Clinical importance of the range of detectable variants between the Oncomine Dx target test and a conventional single-gene test for EGFR mutation

Comparison of diagnostic performance between Oncomine Dx target test and AmoyDx panel for detecting actionable mutations in lung cancer

Companion diagnostic (CDx) tests play important roles in identifying oncogenic driver genes and tailoring effective molecularly targeted therapies for lung cancer patients. In Japan, the Oncomine Dx target test (ODxTT) and the AmoyDx pan lung cancer PCR panel (AmoyDx) are prominent CDx tests and only one of these tests is covered by the domestic insurance system. However, these CDx tests cover different target regions and apply different technologies (ODxTT is amplicon-based next-generation sequencing and AmoyDx is multiplex PCR-based assay), which may lead to missing of actionable mutations affecting patient prognosis. Here, we performed a direct comparison analysis of 1059 genetic alterations of eight driver genes from 131 samples and evaluated the concordance between two CDx tests for detecting actionable variants and fusions. When excluding the eight uncovered variants (ODxTT: two variants, AmoyDx: six variants), the overall percent agreement was 97.6% (1026/1051) with 89.0% of overall positive percent agreement (89/100) and 98.5% of overall negative percent agreement (937/951). Of the 25 discordant genetic alterations, two were undetected despite being covered in the AmoyDx (one EGFR variant and one ROS1 fusion). Furthermore, there were potential false positives in the ODxTT (nine MET exon 14 skippings) and in the AmoyDx (five variants, six ROS1 and three RET fusions). These potential false positives in the AmoyDx likely due to non-specific amplification, which was validated by the unique molecular barcoding sequencing. The ODxTT missed two uncovered EGFR rare variants, which was visually confirmed in the raw sequencing data. Our study provides insights into real-world performance of CDx tests for lung cancer and ensures reliability to advance precision medicine.

The Current Achievements of Multi-Gene Panel Tests in Clinical Settings for Patients with Non-Small-Cell Lung Cancer

Some multi-gene panel tests have been implemented in clinical settings to guide targeted therapy in non-small-cell lung cancer (NSCLC) in Japan. The current performance of multi-gene panel tests under the condition that the Oncomine Dx Target Test (ODxTT) and Amoy Dx® Pan Lung Cancer PCR panel (AmoyDx-multi) are available remains relatively unknown. We retrospectively reviewed consecutive patients with NSCLC, whose FFPE samples were considered for genetic testing. We assessed the submission rates, the success rates, and the driver oncogene detection rates of multi-gene panel tests. A total of 225 patients were histologically newly diagnosed with NSCLC or diagnosed with a recurrence of NSCLC without a previous multi-gene panel test at our institution. Among the 225 patients, the FFPE samples of 212 patients (94.2%) were submitted for multi-gene panel testing, including 191 samples (84.9%) for the ODxTT and 21 samples (9.3%) for the AmoyDx-multi. Among the 212 samples submitted to multi-gene panel tests, the success rate was 99.5% (211/212). The detection rate of driver oncogene alterations for all histologies was 52.4% (111/212), and that for adenocarcinoma was 69.7% (106/152). A favorable submission rate and success rate of multi-gene panel tests were shown, along with a favorable detection rate in recent clinical settings.

TCGAnalyzeR: An Online Pan-Cancer Tool for Integrative Visualization of Molecular and Clinical Data of Cancer Patients for Cohort and Associated Gene Discovery

For humans, the parallel processing capability of visual recognition allows for faster comprehension of complex scenes and patterns. This is essential, especially for clinicians interpreting big data for whom the visualization tools play an even more vital role in transforming raw big data into clinical decision making by managing the inherent complexity and monitoring patterns interactively in real time. The Cancer Genome Atlas (TCGA) database's size and data variety challenge the effective utilization of this valuable resource by clinicians and biologists. We re-analyzed the five molecular data types, i.e., mutation, transcriptome profile, copy number variation, miRNA, and methylation data, of ~11,000 cancer patients with all 33 cancer types and integrated the existing TCGA patient cohorts from the literature into a free and efficient web application: TCGAnalyzeR. TCGAnalyzeR provides an integrative visualization of pre-analyzed TCGA data with several novel modules: (i) simple nucleotide variations with driver prediction; (ii) recurrent copy number alterations; (iii) differential expression in tumor versus normal, with pathway and the survival analysis; (iv) TCGA clinical data including metastasis and survival analysis; (v) external subcohorts from the literature, curatedTCGAData, and BiocOncoTK R packages; (vi) internal patient clusters determined using an iClusterPlus R package or signature-based expression analysis of five molecular data types. TCGAnalyzeR integrated the multi-omics, pan-cancer TCGA with ~120 subcohorts from the literature along with clipboard panels, thus allowing users to create their own subcohorts, compare against existing external subcohorts (MSI, Immune, PAM50, Triple Negative, IDH1, miRNA, metastasis, etc.) along with our internal patient clusters, and visualize cohort-centric or gene-centric results interactively using TCGAnalyzeR.