Worldwide analysis of actionable genomic alterations in lung cancer and targeted pharmacogenomic strategies

Abstract

Based on data from the Global Cancer Statistics 2022, lung cancer stands as the most lethal cancer worldwide, with age-adjusted incidence and mortality rates of 23.6 and 16.9 per 100,000 people, respectively. Despite significant strides in precision oncology driven by large-scale international research consortia, there remains a critical need to deepen our understanding of the genomic landscape across diverse racial and ethnic groups. To address this challenge, we performed comprehensive in silico analyses and data mining to identify pathogenic variants in genes that drive lung cancer. We subsequently calculated the allele frequencies and assessed the deleteriousness of these oncogenic variants among populations such as African, Amish, Ashkenazi Jewish, East and South Asian, Finnish and non-Finnish European, Latino, and Middle Eastern. Our analysis examined 117,707 variants within 86 lung cancer-associated genes across 75,109 human genomes, uncovering 8042 variants that are known or predicted to be pathogenic. We prioritized variants based on their allele frequencies and deleterious scores, and identified those with potential significance for response to anti-cancer therapies through in silico drug simulations, current clinical pharmacogenomic guidelines, and ongoing late-stage clinical trials targeting lung cancer-driving proteins. In conclusion, it is crucial to unite global efforts to create public health policies that emphasize prevention strategies and ensure access to clinical trials, pharmacogenomic testing, and cancer research for these groups in developed nations.

Fig. 1

Epidemiology of lung cancer. (A) Heatmap and ranking of estimated age-standardized incidence rate of lung cancer per 100,000 inhabitants worldwide. (B) Heatmap and ranking of estimated age-standardized mortality rate of lung cancer per 100,000 inhabitants worldwide. ASR: age-standardized rate.

Fig. 2

Lung cancer driver genes, oncogenic variants, and CADD deleteriousness scores. (A) Features of lung cancer driver genes, oncogenic variants, consensus role and CADD deleteriousness scores. (B) Bean plots of CADD deleteriousness scores of the lung oncogenic variome, and ranking of known and predicted oncogenic variants with the highest CADD deleteriousness scores. (C) Ranking of the lung cancer driver genes with the highest number of oncogenic variants and their mean CADD deleteriousness scores.

Fig. 3

Functional enrichment analysis. (A) Heatmap of lung cancer driver genes with oncogenic variants being part of oncogenes, tumor suppressor genes, cell cycle genes, DNA repair genes, kinome, metastatic genes, cancer immunotherapy genes, and genes encoding RNA-binding proteins. (B) Manhattan plot of the most significant GO biological processes (n = 201), KEGG signaling pathways (n = 47), Reactome signaling pathways (n = 20), and WikiPathways (n = 38). (C) Most relevant (Benjamini-Hochberg FDR q-value <0.001) GO biological processes, KEGG signaling pathways, Reactome signaling pathways, and WikiPathways where the lung cancer driver genes with oncogenic variants were involved.

Fig. 4

Lung cancer oncogenic variants with the highest allele frequencies and CADD deleteriousness scores. Scatter plots and ranking of the known and predicted oncogenic variants with the highest allele frequencies and CADD deleteriousness scores (>15) from the European Finnish, European non-Finnish, Latino, East Asian, South Asian, African, Middle Eastern, Ashkenazi Jewish, and Amish populations.

Fig. 5

Landscape of therapeutic strategies based on precision oncology. (A) Current clinical pharmacogenomic guidelines for lung cancer focused on efficacy and toxicity. (B) Circos plot showing in silico drug prescriptions of responsive effects targeting lung cancer actionable genomic alterations. (C) Sankey plot of phase III and IV clinical trials for lung cancer connecting therapeutic targets (n = 11), drugs (n = 34), and mechanisms of action (n = 6).