Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents.

临床前模型一直是癌症研究的主力，产生了大量的药物反应数据。不幸的是，事实证明，将这些数据集衍生的反应生物标志物转化为人类肿瘤特别具有挑战性。为了应对这一挑战，我们开发了 TRANSACT，这是一个计算框架，它构建了一个共识空间来捕获临床前模型和人类肿瘤常见的生物过程，并利用该空间构建药物反应预测因子，从而稳健地从临床前模型转移到人类肿瘤。与四种竞争方法（包括两种深度学习方法）相比，TRANSACT 在癌症基因组图谱上的 23 个药物预测挑战和 Hartwig 医学基金会的 226 个转移性肿瘤挑战中表现出色。我们证明，反应预测为许多具有高度临床重要性的疗法提供了稳健的性能：铂类化疗、吉西他滨和紫杉醇。与其他方法相比，我们通过正确识别靶向治疗的已知生物标志物来证明 TRANSACT 预测因子的可解释性，并且我们提出了介导两种化疗药物耐药性的潜在机制。