Combined PARP and Dual Topoisomerase Inhibition Potentiates Genome Instability and Cell Death in Ovarian Cancer.

Although ovarian cancer is a rare disease, it constitutes the fifth leading cause of cancer death among women. It is of major importance to develop new therapeutic strategies to improve survival. Combining P8-D6, a novel dual topoisomerase inhibitor with exceptional anti-tumoral properties in ovarian cancer and compounds in preclinical research, and olaparib, a PARP inhibitor targeting DNA damage repair, is a promising approach. P8-D6 induces DNA damage that can be repaired by base excision repair or homologous recombination in which PARP plays a major role. This study analyzed benefits of combining P8-D6 and olaparib treatment in 2D and 3D cultures with ovarian cancer cells. Measurement of viability, cytotoxicity and caspase activity were used to assess therapy efficacy and to calculate the combination index (CI). Further DNA damage was quantified using the biomarkers RAD51 and γH2A.X. The combinational treatment led to an increased caspase activity and reduced viability. CI values partially show synergisms in combinations at 100 nM and 500 nM P8-D6. More DNA damage accumulated, and spheroids lost their membrane integrity due to the combinational treatment. While maintaining the same therapy efficacy as single-drug therapy, doses of P8-D6 and olaparib can be reduced in combinational treatments. Synergisms can be seen in some tested combinations. In summary, the combination therapy indicates benefits and acts synergistic at 100 nM and 500 nM P8-D6.

Bivariate cumulative probit model for the comparison of neuronal encoding hypotheses.

Understanding the way stimulus properties are encoded in the nerve cell responses of sensory organs is one of the fundamental scientific questions in neurosciences. Different neuronal coding hypotheses can be compared by use of an inverse procedure called stimulus reconstruction. Here, based on different attributes of experimentally recorded neuronal responses, the values of certain stimulus properties are estimated by statistical classification methods. Comparison of stimulus reconstruction results then allows to draw conclusions about relative importance of covariate features. Since many stimulus properties have a natural order and can therefore be considered as ordinal, we introduce a bivariate ordinal probit model to obtain classifications for the combination of light intensity and velocity of a visual dot pattern based on different covariates extracted from recorded spike trains. For parameter estimation, we develop a Bayesian Gibbs sampler and incorporate penalized splines to model nonlinear effects. We compare the classification performance of different individual cell covariates and simple features of groups of neurons and find that the combination of at least two covariates increases the classification performance significantly. Furthermore, we obtain a non-linear effect for the first spike latency. The model is compared to a naïve Bayesian stimulus estimation method where it yields comparable misclassification rates for the given dataset. Hence, the bivariate ordinal probit model is shown to be a helpful tool for stimulus reconstruction particularly thanks to its flexibility with respect to the number of covariates as well as their scale and effect type.