Table 1_Development and validation of mRNA expression-based classifiers to predict low-risk thyroid tumors.docx

BackgroundMolecular variants and fusions in thyroid nodules can provide prognostic information at a population level. However, thyroid cancers harboring the same molecular alterations may exhibit diverse clinical behavior. Leveraging exome-enriched gene expression analysis may overcome the limitations seen in models based on a small number of point mutations or fusions. Here, we developed and validated mRNA-based classifiers with high negative predictive values to preoperatively rule out thyroid tumor invasion and lymph node metastases.
Materials and methodsIn this retrospective cohort study, histopathology reports from the Afirma Genomic Sequencing Classifier (GSC) algorithm training and consecutive thyroid cancer patients with Bethesda III–VI thyroid nodules in clinical practice (total 697 and ~50%, respectively) were scored for invasion and metastases. mRNA expression-based classifiers were developed utilizing literature-derived signatures as well as differentially expressed genes between samples with or without clinically significant invasion/metastases as the basic building blocks. Machine learning algorithms were employed to develop the final candidate classifiers. The final locked classifiers were validated on a retrospective cohort of 259 patients with Afirma testing who had thyroid surgery and had invasion and metastasis scores assigned based on histopathology while blinded to the classifier results.
ResultsA total of 697 (88% female) patient Afirma samples and scored histology reports were used for classifier development. In development, patients had a median age of 51 years. Ten percent of samples were assigned a high risk for invasion label, and 11.3% were assigned a high risk for lymph node metastasis (LNM) label. A low-risk invasion classifier result was assigned to 41.3% of the cohort with a negative predictive value (NPV) of 97.6%, and a low-risk LNM classifier result was assigned to 49.8% of the cohort with an NPV of 98.6%. In the validation cohort, made up of 75% women with a median age of 53 years, 51% of the samples were ruled out for high risk for invasion label with a 99% [95–100] NPV, and 53% were ruled out for high risk for LNM label with 100% [97–100] NPV.
DiscussionGene expression-based classifiers that confidently, preoperatively rule out thyroid tumor invasion and lymph node metastasis may help personalize the surgical approach for individuals, reducing overtreatment, surgical complications, and postoperative hypothyroidism.

研究背景：甲状腺结节中的分子变异与融合基因可在人群层面提供预后信息。然而，携带相同分子改变的甲状腺癌可能表现出多样的临床行为。利用外显子组富集基因表达分析，或可克服基于少数点突变或融合基因模型的局限性。本研究开发并验证了具备高阴性预测值（negative predictive value, NPV）的mRNA分类器，用于术前排除甲状腺肿瘤侵袭与淋巴结转移。

材料与方法：本研究为回顾性队列研究，纳入Afirma基因组测序分类器（Afirma Genomic Sequencing Classifier, GSC）算法训练队列，以及临床实践中符合Bethesda III~VI类甲状腺结节的连续性甲状腺癌患者（分别为697例和约占50%），对其侵袭与转移情况进行评分。基于mRNA表达的分类器以文献来源的特征集，以及伴或不伴临床显著侵袭/转移的样本间差异表达基因作为基本构建单元开发。采用机器学习算法构建最终候选分类器。最终定型的分类器在一项回顾性队列中进行验证：该队列包含259例接受Afirma检测、并行甲状腺手术的患者，其侵袭与转移评分基于组织病理学结果确定，且分类器结果对其设盲。

结果：本研究共纳入697例（女性占88%）Afirma样本及评分后的组织学报告用于分类器开发。开发队列患者的中位年龄为51岁。10%的样本被标记为高侵袭风险，11.3%的样本被标记为高淋巴结转移（lymph node metastasis, LNM）风险。41.3%的队列被分配为低侵袭风险分类结果，其阴性预测值为97.6%；49.8%的队列被分配为低LNM风险分类结果，其阴性预测值为98.6%。验证队列中，女性占75%，中位年龄为53岁。51%的样本被排除高侵袭风险，其阴性预测值为99%[95~100]；53%的样本被排除高LNM风险，其阴性预测值为100%[97~100]。

讨论：能够在术前准确排除甲状腺肿瘤侵袭与淋巴结转移的基于基因表达的分类器，或可帮助实现个体化手术方案，减少过度治疗、手术并发症及术后甲状腺功能减退症。