Genetics of CD33 in Alzheimer's disease and acute myeloid leukemia.

The CD33 single-nucleotide polymorphism (SNP) rs3865444 has been associated with the risk of Alzheimer's disease (AD). Rs3865444 is in linkage disequilibrium with rs12459419 which has been associated with efficacy of an acute myeloid leukemia (AML) chemotherapeutic agent based on a CD33 antibody. We seek to evaluate the extent to which CD33 genetics in AD and AML can inform one another and advance human disease therapy. We have previously shown that these SNPs are associated with skipping of CD33 exon 2 in brain mRNA. Here, we report that these CD33 SNPs are associated with exon 2 skipping in leukocytes from AML patients and with a novel CD33 splice variant that retains CD33 intron 1. Each copy of the minor rs12459419T allele decreases prototypic full-length CD33 expression by ∼ 25% and decreases the AD odds ratio by ∼ 0.10. These results suggest that CD33 antagonists may be useful in reducing AD risk. CD33 inhibitors may include humanized CD33 antibodies such as lintuzumab which was safe but ineffective in AML clinical trials. Here, we report that lintuzumab downregulates cell-surface CD33 by 80% in phorbol-ester differentiated U937 cells, at concentrations as low as 10 ng/ml. Overall, we propose a model wherein a modest effect on RNA splicing is sufficient to mediate the CD33 association with AD risk and suggest the potential for an anti-CD33 antibody as an AD-relevant pharmacologic agent.

Engineering potent chimeric antigen receptor T cells by programming signaling during T-cell activation.

Programming cell signaling during T-cell activation represents a simple strategy for improving the potency of therapeutic T-cell products. Stim-R technology (Lyell Immunopharma) is a customizable, degradable synthetic cell biomimetic that emulates physiologic, cell-like presentation of signal molecules to control T-cell activation. A breadth of Stim-R formulations with different anti-CD3/anti-CD28 (αCD3/αCD28) antibody densities and stoichiometries were screened for their effects on multiple metrics of T-cell function. We identified an optimized formulation that produced receptor tyrosine kinase-like orphan receptor 1 (ROR1)-targeted chimeric antigen receptor (CAR) T cells with enhanced persistence and polyfunctionality in vitro, as assessed in repeat-stimulation assays, compared with a benchmark product generated using a conventional T-cell-activating reagent. In transcriptomic analyses, CAR T cells activated with Stim-R technology showed downregulation of exhaustion-associated gene sets and retained a unique subset of stem-like cells with effector-associated gene signatures following repeated exposure to tumor cells. Compared with the benchmark product, CAR T cells activated using the optimized Stim-R technology formulation exhibited higher peak expansion, prolonged persistence, and improved tumor control in a solid tumor xenograft model. Enhancing T-cell products with Stim-R technology during T-cell activation may help improve therapeutic efficacy against solid tumors.

Aging as accelerated accumulation of somatic variants: whole-genome sequencing of centenarian and middle-aged monozygotic twin pairs.

It has been postulated that aging is the consequence of an accelerated accumulation of somatic DNA mutations and that subsequent errors in the primary structure of proteins ultimately reach levels sufficient to affect organismal functions. The technical limitations of detecting somatic changes and the lack of insight about the minimum level of erroneous proteins to cause an error catastrophe hampered any firm conclusions on these theories. In this study, we sequenced the whole genome of DNA in whole blood of two pairs of monozygotic (MZ) twins, 40 and 100 years old, by two independent next-generation sequencing (NGS) platforms (Illumina and Complete Genomics). Potentially discordant single-base substitutions supported by both platforms were validated extensively by Sanger, Roche 454, and Ion Torrent sequencing. We demonstrate that the genomes of the two twin pairs are germ-line identical between co-twins, and that the genomes of the 100-year-old MZ twins are discerned by eight confirmed somatic single-base substitutions, five of which are within introns. Putative somatic variation between the 40-year-old twins was not confirmed in the validation phase. We conclude from this systematic effort that by using two independent NGS platforms, somatic single nucleotide substitutions can be detected, and that a century of life did not result in a large number of detectable somatic mutations in blood. The low number of somatic variants observed by using two NGS platforms might provide a framework for detecting disease-related somatic variants in phenotypically discordant MZ twins.

Intra-Tumoral Activation of Endosomal TLR Pathways Reveals a Distinct Role for TLR3 Agonist Dependent Type-1 Interferons in Shaping the Tumor Immune Microenvironment.

Toll-like receptor (TLR) agonists have received considerable attention as therapeutic targets for cancer immunotherapy owing to their ability to convert immunosuppressive tumor microenvironments towards a more T-cell inflamed phenotype. However, TLRs differ in their cell expression profiles and intracellular signaling pathways, raising the possibility that distinct TLRs differentially influence the tumor immune microenvironment. Using single-cell RNA-sequencing, we address this by comparing the tumor immune composition of B16F10 melanoma following treatment with agonists of TLR3, TLR7, and TLR9. Marked differences are observed between treatments, including decreased tumor-associated macrophages upon TLR7 agonist treatment. A biased type-1 interferon signature is elicited upon TLR3 agonist treatment as opposed to a type-2 interferon signature with TLR9 agonists. TLR3 stimulation was associated with increased macrophage antigen presentation gene expression and decreased expression of PD-L1 and the inhibitory receptors Pirb and Pilra on infiltrating monocytes. Furthermore, in contrast to TLR7 and TLR9 agonists, TLR3 stimulation ablated FoxP3 positive CD4 T cells and elicited a distinct CD8 T cell activation phenotype highlighting the potential for distinct synergies between TLR agonists and combination therapy agents.

Combinatorial immunotherapy induces tumor-infiltrating CD8+ T cells with distinct functional, migratory, and stem-like properties.

BACKGROUND: Programmed death (ligand) 1 (PD-(L)1) blockade and OX40/4-1BB costimulation have been separately evaluated in the clinic to elicit potent antitumor T cell responses. The precise mechanisms underlying single agent activity are incompletely understood. It also remains unclear if combining individual therapies leads to synergism, elicits novel immune mechanisms, or invokes additive effects. METHODS: We performed high-dimensional flow cytometry and single-cell RNA sequencing-based immunoprofiling of murine tumor-infiltrating lymphocytes (TILs) isolated from hosts bearing B16 or MC38 syngeneic tumors. This baseline infiltrate was compared to TILs after treatment with either anti-PD-(L)1, anti-OX40, or anti-4-1BB as single agents or as double and triple combinatorial therapies. Fingolimod treatment and CXCR3 blockade were used to evaluate the contribution of intratumoral versus peripheral CD8+ T cells to therapeutic efficacy. RESULTS: We identified CD8+ T cell subtypes with distinct functional and migratory signatures highly predictive of tumor rejection upon treatment with single agent versus combination therapies. Rather than reinvigorating terminally exhausted CD8+ T cells, OX40/4-1BB agonism expanded a stem-like PD-1loKLRG-1+Ki-67+CD8+ T cell subpopulation, which PD-(L)1 blockade alone did not. However, PD-(L)1 blockade synergized with OX40/4-1BB costimulation by dramatically enhancing stem-like TIL presence via a CXCR3-dependent mechanism. CONCLUSIONS: Our findings provide new mechanistic insights into the interplay between components of combinatorial immunotherapy, where agonism of select costimulatory pathways seeds a pool of stem-like CD8+ T cells more responsive to immune checkpoint blockade (ICB).

Baseline Frequency of Inflammatory Cxcl9-Expressing Tumor-Associated Macrophages Predicts Response to Avelumab Treatment.

The tumor microenvironment is rich with immune-suppressive macrophages that are associated with cancer progression and resistance to immune checkpoint therapy. Using pre-treatment tumor biopsies complemented with single-cell RNA sequencing (RNA-seq), we characterize intratumoral immune heterogeneity to unveil potential mechanisms of resistance to avelumab (anti-PD-L1). We identify a proinflammatory F480+MHCII+Ly6Clo macrophage population that is associated with response rather than resistance to avelumab. These macrophages are the primary source of the interferon-inducible chemokine Cxcl9, which facilitates the recruitment of protective Cxcr3+ T cells. Consequently, the efficacy of avelumab in mouse tumor models is dependent on Cxcr3 and Cxcl9, and baseline levels of Cxcl9 in patients treated with avelumab are associated with clinical response and overall survival. These data suggest that, within the broadly immune-suppressive macrophage compartment, a pro-inflammatory population exists that promotes responsiveness to PD-L1 blockade.

Peripheral immune-based biomarkers in cancer immunotherapy: can we realize their predictive potential?

The immunologic landscape of the host and tumor play key roles in determining how patients will benefit from immunotherapy, and a better understanding of these factors could help inform how well a tumor responds to treatment. Recent advances in immunotherapy and in our understanding of the immune system have revolutionized the treatment landscape for many advanced cancers. Notably, the use of immune checkpoint inhibitors has demonstrated durable responses in various malignancies. However, the response to such treatments is variable and currently unpredictable, the availability of predictive biomarkers is limited, and a substantial proportion of patients do not respond to immune checkpoint therapy. Identification and investigation of potential biomarkers that may predict sensitivity to immunotherapy is an area of active research. It is envisaged that a deeper understanding of immunity will aid in harnessing the full potential of immunotherapy, and allow appropriate patients to receive the most appropriate treatments. In addition to the identification of new biomarkers, the platforms and assays required to accurately and reproducibly measure biomarkers play a key role in ensuring consistency of measurement both within and between patients. In this review we discuss the current knowledge in the area of peripheral immune-based biomarkers, drawing information from the results of recent clinical studies of a number of different immunotherapy modalities in the treatment of cancer, including checkpoint inhibitors, bispecific antibodies, chimeric antigen receptor T cells, and anti-cancer vaccines. We also discuss the various technologies and approaches used in detecting and measuring circulatory biomarkers and the ongoing need for harmonization.

Immunomodulatory activity of humanized anti-IL-7R monoclonal antibody RN168 in subjects with type 1 diabetes.

BACKGROUND: The cytokine IL-7 is critical for T cell development and function. We performed a Phase Ib study in patients with type 1 diabetes (T1D) to evaluate how blockade of IL-7 would affect immune cells and relevant clinical responses. METHODS: Thirty-seven subjects with T1D received s.c. RN168, a monoclonal antibody that blocks the IL -7 receptor α (IL7Rα) in a dose-escalating study. RESULTS: Between 90% and 100% IL-7R occupancy and near-complete inhibition of pSTAT5 was observed at doses of RN168 1 mg/kg every other week (Q2wk) and greater. There was a significant decline in CD4+ and CD8+ effector and central memory T cells and CD4+ naive cells, but there were fewer effects on CD8+ naive T cells. The ratios of Tregs to CD4+ or CD8+ effector and central memory T cells versus baseline were increased. RNA sequencing analysis showed downmodulation of genes associated with activation, survival, and differentiation of T cells. Expression of the antiapoptotic protein Bcl-2 was reduced. The majority of treatment-emergent adverse events (TEAEs) were mild and not treatment related. Four subjects became anti-EBV IgG+ after RN168, and 2 had symptoms of active infection. The immunologic response to tetanus toxoid was preserved at doses of 1 and 3 mg/kg Q2wk but reduced at higher doses. CONCLUSIONS: This trial shows that, at dosages of 1-3 mg/kg, RN168 selectively inhibits the survival and activity of memory T cells while preserving naive T cells and Tregs. These immunologic effects may serve to eliminate pathologic T cells in autoimmune diseases. TRIAL REGISTRATION: NCT02038764. FUNDING: Pfizer Inc.

Productive common light chain libraries yield diverse panels of high affinity bispecific antibodies.

The commercial success of bispecific antibodies generally has been hindered by the complexities associated with generating appropriate molecules for both research scale and large scale manufacturing purposes. Bispecific IgG (BsIgG) based on two antibodies that use an identical common light chain can be combined with a minimal set of Fc mutations to drive heavy chain heterodimerization in order to address these challenges. However, the facile generation of common light chain antibodies with properties similar to traditional monoclonal antibodies has not been demonstrated and they have only been used sparingly. Here, we describe the design of a synthetic human antibody library based on common light chains to generate antibodies with biochemical and biophysical properties that are indistinguishable to traditional therapeutic monoclonal antibodies. We used this library to generate diverse panels of well-behaved, high affinity antibodies toward a variety of epitopes across multiple antigens, including mouse 4-1BB, a therapeutically important T cell costimulatory receptor. Over 200 BsIgG toward 4-1BB were generated using an automated purification method we developed that enables milligram-scale production of BsIgG. This approach allowed us to identify antibodies with a wide range of agonistic activity that are being used to further investigate the therapeutic potential of antibodies targeting one or more epitopes of 4-1BB.

A complete algorithm to resolve ambiguity for intersubunit NOE assignment in structure determination of symmetric homo-oligomers.

Assignment of nuclear Overhauser effect (NOE) data is a key bottleneck in structure determination by NMR. NOE assignment resolves the ambiguity as to which pair of protons generated the observed NOE peaks, and thus should be restrained in structure determination. In the case of intersubunit NOEs in symmetric homo-oligomers, the ambiguity includes both the identities of the protons within a subunit, and the identities of the subunits to which they belong. This paper develops an algorithm for simultaneous intersubunit NOE assignment and C(n) symmetric homo-oligomeric structure determinations, given the subunit structure. By using a configuration space framework, our algorithm guarantees completeness, in that it identifies structures representing, to within a user-defined similarity level, every structure consistent with the available data (ambiguous or not). However, while our approach is complete in considering all conformations and assignments, it avoids explicit enumeration of the exponential number of combinations of possible assignments. Our algorithm can draw two types of conclusions not possible under previous methods: (1) that different assignments for an NOE would lead to different structural classes, or (2) that it is not necessary to uniquely assign an NOE, since it would have little impact on structural precision. We demonstrate on two test proteins that our method reduces the average number of possible assignments per NOE by a factor of 2.6 for MinE and 4.2 for CCMP. It results in high structural precision, reducing the average variance in atomic positions by factors of 1.5 and 3.6, respectively.

PFAAT version 2.0: a tool for editing, annotating, and analyzing multiple sequence alignments.

BACKGROUND: By virtue of their shared ancestry, homologous sequences are similar in their structure and function. Consequently, multiple sequence alignments are routinely used to identify trends that relate to function. This type of analysis is particularly productive when it is combined with structural and phylogenetic analysis. RESULTS: Here we describe the release of PFAAT version 2.0, a tool for editing, analyzing, and annotating multiple sequence alignments. Support for multiple annotations is a key component of this release as it provides a framework for most of the new functionalities. The sequence annotations are accessible from the alignment and tree, where they are typically used to label sequences or hyperlink them to related databases. Sequence annotations can be created manually or extracted automatically from UniProt entries. Once a multiple sequence alignment is populated with sequence annotations, sequences can be easily selected and sorted through a sophisticated search dialog. The selected sequences can be further analyzed using statistical methods that explicitly model relationships between the sequence annotations and residue properties. Residue annotations are accessible from the alignment viewer and are typically used to designate binding sites or properties for a particular residue. Residue annotations are also searchable, and allow one to quickly select alignment columns for further sequence analysis, e.g. computing percent identities. Other features include: novel algorithms to compute sequence conservation, mapping conservation scores to a 3D structure in Jmol, displaying secondary structure elements, and sorting sequences by residue composition. CONCLUSION: PFAAT provides a framework whereby end-users can specify knowledge for a protein family in the form of annotation. The annotations can be combined with sophisticated analysis to test hypothesis that relate to sequence, structure and function.

Structure determination of symmetric homo-oligomers by a complete search of symmetry configuration space, using NMR restraints and van der Waals packing.

Structural studies of symmetric homo-oligomers provide mechanistic insights into their roles in essential biological processes, including cell signaling and cellular regulation. This paper presents a novel algorithm for homo-oligomeric structure determination, given the subunit structure, that is both complete, in that it evaluates all possible conformations, and data-driven, in that it evaluates conformations separately for consistency with experimental data and for quality of packing. Completeness ensures that the algorithm does not miss the native conformation, and being data-driven enables it to assess the structural precision possible from data alone. Our algorithm performs a branch-and-bound search in the symmetry configuration space, the space of symmetry axis parameters (positions and orientations) defining all possible C(n) homo-oligomeric complexes for a given subunit structure. It eliminates those symmetry axes inconsistent with intersubunit nuclear Overhauser effect (NOE) distance restraints and then identifies conformations representing any consistent, well-packed structure to within a user-defined similarity level. For the human phospholamban pentamer in dodecylphosphocholine micelles, using the structure of one subunit determined from a subset of the experimental NMR data, our algorithm identifies a diverse set of complex structures consistent with the nine intersubunit NOE restraints. The distribution of determined structures provides an objective characterization of structural uncertainty: backbone RMSD to the previously determined structure ranges from 1.07 to 8.85 A, and variance in backbone atomic coordinates is an average of 12.32 A(2). Incorporating vdW packing reduces structural diversity to a maximum backbone RMSD of 6.24 A and an average backbone variance of 6.80 A(2). By comparing data consistency and packing quality under different assumptions of oligomeric number, our algorithm identifies the pentamer as the most likely oligomeric state of phospholamban, demonstrating that it is possible to determine the oligomeric number directly from NMR data. Additional tests on a number of homo-oligomers, from dimer to heptamer, similarly demonstrate the power of our method to provide unbiased determination and evaluation of homo-oligomeric complex structures.

ICOS Promotes the Function of CD4+ Effector T Cells during Anti-OX40-Mediated Tumor Rejection.

ICOS is a T-cell coregulatory receptor that provides a costimulatory signal to T cells during antigen-mediated activation. Antitumor immunity can be improved by ICOS-targeting therapies, but their mechanism of action remains unclear. Here, we define the role of ICOS signaling in antitumor immunity using a blocking, nondepleting antibody against ICOS ligand (ICOS-L). ICOS signaling provided critical support for the effector function of CD4(+) Foxp3(-) T cells during anti-OX40-driven tumor immune responses. By itself, ICOS-L blockade reduced accumulation of intratumoral T regulatory cells (Treg), but it was insufficient to substantially inhibit tumor growth. Furthermore, it did not impede antitumor responses mediated by anti-4-1BB-driven CD8(+) T cells. We found that anti-OX40 efficacy, which is based on Treg depletion and to a large degree on CD4(+) effector T cell (Teff) responses, was impaired with ICOS-L blockade. In contrast, the provision of additional ICOS signaling through direct ICOS-L expression by tumor cells enhanced tumor rejection and survival when administered along with anti-OX40 therapy. Taken together, our results showed that ICOS signaling during antitumor responses acts on both Teff and Treg cells, which have opposing roles in promoting immune activation. Thus, effective therapies targeting the ICOS pathway should seek to promote ICOS signaling specifically in effector CD4(+) T cells by combining ICOS agonism and Treg depletion. Cancer Res; 76(13); 3684-9. ©2016 AACR.

Effect of genome and environment on metabolic and inflammatory profiles.

Twin and family studies have established the contribution of genetic factors to variation in metabolic, hematologic and immunological parameters. The majority of these studies analyzed single or combined traits into pre-defined syndromes. In the present study, we explore an alternative multivariate approach in which a broad range of metabolic, hematologic, and immunological traits are analyzed simultaneously to determine the resemblance of monozygotic (MZ) twin pairs, twin-spouse pairs and unrelated, non-cohabiting individuals. A total of 517 participants from the Netherlands Twin Register, including 210 MZ twin pairs and 64 twin-spouse pairs, took part in the study. Data were collected on body composition, blood pressure, heart rate, and multiple biomarkers assessed in fasting blood samples, including lipid levels, glucose, insulin, liver enzymes, hematological measurements and cytokine levels. For all 51 measured traits, pair-wise Pearson correlations, correcting for family relatedness, were calculated across all the individuals in the cohort. Hierarchical clustering techniques were applied to group the measured traits into sub-clusters based on similarity. Sub-clusters were observed among metabolic traits and among inflammatory markers. We defined a phenotypic profile as the collection of all the traits measured for a given individual. Average within-pair similarity of phenotypic profiles was determined for the groups of MZ twin pairs, spouse pairs and pairs of unrelated individuals. The average similarity across the full phenotypic profile was higher for MZ twin pairs than for spouse pairs, and lowest for pairs of unrelated individuals. Cohabiting MZ twins were more similar in their phenotypic profile compared to MZ twins who no longer lived together. The correspondence in the phenotypic profile is therefore determined to a large degree by familial, mostly genetic, factors, while household factors contribute to a lesser degree to profile similarity.

NMR structural inference of symmetric homo-oligomers.

Symmetric homo-oligomers represent a majority of proteins, and determining their structures helps elucidate important biological processes, including ion transport, signal transduction, and transcriptional regulation. In order to account for the noise and sparsity in the distance restraints used in Nuclear Magnetic Resonance (NMR) structure determination of cyclic (C(n)) symmetric homo-oligomers, and the resulting uncertainty in the determined structures, we develop a Bayesian structural inference approach. In contrast to traditional NMR structure determination methods, which identify a small set of low-energy conformations, the inferential approach characterizes the entire posterior distribution of conformations. Unfortunately, traditional stochastic techniques for inference may under-sample the rugged landscape of the posterior, missing important contributions from high-quality individual conformations and not accounting for the possible aggregate effects on inferred quantities from numerous unsampled conformations. However, by exploiting the geometry of symmetric homo-oligomers, we develop an algorithm that provides provable guarantees for the posterior distribution and the inferred mean atomic coordinates. Using experimental restraints for three proteins, we demonstrate that our approach is able to objectively characterize the structural diversity supported by the data. By simulating spurious and missing restraints, we further demonstrate that our approach is robust, degrading smoothly with noise and sparsity.

Predicting kinase selectivity profiles using Free-Wilson QSAR analysis.

Kinases are involved in a variety of diseases such as cancer, diabetes, and arthritis. In recent years, many kinase small molecule inhibitors have been developed as potential disease treatments. Despite the recent advances, selectivity remains one of the most challenging aspects in kinase inhibitor design. To interrogate kinase selectivity, a panel of 45 kinase assays has been developed in-house at Pfizer. Here we present an application of in silico quantitative structure activity relationship (QSAR) models to extract rules from this experimental screening data and make reliable selectivity profile predictions for all compounds enumerated from virtual libraries. We also propose the construction of R-group selectivity profiles by deriving their activity contribution against each kinase using QSAR models. Such selectivity profiles can be used to provide better understanding of subtle structure selectivity relationships during kinase inhibitor design.