No abstract

Adaptive immune response modulation has taken a central position in cancer therapy in recent decades. Treatment with immune checkpoint inhibitors (ICIs) is now indicated in many cancer types with exceptional results. The two major inhibitory pathways involved are cytotoxic T-lymphocyte-associated protein 4 (CTLA4) and programmed cell death protein 1 (PD-1). Unfortunately, immune activation is not tumor-specific, and as a result, most patients will experience some form of adverse reaction. Most immune-related adverse events (IRAEs) involve the skin and gastrointestinal (GI) tract; however, any organ can be involved. Cardiotoxicity ranges from arrhythmias to life-threatening myocarditis with very high mortality rates. To date, most treatments of ICI cardiotoxicity include immune suppression, which is also not cardiac-specific and may result in hampering of tumor clearance. Understanding the mechanisms behind immune activation in the heart is crucial for the development of specific treatments. Histological data and other models have shown mainly CD4 and CD8 infiltration during ICI-induced cardiotoxicity. Inhibition of CTLA4 seems to result in the proliferation of more diverse T0cell populations, some of which with autoantigen recognition. Inhibition of PD-1 interaction with PD ligand 1/2 (PD-L1/PD-L2) results in release from inhibition of exhausted self-recognizing T cells. However, CTLA4, PD-1, and their ligands are expressed on a wide range of cells, indicating a much more intricate mechanism. This is further complicated by the identification of multiple co-stimulatory and co-inhibitory signals, as well as the association of myocarditis with antibody-driven myasthenia gravis and myositis IRAEs. In this review, we focus on the recent advances in unraveling the complexity of the mechanisms driving ICI cardiotoxicity and discuss novel therapeutic strategies for directly targeting specific underlying mechanisms to reduce IRAEs and improve outcomes.

No abstract

Conservation is a strong predictor for the pathogenicity of single-nucleotide variants (SNVs). However, some positions that present complex conservation patterns across vertebrates stray from this paradigm. Here, we analyzed the association between complex conservation patterns and the pathogenicity of SNVs in the 115 disease-genes that had sufficient variant data. We show that conservation is not a one-rule-fits-all solution since its accuracy highly depends on the analyzed set of species and genes. For example, pairwise comparisons between the human and 99 vertebrate species showed that species differ in their ability to predict the clinical outcomes of variants among different genes using conservation. Furthermore, certain genes were less amenable for conservation-based variant prediction, while others demonstrated species that optimize prediction. These insights led to developing EvoDiagnostics, which uses the conservation against each species as a feature within a random-forest machine-learning classification algorithm. EvoDiagnostics outperformed traditional conservation algorithms, deep-learning based methods and most ensemble tools in every prediction-task, highlighting the strength of optimizing conservation analysis per-species and per-gene. Overall, we suggest a new and a more biologically relevant approach for analyzing conservation, which improves prediction of variant pathogenicity.