Journal of bioinformatics and systems biology : Open access最新文献

{"title":"Abstract 171: Cloud-based bulk and single-cell RNAseq pipelines in the Terra platform for the Lung PCA","authors":"Christie L. Husted, F. Aguet, C. Shea, A. Gower, William J. Mischler, Y. Koga, R. Hong, S. Dubinett, A. Spira, S. Mazzilli, E. Cerami, I. Leshchiner, M. Lenburg, G. Getz, J. Beane, Joshua D. Campbell","doi":"10.1158/1538-7445.AM2021-171","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-171","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86554885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 3: Deep learning-based integration of esophageal cancer morphology with genomics","authors":"R. Verma, Wei Wu, Neeraj Kumar, Elizabeth A. Yu, Won-Tak Choi, S. Umetsu, T. Bivona","doi":"10.1158/1538-7445.AM2021-3","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-3","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"153 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86134801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 222: Towards patient-specific optimization of neoadjuvant treatment protocols for breast cancer based on image-based fluid dynamics","authors":"Chengyue Wu, D. Hormuth, F. Pineda, G. Karczmar, T. Yankeelov","doi":"10.1158/1538-7445.AM2021-222","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-222","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74885438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 168: Computational model for prediction of actionable drug combinations in cancer","authors":"Sairahul R Pentaparthi, Brandon Burgman, Song Yi","doi":"10.1158/1538-7445.AM2021-168","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-168","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"97 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78093613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 196: Redefining cancer subtypes using multi-omics and deep learning","authors":"A. Akalin, B. Uyar, J. Ronen, V. Franke","doi":"10.1158/1538-7445.AM2021-196","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-196","url":null,"abstract":"Cancer is a heterogeneous collection of diseases traditionally classified by the tissue of origin. The diversity of the molecular profiles of cancers has a big impact on the way patients are diagnosed and treated, how they respond to their prescribed treatments, the duration of survival after diagnosis, and factors such as remission, recurrence, or spread (metastasis) of the disease. While such diagnostic and prognostic outcomes are potentially predictable by taking a closer look into the changes of the genome, epigenome, transcriptome, proteome, and various other omics platforms, the contemporary cancer treatments still predominantly don9t make the best use of such multi-omics profiling of patient samples. Therefore, multi-omics profiling of cancers holds great potential to define a molecularly coherent subtype definition of cancers in order to achieve the eventual goal of matching the best possible treatment to the subgroup of patients. However, the current subtypes from consortiums such as TCGA have been defined by heterogeneous methods and molecular markers by different teams. A subset of these studies have not attempted to characterize molecular subtypes, but rather taken histopathologically defined subtypes as the gold standard and tried to characterize molecular features of these subtypes. Here we evaluate TCGA cancer subtypes based on the molecular profile coherence score. This novel metric combines survival statistics, pathways information, tumor purity estimates, and mutational signatures. We expect that subtypes that are patient subgroups should display molecular signature homogeneity. We evaluate TCGA subtypes from 21 cancers using these criteria and compare the subtypes with our own definition using multi-omics data in a deep learning framework. We have refined the several subtypes from multiple cancers towards more molecularly coherent patient subgroups. Citation Format: Altuna Akalin, Bora Uyar, Jonathan Ronen, Vedran Franke. Redefining cancer subtypes using multi-omics and deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 196.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"284 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76851005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 202: Identification of survival associated hub genes in prostate cancer patients from the TCGA database","authors":"N. Reyes, R. Tiwari, J. Geliebter","doi":"10.1158/1538-7445.AM2021-202","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-202","url":null,"abstract":"Background: Prostate cancer is the most frequently diagnosed malignancy and the fourth leading cause of cancer-related death in the global male population. Although the disease has a relatively low mortality rate with some patients surviving for 10-20 years after treatment, others respond poorly to treatment and die of metastatic disease within 2-3 years. Therefore, there is an urgent need to develop strategies to identify patients with clinically significant prostate cancer requiring aggressive treatment to improve survival, while sparing others unnecessary side effects. The purpose of this study was to identify survival associated genes in prostate cancer patients from the TCGA database using bioinformatics tools. Methods: Data from prostate cancer patients in the TCGA database were divided into two study groups: a high and a low expression group, relative to the median expression. The Gene Expression Profiling Interactive Analysis (GEPIA2) tool was used for the identification of the most differential survival genes. Metascape bioinformatics tool was subsequently used for clustering of genes based on processes, pathway enrichment analysis, and construction of Protein-Protein Interaction (PPI) network. Metascape was also used for molecular Complex Detection (MCODE) to identify the genes with the highest degree of connection, known as hub genes, and to screen modules of the PPI network. Results: Bioinformatics analysis allowed the identification of 361 genes whose expression levels were significantly associated with overall survival in prostate cancer patients from the TCGA. Survival associated genes were primarily enriched in mRNA processing, DNA repair, ncRNA processing, DNA replication, macromolecule methylation, among others. The 12 most connected genes were selected as hub genes and Kaplan-Meier analysis was used to verify survival associated with this set of genes. Hub genes included several splicing factors and components of the processing machinery of cellular pre-mRNAs. Conclusions: These hub genes may reveal basic mechanisms underlying the development of clinically relevant prostate cancer and contribute to the identification of novel markers for prognosis of this cancer. Citation Format: Niradiz Reyes, Raj Tiwari, Jan Geliebter. Identification of survival associated hub genes in prostate cancer patients from the TCGA database [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 202.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"201 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76923369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract LB017: PDX Finder: An open and global catalogue of patient-derived xenograft models","authors":"Zinaida Perova, Csaba Halmagyi, Abayomi Mosaku, N. Conte, J. Mason, Alex Follette, Ross Thorne, Mauricio Martinez, S. Neuhauser, D. Begley, D. Krupke, H. Parkinson, T. Meehan, C. Bult","doi":"10.1158/1538-7445.AM2021-LB017","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-LB017","url":null,"abstract":"Patient-derived tumor xenograft (PDX) models are a critical oncology platform for cancer research, drug development and personalized medicine. Because of the heterogeneous nature of PDXs repositories, finding models of interest is a challenge. The Jackson Laboratory and EMBL-EBI are developing PDX Finder, the world9s largest open PDX database containing millions of phenomic information from over 4300 models (www.pdxfinder.org, PMID: 30535239). In support of this initiative, we developed the PDX Minimal Information standard (PDX-MI) which defines metadata necessary to describe models (PMID: 29092942). Within PDX Finder, critical attributes like diagnosis, drug names or genes are harmonized into a cohesive ontological data model based on PDX-MI. An intuitive search and faceted search interface allow users to select models based on clinical/PDX attributes, tumor markers, dataset availability and/or drug dosing results. We provide PDX, patient, drug and molecular data detail pages where all available information can be browsed and downloaded. To further facilitate user9s model selection, we are linking key external resources like publication platforms and cancer-specific annotation tools enabling exploration and prioritization of PDX variation data (COSMIC, CIViC, OncoMx, OpenCRAVAT). Links to originating resource protocols and contact information are provided, facilitating data understanding and further collaboration. Alongside database development activities, PDX Finder has undertaken activities to tackle areas of standards and tool development, data integration and outreach. PDX Finder provides key expertise and software components to support several worldwide consortia including PDXNet, PDMR and EurOPDX. We are driving the development of, and promoting the use of descriptive standards to facilitate data interoperability and promote global sharing of models. Our standard has become established in the community for data exchange, adopted by PDX providers, consortia, and informatics tools integrating PDX data. It has been re-used by different initiatives in the context of data collection and data modeling allowing adherence to the FAIR data principles - Findability, Accessibility, Interoperability and Reusability. PDX Finder is increasing awareness of PDX models, facilitating data integration, and enabling international collaboration, maximizing the investment in, and translational capabilities of these important models of human cancer. PDX Finder is freely available under an Apache 2 license (github.com/pdxfinder). Work supported by NCI U24 CA204781 01 (ended 31Aug2020), U24 CA253539, and R01 CA089713. Citation Format: Zinaida Perova, Csaba Halmagyi, Abayomi Mosaku, Nathalie Conte, Jeremy Mason, Alex Follette, Ross Thorne, Mauricio Martinez, Steven Neuhauser, Dale Begley, Debra Krupke, Helen Parkinson, Terrence Meehan, Carol Bult. PDX Finder: An open and global catalogue of patient-derived xenograft models [abstract]. In: Proceedings of the America","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73803204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 154: Improving MultiOmyxTMAnalytics cell classification workflow efficiency by Invariant Information Clustering on historical data","authors":"V. Reddy, Nicholas Stavrou, M. Nagy, Q. Au","doi":"10.1158/1538-7445.AM2021-154","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-154","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73476745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 219: Identifying genetic interactions resulting form diverse biological mechanisms to inform cancer drug development","authors":"Manuel García-Quismondo, O. Elemento, Neel S. Madhukar, Coryandar Gilvary","doi":"10.1158/1538-7445.AM2021-219","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-219","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86640461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract LB015: Clinical evaluation of The Paris System-based artificial intelligence algorithm for reporting urinary cytopathology","authors":"Wei-Lei Yang, Jen-Fan Hang, Chi-Bin Li, Ching-Ming Lee, Yi-Sheng Lin, T. Tsao, M. Chang, Y. Ou, Tien-Jen Liu","doi":"10.1158/1538-7445.AM2021-LB015","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-LB015","url":null,"abstract":"BACKGROUND: The Paris System (TPS) for Reporting Urinary Cytology provides standardized diagnostic criteria for urinary tract cytology specimens, focusing on the detection of high-grade urothelial carcinoma (HGUC). Since the publication in 2016, numerous studies have reported a decrease in atypical diagnosis and a significant improvement in the detection of HGUC after adopting TPS. However, the major challenges include labor-intensive screening and interobserver variations. Artificial intelligence (AI) in medical imaging analysis is an emerging tool for ancillary diagnosis. To this end, we have developed an AI algorithm and conducted a retrospective study to evaluate the AI-assisted urine cytology reporting workflow. METHODS: A total of 131 urine cytology slides from bladder cancer patients, either first diagnosis or post-treatment follow-up, were retrieved and digitized as whole slide images (WSIs). A deep learning-based computational model was used to analyze these WSIs. Candidate urothelial cells were automatically highlighted and classified into high-risk and low-risk atypia categories in each sample based on TPS criteria. Slide-wide statistical data, including a total number of high-risk and low-risk cells, nuclear-cytoplasmic ratio (N:C ratio) and nuclear area for each cell, and the distribution and mean values of these variables, were also provided. In a blind study, a cytotechnologist and a cytopathologist parallelly reviewed the AI-annotated images and quantitative data for each WSI sample. Suspicious for HUGC and HGUC were considered to be \"positive\" and the other diagnostic categories were considered to be \"negative\" according to whether trigger cystoscopy. The results were compared with the final diagnosis reviewed by a senior cytopathologist via microscopy to evaluate the performance of the AI-assisted model. RESULTS: There were 35 positive and 96 negative urine cytology samples based on the final diagnosis. The AI algorithm annotated a total of 26,502 cells and a mean of 757.2 cells at cancer risk from all positive samples and a total of 950 cells and a mean of 9.9 cells at cancer risk from all negative samples. The mean N:C ratio was 0.68 for high-risk atypical cells and 0.56 for low-risk atypical cells. The performance of the AI-assisted reports of the cytotechnologist was 88.6% sensitivity, 97.9% specificity, 93.9% positive prediction value (PPV), and 95.9% negative prediction value (NPV) and the cytopathologist was 91.4% sensitivity, 95.8% specificity, 88.9% PPV, and 96.8% NPV. CONCLUSIONS: We demonstrated an AI algorithm that can effectively assist the reporting of urine cytology by classifying urothelial cells at cancer risk and calculating quantitative data using WSI analysis. Integrating this AI model into clinical urine cytology workflow supported TPS for reporting urinary cytopathology, reduced the interobserver variations, and may potentially reduce the human labor for screening. Citation Format: Wei-Lei Yang, Jen-Fan Han","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85076691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}