The Personalized Inherited Signature Predisposing to Non-Small-Cell Lung Cancer in Non-Smokers

Abstract

Lung cancer (LC) continues to be an important public health problem, being the most common form of cancer and a major cause of cancer deaths worldwide. Despite the great bulk of research to identify genetic susceptibility genes by genome-wide association studies, only few loci associated to nicotine dependence have been consistently replicated. Our previously published study in few phenotypically discordant sib-pairs identified a combination of germline truncating mutations in known cancer susceptibility genes in never-smoker early-onset LC patients, which does not present in their healthy sib. These results firstly demonstrated the presence of an oligogenic combination of disrupted cancer-predisposing genes in non-smokers patients, giving experimental support to a model of a "private genetic epidemiology". Here, we used a combination of whole-exome and RNA sequencing coupled with a discordant sib's model in a novel cohort of pairs of never-smokers early-onset LC patients and in their healthy sibs used as controls. We selected rare germline variants predicted as deleterious by CADD and SVM bioinformatics tools and absent in the healthy sib. Overall, we identified an average of 200 variants per patient, about 10 of which in cancer-predisposing genes. In most of them, RNA sequencing data reinforced the pathogenic role of the identified variants showing: (i) downregulation in LC tissue (indicating a "second hit" in tumor suppressor genes); (ii) upregulation in cancer tissue (likely oncogene); and (iii) downregulation in both normal and cancer tissue (indicating transcript instability). The combination of the two techniques demonstrates that each patient has an average of six (with a range from four to eight) private mutations with a functional effect in tumor-predisposing genes. The presence of a unique combination of disrupting events in the affected subjects may explain the absence of the familial clustering of non-small-cell lung cancer. In conclusion, these findings indicate that each patient has his/her own "predisposing signature" to cancer development and suggest the use of personalized therapeutic strategies in lung cancer.

Keywords: germline variants; lung cancer susceptibility; next-generation sequencing; oligogenic model; whole-exome sequencing.

Figure 1

Flowchart illustrating filtering process and variants selection.

Figure 2

Average number of somatic mutations in tumor lung tissues. Missense mutations have a numerical range from 797 to 1542 (in white); indel mutations range from 85 to 735 (in gray); and, in black, the nonsense mutations have a range from 32 to 56.

Figure 3

RNA transcript levels in normal and tumor tissue pairs of 16 candidate germline mutated genes. RNA levels were expressed as FPKM value in tumor tissue (T), in the normal counterpart (N) and in the group of normal tissues (C). ACACA, ANGPTL4, BUB1B, FBN2, MEN1, MMP14, TP73, and WWTR1 showed a downregulation in lung cancer tissue (panel (A)); ACAP2, ENO3, and PSCA showed an upregulation in lung cancer tissue (panel (B)); AMIGO3, CARS, DEPTOR, IQGAP2, and RNASEL were downregulated in both normal and cancer tissue (panel (C)).

Figure 4

Results of GO analysis of upregulated (panel (A)) and downregulated (panel (B)) transcripts in lung tumor tissues compared with lung normal tissues (all components with >10% and p-value < 0.05).

Figure 5

Networks among germline mutated genes. Three independent networks involving mutated genes were identified in different cases. Network nodes (colored spheres) represent proteins (empty nodes = proteins of unknown 3D structure; filled nodes = a 3D structure is known or predicted). Straight lines connecting the nodes represent protein–protein associations (blue lines = known interactions from curated databases; purple lines = known experimentally determined interactions; dark green lines = predicted interactions such as neighborhood gene; light green = predicted interactions by text mining; black lines = co-expression).

Figure 6

Expanded network among EPHB6, ACACA, ENO3, CARS, and ACAP2 genes. Network nodes (colored spheres) represent proteins (empty nodes = proteins of unknown 3D structure; filled nodes = a 3D structure is known or predicted). Straight lines connecting the nodes represent protein–protein associations (light blue lines = known interactions from curated databases; purple lines = known experimentally determined interactions; dark green lines = predicted interactions such as neighborhood gene; light green = predicted interactions by text mining; black lines = co-expression; dark blue lines = gene co-occurrence; red lines = gene fusion).