Integrating transcriptomic, proteomic and pharmacogenomic data to inform individualized therapy in cancers

整合转录组学、蛋白质组学和药物基因组学数据，为癌症个体化治疗提供信息

• 批准号: 9925076
• 负责人: Bin Chen
• 金额: $ 16.91万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9925076
• 关键词: Address; Adopted; Antineoplastic Agents; Award; Basal cell carcinoma; Big Data; Big Data Methods; Bioinformatics; Biological Markers; Cancer Biology; Cancer Patient; Cancer cell line; Cell Line; Clinic; Clinical; Clinical Trials; Code; Communities; Computing Methodologies; Consumption; Data; Data Set; Databases; Disease; Ewings sarcoma; Expression Profiling; Gene Expression; Gene Proteins; Genomic Data Commons; Goals; Individual; Knowledge; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of liver; Mentors; Meta-Analysis; Methods; Molecular; Molecular Profiling; Mus; Mutation; Non-Malignant; Normal tissue morphology; Patients; Pattern; Pharmaceutical Preparations; Pharmacogenomics; Precision Medicine Initiative; Primary carcinoma of the liver cells; Probability; Proteomics; Research; Resources; Sampling; Scientist; Source; System; Therapeutic; Time; Tissue Sample; Translating; Treatment outcome; Tumor Tissue; Universities; Validation; Work; Xenograft procedure; actionable mutation; base; big-data science; c-myc Genes; cancer cell; cancer clinical trial; cancer genomics; cancer therapy; data sharing; drug candidate; drug efficacy; drug sensitivity; efficacy testing; genetic signature; individual patient; individualized medicine; learning classifier; malignant breast neoplasm; molecular marker; mouse model; novel; oncotype; optimal treatments; personalized cancer therapy; personalized medicine; pre-clinical; predictive marker; protein expression; response; statistics; tool; transcriptomics; triple-negative invasive breast carcinoma; tumor

PROJECT SUMMARY As a computational biologist, my long-term goal is to develop methods and tools to discover new or better therapeutics for cancers. In the past few years, I have identified drug-repositioning candidates for a number of primary cancers using Big Data approaches. These candidates have been validated successfully in preclinical mouse models. To maximize the utility of Big Data, I plan to translate the findings into therapeutics; therefore, I propose to develop methods to utilize transcriptomic, proteomic and pharmacogenomic data to inform individualized therapy in cancers. Current preclinical and clinical approaches including the NCI MATCH trial select therapies primarily based on actionable mutations, yet patients may have no actionable mutations or multiple actionable mutations that are hard to prioritize, suggesting the need for other different types of molecular biomarkers. The recent efforts have enabled the large-scale identification of various types of molecular biomarkers through correlating drug sensitivity with molecular profiles of pre-treatment cancer cell lines. Computational methods to match these biomarkers to individual patients to inform therapy in the clinic are thus in high demand. The objective of this award is therefore to develop computational approaches to identify therapeutics for individual patients by leveraging large-scale biomarkers identified from cancer cell lines. Through conducing this research, I expect to expand my knowledge in cancer clinical trials, cancer genomics, cancer biology, and statistics. To achieve the goal, I have gathered seven renowned experts from different fields related to Big Data Science as mentors/advisors/collaborators: Primary Mentor Dr. Atul Butte in translational bioinformatics from UCSF, Co-mentor Dr. Samuel So in cancer biology from Stanford University, Co-mentor Dr. Mark Segal in statistics from UCSF, Advisor Dr. Andrei Goga in cancer biology from UCSF, Advisor Dr. Laura Esserman in breast cancer trials from UCSF, Collaborator Dr. John Gordan in liver cancer trials from UCSF and Collaborator Dr. Xin Chen in cancer biology from UCSF. With the support from my world- class mentors, advisors and collaborators, this award will prepare me to be a leader in developing big data methods that are broadly impactful.

项目摘要 作为一名计算生物学家，我的长期目标是开发方法和工具， 癌症的治疗方法。在过去的几年里，我已经确定了一些药物重新定位的候选人， 使用大数据方法的原发性癌症。这些候选药物已在临床前试验中成功验证 小鼠模型。为了最大限度地利用大数据，我计划将研究结果转化为治疗方法;因此，我 建议开发利用转录组学、蛋白质组学和药物基因组学数据的方法， 癌症的个体化治疗目前的临床前和临床方法，包括NCI MATCH试验 主要基于可操作的突变选择疗法，但患者可能没有可操作的突变，或 多个可操作的突变，很难优先考虑，这表明需要其他不同类型的 分子生物标志物最近的努力使得能够大规模查明各种类型的 通过将药物敏感性与治疗前癌细胞的分子谱相关联的分子生物标志物 线将这些生物标志物与个体患者相匹配的计算方法，以告知临床治疗 因此需求量很大。因此，该奖项的目标是开发计算方法， 通过利用从癌细胞中鉴定大规模生物标志物来鉴定用于个体患者的治疗剂 线通过进行这项研究，我希望扩大我在癌症临床试验方面的知识， 基因组学癌症生物学和统计学为了实现这一目标，我召集了七位来自 作为导师/顾问/合作者的大数据科学相关的不同领域：主要导师Atul Butte博士 来自加州大学旧金山分校的翻译生物信息学，来自斯坦福大学的癌症生物学联合导师Samuel So博士， 加州大学旧金山分校统计学的共同导师马克·西格尔博士，加州大学旧金山分校癌症生物学的顾问安德烈·戈加博士， UCSF乳腺癌试验顾问Laura Esserman博士，肝癌合作者John Gordan博士 来自加州大学旧金山分校的癌症生物学合作者Xin Chen博士。在我的世界的支持下- 类导师，顾问和合作者，这个奖项将准备我是一个领导者在开发大数据 具有广泛影响力的方法。

项目成果

AICM: A Genuine Framework for Correcting Inconsistency Between Large Pharmacogenomics Datasets.

AICM：纠正大型药物基因组数据集之间不一致的真正框架。

• 发表时间: 2019
• 期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
• 作者: Hu,ZhiyueTom;Ye,Yuting;Newbury,PatrickA;Huang,Haiyan;Chen,Bin
• 通讯作者: Chen,Bin

OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features.

• DOI: 10.1038/s41596-020-00430-z
• 发表时间: 2021-03
• 期刊: Nature protocols
• 影响因子: 14.8
• 作者: Zeng B;Glicksberg BS;Newbury P;Chekalin E;Xing J;Liu K;Wen A;Chow C;Chen B
• 通讯作者: Chen B

Deciphering COVID-19 host transcriptomic complexity and variations for therapeutic discovery against new variants.

• DOI: 10.1016/j.isci.2022.105068
• 发表时间: 2022-10-21
• 期刊: ISCIENCE
• 影响因子: 5.8
• 作者: Xing, Jing;Shankar, Rama;Ko, Meehyun;Zhang, Keke;Zhang, Sulin;Drelich, Aleksandra;Paithankar, Shreya;Chekalin, Eugene;Chua, Mei-Sze;Rajasekaran, Surender;Tseng, Chien-Te Kent;Zheng, Mingyue;Kim, Seungtaek;Chen, Bin
• 通讯作者: Chen, Bin

Selecting precise reference normal tissue samples for cancer research using a deep learning approach.

使用深度学习方法为癌症研究选择精确的参考正常组织样本。

• DOI: 10.1186/s12920-018-0463-6
• 发表时间: 2019
• 期刊: BMC medical genomics
• 影响因子: 2.7
• 作者: Zeng,WilliamZD;Glicksberg,BenjaminS;Li,Yangyan;Chen,Bin
• 通讯作者: Chen,Bin

Published Anti-SARS-CoV-2 In Vitro Hits Share Common Mechanisms of Action that Synergize with Antivirals.

已发表的抗 SARS-CoV-2 体外热门药物具有与抗病毒药物协同作用的共同作用机制。

• DOI: 10.1101/2021.03.04.433931
• 发表时间: 2021
• 期刊: bioRxiv : the preprint server for biology
• 作者: Xing,Jing;Paithankar,Shreya;Liu,Ke;Uhl,Katie;Li,Xiaopeng;Ko,Meehyun;Kim,Seungtaek;Haskins,Jeremy;Chen,Bin
• 通讯作者: Chen,Bin