Use of yeast chemigenomics and COXEN informatics in preclinical evaluation of anticancer agents.

Bladder cancer metastasis is virtually incurable with current platinum-based chemotherapy. We used the novel COXEN informatic approach for in silico drug discovery and identified NSC-637993 and NSC-645809 (C1311), both imidazoacridinones, as agents with high-predicted activity in human bladder cancer. Because even highly effective monotherapy is unlikely to cure most patients with metastasis and NSC-645809 is undergoing clinical trials in other tumor types, we sought to develop the basis for use of C1311 in rational combination with other agents in bladder cancer. Here, we demonstrate in 40 human bladder cancer cells that the in vitro cytotoxicity profile for C1311 correlates with that of NSC-637993 and compares favorably to that of standard of care chemotherapeutics. Using genome-wide patterns of synthetic lethality of C1311 with open reading frame knockouts in budding yeast, we determined that combining C1311 with a taxane could provide mechanistically rational combinations. To determine the preclinical relevance of these yeast findings, we evaluated C1311 singly and in doublet combination with paclitaxel in human bladder cancer in the in vivo hollow fiber assay and observed efficacy. By applying COXEN to gene expression data from 40 bladder cancer cell lines and 30 human tumors with associated clinical response data to platinum-based chemotherapy, we provide evidence that signatures of C1311 sensitivity exist within nonresponders to this regimen. Coupling COXEN and yeast chemigenomics provides rational combinations with C1311 and tumor genomic signatures that can be used to select bladder cancer patients for clinical trials with this agent.

Comparison of global versus epidermal growth factor receptor pathway profiling for prediction of lapatinib sensitivity in bladder cancer.

Chemotherapy for metastatic bladder cancer is rarely curative. The recently developed small molecule, lapatinib, a dual epidermal growth factor receptor (EGFR)/human epidermal growth factor receptor-2 receptor tyrosine kinase inhibitor, might improve this situation. Recent findings suggest that identifying which patients are likely to benefit from targeted therapies is beneficial, although controversy remains regarding what types of evaluation might yield optimal candidate biomarkers of sensitivity. Here, we address this issue by developing and comparing lapatinib sensitivity prediction models for human bladder cancer cells. After empirically determining in vitro sensitivities (drug concentration necessary to cause a 50% growth inhibition) of a panel of 39 such lines to lapatinib treatment, we developed prediction models based on profiling the baseline transcriptome, the phosphorylation status of EGFR pathway signaling targets, or a combination of both data sets. We observed that models derived from microarray gene expression data showed better prediction performance (93%-98% accuracy) compared with models derived from EGFR pathway profiling of 23 selected phosphoproteins known to be involved in EGFR-driven signaling (54%-61% accuracy) or from a subset of the microarray data for transcripts in the EGFR pathway (86% accuracy). Combining microarray data and phosphoprotein profiling provided a combination model with 98% accuracy. Our findings suggest that transcriptome-wide profiling for biomarkers of lapatinib sensitivity in cancer cells provides models with excellent predictive performance and may be effectively combined with EGFR pathway phosphoprotein profiling data. These results have significant implications for the use of such tools in personalizing the approach to cancers treated with EGFR-directed targeted therapies.

Concordant gene expression signatures predict clinical outcomes of cancer patients undergoing systemic therapy.

Conventional development of multivariate gene expression models (GEM) predicting therapeutic response of cancer patients is based on analysis of patients treated with specific regimens, which limits generalization to different or novel drug combinations. We overcome this limitation by developing GEMs based on in vitro drug sensitivities and microarray analyses of the NCI-60 cancer cell line panel. These GEMs were evaluated in blind fashion as predictors of tumor response and/or patient survival in seven independent cohorts of patients with breast (n = 275), bladder (n = 59), and ovarian (n= 143) cancer treated with multiagent chemotherapy, of which 233 patients were from prospectively enrolled clinical trials. In all studies, GEMs effectively stratified tumor response and patient survival independent of established clinical and pathologic tumor variables. In bladder cancer patients treated with neoadjuvant methotrexate, vinblastine, Adriamycin (doxorubicin), and cisplatin, the 3-year overall survival for those with favorable GEM scores was 81% versus 33% for those with less favorable scores (P = 0.002). GEMs for breast cancer patients treated with 5-fluorouracil, Adriamycin (doxorubicin), and cyclophosphamide and ovarian cancer patients treated with platinum-containing regimens also stratified patient survival [5-year overall survival 100% versus 74% (P = 0.05) and 3-year overall survival 68% versus 43% (P = 0.008), respectively]. Importantly, clinical prediction using our in vitro GEM was superior to that of conventionally derived GEMs. We show a facile yet effective approach to GEM derivation that identifies patients most likely to benefit from selected multiagent therapy. Use of such in vitro-based GEMs may provide a robust and generalizable approach to personalized cancer therapy.

A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery.

The U.S. National Cancer Institute has used a panel of 60 diverse human cancer cell lines (the NCI-60) to screen >100,000 chemical compounds for anticancer activity. However, not all important cancer types are included in the panel, nor are drug responses of the panel predictive of clinical efficacy in patients. We asked, therefore, whether it would be possible to extrapolate from that rich database (or analogous ones from other drug screens) to predict activity in cell types not included or, for that matter, clinical responses in patients with tumors. We address that challenge by developing and applying an algorithm we term "coexpression extrapolation" (COXEN). COXEN uses expression microarray data as a Rosetta Stone for translating from drug activities in the NCI-60 to drug activities in any other cell panel or set of clinical tumors. Here, we show that COXEN can accurately predict drug sensitivity of bladder cancer cell lines and clinical responses of breast cancer patients treated with commonly used chemotherapeutic drugs. Furthermore, we used COXEN for in silico screening of 45,545 compounds and identify an agent with activity against human bladder cancer.

Prediction of drug combination chemosensitivity in human bladder cancer.

The choice of therapy for metastatic cancer is largely empirical because of a lack of chemosensitivity prediction for available combination chemotherapeutic regimens. Here, we identify molecular models of bladder carcinoma chemosensitivity based on gene expression for three widely used chemotherapeutic agents: cisplatin, paclitaxel, and gemcitabine. We measured the growth inhibition elicited by these three agents in a series of 40 human urothelial cancer cell lines and correlated the GI(50) (50% of growth inhibition) values with quantitative measures of global gene expression to derive models of chemosensitivity using a misclassification-penalized posterior approach. The misclassification-penalized posterior-derived models predicted the growth response of human bladder cancer cell lines to each of the three agents with sensitivities of between 0.93 and 0.96. We then developed an in silico approach to predict the cellular growth responses for each of these agents in the clinically relevant two-agent combinations. These predictions were prospectively evaluated on a series of 15 randomly chosen bladder carcinoma cell lines. Overall, 80% of the predicted combinations were correct (P = 0.0002). Together, our results suggest that chemosensitivity to drug combinations can be predicted based on molecular models and provide the framework for evaluation of such models in patients undergoing combination chemotherapy for cancer. If validated in vivo, such predictive models have the potential to guide therapeutic choice at the level of an individual's tumor.

Isoflurane pretreatment supports hemodynamics and leukocyte rolling velocities in rat mesentery during lipopolysaccharide-induced inflammation.

UNLABELLED: We hypothesized that the protective effects of isoflurane (ISO) pretreatment on the vasculature may be attributed, in part, to altered leukocyte-endothelial interactions. Rats were anesthetized with pentobarbital and then randomized into four groups: control, ISO-control (pretreatment with 30 min of 1.4% ISO), lipopolysaccharide (LPS; 10 mg/kg IV), and ISO-LPS (ISO pretreatment and then LPS). The mesentery was prepared for intravital videomicroscopy. Mean arterial blood pressure (MAP), along with microcirculatory variables that included postcapillary venular and arteriolar blood flow velocity and leukocyte dynamics (number of rolling and adherent leukocytes and individual rolling leukocyte velocities), were measured hourly (baseline and at 0-4 h). In LPS rats, ISO pretreatment significantly (P < 0.05) attenuated the decrease in MAP at 2 and 4 h after LPS and increased leukocyte rolling velocities after 2-4 h. Four hours after LPS, leukocyte rolling velocities were >200% more rapid (63.7 +/- 27.6 microm/s versus 19.8 +/- 6.4 micro m/s) in ISO-LPS versus LPS rats. In control rats, ISO pretreatment had no effect on MAP or leukocyte rolling velocities but increased the number of rolling leukocytes. ISO pretreatment had no effect on arteriolar and postcapillary venular blood flow velocity in LPS rats or leukocyte adherence in LPS or control rats. In conclusion, ISO pretreatment supported hemodynamics and increased leukocyte rolling velocities but did not alter the number of rolling or adherent leukocytes in the mesenteric microcirculation during LPS-induced inflammation. IMPLICATIONS: Isoflurane pretreatment supported hemodynamics and increased leukocyte rolling velocities in the mesenteric microcirculation during lipopolysaccharide-induced inflammation. Faster rolling velocities may reduce the incidence of inflammation by decreasing leukocyte-endothelial interactions and cellular injury.