bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 759: RNA Complete BCT: A novel blood collection tube targeting circulating RNA and extracellular vesicles
Authors: George Nicholas M. , Bartron Lisa , Binbuga Nursen , Salamifar Sean , Seong Eunju 
Published: September 2020

Abstract
Precision medicine, specifically in medical oncology, has been significantly enabled and the pace accelerated with the advent of liquid biopsy-based diagnostics. While liquid biopsy assays targeting circulating cell-free DNA (cfDNA) provide useful diagnostic information, such as mutational burden, they are limited to a static assessment of the disease state due to cfDNA derivation from dying cells. Assays exploiting circulating extracellular vesicles (EVs) and EV-associated cargo, such as cell-free RNA (cfRNA), provide a comprehensive and dynamic view of the disease as these analytes derive from both living and dying cells. A significant hurdle in making liquid biopsy testing with EVs and cfRNA commonplace is donor specimen integrity. Collected patient blood samples undergo time-dependent degradation leading to increases in disease non-specific EVs and cfRNA thereby compromising the accuracy and reproducibility of analyte measurement. To overcome this vulnerability we have developed the Streck RNA Complete BCT, a first of its kind blood collection tube intended for stabilizing draw-time concentrations of extracellular vesicles and cell-free RNA. Intended uses include direct analysis of EV populations and indirect analysis of cfRNA through RT-PCR or Next-generation Sequencing-based methods. Incorporation of this blood collection tube into the pre-analytical workflow will allow for delayed sample processing without effect on sample integrity or downstream analyte analysis.
Citation Format: Nicholas M. George, Lisa Bartron, Nursen Binbuga, Sean Salamifar, Eunju Seong. RNA Complete BCT: A novel blood collection tube targeting circulating RNA and extracellular vesicles [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 759.
Abstract 789: Development and validation of a capture based NGS assay that enables the detection of Epstein Barr virus from gastric cancer patients' tumor tissues
Authors: Hu Yajie , Yang Bo , Wang Lei , Liu Shiyi , Liang Lei , Wang Jie , Li Fugen , Sun Yu 
Published: September 2020

Abstract
Background: Gastric cancer patients with positive test for Epstein Barr virus (EBV) in their tumors have been reported to benefit significantly from anti-PD-1 therapy. Current clinical testing relies on fluorescent in situ hybridization of EBV-encoded small RNA (EBER). NGS assays have been increasingly used in the clinical settings to make therapeutic recommendations to cancer patients due to its versatility in detecting various types of genomic alterations. Detecting EBV by NGS is highly desired.
Method: DNA was extracted from the FFPE samples of 214 gastric cancer patients' tumors, and profiled by the comprehensive NGS assay. A classification algorithm was developed using 131 gastric cancer samples (EBV Pos, n = 31; EBV Neg, n = 100) with known EBV status derived from EBER. The algorithm and the predetermined cutoff was then applied to an independent sample set (EBV Pos, n = 30; EBV Neg, n = 53) to evaluate the test performance.
Results: In the training set, we achieved a sensitivity of 93.5% at a specificity of 100%. In the validation set, we achieved a sensitivity of 93.3% at a specificity of 100%. Using the same algorithm and cutoff, in the patients with tumor content above 20%, we achieve a sensitivity of 97.9% at a specificity of 100%.
Conclusion: We developed a method to detect EBV in gastric tumors' FFPE samples through a capture-based NGS assay. We also demonstrated strong concordance with an orthogonal assay (EBER) in detecting EBV.
Citation Format: Yajie Hu, Bo Yang, Lei Wang, Shiyi Liu, Lei Liang, Jie Wang, Fugen Li, Yu Sun. Development and validation of a capture based NGS assay that enables the detection of Epstein Barr virus from gastric cancer patients' tumor tissues [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 789.
Abstract 824: Uncommon EGFR mutations are associated with higher tumor mutation burden (TMB) in non-small cell lung cancer patients
Authors: Shen Xudong , Zhang Hushan , Huang Depei 
Published: September 2020

Abstract
Background: Epidermal growth factor receptor (EGFR) mutation is regarded as a predictor for poor clinical response to immunotherapy in non-small cell lung cancer (NSCLC), for which common EGFR mutations (the L858R point mutation and exon 19 deletions) generally exist lower tumor mutation burden (TMB) level. On the other side, uncommon EGFR mutations exist variable efficacy to tyrosine kinase inhibitors (TKIs) and the treatment for these patients is still controversial. Therefore, we conducted this study to investigate the correlation between uncommon EGFR mutations and TMB.
Methods: The tissue samples from a Chinese cohort of 2177 lung adenocarcinoma (LAC) and 500 lung squamous cell carcinoma (LSCC) patients were detected by a 381 genes NGS panel from 3D Medicines, and TMB values were obtained from the NGS sequencing data. We evaluated the correlation between common and uncommon mutations in EGFR and TMB in lung adenocarcinoma and squamous cell carcinoma, respectively.
Results: In lung adenocarcinoma, patients with uncommon EGFR mutations (n = 208) had a higher TMB level compared with common EGFR mutations (n = 1027) (medium TMB 6.14 Muts/Mb vs 4.84 Muts/Mb, P = 0.0016), but a lower TMB level than patients without EGFR mutations (n = 942) (medium TMB 6.14 Muts/Mb vs 7.26 Muts/Mb, P = 0.0176). In lung squamous cell carcinoma, the TMB level of uncommon EGFR mutations (n = 21) was significantly higher than common EGFR mutations (n = 20) (medium TMB 12.90 Muts/Mb vs 5.40 Muts/Mb, P &lt; 0.0001). Meanwhile, the medium value of TMB in uncommon mutations group was slightly higher than non-mutations group (n = 459), however, there was no significant difference between the two groups (medium TMB 12.90 Muts/Mb vs 11.29 Muts/Mb, P = 0.2186).
Conclusions: Uncommon mutations in EGFR are associated with higher TMB level than common mutations in both lung adenocarcinoma and squamous cell carcinoma.
Citation Format: Xudong Shen, Hushan Zhang, Depei Huang. Uncommon EGFR mutations are associated with higher tumor mutation burden (TMB) in non-small cell lung cancer patients [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 824.
Abstract 832: Construction and optimization of gene expression signatures for prediction of survival in two-arm clinical trials
Authors: Theilhaber Joachim , Chiron Marielle , Dreymann Jennifer , Bergstrom Donald , Pollard Jack 
Published: September 2020

Abstract
Gene expression signatures for the prediction of differential survival of patients undergoing anti-cancer therapies are of great interest because they can be used to prospectively stratify patients entering new clinical trials, or to determine optimal treatment for patients in more routine clinical settings. Unlike prognostic signatures however, predictive signatures require training set data from clinical studies with at least two treatment arms, and the methodology for constructing and optimizing predictive signatures has been less prominently explored. Focusing on the “use case” of a two-arm clinical trial for metastatic colorectal cancer (CRC) patients treated with the anti-angiogenic molecule aflibercept, we present tools for the construction of signatures predictive of progression-free survival based on cross-validated multivariate Cox models. The work is organized around a specific approach called subtype correlation, which leverages a priori knowledge of molecular subtypes of tumors for a given indication. The tools and concepts presented here include the so-called differential log-hazard ratio, the survival scatter plot, the hazard ratio receiver operating characteristic, the area between curves and the patient selection matrix. In the CRC use case, the resulting signature stratifies the patient population into “sensitive” and “relatively-resistant” groups achieving a more than two-fold difference in the aflibercept-to-control hazard ratios across signature-defined patient groups. Through cross-validation and resampling the probability of generalization of the signature to similar CRC data sets is predicted to be high. The tools presented here should be of general use for building and using predictive multivariate signatures in oncology and in other therapeutic areas.
Citation Format: Joachim Theilhaber, Marielle Chiron, Jennifer Dreymann, Donald Bergstrom, Jack Pollard. Construction and optimization of gene expression signatures for prediction of survival in two-arm clinical trials [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 832.
Abstract 840: Pathway enrichment analysis of gene expression data from formalin-fixed paraffin embedded (FFPE) samples using the GeoMx™ DSP Platform
Authors: Hood Tressa R. , Reeves Jason , Norgaard Zach , Hoang Margaret , Warren Sarah , Piazza Erin , Boykin Rich , Beechem Joseph 
Published: September 2020

Abstract
The GeoMx Digital Spatial Profiling (DSP) Platform enables robust detection of high-plex protein and RNA expression from user-defined sections within FFPE samples. As the number of targets detected within such tissues increases, it becomes important to apply systems biology strategies in order to better interpret the complex biology of the tumor microenvironment.
In this study, we investigated the expression profiles of more than 1600 genes by utilizing more than 10,000 DSP-specific in situ hybridized (ISH) probes on FFPE samples. The bioinformatics tools we have development are enabling us to move beyond single-gene profiling to a better understanding of pathway-based expression. Our study includes colorectal cancer patient samples from which we have matching bulk RNA sequencing and NanoString analysis to compare. The panel of genes profiled have a strong focus around capturing biological signaling along canonical signaling pathways and cell-intrinsic signaling from immune cells and other cell types. We demonstrate the ability to leverage foundational pathway interrogation tools, including Reactome, with the data to capture spatially-resolved pathway interactions and signaling within FFPE tissues.
As we look towards the future of the GeoMx platform and high-plex RNA profiling of tissue samples, these experiments highlight not only the need but the capacity for this platform to derive deep understanding of the biology within and across a single slide of tissue. These experiments are being used to drive development of the software features within the GeoMx ecosystem, which will provide further support for pathway-level exploration of expression when working with highly multiplexed reagents in future platform offerings.
GeoMx™ DSP technology is for Research Use Only and not for use in diagnostic procedures.
Citation Format: Tressa R. Hood, Jason Reeves, Zach Norgaard, Margaret Hoang, Sarah Warren, Erin Piazza, Rich Boykin, Joseph Beechem. Pathway enrichment analysis of gene expression data from formalin-fixed paraffin embedded (FFPE) samples using the GeoMx™ DSP Platform [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 840.
Abstract 844: RNA binding domains of nucleolin exhibit specificity in driving nucleolin-miRNA interactions: An in silico modeling and RNA-protein docking study
Authors: San Avdar , Saxena Anjana , Singh Shaneen 
Published: September 2020

Abstract
Nucleolin (NCL) is a stress responsive multifunctional RNA binding protein (RBP) that plays important roles in gene transcription and RNA metabolism. NCL levels and similarly many of NCL RNA targets are dysregulated in multiple human cancers. NCL- RNA interactions are driven by its four RNA binding domains (RBDs). Despite the myriad NCL functions that involve its RNA-binding properties, the mechanisms driving these interactions are poorly understood. Partial three-dimensional structural information available on NCL RBDs in the RCSB PDB database, is not sufficient to provide a comprehensive understanding of NCL- RNA interactions. To get a better insight into RNA binding specificity for its diverse targets, we have built upon the existing NCL-RBDs structures to generate multiple combinations of RBDs in tandem, using in silico modeling approaches. In this study, we analyze NCL-miRNA interactions in depth with a focus on a subset of miRNAs that are implicated in breast cancer. Using both template-based and ab initio approaches, we have generated complete and robust structural models of all 4 NCL RBDs in tandem and in combinations of two different RBDs in sequence. All models were then tested using a variety of structural quality evaluation programs. Models with top validation scores were used in RNA-protein docking algorithms and assessed for interaction with specific miRNA. Our docking analyses have generated a comprehensive map of the miRNA-NCL protein interface sites for each individual miRNA and consistently predict specific RBDs in NCL-miRNA binding. We have also identified critical residues on the NCL-RBDs that may drive the miRNA binding in an RBD-type specific manner. Our results corroborate previous studies on RBDs from other RBPs. Structural information derived from this study provides a valuable perspective for future experiments. Identification of consensus on RNA binding motifs on NCL RBDs is critical in elucidating RNA target specificity by NCL. Computational analyses provide time and cost-effective benefits over experimental techniques; the in silico predictions are important in designing rational experiments for the future investigations confirming the NCL-miRNA interactions. Our study provides the foundational steps for establishing consensus motifs on NCL-RBDs that potentially direct RNA target specificity. Targeting these specific binding sites will provide new approaches to regulate NCL functions in gene expression during tumorigenesis.
Citation Format: Avdar San, Anjana Saxena, Shaneen Singh. RNA binding domains of nucleolin exhibit specificity in driving nucleolin-miRNA interactions: An in silico modeling and RNA-protein docking study [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 844.
Abstract 846: Microsatellite instability determined by clinical targeted next generation sequencing
Authors: Yeh Ya-Chi , Chen Chien-Hung , Chen Kuan-Ying , Chen Ying-Ja , Chen Shu-Jen 
Published: September 2020

Abstract
Microsatellite instability (MSI)/mismatch repair (MMR) status is a positive predictor for immune checkpoint inhibitors. Conventional detection method for MSI status relies on polymerase chain reaction (PCR) of 5 microsatellite loci. With the advancement of next-generation sequencing (NGS) applied to clinical samples, we developed a MSI prediction algorithm to determine MSI status from large-panel clinical targeted NGS data accounting for more than 500 microsatellite loci. A set of colorectal cancer clinical samples with known MSI status by PCR were used to train a MSI prediction model, which predicts a sample as microsatellite instability high (MSI-H) or microsatellite stable (MSS). The model was constructed based on the change of number of repeats of different lengths from a pooled MSS baseline across microsatellites in a comprehensive cancer genomic panel targeting 440 genes. Using a quantitative measure based on more than 500 microsatellites loci, the model achieves high robustness and efficiency for MSI status detection from NGS data without matched normal. An independent dataset of clinical samples across various cancer types were used to validate the model showing an accuracy of 96.8%. In summary, MSI status can be determined by the MSI prediction model from clinical targeted NGS panel without normal paired sample.
Citation Format: Ya-Chi Yeh, Chien-Hung Chen, Kuan-Ying Chen, Ying-Ja Chen, Shu-Jen Chen. Microsatellite instability determined by clinical targeted next generation sequencing [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 846.
Abstract 858: PrismML: A machine learning platform to query genotype-phenotype patterns in large genomics studies
Authors: Reddy Anupama , Flemming Daisy , Selitsky Sara , Pavel Ana Brandusa , Alexe Gabriela , Bhanot Gyan 
Published: September 2020

Abstract
Background: Large-scale genomics studies (e.g. AACR Project GENIE, TCGA, TopMed) have sequenced thousands of patients in an attempt to understand disease associated genomic variables and their clinical correlates. Existing online platforms (e.g. cBioPortal) enable simple gene-based queries, but do not allow more complex modeling to understand disease pathogenesis, risk and outcome. There is an urgent need to build an interactive, modular and scalable platform that enables users to perform multivariate machine learning on existing genomic data.
Results: We have built a platform, PrismML, that enables a user to interactively query a dataset, and to run a multitude of machine learning tools, from simple statistical tests for differential analysis to multivariate modeling to predict clinical response, or mortality-risk. Since machine learning models are computationally intensive, we have used the power of cloud computing to make the analyses faster and scalable.
Key feature of our platform are: (1) availability of extensive statistical and machine learning methods; (2) implementation of best practices for machine learning, e.g. cross-validation; (3) graphical querying of results to understand the interplay among features. Users can choose to analyze existing data/studies, or upload their own data. Examples of possible queries: “identify genomic features that distinguish metastasis from primary tumors, either in a single cancer or pan-cancer”, or, “build a machine learning model to predict survival within ER- breast cancer patients”.
In addition, there is also an acute need to integrate the knowledge extracted from the multitude of data types. To this end, we have integrated multiple data types into gene-scores, and have incorporated known biological/functional information by integrating gene-scores into pathway-scores.
Summary: PrismML is an interactive and flexible platform to bring the power of machine learning and statistical modeling to the genomics community. This is an active area of development with multiple ongoing features, such as integrating multiple datasets to increase statistical power in rare diseases, and to enable subsetting large diseases to identify prognostic features.
Citation Format: Anupama Reddy, Daisy Flemming, Sara Selitsky, Ana Brandusa Pavel, Gabriela Alexe, Gyan Bhanot. PrismML: A machine learning platform to query genotype-phenotype patterns in large genomics studies [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 858.
Abstract 862: A method to create aggregate histogram representations for machine learning from small sample size medical sensor time series
Authors: Moss Nick , Oakley-Girvan Ingrid , Longmire Michelle R. 
Published: September 2020

Abstract
Background: Machine learning has been used to successfully develop biomarkers from data collected from a wide range of commodity and specialized sensor devices. Typical machine learning algorithms, such as deep learning, require several thousand to on the order of millions of samples of data to work most effectively. However, in many studies with limited numbers of patients and data samples, such methods cannot be used.
Objective: To solve this problem, we have developed a specialized algorithm that can convert time series signals into a set of histogram signatures that can then be used with unsupervised methods such as clustering to create biomarkers.
Methods: Our methods decompose the time series data into time windows and analyze aggregate mean and variance and other statistical qualities over these windows.
Results: We will describe our approach and show how we have applied it along with genetic algorithms to perform auto-tuning for novel biomarkers and endpoints.
Conclusions: We will be able to develop a highly accurate biomarker to detect gate imbalance and impairment from limited accelerometer data.
Citation Format: Nick Moss, Ingrid Oakley-Girvan, Michelle R. Longmire. A method to create aggregate histogram representations for machine learning from small sample size medical sensor time series [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 862.
Abstract 867: Prostate cancer: AI-markers for detecting, grading, and prognosticating 5-year survival post-biopsy
Authors: Laifenfeld Daphna , Reouven Cobi , Gross Inbal , Heled Ronen , Shalev Varda , Sandbank Judith , Linhart Chaim 
Published: September 2020

Abstract
Adenocarcinoma of the prostate is a diverse disease with multiple treatment options and inconsistent outcomes, and is the second most common cancer diagnosed in men, with approximately one in nine men diagnosed in their lifetime. The recent development and regulatory approval of whole slide image (WSI) scanning systems, together with the advent of robust computational approaches such as artificial-intelligence (AI) and machine learning, enable the development of new AI-based algorithmic markers (“AI markers”) that can be used to support the pathologist in their diagnostic process, as well as to better predict outcome and thus disease management in prostate cancer patients. Through a collaboration with Maccabi Healthcare Services, we have developed AI-based diagnostic tools that can correctly identify multiple features within prostate core needle biopsies (PCNBs), including adenocarcinoma, Gleason grade, inflammation, high grade prostatic intraepithelial neoplasia, and others. Combining these features with clinical and molecular data, we have developed multi-feature AI markers that prognosticate 5-year survival post-biopsy using thousands of slides from over 500 patients. In addition, we identify explanatory features associated with pre-biopsy PSA levels that together may help guide treatment decisions. Using AI on a combination of multiple data types, including lab tests, clinical outcome, and tissue biopsy, we demonstrate a powerful approach for the development of novel prognostic algorithms. These algorithms constitute low-cost accessible AI markers that are tissue-sparing and can be easily applied in clinical settings for optimal patient stratification for prospective clinical trials, as well as for different treatment options in prostate cancer. Further, this approach can be extended to additional cancer tissues, with the potential to revolutionize the field of precision medicine in oncology.
Citation Format: Daphna Laifenfeld, Cobi Reouven, Inbal Gross, Ronen Heled, Varda Shalev, Judith Sandbank, Chaim Linhart. Prostate cancer: AI-markers for detecting, grading, and prognosticating 5-year survival post-biopsy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 867.