bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 1618: Breast cancer screening in low and lower - middle income country : A program in Vietnam
Authors: Dang Voc Tai , Dao Tu Van , Tran Thuan Van , Tran Huong Thi 
Published: August 2019

Abstract
Background: From 1980 to 2010, new breast cancer cases increased by more than 50% worldwide. Disease burden increased even more rapidly in low- and middle-income countries (LMICs), where over half of breast cancer cases now occur. In Vietnam, approximately 15229 new cases of breast cancer were diagnosed with 6103 resulted deaths in 2018. Breast cancer can be detected early by screening; however, it is not covered by medical insurance. Mammogram is currently one of the most reliable screening tools for breast cancer. Regular mammograms can help detect breast cancer early, thus allowing for early treatment Purposes: To raise public awareness on breast cancer prevention and early detection, while promoting a habitual routine of breast self - examination and breast cancer screening participation; to call on companies to include breast cancer screening on the periodic health examination for female employees. Methods: We collected data on risk factors of breast cancer. Breast Imaging-Reporting and Data System (BI-RADS fifth edition) scores from the radiology reports were utilized. The information collected from women who completed the questionnaires included: age, BMI, family history of breast cancer, personal history of breast or ovarian cancer, number of pregnancies, number of babies born, number of abortion, number of miscarriage, stillbirth, hormonal medication use (hormone replacement therapy and daily oral contraceptive), age of menarche, menopause, smoking habit, and breast self-examination results. Results: There were 10,000 Vietnamese women who were over 40 years old (over 35 years old if their mother or their sister had breast cancer) participated in this program. Rate for regular breast self-examination was 19.1%. In the 40 to 55 years-old age group, the rate was 70%, 2.2% for women aged 35-39 years (with a family history of breast cancer). Eight women were diagnosed with breast cancer, the incidence rate was 80 per 100,000 population, respectively. Conclusions: The total fee for a breast cancer screening in Viet Nam is approximately $25 ($20 for a mammogram and $5 for consultation fee). This remains one of the best solutions to detect breast cancer early and reduce the overall burden of cancer treatment costs on the already strained Vietnamese health system.
Note: This abstract was not presented at the meeting.
Citation Format: Voc Tai Dang, Tu Van Dao, Thuan Van Tran, Huong Thi Tran. Breast cancer screening in low and lower - middle income country : A program in Vietnam [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 1618.
Abstract 1621: Multiplex analysis of proteins for cancer diagnosis: A case study using ActivSignal technology
Authors: Alexandrov Ilya , Mackenzie Malcolm , Brandina Irina 
Published: August 2019

Abstract
Pancreatic cancer is one of the deadliest types of cancer, killing over 50,000 each year in the US, and with a five-year survival rate below 8%. However, currently there are no reliable diagnostic tests for pancreatic cancer, and the great majority of cases are detected at a late stage, with bleak mortality rates as a result. The ActivSignal IPAD platform monitors activity of 26 cancer signaling pathways in multiplex, by examining phosphorylation status or expression of 70 protein targets. The technology utilizes pairs of antibodies per each protein target to ensure high specificity. These antibody pairs are carefully tested and selected. Detection will only take place if both antibodies in a pair bind to their specific target molecule during the analysis phase - avoiding false hits. The ActivSignal IPAD platform can be used to profile cell culture samples, tissues and different blood fractions. The IPAD assay requires a very small amount of sample. All these characteristics make it potentially adaptable for diagnostics, simultaneously profiling multiple proteins targets. We have tested the assay in a preliminary analysis of human pancreatic cancer patients. Several control and pancreatic ductal adenocarcinoma (PDAC) blood samples were used for analysis. The assay can be performed on a very small volume of material, and 100 microliters of each sample was obtained from a collaborating academic medical center. The profiling was performed on 40 previously identified, relevant proteins. The comparison of Normal controls and PDAC patient samples gave a clear pattern that discriminated these two population groups with a great degree of certainty. Thus, giving a proof of concept that the assay can be used for diagnostic development for pancreatic cancer detection, an urgent unmet need.
Citation Format: Ilya Alexandrov, Malcolm Mackenzie, Irina Brandina. Multiplex analysis of proteins for cancer diagnosis: A case study using ActivSignal technology [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 1621.
Abstract 1625: Challenges of screening applications for early detection of cancer using liquid biopsy
Authors: Kermani Bahram G. , Huecker Sarah , Kirsch Stefan , Polzer Bernhard 
Published: August 2019

Abstract
In the recent years, early detection of cancer using next-generation sequencing (NGS) has been en vogue, due to its potential to drastically improve the survival rate of cancer patients. Liquid biopsy is often viewed as the modus operandi for early detection, as taking biopsies from the actual organs is not practical for widespread screening tests. Among others, two main approaches attempted for early detection are based on cell-free tumor DNA (ctDNA) and circulating tumor cells (CTCs). The rarity of ctDNA and CTC is often debated as a major potential challenge for detection of cancer at early stages. In this work, we will discuss several other challenges that are necessary to address for developing a practical and high-quality screening application for early-stage detection of cancer. We will also provide a framework in which much of these challenges can be addressed. While a reasonable sensitivity is needed for such test to be of value, the most prominent matter regarding screening for cancer is specificity. Due to low prevalence of cancer, what may appear as a high specificity (e.g., 99%) could actually be insufficient, as it translates to a low positive predictive value (PPV) (e.g., in the 10% range). A low PPV would result in a large number of false positives in the test. These false positives would cause follow-up tests that are not only costly, but also could trigger unnecessary anxiety for the patients. A comprehensive validation process is another critical element in early detection. While it is tempting to perform validation based, solely, on groups of Healthy and Cancer patients, it can be shown that some signals caused by Benign conditions could highly resemble those of Cancer patients, and therefore result in false positives. We will show that distinguishing Benign from Cancer necessitates detection of more complex patterns, as opposed to simplistic measures such as genome-wide copy number variation (CNV). Yet another challenge in a liquid biopsy test is identification of the tissue-of-origin. Due to the mixed-signal problem inherent in ctDNA-based technologies, we argue that they face a higher challenge, as compared to CTC-based counterparts, for obtaining this goal. In conclusion, we will show how a system based on CTCs, whole genome sequencing (WGS), and artificial intelligence (AI) could provide an appropriate framework for addressing much of the challenges in early detection of cancer for a true screening application with the specificity in the 99.99% range.
Citation Format: Bahram G. Kermani, Sarah Huecker, Stefan Kirsch, Bernhard Polzer. Challenges of screening applications for early detection of cancer using liquid biopsy [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 1625.
Abstract 1643: Discovery pipeline of cancer transcriptomic biomarkers using machine learning
Authors: Kim Eileen 
Published: August 2019

Abstract
Introduction: Cancer affects millions of people and causes nearly 1 in 6 deaths worldwide. Precision oncology holds potential in guiding cancer treatment on a molecular basis and elucidating targetable cancer pathways. This study aims to create a discovery pipeline that uses cancer patients’ transcriptomes and machine learning algorithms to predict cancer patient survival.
Methods: Transcriptomic and phenotypic data from cancer patients were downloaded from The Cancer Genome Atlas (TCGA). Initial genes were selected using univariate cox proportional hazard models to predict overall survival across all cancer types. Co-expressed selected genes were clustered, creating gene sets per cancer subtype. Then, an amalgam of LASSO and elastic-net regularized generalized linear models (GLMNET), gene bootstrapping, random forest without/with shadow features (Boruta), and recursive feature elimination were used. Monte Carlo iterations were performed for GLMNET to assess model stability and over-/underfitting.
Results: The highest hazard ratios (HRs) and lowest p-values resulted from the univariate cox proportional hazards model (e.g. for the RASGEF1A gene in a uterine serous carcinoma model, HR=2.8e8, p=0.005) and subsequent GLMNET models (e.g. 392 genes for uterine endometrioid carcinoma, HR=2.89e8, p=0.00873). Boruta before GLMNET yielded lower HRs and higher p-values (e.g. 11 genes for uterine serous carcinoma, HR=2, p=0.0517). Bootstrapping genes before GLMNET generally yielded much lower HRs and higher p-values.
Conclusions: We were able to model survival risk in cancer patients with a variety of methods. The best method so far is univariate gene selection and GLMNET, although random forest is promising. Next steps include exploring random forest models further, investigating the biology of prognostic models, using disease-specific survival, analyzing possible confounders, using Breslow-Wilcoxon or accelerated failure time models, replacing histological with molecular-signature-based cancer subtypes, and exploring alternative methods for prognosis prediction. Supported by the Medical Scholars Program, Medical College of Georgia, Augusta, GA.
Citation Format: Eileen Kim. Discovery pipeline of cancer transcriptomic biomarkers using machine learning [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 1643.
Abstract 1644: Pancreatic cancer survival prediction using machine learning and comparing its performance with TNM staging system and prognostic nomograms
Authors: Osman Mohamed H. 
Published: August 2019

Abstract
Background:
Pancreatic cancer is one of the deadliest cancers with a 5-year survival rate of only 6%. Patients with cancer often want to know how long they have left to live and it’s the first question patients ask after diagnosis. Reliable predictions are very important for achieving more personalized care and better management. However, the accuracy of physicians' clinical predictions of survival is poor. Predicting pancreatic cancer survival is challenging due to different tumor characteristics, treatments and patient populations. In this study, we evaluate the performance of Machine Learning (ML) in survival prediction compared to TNM system and published nomograms.
Methods:
Pancreatic cancer patients were identified through the Surveillance, Epidemiology and End Results database (SEER). Clinical data of the patients were extracted including: age, sex, race, marital status, tumor site, tumor histology, grade, tumor size, extension, Lymph Node (L.N) involvement, metastasis, cancer sequence, TNM stage, surgery, radiotherapy, chemotherapy, examined (L.N), positive (L.N) and survival months. Patients’ records were randomly divided into a training set (80%) and a validation set (20%). ML algorithms were tested to predict survival at 6, 12 and 24 months as well as TNM staging system and two nomograms.
Results:
A total number of 22,700 patients were identified through SEER from 2004 – 2013 with median survival of 12 months. The most common primary sites were head of pancreas (62.8%), tail (13.2%), and body (10.7%). Random Forests algorithm achieved better results compared to other models. The trained model yielded Area under the Receiver Operating Characteristic Curve (AUCs) of 86.6% at 6 months, 83.4% at 12 months and 82.2% at 24 months. Compared to TNM staging system which achieved AUCs of 66.6% at 6 months, 65.5% at 12 months and 57% at 24 months. The nomograms achieved AUCs of 71.1% and 55.2% in predicting 1-year-survival.
Sensitivity of the trained model were 80%, 75% and 79% at 6, 12 and 24 months, respectively. The most important characteristics which influenced the model prediction performance were: age at diagnosis, surgery and tumor size.
Conclusion:
Machine learning achieved a much better performance compared to TNM staging system and prognostic nomograms. Improved survival prediction can help in in making better treatment decisions and planning social and care needs.
Citation Format: Mohamed H. Osman. Pancreatic cancer survival prediction using machine learning and comparing its performance with TNM staging system and prognostic nomograms [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 1644.
Abstract 1653: Application of high-density tiling CRISPR screen technology to identify chromatin remodeler complex targets for drug discovery
Authors: Soares Luis , Bocker Michael , Zhou Qianhe , Kadoch Cigall , Chan Ho Man 
Published: August 2019

Abstract
The mammalian SWI/SNF (mSWI/SNF or BAF) complexes are widely mutated in different human pathologies, presenting new therapeutic opportunities in a range of genetically well-defined cancers, including lung, endometrial, malignant rhabdoid tumor, synovial sarcoma, and others. In each of these settings, disruption of different SWI/SNF subunits have been shown to contribute to the tumorigenic potential via distinct mechanisms. This observation suggests the potential for a range of therapeutic targeting strategies to effectively inhibit cancer cell proliferation. Using high-density tiling sgRNA CRISPR/Cas9 screens, we have developed large-scale efforts to systematically identify critical protein functional domains within chromatin remodeling complex components to inform drug discovery strategies across several different cancer indications. Using this approach, we have successfully identified and validated critical functional domains in mSWI/SNF complex components with promising drug target potential in indications such as BRG1-mutated non-small cell lung adenocarcinoma and synovial sarcoma.
Citation Format: Luis Soares, Michael Bocker, Qianhe Zhou, Cigall Kadoch, Ho Man Chan. Application of high-density tiling CRISPR screen technology to identify chromatin remodeler complex targets for drug discovery [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 1653.
Abstract 1679: Neoepitope screening in colorectal cancer patients by custom exome panel profiling somatic mutations
Authors: Venkatramanan Raghavee , Deutsch Kerry , Golez Inah , Shroff Pallavi , Seale Spencer , Ralph Brendan , Dow Sally. , Anderson Evan , Tahir Saman , Malge Anjali , Braastad Corey , Anderson Steven 
Published: August 2019

Abstract
Colorectal cancer (CRC) is one of the major causes of global mortality. Recent clinical advances in T cell therapy using antibodies that block inhibitory T cell signals have been transforming cancer therapy. Studies show that somatic mutations in protein coding regions produce tumor-specific antigens, or neoantigens. CRC warrants deeper investigation into the non-synonymous exonic mutations that seem to be drivers in tumor development. Tumor mutation burden (TMB) and the abundance of neoantigens are highly correlated and it has been shown that TMB can be estimated using targeted sequencing panels. The aim of this study is to investigate 10 matched tumor/normal CRC samples using a panel targeting 422 genes covering about 1.7 Mb to screen for potential neoepitope candidates. Although sequencing and bioinformatics pipelines are developed to effectively filter neoantigens, it is still unclear which antigens elicit a T cell response. A review of the tools used in neoepitope prediction shows that it is not only a very complex task, but demands high sequencing depth. Comparing currently available tools will enable us to understand the strengths and shortcomings of each algorithm, and develop a custom pipeline framework.
Note: This abstract was not presented at the meeting.
Citation Format: Raghavee Venkatramanan, Kerry Deutsch, Inah Golez, Pallavi Shroff, Spencer Seale, Brendan Ralph, Sally. Dow, Evan Anderson, Saman Tahir, Anjali Malge, Corey Braastad, Steven Anderson. Neoepitope screening in colorectal cancer patients by custom exome panel profiling somatic mutations [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 1679.
Abstract 1687: Identification of bladder cancer subtypes with different sensitivities to immunotherapy
Authors: Chu In-Sun , Song Bic-Na , Leem Sun-Hee 
Published: August 2019

Abstract
Bladder urothelial carcinoma (BLCA) is pathologically categorized as non-muscle-invasive (NMIBC) and muscle-invasive (MIBC) bladder cancers. But its clinical heterogeneity has been suggested that biologically relevant subtypes may exist within and between NMIBC and MIBC. Here we identified that BLCA could be subgrouped into four major classes with distinct molecular characteristics regardless of previously known clinicopathological factors, using a series of unsupervised learning approaches. The predictor to discriminate distinct molecular subgroups of BLCA was constructed in a training cohort and validated in six independent cohorts with a total of 1,992 samples. The predictor showed a prognostic significance of the four subtype of BLCA, especially aggressive clinical behavior of the class 3 subtype. Interestingly, class 3 subtype has distinct biological features associated with potential responsiveness to immunotherapy, such as high somatic mutation rates and alterations of genes involved in DNA damage response and repair (DDR). Analyses of anti-PD-L1-treated BLCA patient tumor samples suggested that class 3 subtype was correlated with response to immune checkpoint inhibitors. Our findings have the potential to lead to the improvement of the current molecular classification system of BLCA and the achievement of better clinical outcomes for high-risk patients with BLCA treated with immunotherapy.
Note: This abstract was not presented at the meeting.
Citation Format: In-Sun Chu, Bic-Na Song, Sun-Hee Leem. Identification of bladder cancer subtypes with different sensitivities to immunotherapy [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 1687.
Abstract 1688: Integrative sequencing discovers a novel PGAP3-SRPK1 gene fusion in sinonasal undifferentiated carcinoma
Authors: McKean Erin L. , Birkeland Andrew C. , Neal Molly E. Heft , Kulkarni Aditi , Foltin Sue K. , Jewell Brittany M. , McHugh Jonathon B. , Marentette Lawence J. , Brenner John Chad 
Published: August 2019

Abstract
Sinonasal Undifferentiated Carcinoma (SNUC), a rare and aggressive tumor arising from the sinonasal cavity, remains a difficult-to-treat disease with poor overall survival. There are no agreed upon treatment algorithms and research efforts have focused on identifying new genetic mutations for novel targeted therapies. To date, targeted sequencing studies have identified IDH2 and SMARCAB1 as potential SNUC driver alterations, however the molecular alterations found in SMARCAB1 wild type tumors are unknown. Here, using whole exome sequencing, we characterize a series of 7 SNUC tumors and a SNUC cell line and discover recurrent aberrations to the SWI/SNF, MAGE and FAT gene families. We also validate a novel ALK mutation, recurrent ERBB2 copy number amplifications and a previously undescribed PGAP3-SRPK1 gene fusion in the SNUC cell line. Further investigation of the PGAP-SRPK1 fusion gene reveals a change in the active site with loss of three of nine active site residues. Functional studies are ongoing to determine the importance of the PGAP3-SRPK1 gene fusion for oncogenic phenotypes. This discovery extends the need for comprehensive characterization of gene family mutation status in SNUC and supports a need to understand the downstream molecular mechanisms in order to improve therapeutic strategies for this disease.
Citation Format: Erin L. McKean, Andrew C. Birkeland, Molly E. Heft Neal, Aditi Kulkarni, Sue K. Foltin, Brittany M. Jewell, Jonathon B. McHugh, Lawence J. Marentette, John Chad Brenner. Integrative sequencing discovers a novel PGAP3-SRPK1 gene fusion in sinonasal undifferentiated carcinoma [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 1688.