Radium-223 for patients with metastatic castration-resistant prostate cancer with asymptomatic bone metastases progressing on first-line abiraterone acetate or enzalutamide: A single-arm phase II trial.

PURPOSE: The paper aims to evaluate the efficacy and safety of 223Ra in patients who progressed after first-line androgen deprivation therapy. PATIENTS AND METHODS: EXCAAPE (NCT03002220) was a multicentre, single-arm, open-label, non-controlled phase IIa trial in 52 patients with metastatic castration-resistant prostate cancer and asymptomatic bone metastases who have progressed on abiraterone acetate or enzalutamide, up to six doses of 223Ra (55 kBq/kg of body weight per month). The primary end-point was radiographic progression-free survival (rPFS). Secondary end-points included rPFS based on androgen receptor splice variant 7 (AR-V7) expression in circulating tumour cells (CTCs), overall survival, and safety. RESULTS: Median rPFS was 5.5 months (95% CI 5.3-5.5). Median rPFS of patients with AR-V7(-) CTCs was longer than that of patients with AR-V7(+) CTCs (5.5 versus 2.2 months, respectively; P = 0.056). Median overall survival was 14.8 months (95% CI 11.2-not reached) and was significantly greater for AR-V7(-) patients than for AR-V7(+) patients (14.8 months versus 3.5 months, respectively; P < 0.01). 223Ra was well tolerated; anaemia and thrombocytopenia were the most common grade 3/4 adverse events (5.8% and 11.5%, respectively). CONCLUSIONS: 223Ra seems to be a reasonable treatment for patients with metastatic castration-resistant prostate cancer and asymptomatic bone metastases progressing on novel hormonal therapy and had an acceptable safety profile.

Type 2 Diabetes-Related Variants Influence the Risk of Developing Prostate Cancer: A Population-Based Case-Control Study and Meta-Analysis.

In this study, we have evaluated whether 57 genome-wide association studies (GWAS)-identified common variants for type 2 diabetes (T2D) influence the risk of developing prostate cancer (PCa) in a population of 304 Caucasian PCa patients and 686 controls. The association of selected single nucleotide polymorphisms (SNPs) with the risk of PCa was validated through meta-analysis of our data with those from the UKBiobank and FinnGen cohorts, but also previously published genetic studies. We also evaluated whether T2D SNPs associated with PCa risk could influence host immune responses by analysing their correlation with absolute numbers of 91 blood-derived cell populations and circulating levels of 103 immunological proteins and 7 steroid hormones. We also investigated the correlation of the most interesting SNPs with cytokine levels after in vitro stimulation of whole blood, peripheral mononuclear cells (PBMCs), and monocyte-derived macrophages with LPS, PHA, Pam3Cys, and Staphylococcus Aureus. The meta-analysis of our data with those from six large cohorts confirmed that each copy of the FTOrs9939609A, HNF1Brs7501939T, HNF1Brs757210T, HNF1Brs4430796G, and JAZF1rs10486567A alleles significantly decreased risk of developing PCa (p = 3.70 × 10-5, p = 9.39 × 10-54, p = 5.04 × 10-54, p = 1.19 × 10-71, and p = 1.66 × 10-18, respectively). Although it was not statistically significant after correction for multiple testing, we also found that the NOTCH2rs10923931T and RBMS1rs7593730 SNPs associated with the risk of developing PCa (p = 8.49 × 10-4 and 0.004). Interestingly, we found that the protective effect attributed to the HFN1B locus could be mediated by the SULT1A1 protein (p = 0.00030), an arylsulfotransferase that catalyzes the sulfate conjugation of many hormones, neurotransmitters, drugs, and xenobiotic compounds. In addition to these results, eQTL analysis revealed that the HNF1Brs7501939, HNF1Brs757210, HNF1Brs4430796, NOTCH2rs10923931, and RBMS1rs7593730 SNPs influence the risk of PCa through the modulation of mRNA levels of their respective genes in whole blood and/or liver. These results confirm that functional TD2-related variants influence the risk of developing PCa, but also highlight the need of additional experiments to validate our functional results in a tumoral tissue context.

An update on the mechanisms related to cell death and toxicity of doxorubicin and the protective role of nutrients.

Doxorubicin (DOX), is a very effective chemotherapeutic agent against cancer whose clinical use is limited by toxicity. Different strategies have been proposed to attenuate toxicity, including combined therapy with bioactive compounds. This review update mechanisms of action and toxicity of doxorubicin and the role of nutrients like vitamins (A, C, E), minerals (selenium) and n-3 polyunsaturated fatty acids. Protective activities against DOX toxicity in liver, kidney, skin, bone marrow, testicles or brain have been reported, but these have not been evaluated for all of the reviewed nutrients. In most cases oxidation-related effects were present either, by reducing ROS levels and/or increasing antioxidant defenses. Antiapoptotic and anti-inflammatory mechanisms are also commonly reported. In some cases, interferences with autophagy and calcium homeostasis also have shown to be affected. Notwithstanding, there is a wide variety in duration and doses of treatment tested for both, compounds and DOX, which make difficult to compare the results of the studies. In spite of the reduction of DOX cardiotoxicity in health models, DOX anti-cancer activity in cancer cell lines or xenograft models usually did not result compromised when this has been evaluated. Importantly, clinical studies are needed to confirm all the observed effects.

A plasma metabolomic signature discloses human breast cancer.

PURPOSE: Metabolomics is the comprehensive global study of metabolites in biological samples. In this retrospective pilot study we explored whether serum metabolomic profile can discriminate the presence of human breast cancer irrespective of the cancer subtype. METHODS: Plasma samples were analyzed from healthy women (n = 20) and patients with breast cancer after diagnosis (n = 91) using a liquid chromatography-mass spectrometry platform. Multivariate statistics and a Random Forest (RF) classifier were used to create a metabolomics panel for the diagnosis of human breast cancer. RESULTS: Metabolomics correctly distinguished between breast cancer patients and healthy control subjects. In the RF supervised class prediction analysis comparing breast cancer and healthy control groups, RF accurately classified 100% both samples of the breast cancer patients and healthy controls. So, the class error for both group in and the out-of-bag error were 0. We also found 1269 metabolites with different concentration in plasma from healthy controls and cancer patients; and basing on exact mass, retention time and isotopic distribution we identified 35 metabolites. These metabolites mostly support cell growth by providing energy and building stones for the synthesis of essential biomolecules, and function as signal transduction molecules. The collective results of RF, significance testing, and false discovery rate analysis identified several metabolites that were strongly associated with breast cancer. CONCLUSIONS: In breast cancer a metabolomics signature of cancer exists and can be detected in patient plasma irrespectively of the breast cancer type.