bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 3257: Polygenic risk score, stage, and mode of detection in relation to breast cancer risk
Authors: Trentham-Dietz Amy , Hampton John M. , Ong Irene M. , Page C David , Gould Michael N. , Haag Jill D. , Newcomb Polly A. , Shull James D. , Burnside Elizabeth S. 
Published: August 2018

Abstract
Identification of increasing numbers of breast cancer risk variants holds promise to improve risk prediction and identify population subgroups that could benefit from targeted prevention and early detection. We aimed to examine whether women with increased polygenic risk are more likely to be diagnosed with symptomatic breast cancer. A case-control study including data collected from interviews, DNA from saliva, and cancer registry data collected between 2001-2007 (4,315 cases, 3,919 controls) was used to construct a polygenic risk score (PRS). Single nucleotide polymorphisms (SNPs, N=98) were selected from recently published genome-wide association studies of breast cancer and breast density, and comparative rat genome studies. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CI) associated with the SNPs. SNPs with p-values ≤0.10 (N=24) were used to construct quintiles of the PRS by multiplying the log-odds of the SNPs by the number of risk alleles; multivariable logistic regression models evaluated all women and strata of women defined by method of detection and stage at diagnosis. Odds ratios were adjusted for age, family history of breast cancer, age at menopause/menopausal status, body mass index, parity, and age at first birth. Overall, PRS quintile categories were associated with breast cancer risk in a dose response manner (Q1: OR=1, reference; Q2 OR=1.36, 95% CI 1.16-1.58; Q3 OR=1.66, 95% CI 1.43-1.93; Q4 OR=1.83, 95% CI 1.58-2.12; Q5 OR=2.58, 95% CI 2.23-2.98). Evaluated on a continuous scale, the PRS was associated with an OR=1.38 for a 1-unit change in the standard deviation (SD), 95% CI 1.32-1.45. Odds ratios were essentially unchanged when stratified by method of detection (p-value=0.40). The odds ratio of breast cancer associated with the continuous PRS was elevated among women diagnosed with advanced breast cancer: DCIS, OR=1.36, 95% CI 1.26-1.47 per 1 SD; localized OR=1.37, 95% CI 1.30-1.45 per 1 SD; regional OR=1.38, 95% CI 1.28-1.48 per 1 SD; and distant OR=1.82, 95% CI 1.39-2.40. These results suggest that polygenic risk scores are strongly related to breast cancer risk, that the association does not vary by method of breast cancer detection, but that the association is strongest for metastatic breast cancer.
Citation Format: Amy Trentham-Dietz, John M. Hampton, Irene M. Ong, C David Page, Michael N. Gould, Jill D. Haag, Polly A. Newcomb, James D. Shull, Elizabeth S. Burnside. Polygenic risk score, stage, and mode of detection in relation to breast cancer risk [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 3257.
Abstract 3267: Cervical microbiota and the urinary metabolome in patients with high-risk and low-risk HPV infections
Authors: Ortiz Gilmary , Chorna Natalyia , Romaguera-Agrait Josefina , Martinez-Ferrer Magaly , Sanchez Maria , Ortiz Ana P. , Guerrero-Preston Rafael , White James R. , Godoy-Vitorino Filipa 
Published: August 2018

Abstract
Genital human papillomavirus (HPV) is the world's most commonly diagnosed sexually transmitted infection, and high-risk HPV types are strongly linked to cervical dysplasia and carcinoma. The microbiota of the human cervico-vaginal tract is the interface between the host and environment, and its constituents may act as determinants of susceptibility for HPV infection. There exists the potential for reliable diagnostic biomarkers for cervical cancer in the cervical microbiome as well as in urine, which would be an important biospecimen due to its abundance and non-invasive collection strategy. In this study, we performed comprehensive metabolomic analysis on urine samples from 65 women (54 HPV+, 38 of whom have cervical intraepithelial neoplasia (CIN), and 11 HPV-) and discuss the potential diagnostic implication and metabolic description of high-risk and low-risk HPV infected patients.
The experimental procedure employed gas chromatography mass spectrometry (GC-MS) on the urine-derived products and HPV genotyping was determined by reverse hybridization with the HPV SPF10-LiPA25 kit. We further characterized resident cervical bacteria in these patients using Illumina sequencing of the V4 region of the 16S ribosomal RNA with a high-resolution bioinformatics methodology (Resphera Insight v2.2) for species-level taxonomic assignment.
The hybridization assay detected 11 low-risk and 10 high-risk HPV types many of them co-occurring in the cervical samples. Lactobacillus iners OTUs were shared by all samples but were significantly more dominant in women with low-risk HPV strains (p-value=0.05), while G. vaginalis, S. sanguinegens and L. jensenii were associated with high-risk strains. CIN1 and CIN3 dysplasic biopsies, detected significant enrichments of Gardnerella vaginalis, L.crispatus and Prevotella amnii (p-values &lt;0.05). Preliminary GC-MS analysis identified elevated levels of palmitic (C16:0) and stearic fatty acids (C18:0) as well as lactic acid, abundant in the HPV+ samples while 5-oxo-proline and Neu5Ac (N-Acetylneuraminic acid) were abundant in the HPV- samples.
Our preliminary data identifies possible microbial and metabolic biomarkers associated with oncogenic HPV and cervical intraepithelial neoplasia in women living in Puerto Rico.
Citation Format: Gilmary Ortiz, Natalyia Chorna, Josefina Romaguera-Agrait, Magaly Martinez-Ferrer, Maria Sanchez, Ana P. Ortiz, Rafael Guerrero-Preston, James R. White, Filipa Godoy-Vitorino. Cervical microbiota and the urinary metabolome in patients with high-risk and low-risk HPV infections [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 3267.
Abstract 3271: A machine learning approach for somatic mutation discovery
Authors: Wood Derrick , White James , Georgiadis Andrew , Emburgh Beth Van , Parpart-Li Sonya , Mitchell Jason , Anagnostou Valsamo , Niknafs Noushin , Karchin Rachel , Papp Eniko , McCord Christine , Loverso Peter , Riley David , Diaz Luis A. , Jones Sian , Sausen Mark , Velculescu Victor E. , Angiuoli Samuel 
Published: August 2018

Abstract
Variability in the accuracy for somatic mutation detection may affect discovery of alterations and therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery method based on machine learning approaches that outperformed other methods using tumor alterations that were experimentally validated (sensitivity of 97% vs 90-99%; positive predictive value of 98% vs 34-92%). Analysis of tumor-normal exome data from 1,376 TCGA samples using this method revealed concordance for 74% of mutation calls but also identified likely false positive and negative changes in TCGA data, including in clinically actionable genes. For melanoma and lung cancer patients treated with immune checkpoint inhibitors, determination of high-quality somatic mutation calls improved mutation load-based predictions of clinical outcome. Integration of high-quality mutation detection in clinical NGS analyses improved the accuracy of test results compared to other clinical sequencing analyses not using these approaches (sensitivity of 100% vs 50-97%; positive predictive value of 100% vs 9-66%). These analyses provide an approach for improved identification of tumor-specific mutations and have important implications for research and clinical management of patients with cancer.
Citation Format: Derrick Wood, James White, Andrew Georgiadis, Beth Van Emburgh, Sonya Parpart-Li, Jason Mitchell, Valsamo Anagnostou, Noushin Niknafs, Rachel Karchin, Eniko Papp, Christine McCord, Peter Loverso, David Riley, Luis A. Diaz, Sian Jones, Mark Sausen, Victor E. Velculescu, Samuel Angiuoli. A machine learning approach for somatic mutation 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 3271.
Abstract 3272: A graph remapping framework for in silico adjudication of SNVs, INDELs, and structural variants from genetic sequencing data
Authors: Lee Dillon , Qiao Yi , Marth Gabor 
Published: August 2018

Abstract
Several state-of-the-art, easy to use tools are available both for short-variant detection (e.g. GATK, FREEBAYES), and structural variant (SV) detection (e.g. LUMPY, MANTRA, DELLY), but these tools often produce divergent variant calls, especially INDELs, and it is very difficult to reconcile such variants into a single, accurate set. Furthermore, while it would be highly desirable to also detect larger, structural variants (SV), existing SV detector packages are typically difficult to integrate, highly resource-intensive to run, and result in call sets that require expert manual review to reduce false positive detection rate.
Our algorithm, GRAPHITE (https://github.com/dillonl/graphite) requires as input a collection of variant calls, made by one or more short-variant or SV detection tools. Typically, this starting set is high sensitivity (i.e. inclusive), but low specificity (i.e. have a high false discovery rate). We then apply a novel “variant adjudication” procedure to discard false positives, while keeping true positive calls. This is accomplished by constructing a graph from these variants (the Variant Graph) representing allelic variants as graph branches, in addition to the branches formed by the current, linear genome reference sequence. Using a graph mapping algorithm (GSSW, a graph extension of the Smith-Waterman alignment algorithm) we developed earlier, we re-map all reads from each of the samples contributing to the candidate calls. We retain candidate variants confirmed by mappings to those branches in the graph that represent the corresponding variant allele, and discard those candidates that were not confirmed by such mappings. This procedure results in a highly specific callset that also maintains the high sensitivity of the inclusive starting callset constructed by multiple primary variant calling methods. Because the graph construction and mapping approach works for most types of SVs in addition to all short variants, variants of all different types can be integrated in a single step.
Here we present the application of this method for cross-validating structural variants calls from Pacific Biosciences data by remapping deep Illumina WGS read sets to Variant Graphs constructed using the candidate Pacific Biosciences variants, as part of the Human Genome Structural Variation Consortium (HGSVC) data analysis project. We also present GRAPHITE's application to improving the accuracy of allele frequency measurement in tumor sequencing data, which is essential for the accurate reconstruction of subclonal evolution in longitudinal tumor samples.
Citation Format: Dillon Lee, Yi Qiao, Gabor Marth. A graph remapping framework for in silico adjudication of SNVs, INDELs, and structural variants from genetic sequencing 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 3272.
Abstract 3273: rkmh: A MinHash toolbox for analyzing HPV coinfections
Authors: Dawson Eric T. , Wagner Sarah , Roberson David , Yeager Meredith , Boland Joseph , Garrison Erik , Schiffman Mark , Raine-Bennet Tina , Lorey Thomas , Castle Phillip , Chanock Stephen , Mirabello Lisa , Durbin Richard 
Published: August 2018

Abstract
Human papillomaviruses are common infections transmitted by direct physical contact. Two sexually transmitted types, HPV16 and HPV18, are responsible for almost 75% of cervical cancer cases worldwide; another 10 types of &gt;200 known types are considered carcinogenic. Among the four HPV16 lineages (A, B, C, and D), 100-fold differences in odds ratios for adenocarcinoma are observed. It is therefore informative to distinguish between infection with HPV16 lineages, even though they differ by as little as 2% of their genome. We present a computational toolkit, rkmh, for characterizing viral coinfections. rkmh uses kmer matching strategies to rapidly determine the most similar type or lineage reference genome for a given read. The proportion of reads matching each reference genome can then be calculated and the ratios of the infecting viruses can be estimated. To assess the performance of rkmh we first simulated 100bp paired end Illumina read sets from the PAVE database of HPV reference genomes, then also evaluated performance on a real HPV16 sample sequenced on the Ion Torrent Proton platform (typical read length 250bp), and a set of 3,660 Oxford Nanopore minION reads generated from two HPV16 reference strains (typical read length over 6500bp). We demonstrate that rkmh can adequately classify HPV infections at the type and lineage level. We discuss further applications of the tool in metagenomics. rkmh is freely available at https://github.com/edawson/rkmh.
Citation Format: Eric T. Dawson, Sarah Wagner, David Roberson, Meredith Yeager, Joseph Boland, Erik Garrison, Mark Schiffman, Tina Raine-Bennet, Thomas Lorey, Phillip Castle, Stephen Chanock, Lisa Mirabello, Richard Durbin. rkmh: A MinHash toolbox for analyzing HPV coinfections [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 3273.
Abstract 329: Highly potent and selective ATM kinase inhibitor M3541: A clinical candidate drug with strong antitumor activity in combination with radiotherapy
Authors: Fuchss Thomas , Mederski Werner W. , Zenke Frank T. , Dahmen Heike , Zimmermann Astrid , Blaukat Andree 
Published: August 2018

Abstract
M3541 is an ATP-competitive inhibitor of the Ataxia telangiectasia mutated (ATM) kinase (IC50 &lt; 1 nM), which targets tumor cell survival and growth by inhibiting double-strand break (DSB) repair as well as checkpoint control. DSB repair is crucial for survival of malignant tumor cells, especially under treatment with DNA damaging chemo- and radiotherapy. As such, the rationale of pharmacological inhibition of ATM is to increase and maintain the extent of unrepaired DNA damage generated by radio-, chemotherapy, and targeted therapies to drive tumor cells into cell death. We have developed an orally administered, sub-nanomolar potent & selective kinase inhibitor of ATM, M3541, for cancer therapy in combination with DNA damaging modalities. Here, we present the optimization of initial hit matter & its structure-activity relationships leading to our clinical candidate M3541, its chemical structure (first-time disclosure) & preclinical characterization using biochemical, cellular & human tumor xenograft models. M3541 sensitizes tumor cell lines to radiation therapy in vitro and strongly enhances the anti-tumor activity of ionizing radiation in vivo. These effects are due to the inhibition of ATM kinase activity as demonstrated by the levels of the phosphorylation of its primary downstream target CHK2 in human tumor cell lines. M3541 is currently being investigated in a multicenter Ph I clinical trial.
Citation Format: Thomas Fuchss, Werner W. Mederski, Frank T. Zenke, Heike Dahmen, Astrid Zimmermann, Andree Blaukat. Highly potent and selective ATM kinase inhibitor M3541: A clinical candidate drug with strong antitumor activity in combination with radiotherapy [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 329.
Abstract 3304: The mutation landscape of cancers serves as a record of early malignant transformation
Authors: Polak Paz , Karlic Rosa , Kubler Kirsten , Foulkes William D. , Getz Gad 
Published: August 2018

Abstract
How the cell lineage influences a tissue's susceptibility to malignant transformation is a fundamental question in cancer biology, which has been barely addressed in cancer genomics thus far. Cell properties are encoded in the cell type-specific chromatin structure and we previously demonstrated that the cell-of-origin (COO) chromatin organization is a key determinant of the landscape of somatic mutations, which accumulated over lifetime serving as a memory of the historical cell lineage (Polak et al, Nature , 2015). We now show that this principle is generalizable to common tumor types and offers insights into the molecular events of cancer initiation. We extended our work to 2,641 genomes from 30 cancer types and epigenetic modifications from 98 normal tissues. In 25 cancer types, the tumor originated from a cell type that was its direct cellular counterpart or a close proxy; in only two, there was no match or a close proxy; and in the remaining three (esophageal, pancreatic ductal and biliary adenocarcinoma) the best matched cell type suggested metaplasia to stomack mucosa like tissue.The cellular context of breast tumor formation was investigated in more detail, showing that the COO, and not the gene inactivation event, determines the subtype. Basal-like tumors appeared to arise from luminal progenitor cells, while all other subtypes arose from mature luminal cells. Furthermore, irrespective of the inactivation mechanism (pathogenic germline, somatic truncating or epigenetic silencing event), all BRCA1/2- and RAD51C-altered basal-like tumors best matched to luminal progenitors while BRCA1/2- and CHEK2 -mutated luminal A/B subtypes best matched mature luminal cells. Finally, we observed that tumor type-specific driver genes reside in genomic regions that are defined by a highly active chromatin environment in their COOs. This highlights their essential role in cell type differentiation and implies the acquisition of somatic mutations early, when the chromatin architecture still reflected the COO. Taken together, our findings shed light on the crucial role of the COO in shaping the mutational landscape and tumor evolution.
Citation Format: Paz Polak, Rosa Karlic, Kirsten Kubler, William D. Foulkes, Gad Getz. The mutation landscape of cancers serves as a record of early malignant transformation [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 3304.
Abstract 3308: Urine epigenetic biomarkers for NSCLC diagnosis
Authors: Lerner Lane , Zheng Lily , Kottorou Anastasia , Chen Chen , Ito Tomoaki , Rodgers Kristen , Lee Beverly , Winn Robert , Benedetti Enrico , Wang Tza- Huei , Brock Malcolm V. , Herman James G. , Hulbert Alicia 
Published: August 2018

Abstract
Purpose:
The National Lung Cancer Screening Trial showed mortality reduction on patients with NSCLC by the use of Low-dose CT screening. The study exhibits an unreasonably high false positive rate of 96.6%, which can to lead to significant morbidity and mortality from unnecessary tests. This study aim to determine if methylated promoter regions of a panel of genes that are correlated with Non-Small Cell Lung Cancer can be detected in urine from patients with lung cancer versus those without.
Materials and Methods:
We conducted a prospective case-control study recruiting subjects from the Lung Cancer Spore Trial. Urine and pre-operative CT scans were obtained from all patients. We processed the urine using the Methylation on Beads assay to isolate and bisulfite treat circulating DNA and then employed Quantitative Methylation Specific Real-Time PCR to detect promoter methylation status of the genes: CDO1, TAC1, HOXA7, HOXA9, SOX17 and ZFP42. Sensitivity, specificity, PPV and NPV values were calculated for each gene methylation status.
Results:
34 patients were studied, including 23 patients with NSCLC and 11 patients with benign non-cancerous lesions. The sensitivity, specificity, PPV and NPV values for lung cancer detection in urine are shown in Table 1. Sensitivities ranged 43-96%, Specificities 64-91%, PPV 71-100% and NPV 40-67%. Promoter methylation of the gene panel CDO1, TAC1, HOXA7, HOXA9, SOX17 and ZPF42 has a 78% sensitivity, 91% specificity, 95% PPV and 67% NPV for lung cancer detection.
Conclusion:
Our study suggests that urine can provide a highly sensitive and specific non-invasive route for lung cancer detection. These results are very promising and could potentially reduce unnecessary morbidity and mortality in people undergoing screening for NSCLC by use of a non-invasive method that could be more accessible on primary care centers. However, further studies and validation with larger sample sizes and different populations are necessary before its application to clinical practice.
Patients with positive gene methylation from urine samples and its accuracy for NSCLC detection.Cancer (n=23)Cancer (n=23)Control (n=11)Control (n=11)nSensitivitynSpecificityPPVNPVCDO114611919353TAC11565010010058SOX1716704648050HOXA711483737940HOXA910431919143ZPF4222969187167Panel of genes18781919567
Citation Format: Lane Lerner, Lily Zheng, Anastasia Kottorou, Chen Chen, Tomoaki Ito, Kristen Rodgers, Beverly Lee, Robert Winn, Enrico Benedetti, Tza- Huei Wang, Malcolm V. Brock, James G. Herman, Alicia Hulbert. Urine epigenetic biomarkers for NSCLC diagnosis [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 3308.