I-Corps: In Silico Prediction of Cancer Drug Susceptibility

I-Corps：癌症药物敏感性的计算机预测

• 批准号: 2116886
• 负责人: Mohsin Jafri
• 金额: $ 5万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2116886&HistoricalAwards=false
• 关键词: Corps; Silico; Prediction; Cancer; Drug

The broader impact/commercial potential of this I-Corps project is the development of computational method that will accurately predict whether a particular genetic variant is susceptible to a particular cancer treatment drug. Drug resistance is the main reason a cancer drug treatment regimen fails to cure a patient’s cancer. With drug resistance occurring in 30-85% of cancers, it accounts for a major part of the approximately $200 billion spent on cancer care in 2020 in the United States. This proposed technology may help to improve the diagnosis and treatment of cancer patients by avoiding rounds of failed chemotherapy by tailoring treatment to the genetic profile of the cancer. By determining the correct drug treatment based on the genetic profile of the cancer, the chances of patient survival may increase, the treatment time may decrease, and the overall cost of treatment may decrease. In addition, the proposed technology may be applicable to the diagnosis and treatment of rare genetic diseases and other personalized medicine applications by identifying variants that lead to the disease phenotype.This I-Corps project is based on the development of a computational method that uses machine learning applied to feature sets derived from molecular simulation to predict the functional consequences of genetic variation. Depending upon the cancer type, cancer drug treatments fail 30-85% of the time because of drug resistant genetic variants. However, only a small number of these variants have been linked to a specific drug treatment. To address this problem, a method was developed that leverages machine learning applied to features of all atom molecular dynamics simulations to predict the specific functional effect and the disruptive severity of genetic variants. This technology may be applied to data extracted from the molecular simulation of the proteins that are cancer drug targets and used to predict the susceptibility of a particular genetic variant of the protein to a specific cancer drug. The proposed technology was used to quantitatively predict cancer drug resistance caused by variants of the oncogene BCL-2 to specific drugs such as Venetoclax. Specifically, the technology was shown to determine which cancer drug will work on which genetic variant leading to targeted therapy tailored to the cancer patient’s genetic profile. In addition, the proposed technology may be used to quantitatively classify newly found or unclassified variants as cancer causing or benign creating the potential of early and more accurate cancer variant diagnosis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

I-Corps项目的更广泛的影响/商业潜力是开发一种计算方法，该方法将准确预测特定的基因变异是否对特定的癌症治疗药物敏感。耐药性是癌症药物治疗方案无法治愈患者癌症的主要原因。由于30-85%的癌症发生耐药性，它占2020年美国癌症治疗支出约2000亿美元的主要部分。这项拟议的技术可能有助于改善癌症患者的诊断和治疗，通过根据癌症的遗传特征定制治疗方案，避免化疗失败。通过根据癌症的遗传特征确定正确的药物治疗，患者生存的机会可能会增加，治疗时间可能会缩短，治疗的总成本可能会降低。此外，所提出的技术可以通过识别导致疾病表型的变异，适用于罕见遗传疾病的诊断和治疗以及其他个性化医疗应用。这个I-Corps项目是基于一种计算方法的开发，该方法将机器学习应用于来自分子模拟的特征集，以预测遗传变异的功能后果。根据癌症类型的不同，癌症药物治疗失败的几率为30-85%，原因是耐药基因变异。然而，这些变异中只有一小部分与特定的药物治疗有关。为了解决这个问题，开发了一种方法，利用机器学习应用于所有原子分子动力学模拟的特征来预测特定的功能效应和遗传变异的破坏性严重程度。该技术可应用于从作为癌症药物靶点的蛋白质的分子模拟中提取的数据，并用于预测蛋白质的特定遗传变异对特定癌症药物的敏感性。该技术被用于定量预测由致癌基因BCL-2变异引起的癌症对特定药物（如Venetoclax）的耐药性。具体来说，这项技术被证明可以确定哪种癌症药物对哪种基因变异有效，从而针对癌症患者的基因特征进行针对性治疗。此外，所提出的技术可用于定量地将新发现或未分类的变异分类为致癌或良性，从而创造早期和更准确的癌症变异诊断的潜力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。