Kinase gene fusions: roles and therapeutic value in progressive and refractory papillary thyroid cancer

Liu, Mian; Chen, Pei; Hu, Hui-Yu; Ouyang, Dengjie; Khushbu, Rooh-Afza; Tan, Hai-Long; Huang, Peng; Shi, Chao

doi:10.1007/s00432-020-03491-5

Cited by 9 publications

(10 citation statements)

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“…fusions can affect the tumor behavior and prognosis of thyroid cancer [9,10]. For example, NTRK1/3 fusions have been reported to be associated with advanced tumor stage and aggressive lymphovascular invasion [11][12][13].…”

Section: Development Of An Rna Sequencing Panel To Detect Gene Fusions In Thyroid Cancermentioning

confidence: 99%

“…Various other gene fusions have also been identified in thyroid cancer, including RET , THADA , NTRK1 , NTRK3 , ALK , BRAF , MET , and FGFR2 [ 8 ]. It is well known that gene fusions can affect the tumor behavior and prognosis of thyroid cancer [ 9 , 10 ]. For example, NTRK1/3 fusions have been reported to be associated with advanced tumor stage and aggressive lymphovascular invasion [ 11 - 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Development of an RNA sequencing panel to detect gene fusions in thyroid cancer

2021

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In addition to mutations and copy number alterations, gene fusions are commonly identified in cancers. In thyroid cancer, fusions of important cancer-related genes have been commonly reported; however, extant panels do not cover all clinically important gene fusions. In this study, we aimed to develop a custom RNA-based sequencing panel to identify the key fusions in thyroid cancer. Our ThyChase panel was designed to detect 87 types of gene fusion. As quality control of RNA sequencing, five housekeeping genes were included in this panel. When we applied this panel for the analysis of fusions containing reference RNA (HD796), three expected fusions (EML4-ALK, CCDC6-RET, and TPM3-NTRK1) were successfully identified. We confirmed the fusion breakpoint sequences of the three fusions from HD796 by Sanger sequencing. Regarding the limit of detection, this panel could detect the target fusions from a tumor sample containing a 1% fusion-positive tumor cellular fraction. Taken together, our ThyChase panel would be useful to identify gene fusions in the clinical field.

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Section: Development Of An Rna Sequencing Panel To Detect Gene Fusions In Thyroid Cancermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of an RNA sequencing panel to detect gene fusions in thyroid cancer

2021

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“…The incidence of thyroid cancer (TC) in the United States (US) was estimated to be over 52,890 cases (12,720 men and 40,170 women) in 2020 and is projected to be the fourth most common cancer by 2030 [ 1 ]. The annual incidence has increased by 211% over the last two decades, mostly due to increased detection of papillary thyroid microcarcinoma (PTMC) [ 2 , 3 , 4 ]. Papillary thyroid cancer (PTC) and follicular thyroid cancer (FTC) account for most thyroid cancers [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

MicroRNA-Based Risk Score for Predicting Tumor Progression Following Radioactive Iodine Ablation in Well-Differentiated Thyroid Cancer Patients: A Propensity-Score Matched Analysis

Toraih

Fawzy

Hussein

et al. 2021

Cancers

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To identify molecular markers that can accurately predict aggressive tumor behavior at the time of surgery, a propensity-matching score analysis of archived specimens yielded two similar datasets of DTC patients (with and without RAI). Bioinformatically selected microRNAs were quantified by qRT-PCR. The risk score was generated using Cox regression and assessed using ROC, C-statistic, and Brier-score. A predictive Bayesian nomogram was established. External validation was performed, and causal network analysis was generated. Within the eight-year follow-up period, progression was reported in 51.5% of cases; of these, 48.6% had the T1a/b stage. Analysis showed upregulation of miR-221-3p and miR-222-3p and downregulation of miR-204-5p in 68 paired cancer tissues (p < 0.001). These three miRNAs were not differentially expressed in RAI and non-RAI groups. The ATA risk score showed poor discriminative ability (AUC = 0.518, p = 0.80). In contrast, the microRNA-based risk score showed high accuracy in predicting tumor progression in the whole cohorts (median = 1.87 vs. 0.39, AUC = 0.944) and RAI group (2.23 vs. 0.37, AUC = 0.979) at the cutoff >0.86 (92.6% accuracy, 88.6% sensitivity, 97% specificity) in the whole cohorts (C-statistics = 0.943/Brier = 0.083) and RAI subgroup (C-statistic = 0.978/Brier = 0.049). The high-score group had a three-fold increased progression risk (hazard ratio = 2.71, 95%CI = 1.86–3.96, p < 0.001) and shorter survival times (17.3 vs. 70.79 months, p < 0.001). Our prognostic microRNA signature and nomogram showed excellent predictive accuracy for progression-free survival in DTC.

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“…It is important to assess the PTC recurrence risk accurately for ensuring patients to receive the most appropriate treatment strategy. Over the past few decades, great efforts have been made in exploring prognostic biomarkers for PTC, in particular gene markers, such as mutations in the BRAF, RAS, PIK3CA, P53, PTEN, P53 and ALK genes ( 6 , 7 ). Although these studies showed promising results, they only focused on the factors driven by mutation and the level of transcription while ignoring the diversity of RNA isoform resulting from posttranscriptional modifications.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis of Prognostic Alternative Splicing Signature Reveals Recurrence Predictor for Papillary Thyroid Cancer

et al. 2021

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BackgroundAlternative splicing (AS) plays a key role in the diversity of proteins and is closely associated with tumorigenicity. The aim of this study was to systemically analyze RNA alternative splicing (AS) and identify its prognostic value for papillary thyroid cancer (PTC).MethodsAS percent-splice-in (PSI) data of 430 patients with PTC were downloaded from the TCGA SpliceSeq database. We successfully identified recurrence-free survival (RFS)-associated AS events through univariate Cox regression, LASSO regression and multivariate regression and then constructed different types of prognostic prediction models. Gene function enrichment analysis revealed the relevant signaling pathways involved in RFS-related AS events. Simultaneously, a regulatory network diagram of AS and splicing factors (SFs) was established.ResultsWe identified 1397 RFS-related AS events which could be used as the potential prognostic biomarkers for PTC. Based on these RFS-related AS events, we constructed a ten-AS event prognostic prediction signature that could distinguish high-and low-risk patients and was highly capable of predicting PTC patient prognosis. ROC curve analysis revealed the excellent predictive ability of the ten-AS events model, with an area under the curve (AUC) value of 0.889; the highest prediction intensity for one-year RFS was 0.923, indicating that the model could be used as a prognostic biomarker for PTC. In addition, the nomogram constructed by the risk score of the ten-AS model also showed high predictive efficiency for the prognosis of PTC patients. Finally, the constructed SF-AS network diagram revealed the regulatory role of SFs in PTC.ConclusionThrough the limited analysis, AS events could be regarded as reliable prognostic biomarkers for PTC. The splicing correlation network also provided new insight into the potential molecular mechanisms of PTC.

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Kinase gene fusions: roles and therapeutic value in progressive and refractory papillary thyroid cancer

Cited by 9 publications

References 120 publications

Development of an RNA sequencing panel to detect gene fusions in thyroid cancer

Development of an RNA sequencing panel to detect gene fusions in thyroid cancer

MicroRNA-Based Risk Score for Predicting Tumor Progression Following Radioactive Iodine Ablation in Well-Differentiated Thyroid Cancer Patients: A Propensity-Score Matched Analysis

Comprehensive Analysis of Prognostic Alternative Splicing Signature Reveals Recurrence Predictor for Papillary Thyroid Cancer

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