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Prediction of EVT6-NTRK3-Dependent Papillary Thyroid Cancer Using Minor Expression Profile
A A Kechin1, A A Ivanov2, A E Kel1
1Institute of Chemical Biology and Fundamental Medicine, Siberian Division of the Russian Academy of Sciences, Novosibirsk, Russia.
Abstract:
Solid tumors resulting from oncogenic stimulation of neurotrophin receptors (TRK) by chimeric proteins are a group of rare tumors of various localization that respond to therapy with targeted drugs entrectinib and larotrectinib. The standard method for detecting chimeric TRK genes in tumor samples today is considered to be next generation sequencing with the determination of the prime structure of the chimeric transcripts. We hypothesized that expression of the chimeric tyrosine kinase proteins in tumors can determine the specific transcriptomic profile of tumor cells. We detected differentially expressed genes allowing distinguishing between TRK-dependent tumors papillary thyroid cancer (TC) from other molecular variants of tumors of this type. Using PCR with reverse transcription (RT-PCR), we identified 7 samples of papillary TC carrying a EVT6-NTRK3 rearrangement (7/215, 3.26%). Using machine learning and the data extracted from TCGA, we developed of a recognition function for predicting the presence of rearrangement in NTRK genes based on the expression of 10 key genes: AUTS2, DTNA, ERBB4, HDAC1, IGF1, KDR, NTRK1, PASK, PPP2R5B, and PRSS1. The recognition function was used to analyze the expression data of the above genes in 7 TRK-dependent and 10 TRK-independent thyroid tumors obtained by RT-PCR. On the test samples from TCGA, the sensitivity was 72.7%, the specificity - 99.6%. On our independent validation samples tested by RT-PCR, sensitivity was 100%, specificity - 70%. We proposed an mRNA profile of ten genes that can classify TC in relation to the presence of driver NTRK-chimeric TRK genes with acceptable sensitivity and specificity.
Insights
Researchers identified a 10-gene mRNA profile to detect NTRK gene fusions in thyroid cancer. This molecular signature aids in identifying TRK-dependent tumors for targeted therapy, improving diagnostic accuracy.
Area of Science:
- Oncology
- Molecular Biology
- Genetics
Background:
- Rare solid tumors driven by oncogenic neurotrophin receptor (TRK) fusions respond to targeted therapies like entrectinib and larotrectinib.
- Current detection relies on next-generation sequencing to identify chimeric TRK transcripts.
- A hypothesis suggests that chimeric tyrosine kinase protein expression creates a unique tumor cell transcriptomic profile.
Purpose of the Study:
- To investigate if a specific transcriptomic profile can distinguish TRK-dependent tumors.
- To develop a predictive model for identifying NTRK gene rearrangements in papillary thyroid cancer (TC).
- To establish an mRNA profile for classifying TC based on driver NTRK-chimeric TRK genes.
Main Methods:
- Utilized reverse transcription PCR (RT-PCR) to identify EVT6-NTRK3 rearrangements in 215 papillary TC samples.
- Employed machine learning on TCGA data to develop a predictive function based on 10 key genes.
- Validated the predictive function on independent RT-PCR data from TRK-dependent and TRK-independent thyroid tumors.
Main Results:
- Identified 7 papillary TC samples (3.26%) with EVT6-NTRK3 rearrangement.
- Developed a 10-gene recognition function (AUTS2, DTNA, ERBB4, HDAC1, IGF1, KDR, NTRK1, PASK, PPP2R5B, PRSS1) for predicting NTRK rearrangements.
- Achieved high sensitivity (100%) and specificity (70%) on independent validation samples, with TCGA data showing 72.7% sensitivity and 99.6% specificity.
Conclusions:
- A 10-gene mRNA expression profile can effectively classify papillary thyroid cancer concerning the presence of driver NTRK-chimeric TRK genes.
- This molecular signature offers a potentially valuable tool for identifying patients eligible for TRK-targeted therapies.
- The developed method demonstrates acceptable sensitivity and specificity for clinical application in TRK fusion detection.
