Table 3_Machine learning-based single-sample molecular classifier for cancer grading.xlsx

Tumor subtyping based on morphological grade is used in cancer treatment and management decision-making and to determine a patient’s prognosis. While low- and high-grade tumors are predictive of patient survival for many cancers, tumors of intermediate morphological grades are considered unreliable due to interobserver variability and thus do not have clear prognostic significance. To address this issue, we devised a molecular-based classifier that uses gene expression data from RNA sequencing (RNA-seq) or microarray profiling to predict high- and low-grade risk groups for breast, lung, and renal cancers. For this classifier, we developed a preprocessing procedure that only required expression data from a single sample, without the need for any batch correction or cohort scaling. This classifier, while trained only on RNA sequencing data, achieves highly accurate risk predictions on both RNA-seq and microarray data. First, the molecular grades (mGrades) predicted by this classifier correlated strongly with the pathologist-assigned histological grades and clinical stage. Next, we showed that mGrades were effective in assessing risk levels for G2 samples. Finally, we identified common and unique biological and genetic features in samples of low and high mGrades across breast, lung, and renal cancers. Gene expression patterns as revealed by the classifier can provide useful information for both research and diagnostic purposes.

基于形态学分级的肿瘤分型现已广泛应用于癌症治疗、临床管理决策制定以及患者预后评估。尽管对于多数癌种而言，低级别与高级别肿瘤能够有效预测患者的生存结局，但处于中间形态学分级的肿瘤却因观察者间差异导致可靠性不足，因此尚无明确的预后参考价值。为解决这一痛点，我们研发了一款基于分子特征的分类器，该分类器可利用RNA测序（RNA-seq）或微阵列表达谱分析获取的基因表达数据，对乳腺癌、肺癌及肾癌的高低级别风险分组进行预测。针对该分类器，我们开发了一套仅需单一样本基因表达数据即可运行的预处理流程，无需进行任何批次校正或队列缩放操作。该分类器虽仅以RNA测序数据进行训练，但在RNA-seq及微阵列数据上均能实现高精度的风险预测。首先，该分类器预测得到的分子分级（mGrades）与病理学家判定的组织学分级及临床分期呈现出极强的相关性。其次，我们证实了分子分级可有效用于评估G2样本的风险等级。最后，我们在乳腺癌、肺癌及肾癌的高低分子分级样本中，分别鉴定出了共通与独特的生物学及遗传学特征。该分类器所揭示的基因表达模式，可为科研与临床诊断领域提供极具价值的参考信息。