Dana-Farber Cancer Institute, Boston, MA, USA.
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Nature. 2021 Oct;598(7880):348-352. doi: 10.1038/s41586-021-03922-4. Epub 2021 Sep 22.
The determination of molecular features that mediate clinically aggressive phenotypes in prostate cancer remains a major biological and clinical challenge. Recent advances in interpretability of machine learning models as applied to biomedical problems may enable discovery and prediction in clinical cancer genomics. Here we developed P-NET-a biologically informed deep learning model-to stratify patients with prostate cancer by treatment-resistance state and evaluate molecular drivers of treatment resistance for therapeutic targeting through complete model interpretability. We demonstrate that P-NET can predict cancer state using molecular data with a performance that is superior to other modelling approaches. Moreover, the biological interpretability within P-NET revealed established and novel molecularly altered candidates, such as MDM4 and FGFR1, which were implicated in predicting advanced disease and validated in vitro. Broadly, biologically informed fully interpretable neural networks enable preclinical discovery and clinical prediction in prostate cancer and may have general applicability across cancer types.
确定介导前列腺癌临床侵袭表型的分子特征仍然是一个主要的生物学和临床挑战。最近在应用于生物医学问题的机器学习模型可解释性方面的进展，可能使临床癌症基因组学中的发现和预测成为可能。在这里，我们开发了 P-NET——一种基于生物学的深度学习模型——通过治疗抵抗状态对前列腺癌患者进行分层，并通过完全模型可解释性评估治疗抵抗的分子驱动因素，以进行治疗靶向。我们证明，P-NET 可以使用分子数据进行癌症状态预测，其性能优于其他建模方法。此外，P-NET 中的生物学可解释性揭示了已建立的和新的分子改变候选物，如 MDM4 和 FGFR1，它们与预测晚期疾病有关，并在体外得到验证。广义而言，基于生物学的完全可解释神经网络可实现前列腺癌的临床前发现和临床预测，并且可能在癌症类型中具有普遍适用性。
