Precision medicine in breast cancer: from the computer to the clinic

乳腺癌精准医学：从计算机到诊所

• 批准号: 493626-2016
• 负责人: HaibeKains, Benjamin
• 金额: $ 18.07万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Health Research Projects
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=621861
• 关键词: Precision; medicine; breast; cancer; computer

Breast cancer is the second leading cause of cancer deaths in Canadian women, with 23,800 new cases and an estimateddeath rate of 21% in Canada in 2013. The advent of personalized medicine, which consists of using molecular data(mutations in cancer-related genes for instance) to tailor treatment to the individual patient, holds the promise to significantlyimprove cancer management in our modern society. However, while thousands of anticancer drugs have been developed,few of them were translated into efficient therapeutic strategies in breast cancer. The primary reason for such a discrepancyis the inability to predict patients response to a given therapeutic drug. There is therefore a dire need for developing newclinical tests that can help clinicians select the most beneficial therapy for each individual breast cancer patient.Predicting drug response is a challenging task due to the complex nature of breast cancer. New molecular features enablingprediction of therapy response, are usually discovered using either immortalized cancer cells or patients tumor biopsies.More recently, patient-derived xenografts (PDX) where human tumors are implanted in mice, also enabled to test drugs in amodel that recapitulate most of the features of real patients tumors. These preclinical models have their own weaknessesand strengths. To date these data have never been combined to develop accurate predictors of drug response. We proposein our project to use our recently published network-based method called SNF to efficiently combine cancer cell lines, PDXand patients tumors to build better predictors of drug response and test them in new patients samples collected withinongoing clinical trials. Upon successfully validation, we will closely work with our collaborators to implement our newcomputational tool in clinical routine with the aim to improve matching of patients to the clinical trials testing the drugs fromwhich they most benefit.

乳腺癌是加拿大女性癌症死亡的第二大原因，2013 年加拿大有 23,800 例新病例，估计死亡率为 21%。个性化医疗的出现，其中包括使用分子数据（癌症相关基因的突变）例如）为个体患者量身定制治疗方案，有望显着改善现代社会的癌症管理。然而，尽管已经开发出数千种抗癌药物，但很少有药物能转化为有效的乳腺癌治疗策略。造成这种差异的主要原因是无法预测患者对给定治疗药物的反应。因此，迫切需要开发新的临床测试，帮助临床医生为每个乳腺癌患者选择最有益的治疗方法。由于乳腺癌的复杂性，预测药物反应是一项具有挑战性的任务。通常使用永生化癌细胞或患者肿瘤活检来发现能够预测治疗反应的新分子特征。最近，将人类肿瘤植入小鼠体内的患者来源异种移植物（PDX）也能够在模型中测试药物，该模型概括了大多数真实患者肿瘤的特征。这些临床前模型有其自身的弱点和优点。迄今为止，这些数据从未被合并来开发药物反应的准确预测因子。我们建议在我们的项目中使用我们最近发布的基于网络的方法（称为 SNF）来有效地将癌细胞系、PDX 和患者肿瘤结合起来，以建立更好的药物反应预测因子，并在正在进行的临床试验中收集的新患者样本中对其进行测试。成功验证后，我们将与合作者密切合作，在临床常规中实施我们的新计算工具，目的是改善患者与测试他们最受益的药物的临床试验的匹配。