The Chk1 inhibitor MK-8776 increases the radiosensitivity of human triple-negative breast cancer by inhibiting autophagy.

MK-8776 is a recently described inhibitor that is highly selective for checkpoint kinase 1 (Chk1), which can weaken the DNA repair capacity in cancer cells to achieve chemo-sensitization. A number of studies show that MK-8776 enhances the cytotoxicity of hydroxyurea and gemcitabine without increasing normal tissue toxicities. Thus far, there is no evidence that MK-8776 can be used as a radiotherapy sensitization agent. In this study, we investigated the effects of MK-8776 on the radiosensitivity of 3 human triple-negative breast cancer (TNBC) cell lines MDA-MB-231, BT-549 and CAL-51. MK-8776 dose-dependently inhibited the proliferation of MDA-MB-231, BT-549 and CAL-51 cells with IC50 values of 9.4, 17.6 and 2.1 µmol/L, respectively. Compared with irradiation-alone treatment, pretreatment with a low dose of MK-8776 (100-400 nmol/L) significantly increased irradiation-induced γH2A.X foci in the 3 TNBC cell lines, suggesting enhanced DNA damage by MK-8776, inhibited the cell proliferation and increased the radiosensitivity of the 3 TNBC cell lines. Similar results were obtained in MDA-MB-231 xenograft tumors in nude mice that received MK-8776 (15 or 40 mg/kg, ip) 26 d after irradiation. To explore the mechanisms underlying the radio-sensitization by MK-8776, we used TEM and found that irradiation significantly increased the numbers of autophagosomes in the 3 TNBC cell lines. Moreover, irradiation markedly elevated the levels of Atg5, and promoted the transformation of LC3-I to LC3-II in the cells. Pretreatment with the low dose of MK-8776 suppressed these effects. The above results suggest that MK-8776 increases human TNBC radiosensitivity by inhibiting irradiation-induced autophagy and that MK-8776 may be a potential agent in the radiosensitization of human TNBC.

In-depth mining of clinical data: the construction of clinical prediction model with R.

This article is the series of methodology of clinical prediction model construction (total 16 sections of this methodology series). The first section mainly introduces the concept, current application status, construction methods and processes, classification of clinical prediction models, and the necessary conditions for conducting such researches and the problems currently faced. The second episode of these series mainly concentrates on the screening method in multivariate regression analysis. The third section mainly introduces the construction method of prediction models based on Logistic regression and Nomogram drawing. The fourth episode mainly concentrates on Cox proportional hazards regression model and Nomogram drawing. The fifth Section of the series mainly introduces the calculation method of C-Statistics in the logistic regression model. The sixth section mainly introduces two common calculation methods for C-Index in Cox regression based on R. The seventh section focuses on the principle and calculation methods of Net Reclassification Index (NRI) using R. The eighth section focuses on the principle and calculation methods of IDI (Integrated Discrimination Index) using R. The ninth section continues to explore the evaluation method of clinical utility after predictive model construction: Decision Curve Analysis. The tenth section is a supplement to the previous section and mainly introduces the Decision Curve Analysis of survival outcome data. The eleventh section mainly discusses the external validation method of Logistic regression model. The twelfth mainly discusses the in-depth evaluation of Cox regression model based on R, including calculating the concordance index of discrimination (C-index) in the validation data set and drawing the calibration curve. The thirteenth section mainly introduces how to deal with the survival data outcome using competitive risk model with R. The fourteenth section mainly introduces how to draw the nomogram of the competitive risk model with R. The fifteenth section of the series mainly discusses the identification of outliers and the interpolation of missing values. The sixteenth section of the series mainly introduced the advanced variable selection methods in linear model, such as Ridge regression and LASSO regression.

Did dose escalated radiotherapy in stage III non-small cell lung cancer improve overall survival?

Radiotherapy has been applied in the treatment for lung cancer patients for many years, especially those with advanced stage disease who cannot be treated with surgery. However, if these advanced stage patients should be treated with standard dosage therapy (60 Gy) or high dosage therapy (>60 Gy) remains a hot debated point. Literature related to this topic would be reviewed here. We believe standard dosage therapy should be strongly recommended, and high dosage therapy might benefit patients with high risk factors patients.