High-throughput molecular analysis in lung cancer: insights into biology and potential clinical applications

Abstract

During the last decade, high-throughput technologies including genomic, epigenomic, transcriptomic and proteomic have been applied to further our understanding of the molecular pathogenesis of this heterogeneous disease, and to develop strategies that aim to improve the management of patients with lung cancer. Ultimately, these approaches should lead to sensitive, specific and noninvasive methods for early diagnosis, and facilitate the prediction of response to therapy and outcome, as well as the identification of potential novel therapeutic targets. Genomic studies were the first to move this field forward by providing novel insights into the molecular biology of lung cancer and by generating candidate biomarkers of disease progression. Lung carcinogenesis is driven by genetic and epigenetic alterations that cause aberrant gene function; however, the challenge remains to pinpoint the key regulatory control mechanisms and to distinguish driver from passenger alterations that may have a small but additive effect on cancer development. Epigenetic regulation by DNA methylation and histone modifications modulate chromatin structure and, in turn, either activate or silence gene expression. Proteomic approaches critically complement these molecular studies, as the phenotype of a cancer cell is determined by proteins and cannot be predicted by genomics or transcriptomics alone. The present article focuses on the technological platforms available and some proposed clinical applications. We illustrate herein how the "-omics" have revolutionised our approach to lung cancer biology and hold promise for personalised management of lung cancer.

FIGURE 1

Schematic representation of “bottom up” or shotgun analysis. A protein mixture is first digested (by trypsin) and the resulting peptides are separated by multidimensional liquid chromatography (typically strong cation exchange followed by reverse-phase separation) coupled online to a mass spectrometer. As they elute, the m/z ratios of the peptides are first determined, followed by one or several mass spectrometry (MS)/MS scans from the most abundant peptide signals (y5, y6 and y7 are m/z values for “y-ions” and b4 is the m/z value for a “b-ion”. The fragment peaks that appear to extend from the amino terminus are termed “b” ions, and those that appear to extend from the C-terminus are termed “y ions”). This cycle is repeated until all of the peptides have eluted from the chromatography column. For each precursor peptide selected for MS/MS, peptides of similar nominal mass are extracted from sequence databases and predicted fragmentation patterns are derived in silico. These patterns are then compared with the experimental fragmentation spectrum to generate correlation scores. Positive identification of a protein is based on the observation of two or more peptides issued from its sequence.

FIGURE 2

Sequencing of DNA from cells recovered in a pleural effusion specimen obtained at the time of acquired erlotinib resistance. After PCR amplification of exon 20, 454 sequencing revealed the presence of the T790M resistance mutation of epidermal growth factor receptor (EGFR) [49] at ~2%. Robust calling of T790M carrying EGFR alleles was ensured by >60,000 × over-sampling. Wt: weight; Mut.: mutation. ——: coverage; green: A; blue: C; black: G; red: T. Reprinted from [68], with permission from the publisher.

FIGURE 3

Added value of genomic to a clinical model predictive of lung cancer. Receiver operating characteristic curves of the combined training and test sets (n=118), consisting of smokers undergoing bronchoscopy for suspicion of lung cancer. The clinical model (······) includes three variables: age, mass size and lymphadenopathy; the clinicogenomic model (——) includes the previous variables and the biomarker score. The area under the curve for the clinical and clinicogenomic models is 0.89 and 0.94, respectively, which represents a significant difference between the two curves (p<0.05). Reproduced from [97], with permission from the publisher.

FIGURE 4

Methylation of the promoter region of TP16 and CDH13 in patients with stage I nonsmall cell lung cancer is associated with early recurrence. When both TP16 and CDH13 were methylated in the tumour and the mediastinal nodes, there was a significantly lower rate of recurrence-free survival (9.1% (95% CI 0.5–33.3)) than if TP16 and CDH13 were unmethylated (61.2% (95% CI, 49.7–70.9); p<0.001). ——: methylated (n=11); ······: unmethylated (n=80). Reproduced from [112], with permission from the publisher.

FIGURE 5

Relationship between predicted chemotherapeutic sensitivity and oncogenic pathway deregulation. a) Probability of oncogenic pathway deregulation as a function of predicted docetaxel sensitivity in a series of lung cancer cell lines (red: sensitive; blue: resistant). b) Lung cancer cell lines showing an increased probability of phosphoinositide 3-kinase (PI3K) activation were more likely to respond to a PI3K inhibitor (p=0.001, log-rank test), as measured by sensitivity to the drug in cell proliferation assays. c) Furthermore, cell lines predicted to be resistant to docetaxel were more likely to be sensitive to PI3K inhibition (p<0.001, log-rank test). Src: avian sarcoma (Schmidt-Ruppin A-2) viral oncogene homologue; Ras: rat sarcoma viral oncogene homologue; E2F3: E2F transcription factor 3; Myc: myelocytomatosis viral oncogene homologue; IC₅₀: half maximal inhibitory concentration. Reproduced from [180], with permission from the publisher.

FIGURE 6

Kaplan–Meier analysis of overall survival in the Eastern Cooperative Oncology Group validation cohort (n=96). These patients had advanced nonsmall cell lung cancer and had been treated first line with erlotinib alone. red; poor event-free fraction; green: good event-free fraction; ------: 95% confidence intervals; |: censored patients. Reproduced from [181], with permission from the publisher.