Pharmacogenetics of lung cancer: An integrative epidemiologic approach
Authors: Margaret Spitz, Xifeng Wu
Journal: Clinical Cancer Research (2008)

PL03-02    Overview.  Treatments for lung cancer have improved survival only marginally over the past few decades. Patients with non-small cell lung cancer (NSCLC) are commonly treated with platinum-based chemoradiotherapy with response rates being unpredictable, even controlling for the known prognostic factors of stage, histology, grade and performance status. Therefore, the ability to predict therapeutic response in these patients is of immense clinical benefit.  Currently only clinical variables are used to guide treatment decisions with modest ability to predict overall survival. Molecular signatures derived from global gene expression profiling have shown promise in predicting clinical outcome, as have pathway-based or genome-wide identification of somatic aberrations using high-density comparative genomic hybridization in tumor tissues.   Integrative Epidemiology.   The annotation of the human genome provides an opportunity to explore the impact of germline genetic variation in determining survival differences in non-small cell lung cancer. Evaluation of genetic variants such as single nucleotide polymorphisms (SNPs) is being recognized as an alternative and complementary approach to tumor molecular profiling and is producing promising results. A unifying premise to applying a molecular epidemiologic approach to outcome prediction is the concept of  Integrative Epidemiology , i.e., that the same genes that are implicated in cancer risk, are also involved in the prediction of patient outcome. This implies that risk prediction and prediction of outcome are part of a continuum. Therefore, by analyzing the genetic make-up of both the tumor and the patient, we can generate a “personalized molecular medicine” profile, or pharmacogenetic profile that can be used to individualize therapy and help understand the functional consequences of chemoprevention, chemotherapy, or radiotherapy response and toxicity. This approach will also enable clinicians to select drugs likely to be most effective and least toxic for each patient. This is the promise and potential of pharmacogenetics.  However, it is unlikely that a single polymorphism in a single gene would have a strong effect on treatment response. We therefore need to move beyond the candidate gene approach to a pathway-based polygenic approach, in order to identify clinically relevant combination of genetic markers. In this way, we can amplify the effects of individual polymorphisms and enhance the predictive power of the genetic variants. In this presentation we provide two examples of employing a pathway based genotyping approach, to assess the combined effects of a panel of polymorphisms that interact in the same pathway, rather than focusing on the candidate gene approach.   DNA repair.  DNA repair may be considered a double-edged sword .  Nucleotide excision repair (NER) is the primary DNA repair pathway responsible for the removal of tobacco-related polycyclic aromatic hydrocarbon-induced DNA adducts. Suboptimal DNA repair capacity, as assessed by the  in vitro  host cell reactivation assay, is an independent risk factor for cancer. Likewise, cisplatin and other platinum agents bind preferentially to DNA, and the level of tissue platinum-DNA  adducts is correlated with clinical outcome. Resistance to platinum agents has been linked to enhanced tolerance and to efficient capacity to repair the resultant DNA damage. On the other hand, reduced capacity for nucleotide excision repair has been shown to be associated with improved response to chemotherapy (due to decreased removal of tissue platinum adducts).  As proof of principle and to gain further insight into the prognostic value of genetic polymorphisms in cisplatin response, we analyzed the associations of 12 functional polymorphisms in 7 key genes involved in NER in 229 patients with advanced NSCLC patients receiving first-line cisplatin-based chemotherapy. Joint analysis was performed to evaluate the cumulative effects of specific pathways. Survival tree analysis was conducted to identify patient subsets with distinctive survival patterns. Several biologically plausible main effects were identified in individual analysis.  Specifically, patients carrying the  RAD23  and  XPA_23  variant-containing genotypes exhibited significantly poorer survival, while the  XPD _751 and  XPC_PAT  and  ERCC1  genotypes were associated with improved survival. We also assumed an additive model and summed the unfavorable genotypes (based on hazard ratios from the main effect analysis that had P values  Inflammation Pathway.  Another proof of principle example lies in the inflammation pathway. A sustained inflammatory response that is either not restrained or is aberrantly regulated, creates a microenvironment abundant in inflammatory cells, growth factors, activated stroma, enhanced angiogenesis, and oxidative stress, conditions that predispose to tumorigenesis. Likewise interaction of lung cancer cells with macrophages promotes both invasiveness and matrix-degrading activity. Therefore variants in the inflammation pathway could be markers both of risk and outcome. We have evaluated a panel of 59 single nucleotide polymorphisms (SNPs) in 37 inflammation-related genes in both risk of lung cancer and in outcome in patients with advanced stages of NSCLC. The interleukin 1B (IL1B) C3954T variant genotype was significantly associated with lung cancer risk, after controlling for false positives. This association was stronger in heavy smokers and in patients with emphysema, suggesting that, in the setting of heavy or prolonged exposure to tobacco or other agents, an aberrant inflammatory response may promote lung damage, eventually leading to lung cancer.  Likewise, we have evaluated the same panel of genes as prognostic factors, and as predictive of radiation toxicity. Specifically we noted that the risk of radiation -related pneumonitis was 3.8 for patients with one or two adverse inflammation genotypes relative to those with no adverse genoytpes, and 9.5 for those with 3 or more adverse genotypes, with a significant gene dosage effect.   Genome Wide Association Studies.  Sequencing of the entire human genome, the mapping of common haplotypes of single-nucleotide polymorphisms (SNPs), and cost-effective genotyping technologies have all made genome-wide association studies feasible. Most genome-wide association studies (GWAS) use dense maps of germline genetic variants such as single nucleotide polymorphisms (SNPs) that cover the whole genome to identify genetic markers of risk in a hypothesis generating mode. While the case-control approach is the study design of choice for such association studies, we propose a complementary approach to exploit the depth and diversity of data derived from genome-wide association (GWA) studies for pharmacogenetic research. Specifically we envision a case series design that follows clinically homogenous subsets of patients enrolled in GWA studies longitudinally for data on response to therapy, toxicity, and survival. It is recognized that identifying such patients receiving similar therapy and follow-up is a challenge and the usable sample size will decrease, but the GWAs have large numbers of case from which to select these usable subsets.  The objective is to construct comprehensive prognostic/prediction profiles of epidemiologic, clinical and genetic data to improve patient outcomes. These data can also be used for replication of findings from other case series analyses. In the longer-term, complementary technologies, such as tissue arrays and tissue lysate arrays from patient tumors, as well as functional genomics to demonstrate the functional effects of the relevant SNP’s (siRNA, miRNA and chemical genomics screens) will help to validate which molecular markers or groups of molecular markers best predict risk, early diagnosis, and response to therapy. Consortial studies can be combined for pooled analyses, and studies across different ethnic groups can provide valuable LD information in the regions of interest. The need for collaborative team science is critical.  Supported by NCI grants CA55769, CA127219, CA70907, CA111646, Flight Attendant Medical Research Institute