American patient training set
The training set of patients accrued at the Indiana University Thoracic Oncology Program consisted of 27 patients with stage Ia-IIIb NSCLC who were evaluable for recurrence at two years of follow-up. All histologies of NSCLC were included. This patient cohort was reflective of a broad patient population seen in an American academic medical center without selection for histology or stage. This approach could be useful because genomic markers derived from this type of study could potentially be applied to clinical decision-making in a general thoracic oncology practice. There were 11 patients who experienced recurrence within two years of resection (group R) and 16 patients who did not (group NR). Patient characteristics, including stage of disease, recurrence status, histology, age at surgery, smoking history, survival after surgery, time to recurrence, gender, and adjuvant therapy are described in . The majority of the NSCLC patients exhibited adenocarcinoma histology (n=19). Mean age of the patients at operation was 61.8 (SD=12.8) years (range 34-81 years).
Description of training set patients
The median time to recurrence (TTR) of group R was 12 months and the median overall survival (OS) was 20 months. This study had a 57-month median follow-up and the 2-year cut-off captured 81% of the recurring patients. Among the NR group, 19% of the patients recurred after 2 years, the recurrences being at 32, 50 and 53 months and these patients were still alive at the time of data collection closure. Age at operation, race, gender and smoking status were analyzed in conjunction with gene expression data in subsequent Cox multivariate analysis (see below).
Genomic markers associated with NSCLC recurrence in the training set
To identify genes that were differentially expressed in recurring patients, genomic microarray analysis of tumor gene expression was performed by Affymetrix U133A chip hybridization. Candidate genomic recurrence markers () were identified from 12,956 evaluable probes in the microarray by statistical analysis of log2
-transformed signals (Welch's T-test; P≤0.001). This analysis resulted in 51 probes corresponding to 44 genes that were differentially expressed between the R and NR groups () (raw data available at Geo database; GSE9971) (Table S1
Differentially Expressed Genes in Recurrent NSCLC (Group R) Compared to Non-Recurrent NSCLC (Group NR) – Differential analysis based on P values <0.001
Hierarchical clustering analysis of training set genes associated with NSCLC recurrence
Hierarchical clustering using the 51 probes associated with recurrence separated the training set patients exhibiting recurrence. Based on recurrence or not at 2 y of follow-up, this clustering identified three subgroups of genes exhibiting up-regulation and one exhibiting down-regulation in recurrence (). The most statistically significant markers up-regulated in recurrence were, in order of significance by Welch's T test, FLJ20343, DKFZp566O084, CACNB3, CYP3A5, and DBN1 (). The first three markers were associated with one cluster of genes up-regulated in patients exhibiting recurrence (Group 1), while CYP3A5 was associated with a second cluster (Group 2) and DBN1 with a third (Group 3). Genes in groups 1-3 were associated with a broad range of T test values (ranging from 0.001 to 0.00001). The most statistically significant markers that were down-regulated in recurring patients were, in order of significance, C14orf118, STAT2, ATF7IP, HIPK3 and HLA-DOA () and this group clustered together (Group 4). Group 4 exhibited a smaller distribution of T test values (ranging from 0.0009 to 0.00008), consistent with the larger size of this group.
Gene expression profile and hierarchical clustering separating the recurrent and non-recurrent groups on the basis of discriminatory genes
Screening of a Korean patient validation set for concordance of candidate genomic markers
We hypothesized that screening of a geographically distant and demographically distinct patient population for recurrence-associated genomic markers would lead to the identification of more reproducible genes for the study of NSCLC prognosis. To find a distinct and larger validation set, the GEO database was screened for NSCLC studies with similar stage grouping and no selection based on histology, performed on a similar genomics platform. A Korean study of NSCLC recurrence-associated genomic markers, GSE8894, met the criteria for comparison with the American training set and was probed for genomic markers associated with recurrence common to both sets (11
). The K-M curve for the Korean patients exhibited a median disease-specific survival time of 69.4 months and a 5-y survival percentage of 56.2%, comparable to but perhaps slightly shorter than North American patients with resected NSCLC (9
) (Fig. S1
Each of the 44 genes identified in the American training set was tested in the Korean data set for significance by univariate and multivariate Cox analysis. The four genes that were most significant by univariate Cox analysis were, in order of significance, DBN1, FLAD1 (PP591), CACNB3 and CCND2 (). The first three genes exhibited P values <0.05 and the fourth, exhibiting a P value of 0.08, was retained for model building. By multivariate analysis, only DBN1 exhibited significance (P=0.0095) (). Nonetheless, two of the genes approached multivariate significance, FLAD1 (P=0.0720) and CCND2 (P=0.0713), while CACNB3 did not (P=0.1813) (). These results indicate that DBN1 is potentially of value as a multivariate marker and the combination of the 4 genes was effective in model building (see below).
Validation of genes using Cox-analysis
Confirmation of DBN1, CACNB3, FLAD1 (PP591) and CCND2 expression in the training set by q-PCR
The utility of prognosis-associated genes identified by microarray analysis is increased if they are also assayable by q-PCR (11
). Increased expression of the DBN1
) genes in the recurrent NSCLC tumors was confirmed in the training set by q-PCR (). Decreased expression of CCND2
in the recurring patients of the training set approached, but did not reach, statistical significance (). These expression values were used to perform subsequent Cox regression analysis of the training set.
q-PCR analysis of DBN1, CACNB3, FLAD1 (PP591) and CCND2
Clinical, genomic, and clinicogenomic modeling of the training and validation sets
A clinical model of the training set was performed, based on histology, pStage, sex, race, and smoking history (). The clinical model was effective at separating the low- and high-risk patients in the training set (P=0.0032). The median RFS for the training set was 17.2 months, while the 5-y % RFS was 83.4 and 28.6% for the low- and high-risk groups, respectively (; ). Nonetheless, application of a similar clinical model to the larger validation cohort was less successful (P=0.0518) (11
). A genomic model developed from the training set was more effective than the clinical model at risk stratification of the training (P<0.0001) () and validation sets (P<0.0001) (). Using the genomic model, the 5-y RFS for the low- and high-risk groups was 92.3 vs. 15.4% and 67.5 vs. 32.8% in the training and validation sets, respectively (). Using the clinicogenomic model, patients were effectively risk-stratified in the training (P<0.0001) and validation sets (P<0.0001) (). Using the clinicogenomic model, the 5-y RFS for the low- and high-risk groups was 92.3 vs. 15.4% and 67.0 vs. 33.3% for the training and validation sets, respectively (). In summary, the genomic and clinicogenomic models exhibit clinical utility because the difference in 5-y RFS is more than 2-fold indicating a substantial clinical effect.
Cox modeling on the training and the validation set
Clinical, genomic, and clinicogenomic modeling of stage I and II patients in the validation set
Because the decision to offer chemotherapy or not is crucial for early stage patients, we reanalyzed the stage I-II patients in the validation set using the 4 genomic markers to develop genomic and clinicogenomic models for this risk group. The genomic model risk-stratified the stage I-II patients (P value < 0.0001; ), exhibiting 5-y RFS of 73.2 vs. 33.8 % for the low- and high-risk groups, respectively ( and ). The clinicogenomic model also risk-stratified stage I-II patients (P value < 0.0001; ), exhibiting 5-y RFS of 69.6 vs. 30.3% for the low- and high-risk groups, respectively ( and ). These results support the utility of the 4 genes for risk model development for stage I-II patients.
Genomic and clinicogenomic modeling of the validation set based on disease-specific OS
Risk stratification on the basis of disease-specific OS is another important test of the utility of the 4 genomic markers. Therefore, genomic and clinicogenomic models were developed. Both models were equally effective risk-stratifying the patients into low- and high-risk groups (P<0.0001) (). Using the genomic and clinicogenomic models, the 5-y disease-specific survival for the low- and high-risk groups was 63.3 vs. 37.0% and 67.3 vs. 44.2%, respectively (). These differences in disease-specific OS were at least 1.5-fold, indicating clinical utility.
Analysis of multivariate marker DBN1
The genomic marker DBN1
was identified as a significant in the genomic and clinicogenomic models of RFS stage I-III, RFS stages I-II and disease-specific OS (Stage I-III RFS genomic HR=1.463 CI 1.088-1.967; Stage I-III RFS clinicogenomic HR=1.758 CI 1.248-2.476; Stage I-II RFS genomic HR=1.455 CI 1.038-2.04; Stage I-II RFS clinicogenomic HR=1.72 CI 1.165-2.541; OS genomic HR=1.484 CI 1.057-2.082; OS clinicogenomic HR=1.627 CI 1.115-2.367; Table S3
). The addition of pT and pN stage information improved the significance of DBN1
for all models (RFS stage I-III, P= 0.0119 to 0.0013; RFS stage I-II, P= 0.0297 to 0.0064; OS, P= 0.0226 to 0.0117). This finding indicates that DBN1
serves as a component of the genomic model that can be improved by the addition of clinical stage.