As in the group analysis, functional pathway analysis was carried out on all significantly (P ≤ 0.005) differentially expressed, unique, annotated genes for each patient (P1, 148 genes; P2, 211 genes; P3, 351 genes; P4, 215 genes) to identify functional pathways significantly (P ≤ 0.05) overrepresented in the cancer samples isolated from each individual patient (Table 7).
Five of the 15 most significantly enriched pathways in P1 are associated with the immune response. More specifically, NFAT (nuclear factor of activated T cells) is a major transcriptional regulator in T cells and recently identified as a potent immunoregulator in cancer development and as a potential target for therapeutic manipulation of the immune response in cancer patients.22 Patient 1 also showed enrichment for the TCR and CD28 signaling pathways. Glutathione metabolism was also identified as a significantly enriched pathway in P1. Glutathione is known to affect the efficacy of antineoplastic interventions mainly through nucleophilic thioether formation or oxidation-reduction reactions.23 The prevalence of enriched immune response and glutathione metabolism pathways may help account, thus far, for the favorable outcome in P1.
Patient 2 displayed pathways that have been implicated strongly in cancer development and invasion. Notch signaling participates in many developmental processes regulating cell differentiation, proliferation, apoptosis, adhesion, epithelial-to-mesenchymal transition, migration, and angiogenesis and can act either as an oncogene or tumor suppressor in a highly context-dependent manner.24 Cell cycle disruption is a typical feature of cancer cells and results in DNA damage.25 Cytoskeleton remodeling is required for cancer cell invasion and metastasis, apparent in most cancers.26 Cell adhesion determines the polarity of cells and maintains the cell architecture in tissues. Cell adhesiveness is generally reduced in cancer to allow for invasiveness, extracellular matrix decomposition, and metastasis.27
Genes in P3 were enriched predominantly for cell cycle regulatory pathways (9 of a total 25 pathways). This is typical for cancer cells at an advanced stage as with P2. Like P1, P3 showed enrichment of interleukin-mediated immune responses and the glutathione metabolism pathway. Interleukin 12 is a powerful coordinator of the innate and adaptive immune responses and has been shown to have promising antitumor effects in murine tumor models.28 Interleukin 12 is currently being investigated as a potential therapeutic agent against cancer.29
The most significantly enriched pathway in P4 was the WNT signaling pathway. The canonical WNT/β-catenin pathway has emerged as a critical regulator in stem cells and has also been associated with cancer in many tissues.30 For P4, this particular WNT pathway involved the frizzled family receptor 7 (FZD7), which was up-regulated. Up-regulation of FZD7 has been reported in gastric and colorectal cancers.31,32 Patient 4 also showed enrichment of apoptotic and survival pathways. In the p53-dependent apoptosis pathway, the BCL2L11 gene (BCL2-like 11-apoptosis facilitator), responsible for cytoplasmic transport of proapoptotic proteins BID, BMF, and BIM, is down-regulated. On the other hand, CDK1 (cyclin-dependent kinase 1) that promotes phosphorylation of the proapoptotic BAD (BCL-2–associated agonist of cell death) was up-regulated in the BAD phosphorylation pathway. This is evidence for deregulation of the apoptosis and survival pathways in P4.
As shown above, both the group and the personalized analyses identified genes and pathways previously implicated in the onset/progression of pancreatic and a broad spectrum of other cancers. We were next interested in determining the degree of overlap among those genes and pathways identified as significant in each of the individual patient analyses and in the group analysis. Interestingly, we found that the degree of overlap is remarkably low. As shown in Figure 3 (see also Supplemental Tables 1 [http://links.lww.com/MPA/A282] and 2 [http://links.lww.com/MPA/A283]), less than 6.5% (average, 3.3%) of the genes identified as significantly differentially expressed between normal and cancer cells isolated from individual patients (personalized profiles) overlap with genes identified as significantly differentially expressed across the combined patient samples (group analysis). Likewise, there is remarkably little overlap among the individual patients. For example, of the combined number of annotated genes identified as significantly differentially expressed in samples P1 and P2 (148 + 211 = 359), there was less than 1% (2 / 359 ≈ 0.006) overlap. Even between P2 and P3, samples that share the largest number of overlapping genes (8 genes), the degree of overlap is only slightly more than 1% (8 / (211 + 351) ≈ 0.014).
Comparison of the most significantly overrepresented pathways identified in the personalized and group analyses resulted in similar results to the gene analyses; that is, there is relatively little overlap between pathways identified as overrepresented in the group analysis versus the personalized analyses. Furthermore, there is remarkably little overlap in overrepresented pathways among individual patients based on the personalized profiles (Fig. 4; Supplemental Table 3 http://links.lww.com/MPA/A284).
The results of the above studies indicate that genes and pathways identified as being most significantly different between normal and cancer samples as determined by the group analysis display little or no overlap with those identified as significant by individual personalized analyses. Likewise, we found little or no overlap in genes and pathways identified as being most significantly different among individual patient samples (personalized analyses).
To determine if our findings were simply an artifact of the relatively high stringency used in identifying significantly differentiated genes (P ≤ 0.005), we recomputed the degree of overlap between the personalized and group analyses with a variety of cutoff values ranging from 0.05 to 0.001 Although as stringency is reduced, the total number of differentially expressed genes increases as expected, the low overlap between genes identified as significant by the group versus the personalized analyses remained remarkably low (Fig. 5).
To address the possibility that our findings may simply be an artifact of the relatively small number of patients examined in our study, we conducted a similar analysis using data from a previously published microarray gene expression analysis of control and cancer tissue samples isolated from 36 patients.16 In this earlier study, replicate assays were carried out on 3 patients, allowing us to compare the most significantly differentiated genes as determined by a group analysis (36 patient samples) versus the significantly differentiated genes determined in personalized analyses of 3 patients. Consistent with our previous findings, the results demonstrate remarkably little overlap between genes identified as significant in the group versus personalized analyses (Fig. 6; Supplemental Table 4 http://links.lww.com/MPA/A285).
Molecular profiling is revolutionizing the way we view and treat cancer. Rather than the traditional tissue-of-origin approach to the classification and treatment of the disease, molecular profiling is providing gene-based diagnostics and therapeutics as a realistic alternative. The identification of key genes/pathways associated with various types of cancer is the foundation for both molecular diagnostics and therapeutics.
The group approach to the identification of key genes/pathways involves combining the molecular profiles of collections of samples from diseased patients to identify shared variant profiles that are distinct from those associated with nondiseased controls (eg, see Clarke et al33). Although this can be a productive approach for the detection of biomarkers and potential therapeutic targets for diseases caused by 1 or a few genes, for diseases caused by aberrations in a variety of alternative genes/pathways, the group approach may be less effective.34
Our results consistently demonstrated little to no overlap between genes/pathways identified in the group analyses relative to those identified in the personalized analyses. For example, consistent with earlier reports,36 our group analysis identified MUC4 as one of the most significantly differentiated expressed genes between the normal and pancreatic cancer samples (Table 2). Indeed, MUC4 has recently been proposed as a prime candidate for targeted drug therapy in pancreatic cancer.37 In our personalized analyses, however, MUC4 was identified as significantly overexpressed in only 1 of the 7 patients examined suggesting that MUC4 therapy would likely not be effective for the majority of the patients examined in our study. Conversely, many of the genes identified as being significantly differentially expressed in individual patients (personalized profiles) were not identified as significant in the group analysis. For example, the most significantly differentially expressed gene in the cancer samples isolated from P1 is PSCA (prostate stem cell antigen). Interestingly, a monoclonal antibody against PSCA is currently being tested in clinical trials for both prostate and pancreatic cancer.38,39 Thus, whereas PSCA targeted therapy might well be expected to be effective for P1, it was not identified as being significantly overexpressed in the group analysis or in the personalized analyses of any of the other patients examined. Similarly, ADAM (a disintegrin and metalloprotease), a gene reported to be overexpressed in a number of human cancers40 and identified as a potential candidate for targeted gene therapy,41 was among the most significantly overexpressed genes in P4 but was not identified as being significantly overexpressed in the group analysis or in the personalized analyses of any of the other patients examined.
Collectively, our results are consistent with earlier findings indicating that, on the molecular level, pancreatic cancer is a highly heterogeneous disease.13 Although targeted gene therapy is believed by many to hold great promise in the treatment of pancreatic and other cancers, a crucial step in the process is the accurate identification of appropriate candidate genes for targeted therapy. Our findings indicate that personalized and not group molecular profiling is the most appropriate approach for the identification of putative candidates for effective targeted gene therapy for pancreatic and perhaps other cancers with heterogeneous molecular etiology.
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