Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Over the last decades, several studies have shown that tumor-related genomic alterations predict tumor prognosis, drug response and toxicity. These observations have led to the development of a number of precision therapies based on individual genomic profiles. As part of these approaches, pharmacogenomics analyses genomic alterations that may predict an efficient therapeutic response. Studying these mutations as biomarkers for predicting drug response is of a great interest to improve precision medicine. Here we conduct a comprehensive review of the main pharmacogenomics biomarkers and genomic alterations affecting enzyme activity, transporter capacity, channels and receptors, and therefore the new advances in CRC precision medicine to select the best therapeutic strategy in populations worldwide, with a focus on Latin America.

Background: There is pressing urgency to identify therapeutic targets and drugs that allow treating COVID-19 patients effectively.Methods: We performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19.Results: We screened 1,584 high-confidence immune system proteins in ACE2 and TMPRSS2 co-expressing cells, finding 25 potential therapeutic targets significantly overexpressed in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs, obtaining several approved drugs, compounds under investigation, and experimental compounds with the highest area under the receiver operating characteristics.Conclusion: After being effectively analyzed in clinical trials, these drugs can be considered for treatment of severe COVID-19 patients. Scripts can be downloaded at https://github.com/muntisa/immuno-drug-repurposing-COVID-19.

ObjectiveThe current study aims to demonstrate independent associations between social, educational and health practice interventions as determinants of exclusive breastfeeding in an urban Ecuadorian population.DesignProspective survival analyses.SettingEcuadorian mother–child dyads in urban settings.ParticipantsWe followed-up 363 mother–baby dyads who attended healthcare centres in Portoviejo, province of Manabi, for a median time (P25–P75) of 125 days (121–130 days).Main outcome measuresWe performed a survival analysis, by setting the time-to-abandonment of exclusive breastfeeding measured in days of life, that is, duration of exclusive breastfeeding, periodically assessed by phone, as the primary outcome. Crude and adjusted mixed-effects Cox proportional hazards model were performed to estimate HRs for each explanatory variable.ResultsThe incidence rate of abandonment of breastfeeding was 8.9 per 1000 person-days in the whole sample. Multivariate analysis indicated the three most significant protective determinants of exclusive breastfeeding were (a) sessions of prenatal breastfeeding education with an HR of 0.7 (95% CI: 0.5 to 0.9) per each extra session, (b) self-perception of milk production, with an HR of 0.4 (95% CI: 0.3 to 0.6) per each increase in the perceived quantity of milk production and (c) receiving early skin-to-skin contact with an HR of 0.1 (95% CI: <0.1 to 0.3) compared with those not receiving such contact, immediately after birth.ConclusionsPrenatal education on breastfeeding, self-perception of sufficient breast-milk production and early skin-to-skin contact appear to be strong protectors of exclusive breastfeeding among urban Ecuadorian mother–baby dyads.