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

{"title":"Abstract 259: Comparison of Illumina NovaSeq 6000 and MGISEQ-2000 in profiling xenograft models","authors":"W. Qian, Chen Xiaobo, H. Li, Sheng Guo","doi":"10.1158/1538-7445.AM2021-259","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-259","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"181 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85004100","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 191: A probabilistic analysis of somatic mutations indicates individual survival outcome classes with AUC near 1.00 for all tested cancer-drug combinations from TCGA and 4 immune checkpoint studies (all having ≥ 20 patients and an outcome ratio < 6)","authors":"J. Friedman","doi":"10.1158/1538-7445.AM2021-191","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-191","url":null,"abstract":"A new computational method to predict cancer treatment outcomes from somatic mutation data was tested. Using this method, treatment outcome success or failure for 78 different cancer-drug combinations (74 from TCGA & 4 from published immune checkpoint inhibitor studies) could be \"predicted\" for each patient with nearly perfect accuracy (AUC values from ROC curves at 1.000 or just below) based solely on individual patients9 somatic mutation information. Predictions worked for all examined cancer-drug combinations with information available for > 20 patients and with a treatment SUCCESS to FAILURE ratio between 1/6 and 6. Calculations disregarded outcome information about the patient for whom an outcome was being predicted, but so far only when calculating their own classification measure. More elaborate, independent calculations are being developed to eliminate the remnants of outcome information from one patient in classification measures calculated for other predicted patients, but these newer, more detailed calculations are ongoing. The methods avoid any (1) fitting of parameters to outcome or data, (2) use of linear algebraic methods, (3) determinations of scale factor values, and (4) use of some typically inaccurate types of experimentally estimated probability values. Instead, they use (1) more accurate metastatistics about an accurately determined type of probability value – the probability that the observed frequency of mutation for a gene differs from random in either separate population of the responder or of the non-responder patients – and (2) an analysis of some underlying causes of modeling bias – examining the sensitivity of how identifying non-random mutation frequencies can be perturbed by changes due to single patients. Statistics entailing extrapolation to an infinite sampling limit were avoided in favor of statistics more applicable to small finite samples. When one patient with a \"known\" outcome was deliberately varied, in a systematic non-random way, critical statistics exhibited consistent changes that differed depending on whether the varied patient belonged to the HIT or MISS outcome class and these changes remained consistent with outcome class when patients of \"unknown\" outcome were varied in a similar way. The analysis provided a quantitative mathematical explanation for why FLAG genes had appeared often in many GWAS and suggested that the mutational burden measure used often as a marker for checkpoint inhibitor studies might suffer from similar complications. Prospective studies are being planned. Citation Format: Jonathan Malcolm Friedman. A probabilistic analysis of somatic mutations indicates individual survival outcome classes with AUC near 1.00 for all tested cancer-drug combinations from TCGA and 4 immune checkpoint studies (all having ≥ 20 patients and an outcome ratio","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"07 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85977738","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 175: Pathway modeling to translate the 27-gene immuno-oncology algorithm into bladder cancer","authors":"R. Seitz, T. Nielsen, B. Schweitzer, D. Hout, D. Ross","doi":"10.1158/1538-7445.AM2021-175","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-175","url":null,"abstract":"Background The 27-gene immuno-oncology (IO) algorithm has demonstrated an association with immune checkpoint inhibitor (ICI) response in TNBC, NSCLC, and metastatic urothelial carcinoma (mUC). The algorithm can be run on data generated from either a qPCR assay or from analysis of whole transcriptome RNA-seq data. It integrates gene expression information from infiltrating inflammatory cells with signatures from surrounding stroma and tumor cells to classify cases into likely responder versus non-responders. We hypothesized that because the algorithm derives its biologic signature from the tumor immune microenvironment (TIME), the classification function and thresholds might translate to other solid tissue types based upon biologic separation of inflammatory phenotypes. Methods Using NSCLC and breast cancer datasets from TCGA, we identified 939 genes that comprise the Mesenchymal (M), Mesenchymal Stem-like (MSL), and Immunomodulatory (IM) gene expression patterns centered around a previously described 101-gene signature (Ring, 2016). We applied this 939 gene set to 433 bladder samples from TCGA (UC) and k-means clustered the genes based upon each of the three centroids. Clinical cases were also organized by k-means clustering (k=3). Pathway analysis was performed (GSEA—UCSD/Broad). We assessed classification of UC cases by looking at enrichment of inflammatory pathways into the IM cluster compared to mesenchymal pathways into the M or MSL clusters. The threshold for responder classification using the 27-gene IO algorithm previously established in TNBC was assessed by quantitating the fraction of cases enriched into the IM cluster (potential responders) as opposed to the M or MSL clusters (potential non-responders). Results The 939 genes centered around the 101-gene signature encoded twenty different physiologic pathways. Ten of these pathways included at least one of the genes from the 27-gene IO algorithm. Significant enrichment of inflammatory cell pathways was seen into the IM cluster as opposed to mesenchymal and reactive fibroblast pathways enriched into the M and MSL clusters. Pathways containing therapeutic targets designed to overcome resistance to ICIs were enriched in the MSL gene expression centroid. The 27-gene IO algorithm threshold applied to the TCGA samples classified 79% as responders in the IM cluster as opposed 16% in the M and MSL. Discussion These results support the hypothesis that gene expression signatures discerning TIME physiology associated with ICI response are tissue agnostic and relevant in multiple solid tissue types. The dramatic enrichment of responders into the IM cluster using previously established thresholds is consistent with appropriate biologic classification of the cases and supports utilizing the 27-gene IO algorithm and established threshold for association with ICI response in treated mUC cohorts. Citation Format: Robert S. Seitz, Tyler J. Nielsen, Brock L. Schweitzer, David R. Hout, Douglas T. Ross. Pat","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76843667","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 265: Evaluating variation in drug efficacy endpoints in a syngeneic mouse model (CT26.WT) under immune checkpoint blockade","authors":"B. Mao, Sheng Guo, D. Ouyang, H. Li","doi":"10.1158/1538-7445.AM2021-265","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-265","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"123 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76199337","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 239: Integrated computational image analysis of cellular and acellular tissue components as a method for detailed tumor tissue mapping and structural patterns recognition","authors":"G. Vasiukov, Tatiana Novitskaya, M. Senosain, A. Menshikh, A. Zijlstra, S. Novitskiy, P. Massion","doi":"10.1158/1538-7445.AM2021-239","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-239","url":null,"abstract":"Tumor microenvironment (TME) represents an integrated system that affects cancer cell behavior and contributes directly to disease outcome. Systemic approach to analysis of TME should uncover its complexity and facilitate discovery of mechanisms orchestrating tumor development and metastasis. Multiplex fluorescence tissue staining followed by spatial analysis of tumor tissue architecture can provide insights to pivotal interactions of cellular and acellular components of TME. Extracellular matrix (ECM is represented mainly by collagen deposition. Number of reports indicates that ECM contribution to TME state not only depends upon amount of accumulated collagen but its geometrical features and spatial orientation of fibers. These characteristics of collagen fibers contribute directly to physical and mechanical properties of tissue and can change tumor growth and metastasis. Current methods of computational image analysis of tissue implement assessment of cellular or acellular components separately. The goal of current work was to develop a new computational tool to perform integrated analysis of fibrous and cellular components of tumor tissue in spatial dependent manner to achieve detailed tumor tissue mapping and structural patterns recognition. To pursue this goal, we generated images of human lung adenocarcinoma tissue characterized by indolent and aggressive behavior. We performed multiplex immunofluorescence staining for following markers: CD3 - marker of T-lymphocytes, PanCytokeratin - marker of epithelial/tumor cells, collagen hybridizing peptide (3Helix) - marker of collagen, DAPI - nuclear counterstain. To develop image analysis pipeline, we utilized an open source graphical interface analytical platform KNIME, where we generated modular workflow. For ECM analysis, we integrated Python written code into KNIME node. Segmentation of collagen fibers was performed using skeletonization with subsequent calculation of geometrical properties (length, alignment, widths) and orientation of each fiber. Data, collected from single cell analysis and ECM architecture assessment, were combined and forwarded to downstream spatial analysis, where distances from cell to cell or cell to ECM were computed and neighborhood analysis was performed. We demonstrated that tumor cells in aggressive adenocarcinoma samples were co-localized with a smaller number of collagen fibers. In addition, length of that fibers was less in comparison to indolent group. Correlation analysis revealed positive correlation between length of collagen fibers and number of tumor cells in indolent group, but we did not observe this phenomenon in indolent group. Developed computational method provides additional dimensionality to tissue image analysis and can reveal underrecognized structural patterns of the tumor microenvironment. Citation Format: Georgii Vasiukov, Tatiana Novitskaya, Maria-Fernanda Senosain, Anna Menshikh, Andries Zijlstra, Sergey Novitskiy, Pierre Massion. Integrated","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76237555","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 197: MONE: A construction for interpreting deep learning features in pathology slides","authors":"Ali Foroughi pour, Jonghanne Park, Jeffrey H. Chuang","doi":"10.1158/1538-7445.AM2021-197","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-197","url":null,"abstract":"Deep learning has become a popular tool for analyzing hematoxylin and eosin (HE 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 197.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"82 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83537626","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 162: Cholesterol metabolism gene expression and prostate cancer-specific outcomes in radiotherapy-treated patients","authors":"S. Winter, S. Halliday, Konrad H. Stopsack, S. Osman, A. Hounsell, G. Prue, S. Jain, E. Allott","doi":"10.1158/1538-7445.AM2021-162","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-162","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90660757","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 160: LASSO-based protein signatures for survival prediction in human cancer cohorts","authors":"Mariam M. Konaté, Ming-Chung Li, L. McShane, Yingdong Zhao","doi":"10.1158/1538-7445.AM2021-160","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-160","url":null,"abstract":"Background: Large-scale multi-omics data characterizing human tumors are increasingly available and can be leveraged to develop a deeper understanding of biological processes and predict clinical outcomes. Reverse-phase protein array (RPPA) is a high-throughput, antibody-based method that provides a more direct assessment of cellular activity compared to DNA and RNA sequencing, which generate data that do not always correlate with protein expression. Multiple studies have demonstrated the prognostic value of RPPA data. Some of these studies have used pathway-driven approaches, relying on prior knowledge from the literature to group proteins into biological pathways, to develop prognostic signatures or predictors of treatment response. Methods: We obtained normalized RPPA data for up to 258 total, cleaved, acetylated, or phosphorylated protein species from The Cancer Proteome Atlas (TCPA). Starting from a published RPPA-based seven-protein signature of receptor tyrosine kinase (RTK) pathway activity in the form of an unweighted sum of the seven protein measurements, shown to have prognostic value in a 445-patient renal clear cell carcinoma cohort (TCGA-KIRC), we demonstrated that strong stratification of patients into high and low risk groups can be achieved by using a statistical approach—LASSO regression—with no a priori biological knowledge, to select from the 233 proteins and optimally combine their RPPA measurements into a weighted risk score. Method performance was assessed using two unbiased approaches: 1) 10 iterations of 3-fold cross-validation for unbiased estimation of hazard ratio and difference in 5-year survival (by Kaplan-Meier method) between predictor-defined high and low risk groups; and 2) a permutation test to evaluate the statistical significance of the cross-validated log-rank statistic. Results: For the first evaluation approach, the median hazard ratio between high and low risk groups across the held-out folds in the cross-validation based on the 7-protein RTK score was 2.4, compared to 3.3 when using the risk score derived by LASSO applied to the training data folds. Furthermore, the median difference in overall survival probability at 5 years based on the LASSO-derived risk score was 32.8%, compared to 25.2% when using the 7-protein RTK score. The permutation test p values were 5.0e-4 for both the RTK pathway-driven and the LASSO data-driven approaches. Finally, we demonstrated the applicability and performance of our approach for overall survival prediction in additional TCGA cohorts; namely, ovarian serous cystadenocarcinoma (TCGA-OVCA), sarcoma (TCGA-SARC), and cutaneous melanoma (TCGA-SKCM). Conclusions: The data-driven nature of our LASSO-based approach makes it versatile and particularly well-suited for the discovery of unexplored protein/disease associations that could aid in therapeutic discovery. Citation Format: Mariam M. Konate, Ming-Chung Li, Lisa McShane, Yingdong Zhao. LASSO-based protein signatures for surv","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74605529","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 208: Development of Evidence Statement curation algorithms to aid cancer variant interpretation","authors":"J. Saliba, Lana M. Sheta, Kilannin Krysiak, Arpad M. Danos, Alex R Marr, Erica K. Barnell, Shahil P. Pema, Wan-Hsin Lin, P. Terraf, Joshua F. McMichael, C. Grisdale, Shruti Rao, S. Kiwala, Adam C. Coffman, A. Wagner, O. Griffith, M. Griffith","doi":"10.1158/1538-7445.AM2021-208","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-208","url":null,"abstract":"The Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase (civicdb.org) is an open access, centralized hub for structured, community curated and expertly moderated relationships between genomic variants and cancer. Evidence is curated from peer-reviewed, published literature and is classified into one of five Types: Predisposing, Diagnostic, Prognostic, Predictive (therapeutic), or Functional. The robustness of the Evidence is conveyed through the assignment of Levels with the first three derived from patient studies (Validated, Clinical, Case Study), Preclinical, generated from in vivo or in vitro data, and Inferential, which describes indirect associations. Each Evidence Item requires an Evidence Statement written in the curator9s own words summarizing the source9s results regarding the variant9s clinical impact. Collaborations with groups like ClinGen have generated a significant influx of new curators, increasing the demand for detailed principles regarding data prioritization in the Evidence Statement in order to streamline the curation process. The curation community would benefit from simpler, visual guides through the complex decisions needed to appropriately and consistently curate Evidence Items. We are devoting significant effort to continue the development of straightforward Evidence curation algorithms (decision trees) similar to those used in clinical molecular testing labs to aid CIViC curators. Previously published guidelines on development of these statements are the basis of our Evidence algorithms. Obvious inflection points for curators are clearly identified with specific details noted for each to optimize decision efficiency. As the predominant Evidence Type comprising 57% of all CIViC submissions, 58% of referenced patient trials, and 92% of Preclinical submissions, Predictive Evidence is the initial focus of our pilot guidelines with Diagnostic and Prognostic to follow. Within the Predictive Evidence Type, clinical trials, case studies, and preclinical Levels each require vastly different Evidence Statement details and ultimately the creation of three separate, uniquely modeled algorithms. The implementation of these algorithms will assist in streamlining both curation and the expert review process. Notably, a template is not being created, as the preservation of curator style and voice is important to maintain the community feel of the database. To ensure the highest level of clarity, our team is utilizing specific novice and experienced curators to assist with the development process. As these algorithms pass the pilot phase, they are being tested as curator training tools. Ultimately, these guidelines will be used to encourage independence in curators and to enhance the Evidence already contained in CIViC. Citation Format: Jason Saliba, Lana Sheta, Kilannin Krysiak, Arpad Danos, Alex Marr, Erica Barnell, Shahil Pema, Wan-Hsin Lin, Panieh Terraf, Joshua F. McMichael, Cameron J. Grisdale, Shruti Rao, Susanna ","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73807692","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 199: Blossom AI: A novel drug discovery app for the prediction of hotspots on multiplex protein protein interaction complexes using random forest algorithms","authors":"Stephanie Zhang, Minsoo Kang","doi":"10.1158/1538-7445.AM2021-199","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-199","url":null,"abstract":"Protein protein interactions (PPIs) form the backbone of signal transduction pathways in diverse physiological processes, mediating the transmission and regulation of oncogenic signals essential to cellular proliferation and survival, thus representing a potential new class of drug targets for anticancer therapeutic discovery. However, several challenges face the targeting of PPIs, including large PPI interface areas, a lack of deep pockets, the presence of noncontiguous binding sites, and a general lack of natural ligands. The presence of hot spots (small subsets of amino acid residues that contribute significantly to free binding energy) makes PPIs amenable to small molecule perturbations, playing essential roles in the stability of protein binding. Effectively identifying which specific interface residues of protein protein complexes form the hot spots is critical for understanding the principles of protein interactions and has broad application prospects in protein design and drug development. This project presents Blossom AI, a novel, user friendly mobile app developed in XCode and CoreML that uses random forest decision tree algorithms (RF) to computationally predict the presence of hotspots on protein complexes within seconds, aiding the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anticancer therapy. Leveraging features such as solvent accessible surface area (ASA), blocks substitution matrix, physicochemical properties (hydrophobicity, polarity, polarizability, propensities), position specific scoring matrix (PSSM) and solvent exposure, the RF is trained through a dataset of 313 mutated interface residues (133 hotspot residues and 180 non hotspot residues) from over 60 protein complexes to produce a training accuracy of 88.75%, validation accuracy of 92.86%, specificity of 87.18%, sensitivity of 75.38%, PPV 94.23%, NPV 86.61%. Blossom is high speed, low cost, and user friendly with significantly improved accuracy over the standard of alanine scanning mutagenesis. Citation Format: Stephanie Zhang, Minsoo Kang. Blossom AI: A novel drug discovery app for the prediction of hotspots on multiplex protein protein interaction complexes using random forest algorithms [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 199.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84434536","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}