Hardwiring Mechanism into Predicting Cancer Phenotypes by Computational Learning

描述（由申请人提供）：尽管有大量可用的全基因组研究和大量统计算法的相应应用，但很少有来自基因组规模数据的生物标志物转化为癌症亚型的改进的临床分类。这种普遍存在的缺点源于普遍使用的“现成”算法和机器学习技术，这些算法和技术是为图像分类和语言处理开发的，它们对系统的底层生物学是幼稚的。此外，对于全基因组数据，相对于潜在的候选生物标志物的数量，样品的数量通常较小，导致独立测试数据的可变准确性，尽管用于发现的样品具有高准确性，这导致临床生物标志物的失败。这个问题--所谓的“维度灾难”--由于急剧增加样本量的高昂成本以及患者分层为更小的亚组进行个性化和精确医疗而进一步加剧。疾病表型产生于由其分子组分的相互作用所定义的选定网络和途径中的独特和特定的扰动。在癌症中，这些扰动可能存在于基因调控网络拓扑结构和状态中，细胞信号传导活性中或代谢条件中。我们假设，通过对癌症生物学的这种先验生物学信息的分析，我们将能够降低模型的复杂性，并建立机械合理的预测模型。为了实现这一假设，我们将开发一个分析框架，将来自网络生物学的机械约束嵌入到统计学习过程本身。因此，本申请将开发一套新的统计学习算法，嵌入（目标1）基因表达调控网络，（目标2）细胞信号传导活性和（目标3）代谢，以分类乳腺癌和前列腺癌。在整个研究过程中，我们将与临床合作者密切合作，以确保我们的方法超越当前的预测和预后模型。最后，由于在我们的研究中，我们还将基于从已经市售的临床测定中获得的基因表达测量结果生成机制分类器（即，MammaPrint®和Decipher®），我们的创新模型和预测器也将随时用于临床翻译。我们的机制驱动分类器将同时具有更高的准确性和可解释性，而不是不考虑疾病的潜在生物学的分类器。此外，在分类器中嵌入生物学机制也将有助于识别特定于每种癌症亚型的替代治疗靶点，从而可能改善患者预后和健康结果。最后，我们将在该项目中进行的分子途径和生物网络的大量管理也将为未来的研究提供强大的资源，我们将开发的方法也将适用于其他癌症和其他人类疾病，如神经退行性疾病，心脏病和糖尿病。

DESCRIPTION (provided by applicant): Few biomarkers derived from genome scale data have translated into improved clinical classification of cancer subtypes, in spite of the wealth of available genome-wide studies and of the corresponding application of numerous statistical algorithms. This widespread shortcoming derives from the pervasive use of "off the shelf" algorithms and machine learning techniques developed for image classification and language processing, which are naïve of the underlying biology of the system. Furthermore, for genome-wide data, the number of samples is often small relative to the number of potential candidate biomarkers, resulting in variable accuracy on independent test data despite high accuracy in the samples used for discovery, which contributes to the failure of clinical biomarkers. This problem - so called "curse of dimensionality" - is further exacerbated by the prohibitive cost of dramatically increasing sample size and by patient stratification into smaller subgroups for personalized and precision medicine. Disease phenotypes arise from distinct and specific perturbations in selected networks and pathways defined by the interactions of their molecular constituents. In cancer, these perturbations may reside in gene regulatory networks topology and state, in cell signaling activity, or in metabolic conditions. We hypothesize that by leveragin such prior biological information on cancer biology we will be able to reduce model complexity and build mechanistically justified predictive models. To pursue this hypothesis, we will develop an analytical framework to embed mechanistic constraints derived from network biology into the statistical learning process itself. Hence, this application will develop a novel suite of statistial learning algorithms that embed (Aim 1) gene expression regulatory networks, (Aim 2) cell signaling activity, and (Aim 3) metabolism to classify breast and prostate cancer. Throughout the study we will work closely with clinical collaborators to ensure that our method improve over and above current predictive and prognostic models. Finally, since in our study we will also generate mechanistic classifiers based on gene expression measurements obtained from clinical assays that are already commercially available (i.e., MammaPrint®, and Decipher®), our innovative models and predictors will be also readily available for clinical translation. Our mechanism-driven classifiers will simultaneously have greater accuracy and interpretability than classifiers developed without regard for the underlying biology of the disease. Furthermore, embedding biological mechanisms in the classifiers will also facilitate the identification of alternative therapeutic targets specific to each cancer subtype, potentially improving patient prognosis and health outcomes. Finally, the substantial curation of molecular pathways and biological networks we will carry on in the project will also provide a powerful resource for futur studies, and the methodologies we will develop will be also applicable to other cancer and other human diseases, like neurodegenerative disorders, hearth disease, and diabetes.