Phase 1 study to determine the safety and dosing of autologous PBMCs modified to present HPV16 antigens (SQZ-PBMC-HPV) in HLA-A*02+ patients with HPV16+ solid tumors.

We conducted a dose escalation Phase 1 study of autologous PBMCs loaded by microfluidic squeezing (Cell Squeeze® technology) with HPV16 E6 and E7 antigens (SQZ-PBMC-HPV), in HLA-A*02+ patients with advanced/metastatic HPV16+ cancers. Preclinical studies in murine models had shown such cells resulted in stimulation and proliferation of antigen specific CD8+ cells, and demonstrated antitumor activity. Administration of SQZ-PBMC-HPV was every 3 weeks. Enrollment followed a modified 3+3 design with primary objectives to define safety, tolerability, and the recommended Phase 2 dose. Secondary and exploratory objectives were antitumor activity, manufacturing feasibility, and pharmacodynamic evaluation of immune responses. Eighteen patients were enrolled at doses ranging from 0.5 × 106 to 5.0 × 106 live cells/kg. Manufacture proved feasible and required < 24 h within the overall vein-to-vein time of 1 - 2 weeks; at the highest dose, a median of 4 doses were administered. No DLTs were observed. Most related TEAEs were Grade 1 - 2, and one Grade 2 cytokine release syndrome SAE was reported. Tumor biopsies in three patients showed 2 to 8-fold increases in CD8+ tissue infiltrating lymphocytes, including a case that exhibited increased MHC-I+ and PD-L1+ cell densities and reduced numbers of HPV+ cells. Clinical benefit was documented for the latter case. SQZ-PBMC-HPV was well tolerated; 5.0 × 106 live cells/kg with double priming was chosen as the recommended Phase 2 dose. Multiple participants exhibited pharmacodynamic changes consistent with immune responses supporting the proposed mechanism of action for SQZ-PBMC-HPV, including patients previously refractory to checkpoint inhibitors.

Functional characterization of 105 factor H variants associated with aHUS: lessons for variant classification.

Atypical hemolytic uremic syndrome (aHUS) is a life-threatening thrombotic microangiopathy that can progress, when untreated, to end-stage renal disease. Most frequently, aHUS is caused by complement dysregulation due to pathogenic variants in genes that encode complement components and regulators. Among these genes, the factor H (FH) gene, CFH, presents with the highest frequency (15% to 20%) of variants and is associated with the poorest prognosis. Correct classification of CFH variants as pathogenic or benign is essential to clinical care but remains challenging owing to the dearth of functional studies. As a result, significant numbers of variants are reported as variants of uncertain significance. To address this knowledge gap, we expressed and functionally characterized 105 aHUS-associated FH variants. All FH variants were categorized as pathogenic or benign and, for each, we fully documented the nature of the pathogenicity. Twenty-six previously characterized FH variants were used as controls to validate and confirm the robustness of the functional assays used. Of the remaining 79 uncharacterized variants, only 29 (36.7%) alter FH expression or function in vitro and, therefore, are proposed to be pathogenic. We show that rarity in control databases is not informative for variant classification, and we identify important limitations in applying prediction algorithms to FH variants. Based on structural and functional data, we suggest ways to circumvent these difficulties and, thereby, improve variant classification. Our work highlights the need for functional assays to interpret FH variants accurately if clinical care of patients with aHUS is to be individualized and optimized.

BNT162b2 Vaccine Encoding the SARS-CoV-2 P2 S Protects Transgenic hACE2 Mice against COVID-19.

BNT162b2 is a highly efficacious mRNA vaccine approved to prevent COVID-19. This brief report describes the immunogenicity and anti-viral protective effect of BNT162b2 in hACE2 transgenic mice. Prime-boost immunization with BNT162b2 elicited high titers in neutralizing antibodies against SARS-CoV-2, which correlated with viral clearance and alleviated lung lesions in these mice after viral challenge.

Abatacept Inhibition of T Cell Priming in Mice by Induction of a Unique Transcriptional Profile That Reduces Their Ability to Activate Antigen-Presenting Cells.

OBJECTIVE: To determine at the phenotypic, functional, and transcriptional levels whether abatacept, a CTLA-4Ig molecule that binds with high affinity to CD80/86 on antigen-presenting cells (APCs) and is used to treat rheumatoid arthritis, induces a state of immunologic tolerance in T cells and dendritic cells in mice. METHODS: We investigated the capacity of abatacept to regulate the development of antigen-specific immunologic tolerance in vivo using murine models of priming and tolerance to generate highly purified antigen-specific T cell populations and CD11c+ APCs. These were combined with detailed immunologic and full genome transcriptional analyses. RESULTS: We found that abatacept inhibited T cell activation, but did not render T cells anergic or lead to the generation of Treg cells. However, it induced a sustained inhibition of T cell activation due to the inability of these cells to progress through the cell cycle following T cell receptor stimulation. We also observed that this state was accompanied by an inhibition of dendritic cell activation due to their reduced licensing by T cells. CONCLUSION: This study provides detailed insight into the mode of action of abatacept, demonstrating that its effectiveness is not due to the induction of T cell tolerance, but rather to a sustained inhibition of T cell activation that results in reduced functionality of APCs, with significant implications for its clinical application.

Gene expression profiling of whole blood in ipilimumab-treated patients for identification of potential biomarkers of immune-related gastrointestinal adverse events.

BACKGROUND: Treatment with ipilimumab, a fully human anti-CTLA-4 antibody approved for the treatment of advanced melanoma, is associated with some immune-related adverse events (irAEs) such as colitis (gastrointestinal irAE, or GI irAE) and skin rash, which are managed by treatment guidelines. Nevertheless, predictive biomarkers that can help identify patients more likely to develop these irAEs could enhance the management of these toxicities. METHODS: To identify candidate predictive biomarkers associated with GI irAEs, gene expression profiling was performed on whole blood samples from 162 advanced melanoma patients at baseline, 3 and 11 weeks after the start of ipilimumab treatment in two phase II clinical trials (CA184004 and CA184007). Overall, 49 patients developed Grade 2 or higher (grade 2+) GI irAEs during the course of treatment. A repeated measures analysis of variance (ANOVA) was used to evaluate the differences in mean expression levels between the GI irAE and No-GI irAE groups of patients at the three time points. RESULTS: In baseline samples, 27 probe sets showed differential mean expression (≥ 1.5 fold, P ≤ 0.05) between the GI irAE and No-GI irAE groups. Most of these probe sets belonged to three functional categories: immune system, cell cycle, and intracellular trafficking. Changes in gene expression over time were also characterized. In the GI irAE group, 58 and 247 probe sets had a ≥ 1.5 fold change in expression from baseline to 3 and 11 weeks after first ipilimumab dose, respectively. In particular, on-treatment expression increases of CD177 and CEACAM1, two neutrophil-activation markers, were closely associated with GI irAEs, suggesting a possible role of neutrophils in ipilimumab-associated GI irAEs. In addition, the expression of several immunoglobulin genes increased over time, with greater increases in patients with grade 2+ GI irAEs. CONCLUSIONS: Gene expression profiling of peripheral blood, sampled before or early in the course of treatment with ipilimumab, resulted in the identification of a set of potential biomarkers that were associated with occurrence of GI irAEs. However, because of the low sensitivity of these biomarkers, they cannot be used alone to predict which patients will develop GI irAEs. Further investigation of these biomarkers in a larger patient cohort is warranted.

An immune-active tumor microenvironment favors clinical response to ipilimumab.

PURPOSE: Ipilimumab, a fully human monoclonal antibody specific to CTLA-4, has been shown to improve overall survival in metastatic melanoma patients. As a consequence of CTLA-4 blockade, ipilimumab treatment is associated with proliferation and activation of peripheral T cells. To better understand various tumor-associated components that may influence the clinical outcome of ipilimumab treatment, gene expression profiles of tumors from patients treated with ipilimumab were characterized. EXPERIMENTAL DESIGN: Gene expression profiling was performed on tumor biopsies collected from 45 melanoma patients before and 3 weeks after the start of treatment in a phase II clinical trial. RESULTS: Analysis of pre-treatment tumors indicated that patients with high baseline expression levels of immune-related genes were more likely to respond favorably to ipilimumab. Furthermore, ipilimumab appeared to induce two major changes in tumors from patients who exhibited clinical activity: genes involved in immune response showed increased expression, whereas expression of genes for melanoma-specific antigens and genes involved in cell proliferation decreased. These changes were associated with the total lymphocyte infiltrate in tumors, and there was a suggestion of association with prolonged overall survival in these patients. Many IFN-γ-inducible genes and Th1-associated markers showed increased expression after ipilimumab treatment, suggesting an accumulation of this particular type of T cell at the tumor sites, which might play an important role in mediating the antitumor activity of ipilimumab. CONCLUSIONS: These results support the proposed mechanism of action of ipilimumab, suggesting that cell-mediated immune responses play an important role in the antitumor activity of ipilimumab.

FDR-FET: an optimizing gene set enrichment analysis method.

Gene set enrichment analysis for analyzing large profiling and screening experiments can reveal unifying biological schemes based on previously accumulated knowledge represented as "gene sets". Most of the existing implementations use a fixed fold-change or P value cutoff to generate regulated gene lists. However, the threshold selection in most cases is arbitrary, and has a significant effect on the test outcome and interpretation of the experiment. We developed a new gene set enrichment analysis method, ie, FDR-FET, which dynamically optimizes the threshold choice and improves the sensitivity and selectivity of gene set enrichment analysis. The procedure translates experimental results into a series of regulated gene lists at multiple false discovery rate (FDR) cutoffs, and computes the P value of the overrepresentation of a gene set using a Fisher's exact test (FET) in each of these gene lists. The lowest P value is retained to represent the significance of the gene set. We also implemented improved methods to define a more relevant global reference set for the FET. We demonstrate the validity of the method using a published microarray study of three protease inhibitors of the human immunodeficiency virus and compare the results with those from other popular gene set enrichment analysis algorithms. Our results show that combining FDR with multiple cutoffs allows us to control the error while retaining genes that increase information content. We conclude that FDR-FET can selectively identify significant affected biological processes. Our method can be used for any user-generated gene list in the area of transcriptome, proteome, and other biological and scientific applications.

SDRS--an algorithm for analyzing large-scale dose-response data.

SUMMARY: Dose-response information is critical to understanding drug effects, yet analytical methods for dose-response assays cannot cope with the dimensionality of large-scale screening data such as the microarray profiling data. To overcome this limitation, we developed and implemented the Sigmoidal Dose Response Search (SDRS) algorithm, a grid search-based method designed to handle large-scale dose-response data. This method not only calculates the pharmacological parameters for every assay, but also provides built-in statistic that enables downstream systematic analyses, such as characterizing dose response at the transcriptome level. AVAILABILITY: Bio::SDRS is freely available from CPAN (www.cpan.org). CONTACTS: ruiruji@gmail.com; bruc@acm.org SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

Transcriptional profiling of the dose response: a more powerful approach for characterizing drug activities.

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data.

Complement activation in the peripheral nervous system following the spinal nerve ligation model of neuropathic pain.

Neuroinflammatory and neuroimmune mechanisms, as exemplified by infiltrating immune cells and activation of resident endothelial/glial cells, respectively, are known to be involved in the establishment and maintenance of chronic pain. An immune system pathway that may be involved in the activation of both immune and glial cells is complement. The complement pathway is made up of a large number of distinct plasma proteins which react with one another to opsonize pathogens and induce a series of inflammatory responses to help fight infection. Cleaved products and complexes produced by complement activation are responsible for a range of effects including mediation of immune infiltration, activation of phagocytes, opsonization/lysis of pathogens and injured cells, and production of vasoactive amines such as histamine and serotonin. Gene-expression microarray-analysis performed on the rat spinal nerve ligation (SNL) model of neuropathic pain revealed that multiple complement components including the C1 inhibitor, C1q alpha, beta, and gamma, C1r, C1s, C2, C3, C4, C7, and factors B, D, H, and P, were up-regulated while DAF was down-regulated. Regulation of C3 and DAF was confirmed by real-time RT-PCR and in situ hybridization. To test the hypothesis that complement plays a role in neuropathic pain, SNL rats were treated with cobra venom factor (CVF) to deplete plasma of complement component C3. Pain behavior was significantly attenuated in SNL rats treated with CVF as was complement activity at the ipsilateral dorsal root ganglia. Our results suggest the complement pathway might be a novel target for the development of neuropathic pain therapeutics.

Construction of a reference gene association network from multiple profiling data: application to data analysis.

MOTIVATION: Gene expression profiling is an important tool for gaining insight into biology. Novel strategies are required to analyze the growing archives of microarray data and extract useful information from them. One area of interest is in the construction of gene association networks from collections of profiling data. Various approaches have been proposed to construct gene networks using profiling data, and these networks have been used in functional inference as well as in data visualization. Here, we investigated a non-parametric approach to translate profiling data into a gene network. We explored the characteristics and utility of the resulting network and investigated the use of network information in analysis of variance models and hypothesis testing. RESULTS: Our work is composed of two parts: gene network construction and partitioning and hypothesis testing using sub-networks as groups. In the first part, multiple independently collected microarray datasets from the Gene Expression Omnibus data repository were analyzed to identify probe pairs that are positively co-regulated across the samples. A co-expression network was constructed based on a reciprocal ranking criteria and a false discovery rate analysis. We named this network Reference Gene Association (RGA) network. Then, the network was partitioned into densely connected sub-networks of probes using a multilevel graph partitioning algorithm. In the second part, we proposed a new, MANOVA-based approach that can take individual probe expression values as input and perform hypothesis testing at the sub-network level. We applied this MANOVA methodology to two published studies and our analysis indicated that the methodology is both effective and sensitive for identifying transcriptional sub-networks or pathways that are perturbed across treatments.