A risk model based on combinatorial messenger RNA and microRNA expression has potential clinical utility as a prognostic indicator of thyroid cancer recurrence, according to researchers.
The findings could facilitate earlier prediction of thyroid cancer recurrence, offering clinicians the ability to improve patient outcomes by tailoring treatment to disease risk and increasing posttreatment surveillance, Hannah R. Nieto, MBBS, MRCS, PhD, an academic clinical lecturer at the Institute of Metabolism and Systems Research at the University of Birmingham, United Kingdom, and colleagues, wrote in The Journal of Clinical Endocrinology & Metabolism.
Clinicians currently predict risk for thyroid cancer recurrence using clinical tools that often restage patients after their cancer treatment, according to the study background.
The strategy is useful in stratifying patients to the level of follow-up and degree of thyroid-stimulating hormone suppression required postoperatively; however, it does not inform the surgeon and patient about risk for recurrence until after all treatment has been completed.
“Understanding which thyroid carcinomas are going to recur, and the functional reasons behind the recurrence, will be key to improving patient care,” the researchers wrote.
Nieto and colleagues analyzed data from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes (mRNA/microRNA) in 46 recurrent vs. 455 nonrecurrent thyroid tumors. Two exonic mutational pipelines were used to identify somatic mutations. Functional gene analysis was performed in cell-based assays in multiple thyroid cell lines. The prognostic value of genes was evaluated with TCGA data sets.
Researchers identified 128 new potential biomarkers associated with recurrence, including 40 messenger RNAs (mRNAs), 39 microRNAs and 59 genetic variants. Among differentially expressed genes, modulation of fibronectin 1 (FN1), integrin alpha-3 (ITGalpha3) and MET proto-oncogene receptor tyrosine kinase (MET) had a “significant impact” on thyroid cancer cell migration.
Similarly, researchers observed ablation of miR-486 and miR-1179 increased migration of TPC-1 and SW1736 cells.
Researchers further utilized genes with a validated functional role and identified a five-gene risk score classifier as an independent predictor of thyroid cancer recurrence. After controlling for age, sex, disease stage, tumor stage and node status, multivariate analysis showed that the five-gene risk score classifier was the sole independent prognostic factor for the entire group of THCA patients.
“With the limited treatment options available, early prediction of recurrent disease should impact favorably on patient outcomes by tailoring treatment to disease risk and increasing posttreatment recurrence surveillance,” the researchers wrote. “Our data suggest that the expression of genes FN1, ITGalpha3, MET, miR-486 and miR-1179 can be useful future prognostic tools in indicating the likelihood of individual papillary thyroid cancer recurrence.”
PERSPECTIVE
As the authors note, current recommendations do not advise the molecular profiling of thyroid cancers at the time of their initial evaluation. For these prognostic data to be impactful, the time and substantial costs involved to send off molecular markers for every thyroid nodule or thyroid cancer need to be worked out.
These data are helpful in adding to the understanding of which molecular drivers are important for thyroid cancers and help inform why we sometimes see such a wide difference in disease persistence/recurrence rates despite similar tumor and patient characteristics.
Angela M. Leung, MD
Associate Professor of Medicine
Division of Endocrinology, Diabetes, and Metabolism
Department of Medicine
UCLA David Geffen School of Medicine
VA Greater Los Angeles Healthcare System
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