BackgroundDeveloping chemotherapy resistant cell lines can help to identify markers of resistance. Instead of using a panel of highly heterogeneous cell lines, we assumed that truly robust and convergent pattern of resistance can be identified in multiple parallel engineered derivatives of only a few parental cell lines.MethodsParallel cell populations were initiated for two breast cancer cell lines (MDA-MB-231 and MCF-7) and these were treated independently for 18 months with doxorubicin or paclitaxel. IC50 values against 4 chemotherapy agents were determined to measure cross-resistance. Chromosomal instability and karyotypic changes were determined by cytogenetics. TaqMan RT-PCR measurements were performed for resistance-candidate genes. Pgp activity was measured by FACS.ResultsAll together 16 doxorubicin- and 13 paclitaxel-treated cell lines were developed showing 2–46 fold and 3–28 fold increase in resistance, respectively. The RT-PCR and FACS analyses confirmed changes in tubulin isofom composition, TOP2A and MVP expression and activity of transport pumps (ABCB1, ABCG2). Cytogenetics showed less chromosomes but more structural aberrations in the resistant cells.ConclusionWe surpassed previous studies by parallel developing a massive number of cell lines to investigate chemoresistance. While the heterogeneity caused evolution of multiple resistant clones with different resistance characteristics, the activation of only a few mechanisms were sufficient in one cell line to achieve resistance.

Transcriptomic analysis of global gene expression in ovarian carcinoma can identify dysregulated genes capable to serve as molecular markers for histology subtypes and survival. The aim of our study was to validate previous candidate signatures in an independent setting and to identify single genes capable to serve as biomarkers for ovarian cancer progression. As several datasets are available in the GEO today, we were able to perform a true meta-analysis. First, 829 samples (11 datasets) were downloaded, and the predictive power of 16 previously published gene sets was assessed. Of these, eight were capable to discriminate histology subtypes, and none was capable to predict survival. To overcome the differences in previous studies, we used the 829 samples to identify new predictors. Then, we collected 64 ovarian cancer samples (median relapse-free survival 24.5 months) and performed TaqMan Real Time Polimerase Chain Reaction (RT-PCR) analysis for the best 40 genes associated with histology subtypes and survival. Over 90% of subtype-associated genes were confirmed. Overall survival was effectively predicted by hormone receptors (PGR and ESR2) and by TSPAN8. Relapse-free survival was predicted by MAPT and SNCG. In summary, we successfully validated several gene sets in a meta-analysis in large datasets of ovarian samples. Additionally, several individual genes identified were validated in a clinical cohort.With $43,000 cases in Europe and $22,000 cases in the United States of America each year, ovarian carcinoma is the eighth most frequent malignant tumor in the female population. Although some improvements were achieved in the 5-year survival due to improved efficiency of surgery and treatment with empirically optimized combinations of cytotoxic drugs, the overall cure rate today remains as low as 30%. The most likely explanation for this is the high heterogeneity of ovarian carcinomas.Subtypes of ovarian cancer are recognized based on grade and on histologic subtypes. While high-grade malignancies grow rapidly, are relatively chemosensitive and evolve without a definitive precursor lesion, low-grade tumors grow more slowly, are more resistant to chemotherapy and share molecular characteristics with other low-malignant potential neoplasms. 1 Expression profiling studies have shown that high-grade tumors cluster separately from low-grade carcinomas and borderline tumors. 2,3 About 90% of epithelial ovarian cancers are clonal. 4 This is also reflected in their classification into four different main histotypes of high-grade serous (resembling normal cells of the fallopian tube), endometrioid (cells of the endometrium), mucinous (endocervix) and clear cell (vagina) cancers. The correlation between the different subtypes and their precursor cells were already confirmed by altered gene expression patterns. 5 These subtypes show further differences regarding their epidemiology, genetic changes, gene expression, tumor markers and chemotherapy response. Meanwhile, similarities were also described between high-grade sero...

BACKGROUND: To date individual markers have failed to correctly predict resistance against anticancer agents in breast cancer. We used gene expression patterns attributable to chemotherapy-resistant cells to detect potential new biomarkers related to anthracycline resistance. One of the genes, PSMB7, was selected for further functional studies and clinical validation. METHODS: We contrasted the expression profiles of four pairs of different human tumour cell lines and of their counterparts resistant to doxorubicin. Observed overexpression of PSMB7 in resistant cell lines was validated by immunohistochemistry. To examine its function in chemoresistance, we silenced the gene by RNA interference (RNAi) in doxorubicin-resistant MCF-7 breast cancer cells, then cell vitality was measured after doxorubicin treatment. Microarray gene expression from GEO raw microarray samples with available progression-free survival data was downloaded, and expression of PSMB7 was used for grouping samples. RESULTS: After doxorubicin treatment, 79.8±13.3% of resistant cells survived. Silencing of PSMB7 in resistant cells decreased survival to 31.8±6.4% (P40.001). A similar effect was observed after paclitaxel treatment. In 1592 microarray samples, the patients with high PSMB7 expression had a significantly shorter survival than the patients with low expression (Po0.001). CONCLUSION: Our findings suggest that high PSMB7 expression is an unfavourable prognostic marker in breast cancer.

Because of the low overall response rates of 10–47% to targeted cancer therapeutics, there is an increasing need for predictive biomarkers. We aimed to identify genes predicting response to five already approved tyrosine kinase inhibitors. We tested 45 cancer cell lines for sensitivity to sunitinib, erlotinib, lapatinib, sorafenib and gefitinib at the clinically administered doses. A resistance matrix was determined, and gene expression profiles of the subsets of resistant vs. sensitive cell lines were compared. Triplicate gene expression signatures were obtained from the caArray project. Significance analysis of microarrays and rank products were applied for feature selection. Ninety-five genes were also measured by RT-PCR. In case of four sunitinib resistance associated genes, the results were validated in clinical samples by immunohistochemistry. A list of 63 top genes associated with resistance against the five tyrosine kinase inhibitors was identified. Quantitative RT-PCR analysis confirmed 45 of 63 genes identified by microarray analysis. Only two genes (ANXA3 and RAB25) were related to sensitivity against more than three inhibitors. The immunohistochemical analysis of sunitinib-treated metastatic renal cell carcinomas confirmed the correlation between RAB17, LGALS8, and EPCAM and overall survival. In summary, we determined predictive biomarkers for five tyrosine kinase inhibitors, and validated sunitinib resistance biomarkers by immunohistochemistry in an independent patient cohort.