The helix-loop-helix protein id1 controls stem cell proliferation during regenerative neurogenesis in the adult zebrafish telencephalon.

The teleost brain has the remarkable ability to generate new neurons and to repair injuries during adult life stages. Maintaining life-long neurogenesis requires careful management of neural stem cell pools. In a genome-wide expression screen for transcription regulators, the id1 gene, encoding a negative regulator of E-proteins, was found to be upregulated in response to injury. id1 expression was mapped to quiescent type I neural stem cells in the adult telencephalic stem cell niche. Gain and loss of id1 function in vivo demonstrated that Id1 promotes stem cell quiescence. The increased id1 expression observed in neural stem cells in response to injury appeared independent of inflammatory signals, suggesting multiple antagonistic pathways in the regulation of reactive neurogenesis. Together, we propose that Id1 acts to maintain the neural stem cell pool by counteracting neurogenesis-promoting signals.

Prediction of clinical response to drugs in ovarian cancer using the chemotherapy resistance test (CTR-test).

BACKGROUND: In order to validate if the test result of the Chemotherapy Resistance Test (CTR-Test) is able to predict the resistances or sensitivities of tumors in ovarian cancer patients to drugs, the CTR-Test result and the corresponding clinical response of individual patients were correlated retrospectively. Results were compared to previous recorded correlations. METHODS: The CTR-Test was performed on tumor samples from 52 ovarian cancer patients for specific chemotherapeutic drugs. Patients were treated with monotherapies or drug combinations. Resistances were classified as extreme (ER), medium (MR) or slight (SR) resistance in the CTR-Test. Combination treatment resistances were transformed by a scoring system into these classifications. RESULTS: Accurate sensitivity prediction was accomplished in 79% of the cases and accurate prediction of resistance in 100% of the cases in the total data set. The data set of single agent treatment and drug combination treatment were analyzed individually. Single agent treatment lead to an accurate sensitivity in 44% of the cases and the drug combination to 95% accuracy. The detection of resistances was in both cases to 100% correct. ROC curve analysis indicates that the CTR-Test result correlates with the clinical response, at least for the combination chemotherapy. Those values are similar or better than the values from a publication from 1990. CONCLUSIONS: Chemotherapy resistance testing in vitro via the CTR-Test is able to accurately detect resistances in ovarian cancer patients. These numbers confirm and even exceed results published in 1990. Better sensitivity detection might be caused by a higher percentage of drug combinations tested in 2012 compared to 1990. Our study confirms the functionality of the CTR-Test to plan an efficient chemotherapeutic treatment for ovarian cancer patients.

New in vitro system to predict chemotherapeutic efficacy of drug combinations in fresh tumor samples.

BACKGROUND: To find the best individual chemotherapy for cancer patients, the efficacy of different chemotherapeutic drugs can be predicted by pretesting tumor samples in vitro via the chemotherapy-resistance (CTR)-Test®. Although drug combinations are widely used among cancer therapy, so far only single drugs are tested by this and other tests. However, several first line chemotherapies are combining two or more chemotherapeutics, leading to the necessity of drug combination testing methods. METHODS: We established a system to measure and predict the efficacy of chemotherapeutic drug combinations with the help of the Loewe additivity concept in combination with the CTR-test. A combination is measured by using half of the monotherapy's concentration of both drugs simultaneously. With this method, the efficacy of a combination can also be calculated based on single drug measurements. RESULTS: The established system was tested on a data set of ovarian carcinoma samples using the combination carboplatin and paclitaxel and confirmed by using other tumor species and chemotherapeutics. Comparing the measured and the calculated values of the combination testings revealed a high correlation. Additionally, in 70% of the cases the measured and the calculated values lead to the same chemotherapeutic resistance category of the tumor. CONCLUSION: Our data suggest that the best drug combination consists of the most efficient single drugs and the worst drug combination of the least efficient single drugs. Our results showed that single measurements are sufficient to predict combinations in specific cases but there are exceptions in which it is necessary to measure combinations, which is possible with the presented system.