Immune and Microbial Signatures Associated with PD-1 Blockade Sensitivity in a Preclinical Model for HPV+ Oropharyngeal Cancer.

The United States is suffering from an epidemic associated with high-risk strains of the Human Papillomavirus (HPV) predominantly responsible for the development of head and neck squamous cell carcinoma (HNSCC). Treatment with immune checkpoint inhibitors targeting programmed death 1 (PD-1) or its ligand PD-L1 has shown poor efficacy in HNSCC patients, observing only a 20-30% response. Therefore, biological marker identification associated with PD-1 blockade response is important to improve prognosis and define novel therapeutics for HNSCC patients. Therapy response was associated with increased frequencies of activated CD27+T cells, activated CD79a+ B cells, antigen-presenting CD74+ dendritic and B cells, and PD-L1+ and PD-L2+ myeloid-derived suppressor cells (MDSCs). The oral microbiota composition differed significantly in mice bearing tongue tumors and treated with anti-PD-1. A higher abundance of Allobaculum, Blautia, Faecalibacterium, Dorea, or Roseburia was associated with response to the therapy. However, an increase in Enterococcus was attributed to tongue tumor-bearing non-responding mice. Our findings indicate that differences in immune phenotypes, protein expression, and bacterial abundance occur as mice develop tongue tumors and are treated with anti-PD-1. These results may have a clinical impact as specific bacteria and immune phenotype could serve as biomarkers for treatment response in HNSCC.

Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool.

PURPOSE: Antimalarial drug resistance is a global public health problem that leads to treatment failure. Synergistic drug combinations can improve treatment outcomes and delay the development of drug resistance. Here, we describe the implementation of a freely available computational tool, Machine Learning Synergy Predictor (MLSyPred©), to predict potential synergy in antimalarial drug combinations. METHODS: The MLSyPred© synergy prediction method extracts molecular fingerprints from the drugs' biochemical structures to use as features and also cleans and prepares the raw data. Five machine learning algorithms (Logistic Regression, Random Forest, Support vector machine, Ada Boost, and Gradient Boost) were implemented to build prediction models. Implementation and application of the MLSyPred© tool were tested using datasets from 1540 combinations of 79 drugs and compounds biologically evaluated in pairs for three strains of Plasmodium falciparum (3D7, HB3, and Dd2). RESULTS: The best prediction models were obtained using Logistic Regression for antimalarials with the strains Dd2 and HB3 (0.81 and 0.70 AUC, respectively) and Random Forest for antimalarials with 3D7 (0.69 AUC). The MLSyPred© tool yielded 45% precision for synergistically predicted antimalarial drug combinations that were annotated and biologically validated, thus confirming the functionality and applicability of the tool. CONCLUSION:  The MLSyPred© tool is freely available and represents a promising strategy for discovering potential synergistic drug combinations for further development as novel antimalarial therapies.

B cells as modulators of HPV+ oropharyngeal cancer in a preclinical model.

Among the different immune cells present within tumors, B cells also infiltrate human papillomavirus-positive (HPV+) oropharyngeal tumors. However, the role of B cells during programmed death-1 (PD-1) blockade in HPV+ oropharyngeal cancer needs to be better defined. By using the preclinical mouse model for HPV+ oropharyngeal cancer (named mEER), we characterized B cells within tumors and determined their functional role in vivo during PD-1 blockade. We determined that treatment naïve tongue-implanted tumors, which we have previously demonstrated to be sensitive to PD-1 blockade, contained high infiltration of CD8+ T cells and low infiltration of B cells whereas flank-implanted tumors, which are resistant to PD-1 blockade, contain a higher frequency of B cells compared to T cells. Moreover, B cell-deficient mice (µMt) and B cell-depleted mice showed a slower tumor growth rate compared to wild-type (WT) mice, and B cell deficiency increased CD8+ T cell infiltration in tumors. When we compared tongue tumor-bearing mice treated with anti-PD-1, we observed that tumors that responded to the therapy contained more T cells and B cells than the ones that did not respond. However, µMt mice treated with PD-1 blockade showed similar tumor growth rates to WT mice. Our data suggest that in untreated mice, B cells have a more pro-tumorigenic phenotype potentially affecting T cell infiltration in the tumors. In contrast, B cells are dispensable for PD-1 blockade efficacy. Mechanistic studies are needed to identify novel targets to promote the anti-tumorigenic function and/or suppress the immunosuppressive function of B cells in HPV+ oropharyngeal cancer.

A complete guide to human microbiomes: Body niches, transmission, development, dysbiosis, and restoration.

Humans are supra-organisms co-evolved with microbial communities (Prokaryotic and Eukaryotic), named the microbiome. These microbiomes supply essential ecosystem services that play critical roles in human health. A loss of indigenous microbes through modern lifestyles leads to microbial extinctions, associated with many diseases and epidemics. This narrative review conforms a complete guide to the human holobiont-comprising the host and all its symbiont populations- summarizes the latest and most significant research findings in human microbiome. It pretends to be a comprehensive resource in the field, describing all human body niches and their dominant microbial taxa while discussing common perturbations on microbial homeostasis, impacts of urbanization and restoration and humanitarian efforts to preserve good microbes from extinction.