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

{"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}

{"title":"Abstract 260: Application of integrated analysis of whole genome sequencing and RNA sequencing to personalized therapy decision making in pediatric and young adult cancer","authors":"Yaoqing Shen, M. Bonakdar, L. Williamson, E. Pleasance, K. Mungall, Richard A. Moore, A. Mungall, S. Yip, Anna F. Lee, C. Dunham, J. Laskin, M. Marra, Steven J. M. Jones, S. Rassekh, R. Deyell","doi":"10.1158/1538-7445.AM2021-260","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-260","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85825829","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 178: Desmosome mutations in melanoma promote cellular proliferation and disease progression","authors":"Maayan Baron, T. Ideker","doi":"10.1158/1538-7445.AM2021-178","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-178","url":null,"abstract":"Desmosomes are transmembrane protein complexes that contribute to cell-cell adhesion in the epithelia and other tissues under mechanical stress. Aberrant desmosome expression is often associated with developmental diseases leading to impaired tissue integrity. Recently, similar findings have been reported in cancer; Mutations in desmosomes genes have been observed in various cancer types including skin cancer, head and neck and lung cancer, however mostly epigenetic alterations have been used to associate desmosomes as suppressors of tumor metastasis. Here, we report that desmosomes are frequently mutated in seven cancer types. In melanoma, we find that over 70% of tumors have non-synonymous mutations in desmosomes, and that the desmosome mutational burden is associated with a strong decrease in mRNA expression levels in primary tumor samples (R = -0.23). Differential gene expression analysis and functional characterizations between mutant and wild-type tumors implicates the mutated cells in promoting cell proliferation at early stages of tumorigenesis. These results emerge uniquely from a systems-level analysis integrating multiple proteins in complexes and multiple cell types in heterogeneous tumors. Citation Format: Maayan Baron, Trey Ideker. Desmosome mutations in melanoma promote cellular proliferation and disease progression [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 178.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86373588","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 203: SCLC-CellMiner: An extensive cell line genomic and pharmacology resource identifies a subgroup of small cell lung cancers sensitive to targeted therapies and immunotherapies","authors":"C. Tlemsani, L. Pongor, Fathi Elloumi, L. Girard, K. Huffman, N. Roper, S. Varma, Augustin Luna, V. Rajapakse, P. Boudou-Rouquette, R. Sebastian, K. Kohn, J. Krushkal, M. Aladjem, B. Teicher, P. Meltzer, W. Reinhold, J. Minna, Anish Thomas, Y. Pommier","doi":"10.1158/1538-7445.AM2021-203","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-203","url":null,"abstract":"The typical low life expectancy and limited therapeutic options for patients with small cell lung cancer (SCLC) caused the National Cancer Institute (NCI) to categorize SCLC as “recalcitrant” cancer. SCLC-CellMiner (https://discover.nci.nih.gov/SclcCellMinerCDB) integrates drug sensitivity and genomic data from 118 patient-derived SCLC cell lines, providing a unique genomic and pharmacological resource. Transcriptomic profiling validates the SCLC consensus nomenclature based on expression of 4 master transcription factors NEUROD1, ASCL1, POU2F3 and YAP1 (NAPY classification) and demonstrate differential transcriptional networks driven by these lineage specific transcription factors. Our analyses reveal transcription networks linking SCLC subtypes with MYC and its paralogs MYCL and MYCN and inactivation of the NOTCH pathway in the neuroendocrine SCLC (N, A & P subgroups). By contrast, YAP1-driven SCLC (SCLC-Y) express the NOTCH pathway and co-express both YAP/TAZ and its negative regulator genes driving the Hippo pathway. SCLC-Y cell lines show the greatest resistance to the standard of care drugs (etoposide, cisplatin and topotecan) while PI3K-AKT-mTOR inhibitors show a higher activity in this subgroup. To explore the immune pathways and the potential value of the transciption factors based classification for selecting SCLC patients likely to respond to immune checkpoint inhibitors, we explored a transcriptome signature based on 18 established native immune response and antigen-presenting genes (APM score). The SCLC-Y cell lines are the only subset expressing innate immune response genes. SCLC-CellMiner is a powerfull tool demonstrating the value of cancer cell line genomic and pharmacological databases. Our analyses suggest the potential genomic molecular classifications to select patients for targeted therapies and immunotherapy, such as patients in the SCLC-Y subgroup who may be most responsive to immune checkpoints modulators. Citation Format: Camille Tlemsani, Lorinc Pongor, Fathi Elloumi, Luc Girard, Kenneth Huffman, Nitin Roper, Sudhir Varma, Augustin Luna, Vinodh Rajapakse, Pascaline Boudou-Rouquette, Robin Sebastian, Kurt Kohn, Julia Krushkal, Mirit Aladjem, Beverly Teicher, Paul Meltzer, William Reinhold, John Minna, Anish Thomas, Yves Pommier. SCLC-CellMiner: An extensive cell line genomic and pharmacology resource identifies a subgroup of small cell lung cancers sensitive to targeted therapies and immunotherapies [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 203.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"96 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89101699","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 192: MutAnt: Mutation annotation machine learning algorithm for pathogenicity evaluation of single nonsynonymous nucleotide substitutions in cancer cells","authors":"Aleksandr Sarachakov, V. Svekolkin, Zoia Antysheva, Jessica H. Brown, A. Bagaev, N. Fowler","doi":"10.1158/1538-7445.AM2021-192","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-192","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":"80083665","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 173: Efficient representations of tumor diversity with paired DNA-RNA aberrations","authors":"Qian Ke, Wikum Dinalankara, L. Younes, D. Geman, L. Marchionni","doi":"10.1158/1538-7445.AM2021-173","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-173","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79764673","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 4: Temporal and spatial topography of cell proliferation in cancer","authors":"Giorgio Gaglia, S. Kabraji, Danae Argyropoulou, Yang Dai, J. Bergholz, S. Coy, Jia-Ren Lin, E. Winer, D. Dillon, Jean J. Zhao, P. Sorger, S. Santagata","doi":"10.1158/1538-7445.AM2021-4","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-4","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86266835","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 234: Risk of sepsis among patients with prostate cancer: A network-based modeling approach","authors":"A. Jazayeri, Niusha Jafari, Christopher C. Yang, N. Nikita, G. Yao","doi":"10.1158/1538-7445.AM2021-234","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-234","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77319839","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}