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Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational approaches. We have built a computational framework called pVACtools that, when paired with a well-established genomics pipeline, produces an end-to-end solution for neoantigen characterization. pVACtools supports identification of altered peptides from different mechanisms, including point mutations, inframe and frameshift insertions and deletions, and gene fusions. Prediction of peptide:MHC binding is accomplished by supporting an ensemble of MHC Class I and II binding algorithms within a framework designed to facilitate the incorporation of additional algorithms. Prioritization of predicted peptides occurs by integrating diverse data, including mutant allele expression, peptide binding affinities, and determination whether a mutation is clonal or subclonal. Interactive visualization via a Web interface allows clinical users to efficiently generate, review, and interpret results, selecting candidate peptides for individual patient vaccine designs. Additional modules support design choices needed for competing vaccine delivery approaches. One such module optimizes peptide ordering to minimize junctional epitopes in DNA vector vaccines. Downstream analysis commands for synthetic long peptide vaccines are available to assess candidates for factors that influence peptide synthesis. All of the aforementioned steps are executed via a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization, and selection using a graphical Web-based interface (pVACviz), and design of DNA vector-based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at http://www.pvactools.org.

Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor–normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.

Blood vessels are formed during development and tissue repair through a plethora of modifiers that coordinate efficient vessel assembly in various cellular settings. Here we used the yeast 2-hybrid approach and demonstrated a broad affinity of connective tissue growth factor (CCN2/CTGF) to C-terminal cystine knot motifs present in key angiogenic regulators Slit3, von Willebrand factor, platelet-derived growth factor-B, and VEGF-A. Biochemical characterization and histological analysis showed close association of CCN2/CTGF with these regulators in murine angiogenesis models: normal retinal development, oxygen-induced retinopathy (OIR), and Lewis lung carcinomas. CCN2/CTGF and Slit3 proteins worked in concert to promote in vitro angiogenesis and downstream Cdc42 activation. A fragment corresponding to the first three modules of CCN2/CTGF retained this broad binding ability and gained a dominant-negative function. Intravitreal injection of this mutant caused a significant reduction in vascular obliteration and retinal neovascularization vs. saline injection in the OIR model. Knocking down CCN2/CTGF expression by short-hairpin RNA or ectopic expression of this mutant greatly decreased tumorigenesis and angiogenesis. These results provided mechanistic insight into the angiogenic action of CCN2/CTGF and demonstrated the therapeutic potential of dominant-negative CCN2/CTGF mutants for antiangiogenesis.

The FDA approved capmatinib and tepotinib on May 6, 2020, and February 3, 2021, respectively. Capmatinib is indicated for patients with metastatic non–small cell lung cancer (mNSCLC) whose tumors have a mutation leading to mesenchymal–epithelial transition (MET) exon 14 skipping as detected by an FDA-approved test. Tepotinib is indicated for mNSCLC harboring MET exon 14 skipping alterations. The approvals were based on trials GEOMETRY mono-1 (capmatinib) and VISION (tepotinib). In GEOMETRY mono-1, overall response rate (ORR) per Blinded Independent Review Committee (BIRC) was 68% [95% confidence interval (CI), 48–84] with median duration of response (DoR) 12.6 months (95% CI, 5.5–25.3) in 28 treatment-naïve patients and 41% (95% CI: 29, 53) with median DoR 9.7 months (95% CI, 5.5–13) in 69 previously treated patients with NSCLC with mutations leading to MET exon 14 skipping. In VISION, ORR per BIRC was 43% (95% CI: 32, 56) with median DoR 10.8 months (95% CI, 6.9–not estimable) in 69 treatment-naïve patients and 43% (95% CI, 33–55) with median DoR 11.1 months (95% CI, 9.5–18.5) in 83 previously-treated patients with NSCLC harboring MET exon 14 alterations. These are the first two therapies to be FDA approved specifically for patients with metastatic NSCLC with MET exon 14 skipping.

h The aim of this study was to examine the relationships between N-acetyltransferase genotypes, pharmacokinetics, and tolerability of granular slow-release para-aminosalicylic acid (GSR-PAS) in tuberculosis patients. The study was a randomized, two-period, open-label, crossover design wherein each patient received 4 g GSR-PAS twice daily or 8 g once daily alternately. The PAS concentration-time profiles were modeled by a one-compartment disposition model with three transit compartments in series to describe its absorption. Patients' NAT1 and NAT2 genotypes were determined by sequencing and restriction enzyme analysis, respectively. The number of daily vomits was modeled by a Poisson probability mass function. Comparisons of other tolerability measures by regimens, gender, and genotypes were evaluated by a linear mixed-effects model. The covariate effects associated with efavirenz, gender, and NAT1*3, NAT1*14, and NAT2*5 alleles corresponded to 25, 37, ؊17, ؊48, and ؊27% changes, respectively, in oral clearance of PAS. The NAT1*10 allele did not influence drug clearance. The time above the MIC of 1 mg/liter was significantly different between the two regimens but not influenced by the NAT1 or NAT2 genotypes. The occurrence and intensity of intolerance differed little between regimens. Four grams of GSR-PAS twice daily but not 8 g once daily ensured concentrations exceeding the MIC (1 mg/liter) throughout the dosing interval; PAS intolerance was not related to maximum PAS concentrations over the doses studied and was not more frequent after once-daily dosing. We confirm that the slow phenotype conferred by the NAT1*14 and NAT1*3 alleles resulted in higher PAS exposure but found no evidence of increased activity of the NAT1*10 allele. p ara-Aminosalicylic acid (PAS) was the first effective antituberculosis agent used to treat pulmonary tuberculosis (1); for a duration of 20 to 25 years, it was part of the standard "first-line" tuberculosis treatment (2). Valued for preventing resistance in companion drugs, it was nonetheless notorious for gastrointestinal intolerance, causing frequent nausea, vomiting, and abdominal discomfort. The replacement of PAS with rifampin and ethambutol was greeted with relief by patients, but with widespread multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis in a number of countries, particularly in the developing world, PAS is again being used to treat drug-resistant tuberculosis.The PAS preparation most commonly used in many countries is the granular slow-release (delayed-release) formulation (GSR-PAS), which prevents premature drug release in the stomach, avoiding the high PAS concentrations considered prone to cause intolerance. Several studies (3-7) have reported PAS concentrations associated with use of GSR-PAS in dosages from 4 g daily to 8 to 12 g daily in divided doses, as recommended by the World Health Organization (8). As PAS is usually considered bacteriostatic, divided dosing aims to provide concentrations consistently exceeding the PAS MIC of approx...

BackgroundThe pandemic influenza A (H1N1) pdm09 virus, avian influenza A (H5N1) virus, and influenza A (H7N9) virus induced severe morbidity and mortality throughout the world. Previous studies suggested a close association between the interferon-induced transmembrane protein-3 (IFITM3) genetic variant rs12252 and influenza. Here, we explored the correlation between the rs12252 and influenza susceptibility and severity using meta-analysis.MethodsRelevant studies published before May 22, 2014 were retrieved from PubMed, ISI web of knowledge, EBSCO, and Cochrane central register of controlled trials databases. Association between rs12252 and influenza susceptibility and severity were determined using statistical analysis of odds ratios (ORs).ResultsA total of four studies consisting of 445 cases and 4180 controls were included in our analysis. Generally, there is increased risk of influenza in subjects carrying rs12252 in the recessive model (CC vs. CT+TT: OR = 2.35, 95% CI: 1.49-3.70, P<0.001), the dominant model (CC+CT vs. TT: OR=1.60, 95% CI: 1.18–2.22, P=0.003), the homozygote comparison (CC vs. TT: OR=4.11, 95% CI: 2.15–7.84, P<0.001), and the allele contrast (C vs. T: OR=1.67, 95% CI: 1.32–2.13, P<0.001). Stratification analysis of ethnicity and severity revealed a significant increase in influenza susceptibility by IFITM3-SNP rs12252 among both Asian and Caucasian population. SNP rs12252 shows significant impact on severe infections (P<0.05), but not on mild influenza. Besides, our result also associated rs12252 with influenza severity (severe vs. mild: OR=2.37, 95% CI: 1.32–4.25, P=0.004), (severe vs. control: OR=2.70, 95% CI: 1.85–3.94, P<0.001).ConclusionOur meta-analysis suggests a significant association between a minor IFITM3 allele (SNP rs12252-C) with severe influenza susceptibility, but not in mild influenza subjects, in both UK Caucasians and Han Chinese population. The rs12252-C allele causes a 23.7% higher chance of infection and also constitutes a risk factor for more severe influenza.

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