The Journal of clinical endocrinology and metabolism · 2026
Long-term Survivors of Anaplastic Thyroid Cancer: A Genomic Predictive Model.
СтатьяМногоцентровое исследованиеПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Greenspun Benjamin C, Metzger Daniel Aryeh, Lee Sally, Pearson Bradley E, Li Jin H, Chen Shuibing, Zarnegar Rasa, Fahey Iii Thomas J, Finnerty Brendan M
Аннотация
CONTEXT: Longer-term survival is possible for some patients with anaplastic thyroid cancer (ATC). However, genomic factors associated with improved survival are poorly characterized.
OBJECTIVE: To develop a mathematical model to predict mutation-based survival risk in ATC.
DESIGN: Retrospective cohort study of 204 ATC samples from the cBioPortal database, divided into 80% training and 20% validation cohorts. Multivariate analysis identified prognostic genes, used to construct a point-based risk model. KEGG pathway enrichment and BRAF subanalyses were performed.
SETTING: Multi-institutional, international genomic database.
PATIENTS OR OTHER PARTICIPANTS: Samples were included if sequencing and survival data were available (n = 204).
INTERVENTION(S): Not applicable.
MAIN OUTCOME MEASURE(S): The prespecified primary outcome was overall survival.
RESULTS: Fourteen genes were associated with increased risk: TET1, MAPK12, ATP10A, PIK3CA, MUC4, PNPLA2, PLD4, EGLN2, BSN, FLNC, RADIL, ZMYND8, FRAS1, RECQL4. More aggressive (n = 37) and less aggressive cohorts (n = 128) were determined using the maximally selected rank statistic, yielding a point threshold of 0.27. The predictive performance of the risk model demonstrated a C-index of 0.74. On Kaplan-Meier analysis, 1-year survival differed for more aggressive patients (0%) compared to less aggressive patients (32%). For the validation cohort, survival remained significantly different between risk cohorts and on BRAF subanalysis. Each risk cohort subsequently underwent KEGG pathway enrichment analysis, which showed significantly increased enrichment across several pathways for more aggressive tumors.
CONCLUSION: This model identifies mutated genes that are associated with the most aggressive ATCs and thus may aid in preoperative risk assessment when evaluating patients for surgery for curative intent.
OBJECTIVE: To develop a mathematical model to predict mutation-based survival risk in ATC.
DESIGN: Retrospective cohort study of 204 ATC samples from the cBioPortal database, divided into 80% training and 20% validation cohorts. Multivariate analysis identified prognostic genes, used to construct a point-based risk model. KEGG pathway enrichment and BRAF subanalyses were performed.
SETTING: Multi-institutional, international genomic database.
PATIENTS OR OTHER PARTICIPANTS: Samples were included if sequencing and survival data were available (n = 204).
INTERVENTION(S): Not applicable.
MAIN OUTCOME MEASURE(S): The prespecified primary outcome was overall survival.
RESULTS: Fourteen genes were associated with increased risk: TET1, MAPK12, ATP10A, PIK3CA, MUC4, PNPLA2, PLD4, EGLN2, BSN, FLNC, RADIL, ZMYND8, FRAS1, RECQL4. More aggressive (n = 37) and less aggressive cohorts (n = 128) were determined using the maximally selected rank statistic, yielding a point threshold of 0.27. The predictive performance of the risk model demonstrated a C-index of 0.74. On Kaplan-Meier analysis, 1-year survival differed for more aggressive patients (0%) compared to less aggressive patients (32%). For the validation cohort, survival remained significantly different between risk cohorts and on BRAF subanalysis. Each risk cohort subsequently underwent KEGG pathway enrichment analysis, which showed significantly increased enrichment across several pathways for more aggressive tumors.
CONCLUSION: This model identifies mutated genes that are associated with the most aggressive ATCs and thus may aid in preoperative risk assessment when evaluating patients for surgery for curative intent.