Genetic characterization of medullary thyroid cancer in childhood survivors of the Chernobyl accident

Minor role of TP53 and TERT promoter mutations in medullary thyroid carcinoma: report of new cases and revision of the literature

PURPOSE

Aims of this study were to investigate the prevalence of TP53 and TERT mutations in Medullary Thyroid carcinoma (MTC) and their role in inducing aggressiveness in positive cases.

METHODS

We performed a literature search in PubMed to identify studies investigating the prevalence of TERT and TP53 mutations in MTC. We also included data on MTC cases (n = 193) obtained at our center and unpublished. The in-silico pathogenicity of the TP53 mutations has been evaluated by predictor tools.

RESULTS

We identified a total of 25 and 11 published papers: all together 1280 cases have been investigated for the presence of TP53 mutations and 974 for TERT promoter mutation. Twenty-five out of 1280 (2%) cases had a TP53 mutation while only 3/974 MTC cases (0.3%) have been found to be positive for TERT promoter mutations. Among all, we identified 19 different TP53 mutations that in 12 cases were demonstrated to have an in silico predicted high pathogenic role and a high impact on protein function. Three non-sense and 4 probably not damaging mutations were also reported. The pathogenic role of the TERT promoter mutations has been previously in vitro determined. No correlation between TP53 and/or TERT mutations and aggressiveness of MTC has been demonstrated.

CONCLUSION

The prevalence of TP53 and TERT promoter mutations is very low in MTC. The reported mutations are pathogenic in the majority of cases. Because of their rarity it is not possible to clarify if they play or not a role in the pathogenesis and/or aggressiveness of this specific thyroid tumor.

Medullary thyroid carcinoma in children: current state of the art and future perspectives

Medullary thyroid carcinoma (MTC) is a distinct type of malignant thyroid tumor in cell origin, biological behavior, and natural history. It accounts for 1.6% of all thyroid cancers and presents either sporadically or as a hereditary disease, the latter occurring as a part of multiple endocrine neoplasia (MEN) 2A and MEN2B syndromes or as a familial MTC disease with no other manifestations. The gene responsible for the hereditary form is the rearranged during transfection (RET) gene, a proto-oncogene located to human chromosome 10. Most pediatric MTC cases have been discovered after genetic testing investigations, leading to the concept of prophylactic surgery in presymptomatic patients. Therefore, the genetic status of the child, along with serum calcitonin levels and ultrasonographic findings, determine the appropriate age for prophylactic surgical intervention. Nevertheless, a diagnosis at an early stage of MTC warrants total thyroidectomy and central lymph node dissection with the addition of lateral/contralateral lymph node dissection depending on the tumor size, ultrasonographic evidence of neck disease, or calcitonin levels. Conversely, locally advanced/unresectable or metastatic MTC is primarily treated with multikinase inhibitors, while more specific RET inhibitors are being tested in clinical trials with promising results.

Ultrasound images-based deep learning radiomics nomogram for preoperative prediction of RET rearrangement in papillary thyroid carcinoma

PURPOSE

To create an ultrasound -based deep learning radiomics nomogram (DLRN) for preoperatively predicting the presence of RET rearrangement among patients with papillary thyroid carcinoma (PTC).

METHODS

We retrospectively enrolled 650 patients with PTC. Patients were divided into the RET/PTC rearrangement group (n = 103) and the non-RET/PTC rearrangement group (n = 547). Radiomics features were extracted based on hand-crafted features from the ultrasound images, and deep learning networks were used to extract deep transfer learning features. The least absolute shrinkage and selection operator regression was applied to select the features of nonzero coefficients from radiomics and deep transfer learning features; then, we established the deep learning radiomics signature. DLRN was constructed using a logistic regression algorithm by combining clinical and deep learning radiomics signatures. The prediction performance was evaluated using the receiver operating characteristic curve, calibration curve, and decision curve analysis.

RESULTS

Comparing the effectiveness of the models by linking the area under the receiver operating characteristic curve of each model, we found that the area under the curve of DLRN could reach 0.9545 (95% confidence interval: 0.9133-0.9558) in the test cohort and 0.9396 (95% confidence interval: 0.9185-0.9607) in the training cohort, indicating that the model has an excellent performance in predicting RET rearrangement in PTC. The decision curve analysis demonstrated that the combined model was clinically useful.

CONCLUSION

The novel ultrasonic-based DLRN has an important clinical value for predicting RET rearrangement in PTC. It can provide physicians with a preoperative non-invasive primary screening method for RET rearrangement diagnosis, thus facilitating targeted patients with purposeful molecular sequencing to avoid unnecessary medical investment and improve treatment outcomes.

RET fusions in solid tumors

The RET proto-oncogene has been well-studied. RET is involved in many different physiological and developmental functions. When altered, RET mutations influence disease in a variety of organ systems from Hirschsprung's disease and multiple endocrine neoplasia 2 (MEN2) to papillary thyroid carcinoma (PTC) and non-small cell lung cancer (NSCLC). Changes in RET expression have been discovered in 30-70% of invasive breast cancers and 50-60% of pancreatic ductal adenocarcinomas in addition to colorectal adenocarcinoma, melanoma, small cell lung cancer, neuroblastoma, and small intestine neuroendocrine tumors. RET mutations have been associated with tumor proliferation, invasion, and migration. RET fusions or rearrangements are somatic juxtapositions of 5' sequences from other genes with 3' RET sequences encoding tyrosine kinase. RET rearrangements occur in approximately 2.5-73% of sporadic PTC and 1-3% of NSCLC patients. The most common RET fusions are CDCC6-RET and NCOA4-RET in PTC and KIF5B-RET in NSCLC. Tyrosine kinase inhibitors are drugs that target kinases such as RET in RET-driven (RET-mutation or RET-fusion-positive) disease. Multikinase inhibitors (MKI) target various kinases and other receptors. Several MKIs are FDA-approved for cancer therapy (sunitinib, sorafenib, vandetanib, cabozantinib, regorafenib, ponatinib, lenvatinib, alectinib) and non-oncologic disease (nintedanib). Selective RET inhibitor drugs LOXO-292 (selpercatinib) and BLU-667 (pralsetinib) are also undergoing phase I/II and I clinical trials, respectively, with preliminary results demonstrating partial response and low incidence of serious adverse events. RET fusions provide a viable therapeutic target for oncologic treatment, and further study is warranted into the prevalence and pathogenesis of RET fusions as well as development of current and new tyrosine kinase inhibitors.