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

High-Definition DNA Methylation Profiles from Breast and Ovarian Carcinoma Cell Lines with Differing Doxorubicin Resistance

Acquired drug resistance represents a frequent obstacle which hampers efficient chemotherapy of cancers. The contribution of aberrant DNA methylation to the development of drug resistant tumor cells has gained increasing attention over the past decades. Hence, the objective of the presented study was to characterize DNA methylation changes which arise from treatment of tumor cells with the chemotherapeutic drug doxorubicin. DNA methylation levels from CpG islands (CGIs) linked to twenty-eight genes, whose expression levels had previously been shown to contribute to resistance against DNA double strand break inducing drugs or tumor progression in different cancer types were analyzed. High-definition DNA methylation profiles which consisted of methylation levels from 800 CpG sites mapping to CGIs around the transcription start sites of the selected genes were determined. In order to investigate the influence of CGI methylation on the expression of associated genes, their mRNA levels were investigated via qRT-PCR. It was shown that the employed method is suitable for providing highly accurate methylation profiles, comparable to those obtained via clone sequencing, the gold standard for high-definition DNA methylation studies. In breast carcinoma cells with acquired resistance against the double strand break inducing drug doxorubicin, changes in methylation of specific cytosines from CGIs linked to thirteen genes were detected. Moreover, similarities between methylation profiles obtained from breast and ovarian carcinoma cell lines with acquired doxorubicin resistance were found. The expression levels of a subset of analyzed genes were shown to be linked to the methylation levels of the analyzed CGIs. Our results provide detailed DNA methylation information from two separate model systems for acquired doxorubicin resistance and suggest the occurrence of similar methylation changes in both systems upon exposure to the drug

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field

APRIL-Deficient Mice Have Normal Immune System Development

APRIL (a proliferation-inducing ligand) is a member of the tumor necrosis factor (TNF) superfamily. APRIL mRNA shows high levels of expression in tumors of different origin and a low level of expression in normal cells. APRIL shares two TNF receptor family members, TACI and BCMA, with another TNF homolog, BLyS/BAFF. BLyS is involved in regulation of B-cell activation and survival and also binds to a third receptor, BR3/BAFF-R, which is not shared with APRIL. Recombinant APRIL and BLyS induce accumulation of B cells in mice, while BLyS deficiency results in severe B-cell dysfunction. To investigate the physiological role of APRIL, we generated mice that are deficient in its encoding gene. APRIL(−/−) mice were viable and fertile and lacked any gross abnormality. Detailed histological analysis did not reveal any defects in major tissues and organs, including the primary and secondary immune organs. T- and B-cell development and in vitro function were normal as well, as were T-cell-dependent and -independent in vivo humoral responses to antigenic challenge. These data indicate that APRIL is dispensable in the mouse for proper development. Thus, BLyS may be capable of fulfilling APRIL's main functions

Additional file 1: of Prediction of clinical response to drugs in ovarian cancer using the chemotherapy resistance test (CTR-test)

Percentage of viable cells and tumor cells. The percentage of viable cells and tumor cells of the different patient samples (Responder or Non-Responder for Monotherapy, Monotherapy plus Avastin or Combination Chemotherapy) is presented. (PDF 29 kb

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