Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Nanostring-Based Multigene Assay to Predict Recurrence for Gastric Cancer Patients after Surgery

  • Jeeyun Lee ,

    Contributed equally to this work with: Jeeyun Lee, Insuk Sohn, In-Gu Do, Kyoung-Mee Kim

    Affiliation Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center Sungkyunkwan University School of Medicine, Seoul, Korea

  • Insuk Sohn ,

    Contributed equally to this work with: Jeeyun Lee, Insuk Sohn, In-Gu Do, Kyoung-Mee Kim

    Affiliation Samsung Cancer Research Institute, Samsung Medical Center, Seoul, Korea

  • In-Gu Do ,

    Contributed equally to this work with: Jeeyun Lee, Insuk Sohn, In-Gu Do, Kyoung-Mee Kim

    Affiliations Samsung Cancer Research Institute, Samsung Medical Center, Seoul, Korea, Department of Pathology, Samsung Medical Center Sungkyunkwan University School of Medicine, Seoul, Korea

  • Kyoung-Mee Kim ,

    Contributed equally to this work with: Jeeyun Lee, Insuk Sohn, In-Gu Do, Kyoung-Mee Kim

    Affiliation Department of Pathology, Samsung Medical Center Sungkyunkwan University School of Medicine, Seoul, Korea

  • Se Hoon Park,

    Affiliation Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center Sungkyunkwan University School of Medicine, Seoul, Korea

  • Joon Oh Park,

    Affiliation Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center Sungkyunkwan University School of Medicine, Seoul, Korea

  • Young Suk Park,

    Affiliation Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center Sungkyunkwan University School of Medicine, Seoul, Korea

  • Ho Yeong Lim,

    Affiliation Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center Sungkyunkwan University School of Medicine, Seoul, Korea

  • Tae Sung Sohn,

    Affiliation Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

  • Jae Moon Bae,

    Affiliation Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

  • Min Gew Choi,

    Affiliation Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

  • Do Hoon Lim,

    Affiliation Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

  • Byung Hoon Min,

    Affiliation Department of Gastroenterology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

  • Joon Haeng Lee,

    Affiliation Department of Gastroenterology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

  • Poong Lyul Rhee,

    Affiliation Department of Gastroenterology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

  • Jae J. Kim,

    Affiliation Department of Gastroenterology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

  • Dong Il Choi,

    Affiliation Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

  • Iain Beehuat Tan,

    Affiliation Cancer and Stem Cell Biology, Duke-NUS Graduate Medical School Singapore, Singapore

  • Kakoli Das,

    Affiliation Genome Institute of Singapore, Singapore, Singapore

  • Patrick Tan,

    Affiliations Cancer and Stem Cell Biology, Duke-NUS Graduate Medical School Singapore, Singapore, Genome Institute of Singapore, Singapore, Singapore, Cancer Science Institute of Singapore, Singapore, Singapore

  • Sin Ho Jung,

    Affiliation Samsung Cancer Research Institute, Samsung Medical Center, Seoul, Korea

  • Won Ki Kang ,

    wkkang@skku.edu) (WK); sungkimm@skku.edu (SK)

    Affiliation Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center Sungkyunkwan University School of Medicine, Seoul, Korea

  •  [ ... ],
  • Sung Kim

    wkkang@skku.edu) (WK); sungkimm@skku.edu (SK)

    Affiliation Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

  • [ view all ]
  • [ view less ]

Nanostring-Based Multigene Assay to Predict Recurrence for Gastric Cancer Patients after Surgery

  • Jeeyun Lee, 
  • Insuk Sohn, 
  • In-Gu Do, 
  • Kyoung-Mee Kim, 
  • Se Hoon Park, 
  • Joon Oh Park, 
  • Young Suk Park, 
  • Ho Yeong Lim, 
  • Tae Sung Sohn, 
  • Jae Moon Bae
PLOS
x

Abstract

Despite the benefits from adjuvant chemotherapy or chemoradiotherapy, approximately one-third of stage II gastric cancer (GC) patients developed recurrences. The aim of this study was to develop and validate a prognostic algorithm for gastric cancer (GCPS) that can robustly identify high-risk group for recurrence among stage II patients. A multi-step gene expression profiling study was conducted. First, a microarray gene expression profiling of archived paraffin-embedded tumor blocks was used to identify candidate prognostic genes (N = 432). Second, a focused gene expression assay including prognostic genes was used to develop a robust clinical assay (GCPS) in stage II patients from the same cohort (N = 186). Third, a predefined cut off for the GCPS was validated using an independent stage II cohort (N = 216). The GCPS was validated in another set with stage II GC who underwent surgery without adjuvant treatment (N = 300). GCPS was developed by summing the product of Cox regression coefficients and normalized expression levels of 8 genes (LAMP5, CDC25B, CDK1, CLIP4, LTB4R2, MATN3, NOX4, TFDP1). A prospectively defined cut-point for GCPS classified 22.7% of validation cohort treated with chemoradiotherapy (N = 216) as high-risk group with 5-year recurrence rate of 58.6% compared to 85.4% in the low risk group (hazard ratio for recurrence = 3.16, p = 0.00004). GCPS also identified high-risk group among stage II patients treated with surgery only (hazard ratio = 1.77, p = 0.0053).

Introduction

Gastric cancers are highly lethal malignancies with five-year survival rates being one of the worst reported for any solid tumors. According to data from the National Cancer Institute Surveillance, Epidemiology and End Results (SEER) Program, the five-year survival for patients with gastric cancer (GC) improved only modestly over the last 50 years, from 12 to 22 percent [1]. The propensity of GC for early metastatic dissemination has been well documented in previous studies [2], [3]. Based on the recent adjuvant phase III trials, survival benefit from adjuvant chemotherapy or chemoradiation therapy has been documented in GC [4][7]. However, 25 to 40% of all surgically resected GC patients still develop recurrences that are not amenable to re-resection [4], [7][9]. For pathologic stage III and IV GC, 5-year disease-free survival rates are very poor (stage IIIA, 57.6%, stage IIIB, 39.6%; and stage IV 26.3%) [8] implicating that these tumors have inherently aggressive behaviour. In contrast, pathologic stage II GC patients have more favorable clinical outcome with 5-year disease free survival rates of 76% −90% following surgery and adjuvant treatment [8], [9]. Nevertheless, there is a wide spectrum of clinical aggressiveness even within the same stage with some patients being cured with surgery alone while some patients recur shortly after surgery and adjuvant chemoradiation therapy. Hence, based on the hypothesis that there is a significant molecular heterogeneity, we designed a large-scaled gene expression profiling study to develop a molecular test which may efficiently discriminate low-risk from high-risk GC groups for recurrence after surgery.

A molecular test that identifies high-risk patients for recurrence may lead to optimized perioperative treatment strategies in GC. The discovery phase included GC patients from all clinical stages treated with chemoradiotherapy (N = 520). Tumor blocks from these patients were subjected to prognostic gene discovery using Whole Genome DASL assay (WG-DASL) (Illumina, San Diego, CA), a microarray gene expression profiling method for formalin-fixed paraffin-embedded tissue (FFPE). The purpose was to develop hypothesis for clinical utility and discover candidate prognostic or internal reference genes that will help design focused gene expression assay. Results from this phase suggested that clinical utility of a gene expression based prognostic algorithm may potentially distinguish a high-risk group among stage II patients.

The objective of the project was to develop a hypothesis with clinical utility and discover candidate prognostic or internal reference genes in order to design focused gene expression assays. The results from the discovery phase suggest that clinical utility of a gene expression-based prognostic algorithm may potentially distinguish a high-risk group among stage II patients. With the use of a robust multistep prognostic algorithm, Gastric Cancer Prognostic Score (GCPS) for stage II GC patients was developed to identify high-risk patients for recurrence after surgery.

Methods

From September 1994 to December 2005, 1,557 GC patients underwent curative gastrectomy at Samsung Medical Center. Among those, 1,107 patients were selected based on following criteria: histologically confirmed adenocarcinoma of the stomach; surgical resection of tumour without macroscopic or microscopic residual disease; age ≥18; pathology stage IB (T2bN0, T1N1 but not T2aN0) to IV, according to the American Joint Committee on Cancer (AJCC) staging system (6th Ed); complete surgical record and treatment record, and patients receiving the INT-0116 regimen as adjuvant treatment [7]. The study was approved by the institutional review board of the Samsung Medical Center, Seoul, South Korea (IRB approval number: SMC 2010-10-025). All study participants provided written informed consent form recommended by the IRB. In the patients who have deceased at the time of study entry, written informed consent forms were waived by the IRB. Study design and patient cohorts are provided according to REMARK guideline (Figure 1A, 1B, File S1, Section 1). Of the cohort of 1,107 patients, a discovery set of 520 patients and a validation set of 587 patients were randomly assigned and allocated to 6 batches stratified by tumor size and year of surgery for WG-DASL assay.

thumbnail
Figure 1. The Consort Diagram.

(A) A multi-step approach gene expression profiling design, (B) Consort Diagram.

https://doi.org/10.1371/journal.pone.0090133.g001

To avoid false-positive conclusions due to over-fitting, prognostic algorithms and their predefined cut-points were tested in independent cohorts that were not used for prognostic gene discovery and algorithm building. A 4-phase study was designed, with 4 pre-defined independent cohorts recruited from the Samsung Medical Center. The first 3 cohorts include patients with similar clinical and pathological features from chemoradiotherapy-treated study cohorts (File S1, Section 2). The first phase (discovery phase) of the study included GC patients from all clinical stages who were treated with chemo-radiotherapy (N = 520) [8]. Tumor blocks from these patients were subjected to prognostic gene discovery using the WG-DASL (Illumina, San Diego, CA), a microarray gene expression profiling method for FFPE [7]. An ad-hoc external validation of the gene set was performed to minimize any bias from single institutional cohort. The second phase (algorithm development) was to translate findings from the first phase into a clinically applicable test format. We chose the nCounter platform (Nanostring Technologies, Seattle, WA), because of its ability to interrogate the expression levels of up to 800 genes using total RNA extracted from FFPE in a single-tube reaction [8]. We screened stage II patients from the first phase (N = 186) for de novo discovery of prognostic genes, selected ideal combinations of genes using the gradient least absolute shrinkage and selection operator (LASSO) algorithm [10], and then built a first-generation GCPS (GCPS-g1) by adding the products of normalized gene expression and coefficients from the Cox model for DFS. In the third cohort of stage II patients (N = 216). In the fourth phase (testing of clinical utility in a surgery-only setting), we tested the potential clinical utility of GCPS in stage II patients treated with surgery only. A time stamp protocol (Figure S12) was developed before processing of this final cohort. We subsequently developed a refined second-generation GCPS (GCPS-g2) (the final gene set) by analyzing the combined stage II cohorts from the second and third phases of the study.

Gene expression profiling using whole genome-DASL assay

Before each gene profiling experiment, tissue samples were randomly allocated to different batches stratified by surgery time (before 2000 vs. after 2000) and tumor size (≤5 cm vs >5 cm) to minimize any variations from the DNA quality. Total RNA was extracted from 2–4 sections of 4-µm thick FFPE sections from representative primary tumor blocks using the High Pure RNA Paraffin kit (Roche Diagnostic, Mannheim, Germany) after removing non-tumor elements by manual macrodissection guided by hematoxylin and eosin stained slides. WG-DASL assay was performed using 200 ng of RNA following the manufacturer's instruction [11]. For nCounter assay, 200 ng of total RNA was hybridized with the custom designed code set of 800 genes for 18 hours at 65°C and processed according to manufacturer's instruction [12]. The data were normalized to average expression levels of 48 internal reference genes selected from microarray experiment. The detailed description of the discovery phase using WG-DASL assay is provided in File S1, Section 3. The comparability in DNA quality of FFPE tissue and fresh frozen tissues using DASL assay was published previously [13], [14].

Prognostic model building and validation

The algorithm for n-Counter-based assay development for clinical utility based on WG-DASL is provided in File S1, Sections 4–6. We used the gradient lasso algorithm to fit a prediction model based on Cox's proportional hazards model for DFS using the probes with marginal p value<0.01 (Figure S2 in File S1) [10]. We used leave one out cross validation with de novo discovery at each leave one out step to assess the performance of the prognostic model within the discovery cohort. Optimal cut-point was determined by creating a plot for p-values for each cut-point for the prognostic score. For validation study, a priori defined algorithm and cut-point values were used. The validation method for GCPS is outlined in File S1, Section 7.

Results

Microarray gene expression profiling of GC patients treated with adjuvant chemoradiotherapy (phase 1)

We performed gene expression profiling of FFPE from the discovery cohort of 520 cases of stage IB–IV GC treated with standard chemoradiotherapy after curative resection using the WG-DASL assay (Figure 1). Among them, 432 samples passed RNA quality control (GEO database GSE 26253) (File S1, Section 3). The primary end point was DFS. Univariate analysis identified 369 probes that were significantly associated with disease-free survival at p<0.01 without adjustment for other clinical variables (File S1, Section 3d). Next, gradient Lasso was used to develop a prognostic algorithm to predict recurrence (File S1, Section 3e). The leave-one-out cross-validation (LOOCV) procedure with de novo discovery of prognostic genes and the building of a prognostic algorithm at each step was used to examine the robustness of the prognostic algorithm. According to prognostic gene signatures (26 genes, File S1, Section 3f) and pathologic stages (localized vs. advanced), 432 patients were categorized into the following groups: low-risk and stage IB/II (N = 145; 5-year DFS, 84.8%), high-risk and stage IB/II (N = 90; 5-year DFS, 61.1%), low-risk and stage III/IV (N = 83; 5-year DFS, 48.9%), and high-risk and stage III/IV (N = 114; 5-year DFS, 36.9%) (Figure 2). As an ad-hoc analysis, we tested this gene signature using gene expression profiling data from the Singapore patient cohort (N = 199) to minimize any inherent bias from a single institution cohort [15]. In the external patient cohort, the gene signature was able to separate the high-risk group (N = 100) from the low-risk group (N = 99) for recurrence with statistical significance (p<0.00001; hazard ratio (HR), 2.3; 95% CI, 1.62–3.28) (Figure 2). These data suggest the main clinical utility of gene expression profiling of GC in the identification of high-risk patients among stage II patients (low vs. high risk stage IB/II, 84.8% vs. 61.1%; low vs. high risk stage III/IV, 48.9% vs. 36.9%). Therefore, for the development of clinical assay and validation, we focused on developing a gene-set which can robustly predict recurrence in stage II patients.

thumbnail
Figure 2. Kaplan-Meier curves for high risk and low risk groups classified by leave one out cross validation procedure.

HR denotes hazard ratio and p-value is calculated from 100 permutations. (A) all stage patients, high vs low risk; (B) according to stage and risk groups; (C) External validation of the initial gene expression profiling.

https://doi.org/10.1371/journal.pone.0090133.g002

Development of Gastric Cancer Prognostic Score (GCPS) for stage II GC using the nCounter assay (phase 2)

We custom designed an nCounter probe set comprised of candidate prognostic genes from WG-DASL microarray data (phase 1), as well as known cancer genes, kinase genes, and G protein-coupled receptor genes. To address the variability problem in the integrity of RNA molecules in archived FFPE due to pre-analytical variables, such as fixation time and age of the blocks, we applied within-sample normalization using a set of 48 internal reference genes selected from microarray data based on minimum variation across cases and lack of association with prognosis (File S1, Section 4). Correlation between hazard ratios of prognostic genes based on nanostring and WG-DASL are provided in File S1, Section 4b, and Figure S3 in File S1.

We profiled 186 stage II patients from the discovery set. After assessing the robustness of prognostic algorithms built by gradient LASSO through LOOCV, we applied gradient LASSO to all 186 patients and identified 8 genes (LAMP5, CDC25B, CDK1, CLIP4, LTB4R2, MATN3, NOX4, and TFDP1) that in combination provided robust prognostic information (Table 1). The GCPS was then developed as a linear combination of the Cox regression estimates and normalized expression levels of these 8 genes. The cut-point analysis demonstrates that the GCPS was most robust in identifying 25% of patients with worst outcomes (File S1, Section 4). We chose a cut-point of 0.2205 for prospective validation in the independent validation cohort.

thumbnail
Table 1. List of genes that constitute Gastric Cancer Prognostic Score and their Cox regression estimates used to calculate the score.

https://doi.org/10.1371/journal.pone.0090133.t001

Validation of GCPS and its predefined cut-point in stage II GC patients treated with chemoradiotherapy (phase 3)

To avoid the potential over-fitting issue associated with cross-validation [16], we validated GCPS with the fixed algorithm and cut-points in an independent patient cohort that was not used in gene discovery. The clinical and pathological features of 216 stage II patients from the validation set were similar to those from the discovery cohort (File S1, Section 6, and Figures S4–6 in File S1). When we applied GCPS-g1 to the validation set, the risk score distribution was very similar, suggesting the robust analytical performance of the assay (File S1, Section 7a, and Figure S7 is File S1). The predefined cut-point (0.2205) for GCPS-g1 classified 22.7% of tumors from the validation set as the high-risk group. The Kaplan Meier estimate of 5-year DFS for the high-risk patients was 58.6%, compared to 85.4% for the low-risk patients (HR for recurrence, 3.16; p = 0.00004) (Figure 3). GCPS was significant in both intestinal- and diffuse- type GCs, as shown in Figure S8 in File S1 (File S1, Section 7b). The multivariate analysis further shows that GCPS-g1 provided additional prognostic information, besides other known factors such as Lauren classification, differentiation grade, age, and surgery type (HR, 3.027; p = 0.00016; Table 2). Therefore, GCPS may be used to identify stage II patients who remain at high risk even after standard adjuvant chemoradiotherapy and who have similar risk of recurrence as stage III patients.

thumbnail
Figure 3. (A) DFS of stage 2 patients according to GCPS.

(B) DFS of stage 2 patients treated with surgery according to GCPS.

https://doi.org/10.1371/journal.pone.0090133.g003

thumbnail
Table 2. Multivariate Cox regression analysis results on the validation set (N = 216).

https://doi.org/10.1371/journal.pone.0090133.t002

GCPS as a prognostic factor for stage II GC patients treated with surgery only based on a prospectively designed protocol (phase 4)

The review of the clinical database identified 306 patients who did not receive postoperative treatment based on shared decision between physicians and patients (File S1, Section 1, Table S1 in File S1). These patients were subjected to exploratory analyses for assessing the prognostic role of GCPS in patients treated with surgery only and testing the hypothesis that the benefit from chemoradiotherapy is limited in high-risk patients defined by GCPS. For this step, we developed GCPS-g2 (Table S8 in File S1), the second-generation GCPS, by analyzing all stage II cases from phases 2 and 3 to maximize the sample size. We prospectively tested the predefined GCPS-g2 algorithm and cut-point as described in the time stamp protocol (Figure S12 in File S1). The GCPS-g2 predicted recurrence in 300 stage II tumors with a hazard ratio of 2.131 (95% CI, 1.428–3.180; p = 0.00021) (HR, 3.16) (Figure S9 in File S1). To minimize the possibility of over-fitting of the algorithm to chemoradiotherapy-treated patients, we also tested the GCPS-g1, which was previously validated in the chemoradiotherapy-treated cohort, in surgery alone cohort (Figure S10 in File S1). The hazard ratio for GCPS-g1 (HR, 1.77; 95% CI, 1.18–2.67; p = 0.0053) is similar to that for GCPS-g2 (Figure 3). Therefore, the GCPS robustly predicted recurrence in stage II GC with or without postoperative treatment. Based on this data, it can be speculated that high-risk stage II patients defined by GCPS did not gain a tremendous benefit from chemoradiotherapy.

Expression of adverse prognostic genes in tumor microenvironments

In all 3 stage II cohorts included in this study, increased expression levels of 4 genes from GCPS (NOX4, LAMP5, MATN3, and CLIP4) were associated with poor prognosis. Since the known functions of these genes suggest their expression in microenvironments rather than actual tumor cells, we performed the nCounter assay for microdissected tumors versus stromal components from 4 representative high-risk tumors (Figure S11 in File S1). The expression of these genes was significantly higher in stromal components, compared to epithelial cancer cells, with NOX4 showing the most pronounced differences (p = 0.04).

Discussion

In an initial discovery phase, we performed WG-DASL in all stage GC patients. Then, based on the WG-DASL data, we observed that the segregation of high risk group from low risk group was most significant in early stage Ib/II patients (low vs. high risk stage IB/II, 84.8% vs. 61.1%; low vs. high risk stage III/IV, 48.9% vs. 36.9%). Therefore, for the development of clinical assay and validation, we focused on developing a gene-set which can robustly predict recurrence in stage II patients. We developed and validated a prognostic algorithm for gastric cancer, GCPS, which can robustly identify high-risk groups for recurrence among stage II patients. GCPS, developed using the nCounter platform, showed the robust performance in FFPE samples. In addition, the inclusion of internal reference genes allowed the application of GCPS to individual patients. Therefore, we suggest that GCPS can be readily applied to routine clinical use. The GCPS of 8 genes (LAMP5, CDC25B, CDK1, CLIP4, LTB4R2, MATN3, NOX4, and TFDP1) were discovered and validated in over 700 stage II GC patients. We found that the GCPS identified high-risk GC patients for recurrence regardless adjuvant treatment and that high-risk stage II GC patients showed similar DFS to stage III patients. Notably, the GCPS predicted recurrence of both Lauren types (diffuse or intestinal) (Figure S8 in File S1).

Our data clearly demonstrate the presence of molecular heterogeneity in GC, which was associated with clinical outcomes but independent of clinicopathologic staging information. Our data indicate that stage IB/II patients had very poor prognosis when their tumors expressed poor-risk gene signatures. There was a difference of 23.7% in 5-year DFS between high-risk and low-risk gene signatures in stage IB/II patients, and 5-year DFS of high-risk stage IB/II patients was below 60%, despite the use of adjuvant chemoradiotherapy (Figure 1). Therefore, it may be necessary to prospectively design a trial to question whether chemoradiotherapy is required for stage IB/II patients with low-risk gene expression profiles. In order to minimize any potential bias from variations in clinical practice or surgery at a single center, we performed an ad hoc external validation of the signature to validate the signature. As shown in the Results, the signature consistently predicted recurrence in Singapore cohort.

Among the 8 final GCPS genes (LAMP5, CDC25B, CDK1, CLIP4, LTB4R2, MATN3, NOX4, and TFDP1), CDC25B and CDK1, which are known to be associated with cell proliferation, were found to correlate with favorable prognosis (negative Cox regression estimates in Table 2). Notably, a similar trend has been observed for colon cancer by a gene expression assay [17], [18]. These findings may reflect the differentiation status of these tumor cells, since normal gastric and colon mucosal epithelial cells have high turnover rates. TFDP1 encodes the transcriptional factor DP-1, which acts as a positive regulator of the G1/S transition during the cell cycle [19], [20]. Particularly in hepatocellular carcinoma, TFDP1 overexpression was substantially associated with disease progression [19]. Since the scope of this current study does not include the functional study of these genes, their biological significance should be investigated in future studies. Recently, Cho et al. has performed the largest gene expression profiling in 213 GC patients using fresh frozen tissues [21]. They identified 6 prognostic genes (CTNBB1, EXOCS3, TOP2A, LBA1, CCL5, and LZTR1) for patient survival after curative resection. However, to the best of our knowledge, GCPS is the only gene set which has now been validated in more than 700 stage II GC patients, regardless Lauren classification (diffuse or intestinal), a known prognostic factor, or adjuvant treatment.

In conclusion, with the use of a multistep approach, we developed 8-gene GCPS, which was able to robustly identify high-risk stage II GC patients for recurrence after surgery regardless of adjuvant treatment. Currently, with the ongoing ARTIST-II trial (NCT#01761461), we plan to validate our GCPS in a prospectively designed phase III trial.

Supporting Information

File S1.

1. Patient characteristics of study cohorts at each step (Table S1). a. Table S1. Patients characteristics. 2. Clinical and pathological characteristics of cases examined at each phase. 3. Detailed description of the discovery step using WG-DASL assay (step 1). a. Figure S1. QA of WG-DASL data. b. Table S2. Comparison of FISH and IHC results for HER2 status in gastric cancer in Step 1. c. Table S3. List of probes that are differentially expressed between HER2-positive and HER2- negative patient groups in Step 1. d. Table S4. List of all probes with univariate p-values<0.01 in Step 1. e. Figure S2. Gradient Lasso algorithm. f. Table S5. List of 26 probes included in the prediction model fitted by the whole data set (n = 432). g. Table S6. Multivariate Cox regression analysis results in gene discovery set (n = 432). 4. Design of focused gene expression assay using nCounter platform. a. Table S7. List of reference genes for nCounter assay. b. Figure S3. Correlation between hazard ratios of prognostic genes based on quantile normalization and self-normalization using WG-DASL assay. 5. nCounter assay and quality control. 6. Selection of cut-off for Gastric Cancer Prognostic Score (GCPS(Figure S4, S5, & S6). a. Figure S4. DFS according to each quartiles of GCPS-g1. b. Figure S5. Cut-point analysis for GCPS-g1. c. Figure S6. DFS according to optimized cut-point of GCPS-g1. 6. Distribution of GCPS between discovery set and validation set. a. Figure S7. Distribution of GCPS-g1 within the discovery and validation set. b. Figure S8. GCPS: intestinal vs diffuse type. 7. Testing of clinical utility of GCPS-g2 in patients treated with surgery only. a. Figure S9. DFS of stage II patients treated with chemoradiotherapy based on quartile of GCPS-g2. b. Figure S10. DFS of stage II patients treated with surgery alone based on quartile of GCPS-g2. c. Figure S11. Expression of adverse prognostic genes included in Gastric Cancer Prognostic Score according to tissue compartments (tumor versus stroma). Normalized expression levels are shown. d. Table S8. List of nCounter probes included in GCPS-g2. 8. Gastric cancer validation study protocol. a. Figure S12. Gastric cancer validation study protocol.

https://doi.org/10.1371/journal.pone.0090133.s001

(DOCX)

Author Contributions

Conceived and designed the experiments: JL WK SK. Performed the experiments: IGD KMK. Contributed reagents/materials/analysis tools: SHP JOP YSP HYL TSS JMB MGC DHL BHM JHL PLR JJK DIC IBT KD PT. Wrote the paper: JL IS IGD WK SK. Performed statistics: IS SHJ.

References

  1. 1. Levi F, Lucchini F, Gonzalez JR, Fernandez E, Negri E, et al. (2004) Monitoring falls in gastric cancer mortality in Europe. Ann Oncol 15: 338–345.
  2. 2. Folli S, Morgagni P, Roviello F, De Manzoni G, Marrelli D, et al. (2001) Risk factors for lymph node metastases and their prognostic significance in early gastric cancer (EGC) for the Italian Research Group for Gastric Cancer (IRGGC). Jpn J Clin Oncol 31: 495–499.
  3. 3. Nigro JJ, Hagen JA, DeMeester TR, DeMeester SR, Theisen J, et al. (1999) Occult esophageal adenocarcinoma: extent of disease and implications for effective therapy. Ann Surg 230: 433–438 discussion 438–440.
  4. 4. Bang YJ, Kim YW, Yang H, Chung HC, Park YK, et al. (2011) Adjuvant capecitabine and oxaliplatin for gastric cancer: results of the Phase III CLASSIC trial. J of Clin Oncol 29 (Abstr LBA 4002).
  5. 5. Bang YJ, Kim YW, Yang HK, Chung HC, Park YK, et al. (2012) Adjuvant capecitabine and oxaliplatin for gastric cancer after D2 gastrectomy (CLASSIC): a phase 3 open-label, randomised controlled trial. Lancet 379: 315–321.
  6. 6. Lee J, Lim do H, Kim S, Park SH, Park JO, et al. (2012) Phase III trial comparing capecitabine plus cisplatin versus capecitabine plus cisplatin with concurrent capecitabine radiotherapy in completely resected gastric cancer with D2 lymph node dissection: the ARTIST trial. J Clin Oncol 30: 268–273.
  7. 7. Macdonald JS, Smalley SR, Benedetti J, Hundahl SA, Estes NC, et al. (2001) Chemoradiotherapy after surgery compared with surgery alone for adenocarcinoma of the stomach or gastroesophageal junction. N Engl J Med 345: 725–730.
  8. 8. Kim S, Lim DH, Lee J, Kang WK, MacDonald JS, et al. (2005) An observational study suggesting clinical benefit for adjuvant postoperative chemoradiation in a population of over 500 cases after gastric resection with D2 nodal dissection for adenocarcinoma of the stomach. Int J Radiat Oncol Biol Phys 63: 1279–1285.
  9. 9. Lee K, Kye M, Jang JS, Lee OJ, Kim T, et al. (2004) Proteomic analysis revealed a strong association of a high level of alpha1-antitrypsin in gastric juice with gastric cancer. Proteomics 4: 3343–3352.
  10. 10. Sohn I, Kim J, Jung SH, Park C (2009) Gradient lasso for Cox proportional hazards model. Bioinformatics 25: 1775–1781.
  11. 11. Bibikova M, Talantov D, Chudin E, Yeakley JM, Chen J, et al. (2004) Quantitative gene expression profiling in formalin-fixed, paraffin-embedded tissues using universal bead arrays. Am J Pathol 165: 1799–1807.
  12. 12. Geiss GK, Bumgarner RE, Birditt B, Dahl T, Dowidar N, et al. (2008) Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol 26: 317–325.
  13. 13. Hoshida Y, Villanueva A, Kobayashi M, Peix J, Chiang DY, et al. (2008) Gene expression in fixed tissues and outcome in hepatocellular carcinoma. N Engl J Med 359: 1995–2004.
  14. 14. Fan JB, Yeakley JM, Bibikova M, Chudin E, Wickham E, et al. (2004) A versatile assay for high-throughput gene expression profiling on universal array matrices. Genome Res 14: 878–885.
  15. 15. Tan IB, Ivanova T, Lim KH, Ong CW, Deng N, et al. (2011) Intrinsic subtypes of gastric cancer, based on gene expression pattern, predict survival and respond differently to chemotherapy. Gastroenterology 141: 476–485, 485 e471–411.
  16. 16. Simon R, Radmacher MD, Dobbin K, McShane LM (2003) Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst 95: 14–18.
  17. 17. Allegra CJ, Paik S, Colangelo LH, Parr AL, Kirsch I, et al. (2003) Prognostic value of thymidylate synthase, Ki-67, and p53 in patients with Dukes' B and C colon cancer: a National Cancer Institute-National Surgical Adjuvant Breast and Bowel Project collaborative study. J Clin Oncol 21: 241–250.
  18. 18. Clark-Langone KM, Wu JY, Sangli C, Chen A, Snable JL, et al. (2007) Biomarker discovery for colon cancer using a 761 gene RT-PCR assay. BMC Genomics 8: 279.
  19. 19. Yasui K, Okamoto H, Arii S, Inazawa J (2003) Association of over-expressed TFDP1 with progression of hepatocellular carcinomas. J Hum Genet 48: 609–613.
  20. 20. Yasui K, Arii S, Zhao C, Imoto I, Ueda M, et al. (2002) TFDP1, CUL4A, and CDC16 identified as targets for amplification at 13q34 in hepatocellular carcinomas. Hepatology 35: 1476–1484.
  21. 21. Cho JY, Lim JY, Cheong JH, Park YY, Yoon SL, et al. (2011) Gene expression signature-based prognostic risk score in gastric cancer. Clin Cancer Res 17: 1850–1857.