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. 2024 Dec 18;15(1):757.
doi: 10.1007/s12672-024-01640-7.

Comparison of targeted next generation sequencing assays in non-small cell lung cancer patients

Affiliations

Comparison of targeted next generation sequencing assays in non-small cell lung cancer patients

Ieva Drejeriene et al. Discov Oncol. .

Abstract

Non-small cell lung cancer (NSCLC) is the most prevalent type of lung cancer the mutational spectrum of which has been extensively characterized. Treatment of patients with NSCLC based on their molecular profile is now part of the standard clinical care. The aim of this study was firstly to investigate two different NGS-based tumor profile genetic tests and secondly to assess the clinical actionability of the mutations and their association with survival and clinicopathological characteristics. Overall, 52 mutations were identified in 31 patients by either one or both assays. The most frequently mutated genes were TP53 (40.4%), KRAS (13.46%) and EGFR (9.62%). TP53 and KRAS mutations were associated with worst overall survival while KRAS was positively correlated with adenocarcinoma. The two methods showed a high concordance for the commonly covered genomic regions (97.14%). Ten mutations were identified in a genomic region exclusively covered by the MEDICOVER Genetics custom tumor profile assay. Likewise, one MET mutation was identified by the Ion Amliseq assay in a genomic region exclusively covered by Ion Amliseq. In conclusion both assays showed highly similar results in the commonly covered genomic areas, however, the MEDICOVER Genetics assay identified additional clinically actionable mutations that can be applied in clinical practice for personalized treatment decision making for patients with NSCLC.

Keywords: clinical utility; mutational profile; next generation sequencing; NSCLC.

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Conflict of interest statement

Declarations. Competing interests: CL: Employed by MEDICOVER Genetics has filed a PCT patent application for Target-enriched multiplexed parallel analysis for assessment of tumor biomarkers (WO2019/008172A1); AA: Employed by MEDICOVER Genetics; has filed a PCT patent application for the Target-enriched multiplexed parallel analysis for assessment of tumor biomarkers (WO2019/008172A1); has filed a PCT patent application for the Enrichment of Targeted Genomic Regions for Multiplexed Par-allel Analysis (WO2019/008148A9); EK: Employed by MEDICOVER Genetics; has filed a PCT pa-tent application for the Target-enriched multiplexed parallel analysis for assessment of tumor biomarkers (WO2019/008172A1); has filed a PCT patent application for the Enrichment of Tar-geted Genomic Regions for Multiplexed Parallel Analysis (WO2019/008148A9); KT: Employed by MEDICOVER Genetics, has filed a PCT patent application for the Enrichment of Targeted Ge-nomic Regions for Multiplexed Parallel Analysis (WO2019/008148A9); AE: Employed by MEDI-COVER Genetics; has filed a PCT patent application for the Target-enriched multiplexed parallel analysis for assessment of tumor biomarkers (WO2019/008172A1); MI: Employed by MEDICOV-ER Genetics; has filed a PCT patent application for the Target-enriched multiplexed parallel analysis for assessment of tumor biomarkers (WO2019/008172A1), has filed a PCT patent applica-tion for the Enrichment of Targeted Genomic Regions for Multiplexed Parallel Analysis (WO2019/008148A9); GK: Employed by MEDICOVER Genetics; has filed a PCT patent application for the Target-enriched multiplexed parallel analysis for assessment of tumor biomarkers (WO2019/008172A1); has filed a PCT patent application for the Enrichment of Targeted Genomic Regions for Multiplexed Parallel Analysis (WO2019/008148A9); PCP: Employed by MEDICOVER Genetics; has filed a PCT patent application for the Target-enriched multiplexed parallel analysis for assessment of tumor biomarkers (WO2019/008172A1); has filed a PCT patent application for the Enrichment of Targeted Genomic Regions for Multiplexed Parallel Analysis (WO2019/008148A9); The rest of the authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
Description of the assays and experimental strategy used. A. List of genes covered in the two assays used for molecular profiling of NSCLC tumor specimens. The IonAmliseq assay covers hotspot regions of 22 genes while the MEDICOVER Genetics custom tumor profile assay covers hotspots and other exonic regions of 49 genes. With red are the genes commonly covered by both assays. B. Venn diagram showing the size of the genomic area covered by both assays as well as their overlapping genomic coverage. C. Experimental strategy followed: FFPE specimens from 51 patients diagnosed with NSCLC were subjected to sectioning. For 39 patients, adequate amount of DNA was extracted from the same set of sections and sent to two labs for subsequent analysis with the Ion Ampliseq and the MEDICOVER assays. For 12 patients DNA derived from different sections of the same FFPE block was sent to the two labs for downstream processing. A total of 39 patients met QC parameters and proceeded to NGS. Available sequencing data were used for concordance analysis and estimation of the molecular profile of each tumor sample
Fig. 2
Fig. 2
Concordance analysis in the commonly covered regions by both assays. A. Frequency of NSCLC tumors with mutations identified by either one or both methods (concordant mutations). B. Venn diagram indicating the number of variants identified by either assay. 34 variants were commonly identified by both assays, while one extra variant was identified by the N MEDICOVER Genetics custom tumor profile assay (the KRAS G13C variant was detect below threshold level at VAF < 5% with the IonAmliseq and thus was excluded). C. Distribution of variant allele frequencies (VAF) for the concordant mutations identified by each assay. D. Variability of mutation detection and VAF for NGS data originating from different sections for the same FFPE biopsy
Fig. 3
Fig. 3
Mutational profile of NSCLC tumors. A. Diagram depicting all the mutations identified by either both or at least one of the two assays. B. Frequency of mutations per gene identified in all patients
Fig. 4
Fig. 4
Distribution of mutations in the most frequently mutated genes. Lollipop plots showing the distribution of mutations across the coding region of A. TP53 B. KRAS C. EGFR
Fig. 5
Fig. 5
Analysis of clinical utility of the two assays. The number of genes with mutations associated with sensitivity to approved therapy, resistance, or low sensitivity to therapy (contra-indicated), sensitivity to drugs approved for other cancer types and inclusion in clinical trials is indicated
Fig. 6
Fig. 6
Overall survival (OS) in patients with and without mutations in KRAS or TP53 in early (I-II) and late (III-IV) stage NSCLC. Red = Stage I-II/mutated, Green = StageI-II/wild type, blue = stageIII-IV/mutated, purple = stage III-IV/wild type

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