Dataset Information

A highly specific and sensitive massive parallel sequencer-based test for somatic mutations in non-small cell lung cancer.

ABSTRACT: Molecular targeting therapy for non-small cell lung cancer (NSCLC) has clarified the importance of mutation testing when selecting treatment regimens. As a result, multiple-gene mutation tests are urgently needed. We developed a next-generation sequencer (NGS)-based, multi-gene test named the MINtS for investigating driver mutations in both cytological specimens and snap-frozen tissue samples. The MINtS was used to investigate the EGFR, KRAS, BRAF genes from DNA, and the ERBB2, and the ALK, ROS1, and RET fusion genes from RNA. We focused on high specificity and sensitivity (?0.99) and even included samples with a cancer cell content of 1%. The MINtS enables testing of more than 100 samples in a single run, making it possible to process a large number of samples submitted to a central laboratory, and reducing the cost for a single sample. We investigated 96 cytological samples and 190 surgically resected tissues, both of which are isolated in daily clinical practice. With the cytological samples, we compared the results for the EGFR mutation between the MINtS and the PNA-LNA PCR clamp test, and their results were 99% consistent. In the snap-frozen tissue samples, 188/190 (99%) samples were successfully analyzed for all genes investigated using both DNA and RNA. Then, we used 200 cytological samples that were serially isolated in clinical practice to assess RNA quality. Using our procedure, 196 samples (98%) provided high-quality RNA suitable for analysis with the MINtS. We concluded that the MINtS test system is feasible for analyzing "druggable" genes using cytological samples and snap-frozen tissue samples. The MINtS will fill a needs for patients for whom only cytological specimens are available for genetic testing.

SUBMITTER: Inoue Y

PROVIDER: S-EPMC5407820 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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A highly specific and sensitive massive parallel sequencer-based test for somatic mutations in non-small cell lung cancer.

Inoue Yoshiaki Y Shiihara Jun J Miyazawa Hitoshi H Ohta Hiromitsu H Higo Megumi M Nagai Yoshiaki Y Kobayashi Kunihiko K Saijo Yasuo Y Tsuchida Masanori M Nakayama Mitsuo M Hagiwara Koichi K

PloS one 20170427 4

Molecular targeting therapy for non-small cell lung cancer (NSCLC) has clarified the importance of mutation testing when selecting treatment regimens. As a result, multiple-gene mutation tests are urgently needed. We developed a next-generation sequencer (NGS)-based, multi-gene test named the MINtS for investigating driver mutations in both cytological specimens and snap-frozen tissue samples. The MINtS was used to investigate the EGFR, KRAS, BRAF genes from DNA, and the ERBB2, and the ALK, ROS1 ...[more]

PMID: 28448556

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Project description:PET-based radiomics have been used to noninvasively quantify the metabolic tumor phenotypes; however, little is known about the relationship between these phenotypes and underlying somatic mutations. This study assessed the association and predictive power of 18F-FDG PET-based radiomic features for somatic mutations in non-small cell lung cancer patients. Methods: Three hundred forty-eight non-small cell lung cancer patients underwent diagnostic 18F-FDG PET scans and were tested for genetic mutations. Thirteen percent (44/348) and 28% (96/348) of patients were found to harbor epidermal growth factor receptor (EGFR) or Kristen rat sarcoma viral (KRAS) mutations, respectively. We evaluated 21 imaging features: 19 independent radiomic features quantifying phenotypic traits and 2 conventional features (metabolic tumor volume and maximum SUV). The association between imaging features and mutation status (e.g., EGFR-positive [EGFR+] vs. EGFR-negative) was assessed using the Wilcoxon rank-sum test. The ability of each imaging feature to predict mutation status was evaluated by the area under the receiver operating curve (AUC) and its significance was compared with a random guess (AUC = 0.5) using the Noether test. All P values were corrected for multiple hypothesis testing by controlling the false-discovery rate (FDRWilcoxon, FDRNoether) with a significance threshold of 10%. Results: Eight radiomic features and both conventional features were significantly associated with EGFR mutation status (FDRWilcoxon = 0.01-0.10). One radiomic feature (normalized inverse difference moment) outperformed all other features in predicting EGFR mutation status (EGFR+ vs. EGFR-negative, AUC = 0.67, FDRNoether = 0.0032), as well as differentiating between KRAS-positive and EGFR+ (AUC = 0.65, FDRNoether = 0.05). None of the features was associated with or predictive of KRAS mutation status (KRAS-positive vs. KRAS-negative, AUC = 0.50-0.54). Conclusion: Our results indicate that EGFR mutations may drive different metabolic tumor phenotypes that are captured in PET images, whereas KRAS-mutated tumors do not. This proof-of-concept study sheds light on genotype-phenotype interactions, using radiomics to capture and describe the phenotype, and may have potential for developing noninvasive imaging biomarkers for somatic mutations.

Dataset Information

A highly specific and sensitive massive parallel sequencer-based test for somatic mutations in non-small cell lung cancer.

Publications

A highly specific and sensitive massive parallel sequencer-based test for somatic mutations in non-small cell lung cancer.

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