In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to identify biomarkers related to survival. We searched the caBIG, GEO and TCGA repositories to identify samples with published gene expression data and survival information. Univariate and multivariate Cox regression analysis, Kaplan-Meier survival plot with hazard ratio and logrank P value are calculated and plotted in R. The complete analysis tool can be accessed online at: www.kmplot.com/lung. All together 1,715 samples of ten independent datasets were integrated into the system. As a demonstration, we used the tool to validate 21 previously published survival associated biomarkers. Of these, survival was best predicted by CDK1 (p<1E-16), CD24 (p<1E-16) and CADM1 (p = 7E-12) in adenocarcinomas and by CCNE1 (p = 2.3E-09) and VEGF (p = 3.3E-10) in all NSCLC patients. Additional genes significantly correlated to survival include RAD51, CDKN2A, OPN, EZH2, ANXA3, ADAM28 and ERCC1. In summary, we established an integrated database and an online tool capable of uni- and multivariate analysis for in silico validation of new biomarker candidates in non-small cell lung cancer.

Cancer patients with tumors of similar grading, staging and histogenesis can have markedly different treatment responses to different chemotherapy agents. So far, individual markers have failed to correctly predict resistance against anticancer agents. We tested 30 cancer cell lines for sensitivity to 5‐fluorouracil, cisplatin, cyclophosphamide, doxorubicin, etoposide, methotrexate, mitomycin C, mitoxantrone, paclitaxel, topotecan and vinblastine at drug concentrations that can be systemically achieved in patients. The resistance index was determined to designate the cell lines as sensitive or resistant, and then, the subset of resistant vs. sensitive cell lines for each drug was compared. Gene expression signatures for all cell lines were obtained by interrogating Affymetrix U133A arrays. Prediction Analysis of Microarrays was applied for feature selection. An individual prediction profile for the resistance against each chemotherapy agent was constructed, containing 42–297 genes. The overall accuracy of the predictions in a leave‐one‐out cross validation was 86%. A list of the top 67 multidrug resistance candidate genes that were associated with the resistance against at least 4 anticancer agents was identified. Moreover, the differential expressions of 46 selected genes were also measured by quantitative RT‐PCR using a TaqMan micro fluidic card system. As a single gene can be correlated with resistance against several agents, associations with resistance were detected all together for 76 genes and resistance phenotypes, respectively. This study focuses on the resistance at the in vivo concentrations, making future clinical cancer response prediction feasible. The TaqMan‐validated gene expression patterns provide new gene candidates for multidrug resistance. Supplementary material for this article can be found on the International Journal of Cancer website at http://www.interscience.wiley.com/jpages/0020‐7136/suppmat.

Purpose: Cisplatin resistance is a major obstacle in the treatment of ovarian carcinoma. ABCC2 is commonly localized in apical cell membranes and could confer cisplatin resistance. Here, we show that ABCC2 can be localized in the cytoplasmic membrane as well as in the nuclear membrane of various human tissues including ovarian carcinoma cells. Experimental Design: For the subcellular detection of ABCC2, immunohistochemistry was done using 41 Federation Internationale des Gynaecologistes et Obstetristes stage III ovarian carcinoma specimens prepared before treatment with cisplatin-based schemes and 35 specimens from the same group after chemotherapy. Furthermore, 11 ovarian carcinoma cell lines as well as tissue microarrays consisting of various human tissues were analyzed. Results: Nuclear membranous localization of ABCC2 was associated with response to first-line chemotherapy at primary (P = 0.0013) and secondary surgery (P = 0.0060). Cases with relapse showed higher nuclear membrane expression at primary (P = 0.0003) and secondary surgery (P = 0.0024). Kaplan-Meier analyses showed that weak nuclear membrane ABCC2 expression before treatment was associated with significantly longer overall (P = 0.04) and progression-free survival (P = 0.001); following chemotherapy, it correlated with significantly longer progression-free survival (P = 0.038). Tissue microarrays confirmed nuclear membranous localization of ABCC2, in particular, in poorly differentiated cells. In ovarian carcinoma cells, it correlated with resistance against cisplatin, whereas localization in the cytoplasmic membrane did not. Conclusions: ABCC2 confers resistance to cisplatin of ovarian carcinoma in cell culture systems and in clinics when expressed in the nuclear membrane. Thus, ABCC2 localization can predict platinum therapy outcome. Furthermore, expression of ABCC2 in nuclear membranes in human tissues is specific for poorly differentiated cells including stem cells.

Betulin and betulinic acid are naturally occurring pentacyclic triterpenes showing cytotoxicity towards a number of cancer cell lines. These compounds can be found in the bark of the many plants. In this report we have compared the cytotoxic activity of crude birch bark extract and purified betulin and betulinic acid towards human gastric carcinoma (EPG85-257) and human pancreatic carcinoma (EPP85-181) drug-sensitive and drug-resistant (daunorubicin and mitoxantrone) cell lines. Our results show significant differences in sensitivity between cell lines depending on the compound used, and suggest that both betulin and betulinic acid can be considered as a promising leads in the treatment of cancer.