Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-160
Mariam M. Konaté, Ming-Chung Li, L. McShane, Yingdong Zhao
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
背景:表征人类肿瘤的大规模多组学数据越来越多，可以用来更深入地了解生物过程和预测临床结果。逆相蛋白阵列(RPPA)是一种高通量、基于抗体的方法，与DNA和RNA测序相比，它提供了更直接的细胞活性评估，DNA和RNA测序产生的数据并不总是与蛋白质表达相关。多项研究证实了RPPA数据的预后价值。其中一些研究使用了途径驱动的方法，依靠文献中的先验知识将蛋白质分组为生物学途径，以开发治疗反应的预后特征或预测因子。方法:我们从癌症蛋白质组图谱(TCPA)中获得了258种总、断裂、乙酰化或磷酸化蛋白的标准化RPPA数据。从已发表的基于rpa的受体酪氨酸激酶(RTK)途径活性的7种蛋白标记(以7种蛋白测量值的未加权和的形式)开始，在445例肾透明细胞癌队列(TCGA-KIRC)中显示出预后价值，我们证明可以通过使用统计方法- lasso回归-在没有先验生物学知识的情况下将患者分为高风险和低风险组。从233种蛋白质中进行选择，并将其RPPA测量结果最佳地结合成加权风险评分。采用两种无偏方法评估方法的性能:1)10次3重交叉验证，以无偏估计预测者定义的高风险组和低风险组之间的风险比和5年生存率差异(通过Kaplan-Meier方法);2)用置换检验来评价交叉验证的对数秩统计量的统计显著性。结果:对于第一种评估方法，基于7蛋白RTK评分的交叉验证中，高风险组和低风险组之间的中位风险比为2.4，而使用LASSO导出的风险评分应用于训练数据折叠时为3.3。此外，基于lasso衍生风险评分的5年总生存率的中位数差异为32.8%，而使用7蛋白RTK评分的中位数差异为25.2%。RTK路径驱动和LASSO数据驱动方法的排列检验p值均为5.0 ~ 4。最后，我们证明了我们的方法在其他TCGA队列中用于总生存预测的适用性和性能;即卵巢浆液性囊腺癌(TCGA-OVCA)、肉瘤(TCGA-SARC)和皮肤黑色素瘤(TCGA-SKCM)。结论:我们基于lasso的方法的数据驱动性质使其具有通用性，特别适合于发现未探索的蛋白质/疾病关联，可以帮助发现治疗方法。引用格式:Mariam M. Konate, Ming-Chung Li, Lisa McShane, Yingdong Zhao。基于lasso的蛋白质特征用于人类癌症群体的生存预测[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志，2021;81(13 -增刊):摘要第160期。
{"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}
Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-208
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
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
癌症变异的临床解释(CIViC)知识库(civicdb.org)是一个开放获取、集中的中心，用于结构化、社区策划和专家调节基因组变异与癌症之间的关系。证据来自同行评审的已发表文献，并分为五种类型之一:易感性、诊断性、预后性、预测性(治疗性)或功能性。证据的稳健性通过以下级别的分配来传达:前三个级别来自患者研究(验证，临床，案例研究)，临床前，来自体内或体外数据，以及描述间接关联的推论。每个证据项目都需要一份用管理者自己的话撰写的证据声明，总结了关于变异临床影响的来源结果。与ClinGen等组织的合作产生了大量新的策展人，增加了对证据声明中有关数据优先级的详细原则的需求，以简化策展过程。策展社区将受益于更简单、直观的指南，通过适当和一致地策展证据项目所需的复杂决策。我们正在投入大量精力，继续开发直接的证据管理算法(决策树)，类似于临床分子检测实验室中用于帮助CIViC策展人的算法。以前发表的关于这些陈述的发展指南是我们证据算法的基础。明确确定了策展人的明显拐点，并为每个拐点记录了具体细节，以优化决策效率。作为主要的证据类型，包括57%的CIViC提交，58%的参考患者试验和92%的临床前提交，预测性证据是我们试点指南的最初重点，随后是诊断和预后。在预测证据类型中，临床试验、案例研究和临床前水平都需要截然不同的证据声明细节，并最终创建三种独立的、独特的建模算法。这些算法的实施将有助于简化策展和专家审查过程。值得注意的是，没有创建模板，因为保存管理员风格和声音对于维护数据库的社区感觉很重要。为了确保最高水平的清晰度，我们的团队正在使用特定的新手和有经验的管理员来协助开发过程。随着这些算法通过试点阶段，它们将作为策展人培训工具进行测试。最终，这些指导方针将用于鼓励策展人的独立性，并加强CIViC中已经包含的证据。引文格式: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 Kiwala, Adam Coffman, Alex Wagner, Obi L. Griffith, Malachi Griffith。证据陈述管理算法的发展，以帮助癌症变异解释[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志，2021;81(13 -增刊):摘要nr 208。
{"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}
Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-199
Stephanie Zhang, Minsoo Kang
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.
{"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}
Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-260
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
{"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}
Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-178
Maayan Baron, T. Ideker
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.
{"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}
Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-203
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
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.
小细胞肺癌(SCLC)患者典型的低预期寿命和有限的治疗选择导致美国国家癌症研究所(NCI)将SCLC归类为“顽固性”癌症。SCLC- cellminer (https://discover.nci.nih.gov/SclcCellMinerCDB)整合了118个患者来源的SCLC细胞系的药物敏感性和基因组数据，提供了独特的基因组和药理学资源。转录组学分析验证了基于4个主要转录因子NEUROD1、ASCL1、POU2F3和YAP1表达的SCLC共识命名法(NAPY分类)，并展示了由这些谱系特异性转录因子驱动的差异转录网络。我们的分析揭示了连接SCLC亚型与MYC及其类似物MYCL和MYCN的转录网络，以及神经内分泌SCLC (N, A和P亚组)中NOTCH通路的失活。相比之下，yap1驱动的SCLC (SCLC- y)表达NOTCH通路，并共同表达YAP/TAZ及其驱动Hippo通路的负调控基因。SCLC-Y细胞系对标准护理药物(依泊苷、顺铂和拓扑替康)表现出最大的耐药性，而PI3K-AKT-mTOR抑制剂在该亚组中表现出更高的活性。为了探索免疫途径和基于转录因子的分类在选择可能对免疫检查点抑制剂有反应的SCLC患者中的潜在价值，我们探索了基于18个已建立的天然免疫反应和抗原呈递基因(APM评分)的转录组特征。SCLC-Y细胞系是唯一表达先天免疫应答基因的亚群。SCLC-CellMiner是一个强大的工具，展示了癌细胞系基因组和药理学数据库的价值。我们的分析表明，潜在的基因组分子分类可以选择靶向治疗和免疫治疗的患者，例如SCLC-Y亚组患者，他们可能对免疫检查点调节剂最敏感。引文格式:Camille Tlemsani, Lorinc Pongor, Fathi Elloumi, Luc Girard, Kenneth Huffman, Nitin Roper, Sudhir Varma, Augustin Luna, Vinodh Rajapakse, Pascaline boudoul - rouquette, Robin Sebastian, Kurt Kohn, Julia Krushkal, Mirit Aladjem, Beverly Teicher, Paul Meltzer, William Reinhold, John Minna, Anish Thomas, Yves Pommier。SCLC-CellMiner:广泛的细胞系基因组学和药理学资源鉴定了对靶向治疗和免疫治疗敏感的小细胞肺癌亚群[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志，2021;81(13 -增刊):摘要第203期。
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Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-192
Aleksandr Sarachakov, V. Svekolkin, Zoia Antysheva, Jessica H. Brown, A. Bagaev, N. Fowler
{"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}
Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-173
Qian Ke, Wikum Dinalankara, L. Younes, D. Geman, L. Marchionni
{"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}
Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-4
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
{"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}
Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-234
A. Jazayeri, Niusha Jafari, Christopher C. Yang, N. Nikita, G. Yao
{"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}
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