Development and validation of mRNA expression-based classifiers to predict low-risk thyroid tumors
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 limitatio...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Endocrinology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1600815/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839628016186032128 |
|---|---|
| author | Allan Golding David Bimston Emma Namiranian Ellen Marqusee Gabriel Correa Evana Valenzuela Scheker Ruochen Jiang Yangyang Hao Mohammed Alshalalfa Jing Huang Joshua P. Klopper Richard T. Kloos Sara Ahmadi |
| author_facet | Allan Golding David Bimston Emma Namiranian Ellen Marqusee Gabriel Correa Evana Valenzuela Scheker Ruochen Jiang Yangyang Hao Mohammed Alshalalfa Jing Huang Joshua P. Klopper Richard T. Kloos Sara Ahmadi |
| author_sort | Allan Golding |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e6fb5078d1ff4af8b9e63c2d95b2723d |
| institution | Matheson Library |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-e6fb5078d1ff4af8b9e63c2d95b2723d2025-07-16T04:12:00ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-07-011610.3389/fendo.2025.16008151600815Development and validation of mRNA expression-based classifiers to predict low-risk thyroid tumorsAllan Golding0David Bimston1Emma Namiranian2Ellen Marqusee3Gabriel Correa4Evana Valenzuela Scheker5Ruochen Jiang6Yangyang Hao7Mohammed Alshalalfa8Jing Huang9Joshua P. Klopper10Richard T. Kloos11Sara Ahmadi12Memorial Healthcare System, Interventional Endocrinology, Hollywood, FL, United StatesMemorial Healthcare System, Endocrine Surgery, Hollywood, FL, United StatesBrigham and Women’s Hospital, Endocrine, Diabetes and Hypertension, Boston, MA, United StatesBrigham and Women’s Hospital, Endocrine, Diabetes and Hypertension, Boston, MA, United StatesMemorial Healthcare System, Interventional Endocrinology, Hollywood, FL, United StatesMemorial Healthcare System, Interventional Endocrinology, Hollywood, FL, United StatesVeracyte, Research Discovery, South San Francisco, CA, United StatesVeracyte, Research Discovery, South San Francisco, CA, United StatesVeracyte, Research Discovery, South San Francisco, CA, United StatesVeracyte, Research Discovery, South San Francisco, CA, United StatesVeracyte, Medical Affairs, South San Francisco, CA, United StatesVeracyte, Medical Affairs, South San Francisco, CA, United StatesBrigham and Women’s Hospital, Endocrine, Diabetes and Hypertension, Boston, MA, United StatesBackgroundMolecular 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.https://www.frontiersin.org/articles/10.3389/fendo.2025.1600815/fullthyroid nodulethyroid cancerAfirmamolecular diagnosticsthyroid tumor prognosismachine learning |
| spellingShingle | Allan Golding David Bimston Emma Namiranian Ellen Marqusee Gabriel Correa Evana Valenzuela Scheker Ruochen Jiang Yangyang Hao Mohammed Alshalalfa Jing Huang Joshua P. Klopper Richard T. Kloos Sara Ahmadi Development and validation of mRNA expression-based classifiers to predict low-risk thyroid tumors Frontiers in Endocrinology thyroid nodule thyroid cancer Afirma molecular diagnostics thyroid tumor prognosis machine learning |
| title | Development and validation of mRNA expression-based classifiers to predict low-risk thyroid tumors |
| title_full | Development and validation of mRNA expression-based classifiers to predict low-risk thyroid tumors |
| title_fullStr | Development and validation of mRNA expression-based classifiers to predict low-risk thyroid tumors |
| title_full_unstemmed | Development and validation of mRNA expression-based classifiers to predict low-risk thyroid tumors |
| title_short | Development and validation of mRNA expression-based classifiers to predict low-risk thyroid tumors |
| title_sort | development and validation of mrna expression based classifiers to predict low risk thyroid tumors |
| topic | thyroid nodule thyroid cancer Afirma molecular diagnostics thyroid tumor prognosis machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1600815/full |
| work_keys_str_mv | AT allangolding developmentandvalidationofmrnaexpressionbasedclassifierstopredictlowriskthyroidtumors AT davidbimston developmentandvalidationofmrnaexpressionbasedclassifierstopredictlowriskthyroidtumors AT emmanamiranian developmentandvalidationofmrnaexpressionbasedclassifierstopredictlowriskthyroidtumors AT ellenmarqusee developmentandvalidationofmrnaexpressionbasedclassifierstopredictlowriskthyroidtumors AT gabrielcorrea developmentandvalidationofmrnaexpressionbasedclassifierstopredictlowriskthyroidtumors AT evanavalenzuelascheker developmentandvalidationofmrnaexpressionbasedclassifierstopredictlowriskthyroidtumors AT ruochenjiang developmentandvalidationofmrnaexpressionbasedclassifierstopredictlowriskthyroidtumors AT yangyanghao developmentandvalidationofmrnaexpressionbasedclassifierstopredictlowriskthyroidtumors AT mohammedalshalalfa developmentandvalidationofmrnaexpressionbasedclassifierstopredictlowriskthyroidtumors AT jinghuang developmentandvalidationofmrnaexpressionbasedclassifierstopredictlowriskthyroidtumors AT joshuapklopper developmentandvalidationofmrnaexpressionbasedclassifierstopredictlowriskthyroidtumors AT richardtkloos developmentandvalidationofmrnaexpressionbasedclassifierstopredictlowriskthyroidtumors AT saraahmadi developmentandvalidationofmrnaexpressionbasedclassifierstopredictlowriskthyroidtumors |