We previously demonstrated that the NR superfamily is a prognostic biomarker set for survival and relapse of lung cancer patients and further provided SHP
as the first single-gene prognostic biomarker for early stage (stage I) of lung cancer patients (20
). These findings prompted us to develop a rationale of investigating the diagnostic potential of the NR superfamily. In the present study, we investigated the potential use of the NR superfamily gene signature for the diagnosis and treatment of lung cancer. Several independent preclinical and clinical approaches were used to demonstrate that NR profiling of lung cancer can be used to do the following: 1) distinguish between normal bronchial epithelial cells and lung cancer; 2) classify different types of lung cancer (e.g.
SCLC); 3) diagnose lung cancer incidence in smokers at risk; and 4) predict NR ligand responsiveness and provide tumor-specific therapeutic targets. Previous work has suggested that a key advantage to developing a biomarker that can diagnose lung cancer in smokers at risk is that it might eliminate the need for other costly tests and even provide a rationale for treating healthy smokers prophylactically (5
). The expression profiling of the NR superfamily takes this strategy one step further by providing a diagnostic biomarker that can detect both the presence and type of lung cancer and that might be used to discover novel therapeutic targets for individual patients.
One of the more intriguing findings from this work was that the NR-specific pattern of mRNA expression in an individual tumor might be used to predict that tumor's response to a given receptor's ligand. As a proof of principle, we tested randomly chosen subsets of NR-positive and -negative cell lines (for AR, ERα, VDR, and PPARγ) and found a remarkable concordance between the presence of a given receptor's mRNA and the cell's growth response to that receptor's ligand. Although the functional data support the notion that mRNA expression may be used in some cases to indicate the presence or absence of functional receptors, mRNA expression may not always be reliable (25
). At present, the paucity of adequate antibodies for most NRs prevents a robust atlas of protein expression. Knowing a given tumor's NRs profile might then be used to guide therapeutic options. As a case in point, we used the cancer cell-specific pattern of PPAR
γ expression to evaluate growth responses to a PPARγ agonist. The potential benefits of targeting PPARγ in cancer (including NSCLC) have been suggested previously (13
). In this study, we showed that the PPAR
γ mRNA profile in tumor cells predicted both the presence of PPARγ protein (as measured by immunoblot) and cell growth inhibition in response to thiazolidinedione. The ability of thiazolidinedione treatment to inhibit PPARγ-positive xenograft tumors provides a promising rationale for the theragnostic use of NR profiling. It is worth noting that treatment and prevention of a number of diseases have been successful by specifically targeting NRs (35
); thus, this approach is standard practice for guiding antihormonal therapy of breast and prostate cancer (41
). Nevertheless, to develop NRs as therapeutic targets for lung cancer treatment, profiling of NR protein expression in pair-matched patient samples from normal lung vs.
corresponding tumors might be advantageous because different NRs are known to be expressed in various cell types in normal lung tissues (25
In addition to the diagnostic and therapeutic potential of the NR profiling in lung cancer, this work also reveals a strategy to study the pathogenesis and progression of the disease. To that end, we note that there was a substantial change in the NR expression profile when comparing normal HBECs and nontumorigenic, immortalized cell lines harboring oncogenic alterations (e.g.
by expressing K-rasV12
, E6/E7, CDK4, and hTERT) to the fully tumorigenic clones (B). The NR expression changes marking the progression of normal cells into cancer cells imply NR regulation of transcriptional networks plays a role in tumorigenesis. Furthermore, the unsupervised clustering of NR expression in the various cancer cell lines revealed the presence of two major classes of NRs that are remarkably similar to the two clusters found previously by analyzing whole-body anatomical expression of NRs in normal tissues (9
). These two NR clusters represent a hierarchical network that governs two distinct physiological processes, development, and metabolism. The finding that the developmental and metabolic NR-dependent transcriptional networks are dysregulated in lung cancer suggests that NRs play an important role in the homeostatic balance that maintains normal differentiated lung tissue. Closer inspection of the NR expression signature revealed specific NRs appear to be associated with specific types of lung cancer and their transcriptional programs. Notable examples included the expression of SHP
in SCLC and also PPAR
γ in NSCLC. SHP is an atypical nuclear receptor that is known for its role in enterohepatic lipid metabolism in which it interacts and negatively regulates the activity of other transcription factors, including many NRs (44
). The expression of a potent transcriptional repressor like SHP in a cell type in which it is not normally expressed may be expected to have significant pathological effects on cell function, and this finding provides a rationale for further mechanistic studies. The role of PPARγ in multiple cancer types has implicated this receptor in the progression of the disease as a therapeutic target (13
), which we have validated further in this study. Finally, by evaluating global changes in gene expression that were associated with specific NRs, we found significant, NR-specific changes in expression of discrete subsets of genes (–). These findings imply that this type of analysis might be used to find the downstream target genes that may also be important in lung cancer pathogenesis.
Collectively, our studies support the idea of targeting individual receptors, particularly those that are already well-documented targets of Food and Drug Administration-approved drugs for treating lung cancer. By profiling all 48 NR family members and validating the preclinical implications of the selected NRs as diagnostic biomarkers and therapeutic targets, our work provides a new theragnostic approach that could simultaneously decide diagnosis as well as guide individual-specific therapeutic treatment schemes in the future.