The intrinsic substrate specificity of the human tyrosine kinome.

Phosphorylation of proteins on tyrosine (Tyr) residues evolved in metazoan organisms as a mechanism of coordinating tissue growth1. Multicellular eukaryotes typically have more than 50 distinct protein Tyr kinases that catalyse the phosphorylation of thousands of Tyr residues throughout the proteome1-3. How a given Tyr kinase can phosphorylate a specific subset of proteins at unique Tyr sites is only partially understood4-7. Here we used combinatorial peptide arrays to profile the substrate sequence specificity of all human Tyr kinases. Globally, the Tyr kinases demonstrate considerable diversity in optimal patterns of residues surrounding the site of phosphorylation, revealing the functional organization of the human Tyr kinome by substrate motif preference. Using this information, Tyr kinases that are most compatible with phosphorylating any Tyr site can be identified. Analysis of mass spectrometry phosphoproteomic datasets using this compendium of kinase specificities accurately identifies specific Tyr kinases that are dysregulated in cells after stimulation with growth factors, treatment with anti-cancer drugs or expression of oncogenic variants. Furthermore, the topology of known Tyr signalling networks naturally emerged from a comparison of the sequence specificities of the Tyr kinases and the SH2 phosphotyrosine (pTyr)-binding domains. Finally we show that the intrinsic substrate specificity of Tyr kinases has remained fundamentally unchanged from worms to humans, suggesting that the fidelity between Tyr kinases and their protein substrate sequences has been maintained across hundreds of millions of years of evolution.

An atlas of substrate specificities for the human serine/threonine kinome.

Protein phosphorylation is one of the most widespread post-translational modifications in biology1,2. With advances in mass-spectrometry-based phosphoproteomics, 90,000 sites of serine and threonine phosphorylation have so far been identified, and several thousand have been associated with human diseases and biological processes3,4. For the vast majority of phosphorylation events, it is not yet known which of the more than 300 protein serine/threonine (Ser/Thr) kinases encoded in the human genome are responsible3. Here we used synthetic peptide libraries to profile the substrate sequence specificity of 303 Ser/Thr kinases, comprising more than 84% of those predicted to be active in humans. Viewed in its entirety, the substrate specificity of the kinome was substantially more diverse than expected and was driven extensively by negative selectivity. We used our kinome-wide dataset to computationally annotate and identify the kinases capable of phosphorylating every reported phosphorylation site in the human Ser/Thr phosphoproteome. For the small minority of phosphosites for which the putative protein kinases involved have been previously reported, our predictions were in excellent agreement. When this approach was applied to examine the signalling response of tissues and cell lines to hormones, growth factors, targeted inhibitors and environmental or genetic perturbations, it revealed unexpected insights into pathway complexity and compensation. Overall, these studies reveal the intrinsic substrate specificity of the human Ser/Thr kinome, illuminate cellular signalling responses and provide a resource to link phosphorylation events to biological pathways.

Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles.

Individual variation in germline and expressed B-cell immunoglobulin (Ig) repertoires has been associated with aging, disease susceptibility, and differential response to infection and vaccination. Repertoire properties can now be studied at large-scale through next-generation sequencing of rearranged Ig genes. Accurate analysis of these repertoire-sequencing (Rep-Seq) data requires identifying the germline variable (V), diversity (D), and joining (J) gene segments used by each Ig sequence. Current V(D)J assignment methods work by aligning sequences to a database of known germline V(D)J segment alleles. However, existing databases are likely to be incomplete and novel polymorphisms are hard to differentiate from the frequent occurrence of somatic hypermutations in Ig sequences. Here we develop a Tool for Ig Genotype Elucidation via Rep-Seq (TIgGER). TIgGER analyzes mutation patterns in Rep-Seq data to identify novel V segment alleles, and also constructs a personalized germline database containing the specific set of alleles carried by a subject. This information is then used to improve the initial V segment assignments from existing tools, like IMGT/HighV-QUEST. The application of TIgGER to Rep-Seq data from seven subjects identified 11 novel V segment alleles, including at least one in every subject examined. These novel alleles constituted 13% of the total number of unique alleles in these subjects, and impacted 3% of V(D)J segment assignments. These results reinforce the highly polymorphic nature of human Ig V genes, and suggest that many novel alleles remain to be discovered. The integration of TIgGER into Rep-Seq processing pipelines will increase the accuracy of V segment assignments, thus improving B-cell repertoire analyses.

Characterization of Diabetogenic CD8+ T Cells: IMMUNE THERAPY WITH METABOLIC BLOCKADE.

Type 1 diabetes mellitus is caused by the killing of insulin-producing ß cells by CD8+T cells. The disease progression, which is chronic, does not follow a course like responses to conventional antigens such as viruses, but accelerates as glucose tolerance deteriorates. To identify the unique features of the autoimmune effectors that may explain this behavior, we analyzed diabetogenic CD8+ T cells that recognize a peptide from the diabetes antigen IGRP (NRP-V7-reactive) in prediabetic NOD mice and compared them to others that shared their phenotype (CD44(+)CD62L(lo)PD-1(+)CXCR3(+)) but negative for diabetes antigen tetramers and to LCMV (lymphocytic choriomeningitis)-reactive CD8+ T cells. There was an increase in the frequency of the NRP-V7-reactive cells coinciding with the time of glucose intolerance. The T cells persisted in hyperglycemic NOD mice maintained with an insulin pellet despite destruction of ß cells. We compared gene expression in the three groups of cells compared with the other two subsets of cells, and the NRP-V7-reactive cells exhibited gene expression of memory precursor effector cells. They had reduced cellular proliferation and were less dependent on oxidative phosphorylation. When prediabetic NOD mice were treated with 2-deoxyglucose to block aerobic glycolysis, there was a reduction in the diabetes antigen versus other cells of similar phenotype and loss of lymphoid cells infiltrating the islets. In addition, treatment of NOD mice with 2-deoxyglucose resulted in improved ß cell granularity. These findings identify a link between metabolic disturbances and autoreactive T cells that promotes development of autoimmune diabetes.

High-resolution antibody dynamics of vaccine-induced immune responses.

The adaptive immune system confers protection by generating a diverse repertoire of antibody receptors that are rapidly expanded and contracted in response to specific targets. Next-generation DNA sequencing now provides the opportunity to survey this complex and vast repertoire. In the present work, we describe a set of tools for the analysis of antibody repertoires and their application to elucidating the dynamics of the response to viral vaccination in human volunteers. By analyzing data from 38 separate blood samples across 2 y, we found that the use of the germ-line library of V and J segments is conserved between individuals over time. Surprisingly, there appeared to be no correlation between the use level of a particular VJ combination and degree of expansion. We found the antibody RNA repertoire in each volunteer to be highly dynamic, with each individual displaying qualitatively different response dynamics. By using combinatorial phage display, we screened selected VH genes paired with their corresponding VL library for affinity against the vaccine antigens. Altogether, this work presents an additional set of tools for profiling the human antibody repertoire and demonstrates characterization of the fast repertoire dynamics through time in multiple individuals responding to an immune challenge.

VDJML: a file format with tools for capturing the results of inferring immune receptor rearrangements.

BACKGROUND: The genes that produce antibodies and the immune receptors expressed on lymphocytes are not germline encoded; rather, they are somatically generated in each developing lymphocyte by a process called V(D)J recombination, which assembles specific, independent gene segments into mature composite genes. The full set of composite genes in an individual at a single point in time is referred to as the immune repertoire. V(D)J recombination is the distinguishing feature of adaptive immunity and enables effective immune responses against an essentially infinite array of antigens. Characterization of immune repertoires is critical in both basic research and clinical contexts. Recent technological advances in repertoire profiling via high-throughput sequencing have resulted in an explosion of research activity in the field. This has been accompanied by a proliferation of software tools for analysis of repertoire sequencing data. Despite the widespread use of immune repertoire profiling and analysis software, there is currently no standardized format for output files from V(D)J analysis. Researchers utilize software such as IgBLAST and IMGT/High V-QUEST to perform V(D)J analysis and infer the structure of germline rearrangements. However, each of these software tools produces results in a different file format, and can annotate the same result using different labels. These differences make it challenging for users to perform additional downstream analyses. RESULTS: To help address this problem, we propose a standardized file format for representing V(D)J analysis results. The proposed format, VDJML, provides a common standardized format for different V(D)J analysis applications to facilitate downstream processing of the results in an application-agnostic manner. The VDJML file format specification is accompanied by a support library, written in C++ and Python, for reading and writing the VDJML file format. CONCLUSIONS: The VDJML suite will allow users to streamline their V(D)J analysis and facilitate the sharing of scientific knowledge within the community. The VDJML suite and documentation are available from https://vdjserver.org/vdjml/ . We welcome participation from the community in developing the file format standard, as well as code contributions.

Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data.

UNLABELLED: Advances in high-throughput sequencing technologies now allow for large-scale characterization of B cell immunoglobulin (Ig) repertoires. The high germline and somatic diversity of the Ig repertoire presents challenges for biologically meaningful analysis, which requires specialized computational methods. We have developed a suite of utilities, Change-O, which provides tools for advanced analyses of large-scale Ig repertoire sequencing data. Change-O includes tools for determining the complete set of Ig variable region gene segment alleles carried by an individual (including novel alleles), partitioning of Ig sequences into clonal populations, creating lineage trees, inferring somatic hypermutation targeting models, measuring repertoire diversity, quantifying selection pressure, and calculating sequence chemical properties. All Change-O tools utilize a common data format, which enables the seamless integration of multiple analyses into a single workflow. AVAILABILITY AND IMPLEMENTATION: Change-O is freely available for non-commercial use and may be downloaded from http://clip.med.yale.edu/changeo. CONTACT: steven.kleinstein@yale.edu.

Integrating B cell lineage information into statistical tests for detecting selection in Ig sequences.

Detecting selection in B cell Ig sequences is critical to understanding affinity maturation and can provide insights into Ag-driven selection in normal and pathologic immune responses. The most common sequence-based methods for detecting selection analyze the ratio of replacement and silent mutations using a binomial statistical analysis. However, these approaches have been criticized for low sensitivity. An alternative method is based on the analysis of lineage trees constructed from sets of clonally related Ig sequences. Several tree shape measures have been proposed as indicators of selection that can be statistically compared across cohorts. However, we show that tree shape analysis is confounded by underlying experimental factors that are difficult to control for in practice, including the sequencing depth and number of generations in each clone. Thus, although lineage tree shapes may reflect selection, their analysis alone is an unreliable measure of in vivo selection. To usefully capture the information provided by lineage trees, we propose a new method that applies the binomial statistical framework to mutations identified based on lineage tree structure. This hybrid method is able to detect selection with increased sensitivity in both simulated and experimental data sets. We anticipate that this approach will be especially useful in the analysis of large-scale Ig sequencing data sets generated by high-throughput sequencing technologies.

pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires.

UNLABELLED: Driven by dramatic technological improvements, large-scale characterization of lymphocyte receptor repertoires via high-throughput sequencing is now feasible. Although promising, the high germline and somatic diversity, especially of B-cell immunoglobulin repertoires, presents challenges for analysis requiring the development of specialized computational pipelines. We developed the REpertoire Sequencing TOolkit (pRESTO) for processing reads from high-throughput lymphocyte receptor studies. pRESTO processes raw sequences to produce error-corrected, sorted and annotated sequence sets, along with a wealth of metrics at each step. The toolkit supports multiplexed primer pools, single- or paired-end reads and emerging technologies that use single-molecule identifiers. pRESTO has been tested on data generated from Roche and Illumina platforms. It has a built-in capacity to parallelize the work between available processors and is able to efficiently process millions of sequences generated by typical high-throughput projects. AVAILABILITY AND IMPLEMENTATION: pRESTO is freely available for academic use. The software package and detailed tutorials may be downloaded from http://clip.med.yale.edu/presto.

Influence of seasonal exposure to grass pollen on local and peripheral blood IgE repertoires in patients with allergic rhinitis.

BACKGROUND: Previous studies of immunoglobulin gene sequences in patients with allergic diseases using low-throughput Sanger sequencing have limited the analytic depth for characterization of IgE repertoires. OBJECTIVES: We used a high-throughput, next-generation sequencing approach to characterize immunoglobulin heavy-chain gene (IGH) repertoires in patients with seasonal allergic rhinitis (AR) with the aim of better understanding the underlying disease mechanisms. METHODS: IGH sequences in matched peripheral blood and nasal biopsy specimens from nonallergic healthy control subjects (n = 3) and patients with grass pollen-related AR taken in season (n = 3) or out of season (n = 4) were amplified and pyrosequenced on the 454 GS FLX+ System. RESULTS: A total of 97,610 IGH (including 8,135 IgE) sequences were analyzed. Use of immunoglobulin heavy-chain variable region gene families 1 (IGHV1) and 5 (IGHV5) was higher in IgE clonotypic repertoires compared with other antibody classes independent of atopic status. IgE repertoires measured inside the grass pollen season were more diverse and more mutated (particularly in the biopsy specimens) and had more evidence of antigen-driven selection compared with those taken outside of the pollen season or from healthy control subjects. Clonal relatedness was observed for IgE between the blood and nasal biopsy specimens. Furthermore in patients with AR, but not healthy control subjects, we found clonal relatedness between IgE and IgG classes. CONCLUSION: This is the first report that exploits next-generation sequencing to determine local and peripheral blood IGH repertoires in patients with respiratory allergic disease. We demonstrate that natural pollen exposure was associated with changes in IgE repertoires that were suggestive of ongoing germinal center reactions. Furthermore, these changes were more often apparent in nasal biopsy specimens compared with peripheral blood and in patients with AR compared with healthy control subjects.

Quantifying selection in high-throughput Immunoglobulin sequencing data sets.

High-throughput immunoglobulin sequencing promises new insights into the somatic hypermutation and antigen-driven selection processes that underlie B-cell affinity maturation and adaptive immunity. The ability to estimate positive and negative selection from these sequence data has broad applications not only for understanding the immune response to pathogens, but is also critical to determining the role of somatic hypermutation in autoimmunity and B-cell cancers. Here, we develop a statistical framework for Bayesian estimation of Antigen-driven SELectIoN (BASELINe) based on the analysis of somatic mutation patterns. Our approach represents a fundamental advance over previous methods by shifting the problem from one of simply detecting selection to one of quantifying selection. Along with providing a more intuitive means to assess and visualize selection, our approach allows, for the first time, comparative analysis between groups of sequences derived from different germline V(D)J segments. Application of this approach to next-generation sequencing data demonstrates different selection pressures for memory cells of different isotypes. This framework can easily be adapted to analyze other types of DNA mutation patterns resulting from a mutator that displays hot/cold-spots, substitution preference or other intrinsic biases.

Detecting selection in immunoglobulin sequences.

The ability to detect selection by analyzing mutation patterns in experimentally derived immunoglobulin (Ig) sequences is a critical part of many studies. Such techniques are useful not only for understanding the response to pathogens, but also to determine the role of antigen-driven selection in autoimmunity, B cell cancers and the diversification of pre-immune repertoires in certain species. Despite its importance, quantifying selection in experimentally derived sequences is fraught with difficulties. The necessary parameters for statistical tests (such as the expected frequency of replacement mutations in the absence of selection) are non-trivial to calculate, and results are not easily interpretable when analyzing more than a handful of sequences. We have developed a web server that implements our previously proposed Focused binomial test for detecting selection. Several features are integrated into the web site in order to facilitate analysis, including V(D)J germline segment identification with IMGT alignment, batch submission of sequences and integration of additional test statistics proposed by other groups. We also implement a Z-score-based statistic that increases the power of detecting selection while maintaining specificity, and further allows for the combined analysis of sequences from different germlines. The tool is freely available at http://clip.med.yale.edu/selection.

The Incidence and Severity of COVID-19 Infection Post Vaccination in Saudi Arabia.

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). Presently, there is ongoing continuous research for more therapeutic options with a wide variety of vaccine availability. However, many people have worried about the vaccine's side effects. Hence, the current study was conducted to determine the prevalence of vaccinated individuals, side effects, and the rate of infectivity post vaccination including the three doses of vaccinations. Methods A cross-sectional questionnaire-based survey was conducted using Google Forms (Google, Inc., Mountain View, CA). Five hundred forty-three individuals participated and reported their status of COVID-19 infection, vaccination, and side effects. All the participants from Saudi Arabia received all the vaccine shots including the booster dose. Results Most of the Saudi nationals were fully vaccinated, and most received Pfizer vaccines for their first and second shots. Pain at the injection site was reported as the most common adverse effect followed by fever, headache, fatigue, and joint pain. Conclusion From the findings, it is concluded that most of the population of Saudi Arabia was vaccinated effectively. Pain at the injection site is identified as the primary adverse effect of vaccination. Most of the population is vaccinated with the Pfizer vaccine. Long-term side effect monitoring is recommended with large population studies to confirm the status of vaccines and adverse effects.

Transcriptomic Maps of Colorectal Liver Metastasis: Machine Learning of Gene Activation Patterns and Epigenetic Trajectories in Support of Precision Medicine.

The molecular mechanisms of the liver metastasis of colorectal cancer (CRLM) remain poorly understood. Here, we applied machine learning and bioinformatics trajectory inference to analyze a gene expression dataset of CRLM. We studied the co-regulation patterns at the gene level, the potential paths of tumor development, their functional context, and their prognostic relevance. Our analysis confirmed the subtyping of five liver metastasis subtypes (LMS). We provide gene-marker signatures for each LMS, and a comprehensive functional characterization that considers both the hallmarks of cancer and the tumor microenvironment. The ordering of CRLMs along a pseudotime-tree revealed a continuous shift in expression programs, suggesting a developmental relationship between the subtypes. Notably, trajectory inference and personalized analysis discovered a range of epigenetic states that shape and guide metastasis progression. By constructing prognostic maps that divided the expression landscape into regions associated with favorable and unfavorable prognoses, we derived a prognostic expression score. This was associated with critical processes such as epithelial-mesenchymal transition, treatment resistance, and immune evasion. These factors were associated with responses to neoadjuvant treatment and the formation of an immuno-suppressive, mesenchymal state. Our machine learning-based molecular profiling provides an in-depth characterization of CRLM heterogeneity with possible implications for treatment and personalized diagnostics.

In Vivo Modeling of CLL Transformation to Richter Syndrome Reveals Convergent Evolutionary Paths and Therapeutic Vulnerabilities.

Transformation to aggressive disease histologies generates formidable clinical challenges across cancers, but biological insights remain few. We modeled the genetic heterogeneity of chronic lymphocytic leukemia (CLL) through multiplexed in vivo CRISPR-Cas9 B-cell editing of recurrent CLL loss-of-function drivers in mice and recapitulated the process of transformation from indolent CLL into large cell lymphoma [i.e., Richter syndrome (RS)]. Evolutionary trajectories of 64 mice carrying diverse combinatorial gene assortments revealed coselection of mutations in Trp53, Mga, and Chd2 and the dual impact of clonal Mga/Chd2 mutations on E2F/MYC and interferon signaling dysregulation. Comparative human and murine RS analyses demonstrated tonic PI3K signaling as a key feature of transformed disease, with constitutive activation of the AKT and S6 kinases, downmodulation of the PTEN phosphatase, and convergent activation of MYC/PI3K transcriptional programs underlying enhanced sensitivity to MYC/mTOR/PI3K inhibition. This robust experimental system presents a unique framework to study lymphoid biology and therapy. SIGNIFICANCE: Mouse models reflective of the genetic complexity and heterogeneity of human tumors remain few, including those able to recapitulate transformation to aggressive disease histologies. Herein, we model CLL transformation into RS through multiplexed in vivo gene editing, providing key insight into the pathophysiology and therapeutic vulnerabilities of transformed disease. This article is highlighted in the In This Issue feature, p. 101.

Cell subset prediction for blood genomic studies.

BACKGROUND: Genome-wide transcriptional profiling of patient blood samples offers a powerful tool to investigate underlying disease mechanisms and personalized treatment decisions. Most studies are based on analysis of total peripheral blood mononuclear cells (PBMCs), a mixed population. In this case, accuracy is inherently limited since cell subset-specific differential expression of gene signatures will be diluted by RNA from other cells. While using specific PBMC subsets for transcriptional profiling would improve our ability to extract knowledge from these data, it is rarely obvious which cell subset(s) will be the most informative. RESULTS: We have developed a computational method (Subset Prediction from Enrichment Correlation, SPEC) to predict the cellular source for a pre-defined list of genes (i.e. a gene signature) using only data from total PBMCs. SPEC does not rely on the occurrence of cell subset-specific genes in the signature, but rather takes advantage of correlations with subset-specific genes across a set of samples. Validation using multiple experimental datasets demonstrates that SPEC can accurately identify the source of a gene signature as myeloid or lymphoid, as well as differentiate between B cells, T cells, NK cells and monocytes. Using SPEC, we predict that myeloid cells are the source of the interferon-therapy response gene signature associated with HCV patients who are non-responsive to standard therapy. CONCLUSIONS: SPEC is a powerful technique for blood genomic studies. It can help identify specific cell subsets that are important for understanding disease and therapy response. SPEC is widely applicable since only gene expression profiles from total PBMCs are required, and thus it can easily be used to mine the massive amount of existing microarray or RNA-seq data.

Taking advantage: high-affinity B cells in the germinal center have lower death rates, but similar rates of division, compared to low-affinity cells.

B lymphocytes producing high-affinity Abs are critical for protection from extracellular pathogens, such as bacteria and parasites. The process by which high-affinity B cells are selected during the immune response has never been elucidated. Although it has been shown that high-affinity cells directly outcompete low-affinity cells in the germinal center (GC), whether there are also intrinsic differences between these cells has not been addressed. It could be that higher affinity cells proliferate more rapidly or are more likely to enter cell cycle, thereby outgrowing lower affinity cells. Alternatively, higher affinity cells could be relatively more resistant to cell death in the GC. By comparing high- and low-affinity B cells for the same Ag, we show here that low-affinity cells have an intrinsically higher death rate than do cells of higher affinity, even in the absence of competition. This suggests that selection in the GC reaction is due at least in part to the control of survival of higher affinity B cells and not by a proliferative advantage conferred upon these cells compared with lower affinity B cells. Control over survival rather than proliferation of low- and high-affinity B cells in the GC allows greater diversity not only in the primary response but also in the memory response.

Protective effect of methanolic extract of Annona squamosa Linn in isoniazid-rifampicin induced hepatotoxicity in rats.

The present study was made to investigate the protective effect of methanolic extract of Annona squamosa on isoniazid-rifampicin-induced hepatotoxicity in rats. Rats were divided into five different groups (n=6), group 1 served as a control, Group 2 received isoniazid (100 mg/kg, i.p.) and co-administered with rifampicin (100 mg/kg, i.p.), in sterile water, group 3 and 4 served as extract treatment groups and received 250 & 500 mg/kg bw, p.o methanolic extract of Annona squamosa and group 5 served as standard group and received silymarin 2.5 mg/kg bw, p.o. All the treatment protocols followed 21 days and after rats were sacrificed blood and liver were used for biochemical and histological studies, respectively. Administration of isoniazid and rifampicin caused a significant elevation in the levels of liver marker enzymes and thiobarbituric acid reactive substances (TBARS, oxidative stress markers) in experimental rats. Administration of methanolic extracts of Annona squamosa significantly prevented isoniazid-rifampicin-induced elevation in the levels of serum diagnostic liver marker enzymes (alanine amino transferase (ALT), aspartate amino transferase (AST), alkaline phosphatase (ALP) and gamma glutamate transpeptidase (γ-GT)), serum bilirubin, and TBARS level in experimental groups of rats. Moreover, total protein and reduced glutathione (GSH) levels were significantly increased in treatment group. The effect of extract was compared with a standard drug, silymarin. The changes in biochemical parameters were supported by histological profile. It is to be concluded that the methanolic extract of Annona squamosa protects against isoniazid and rifampicin-induced oxidative liver injury in rats.

Activation of Notch and Myc Signaling via B-cell-Restricted Depletion of Dnmt3a Generates a Consistent Murine Model of Chronic Lymphocytic Leukemia.

Chronic lymphocytic leukemia (CLL) is characterized by disordered DNA methylation, suggesting these epigenetic changes might play a critical role in disease onset and progression. The methyltransferase DNMT3A is a key regulator of DNA methylation. Although DNMT3A somatic mutations in CLL are rare, we found that low DNMT3A expression is associated with more aggressive disease. A conditional knockout mouse model showed that homozygous depletion of Dnmt3a from B cells results in the development of CLL with 100% penetrance at a median age of onset of 5.3 months, and heterozygous Dnmt3a depletion yields a disease penetrance of 89% with a median onset at 18.5 months, confirming its role as a haploinsufficient tumor suppressor. B1a cells were confirmed as the cell of origin of disease in this model, and Dnmt3a depletion resulted in focal hypomethylation and activation of Notch and Myc signaling. Amplification of chromosome 15 containing the Myc gene was detected in all CLL mice tested, and infiltration of high-Myc-expressing CLL cells in the spleen was observed. Notably, hyperactivation of Notch and Myc signaling was exclusively observed in the Dnmt3a CLL mice, but not in three other CLL mouse models tested (Sf3b1-Atm, Ikzf3, and MDR), and Dnmt3a-depleted CLL were sensitive to pharmacologic inhibition of Notch signaling in vitro and in vivo. Consistent with these findings, human CLL samples with lower DNMT3A expression were more sensitive to Notch inhibition than those with higher DNMT3A expression. Altogether, these results suggest that Dnmt3a depletion induces CLL that is highly dependent on activation of Notch and Myc signaling. SIGNIFICANCE: Loss of DNMT3A expression is a driving event in CLL and is associated with aggressive disease, activation of Notch and Myc signaling, and enhanced sensitivity to Notch inhibition.

Phase II study of pembrolizumab in refractory esophageal cancer with correlates of response and survival.

BACKGROUND: Immune checkpoint inhibitors have revolutionized cancer treatment, but the benefits in refractory patients with esophageal cancer have been modest. Predictors of response as well as new targets for novel therapeutic combinations are needed. In this phase 2 clinical trial, we tested single-agent pembrolizumab in patients with advanced esophageal cancer, who received at least one prior line of therapy. METHODS: Pembrolizumab 200 mg every 3 weeks was tested in 49 patients with refractory esophageal cancer: 39 with adenocarcinoma and 10 with esophageal squamous cell carcinoma. Major endpoints were radiological response by Immune-related Response Evaluation Criteria In Solid Tumors and survival. Tumor samples were evaluated for programmed cell death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), and immune contexture by both NanoString mRNA expression analysis and flow cytometry. Peripheral blood mononuclear cells and a panel of circulating chemokines were also analyzed. RESULTS: The overall response rate (ORR) was 8% (4 of 49 patients; 95% CI 2.3% to 19.6%). Median overall survival (OS) was 5.8 months (95% CI 4.0 to 9.5). ORR and OS were not associated with histology. For PD-L1-positive patients, ORR was 13.3% (95% CI 1.7% to 40.5%) and median OS was 7.9 months (95% CI 4.7 to 15.5). A trend toward improved OS was observed in seven patients with a TMB ≥10 mut/Mb (p=0.086). Tumors with a PD-L1 Combined Positive Score ≥1 showed enrichment of LAG3 (p=0.005) and IDO1 (p=0.04) gene expression. Baseline levels of circulating CXCL10, interleukin 2 (IL2) receptor α (IL2RA) and IL6 were associated with survival: CXCL10 favorably, (HR 0.37, p=0.002 (progression-free survival); HR 0.55, p=0.018 (OS)); IL2RA and IL6 unfavorably (HR 1.57, p=0.020 for IL6 (OS); HR 2.36, p=0.025 for IL2RA (OS)). CONCLUSIONS: Pembrolizumab monotherapy was modestly effective in refractory esophageal cancer. Circulating CXCL10 at baseline appeared to be a robust predictor of response. Other T cell exhaustion markers are upregulated in PD-L1-positive patients, suggesting that immunotherapy combinations such as anti-LAG3/programmed cell death protein 1 (PD-1) or anti-IDO1/PD-1 may be of promise in refractory esophageal cancer.