Lung Cancer Proteogenomics: Shaping the Future of Clinical Investigation

Abstract

Background: Lung cancer is associated with a high incidence of mortality worldwide. Molecular mechanisms governing the disease have been explored by genomic studies; however, several aspects remain elusive. The integration of genomic profiling with in-depth proteomic profiling has introduced a new dimension to lung cancer research, termed proteogenomics. The aim of this review article was to investigate proteogenomic approaches in lung cancer, focusing on how elucidation of proteogenomic features can evoke tangible clinical outcomes.

Methods: A strict methodological approach was adopted for study selection and key article features included molecular attributes, tumor biomarkers, and major hallmarks involved in oncogenesis.

Results: As a consensus, in all studies it becomes evident that proteogenomics is anticipated to fill significant knowledge gaps and assist in the discovery of novel treatment options. Genomic profiling unravels patient driver mutations, and exploration of downstream effects uncovers great variability in transcript and protein correlation. Also, emphasis is placed on defining proteogenomic traits of tumors of major histological classes, generating a diverse portrait of predictive markers and druggable targets.

Conclusions: An up-to-date synthesis of landmark lung cancer proteogenomic studies is herein provided, underpinning the importance of proteogenomics in the landscape of personalized medicine for combating lung cancer.

Keywords: NSCLC; SCLC; aberrant protein expression; genetic alterations; lung cancer; multi-omics; proteogenomics.

Figure 1

Overview of lung cancer proteogenomic studies. (a) Schematic representation of a proteogenomics workflow commonly adopted by the majority of investigated articles. (b) Boxplot of geographic distribution of patient population cohorts by continent (only studies with adequate data availability were included). Multi − Continent: North America, Europe, and Asia.

Figure 2

Mutational landscape of patient cohorts from reviewed articles. (a) Top 10 driver mutations based on frequency (%) of appearance in the examined cohorts *. (b,c) Relative incidence of these top 10 driver mutations in different ethnicities and lung cancer types, as defined by histological evidence. * Mutation data were publicly available in 5 studies, involving a total of 716 patients. Han: Han Chinese; ASCC: Adenosquamous Carcinoma; LUAD: Lung Adenocarcinoma; SCC: Squamous Cell Carcinoma.

Figure 3

Key proteogenomic findings derived from reviewed articles. (a) Boxplot depicting gene-wise mRNA-protein correlation in tumor samples of cohorts corresponding to the references on the x-axis [32,33,34,39]. Circles represent outlier and central (black) dots represent median values. The y-axis values represent mRNA-protein correlation expressed as Spearman’s correlation. (b) Incidence of top 5 differentially expressed proteins in the examined paper cohort. Only studies with adequate data availability were included.