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. 2021 Apr 30;13(9):2172.
doi: 10.3390/cancers13092172.

Genomic Characterization of Concurrent Alterations in Non-Small Cell Lung Cancer (NSCLC) Harboring Actionable Mutations

Antonio Passaro  1 , Ilaria Attili  1 , Alessandra Rappa  2 , Davide Vacirca  2 , Alberto Ranghiero  2 , Caterina Fumagalli  2 , Juliana Guarize  3 , Lorenzo Spaggiari  3   4 , Filippo de Marinis  1 , Massimo Barberis  2 , Elena Guerini-Rocco  2   4
Affiliations

Genomic Characterization of Concurrent Alterations in Non-Small Cell Lung Cancer (NSCLC) Harboring Actionable Mutations

Antonio Passaro et al. Cancers (Basel). .

Abstract

An increasing number of driver genomic alterations with potential targeted treatments have been identified in non-small cell lung cancer (NSCLC). Much less is known about the incidence and different distribution of concurrent alterations, as identified by comprehensive genomic profiling in oncogene-addicted NSCLCs. Genomic data from advanced NSCLC consecutively analyzed using a broad next-generation sequencing panel were retrospectively collected. Tumors harboring at least one main actionable gene alteration were categorized according to the presence/absence of concurrent genomic aberrations, to evaluate different patterns among the main oncogene-addicted NSCLCs. Three-hundred-nine actionable gene alterations were identified in 284 advanced NSCLC patients during the study period. Twenty-five tumor samples (8%) displayed concurrent alterations in actionable genes. Co-occurrences involving any pathogenic variant or copy number variation (CNV) were identified in 82.8% of cases. Overall, statistically significant differences in the number of concurrent alterations, and the distribution of TP53, STK11, cyclines and receptor tyrosin kinase (RTK) aberrations were observed across the eight actionable gene groups. NGS analyses of oncogene-addicted NSCLCs showed a different distribution and pattern of co-alteration profiles. Further investigations are needed to evaluate the prognostic and treatment-related impact of these concurrent alterations, hooked to the main gene aberrations.

Keywords: ALK; BRAF; EGFR; ERBB2; KRAS; MET; RET; co-mutation; next generation sequencing; predictive biomarker.

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Conflict of interest statement

Passaro has received personal fees from AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Merck Sharp & Dohme, Jansenn, Novartis, Pfizer and Roche, outside the submitted work. De Marinis has received honoraria or consulting fees from AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Merck Sharp & Dohme, Pfizer, Novartis, Takeda, Xcovery and Roche, outside the submitted work. Guerini Rocco has received personal fees from ThermoFisher Scientific, Novartis, AstraZeneca and Roche outside the submitted work; non-financial support from ThermoFisher Scientific, AstraZeneca, Roche, Novartis, Biocartis and Illumina outside the submitted work. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Distribution and co-occurrence of actionable and concurrent gene alterations. Oncoprint plots showing the distribution of actionable gene alterations, concurrent actionable alterations and other co-occuring gene alterations identified in more than 5% of the cases. The type of alteration is color-coded according to the legend below. Co-occurrences of actionable gene alterations included MET amplification, KRAS and EGFR mutations. The most frequent concurrent alterations were TP53 and STK11 mutations. Oncoprinter tool—cBioportal (https://www.cbioportal.org/oncoprinter, accessed on 10 March 2021).
Figure 2
Figure 2
Number of concurrent gene alterations across the eight actionable gene groups. Each bar represents the number of concurrent gene alterations across the eight actionable gene groups. The median number and the range of concurrent gene alterations identified in each actionable gene group are reported in the legend on the right. Statistically significant differences in the distribution of the median number of total concurrent alterations were seen across the actionable gene groups, with the highest median number of concurrent alterations identified in MET deregulated tumors. ALK, ROS1 and RET-positive tumors displayed the lowest median number of co-occurrences. One-way ANOVA test for variance analysis. * p ≤ 0.05; ** p < 0.01; *** p < 0.001
Figure 3
Figure 3
Genes and/or pathways affected by the concurrent alterations identified in the eight actionable gene groups. Each bar represents one actionable gene group. The frequency of genes and/or pathways affected by the concurrent alterations were reported for each actionable gene group (A) and actionable gene subclass (B) and color-coded according to the legend below. Distinctive concurrent gene and/or pathway alterations were identified in the different actionable gene groups. In particular, TP53 mutations were more frequently detected in EGFR, ERBB2 and RET-positive tumors. Mutations in the STK11 gene were more frequent in cases harboring KRAS and BRAF mutations. No significant differences were seen within actionable gene subclasses.
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