A proteomic profiling of laser-microdissected lung adenocarcinoma cells of early lepidic-types

Abstract

Background

In the new pathologic classification of lung adenocarcinoma proposed by IASLC/ATS/ERS in 2011, lepidic type adenocarcinomas are constituted by three subtypes; adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and lepidic predominant invasive adenocarcinoma (LPIA). Although these subtypes are speculated to show sequential progression from preinvasive lesion to invasive lung cancer, changes of protein expressions during these processes have not been fully studied yet. This study aims to glimpse a proteomic view of the early lepidic type lung adenocarcinomas.

Methods

A total of nine formalin-fixed and paraffin-embedded (FFPE) lepidic type lung adenocarcinoma tissues were selected from our archives, three tissues each in AIS, MIA and LPIA. The tumor and peripheral non-tumor cells in these FFPE tissues were collected with laser microdissection (LMD). Using liquid chromatography-tandem mass spectrometry (MS/MS), protein compositions were compared with respect to the peptide separation profiles among tumors collected from three types of tissues, AIS, MIA and LPIA. Proteins identified were semi-quantified by spectral counting-based or identification-based approach, and statistical evaluation was performed by pairwise G-tests.

Results

A total of 840 proteins were identified. Spectral counting-based semi-quantitative comparisons of all identified proteins through AIS to LPIA have revealed that the protein expression profile of LPIA was significantly differentiated from other subtypes. 70 proteins including HPX, CTTN, CDH1, EGFR, MUC1 were found as LPIA-type marker candidates, 15 protein candidates for MIA-type marker included CRABP2, LMO7, and RNPEP, and 26 protein candidates for AIS-type marker included LTA4H and SOD2. The STRING gene set enrichment resulted from the protein-protein interaction (PPI) network analysis suggested that AIS was rather associated with pathways of focal adhesion, adherens junction, tight junction, that MIA had a strong association predominantly with pathways of proteoglycans in cancer and with PI3K-Akt. In contrast, LPIA was associated broadly with numerous tumor-progression pathways including ErbB, Ras, Rap1 and HIF-1 signalings.

Conclusions

The proteomic profiles obtained in this study demonstrated the technical feasibility to elucidate protein candidates differentially expressed in FFPE tissues of LPIA. Our results may provide candidates of disease-oriented proteins which may be related to mechanisms of the early-stage progression of lung adenocarcinoma.

Kato et al. Clinical and Translational Medicine (2015) 4:24

DOI 10.1186/s40169-015-0064-3

http://crossmark.crossref.org/dialog/?doi=10.1186/s40169-015-0064-3&domain=pdf

Web End = Yasufumi Kato1, Haruhiko Nakamura2, Hiromasa Tojo3, Masaharu Nomura1, Toshitaka Nagao4, Takeshi Kawamura5, Tatsuhiko Kodama5, Tatsuo Ohira1, Norihiko Ikeda1, Thomas Fehniger6,7, Gyrgy Marko-Varga1,6,7,Toshihide Nishimura1,6* and Harubumi Kato1,8

Abstract

Background: In the new pathologic classification of lung adenocarcinoma proposed by IASLC/ATS/ERS in 2011, lepidic type adenocarcinomas are constituted by three subtypes; adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and lepidic predominant invasive adenocarcinoma (LPIA). Although these subtypes are speculated to show sequential progression from preinvasive lesion to invasive lung cancer, changes of protein expressions during these processes have not been fully studied yet. This study aims to glimpse a proteomic view of the early lepidic type lung adenocarcinomas.

Methods: A total of nine formalin-fixed and paraffin-embedded (FFPE) lepidic type lung adenocarcinoma tissues were selected from our archives, three tissues each in AIS, MIA and LPIA. The tumor and peripheral non-tumor cells in these FFPE tissues were collected with laser microdissection (LMD). Using liquid chromatography-tandem mass spectrometry (MS/MS), protein compositions were compared with respect to the peptide separation profiles among tumors collected from three types of tissues, AIS, MIA and LPIA. Proteins identified were semi-quantified by spectral counting-based or identification-based approach, and statistical evaluation was performed bypairwise G-tests.

Results: A total of 840 proteins were identified. Spectral counting-based semi-quantitative comparisons of all identified proteins through AIS to LPIA have revealed that the protein expression profile of LPIA was significantly differentiated from other subtypes. 70 proteins including HPX, CTTN, CDH1, EGFR, MUC1 were found as LPIA-type marker candidates, 15 protein candidates for MIA-type marker included CRABP2, LMO7, and RNPEP, and 26 protein candidates for AIS-type marker included LTA4H and SOD2. The STRING gene set enrichment resulted from the protein-protein interaction (PPI) network analysis suggested that AIS was rather associated with pathways of focal adhesion, adherens junction, tight junction, that MIA had a strong association predominantly with pathways of proteoglycans in cancer and with PI3K-Akt. In contrast, LPIA was associated broadly with numerous tumor-progression pathways including ErbB, Ras, Rap1 and HIF-1 signalings.

Conclusions: The proteomic profiles obtained in this study demonstrated the technical feasibility to elucidate protein candidates differentially expressed in FFPE tissues of LPIA. Our results may provide candidates of disease-oriented proteins which may be related to mechanisms of the early-stage progression of lung adenocarcinoma.

Keywords: Lung cancer; Adenocarcinoma; Lepidic type adenocarcinoma; Adenocarcinoma in situ; Minimally invasive adenocarcinoma; Comparative proteomics; Formalin-fixed and paraffin-embedded tissue sections; Laser microdissection; Mass spectrometry; Protein-protein interaction

* Correspondence: mailto:[email protected]

Web End [email protected]

1Department of Thoracic and Thyroid Surgery, Tokyo Medical University, Tokyo, Japan

6Center of Excellence in Biological and Medical Mass Spectrometry, Lund University, BMC, Lund, SwedenFull list of author information is available at the end of the article

2015 Kato et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0

Web End =http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

RESEARCH ARTICLE Open Access

A proteomic profiling of laser-microdissected lung adenocarcinoma cells of early lepidic-types

Kato et al. Clinical and Translational Medicine (2015) 4:24 Page 2 of 13

Background

Lung cancer is the leading cause of cancer-related mortality worldwide [1]. In Japan, annual deaths from lung cancer are increasing and currently approach about 70,000 [2], while in the United States with a recent decreasing trend in mortality, more than 160,000 succumb annually [3]. In an increasing trend worldwide, advances in chest high-resolution computed tomography (HRCT) scanning technology have enabled the localization of small adenocarcinoma nodules [4] at an earlier and potentially more curable stage of development than previously possible [5]. There are 90 million current and ex-smokers in the United States who are at increased risk of lung cancer. The published data from the National Lung Screening Trial (NLST) suggest that yearly screening with low-dose thoracic CT scan in heavy smokers can reduce lung cancer mortality by 20 % and all-cause mortality by 7-% [6].

In 2011, the new pathologic classification of lung adeno-carcinoma was proposed by the International Association for the Study of Lung Cancer (IASLC), the American Thoracic Society (ATS) and the European Respiratory Society (ERS) [7]. In the new classification, the concept of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) were newly introduced and the term bronchioloalveolar carcinoma (BAC) was abolished. Additionally, invasive adenocarcinomas were categorized into 6 subtypes, lepidic, acinar, papillary, micropapillary, solid, and variants, according to the predominant histo-logic pattern. Both AIS and MIA were defined as tumors 3 cm in size. AIS is a preinvasive lesion showing pure lepidic growth without invasion. MIA is also lepidic predominant tumor but with 5 mm invasion. LPIA is an invasive adenocarcinoma showing former nonmucinous BAC pattern with > 5 mm invasion. These 3 lepidic type adenocarcinomas are speculated to show step-wise progression from AIS, MIA, to LPIA. After complete re-section of AIS or MIA, usually 100 % of recurrence-free 5-year survival can be obtained [7], while some recurrent cases are found after resection of LPIA [810]. Since postoperative prognoses between the AIS plus MIA group and LPIA are different, differential protein expressions associated with invasiveness of cancer cells in each subtype should play important roles to determine local recurrences and survivals. However, precise proteomic analyses using individual cells in these early adenocarcinomas have not yet been performed. To the best of our knowledge, this is the first report performing proteomic analysis using micro-dissected early phase lung adenocarcinoma cells.

Recent advancements in shotgun sequencing and quantitative mass spectrometry for protein analyses could make proteomics amenable to clinical biomarker discovery [11, 12]. Laser microdissection (LMD) made it possible to collect target cells from a variety of formalin fixed

paraffin embedded (FFPE) cancer tissues. This study attempts to capture a proteomic view of LPIA in comparison with other early stage lung adenocarcimomas by utilizing a label-free identification-based (or spectral counting-based) semi-quantitative shotgun proteomics approach following LMD [1319].

Results and discussion

Group comparisons by Rsc and G-statisticsWe used Abacus [20] to select high-scoring proteins using the thresholds of PeptideProphet probability > 0.99 and ProteinProphet probability > 0.9 as described in MATERIALS and METHODS, resulting in identifying a total of 840 proteins and obtaining their values of raw fold change in log2 (Rsc). For G-test (p < 0.05) [21], the raw SpCs of all patients in each group were pooled, thereby improving the performance of G-test and decreasing false positive rates significantly [15, 22]. Next, the values of Rsc that is a measure of fold changes for protein expression levels were calculated as described in Materials and Methods using the spectral counts of these proteins.

The full lists of 840 proteins identified were provided as Additional file 1: Table S3. Proteins in LPIA, MIA and AIS identified under SpCtotal > 2 for a protein were 789,

607, and 544, respectively, and were subjected to gene ontology (GO) analysis by using PANTHER Ver. 10.0 (http://www.pantherdb.org/

Web End =http://www.pantherdb.org/). Results of (A) biological processes and (B) protein classes are shown in Fig. 1.

A marker candidate for the LPIA-type was chosen under the following criterions so that a protein had the pairwise p-value < 0.05 in G-test and Rsc < 1 against MIA and AI, [LPIA] (the retative abundance in spectral count) higher than [MIA] and [AIS], and total spectral counts throughout the desease states > 5. Table 1 summarizes 70 protein candidates thus obtained for LPIA from total 840 proteins identified, which are listed in increasing order of the Rsc (LPIA vs. MIA) values; the negatively larger the Rsc value of a given protein, the greater its expression level in LPIA compared with MIA and AIS. Those included beta-actin-like protein 2 (ACTBL2), tubulin alpha-1C chain (TUBA1C), band 7 protein family protein, HLA class I histocompatibility antigen, A-2 alpha chain (HLA-A), ARPC4-TTLL3 fusion protein, epiplakin (EPPK1), synaptogyrin-2 (SYNGR2), hemopexin (HPX), small nuclear ribonucleoprotein G-like protein (SNRPF), src substrate cortactin (CTTN), cadherin-1 (CDH1) (known as E-cadherin), epidermal growth factor receptor (EGFR), mucin-1 (MUC1), and promyelocytic leukemia protein (PML). The high expression of beta-actin-like protein 2 (ACTBL2) and tubulin alpha-1C chain (TUBA1C) might be related to active actin polymerization associated with invasiveness of LPIA.

Src substrate cortactin (CTTN), epidermal growth factor receptor (EGFR) and mucin-1 (MUC1) expressed in

Kato et al. Clinical and Translational Medicine (2015) 4:24 Page 3 of 13

Fig. 1 Gene ontology (GO) analysis for three cancer groups, AIS, MIA and LPIA, in which utilized were 544, 607, and 789 proteins, respectively,

identified with SpC > 2. a Biological process: 1, cellular component organization or biogenesis (GO:0071840); 2, cellular process (GO:0009987); 3,

localization (GO:0051179); 4, apoptotic process (GO:0006915); 5, reproduction (GO:0000003); 6, biological regulation (GO:0065007); 7, response to

stimulus (GO:0050896); 8, developmental process (GO:0032502); 9, multicellular organismal process (GO:0032501); 10, biological adhesion

(GO:0022610); 11, metabolic process (GO:0008152); 12, immune system process (GO:0002376). b Protein class: 1 extracellular matrix protein

(PC00102); 2, protease (PC00190); 3, cytoskeletal protein (PC00085); 4, transporter (PC00227); 5, transmembrane receptor regulatory/adaptor

protein (PC00226); 6, transferase (PC00220); 7, oxidoreductase (PC00176); 8, lyase (PC00144); 9, cell adhesion molecule (PC00069); 10, ligase

(PC00142); 11, nucleic acid binding (PC00171); 12, signaling molecule (PC00207); 13, enzyme modulator (PC00095); 14, calcium-binding protein

(PC00060); 15, defense/immunity protein (PC00090); 16, hydrolase (PC00121); 17, transfer/carrier protein (PC00219); 18, membrane traffic protein

(PC00150); 19, phosphatase (PC00181); 20, transcription factor (PC00218); 21, chaperone (PC00072); 22, cell junction protein (PC00070); 23,

surfactant (PC00212); 24, structural protein (PC00211); 25, kinase (PC00137); 26, storage protein (PC00210); 27, receptor (PC00197); 28,

isomerase (PC00135)

LPIA might reflect its invasiveness with aggressive proliferation. Invasive carcinoma cells degrade and invade through the extracellular matrix (ECM) by invadopodia, where an EGFRSrcArgcortactin pathway is considered to mediate functional maturation of invadopodia [2325]. Overexpression of cortactin protein (CTTN) has been currently considered to be an important biomarker for invasive cancers because of its frequent link to various invasive cancers, including melanoma, colorectal, and glioblastoma [25].

Proteins expressed increasingly along the disease stages from AIS to LPIA, which might be considered to be disease progression-related, included were alpha-enolase (ENO1), plectin (PLEC), major vault protein (MVP), heterogeneous

nuclear ribonucleoprotein M (HNRNPM), 14-3-3 protein sigma (SFN), lysophosphatidylcholine acyltransferase 1 (LPCAT1), anterior gradient protein 2 homolog (AGR2), phospholipase D3 (PLD3), hypoxia up-regulated protein 1 (HYOU1), fatty acid synthase (FASN), programmed cell death protein 6 (PDCD6), and ethylmalonyl-CoA decarboxylase (ECHDC1). Among proteins expressed characteristically in the AIS and MIA disease stages, leukotriene A-4 hydrolase (LTA4H) in AIS and cellular retinoic acid-binding protein 2 (CRABP2) in MIA, respectively, were representative. Proteins significant to AIS and MIA are provided in Additional file 1: Table S1 and S2. Enhanced AGR2 expression has been observed in most human adenocarcinomas,

Table 1 Seventy protein candidates characterizing LPIA listed in increasing order of the Rsc (LPIA vs. MIA) values; the negatively larger the Rsc value of a given protein, the greater its expression level in LPIA compared with MIA and AIS

Spectral counts

(SpCs)

Relative %

thougtout stages

Fold change

in log2 (RSC)

p-value in G-test

No Accession

Number/Code

Gene ID Description Protein

length

(AA)

LPIA MIA AIS Total [LPIA] [MIA] [AIS] LPIA

vs MIA

LPIA

vs AIS

LPIA

vs pN

LPIA

vs MIA

LPIA

vs AIS

LPIA

vs pN

1 HIP000323690 Band 7 protein

family protein

Band7 protein family protein 2858 77 0 0 77 100.0 0.0 0.0 -5.816 -5.758 -4.482 2.33E-22 5.80E-22 1.67E-13

2 P01892 HLA-A HLA class I histocompatibility antigen,

A-2 alpha chain

365 34 0 0 34 100.0 0.0 0.0 -4.663 -4.605 -3.329 3.75E-10 5.59E-10 2.94E-06

471 32 0 6 38 84.2 0.0 15.8 -4.579 -1.984 -3.244 1.38E-09 9.10E-05 6.39E-06

4 P31948 STIP1 Stress-induced-phosphoprotein 1 543 27 0 5 32 84.4 0.0 15.6 -4.344 -1.963 -3.009 3.63E-08 3.43E-04 4.45E-055 P10253 GAA Lysosomal alpha-glucosidase 952 26 0 0 26 100.0 0.0 0.0 -4.292 -4.233 -2.957 6.99E-08 9.48E-08 6.57E-056 Q07065 CKAP4 Cytoskeleton-associated protein 4 602 24 0 5 29 82.8 0.0 17.2 -4.181 -1.801 -2.847 2.58E-07 1.43E-03 1.43E-047 P17858 PFKL 6-phosphofructokinase, liver type 780 22 0 2 24 91.7 0.0 8.3 -4.062 -2.625 -2.727 9.55E-07 9.43E-05 3.11E-048 Q07960 ARHGAP1 Rho GTPase-activating protein 1 439 20 0 0 20 100.0 0.0 0.0 -3.932 -3.874 -2.598 3.53E-06 4.47E-06 6.77E-049 P06865 HEXA Beta-hexosaminidase subunit alpha 529 19 0 0 19 100.0 0.0 0.0 -3.863 -3.804 -2.528 6.80E-06 8.49E-06 9.99E-0410 P53007 SLC25A1 Tricarboxylate transport protein,

mitochondrial

3 Q15233 NONO Non-POU domain-containing

octamer-binding protein

311 18 0 0 18 100.0 0.0 0.0 -3.790 -3.731 -2.455 1.31E-05 1.61E-05 1.47E-03

11 P36871 PGM1 Phosphoglucomutase-1 562 18 0 0 18 100.0 0.0 0.0 -3.790 -3.731 -2.455 1.31E-05 1.61E-05 1.47E-0312 A0A0A6YYG9 ARPC4-TTLL3 ARPC4-TTLL3 fusion protein 625 18 0 0 18 100.0 0.0 0.0 -3.790 -3.731 -2.455 1.31E-05 1.61E-05 1.47E-0313 Q96HE7 ERO1L ERO1-like protein alpha 468 16 0 0 16 100.0 0.0 0.0 -3.631 -3.573 -2.296 4.84E-05 5.84E-05 3.21E-0314 Q02218 OGDH 2-oxoglutarate dehydrogenase,

mitochondrial

1023 16 0 1 17 94.1 0.0 5.9 -3.631 -2.725 -2.296 4.84E-05 5.45E-04 3.21E-03

15 P58107 EPPK1 Epiplakin 5090 15 0 0 15 100.0 0.0 0.0 -3.545 -3.487 -2.210 9.32E-05 1.11E-04 4.75E-0316 P49588 AARS AlaninetRNA ligase, cytoplasmic 968 15 0 0 15 100.0 0.0 0.0 -3.545 -3.487 -2.210 9.32E-05 1.11E-04 4.75E-0317 P16615 ATP2A2 Sarcoplasmic/endoplasmic

reticulum calcium ATPase 2

997 15 0 0 15 100.0 0.0 0.0 -3.545 -3.487 -2.210 9.32E-05 1.11E-04 4.75E-03

340 14 0 0 14 100.0 0.0 0.0 -3.453 -3.395 -2.119 1.80E-04 2.11E-04 7.01E-03

19 O43760 SYNGR2 Synaptogyrin-2 224 14 0 0 14 100.0 0.0 0.0 -3.453 -3.395 -2.119 1.80E-04 2.11E-04 7.01E-0320 O60701 UGDH UDP-glucose 6-dehydrogenase 494 12 0 0 12 100.0 0.0 0.0 -3.250 -3.192 -1.916 6.66E-04 7.66E-04 1.53E-0221 Q5T2N8 ATAD3C ATPase family AAA domain-containing

protein 3C

18 P62873 GNB1 Guanine nucleotide-binding protein

G(I)/G(S)/G(T) subunit beta-1

411 11 0 0 11 100.0 0.0 0.0 -3.137 -3.079 -1.802 1.28E-03 1.46E-03 2.27E-02

22 P46782 RPS5 40S ribosomal protein S5 204 11 0 0 11 100.0 0.0 0.0 -3.137 -3.079 -1.802 1.28E-03 1.46E-03 2.27E-0223 Q8NBJ7 SUMF2 Sulfatase-modifying factor 2 301 11 0 2 13 84.6 0.0 15.4 -3.137 -1.700 -1.802 1.28E-03 3.46E-02 2.27E-0224 Q96AE4 FUBP1 Far upstream element-binding protein 1 644 10 0 0 10 100.0 0.0 0.0 -3.014 -2.956 -1.679 2.48E-03 2.78E-03 3.36E-02

Katoetal.ClinicalandTranslationalMedicine(2015) 4:24 Page4of13

Table 1 Seventy protein candidates characterizing LPIA listed in increasing order of the Rsc (LPIA vs. MIA) values; the negatively larger the Rsc value of a given protein, the greater its expression level in LPIA compared with MIA and AIS (Continued)

25 P46783 RPS10 40S ribosomal protein S10 165 10 0 0 10 100.0 0.0 0.0 -3.014 -2.956 -1.679 2.48E-03 2.78E-03 3.36E-0226 P17516 AKR1C4 Aldo-keto reductase family 1 member C4 323 10 0 0 10 100.0 0.0 0.0 -3.014 -2.956 -1.679 2.48E-03 2.78E-03 3.36E-0227 P15531 NME1 Nucleoside diphosphate kinase A 152 10 0 0 10 100.0 0.0 0.0 -3.014 -2.956 -1.679 2.48E-03 2.78E-03 3.36E-0228 P15428 HPGD 15-hydroxyprostaglandin

dehydrogenase [NAD(+)]

266 10 0 0 10 100.0 0.0 0.0 -3.014 -2.956 -1.679 2.48E-03 2.78E-03 3.36E-02

29 O43776 NARS AsparaginetRNA ligase, cytoplasmic 548 10 0 0 10 100.0 0.0 0.0 -3.014 -2.956 -1.679 2.48E-03 2.78E-03 3.36E-0230 P68036 UBE2L3 Ubiquitin-conjugating enzyme E2 L3 154 9 0 0 9 100.0 0.0 0.0 -2.880 -2.821 -1.545 4.78E-03 5.30E-03 4.97E-0231 P54802 NAGLU Alpha-N-acetylglucosaminidase 743 9 0 0 9 100.0 0.0 0.0 -2.880 -2.821 -1.545 4.78E-03 5.30E-03 4.97E-0232 P11586 MTHFD1 C-1-tetrahydrofolate synthase, cytoplasmic 935 9 0 0 9 100.0 0.0 0.0 -2.880 -2.821 -1.545 4.78E-03 5.30E-03 4.97E-0233 P02790 HPX Hemopexin 462 9 0 0 9 100.0 0.0 0.0 -2.880 -2.821 -1.545 4.78E-03 5.30E-03 4.97E-0234 A8MWD9 SNRPF Small nuclear ribonucleoprotein G-like protein 76 9 0 0 9 100.0 0.0 0.0 -2.880 -2.821 -1.545 4.78E-03 5.30E-03 4.97E-0235 Q9Y3U8 RPL36 60S ribosomal protein L36 105 8 0 0 8 100.0 0.0 0.0 -2.732 -2.673 -1.397 9.22E-03 1.01E-02 7.37E-0236 Q14247 CTTN Src substrate cortactin 634 8 0 0 8 100.0 0.0 0.0 -2.732 -2.673 -1.397 9.22E-03 1.01E-02 7.37E-0237 P62491 RAB11A Ras-related protein Rab-11A 216 8 0 0 8 100.0 0.0 0.0 -2.732 -2.673 -1.397 9.22E-03 1.01E-02 7.37E-0238 P56192 MARS MethioninetRNA ligase, cytoplasmic 900 8 0 0 8 100.0 0.0 0.0 -2.732 -2.673 -1.397 9.22E-03 1.01E-02 7.37E-0239 P12830 CDH1 Cadherin-1 882 8 0 0 8 100.0 0.0 0.0 -2.732 -2.673 -1.397 9.22E-03 1.01E-02 7.37E-0240 P05166 PCCB Propionyl-CoA carboxylase beta chain, mitochondrial 559 8 0 0 8 100.0 0.0 0.0 -2.732 -2.673 -1.397 9.22E-03 1.01E-02 7.37E-0241 O75347 TBCA Tubulin-specific chaperone A 108 8 0 0 8 100.0 0.0 0.0 -2.732 -2.673 -1.397 9.22E-03 1.01E-02 7.37E-0242 O43684 BUB3 Mitotic checkpoint protein BUB3 328 8 0 0 8 100.0 0.0 0.0 -2.732 -2.673 -1.397 9.22E-03 1.01E-02 7.37E-0243 Q9UM22 EPDR1 Mammalian ependymin-related protein 1 224 7 0 0 7 100.0 0.0 0.0 -2.567 -2.508 -1.232 1.78E-02 1.93E-02 1.09E-0144 Q14376 GALE UDP-glucose 4-epimerase 348 7 0 0 7 100.0 0.0 0.0 -2.567 -2.508 -1.232 1.78E-02 1.93E-02 1.09E-0145 P48637 GSS Glutathione synthetase 474 7 0 0 7 100.0 0.0 0.0 -2.567 -2.508 -1.232 1.78E-02 1.93E-02 1.09E-0146 P47897 QARS GlutaminetRNA ligase 775 7 0 0 7 100.0 0.0 0.0 -2.567 -2.508 -1.232 1.78E-02 1.93E-02 1.09E-0147 P15941 MUC1 Mucin-1 475 7 0 0 7 100.0 0.0 0.0 -2.567 -2.508 -1.232 1.78E-02 1.93E-02 1.09E-0148 O60763 USO1 General vesicular transport factor p115 962 7 0 0 7 100.0 0.0 0.0 -2.567 -2.508 -1.232 1.78E-02 1.93E-02 1.09E-0149 P46977 STT3A Dolichyl-diphosphooligosaccharideprotein

glycosyltransferase subunit STT3A

705 12 1 0 13 92.3 7.7 0.0 -2.402 -3.192 -1.916 4.91E-03 7.66E-04 1.53E-02

50 O43488 AKR7A2 Aflatoxin B1 aldehyde reductase member 2 358 12 1 1 14 85.7 7.1 7.1 -2.402 -2.344 -1.916 4.91E-03 5.59E-03 1.53E-0251 Q9Y3I0 C22orf28 tRNA-splicing ligase RtcB homolog 505 6 0 0 6 100.0 0.0 0.0 -2.380 -2.322 -1.045 3.45E-02 3.70E-02 1.63E-0152 Q9NRV9 HEBP1 Heme-binding protein 1 189 6 0 0 6 100.0 0.0 0.0 -2.380 -2.322 -1.045 3.45E-02 3.70E-02 1.63E-0153 Q9BPW8 NIPSNAP1 Protein NipSnap homolog 1 284 6 0 0 6 100.0 0.0 0.0 -2.380 -2.322 -1.045 3.45E-02 3.70E-02 1.63E-0154 P49419 ALDH7A1 Alpha-aminoadipic semialdehyde

dehydrogenase

539 6 0 0 6 100.0 0.0 0.0 -2.380 -2.322 -1.045 3.45E-02 3.70E-02 1.63E-01

55 P29590 PML Protein PML 633 6 0 0 6 100.0 0.0 0.0 -2.380 -2.322 -1.045 3.45E-02 3.70E-02 1.63E-01

Katoetal.ClinicalandTranslationalMedicine(2015) 4:24 Page5of13

Table 1 Seventy protein candidates characterizing LPIA listed in increasing order of the Rsc (LPIA vs. MIA) values; the negatively larger the Rsc value of a given protein, the greater its expression level in LPIA compared with MIA and AIS (Continued)

56 P14868 DARS AspartatetRNA ligase, cytoplasmic 501 6 0 0 6 100.0 0.0 0.0 -2.380 -2.322 -1.045 3.45E-02 3.70E-02 1.63E-0157 P00533 EGFR Epidermal growth factor receptor 705 6 0 0 6 100.0 0.0 0.0 -2.380 -2.322 -1.045 3.45E-02 3.70E-02 1.63E-0158 Q02252 ALDH6A1 Methylmalonate-semialdehyde dehydrogenase

[acylating], mitochondrial

535 29 4 9 42 69.0 9.5 21.4 -2.372 -1.348 -3.108 2.84E-05 4.68E-03 2.05E-05

59 Q99829 CPNE1 Copine-1 537 26 4 7 37 70.3 10.8 18.9 -2.221 -1.510 -2.957 1.38E-04 3.51E-03 6.57E-05

60 Q15363 TMED2 Transmembrane emp24

domain-containing protein 2

201 10 1 0 11 90.9 9.1 0.0 -2.166 -2.956 -1.679 1.57E-02 2.78E-03 3.36E-02

61 P35221 CTNNA1 Catenin alpha-1 906 28 5 10 43 65.1 11.6 23.3 -2.072 -1.165 -3.059 1.61E-04 1.31E-02 3.02E-0562 O75340 PDCD6 Programmed cell death protein 6 191 30 6 3 39 76.9 15.4 7.7 -1.953 -2.665 -3.155 1.78E-04 5.33E-06 1.39E-0563 O00764 PDXK Pyridoxal kinase 312 11 2 2 15 73.3 13.3 13.3 -1.759 -1.700 -1.802 3.11E-02 3.46E-02 2.27E-0264 P13667 PDIA4 Protein disulfide-isomerase A4 645 70 18 26 114 61.4 15.8 22.8 -1.735 -1.174 -4.346 1.64E-07 7.79E-05 2.52E-1265 Q9NTX5 ECHDC1 Ethylmalonyl-CoA decarboxylase 307 28 7 1 36 77.8 19.4 2.8 -1.671 -3.487 -3.059 1.17E-03 4.05E-07 3.02E-0566 Q7Z4W1 DCXR L-xylulose reductase 244 23 7 0 30 76.7 23.3 0.0 -1.400 -4.065 -2.788 9.91E-03 6.51E-07 2.11E-0467 Q9Y2Q3 GSTK1 Glutathione S-transferase kappa 1 226 14 4 3 21 66.7 19.0 14.3 -1.383 -1.629 -2.119 4.58E-02 2.20E-02 7.01E-0368 P49748 ACADVL Very long-chain specific acyl-CoA

dehydrogenase, mitochondrial

655 30 10 12 52 57.7 19.2 23.1 -1.319 -1.024 -3.155 4.98E-03 2.05E-02 1.39E-05

750 16 5 0 21 76.2 23.8 0.0 -1.309 -3.573 -2.296 4.13E-02 5.84E-05 3.21E-03

70 O95994 AGR2 Anterior gradient protein 2 homolog 175 58 23 14 95 61.1 24.2 14.7 -1.135 -1.746 -4.079 5.21E-04 1.57E-06 2.65E-10

Katoetal.ClinicalandTranslationalMedicine(2015) 4:24 Page6of13

69 P42224 STAT1 Signal transducer and activator

of transcription 1- alpha/beta

Kato et al. Clinical and Translational Medicine (2015) 4:24 Page 7 of 13

including pancreas, lung, ovary, breast and prostate, frequently suggesting its association with tumor progression and metastasis [13, 14, 2630]. The HYOU1 protein (hypoxia up-regulated 1, alternatively known as Orp150), belonging to the heat shock protein 70 family, demonstrated its increased expression in prostate, bladder and invasive breast cancer, is suggested to be associated with tumor invasiveness [31].

Protein-protein network analysis of expressed proteins Network analysis of significant proteins is also helpful to understand how they interplay with other key proteins and pathways. In this study the network analysis of protein-protein interaction was performed by utilizing the STRING database version 10 [32, 33]. Therein only experiments, databases and text mining were utilized to avoid less confident predicted interactions. The STRING PPI networks were obtained by applying 70 proteins expressed significantly to LPIA (given in Table 1) and shown in Fig. 2 (also given as Additional file 2: Figure S1). The STRING PPI networks obtained for AIS and MIA are provided as Additional file 3: Figure S2 and Additional file 4: Figure S3. Figure 3 illustrates results of the STRING gene set enrichments (GSEs) for LPIA, MIA, and AIS obtained against cancer related KEGG pathways, which were elucidated with their significance rank p < 0.05 after correction by false discovery rate (FDR). All results are provided in Additional file 1: Table S4. Enrichments on AIS indicated the strong association with pathways of focal adhesion (p = 2.69 1016), adherens junction (p = 6.45 1012) and leukocyte transendothelial migration (p = 9.79 1013).

MIA was found to be associated with PI3K-Akt signaling (p = 8.25 106) and predominantly with proteoglycans in cancer (p = 3.99 1017). In contrast, LPIA was associated broadly with numerous cancer-related pathways which included proteoglycans in cancer (p = 5.94 105), ErbB signaling (p = 2.99 103), Ras signaling (p = 5.77 103), Rap1 signaling (p = 9.72 104), chemokine signaling (p = 7.23 105), and HIF-1 signaling (p = 5.77 103).

Proteoglycans are known to be important molecular effectors of cell surface and pericellular microenvironments and to have multiple functions in cancer and angiogenesis by interacting with both ligands and receptors that regulate neoplastic growth and neovascularization [34]. Molecules participating in the proteoglycan-related cancer pathway were denoted by red circles in Additional file 3: Figure S2. The ErbB signaling pathway is associated with many cancer pathways. The ErbB family belong epidermal growth factor receptors which play an important role in tumor growth. Over-expression of EGFR occurs around 60 % of non-small cell lung cancer (NSCLC), in which adenocarcinoma has the higher frequency [35]. Hypoxia-inducible factors (HIFs) regulate the transcription of genes that mediate the response to hypoxia (reduced O2 availability) [36]. It is

considered that diverse products of HIF-1 action such as induction of the Met protein, hepatocyte growth factor (HGF), followed by Met receptor activation may result in the poor prognosis attached to hypoxic tumors, which indeed turn out to be more aggressive that their well-oxygenerated counterparts. Molecules participating in the ErbB and HIF-1 signaling pathways were denoted by orange and red circles, respectively, in Fig. 2. Numerous clinical data demonstrated that increased levels of HIF-1 proteins consequenced a poor prognosis and increased patient mortality in many different human cancers including NSCLC [37].

Conclusion

Former localized BAC (2 cm) lesions have been histo-logically classified into types A, B and C by Noguchi et al. based on finding of local cancer progression [38]. These lesions, now identified as AIS, MIA or LPIA usually show focal ground-glass opacity (GGO) on chest HRCT (high resolution computed tomography). Generally, AIS shows pure GGO, and representative MIA and LPIA lesions show GGO with some intratumoral areas of collapsed shadow suggesting invasion. There are multiple studies [39, 40] describing that limited lung resection including wedge resection or segmentectomy can cure early adenocarcinomas showing pure GGO. From histologic and radiologic points of view, it is hypothesized that preinvasive AIS progresses to the invasive lesions MIA and LPIA sequentially. Postsurgical 5-year recurrence-free survival rates for AIS and MIA are 100 %, while these for LPIA ranged 71.9 to 93.8 % [810].

The molecular biological background predisposing the worse prognosis of LPIA compared with AIS and MIA may be in part due to the forms of altered protein expressions found in our present study. Proteins appearing in the step from AIS to MIA are probably important at the initial step of microinvasion. As LPIA prepares characteristics of matured lung cancer, it is reasonable that LPIA expresses a variety of proteins associated with cancer invasion. We believe that some of these proteins are candidates for molecular target therapy to suppress local invasion or distant metastases.

In the new adenocarcinoma subtyping, prognoses of solid or micropapillary predominant invasive adenocarcinomas were reported to be apparently worse than these of other subtypes including lepidic type adenocarcinomas [10, 41, 42]. We imagine that in the future comparative proteomic analyses such as that presented here will contribute to elucidate protein expressions determining malignant grade of various lung adenocarcinoma subtypes, which will further provide important knowledge to understand the carcino-genetic process and tumor lineages of lung adenocarcinomas for the benefit of patients with more efficient diagnosis and treatment of these tumors.

Kato et al. Clinical and Translational Medicine (2015) 4:24 Page 8 of 13

Fig. 2 The high-resolution evidence-view of STRING PPI networks obtained on LPIA by using 70 proteins significantly expressed (listed in Table 1),

which were generated using default setting in network depth of 50 interactions under medium confidence (0.4) and standard criteria for linkage

only with experiments, databases, and textmining

Method

Ethics approvalThe study protocol conformed to the principles of the Declaration of Helsinki. All patients were provided with informed consent and the study protocol was approved by Tokyo Medical University Hospital institutional ethics committee.

FFPE tissues and sample preparationSurgically removed lung tissues were fixed with a buffered formalin solution containing 10 15-% methanol, and embedded by a conventional method. Archived paraffin blocks of formalin-fixed adenocarcinoma tissues obtained from cases of AIS (n = 3), MIA (n = 3), and

Kato et al. Clinical and Translational Medicine (2015) 4:24 Page 9 of 13

Fig. 3 Cancer-related pathways associated with AIS, MIA and LPIA, which were obtained by the STRING enrichment analysis under the statistical

significance of p <0.05

LPIA (n = 3), which were retrieved with the approval from Ethical Committee of Tokyo Medical University Hospital (Acception No. 1964). Patients characteristics are listed in Table 2. Paraffin blocks were cut into 4-m sections for diagnosis and 10-m sections for proteomics. The 10-m sections were stained with only haematoxylin. Three pathologists (M.N., Y.K.) independently confirmed adenocarcinoma subtypes using the 4-m sections stained with haematoxylin-eosin (HE).

Laser capture and protein solubilizationCancerous lesions were identified on serial tissue sections stained with hematoxylin and eosin (HE). For proteomic analysis, a 10-m thick section prepared from the same

tissue block was attached onto DIRECTOR slides (Onco-PlexDx, Rockville, MD, USA), de-paraffinized twice with xylene for 5-min, rehydrated with graded ethanol solutions and distilled water, and stained by hematoxylin. Those slides were air-dried and subjected to laser micro-dissection with a Leica LMD6000 (Leica Micro-systems GmbH, Ernst-Leitz-Strasse, Wetzlar, Germany). At least 30,000 cells (8.17 0.03 mm2) per a tissue were collected directly into a 1.5-mL low-binding plastic tube. From individual three types tissues non-cancerous lesions far from tumors were also collect the same numbers of cells as the pseudo-normal (pN) group (n = 3). Representative HE-stained images of adenocarcinoma tissues of AIS, MIA and LPIA were shown in Fig. 4 together with examples of

Kato et al. Clinical and Translational Medicine (2015) 4:24 Page 10 of 13

Table 2 Patients characteristicsPatient number Age Sex CEA (ng/ml) Location Tumor size on CT (mm) Histological type TNM classification EGFR mutation 2 55 M 3.9 Rt.S1 11 pseudo-Normala AIS T1aN0M0 Unkown4 59 W 8.1 Lt.S1 + 2 15 pseudo-Normala LPIA T1bN0M0 Unkown9 55 W 5.3 Rt.S4 37 pseudo-Normala LPIA T1bN0M0 Unkown1 63 W 1.9 Rt.S2 15 Adenocarcinoma AIS T1aN0M0 Unkown2 55 M 3.9 Rt.S1 11 Adenocarcinoma AIS T1aN0M0 Unkown3 56 W 2 Lt.S8 10 Adenocarcinoma AIS T1aN0M0 Unkown4 59 W 8.1 Lt.S3 28 Adenocarcinoma MIA T1bN0M0 Unkown5 73 W 5.4 Rt.S5 25 Adenocarcinoma MIA T1bN0M0 Unkown6 74 W 1.5 Lt.S3 25 Adenocarcinoma MIA T1bN0M0 L858R7 78 W 1.5 Rt.S3 37 Adenocarcinoma LPIA T2aN0M0 L858R8 72 W 0.6 Rt.S1 51 Adenocarcinoma LPIA T2aN0M0 Unkown9 55 W 5.3 Rt.S4 37 Adenocarcinoma LPIA T1bN0M0 Unkown

alesions judged pathologically as non-cancerous regions adjacent to tumors

targeted lesions before and after laser-microdissections (LMD). Proteins were extracted and digested with trypsin using Liquid Tissue MS Protein Prep kits (OncoPlexDx, Rockville, MD, USA) according to the manufacturers protocol [43].

Exploratory liquid chromatography-tandem mass spectrometryThe digested samples were analyzed in triplicate and in random order by liquid chromatography (LC)-tandem mass spectrometry (MS/MS) using reverse-phase liquid chromatography (RP-LC) interfaced with a LTQ-Orbitrap XL hybrid mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) via a closed nano-electrospray device (ADVANCE Captive Spray Source; AMR Inc. Japan). The RP-LC system consisted of Paradigm MS4 (Michrom Bioresources, USA), a peptide Cap-Trap cartridge (0.3 5.0 mm) and a capillary separation column (an L-column Micro of 0.1 150 mm packed with reverse phase L-C18 gels of 3 m in diameter and 12-nm pore size, (CERI, Tokyo, Japan)). An autosampler (HTC-PAL, CTC Analytics, Switzerland) loaded an aliquot of samples onto the trap, which then was washed with solvent A (98 % distilled water with 2 % acetonitrile and 0.1 % formic acid) for concentrating peptides on the trap and desalting. Subsequently, the trap was connected in series to the separation column, and the whole columns were developed for 100 min with a linear acetonitrile concentration gradient made from 5 to 35 % solvent B (10 % distilled water and 90 % acetonitrile containing 0.1 % formic acid) at the flow-rate of 300 nL/min.

An LTQ was operated in the data-dependent MS/MS mode to automatically acquire up to three successive MS/ MS scans in the centroid mode. The three most intense precursor ions for these MS/MS scans could be selected from a high-resolution MS spectrum (a survey scan) that

an Orbitrap previously acquired during a predefined short time window in the profile mode at the resolution of 30,000 and the lock mass of m/z 536.1654 in the m/z range of 350 to 1500. The sets of acquired high-resolution MS and MS/MS spectra for peptides were converted to single data files and they were merged into Mascot generic format files for database searching.

Protein identificationTo extract protein candidates characterizing lepidic type adenocarcinoma from the shotgun proteomic datasets experimentally acquired, consisting of 36 runs (triplicate runs of 12 samples from 4 groups, AIS, MIA, LPIA, and pseudo-normal, N), we have utilized a label-free spectral counting approach for proteomic data analysis on protein identification and semi-quantitative comparison.

Tha mass spectral raw data were analyzed using the one-path method of X! Tandem wth a k-score plugin in Trans-Proteomic pipeline (TPP) [44, 45] against the combined protein fasta file from Human-Invitational database (HInvDB) [46], RefSeq, and UniProtKB/Swiss-Prot appended with reversed decoy sequences. Peptide mass tolerance was 10 ppm, fragment mass tolerance 0.5 Da, and up to two missed cleavages and non-tryptic cleavage at one end of a peptide were allowed. Methionine oxidation is considered as variable modification. The output files from the search engine were converted to the pepXML files and subjected to peptide-spectrum match (PSM) posterior probability calculation with PeptideProphet [47] and then to Protein-Prophet for identification at the protein level in Transproteomic pipeline (TPP) [44, 45].

Semi-quantitative comparisonFor calculating spectral counts (SpCs) at the protein level, triplicate X!Tandem [48] results for each patient were simultaneously analyzed with PeptideProphet and the single

Kato et al. Clinical and Translational Medicine (2015) 4:24 Page 11 of 13

a)

b)

c)

Fig. 4 Representative HE-stained images of a) adenocarcinoma in

situ (AIS), b) minimally invasive adenocarcinoma (MIA), and c) lepidic

predominant invasive adenocarcinoma (LPIA). In AIS, no foci of

invasion or scarring could not be seen, and atypical pneumocytes

were proliferating along the slightly thickened alveolar wall. In MIA,

showing a small area of invasion (<0.5 cm), tumor cells grew mostly

in lepidic pattern along the surface of alveolar walls. In LPIA, showing a

larger area of invasion (0.5 cm), type II pneumocytes and Clara cells

were proliferating along the surface of thickened alveolar walls.

Alveolar epitheliums are substituted in tumor cells, together with

examples of targeted lesions before and after

laser-microdissections (LMD)

http://www.clintransmed.com/content/supplementary/s40169-015-0064-3-s1.doc

Web End =Additional file 1: Table S1. Proteins characteristic to AIS, which were under p < 0.05 (AIS vs LPIA), Rsc > 1 (LPIA vs. AIS), and [AIS] greater than [LPIA] and [MIA]. Table S2. Proteins characteristic to MIA, which were under p < 0.05 (MIA vs LPIA), Rsc > 1 (LPIA vs. MIA), and [MIA] greater than [LPIA] and [AIS]. Table S3. The list of total 840 proteins identified. Table S4. The STRING network enrichment results on KEGG pathways.

http://www.clintransmed.com/content/supplementary/s40169-015-0064-3-s2.ppt

Web End =Additional file 2: Figure S1. The high-resolution evidence-view of STRING PPI networks obtained on LPIA by using 70 proteins significantly expressed (listed in Table 1), which were generated using default setting in network depth of 50 interactions under medium confidence (0.4) and standard criteria for linkage only with experiments, databases, and text mining.

http://www.clintransmed.com/content/supplementary/s40169-015-0064-3-s3.ppt

Web End =Additional file 3: Figure S2. The high-resolution evidence-view of STRING PPI networks obtained on AIS by using 26 proteins significantly

output file was subjected to ProteinProphet [49]. In addition, all PeptideProphet results were simultaneously analyzed with ProteinProphet. Then, all PeptideProphet and ProtinProphet results were used for extracting significant

proteins and computing SpCs with Abacus [20] using the following thresholds: maxiniProb threshold = 0.99 for the minimum PeptideProphet score, and Combined File Prob threshold = 0.9 for the minimum ProteinProphet score in the combined file.

Fold changes of expressed proteins in the base 2 logarithmic scale (RSC) were calculated using spectral counting as described [15]. Proteins expressed significantly between two groups were chosen so that their RSC satisfy >1 or < 1, which correspond to their fold changes >2 or <0.5, and p-value < 0.05 in G-test [19]. Although G-test does not require replicates, spectral counts for each protein from trip-licates were pooled and used for G-statistic calculation using a two-way contingency table arranged in two rows for a target protein and any other proteins, and two columns for cancer groups on an Excel macro. The Yates correction for continuity was applied to the 2 2 tables. The correction could enable us to handle the data containing small spectral counts including zero.

Network analysis of protein-protein interactions Network analysis of protein-protein interactions was carried out by using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database version 10 [32, 33], in which nodes are proteins and edges are the predicted functional associations based on primary databases comprising of KEGG and GO, and primary literature. STRING can predict these interactions based on neighbourhood, gene fusion products, homology and similarity of coexpression patterning, experiments, databases and textmining. Network interaction scores for each node are expressed as a joint probability derived from cu-rated databases of experimental information, textmining and computationally predicted by genetic proximity [32]. In this study, STRING networks were calculated under the criteria for linkage only with experiments, databases, and textmining with the default settings - medium confidence score: 0.400, network depth: 0 and up to 50 interactions.

Additional files

Kato et al. Clinical and Translational Medicine (2015) 4:24 Page 12 of 13

expressed (listed in Additional file 1: Table S1), which were generated using default setting in network depth of 50 interactions under medium confidence (0.4) and standard criteria for linkage only with experiments, databases, and text mining.

http://www.clintransmed.com/content/supplementary/s40169-015-0064-3-s4.ppt

Web End =Additional file 4: Figure S3. The high-resolution evidence-view of STRING PPI networks obtained on MIA by using 15 proteins significantly expressed (listed in Additional file 1: Table S2), which were generated using default setting in network depth of 50 interactions under medium confidence (0.4) and standard criteria for linkage only with experiments, databases, and text mining.

AbbreviationsBAC: Bronchioloaveolar carcinoma; AIS: Adenocarcinoma in situ;

MIA: Minimally invasive adenocarcinoma; LPIA: Lepidic predominant invasive adenocarcinoma; NSCLC: Non-small cell lung carcinoma; LC: Liquid chromatography; MS: Mass spectrometry; FFPE: Formalin-fixed paraffin embedded; LMD: Laser microdissection; HPLC: High pressure liquid chromatography; MS/MS: Tandem mass spectrometry; SpC: Spectral count; STRING: Search tool for the retrieval of interacting genes/proteins database; KEGG: Kyoto encyclopedia of genes and genomes; GO: Gene ontology;

GSE: Gene set enrichment.

Competing interestsThe authors declare that they have no competing interests.

Authors contributionsConceived and designed the experiments: HK MN TN TKO NI TF GMV. Selection of patients: YK MN TNA. Performed the experiments: YK TNA KN. Analyzed the data: TN HT TKA. Wrote the paper: TN HT YK. All authors read and approved the final manuscript.

AcknowledgementsThe authors wish to thank all the members of both the Department of Thoracic and Thyroid Surgery, and the Division of Diagnostic Pathology, Tokyo Medical University. This work was supported in part by the research grant support from the Akaeda Medical Research Foundation.

Author details

1Department of Thoracic and Thyroid Surgery, Tokyo Medical University, Tokyo, Japan. 2Department of Chest Surgery, St. Mariana University School of Medicine, Kanagawa, Japan. 3Department of Biophysics and Biochemistry, Osaka University Graduate School of Medicine, Suita, Japan. 4Division of Diagnostic Pathology, Tokyo Medical University Hospital, Tokyo, Japan.

5Laboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.

6Center of Excellence in Biological and Medical Mass Spectrometry, Lund University, BMC, Lund, Sweden. 7Clinical Protein Science & Imaging, Biomedical Center, Department of Biomedical Engineering, Lund University, BMC, Lund, Sweden. 8Niizashiki Central General Hospital, Saitama, Japan.

Received: 29 January 2015 Accepted: 19 June 2015

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