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. 2013 Sep 12;8(9):e73484.
doi: 10.1371/journal.pone.0073484. eCollection 2013.

Identification and Characterization of Cancer Mutations in Japanese Lung Adenocarcinoma Without Sequencing of Normal Tissue Counterparts

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Free PMC article

Identification and Characterization of Cancer Mutations in Japanese Lung Adenocarcinoma Without Sequencing of Normal Tissue Counterparts

Ayako Suzuki et al. PLoS One. .
Free PMC article

Abstract

We analyzed whole-exome sequencing data from 97 Japanese lung adenocarcinoma patients and identified several putative cancer-related genes and pathways. Particularly, we observed that cancer-related mutation patterns were significantly different between different ethnic groups. As previously reported, mutations in the EGFR gene were characteristic to Japanese, while those in the KRAS gene were more frequent in Caucasians. Furthermore, during the course of this analysis, we found that cancer-specific somatic mutations can be detected without sequencing normal tissue counterparts. 64% of the germline variants could be excluded using a total of 217 external Japanese exome datasets. We also show that a similar approach may be used for other three ethnic groups, although the discriminative power depends on the ethnic group. We demonstrate that the ATM gene and the PAPPA2 gene could be identified as cancer prognosis related genes. By bypassing the sequencing of normal tissue counterparts, this approach provides a useful means of not only reducing the time and cost of sequencing but also analyzing archive samples, for which normal tissue counterparts are not available.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Identification and characterization of the putative cancer-related genes using 97 cancer exomes.
SNVs in the PREX1 (A) and PREX2 (B) genes are represented in the boxes. The protein domains in which the enrichments of the SNVs were statistically significant are represented in orange boxes (also see Materials and Method). DH-domain: Dbl homology (DH) domain; PH: Pleckstrin homology domain; D: DEP domain; P: PDZ/DHR/GLGF.
Figure 2
Figure 2. The EGFR/Ras pathways in Japanese and Caucasian populations.
(A) Mutation patterns in the endometrial cancer pathway that was detected in the enrichment analysis are shown. The size of the circle represents the population of the cancers harboring the SNVs in the corresponding gene (percentage is also shown in the margin). SNVs in this study and the external dataset in Caucasian populations are shown in red and blue circles, respectively. n.a.: mutation frequencies were not available. (B) Comparison of mutation ratio of EGFR, KRAS and TP53 genes among both datasets. The p-values were calculated by two-sample test for equality of proportions.
Figure 3
Figure 3. Fidelity of the germline SNV detection in cancer exome analysis.
Somatic SNV candidates were identified by using 26 cancer exomes and each normal counterpart. Correct somatic SNVs and false positives were shown in pink and blue bars, respectively. The 26 cancers used for the analysis were sorted by the increasing total number of SNVs (x-axis).
Figure 4
Figure 4. Discriminative powers of detecting germline SNVs using external references.
(A) The power of detecting germline SNVs considering mutual overlap between other Japanese individuals. Sensitivity represents the proportion of germline SNVs correctly detected. The datasets used to exclude the germline SNVs are shown on the x axis. The inset represents the extrapolation of the graph. Fitting curve of the graph is also shown. (B) Discriminative powers of three different ethnic groups for the germline SNVs in 97 Japanese cancers. Sensitivities for detecting germline SNVs are shown by the following colors; green: Chinese; purple: Yoruba; orange: Caucasian.
Figure 5
Figure 5. Identification of the putative prognosis-related genes.
(A) SNVs in the ATM gene. The SNVs that were identified in the initial screening and those remaining after the Sanger sequencing validation of the normal-tissue counterpart were shown in black and red, respectively. TAN: Telomere-length maintenance and DNA damage repair; PI3_PI4 kinase: Phosphatidylinositol 3-/4-kinase, catalytic. (B) Survival analysis of patients with and without ATM SNVs. The datasets before and after the Sanger sequencing validation are represented by black and red lines, respectively. Statistical significance was calculated using a log-rank test (P < 0.05). Note that the survival differences for individuals with SNVs in the non-Sanger-validated dataset were significant before the Sanger validation. (C, D) Results of a similar analysis as that described in A and B for the PAPPA2 gene. In this case, the patients with the SNVs showed better prognoses. ConA like sub: Concanavalin A-like lectin/glucanase, subgroup; N: Notch dimain; Peptidase M43: Peptidase M43, pregnancy-associated plasma-A.

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Grant support

This work was supported by JSPS KAKENHI Grant number 24300345. This work was also supported by MEXT KAKENHI Grant Number 221S0002. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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