Maximal androgen blockade in combination with methotrexate for treatment of metastatic prostate cancer

Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Prostate cancer is the most common type of cancer in men. The disease presents good survival rates if treated at the early stages. However, the evolution of the disease in its most aggressive variant remains without effective therapeutic answers. Therefore, the identification of novel effective therapeutics is urgently needed. On these premises, we developed a series of machine learning models, based on compounds with reported highly homogeneous cell-based antiproliferative assay data, able to predict the activity of ligands towards the PC-3 and DU-145 prostate cancer cell lines. The data employed in the development of the computational models was finely-tuned according to a series of thresholds for the classification of active/inactive compounds, to the number of features to be implemented, and by using 10 different machine learning algorithms. Models’ evaluation allowed us to identify the best combination of activity thresholds and ML algorithms for the classification of active compounds, achieving prediction performances with MCC values above 0.60 for PC-3 and DU-145 cells. Moreover, in silico models based on the combination of PC-3 and DU-145 data were also developed, demonstrating excellent precision performances. Finally, an analysis of the activity annotations reported for the ligands in the curated datasets were conducted, suggesting associations between cellular activity and biological targets that might be explored in the future for the design of more effective prostate cancer antiproliferative agents.

Prostate cancer-specific hallmarks of amino acids metabolism: Towards a paradigm of precision medicine

So far multiple differences in prostate cancer-specific amino acids metabolism have been discovered. Moreover, attempts to utilize these alterations for prostate cancer diagnosis and treatment have been made. The prostate cancer metabolism and biosynthesis of amino acids are particularly focused on anaplerosis more than on energy production. Other crucial requirements on amino acids pool come from the serine, one‑carbon cycle, glycine synthesis pathway and folate metabolism forming major sources of interproducts for synthesis of nucleobases necessary for rapidly proliferating cells. Considering the lack of some amino acids biosynthetic pathways and/or their extraordinary importance for prostate cancer cells, there is a widespread potential for targeted therapeutic applications with no effect on non-malignant cells. This review summarizes the up-to-date knowledge of the importance of amino acids for prostate cancer pathogenesis with a special emphasis on potential applications of metabolic variabilities in the new oncologic paradigm of precision medicine.

Homocysteine-reducing B vitamins and ischemic heart disease: a separate-sample Mendelian randomization analysis

BACKGROUND/OBJECTIVES

Observationally, homocysteine is positively associated with ischemic heart disease (IHD) and unhealthy lipids; folate and vitamin B12, which reduce homocysteine, are associated with lower IHD risk and healthy lipids. Randomized controlled trials have shown no benefits of folate and vitamin B12 for IHD. To clarify the role of these potential targets of intervention in IHD we assessed how genetically determined homocysteine, folate and vitamin-B12-affected IHD and lipids.

SUBJECTS/METHODS

Separate-sample instrumental variable analysis with genetic instruments, that is, Mendelian randomization, was used to obtain unconfounded estimates (based on strongly related single-nucleotide polymorphisms (SNPs)) using CARDIoGRAMplusC4D, a large coronary artery disease/myocardial infarction (CAD/MI) case (n=64 374)-control (n=130 681) study with extensive genotyping, and the Global Lipids Genetics Consortium Results (n=196 475).

RESULTS

Homocysteine was unrelated to CAD/MI (odds ratio (OR) 1.07 per log-transformed s.d., 95% confidence interval (CI) 0.96 to 1.19) based on 14 SNPs, as was folate (OR 1.18 per s.d., 95% CI 0.80 to 1.75) based on rs153734, and vitamin B12 (OR 0.98 per log-transformed s.d., 95% CI 0.85 to 1.14) based on rs602662, rs9473555, rs526934 and rs11254363. Homocysteine and folate were not clearly associated with lipids, vitamin B12 was associated with higher inverse normal transformed low-density lipoprotein cholesterol (0.07, 95% CI 0.02 to 0.12) and triglycerides (0.05, 95% CI 0.004 to 0.09).

CONCLUSIONS

Our findings do not corroborate the observed positive association of homocysteine or negative associations of folate and vitamin B12 with CAD/MI. Vitamin B12 might be associated with an unfavorable lipid profile.