Citations

Abstract Purpose: Variations in genes related to biological activity of anticancer drugs could influence treatment responses and lung cancer prognosis. Genetic variants in four biological pathways, that is, glutathione metabolism, DNA repair, cell cycle, and epidermal growth factor receptor (EGFR), were systematically investigated to examine their association with survival in advanced stage non–small cell lung cancer (NSCLC) treated with chemotherapy. Experimental Design: A total of 894 tagging single-nucleotide polymorphisms (SNP) in 70 genes from the four pathways were genotyped and analyzed in a 1,076-patient cohort. Association with overall survival was analyzed at SNP and whole-gene levels within all patients and major chemotherapy agent combination groups. Results: A poorer overall survival was observed in patients with genetic variations in GSS (glutathione pathway) and MAP3K1 (EGFR pathway; HR = 1.45; 95% CI = 1.20–1.77 and HR = 1.25; 95% CI = 1.05–1.50, respectively). In the stratified analysis on patients receiving platinum plus taxane treatment, we observed a hazardous effect on overall survival by the MAP3K1 variant (HR = 1.38; 95% CI = 1.11–1.72) and a protective effect by RAF1 (HR = 0.64; 95% CI = 0.50–0.82) in the EGFR pathway. In patients receiving platinum plus gemcitabine treatment, RAF1 and GPX5 (glutathione pathway) genetic variations showed protective effects on survival (HR = 0.54; 95% CI = 0.38–0.77; HR = 0.67; 95% CI = 0.52–0.85, respectively); in contrast, NRAS (EGFR pathway) and GPX7 (glutathione pathway) variations showed hazardous effects on overall survival (HR = 1.91; 95% CI = 1.30–2.80; HR = 1.83; 95% CI = 1.27–2.63, respectively). All genes that harbored these significant SNPs remained significant by whole-gene analysis. Conclusion: Common genetic variations in genes of EGFR and glutathione pathways may be associated with overall survival among patients with advanced stage NSCLC treated with platinum, taxane, and/or gemicitabine combinations. Clin Cancer Res; 17(11); 3830–40. ©2011 AACR.

Abstract Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr LB-186.

Abstract The androgen receptor (AR) is the principal target for treatment of non–organ-confined prostate cancer (PCa). Androgen deprivation therapies (ADT) directed against the AR ligand–binding domain do not fully inhibit androgen-dependent signaling critical for PCa progression. Thus, information that could direct the development of more effective ADTs is desired. Systems and bioinformatics approaches suggest that considerable variation exists in the mechanisms by which AR regulates expression of effector genes, pointing to a role for secondary transcription factors. A combination of microarray and in silico analyses led us to identify a 158-gene signature that relies on AR along with the transcription factor SRF (serum response factor), representing less than 6% of androgen-dependent genes. This AR-SRF signature is sufficient to distinguish microdissected benign and malignant prostate samples, and it correlates with the presence of aggressive disease and poor outcome. The AR-SRF signature described here associates more strongly with biochemical failure than other AR target gene signatures of similar size. Furthermore, it is enriched in malignant versus benign prostate tissues, compared with other signatures. To our knowledge, this profile represents the first demonstration of a distinct mechanism of androgen action with clinical relevance in PCa, offering a possible rationale to develop novel and more effective forms of ADT. Cancer Res; 71(5); 1978–88. ©2011 AACR.

Abstract Background: Lung cancer is commonly treated with platinum compounds. The “glutathione pathway” participates in the metabolism of platinum compounds. We set out to test the hypotheses that single nucleotide polymorphisms (SNPs) or copy number polymorphisms for genes within the glutathione pathway might influence survival in lung cancer patients treated with these drugs. Methods: Germline DNA samples from 973 lung cancer patients were genotyped for 290 glutathione pathway SNPs. GSTT1 copy number was also assayed. We determined the association of these polymorphisms with survival for lung cancer patients, followed by functional genomic validation. Results: We observed suggestive associations between survival and GSTT1 copy number (P = 0.017), and GSTA5, GSTM4, and ABCC4 SNPs, adjusted for covariates (P = 0.018, 0.002, and 0.002, respectively) or not (P = 0.005, 0.011, and 0.002). One hundred lymphoblastoid cell lines were then treated with cisplatin, and IC50 values were significantly associated with the GSTM4 SNP (P = 0.019). Furthermore, GSTM4, GSTT1, and ABCC4 overexpression significantly decreased cisplatin sensitivity in lung cancer and HEK293T cell lines. Conclusions: These results suggest that GSTM4 polymorphisms are biomarkers for the prediction of cisplatin response. ABCC4 polymorphisms, as well as GSTT1 copy number, may also help to predict cisplatin response, but further validation is required. These results represent a step toward the individualized chemotherapy of lung cancer. Cancer Epidemiol Biomarkers Prev; 19(3); 811–21

Abstract Purpose: We have previously mapped a major susceptibility locus influencing familial lung cancer risk to chromosome 6q23-25. However, the causal gene at this locus remains undetermined. In this study, we further refined this locus to identify a single candidate gene, by fine mapping using microsatellite markers and association studies using high-density single nucleotide polymorphisms (SNP). Experimental Design: Six multigenerational families with five or more affected members were chosen for fine-mapping the 6q linkage region using microsatellite markers. For association mapping, we genotyped 24 6q-linked cases and 72 unrelated noncancer controls from the Genetic Epidemiology of Lung Cancer Consortium resources using the Affymetrix 500K chipset. Significant associations were validated in two independent familial lung cancer populations: 226 familial lung cases and 313 controls from the Genetic Epidemiology of Lung Cancer Consortium, and 154 familial cases and 325 controls from Mayo Clinic. Each familial case was chosen from one high-risk lung cancer family that has three or more affected members. Results: A region-wide scan across 6q23-25 found significant association between lung cancer susceptibility and three single nucleotide polymorphisms in the first intron of the RGS17 gene. This association was further confirmed in two independent familial lung cancer populations. By quantitative real-time PCR analysis of matched tumor and normal human tissues, we found that RGS17 transcript accumulation is highly and consistently increased in sporadic lung cancers. Human lung tumor cell proliferation and tumorigenesis in nude mice are inhibited upon knockdown of RGS17 levels. Conclusion: RGS17 is a major candidate for the familial lung cancer susceptibility locus on chromosome 6q23-25.

Purpose Gene expression profiling for outcome prediction of non–small-cell lung cancer (NSCLC) remains clouded by heterogeneous and unvalidated results. This study applied multivariate approaches to identify and evaluate value-added gene expression signatures in two types of NSCLC. Materials and Methods Two NSCLC oligonucleotide microarray data sets of adenocarcinoma and squamous cell carcinoma were used as training sets to select prognostic genes independent of conventional predictors. The top 50 genes from each set were used to predict the outcomes of two independent validation data sets of 84 and 91 NSCLC cases. Results Adenocarcinomas with the 50-gene signature from adenocarcinoma in both validation data sets had a 2.4-fold (95% CI, 1.3 to 4.4 and 1.0 to 5.8) increased mortality after adjustment for conventional predictors. Squamous cell carcinoma with this high-risk signature had an adjusted risk of 1.1 (95% CI, 0.4 to 3.2) in one data set and 2.5 (95% CI, 1.1 to 5.8) in another consisting of stage I tumors. Adenocarcinoma with the 50-gene signature from squamous cell carcinoma had an elevated risk of 3.5 (95% CI, 1.4 to 9.0) after adjustment for conventional predictors. Squamous cell carcinoma with this high risk signature had an adjusted risk of 1.8 (95% CI, 0.7 to 4.6). Despite the little overlap in individual genes, the two gene signatures had significant functional connectedness in molecular pathways. Conclusion Two non-overlapping but functionally related gene expression signatures provide consistently improved survival prediction for NSCLC regardless of histologic cell type. Multiple sets of genes may exist for NSCLC with predictive value, but ones with independent predictive value beyond clinical predictors will be required for clinical translation.

Abstract DNA microarray has been widely used in cancer research to better predict clinical outcomes and potentially improve patient management. The new approach provides accurate tumor classification and outcome predictions, such as tumor stage, metastatic status, and patient survival, and offers some hope for individualized medicine. However, growing evidence suggests that gene-based prediction is not stable and little is known about the prediction power of gene expression profiles compared with well-known clinical and pathologic predictors. This review summarized up-to-date publications in microarray-based lung cancer clinical outcome prediction and conducted secondary analyses for those with sufficient sample sizes and associated clinical information. Among the most commonly used analytic approaches, unsupervised clustering mainly recaptures tumor histology and provides variable degrees of prediction for tumor stage, lymph node status, or survival. Overall, most studies lack an independent validation. Supervised learning and testing generally offer a better prediction. Noted is that when conventional predictors of age, gender, stage, cell type, and tumor grade are considered collectively, the predictive advantage of the gene expression profiles diminishes. We conclude that outcome prediction from gene expression signatures selected by current analytic approaches can be mostly explained by well-known conventional predictors, particularly histologic subtype and grade of differentiation. A strategy for establishing independent or more accurate signatures is commented. (Cancer Epidemiol Biomarkers Prev 2006;15(11):2063–8)