Age- and sex-specific genomic profiles in non-small cell lung cancer.

CONTEXT: Gene expression profiling may be useful in examining differences underlying age- and sex-specific outcomes in non-small cell lung cancer (NSCLC). OBJECTIVE: To describe clinically relevant differences in the underlying biology of NSCLC based on patient age and sex. DESIGN, SETTING, AND PATIENTS: Retrospective analysis of 787 patients with predominantly early stage NSCLC performed at Duke University, Durham, North Carolina, from July 2008 to June 2009. Lung tumor samples with corresponding microarray and clinical data were used. All patients were divided into subgroups based on age (< 70 vs > or = 70 years old) or sex. Gene expression signatures representing oncogenic pathway activation and tumor biology/microenvironment status were applied to these samples to obtain patterns of activation/deregulation. MAIN OUTCOME MEASURES: Patterns of oncogenic and molecular signaling pathway activation that are reproducible and correlate with 5-year recurrence-free patient survival. RESULTS: Low- and high-risk patient clusters/cohorts were identified with the longest and shortest 5-year recurrence-free survival, respectively, within the age and sex NSCLC subgroups. These cohorts of NSCLC demonstrate similar patterns of pathway activation. In patients younger than 70 years, high-risk patients, with the shortest recurrence-free survival, demonstrated increased activation of the Src (25% vs 6%; P<.001) and tumor necrosis factor (76% vs 42%; P<.001) pathways compared with low-risk patients. High-risk patients aged 70 years or older demonstrated increased activation of the wound healing (40% vs 24%; P = .02) and invasiveness (64% vs 20%; P<.001) pathways compared with low-risk patients. In women, high-risk patients demonstrated increased activation of the invasiveness (99% vs 2%; P<.001) and STAT3 (72% vs 35%; P<.001) pathways while high-risk men demonstrated increased activation of the STAT3 (87% vs 18%; P<.001), tumor necrosis factor (90% vs 46%; P<.001), EGFR (13% vs 2%; P = .003), and wound healing (50% vs 22%; P<.001) pathways. Multivariate analyses confirmed the independent clinical relevance of the pathway-based subphenotypes in women (hazard ratio [HR], 2.02; 95% confidence interval [CI], 1.34-3.03; P<.001) and patients younger than 70 years (HR, 1.83; 95% CI, 1.24-2.71; P = .003). All observations were reproducible in split sample analyses. CONCLUSIONS: Among a cohort of patients with NSCLC, subgroups defined by oncogenic pathway activation profiles were associated with recurrence-free survival. These findings require validation in independent patient data sets.

Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer.

CONTEXT: Gene expression profiling may be useful for prognostic and therapeutic strategies in breast carcinoma. OBJECTIVES: To demonstrate the value in integrating genomic information with clinical and pathological risk factors, to refine prognosis, and to improve therapeutic strategies for early stage breast cancer. DESIGN, SETTING, AND PATIENTS: Retrospective study of patients with early stage breast carcinoma who were candidates for adjuvant chemotherapy; 964 clinically annotated breast tumor samples (573 in the initial discovery set and 391 in the validation cohort) with corresponding microarray data were used. All patients were assigned relapse risk scores based on their respective clinicopathological features. Signatures representing oncogenic pathway activation and tumor biology/microenvironment status were applied to these samples to obtain patterns of deregulation that correspond with relapse risk scores to refine prognosis with the clinicopathological prognostic model alone. Predictors of chemotherapeutic response were also applied to further characterize clinically relevant heterogeneity in early stage breast cancer. MAIN OUTCOME MEASURES: Gene expression signatures and clinicopathological variables in early stage breast cancer to determine a refined estimation of relapse-free survival and sensitivity to chemotherapy. RESULTS: In the initial data set of 573 patients, prognostically significant clusters representing patterns of oncogenic pathway activation and tumor biology/microenvironment states were identified within the low-risk (log-rank P = .004), intermediate-risk (log-rank P = .01), and high-risk (log-rank P = .003) model cohorts, representing clinically important genomic subphenotypes of breast cancer. As an example, in the low-risk cohort, of 6 prognostically significant clusters, patients in cluster 4 had an inferior relapse-free survival vs patients in cluster 1 (log-rank P = .004) and cluster 5 (log-rank P = .03). Median relapse-free survival for patients in cluster 4 was 16 months less than for patients in cluster 1 (95% CI, 7.5-24.5 months) and 19 months less than for patients in cluster 5 (95% CI, 10.5-27.5 months). Multivariate analyses confirmed the independent prognostic value of the genomic clusters (low risk, P = .05; high risk, P = .02). The reproducibility and validity of these patterns of pathway deregulation in predicting relapse risk was established using related but not identical clusters in the independent validation cohort. The prognostic clinicogenomic clusters also have unique sensitivity patterns to commonly used cytotoxic therapies. CONCLUSIONS: These results provide preliminary evidence that incorporation of gene expression signatures into clinical risk stratification can refine prognosis. Prospective studies are needed to determine the value of this approach for individualizing therapeutic strategies.

Using Drosophila melanogaster to identify chemotherapy toxicity genes.

The severity of the toxic side effects of chemotherapy shows a great deal of interindividual variability, and much of this variation is likely genetically based. Simple DNA tests predictive of toxic side effects could revolutionize the way chemotherapy is carried out. Due to the challenges in identifying polymorphisms that affect toxicity in humans, we use Drosophila fecundity following oral exposure to carboplatin, gemcitabine and mitomycin C as a model system to identify naturally occurring DNA variants predictive of toxicity. We use the Drosophila Synthetic Population Resource (DSPR), a panel of recombinant inbred lines derived from a multiparent advanced intercross, to map quantitative trait loci affecting chemotoxicity. We identify two QTL each for carboplatin and gemcitabine toxicity and none for mitomycin. One QTL is associated with fly orthologs of a priori human carboplatin candidate genes ABCC2 and MSH2, and a second QTL is associated with fly orthologs of human gemcitabine candidate genes RRM2 and RRM2B. The third, a carboplatin QTL, is associated with a posteriori human orthologs from solute carrier family 7A, INPP4A&B, and NALCN. The fourth, a gemcitabine QTL that also affects methotrexate toxicity, is associated with human ortholog GPx4. Mapped QTL each explain a significant fraction of variation in toxicity, yet individual SNPs and transposable elements in the candidate gene regions fail to singly explain QTL peaks. Furthermore, estimates of founder haplotype effects are consistent with genes harboring several segregating functional alleles. We find little evidence for nonsynonymous SNPs explaining mapped QTL; thus it seems likely that standing variation in toxicity is due to regulatory alleles.

The genetic architecture of methotrexate toxicity is similar in Drosophila melanogaster and humans.

The severity of the toxic side effects of chemotherapy varies among patients, and much of this variation is likely genetically based. Here, we use the model system Drosophila melanogaster to genetically dissect the toxicity of methotrexate (MTX), a drug used primarily to treat childhood acute lymphoblastic leukemia and rheumatoid arthritis. We use the Drosophila Synthetic Population Resource, a panel of recombinant inbred lines derived from a multiparent advanced intercross, and quantify MTX toxicity as a reduction in female fecundity. We identify three quantitative trait loci (QTL) affecting MTX toxicity; two colocalize with the fly orthologs of human genes believed to mediate MTX toxicity and one is a novel MTX toxicity gene with a human ortholog. A fourth suggestive QTL spans a centromere. Local single-marker association scans of candidate gene exons fail to implicate amino acid variants as the causative single-nucleotide polymorphisms, and we therefore hypothesize the causative variation is regulatory. In addition, the effects at our mapped QTL do not conform to a simple biallelic pattern, suggesting multiple causative factors underlie the QTL mapping results. Consistent with this observation, no single single-nucleotide polymorphism located in or near a candidate gene can explain the QTL mapping signal. Overall, our results validate D. melanogaster as a model for uncovering the genetic basis of chemotoxicity and suggest the genetic basis of MTX toxicity is due to a handful of genes each harboring multiple segregating regulatory factors.

Characterizing the clinical relevance of an embryonic stem cell phenotype in lung adenocarcinoma.

PURPOSE: Cancer cells possess traits reminiscent of those ascribed to normal stem cells. It is unclear whether these phenotypic similarities are the result of a common biological phenotype, such as regulatory pathways. EXPERIMENTAL DESIGN: Lung cancer cell lines with corresponding gene expression data and genes associated with an embryonic stem cell identity were used to develop a signature of embryonic stemness (ES) activity specific to lung adenocarcinoma. Biological characteristics were elucidated as a function of cancer biology/oncogenic pathway dysregulation. The ES signature was applied to three independent early-stage (I-IIIa) lung adenocarcinoma data sets with clinically annotated gene expression data. The relationship between the ES phenotype and cisplatin (current standard of care) sensitivity was evaluated. RESULTS: Pathway analysis identified specific regulatory networks [Ras (P = 0.0005), Myc (P = 0.0224), wound healing (P < 0.0001), chromosomal instability (P < 0.0001), and invasiveness (P < 0.0001)] associated with the ES phenotype. The prognostic relevance of the ES signature, as related to patient survival, was characterized in three cohorts [CALGB 9761 (n = 82; P = 0.0001), National Cancer Institute Director's Challenge Consortium (n = 442; P = 0.0002), and Duke (n = 45; P = 0.06)]. The ES signature was not prognostic in prostate, breast, or ovarian adenocarcinomas. Lung tumors (n = 569) and adenocarcinoma cell lines (n = 31) expressing the ES phenotype were more likely to be resistant to cisplatin (P < 0.0001 and P = 0.006, respectively). CONCLUSIONS: Lung adenocarcinomas that share a common gene expression pattern with normal human embryonic stem cells were associated with decreased survival, increased biological complexity, and increased likelihood of resistance to cisplatin. This indicates the aggressiveness of these tumors.

An integrated approach to the prediction of chemotherapeutic response in patients with breast cancer.

BACKGROUND: A major challenge in oncology is the selection of the most effective chemotherapeutic agents for individual patients, while the administration of ineffective chemotherapy increases mortality and decreases quality of life in cancer patients. This emphasizes the need to evaluate every patient's probability of responding to each chemotherapeutic agent and limiting the agents used to those most likely to be effective. METHODS AND RESULTS: Using gene expression data on the NCI-60 and corresponding drug sensitivity, mRNA and microRNA profiles were developed representing sensitivity to individual chemotherapeutic agents. The mRNA signatures were tested in an independent cohort of 133 breast cancer patients treated with the TFAC (paclitaxel, 5-fluorouracil, adriamycin, and cyclophosphamide) chemotherapy regimen. To further dissect the biology of resistance, we applied signatures of oncogenic pathway activation and performed hierarchical clustering. We then used mRNA signatures of chemotherapy sensitivity to identify alternative therapeutics for patients resistant to TFAC. Profiles from mRNA and microRNA expression data represent distinct biologic mechanisms of resistance to common cytotoxic agents. The individual mRNA signatures were validated in an independent dataset of breast tumors (P = 0.002, NPV = 82%). When the accuracy of the signatures was analyzed based on molecular variables, the predictive ability was found to be greater in basal-like than non basal-like patients (P = 0.03 and P = 0.06). Samples from patients with co-activated Myc and E2F represented the cohort with the lowest percentage (8%) of responders. Using mRNA signatures of sensitivity to other cytotoxic agents, we predict that TFAC non-responders are more likely to be sensitive to docetaxel (P = 0.04), representing a viable alternative therapy. CONCLUSIONS: Our results suggest that the optimal strategy for chemotherapy sensitivity prediction integrates molecular variables such as ER and HER2 status with corresponding microRNA and mRNA expression profiles. Importantly, we also present evidence to support the concept that analysis of molecular variables can present a rational strategy to identifying alternative therapeutic opportunities.

Pharmacogenomic strategies provide a rational approach to the treatment of cisplatin-resistant patients with advanced cancer.

PURPOSE: Standard treatment for advanced non-small-cell lung cancer (NSCLC) includes the use of a platinum-based chemotherapy regimen. However, response rates are highly variable. Newer agents, such as pemetrexed, have shown significant activity as second-line therapy and are currently being evaluated in the front-line setting. We utilized a genomic strategy to develop signatures predictive of chemotherapeutic response to both cisplatin and pemetrexed to provide a rational approach to effective individualized medicine. METHODS: Using in vitro drug sensitivity data, coupled with microarray data, we developed gene expression signatures predicting sensitivity to cisplatin and pemetrexed. Signatures were validated with response data from 32 independent ovarian and lung cancer cell lines as well as 59 samples from patients previously treated with cisplatin. RESULTS: Genomic-derived signatures of cisplatin and pemetrexed sensitivity were shown to accurately predict sensitivity in vitro and, in the case of cisplatin, to predict treatment response in patients treated with cisplatin. The accuracy of the cisplatin predictor, based on available clinical data, was 83.1% (sensitivity, 100%; specificity 57%; positive predictive value, 78%; negative predictive value, 100%). Interestingly, an inverse correlation was seen between in vitro cisplatin and pemetrexed sensitivity, and importantly, between the likelihood of cisplatin and pemetrexed response in patients. CONCLUSION: The use of genomic predictors of response to cisplatin and pemetrexed can be incorporated into strategies to optimize therapy for advanced solid tumors.