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

乳腺癌是导致加拿大女性死亡的第二大癌症原因，2013年加拿大新增23800例乳腺癌病例，估计死亡率为21%。个性化医学的出现，包括使用分子数据(例如癌症相关基因的突变)为个体患者量身定做治疗，有望显著改善我们现代社会的癌症管理。然而，尽管已经开发了数千种抗癌药物，但很少有药物被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来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质，预测药物反应是一项具有挑战性的任务。新的分子特征能够预测治疗反应，通常是通过永生化癌细胞或患者肿瘤活检发现的。最近，将人类肿瘤移植到小鼠体内的患者衍生异种移植(PDX)也能够在模型中测试药物，概括了真实患者肿瘤的大多数特征。这些临床前模型各有优缺点。到目前为止，这些数据从未结合起来开发出准确的药物反应预测指标。在我们的项目中，我们建议使用我们最近发表的基于网络的方法SNF来有效地结合癌细胞系、PDX和患者肿瘤，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进行测试。在成功验证后，我们将与我们的合作者密切合作，在临床常规中实施我们的新计算工具，以提高患者与测试他们最大受益的药物的临床试验的匹配度。

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.