A major goal in human genetics is to understand the role of common genetic variants in susceptibility to common diseases. This will require characterizing the nature of gene variation in human populations, assembling an extensive catalogue of single-nucleotide polymorphisms (SNPs) in candidate genes and performing association studies for particular diseases. At present, our knowledge of human gene variation remains rudimentary. Here we describe a systematic survey of SNPs in the coding regions of human genes. We identified SNPs in 106 genes relevant to cardiovascular disease, endocrinology and neuropsychiatry by screening an average of 114 independent alleles using 2 independent screening methods. To ensure high accuracy, all reported SNPs were confirmed by DNA sequencing. We identified 560 SNPs, including 392 coding-region SNPs (cSNPs) divided roughly equally between those causing synonymous and non-synonymous changes. We observed different rates of polymorphism among classes of sites within genes (non-coding, degenerate and non-degenerate) as well as between genes. The cSNPs most likely to influence disease, those that alter the amino acid sequence of the encoded protein, are found at a lower rate and with lower allele frequencies than silent substitutions. This likely reflects selection acting against deleterious alleles during human evolution. The lower allele frequency of missense cSNPs has implications for the compilation of a comprehensive catalogue, as well as for the subsequent application to disease association.

Chromatin regulators play fundamental roles in the regulation of gene expression and chromosome maintenance, but the regions of the genome where most of these regulators function has not been established. We explored the genome-wide occupancy of four different chromatin regulators encoded in Saccharomyces cerevisiae. The results reveal that the histone acetyltransferases Gcn5 and Esa1 are both generally recruited to the promoters of active protein-coding genes. In contrast, the histone deacetylases Hst1 and Rpd3 are recruited to specific sets of genes associated with distinct cellular functions. Our results provide new insights into the association of histone acetyltransferases and histone deacetylases with the yeast genome, and together with previous studies, suggest how these chromatin regulators are recruited to specific regions of the genome.

To determine whether combination therapy with NHS-muIL12 and the anti-programmed death ligand 1 (PD-L1) antibody avelumab can enhance antitumor efficacy in preclinical models relative to monotherapies. BALB/c mice bearing orthotopic EMT-6 mammary tumors and μMt mice bearing subcutaneous MC38 tumors were treated with NHS-muIL12, avelumab, or combination therapy; tumor growth and survival were assessed. Tumor recurrence following remission and rechallenge was evaluated in EMT-6 tumor-bearing mice. Immune cell populations within spleen and tumors were evaluated by FACS and IHC. Immune gene expression in tumor tissue was profiled by NanoString® assay and plasma cytokine levels were determined by multiplex cytokine assay. The frequency of tumor antigen-reactive IFNγ-producing CD8 T cells was evaluated by ELISpot assay. NHS-muIL12 and avelumab combination therapy enhanced antitumor efficacy relative to either monotherapy in both tumor models. Most EMT-6 tumor-bearing mice treated with combination therapy had complete tumor regression. Combination therapy also induced the generation of tumor-specific immune memory, as demonstrated by protection against tumor rechallenge and induction of effector and memory T cells. Combination therapy enhanced cytotoxic NK and CD8 T-cell proliferation and T-bet expression, whereas NHS-muIL12 monotherapy induced CD8 T-cell infiltration into the tumor. Combination therapy also enhanced plasma cytokine levels and stimulated expression of a greater number of innate and adaptive immune genes compared with either monotherapy. These data indicate that combination therapy with NHS-muIL12 and avelumab increased antitumor efficacy in preclinical models, and suggest that combining NHS-IL12 and avelumab may be a promising approach to treating patients with solid tumors. .

BackgroundBintrafusp alfa is a first-in-class bifunctional fusion protein composed of the extracellular domain of transforming growth factor (TGF)-βRII (a TGF-β ‘trap’) fused to a human IgG1 mAb blocking programmed cell death ligand 1. This is the largest analysis of patients with advanced, pretreated human papillomavirus (HPV)-associated malignancies treated with bintrafusp alfa.MethodsIn these phase 1 (NCT02517398) and phase 2 trials (NCT03427411), 59 patients with advanced, pretreated, checkpoint inhibitor-naive HPV-associated cancers received bintrafusp alfa intravenously every 2 weeks until progressive disease, unacceptable toxicity, or withdrawal. Primary endpoint was best overall response per Response Evaluation Criteria in Solid Tumors (RECIST) V.1.1; other endpoints included safety.ResultsAs of April 17, 2019 (phase 1), and October 4, 2019 (phase 2), the confirmed objective response rate per RECIST V.1.1 in the checkpoint inhibitor-naive, full-analysis population was 30.5% (95% CI, 19.2% to 43.9%; five complete responses); eight patients had stable disease (disease control rate, 44.1% (95% CI, 31.2% to 57.6%)). In addition, three patients experienced a delayed partial response after initial disease progression, for a total clinical response rate of 35.6% (95% CI, 23.6% to 49.1%). An additional patient with vulvar cancer had an unconfirmed response. Forty-nine patients (83.1%) experienced treatment-related adverse events, which were grade 3/4 in 16 patients (27.1%). No treatment-related deaths occurred.ConclusionBintrafusp alfa showed clinical activity and manageable safety and is a promising treatment in HPV-associated cancers. These findings support further investigation of bintrafusp alfa in patients with advanced, pretreated HPV-associated cancers.

Direct physical information that describes where transcription factors, nucleosomes, modified histones, RNA polymerase II and other key proteins interact with the genome provides an invaluable mechanistic foundation for understanding complex programs of gene regulation. We present a method, joint binding deconvolution (JBD), which uses additional easily obtainable experimental data about chromatin immunoprecipitation (ChIP) to improve the spatial resolution of the transcription factor binding locations inferred from ChIP followed by DNA microarray hybridization (ChIP-Chip) data. Based on this probabilistic model of binding data, we further pursue improved spatial resolution by using sequence information. We produce positional priors that link ChIP-Chip data to sequence data by guiding motif discovery to inferred protein-DNA binding sites. We present results on the yeast transcription factors Gcn4 and Mig2 to demonstrate JBD's spatial resolution capabilities and show that positional priors allow computational discovery of the Mig2 motif when a standard approach fails.

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

A detailed understanding of the circulating pathogens in a particular geographic location aids in effectively utilizing targeted, rapid diagnostic assays, thus allowing for appropriate therapeutic and containment procedures. This is especially important in regions prevalent for highly pathogenic viruses co-circulating with other endemic pathogens such as the malaria parasite. The importance of biosurveillance is highlighted by the ongoing Ebola virus disease outbreak in West Africa. For example, a more comprehensive assessment of the regional pathogens could have identified the risk of a filovirus disease outbreak earlier and led to an improved diagnostic and response capacity in the region. In this context, being able to rapidly screen a single sample for multiple pathogens in a single tube reaction could improve both diagnostics as well as pathogen surveillance. Here, probes were designed to capture identifying filovirus sequence for the ebolaviruses Sudan, Ebola, Reston, Taï Forest, and Bundibugyo and the Marburg virus variants Musoke, Ci67, and Angola. These probes were combined into a single probe panel, and the captured filovirus sequence was successfully identified using the MiSeq next-generation sequencing platform. This panel was then used to identify the specific filovirus from nonhuman primates experimentally infected with Ebola virus as well as Bundibugyo virus in human sera samples from the Democratic Republic of the Congo, thus demonstrating the utility for pathogen detection using clinical samples. While not as sensitive and rapid as real-time PCR, this panel, along with incorporating additional sequence capture probe panels, could be used for broad pathogen screening and biosurveillance.

• Transcriptome analysis of CD206+ and CD206-macrophages reveals differential phenotype. • CD06+ macrophages coexpressing either MORC4 or SERPINH1 show a positive correlation with patient survival. • CD206-macrophages expressing MHCII are correlated with negative patient survival.