Partitioning heritability by functional annotation using genome-wide association summary statistics

Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here we analyze a broad set of functional elements, including cell type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits with an average sample size of 73,599. To enable this analysis, we introduce a new method, stratified LD score regression, for partitioning heritability from GWAS summary statistics while accounting for linked markers. This new method is computationally tractable at very large sample sizes and leverages genome-wide information. Our findings include a large enrichment of heritability in conserved regions across many traits, a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers and many cell type-specific enrichments, including significant enrichment of central nervous system cell types in the heritability of body mass index, age at menarche, educational attainment and smoking behavior

Impact of Previously Unrecognized HLA Mismatches Using Ultrahigh Resolution Typing in Unrelated Donor Hematopoietic Cell Transplantation

PURPOSE: Ultrahigh resolution (UHR) HLA matching is reported to result in better outcomes following unrelated donor hematopoietic cell transplantation, improving survival and reducing post-transplant complications. However, most studies included relatively small numbers of patients. Here we report the findings from a large, multicenter validation study. METHODS: UHR HLA typing was available on 5,140 conventionally 10 out of 10 HLA-matched patients with malignant disease transplanted between 2008 and 2017. RESULTS: After UHR HLA typing, 82% of pairs remained 10 out of 10 UHR-matched; 12.3% of patients were 12 out of 12 UHR HLA-matched. Compared with 12 out of 12 UHR-matched patients, probabilities of grade 2-4 acute graft-versus-host disease (aGVHD) were significantly increased with UHR mismatches (overall P = .0019) and in those patients who were HLA-DPB1 T-cell epitope permissively mismatched or nonpermissively mismatched (overall P = .0011). In the T-cell-depleted subset, the degree of UHR HLA mismatch was only associated with increased transplant-related mortality (TRM) (overall P = .0068). In the T-cell-replete subset, UHR HLA matching was associated with a lower probability of aGVHD (overall P = .0020); 12 out of 12 UHR matching was associated with reduced TRM risk when compared with HLA-DPB1 T-cell epitope permissively mismatched patients, whereas nonpermissive mismatching resulted in a greater risk (overall P = .0003). CONCLUSION: This study did not confirm that UHR 12 out of 12 HLA matching increases the probability of overall survival but does demonstrate that aGVHD risk, and in certain settings TRM, is lowest in UHR HLA-matched pairs and thus warrants consideration when multiple 10 out of 10 HLA-matched donors of equivalent age are available

Managing toxicities associated with immune checkpoint inhibitors: consensus recommendations from the Society for Immunotherapy of Cancer (SITC) Toxicity Management Working Group.

Cancer immunotherapy has transformed the treatment of cancer. However, increasing use of immune-based therapies, including the widely used class of agents known as immune checkpoint inhibitors, has exposed a discrete group of immune-related adverse events (irAEs). Many of these are driven by the same immunologic mechanisms responsible for the drugs\u27 therapeutic effects, namely blockade of inhibitory mechanisms that suppress the immune system and protect body tissues from an unconstrained acute or chronic immune response. Skin, gut, endocrine, lung and musculoskeletal irAEs are relatively common, whereas cardiovascular, hematologic, renal, neurologic and ophthalmologic irAEs occur much less frequently. The majority of irAEs are mild to moderate in severity; however, serious and occasionally life-threatening irAEs are reported in the literature, and treatment-related deaths occur in up to 2% of patients, varying by ICI. Immunotherapy-related irAEs typically have a delayed onset and prolonged duration compared to adverse events from chemotherapy, and effective management depends on early recognition and prompt intervention with immune suppression and/or immunomodulatory strategies. There is an urgent need for multidisciplinary guidance reflecting broad-based perspectives on how to recognize, report and manage organ-specific toxicities until evidence-based data are available to inform clinical decision-making. The Society for Immunotherapy of Cancer (SITC) established a multidisciplinary Toxicity Management Working Group, which met for a full-day workshop to develop recommendations to standardize management of irAEs. Here we present their consensus recommendations on managing toxicities associated with immune checkpoint inhibitor therapy

The Genomic and Immune Landscapes of Lethal Metastatic Breast Cancer.

The detailed molecular characterization of lethal cancers is a prerequisite to understanding resistance to therapy and escape from cancer immunoediting. We performed extensive multi-platform profiling of multi-regional metastases in autopsies from 10 patients with therapy-resistant breast cancer. The integrated genomic and immune landscapes show that metastases propagate and evolve as communities of clones, reveal their predicted neo-antigen landscapes, and show that they can accumulate HLA loss of heterozygosity (LOH). The data further identify variable tumor microenvironments and reveal, through analyses of T cell receptor repertoires, that adaptive immune responses appear to co-evolve with the metastatic genomes. These findings reveal in fine detail the landscapes of lethal metastatic breast cancer.CRUK

Antifibrotic Effects of the Dual CCR2/CCR5 Antagonist Cenicriviroc in Animal Models of Liver and Kidney Fibrosis

Background & Aims Interactions between C-C chemokine receptor types 2 (CCR2) and 5 (CCR5) and their ligands, including CCL2 and CCL5, mediate fibrogenesis by promoting monocyte/macrophage recruitment and tissue infiltration, as well as hepatic stellate cell activation. Cenicriviroc (CVC) is an oral, dual CCR2/CCR5 antagonist with nanomolar potency against both receptors. CVC’s anti-inflammatory and antifibrotic effects were evaluated in a range of preclinical models of inflammation and fibrosis. Methods Monocyte/macrophage recruitment was assessed in vivo in a mouse model of thioglycollate-induced peritonitis. CCL2-induced chemotaxis was evaluated ex vivo on mouse monocytes. CVC’s antifibrotic effects were evaluated in a thioacetamide-induced rat model of liver fibrosis and mouse models of diet-induced non-alcoholic steatohepatitis (NASH) and renal fibrosis. Study assessments included body and liver/kidney weight, liver function test, liver/kidney morphology and collagen deposition, fibrogenic gene and protein expression, and pharmacokinetic analyses. Results CVC significantly reduced monocyte/macrophage recruitment in vivo at doses ≥20 mg/kg/day (p < 0.05). At these doses, CVC showed antifibrotic effects, with significant reductions in collagen deposition (p < 0.05), and collagen type 1 protein and mRNA expression across the three animal models of fibrosis. In the NASH model, CVC significantly reduced the non-alcoholic fatty liver disease activity score (p < 0.05 vs. controls). CVC treatment had no notable effect on body or liver/kidney weight. Conclusions CVC displayed potent anti-inflammatory and antifibrotic activity in a range of animal fibrosis models, supporting human testing for fibrotic diseases. Further experimental studies are needed to clarify the underlying mechanisms of CVC’s antifibrotic effects. A Phase 2b study in adults with NASH and liver fibrosis is fully enrolled (CENTAUR Study 652-2-203; NCT02217475)

Molecular imaging of cell death in vivo by a novel small molecule probe

Apoptosis has a role in many medical disorders, therefore assessment of apoptosis in vivo can be highly useful for diagnosis, follow-up and evaluation of treatment efficacy. ApoSense is a novel technology, comprising low molecular-weight probes, specifically designed for imaging of cell death in vivo. In the current study we present targeting and imaging of cell death both in vitro and in vivo, utilizing NST-732, a member of the ApoSense family, comprising a fluorophore and a fluorine atom, for both fluorescent and future positron emission tomography (PET) studies using an 18F label, respectively. In vitro, NST-732 manifested selective and rapid accumulation within various cell types undergoing apoptosis. Its uptake was blocked by caspase inhibition, and occurred from the early stages of the apoptotic process, in parallel to binding of Annexin-V, caspase activation and alterations in mitochondrial membrane potential. In vivo, NST-732 manifested selective uptake into cells undergoing cell-death in several clinically-relevant models in rodents: (i) Cell-death induced in lymphoma by irradiation; (ii) Renal ischemia/reperfusion; (iii) Cerebral stroke. Uptake of NST-732 was well-correlated with histopathological assessment of cell-death. NST-732 therefore represents a novel class of small-molecule detectors of apoptosis, with potential useful applications in imaging of the cell death process both in vitro and in vivo

A unified data representation theory for network visualization, ordering and coarse-graining

Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we show an elegant, information theoretic data representation approach as a unified solution of network visualization, data ordering and coarse-graining. The optimal representation is the hardest to distinguish from the original data matrix, measured by the relative entropy. The representation of network nodes as probability distributions provides an efficient visualization method and, in one dimension, an ordering of network nodes and edges. Coarse-grained representations of the input network enable both efficient data compression and hierarchical visualization to achieve high quality representations of larger data sets. Our unified data representation theory will help the analysis of extensive data sets, by revealing the large-scale structure of complex networks in a comprehensible form.Comment: 13 pages, 5 figure