 10,000 TCGA samples in an effort to identify somatic variants that are associated with alternative splicing patterns within these tumors. In order to perform this analysis, we obtained all tumor samples that had somatic variant VCF files and RNA-seq bam files from the Genomic Data Commons (GDC). Included within these samples are more than 700 solid tissue normal samples, which will be used as a panel of normals in order to filter out variants and splicing events that are not tumor specific. We plan to present on the significance of our findings when analyzing these samples within their respective tumor type and across all tumor types. We also plan to present on whether these aberrant splicing events that result from somatic mutations might interfere with predicted neoantigens pVAC-Seq (Hundal et al. 2016). RegTools is freely available and open source (www.regtools.org).
Citation Format: Kelsy C. Cotto, Yang-Yang Feng, Zachary L. Skidmore, Obi L. Griffith, Malachi Griffith. Application of RegTools to TCGA samples for the identification of tumor-specific splice variants [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2487.