Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in-depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray versus RNA sequencing data) and technical and uncharacterized biological variance between training and test data can pose challenges in classifying individual samples. In this article, we provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in a clinical diagnostic setting. We describe a framework for integrating several methods for quality control, normalization, batch correction, classification and reporting, and develop a use case of the framework in breast cancer.

中文翻译：

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为癌症分子分型建立灵活而强大的分析框架

分子亚型对于推断肿瘤侵袭性和预测预后至关重要。在实践中，肿瘤分析需要深入了解涉及处理和分析生成数据的生物信息学工具。此外，数据不兼容性（例如，微阵列与RNA测序数据）以及训练和测试数据之间的技术和未表征的生物学差异可能会给个体样本的分类带来挑战。在本文中，我们提供了在临床诊断环境中实施人类癌症分子分析生物信息学框架的路线图。我们描述了一个集成多种质量控制、标准化、批量校正、分类和报告方法的框架，并开发了该框架在乳腺癌中的用例。