Background Thyroid cancer represents approximately 90% of endocrine cancers. Difficulties in diagnosis and low inter-observer agreement are sometimes encountered, especially in the distinction between the follicular variant of papillary thyroid carcinoma (fvptc) and other follicular-patterned lesions, and can present significant challenges. In the present proof-of-concept study, we report a gene-expression assay using NanoString nCounter technology (NanoString Technologies, Seattle, WA, U.S.A.) that might aid in the differential diagnosis of thyroid neoplasms based on gene-expression signatures. Methods Our cohort included 29 patients with classical papillary thyroid carcinoma (ptc), 13 patients with fvptc, 14 patients with follicular thyroid carcinoma (ftc), 14 patients with follicular adenoma (fa), and 14 patients without any abnormality. We developed a 3-step classifier that shows good correlation with the pathologic diagnosis of various thyroid neoplasms. Step 1 differentiates normal from abnormal thyroid tissue; step 2 differentiates benign from malignant lesions; and step 3 differentiates the common malignant entities ptc, ftc, and fvptc. Results Using our 3-step classifier approach based on selected genes, we developed an algorithm that attempts to differentiate thyroid lesions with varying levels of sensitivity and specificity. Three genes—namely SDC4, PLCD3, and NECTIN4/PVRL4—were the most informative in distinguishing normal from abnormal tissue with a sensitivity and a specificity of 100%. One gene, SDC4, was important for differentiating benign from malignant lesions with a sensitivity of 89% and a specificity of 92%. Various combinations of genes were required to classify specific thyroid neoplasms. Conclusions This preliminary proof-of-concept study suggests a role for nCounter technology, a digital gene expression analysis technique, as an adjunct assay for the molecular diagnosis of thyroid neoplasms.

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The risk assessment classification schemes for gastrointestinal stromal tumors (GIST) include tumor site, size, mitotic count and variably tumour rupture. Heterogeneity in high risk GIST poses limitations for current classification schemes.This study aims to demonstrate the clinical utility of risk stratification by gene expression profiling (GEP) using Nanostring technology. MethodFifty-six GIST cases were analyzed using a 231 gene expression panel. GEP results were correlated with clinical and pathological data. The prognostic performance was assessed in 34 patients with available survival data using ROC curves, Kaplan Meier Survival curves and compared with traditional risk assessment schemes.Volcano plot analysis identified seven genes with significantly higher expression (FDR < .05) in high risk than in non-high risk tumors, namely, TYMS, CDC2, TOP2A, CCNA2, E2F1, PCNA, and BIRC5. Together, these transcripts exhibited significantly higher expression in high risk tumors than in intermediate (P < .01), low (P < .001), and very low (P = 0.01) risk tumors. Receiver operating characteristic curve analysis demonstrated area under the curve (AUC) to be 0.858 for the separation of high risk and non-high risk tumors. Kaplan-Meier survival analysis demonstrated improved risk stratification (log-rank test P < .001) compared to the current risk assessment classification (P = 0.231). ConclusionIn addition to current clinical and histology-based risk classification for patients with GIST, gene expression may offer complementary prognostic information.

BackgroundTargeted therapy of patients with non-small cell lung cancer (NSCLC) who harbour sensitising mutations by tyrosine kinase inhibitors (TKIs) has been found more effective than traditional chemotherapies. However, target genes status (eg, epidermal growth factor receptor (EGFR) TKIs sensitising and resistant mutations) need to be tested for choosing appropriate TKIs. This study is to investigate the performance of a liquid biopsy-based targeted capture sequencing assay on the molecular analysis of NSCLC.MethodsPlasma samples from patients with NSCLC who showed resistance to the first/second-generation EGFR TKIs treatment were collected. The AVENIO ctDNA Expanded Kit is a 77 pan-cancer genes detection assay that was used for detecting EGFR TKIs resistance-associated gene mutations. Through comparison of the EGFR gene testing results from the Cobas EGFR Mutation Test v2, and UltraSEEK Lung Panel, the effectiveness of the targeted capture sequencing assay was verified.ResultsA total of 24 plasma cell-free DNA (cfDNA) samples were tested by the targeted capture sequencing assay. 33.3% (8/24) cfDNA samples were positive for EGFR exon 20 p.T790M which leads to EGFR dependent TKIs resistance. 8.3% (2/24) and 4.2% (1/24) samples were positive for mesenchymal-epithelial transition gene amplification and B‐Raf proto‐oncogene, serine/threonine kinase exon 15 p.V600E mutations which lead to EGFR independent TKIs resistance. The median value of the p.T790M variant allele fraction and variant copy numbers was 2% and 36.10 copies/mL plasma, respectively. The next-generation sequencing test showed higher than 90% concordance with either MassArray or qPCR-based methods for detecting either EGFR TKIs sensitising or resistance mutations.ConclusionThe targeted capture sequencing test can support comprehensive molecular analysis needed for TKIs treatment, which is promising to be clinically applied for the improved precision treatment of NSCLC.

PurposeThe risk assessment classification schemes for gastrointestinal stromal tumors (GIST) includes tumor site, size, mitotic count and variably tumour rupture. Heterogeneity in high risk GIST poses limitations for current classification schemes. Objective This study aims to demonstrate the clinical utility of risk stratification by gene expression profiling (GEP) using Nanostring technology. MEthodFifty-six GIST cases were analyzed using a 231 gene expression panel. GEP results were correlated with clinical and pathological data. The prognostic performance was assessed in 34 patients with available survival data using ROC curves, Kaplan Meier Survival curves and compared with traditional risk assessment schemes. Results Volcano plot analysis identified seven genes with significantly higher expression (FDR < .05) in high risk than in non-high risk tumors, namely, TYMS, CDC2, TOP2A, CCNA2, E2F1, PCNA, and BIRC5. Together, these transcripts exhibited significantly higher expression in high risk tumors than in intermediate ( P < .001), low ( P < .001), and very low ( P = 0.01) risk tumors. Receiver operating characteristic curve analysis demonstrated area under the curve (AUC) to be 0.861 for the separation of high risk and non-high risk tumors. Kaplan-Meier survival analysis demonstrated improved risk stratification (log-rank test P < .001) compared to the current risk assessment classification ( P = 0.18). Conclusion In addition to current clinical and histology-based risk classification for patients with GIST, gene expression may offer complementary prognostic information.