Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries.

BACKGROUND: We developed machine learning models to understand the predictors of shorter-, intermediate-, and longer-term mortality among hemodialysis (HD) patients affected by COVID-19 in four countries in the Americas. METHODS: We used data from adult HD patients treated at regional institutions of a global provider in Latin America (LatAm) and North America who contracted COVID-19 in 2020 before SARS-CoV-2 vaccines were available. Using 93 commonly captured variables, we developed machine learning models that predicted the likelihood of death overall, as well as during 0-14, 15-30, > 30 days after COVID-19 presentation and identified the importance of predictors. XGBoost models were built in parallel using the same programming with a 60%:20%:20% random split for training, validation, & testing data for the datasets from LatAm (Argentina, Columbia, Ecuador) and North America (United States) countries. RESULTS: Among HD patients with COVID-19, 28.8% (1,001/3,473) died in LatAm and 20.5% (4,426/21,624) died in North America. Mortality occurred earlier in LatAm versus North America; 15.0% and 7.3% of patients died within 0-14 days, 7.9% and 4.6% of patients died within 15-30 days, and 5.9% and 8.6% of patients died > 30 days after COVID-19 presentation, respectively. Area under curve ranged from 0.73 to 0.83 across prediction models in both regions. Top predictors of death after COVID-19 consistently included older age, longer vintage, markers of poor nutrition and more inflammation in both regions at all timepoints. Unique patient attributes (higher BMI, male sex) were top predictors of mortality during 0-14 and 15-30 days after COVID-19, yet not mortality > 30 days after presentation. CONCLUSIONS: Findings showed distinct profiles of mortality in COVID-19 in LatAm and North America throughout 2020. Mortality rate was higher within 0-14 and 15-30 days after COVID-19 in LatAm, while mortality rate was higher in North America > 30 days after presentation. Nonetheless, a remarkable proportion of HD patients died > 30 days after COVID-19 presentation in both regions. We were able to develop a series of suitable prognostic prediction models and establish the top predictors of death in COVID-19 during shorter-, intermediate-, and longer-term follow up periods.

Protein Kinase C Alpha (PKCα) overexpression leads to a better response to retinoid acid therapy through Retinoic Acid Receptor Beta (RARß) activation in mammary cancer cells.

PURPOSE: Retinoids have proved to be effective for hematologic malignancies treatment but till nowadays, their use as single agent for the solid tumor's management is still controversial. All-trans retinoic acid (ATRA), the main active metabolite of vitamin A, exerts non-genomic interactions with different members of the protein kinase C (PKC) family, recognized modulators of different tumor progression pathways. To determine whether a group of patients could become benefited employing a retinoid therapy, in this study we have evaluated whether PKCα expression (a poor prognosis marker in breast cancer) could sensitizes mammary cells to ATRA treatment. METHODS: PKCα overexpression was achieved by stable transfection and confirmed by western blot. Transfected PKC functionality was determined by nuclear translocation-induction and confocal microscopy. In vitro proliferation was evaluated by cell counting and cell cycle distribution was analyzed by flow cytometry. In vivo studies were performed to evaluate orthotopic tumor growth and experimental lung colonization. Retinoic acid response elements (RARE) and AP1 sites-dependent activity was studied by gene reporter assays and retinoic acid receptors (RARs) were measured by RT-qPCR. RESULTS: Our findings suggest that high PKCα levels improve the differentiation response to ATRA in a RAR signaling-dependent manner. Moreover, RARß expression appears to be critical to induce ATRA sensitization, throughout AP1 trans-repression. CONCLUSION: Here we propose that retinoids could lead a highly personalized anticancer treatment, bringing benefits to patients with aggressive breast tumors resulting from high PKCα expression but, an adequate expression of the RARß receptor is required to ensure the effect on this process.

Microenvironment and endocrine resistance in breast cancer: Friend or foe?

Breast cancer affects one in eight women around the world. Seventy five percent of these patients have tumors that are estrogen receptor positive and as a consequence receive endocrine therapy. However, about one third eventually develop resistance and cancer reappears. In the last decade our vision of cancer has evolved to consider it more of a tissue-related disease than a cell-centered one. This editorial argues that we are only starting to understand the role the tumor microenvironment plays in therapy resistance in breast cancer. The development of new therapeutic strategies that target the microenvironment will come when we clearly understand this extremely complicated scenario. As such, and as a scientific community, we have extremely challenging work ahead. We share our views regarding these matters.