bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 1641: Generation and evaluation of medical synthetic data
Authors: Goncalves Andre R. , Ray Priyadip , Soper Braden , Myneni Madhumita , Stevens Jennifer L. , Coyle Linda M. , Sales Ana Paula 
Published: August 2019

Abstract
While machine learning (ML) has shown some promise in medical research, its actual impact has been limited relative to other application domains. One reason for this disparity is the lack of high-quality, patient-level data available to the broader ML research community. Such datasets are often not made available due to protections around patient privacy. To overcome these obstacles, high-quality, synthetic datasets could be leveraged to accelerate methodological developments in the application of ML to biomedical research. Clinical data in the form of electronic health records present a rich data source to be used for synthetic data generation. Such data can be high dimensional and predominantly categorical, which poses multiple challenges from a modeling perspective. In this paper, we evaluate four classes of synthetic data generation techniques, as well as several metrics for evaluating the quality of the synthetic data. While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets from the publicly available Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast cancer cases diagnosed in the year of 2010, which includes over 26000 individual cases. Finally, we discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of synthetic medical data.
Citation Format: Andre R. Goncalves, Priyadip Ray, Braden Soper, Madhumita Myneni, Jennifer L. Stevens, Linda M. Coyle, Ana Paula Sales. Generation and evaluation of medical synthetic data [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 1641.
Abstract 1648: Deep learning tissue "fingerprints" to identify patients and predict clinical subtypes of breast cancer from digital pathology images
Authors: Rawat Rishi , Sha Fei , Shibata Darryl , Ruderman Daniel , Agus David 
Published: August 2019

Abstract
Inspired by facial recognition, we define an analogous problem in digital pathology called tissue matching. The objective is to match histology image patches to the patient. Unlike many classification problems in pathology, tissue matching comes with its own ground truth, and does not require subjective annotation. Using tissue-matching via deep neural networks, we developed strategies to overcome common sources of noise in histology data, including H&E and slide scanner variability. We demonstrate that the typical approach of increasing the training set size, in the current study up to 12,000 tissue cores, yields only moderate gains in test accuracy, while including a loss term promoting style-invariance cooperatively improves the impact of scaling training set size. Our best neural network learned features that matched patients from the test set of H&E image patches with 93% accuracy (n=104 patients, baseline accuracy &lt;1%). Leveraging this network’s identification ability, we applied its internal representation as a feature vector (tissue fingerprint) to apply to other problems in pathology. We found that a fingerprint-based classifier could predict estrogen receptor status from breast cancer whole-slides on two separate cohorts with high accuracy (AUCROC=0.89), a sizeable improvement over previous approaches. This work presents a novel, scalable, and transparent approach to train neural networks to identify distinctive features of pathology images. These features have demonstrated utility in classifying biomarkers from morphology and have the potential to assist in a number of difficult tasks in pathology, including tissue subtype classification and patient tumor similarity assessments based on morphometry.
Citation Format: Rishi Rawat, Fei Sha, Darryl Shibata, Daniel Ruderman, David Agus. Deep learning tissue "fingerprints" to identify patients and predict clinical subtypes of breast cancer from digital pathology images [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 1648.
Abstract 1652: Evaluating cell lines andorganoidsas models for metastatic cancer through integrative analysis of open genomic data
Authors: Liu Ke , Newbury Patrick , Glicksberg Benjamin , Zeng William , Andrechek Eran , Chen Bin 
Published: August 2019

Abstract
Metastasis is the most common cause of cancer-related death and, as such, there is an urgent need to discover new therapies to treat metastasized cancers. Cancer cell lines are widely-used models to study cancer biology and test drug candidates. However, it is still unknown to what extent they adequately resemble the disease in patients. The recent accumulation of large-scale genomic data in cell lines, mouse models, and patient tissue samples provides an unprecedented opportunity to evaluate the suitability of cell lines for metastatic cancer research. In this work, we used breast cancer as a case study. The comprehensive comparison of the genetic profiles of 57 breast cancer cell lines with those of metastatic breast cancer samples revealed substantial genetic differences. In addition, we identified cell lines that more closely resemble different subtypes of metastatic breast cancer. Surprisingly, a combined analysis of mutation, copy number variation and gene expression data suggested that MDA-MB-231, the most commonly used triple negative cell line for metastatic breast cancer research, had little genomic similarity with Basal-like metastatic breast cancer samples. We further compared cell lines with organoids, a new type of preclinical model which are becoming more popular in recent years. We found that organoids outperformed cell lines in resembling the transcriptome of metastatic breast cancer samples. However, additional differential expression analysis suggested that both types of models could not mimic the effects of tumor microenvironment and meanwhile had their own bias towards modeling specific biological processes. Our work provides a guide of cell line selection in metastasis-related study and sheds light on the potential of organoids in translational research.
Note: This abstract was not presented at the meeting.
Citation Format: Ke Liu, Patrick Newbury, Benjamin Glicksberg, William Zeng, Eran Andrechek, Bin Chen. Evaluating cell lines andorganoidsas models for metastatic cancer through integrative analysis of open genomic data [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 1652.
Abstract 1655: A structural approach for prioritizing variants in personalized Oncology
Authors: Wong Siao-Han , Benedikt Brors 
Published: August 2019

Abstract
In personalized oncology, treatment strategies take individualized genomic aberrations into consideration and relies heavily on how the cancer genomes are interpreted. With immense efforts put into cohort studies, knowledge of pathogenicity at the gene level had been accumulated. On the other hand, the increasing amount of personalized clinical trials and the accompanying need of interpreting pathogenecity at the sub-gene level emphasize the importance of resolving heterogeneous significance within genes.
There had been some initiatives where people identified mutation hotspots within genes, looking at either highly recurrent single residues or mutation clusters. The hotspot clusters can be inferred from protein sequences as well as from protein structures, where residues distant in sequence may be brought together through protein folding. Nonetheless, these information are not well utilized in routine clinical settings yet.
In this study, we aim to build a consensus list of prevalent mutational events for molecular stratification of patients. This is achieved by collecting sequence and structural mutation hotspots from different algorithms, and further combine structural information to identify their neighboring residues in protein structures. Specifically, around 1400 residue hotspots are collected from CancerHotspots and DominoEffect. An additional of around 250 structural hotspots are identified by three structural algorithms, namely 3DHotSpots, HotMAPS and HotSpot3D. These results are mostly derived from TCGA PanCancer cohorts, with CancerHotspots included more other studies in the analysis. With this consensus information in place, we further set up the mechanism to map from genomic variants to protein sequence and protein structures. This enables us to annotate the variants found in patient genome and see whether they co-localize with hotspot residues, in their structural neighborhood, or in structural informative domains like interaction interfaces.
In summary, a higher prevalence of variants might imply higher significance and more well-studied roles than other low prevalent mutant alleles. Transferring this knowledge from the public cohorts helps researchers quickly prioritize variants found in patient genomes. Annotating structural significance of variants further provides us with more confidence in identifying driving events. With these procedures automated, the goal is to help facilitate clinical evaluations to meet the growing number of patients recruited to personalized oncology trials.
Citation Format: Siao-Han Wong, Brors Benedikt. A structural approach for prioritizing variants in personalized Oncology [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 1655.
Abstract 1656: Systematic identification of cancer therapeutic liabilities through adaptive functional proteomics
Authors: Li Jun , Zhao Wei , Lu Yiling , Mills Gordon B. , Liang Han 
Published: August 2019

Abstract
Reverse-phase protein arrays (RPPAs) offer a powerful functional proteomic approach to evaluate biomarkers and mechanisms underlying sensitivity and resistance to cancer therapy. This rapidly maturing quantitative antibody-based assay can assess a large number of protein markers in many samples in a cost-effective, sensitive and high-throughput manner. MD Anderson Cancer Center has been a leader in the technology, and our platform currently contains ~300 protein markers, covering all major signaling pathways. We have generated and compiled the RPPA data of cell lines that were treated with &gt;150 compounds under multi-dosage, replicates or triplicates up to 8 time points in 2D/3D cell culture. In total, we have &gt;25,000 perturbed samples profiled using our RPPA platform. From these adaptive RPPA data, we developed computational algorithms to infer therapeutic liabilities including synthetic lethality and related combined therapies in a systematic way. Representative examples include PARP and MEK inhibitor combination showing synergistic activities in RAS mutant tumors. We further built an integrated data portal that contains user-friendly analytic and visualization tools for a broad biomedical community to utilize these data.
Citation Format: Jun Li, Wei Zhao, Yiling Lu, Gordon B. Mills, Han Liang. Systematic identification of cancer therapeutic liabilities through adaptive functional proteomics [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 1656.
Abstract 1658: Performance evaluation of assembly based structural variation discovery in paired tumor normal samples
Authors: Xiao Chunlin 
Published: August 2019

Abstract
Structural variations (SVs) are well known to contribute to genetic diversity of human populations, affect biological functions, and cause various human disorders. However, accurately identifying SVs with correct sizes and locations in the human genome, particularly in cancer samples, remains a challenge due to the complexities of the human genome, limitations of sequencing technologies, and drawbacks of analysis methods. Recent advancement of next-generation sequencing technologies has significantly reduced the sequencing cost, while substantially increased the lengths of the sequencing reads. Therefore, using de novo assembly based approaches for discovering a full spectrum of SVs in human genome becomes tempting. While various assembly methods have been developed and proposed for general use by the community, the relative efficiency and predictive accuracy of SVs calling based on these assembly methods have not been fully evaluated. In this study, we applied several popular de novo assembly tools to the sequencing read data that were generated using multiple sequencing technologies with technical replicates for a paired tumor-normal samples. Assemblies and SVs callsets were generated, and repeatability in the SVs of the technical replicates and reproducibility across sequencing sites were evaluated. Results showed that there were substantial variabilities between SVs callsets based on various assemblies within sequencing platform and cross sequencing sites. We also observed considerable variabilities between assembly based SVs callsets and alignment based SVs callsets. The sources of these variabilities will be further discussed. These results allow better understanding of the impacts of de novo assembly methods on SVs calling, thus providing a better insight to precision medicine.
Citation Format: Chunlin Xiao. Performance evaluation of assembly based structural variation discovery in paired tumor normal samples [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 1658.
Abstract 166: MMP12 promotes EMT-mediated tumor aggressiveness in lung cancer cells
Authors: Han Na-Kyung , Lee Yoon-Jin , Lee Hae-June 
Published: August 2019

Abstract
Epithelial mesenchymal transition (EMT) plays a pivotal role in cancer progression and metastasis. Matrix metalloproteinases (MMPs) have a major role in remodeling the extracellular matrix (ECM) and are implicated in the invasion and metastasis of various malignancies. MMP12 is involved in many pathological processes including cancer, but has not been fully understood yet. To investigate the role of MMP12 in lung cancer cells, we examined the effect of overexpression or knockdown of MMP12 in A549 cells and measured the protein expression level of EMT markers. MMP12 overexpression induced considerable EMT phenotypic changes compared to the control group, both in vitro and in vivo. The mRNA levels of EMT markers, snail2/slug, snail1, zeb1, TGFβ-1, and fibronectin, increased significantly upon MMP12 overexpression. We observed that changes in the expression level of ECM components such as, collagen, lamin, and proteoglycans led to MMP12 overexpression-induced EMT. These alterations promoted the aggressiveness of the A549 cells, evident from changes in functional properties of the cell, such as proliferation, migration, invasion, and adhesion capacity. On the contrary, MMP12 suppression abrogated malignancy of A549 cells. MMP12 knockdown in A549 cells led to decreased mRNA expression of EMT marker proteins associated with adherence, invasion, and migration. Our results suggest that MMP12 is a key regulator of EMT in non-small cell lung cancer (NSCLC). MMP12 overexpression in A549 cells results in changes in the expression of EMT markers and ECM composition. Interestingly, the dynamic changes in the ECM components by MMP12 overexpression cause EMT reprogramming and induce malignant functional properties. Thus, we suggest that the control of MMP12 expression can be a potential target for new therapeutic strategies of NSCLC.
Citation Format: Na-Kyung Han, Yoon-Jin Lee, Hae-June Lee. MMP12 promotes EMT-mediated tumor aggressiveness in lung 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 166.
Abstract 1664: Comparison of RNA sequencing data generated from formalin-fixed, paraffin-embedded (FFPE) papillary thyroid carcinoma samples using different library preparation methods
Authors: Dragon Julie , Barrantes Ramiro , Hoffman Jessica , Tighe Scott 
Published: August 2019

Abstract
Comparison of RNA sequencing data generated from formalin-fixed, paraffin-embedded (FFPE) papillary thyroid carcinoma samples using different library preparation methods. Julie Dragon, Ramiro Barrantes, Jessica Hoffman, and Scott Tighe.
Genomics research requires a significant sample size to provide robust biological signal, leaving researchers clamoring for access to large sample sets. As this research continues to expand into the clinical arena, the demand to sequence RNA from banked tissue samples, such as formalin-fixed paraffin-embedded blocks, is unavoidable. Readily available, these samples represent a wealth of both patients and disease variation. However, recovering DNA and RNA from such samples can be challenging depending on age of the sample block and fixture protocols, which influence nucleic acid degradation and ultimately our ability to synthesize RNA-Seq libraries. To fulfill the need for increased recovery of quality reads, several manufacturers have developed solutions to address these challenges, including FFPE-specific extraction kits, as well as library synthesis and quality control reagents.
In this study, we analyzed the quality and outcome of RNA-Seq data generated from RNA from two library synthesis kits applied to FFPE-derived human thyroid tumors with storage times from 4-12 years. The data processing pipeline included adaptor and poor quality base trimming (trimommatic), removal of rRNA reads (sortMeRNA), genomic alignment to hg19 (HiSat2), and read distribution and alignment quality assessment (RNASeqQC) (Adiconis and Levin, 2013). Differential expression was evaluated using DESeq2 (Love et al, 2014).
Results indicated that of the reagents tested, it is possible to improve capture of the usable RNA data, and biological signal, with improved control of rRNA depletion and read duplication with newly developed library preparation kits specifically designed for FFPE samples. Moreover, our analysis illustrates some features of the tested synthesis kits that enable one to perform better for FFPE samples with highly varying quality, and quality metrics to consider when evaluating FFPE samples for sequencing.
Citation Format: Julie Dragon, Ramiro Barrantes, Jessica Hoffman, Scott Tighe. Comparison of RNA sequencing data generated from formalin-fixed, paraffin-embedded (FFPE) papillary thyroid carcinoma samples using different library preparation methods [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 1664.
Abstract 1670: A hidden markov modeling approach for identifying tumor subclones in next-generation sequencing studies
Authors: Zhu Bin , Choo-Wosoba Hyoyoung , Albert Paul 
Published: August 2019

Abstract
Allele-specific copy number alteration (ASCNA) analysis is for identifying copy number abnormalities in tumor cells. Unlike normal cells, tumor cells are heterogeneous with a combination of dominant and minor subclones with distinct copy number profiles. Estimating the clonal proportion and identifying main and subclone genotypes across the genome is important for understanding tumor progression. Several ASCNA tools have recently been developed, but they have been limited to the identification of subclone regions, and not the genotype of the subclones. In this paper, we propose subHMM, a hidden Markov model-based approach that estimates both subclone region as well as region-specific subclone genotype and clonal proportion. We specify a hidden state variable representing the conglomeration of clonal genotypes and subclone status. We propose a two-step algorithm for parameter estimation, where in the first step, a standard hidden Markov model with this conglomerated state space is fit. Then, in the second step, region-specific estimates of the clonal proportions are obtained by maximizing region-specific pseudo-likelihoods. We apply subHMM to a renal cell carcinoma dataset from The Cancer Genome Atlas. In addition, we conduct simulation studies that show the good performance of the proposed approach.
Citation Format: Bin Zhu, Hyoyoung Choo-Wosoba, Paul Albert. A hidden markov modeling approach for identifying tumor subclones in next-generation sequencing studies [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 1670.
Abstract 1671: Characterization of a gene signature predictive of cancer patient survival prognosis and chemo-response
Authors: Tran Paul , Li Shuchun , Liu Hai-Tao , Tran Lynn , Purohit Sharad , Dun Boying , She Jin-Xiong 
Published: August 2019

Abstract
Precision oncology aims to match cancer patients with drugs based on their unique molecular profiles. The MATCH trial results indicate ~80% of cancer patients do not benefit from screening cancer patients for actionable genomic aberrations. We aim to identify potentially druggable gene signatures that are molecular drivers of poor survival outcome for these cancer patients. We analyze data from The Cancer Genome Atlas (TCGA) to identify patient groups with differential survival characteristics using gene expression data. We applied a univariate Cox regression model to identify genes associated with patient survival and identified a 67 gene signature of highly correlated genes significant for cell cycle related gene ontologies on over-representation analysis. Patients from the TCGA dataset whose tumors have a higher gene expression score are more likely to have worse survival prognosis compared to tumors with a lower gene expression score, demonstrated through Kaplan-Meier survival analysis and univariate and multivariate Cox proportional hazard models. This suggests we have identified a co-regulated gene expression network associated with cellular proliferation which predicts overall and recurrence-free survival in many of the cancers in the TCGA dataset. We further analyzed data from the NCI-60 Human Tumor Cell Lines Screen and found the cellular proliferation gene signature expression is correlated with response to cell cycle inhibitors. We then validated this finding using resistant cell lines and gene knockdown models in vitro. This gene signature may provide an important tool for selecting patients for chemotherapy administration.
Citation Format: Paul Tran, Shuchun Li, Hai-Tao Liu, Lynn Tran, Sharad Purohit, Boying Dun, Jin-Xiong She. Characterization of a gene signature predictive of cancer patient survival prognosis and chemo-response [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 1671.