Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-328
Kelton A. Schleyer, Ben Fetrow, P. Z. Fatland, Jun Liu, Maya Chaaban, Biwu Ma, Lina Cui
{"title":"Abstract 328: Selective fluorogenic probe for rapid detection of cathepsin L activity","authors":"Kelton A. Schleyer, Ben Fetrow, P. Z. Fatland, Jun Liu, Maya Chaaban, Biwu Ma, Lina Cui","doi":"10.1158/1538-7445.AM2021-328","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-328","url":null,"abstract":"","PeriodicalId":9563,"journal":{"name":"Cancer Chemistry","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81516118","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-SY07-02
J. Barnholtz-Sloan
Glioblastoma (GBM) is the most common type of primary malignant tumor in adults and contributes disproportionately to cancer morbidity and mortality. Understanding its molecular pathogenesis is crucial for improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomics data on 99-treatment naive GBMs provided insights to GBM biology. Multiple key findings emerged that were not known from traditional genomic analyses: (1) Analysis of protein phosphorylation identified key signaling intermediates in the RTK/RAS path-way common to multiple RTK genomic alterations (PTPN11 and PLCG1), potentially offering common therapeutic targets for different oncogenic drivers in GBM. (2) Phosphoproteomics also identified potential druggable targets based on kinase-substrate pathway analysis, as well as novel phosphoprotein targets associated with the regulation of telomere length by ATRX in IDH mutants. (3) TP53 protein abundance in GBM is regulated by protein/phosphoprotein effectors (4) Four immune GBM subtypes exist, characterized by distinct immune cell population differences – Immune High and Immune Low phenotypes in GBM were driven by tumor-associated macrophage markers, and associated with distinct epigenetic modifications and histone acetylation patterns. (5) The mesenchymal subtype displays EMT signatures specific in tumor cells, distinct from infiltrating immune cells. (6) Histone H2B acetylation and immune-low GBM was driven largely by BRDs, CREBBP, and EP300. (7) Identification of key metabolic changes in IDH mutants facilitating the accumulation of onco-metabolite 2-HG were also associated with lipidomic changes. This work identifies additional therapeutic channels for GBM and novel information useful for more accurate stratification patients for effective treatment. Citation Format: Jill S. Barnholtz-Sloan. Gaining biologic insights into glioblastoma using proteomics [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 SY07-02.
胶质母细胞瘤(GBM)是成人中最常见的原发性恶性肿瘤类型，并对癌症发病率和死亡率有不成比例的贡献。了解其分子发病机制对提高诊断和治疗具有重要意义。对99次治疗的初始GBM的基因组学、蛋白质组学、翻译后修饰和代谢组学数据进行综合分析，为GBM生物学提供了新的见解。传统基因组分析中不知道的多个关键发现:(1)蛋白质磷酸化分析确定了多个RTK基因组改变(PTPN11和PLCG1)共同的RTK/RAS通路中的关键信号中间体，可能为GBM中不同的致癌驱动因素提供共同的治疗靶点。(2)磷酸化蛋白质组学还通过激酶-底物通路分析发现了潜在的可药物靶点，以及IDH突变体中与ATRX调节端粒长度相关的新型磷酸化蛋白靶点。(3) GBM中TP53蛋白丰度受蛋白/磷蛋白效应物调控。(4)GBM存在四种免疫亚型，其特征是免疫细胞群差异明显——GBM中的免疫高表型和免疫低表型由肿瘤相关巨噬细胞标志物驱动，并与不同的表观遗传修饰和组蛋白乙酰化模式相关。(5)间充质亚型在肿瘤细胞中表现出特异性的EMT特征，与浸润性免疫细胞不同。(6)组蛋白H2B乙酰化和免疫低GBM主要由brd、CREBBP和EP300驱动。(7) IDH突变体中促进肿瘤代谢物2-HG积累的关键代谢变化也与脂质组学变化有关。这项工作确定了GBM的额外治疗渠道和新信息，有助于更准确地分层患者进行有效治疗。引用格式:Jill S. Barnholtz-Sloan。利用蛋白质组学获得胶质母细胞瘤的生物学见解[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):SY07-02。
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Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-294
Sanne Holt, Sophie C Vermond, M. Hazenoot, Rene J. McLaughlin, Marco Guadagnoli, M. Vlaming
{"title":"Abstract 294:In vitroefficacy studies to support engineered T cell therapies","authors":"Sanne Holt, Sophie C Vermond, M. Hazenoot, Rene J. McLaughlin, Marco Guadagnoli, M. Vlaming","doi":"10.1158/1538-7445.AM2021-294","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-294","url":null,"abstract":"","PeriodicalId":9563,"journal":{"name":"Cancer Chemistry","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":"81215723","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.AM2021-273
Catherine M. Ade, Y. Qi, Sudipto Das, K. Hanada, Tapan Maity, Xu Zhang, T. Andresson, U. Guha, J. Yang
{"title":"Abstract 273: A mass spectrometry survey of frequent HLA alleles successfully presenting common tumor specific mutations for immune recognition","authors":"Catherine M. Ade, Y. Qi, Sudipto Das, K. Hanada, Tapan Maity, Xu Zhang, T. Andresson, U. Guha, J. Yang","doi":"10.1158/1538-7445.AM2021-273","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-273","url":null,"abstract":"","PeriodicalId":9563,"journal":{"name":"Cancer Chemistry","volume":"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":"78974774","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.AM2021-284
V. Ouellette, Atziri Corin Chavez Alvarez, S. Fortin
{"title":"Abstract 284: Design, synthesis and biological evaluation of novel water-soluble salts of the antimitotic prodrugs 4-(3-alkyl-2-oxoimidazolidin-1-yl)-N-phenylbenzenesulfonamides selectively bioactivated by cytochrome p450 1A1 in breast cancer cells","authors":"V. Ouellette, Atziri Corin Chavez Alvarez, S. Fortin","doi":"10.1158/1538-7445.AM2021-284","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-284","url":null,"abstract":"","PeriodicalId":9563,"journal":{"name":"Cancer Chemistry","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82969909","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.AM2021-325
M. Dong, Karim Aljakouch, K. Böpple, Bernd Winkler, J. Schüler, F. Essmann, H. Kopp, J. Krijgsveld, W. Aulitzky
{"title":"Abstract 325: Nascent proteome analysis of tumor cells and their microenvironment in cultured human tumor tissues","authors":"M. Dong, Karim Aljakouch, K. Böpple, Bernd Winkler, J. Schüler, F. Essmann, H. Kopp, J. Krijgsveld, W. Aulitzky","doi":"10.1158/1538-7445.AM2021-325","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-325","url":null,"abstract":"","PeriodicalId":9563,"journal":{"name":"Cancer Chemistry","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88006121","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.AM2021-279
C. Brami, Ellaine Salvador, A. Kessler, M. Burek, T. Voloshin, M. Giladi, R. Ernestus, M. Löhr, C. Förster, C. Hagemann
{"title":"Abstract 279: Transient opening of the blood brain barrier by Tumor Treating Fields (TTFields)","authors":"C. Brami, Ellaine Salvador, A. Kessler, M. Burek, T. Voloshin, M. Giladi, R. Ernestus, M. Löhr, C. Förster, C. Hagemann","doi":"10.1158/1538-7445.AM2021-279","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-279","url":null,"abstract":"","PeriodicalId":9563,"journal":{"name":"Cancer Chemistry","volume":"28 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91499976","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.AM2021-19
Qimin Quan, J. Ritchey, J. Wilkinson, John Geanacopoulos, Alaina Kaiser, J. Boyce
{"title":"Abstract 19: Proteome-wide biomarker discovery using digital MosaicNeedles","authors":"Qimin Quan, J. Ritchey, J. Wilkinson, John Geanacopoulos, Alaina Kaiser, J. Boyce","doi":"10.1158/1538-7445.AM2021-19","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-19","url":null,"abstract":"","PeriodicalId":9563,"journal":{"name":"Cancer Chemistry","volume":"12369 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79470443","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.AM2021-278
Weidong Xie, Xiaoyan Cheng, Zhengfang Ding, R. Deng, D. Gu
Drug discovery is resource intensive, and involves typical timelines of 10-20 years and costs that range from US$0.5 billion to US$2.6 billion. Artificial intelligence (AI) has recently started to gear-up its application in various sectors of the society and the pharmaceutical industry as a frontrunner beneficiary.Artificial intelligence can accelerate drug discovery and reduce costs by facilitating the rapid screening and identification of compounds. We have developed DM-AI drug discovery platform, including convolutional neural networks, decision treealgorithm, reinforcement learning, generative adversarial networks, big data, and knowledge graphs, along with structure and ligand-based high-throughput virtual screening , for new drug discovery and development. DM-AI optimizes biological activity,toxicity,physicochemical property. We used DM-AI to discover potent inhibitors of SHP2, PIM1, DNA-PK, kinases target implicated in solid tumor and other diseases.We started to train a biological activity prediction model on a database of the given target kinase inhibitors (positive set) and non-kinase targets molecules (negative set), and then predicted the activity of existing million data sets, obtained an initial output of thousands structures. We then evaluated these structures using a pharmacophore reward model on the basis of virtual chemical spaces of kinase inhibitors in complex with target protein. To narrow our focus to a smaller set of molecules for analysis, compounds with higher score were filtered to remove patents and applications molecules, also remove molecules bearing structural alerts and reactive groups.By day 7 after target selection, We had selected dozens structures with structural diversity for experimental validation. and by day 28, they were tested for in vitro inhibitory activity in an enzymatic kinase assay, active compounds accounted for up to 65% in some target models. This illustrates the utility of our DM-AI drug discovery platform for the successful, rapid discovery of drug candidates. Citation Format: Weidong Xie, Xing Cheng, Zhengfang Ding, Riqiang Deng, Dawei Gu. Artificial intelligence accelerate drug discovery [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 278.
{"title":"Abstract 278: Artificial intelligence accelerate drug discovery","authors":"Weidong Xie, Xiaoyan Cheng, Zhengfang Ding, R. Deng, D. Gu","doi":"10.1158/1538-7445.AM2021-278","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-278","url":null,"abstract":"Drug discovery is resource intensive, and involves typical timelines of 10-20 years and costs that range from US$0.5 billion to US$2.6 billion. Artificial intelligence (AI) has recently started to gear-up its application in various sectors of the society and the pharmaceutical industry as a frontrunner beneficiary.Artificial intelligence can accelerate drug discovery and reduce costs by facilitating the rapid screening and identification of compounds. We have developed DM-AI drug discovery platform, including convolutional neural networks, decision treealgorithm, reinforcement learning, generative adversarial networks, big data, and knowledge graphs, along with structure and ligand-based high-throughput virtual screening , for new drug discovery and development. DM-AI optimizes biological activity,toxicity,physicochemical property. We used DM-AI to discover potent inhibitors of SHP2, PIM1, DNA-PK, kinases target implicated in solid tumor and other diseases.We started to train a biological activity prediction model on a database of the given target kinase inhibitors (positive set) and non-kinase targets molecules (negative set), and then predicted the activity of existing million data sets, obtained an initial output of thousands structures. We then evaluated these structures using a pharmacophore reward model on the basis of virtual chemical spaces of kinase inhibitors in complex with target protein. To narrow our focus to a smaller set of molecules for analysis, compounds with higher score were filtered to remove patents and applications molecules, also remove molecules bearing structural alerts and reactive groups.By day 7 after target selection, We had selected dozens structures with structural diversity for experimental validation. and by day 28, they were tested for in vitro inhibitory activity in an enzymatic kinase assay, active compounds accounted for up to 65% in some target models. This illustrates the utility of our DM-AI drug discovery platform for the successful, rapid discovery of drug candidates. Citation Format: Weidong Xie, Xing Cheng, Zhengfang Ding, Riqiang Deng, Dawei Gu. Artificial intelligence accelerate drug discovery [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 278.","PeriodicalId":9563,"journal":{"name":"Cancer Chemistry","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85813141","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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