Respiratory complex I regulates dendritic cell maturation in explant model of human tumor immune microenvironment.

BACKGROUND: Combining cytotoxic chemotherapy or novel anticancer drugs with T-cell modulators holds great promise in treating advanced cancers. However, the response varies depending on the tumor immune microenvironment (TIME). Therefore, there is a clear need for pharmacologically tractable models of the TIME to dissect its influence on mono- and combination treatment response at the individual level. METHODS: Here we establish a patient-derived explant culture (PDEC) model of breast cancer, which retains the immune contexture of the primary tumor, recapitulating cytokine profiles and CD8+T cell cytotoxic activity. RESULTS: We explored the immunomodulatory action of a synthetic lethal BCL2 inhibitor venetoclax+metformin drug combination ex vivo, discovering metformin cannot overcome the lymphocyte-depleting action of venetoclax. Instead, metformin promotes dendritic cell maturation through inhibition of mitochondrial complex I, increasing their capacity to co-stimulate CD4+T cells and thus facilitating antitumor immunity. CONCLUSIONS: Our results establish PDECs as a feasible model to identify immunomodulatory functions of anticancer drugs in the context of patient-specific TIME.

Characterization and clinical impact of the tumor microenvironment in post-transplant aggressive B-cell lymphomas.

Post-transplant lymphoproliferative disorders (PTLD) are iatrogenic immune deficiency-associated lymphoid/plasmacytic proliferations developing due to immunosuppression in solid organ or hematopoietic stem cell allograft patients. PTLD are characterized by abnormal proliferation of lymphoid cells and have a heterogeneous clinical behavior. We profiled expression of >700 tumor microenvironment (TME)-related genes in 75 post-transplant aggressive B-cell lymphomas (PTABCL). Epstein-Barr virus (EBV)-positive PT-ABCL clustered together and were enriched for type I interferon pathway and antiviral-response genes. Additionally, a cytotoxicity gene signature associated with EBV-positivity and favorable overall survival (OS) (hazard ratio =0.61; P=0.019). In silico immunophenotyping revealed two subgroups with distinct immune cell compositions. The inflamed subgroup with higher proportions of immune cells had better outcome compared to noninflamed subgroup (median OS >200.0 vs. 15.2 months; P=0.006). In multivariable analysis with EBV status, International Prognostic Index, and rituximab-containing treatment, inflamed TME remained as an independent predictor for favorable outcome. We also compared TME between post-transplant and immunocompetent host diffuse large B-cell lymphomas (n=75) and discovered that the proportions of T cells were lower in PT-diffuse large B-cell lymphomas. In conclusion, we provide a comprehensive phenotypic characterization of PT-ABCL, highlighting the importance of immune cell composition of TME in determining the clinical behavior and prognosis of PT-ABCL.

Machine Learning of Bone Marrow Histopathology Identifies Genetic and Clinical Determinants in Patients with MDS.

In myelodysplastic syndrome (MDS) and myeloproliferative neoplasm (MPN), bone marrow (BM) histopathology is assessed to identify dysplastic cellular morphology, cellularity, and blast excess. Yet, other morphologic findings may elude the human eye. We used convolutional neural networks to extract morphologic features from 236 MDS, 87 MDS/MPN, and 11 control BM biopsies. These features predicted genetic and cytogenetic aberrations, prognosis, age, and gender in multivariate regression models. Highest prediction accuracy was found for TET2 [area under the receiver operating curve (AUROC) = 0.94] and spliceosome mutations (0.89) and chromosome 7 monosomy (0.89). Mutation prediction probability correlated with variant allele frequency and number of affected genes per pathway, demonstrating the algorithms' ability to identify relevant morphologic patterns. By converting regression models to texture and cellular composition, we reproduced the classical del(5q) MDS morphology consisting of hypolobulated megakaryocytes. In summary, this study highlights the potential of linking deep BM histopathology with genetics and clinical variables. SIGNIFICANCE: Histopathology is elementary in the diagnostics of patients with MDS, but its high-dimensional data are underused. By elucidating the association of morphologic features with clinical variables and molecular genetics, this study highlights the vast potential of convolutional neural networks in understanding MDS pathology and how genetics is reflected in BM morphology. See related commentary by Elemento, p. 195.

Oncogenic Ras Disrupts Epithelial Integrity by Activating the Transmembrane Serine Protease Hepsin.

Ras proteins play a causal role in human cancer by activating multiple pathways that promote cancer growth and invasion. However, little is known about how Ras induces the first diagnostic features of invasion in solid tumors, including loss of epithelial integrity and breaching of the basement membrane (BM). In this study, we found that oncogenic Ras strongly promotes the activation of hepsin, a member of the hepsin/TMPRSS type II transmembrane serine protease family. Mechanistically, the Ras-dependent hepsin activation was mediated via Raf-MEK-ERK signaling, which controlled hepsin protein stability through the heat shock transcription factor-1 stress pathway. In Ras-transformed three-dimensional mammary epithelial culture, ablation of hepsin restored desmosomal cell-cell junctions, hemidesmosomes, and BM integrity and epithelial cohesion. In tumor xenografts harboring mutant KRas, silencing of hepsin increased local invasion concomitantly with accumulation of collagen IV. These findings suggest that hepsin is a critical protease for Ras-dependent tumorigenesis, executing cell-cell and cell-matrix pathologies important for early tumor dissemination. SIGNIFICANCE: These findings identify the cell-surface serine protease hepsin as a potential therapeutic target for its role in oncogenic Ras-mediated deregulation of epithelial cell-cell and cell-matrix interactions and cohesion of epithelial structure.

Somatic mutations and T-cell clonality in patients with immunodeficiency.

Common variable immunodeficiency and other late-onset immunodeficiencies often co-manifest with autoimmunity and lymphoproliferation. The pathogenesis of most cases is elusive, as only a minor subset harbors known monogenic germline causes. The involvement of both B and T cells is however implicated. To study whether somatic mutations in CD4+ and CD8+ T cells associate with immunodeficiency, we recruited 17 patients and 21 healthy controls. Eight patients had late-onset common variable immunodeficiency and nine patients other immunodeficiency and/or severe autoimmunity. In total, autoimmunity occurred in 94% and lymphoproliferation in 65%. We performed deep sequencing of 2533 immune-associated genes from CD4+ and CD8+ cells. Deep T-cell receptor beta sequencing was used to characterize CD4+ and CD8+ T-cell receptor repertoires. The prevalence of somatic mutations was 65% in all immunodeficiency patients, 75% in common variable immunodeficiency and 48% in controls. Clonal hematopoiesis-associated variants in both CD4+ and CD8+ cells occurred in 24% of immunodeficiency patients. Results demonstrated mutations in known tumor suppressors, oncogenes, and genes that are critical for immune- and proliferative functions, such as STAT5B (two patients), C5AR1 (two patients), KRAS (one patient), and NOD2 (one patient). Additionally, as a marker of T-cell receptor repertoire perturbation, common variable immunodeficiency patients harbored increased frequencies of clones with identical complementarity determining region 3 sequences despite unique nucleotide sequences when compared to controls. In conclusion, somatic mutations in genes implicated for autoimmunity and lymphoproliferation are common in CD4+ and CD8+ cells of patients with immunodeficiency. They may contribute to immune dysregulation in a subset of immunodeficiency patients.

STAT3 Mutation Is Associated with STAT3 Activation in CD30+ ALK- ALCL.

Peripheral T-cell lymphomas (PTCL) are a heterogeneous, and often aggressive group of non-Hodgkin lymphomas. Recent advances in the molecular and genetic characterization of PTCLs have helped to delineate differences and similarities between the various subtypes, and the JAK/STAT pathway has been found to play an important oncogenic role. Here, we aimed to characterize the JAK/STAT pathway in PTCL subtypes and investigate whether the activation of the pathway correlates with the frequency of STAT gene mutations. Patient samples from AITL (n = 30), ALCL (n = 21) and PTCL-NOS (n = 12) cases were sequenced for STAT3, STAT5B, JAK1, JAK3, and RHOA mutations using amplicon sequencing and stained immunohistochemically for pSTAT3, pMAPK, and pAKT. We discovered STAT3 mutations in 13% of AITL, 13% of ALK+ ALCL, 38% of ALK- ALCL and 17% of PTCL-NOS cases. However, no STAT5B mutations were found and JAK mutations were only present in ALK- ALCL (15%). Concurrent mutations were found in all subgroups except ALK+ ALCL where STAT3 mutations were always seen alone. High pY-STAT3 expression was observed especially in AITL and ALCL samples. When studying JAK-STAT pathway mutations, pY-STAT3 expression was highest in PTCLs harboring either JAK1 or STAT3 mutations and CD30+ phenotype representing primarily ALK- ALCLs. Further investigation is needed to elucidate the molecular mechanisms of JAK-STAT pathway activation in PTCL.

The human long non-coding RNA gene RMRP has pleiotropic effects and regulates cell-cycle progression at G2.

RMRP was the first non-coding nuclear RNA gene implicated in a disease. Its mutations cause cartilage-hair hypoplasia (CHH), an autosomal recessive skeletal dysplasia with growth failure, immunodeficiency, and a high risk for malignancies. This study aimed to gain further insight into the role of RNA Component of Mitochondrial RNA Processing Endoribonuclease (RMRP) in cellular physiology and disease pathogenesis. We combined transcriptome analysis with single-cell analysis using fibroblasts from CHH patients and healthy controls. To directly assess cell cycle progression, we followed CHH fibroblasts by pulse-labeling and time-lapse microscopy. Transcriptome analysis identified 35 significantly upregulated and 130 downregulated genes in CHH fibroblasts. The downregulated genes were significantly connected to the cell cycle. Multiple other pathways, involving regulation of apoptosis, bone and cartilage formation, and lymphocyte function, were also affected, as well as PI3K-Akt signaling. Cell-cycle studies indicated that the CHH cells were delayed specifically in the passage from G2 phase to mitosis. Our findings expand the mechanistic understanding of CHH, indicate possible pathways for therapeutic intervention and add to the limited understanding of the functions of RMRP.

Gain-of-function CEBPE mutation causes noncanonical autoinflammatory inflammasomopathy.

BACKGROUND: CCAAT enhancer-binding protein epsilon (C/EBPε) is a transcription factor involved in late myeloid lineage differentiation and cellular function. The only previously known disorder linked to C/EBPε is autosomal recessive neutrophil-specific granule deficiency leading to severely impaired neutrophil function and early mortality. OBJECTIVE: The aim of this study was to molecularly characterize the effects of C/EBPε transcription factor Arg219His mutation identified in a Finnish family with previously genetically uncharacterized autoinflammatory and immunodeficiency syndrome. METHODS: Genetic analysis, proteomics, genome-wide transcriptional profiling by means of RNA-sequencing, chromatin immunoprecipitation (ChIP) sequencing, and assessment of the inflammasome function of primary macrophages were performed. RESULTS: Studies revealed a novel mechanism of genome-wide gain-of-function that dysregulated transcription of 464 genes. Mechanisms involved dysregulated noncanonical inflammasome activation caused by decreased association with transcriptional repressors, leading to increased chromatin occupancy and considerable changes in transcriptional activity, including increased expression of NLR family, pyrin domain-containing 3 protein (NLRP3) and constitutively expressed caspase-5 in macrophages. CONCLUSION: We describe a novel autoinflammatory disease with defective neutrophil function caused by a homozygous Arg219His mutation in the transcription factor C/EBPε. Mutated C/EBPε acts as a regulator of both the inflammasome and interferome, and the Arg219His mutation causes the first human monogenic neomorphic and noncanonical inflammasomopathy/immunodeficiency. The mechanism, including widely dysregulated transcription, is likely not unique for C/EBPε. Similar multiomics approaches should also be used in studying other transcription factor-associated diseases.

Breast cancer outcome prediction with tumour tissue images and machine learning.

PURPOSE: Recent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input. METHODS: Utilising tissue microarray (TMA) samples obtained from the primary tumour of patients (N = 1299) within a nationwide breast cancer series with long-term-follow-up, we train and validate a machine learning method for patient outcome prediction. The prediction is performed by classifying samples into low or high digital risk score (DRS) groups. The outcome classifier is trained using sample images of 868 patients and evaluated and compared with human expert classification in a test set of 431 patients. RESULTS: In univariate survival analysis, the DRS classification resulted in a hazard ratio of 2.10 (95% CI 1.33-3.32, p = 0.001) for breast cancer-specific survival. The DRS classification remained as an independent predictor of breast cancer-specific survival in a multivariate Cox model with a hazard ratio of 2.04 (95% CI 1.20-3.44, p = 0.007). The accuracy (C-index) of the DRS grouping was 0.60 (95% CI 0.55-0.65), as compared to 0.58 (95% CI 0.53-0.63) for human expert predictions based on the same TMA samples. CONCLUSIONS: Our findings demonstrate the feasibility of learning prognostic signals in tumour tissue images without domain knowledge. Although further validation is needed, our study suggests that machine learning algorithms can extract prognostically relevant information from tumour histology complementing the currently used prognostic factors in breast cancer.

Immune cell constitution in bone marrow microenvironment predicts outcome in adult ALL.

As novel immunological treatments are gaining a foothold in the treatment of acute lymphoblastic leukemia (ALL), it is elemental to examine ALL immunobiology in more detail. We used multiplexed immunohistochemistry (mIHC) to study the immune contexture in adult precursor B cell ALL bone marrow (BM). In addition, we developed a multivariate risk prediction model that stratified a poor survival group based on clinical parameters and mIHC data. We analyzed BM biopsy samples of ALL patients (n = 52) and healthy controls (n = 14) using mIHC with 30 different immunophenotype markers and computerized image analysis. In ALL BM, the proportions of M1-like macrophages, granzyme B+CD57+CD8+ T cells, and CD27+ T cells were decreased, whereas the proportions of myeloid-derived suppressor cells and M2-like macrophages were increased. Also, the expression of checkpoint molecules PD1 and CTLA4 was elevated. In the multivariate model, age, platelet count, and the proportion of PD1+TIM3+ double-positive CD4+ T cells differentiated a poor survival group. These results were validated by flow cytometry in a separate cohort (n = 31). In conclusion, the immune cell contexture in ALL BM differs from healthy controls. CD4+PD1+TIM3+ T cells were independent predictors of poor outcome in our multivariate risk model, suggesting that PD1 might serve as an attractive immuno-oncological target in B-ALL.

Aggressive natural killer-cell leukemia mutational landscape and drug profiling highlight JAK-STAT signaling as therapeutic target.

Aggressive natural killer-cell (NK-cell) leukemia (ANKL) is an extremely aggressive malignancy with dismal prognosis and lack of targeted therapies. Here, we elucidate the molecular pathogenesis of ANKL using a combination of genomic and drug sensitivity profiling. We study 14 ANKL patients using whole-exome sequencing (WES) and identify mutations in STAT3 (21%) and RAS-MAPK pathway genes (21%) as well as in DDX3X (29%) and epigenetic modifiers (50%). Additional alterations include JAK-STAT copy gains and tyrosine phosphatase mutations, which we show recurrent also in extranodal NK/T-cell lymphoma, nasal type (NKTCL) through integration of public genomic data. Drug sensitivity profiling further demonstrates the role of the JAK-STAT pathway in the pathogenesis of NK-cell malignancies, identifying NK cells to be highly sensitive to JAK and BCL2 inhibition compared to other hematopoietic cell lineages. Our results provide insight into ANKL genetics and a framework for application of targeted therapies in NK-cell malignancies.

Deep learning based tissue analysis predicts outcome in colorectal cancer.

Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79-3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28-2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30-2.15; AUC 0.57) in the stratification into low- and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.

Somatic STAT3 mutations in Felty syndrome: an implication for a common pathogenesis with large granular lymphocyte leukemia.

Felty syndrome is a rare disease defined by neutropenia, splenomegaly, and rheumatoid arthritis. Sometimes the differential diagnosis between Felty syndrome and large granular lymphocyte leukemia is problematic. Recently, somatic STAT3 and STAT5B mutations were discovered in 30-40% of patients with large granular lymphocyte leukemia. Herein, we aimed to study whether these mutations can also be detected in Felty syndrome, which would imply the existence of a common pathogenic mechanism between these two disease entities. We collected samples and clinical information from 14 Felty syndrome patients who were monitored at the rheumatology outpatient clinic for Felty syndrome. Somatic STAT3 mutations were discovered in 43% (6/14) of Felty syndrome patients with deep amplicon sequencing targeting all STAT3 exons. Mutations were located in the SH2 domain of STAT3, which is a known mutational hotspot. No STAT5B mutations were found. In blood smears, overrepresentation of large granular lymphocytes was observed, and in the majority of cases the CD8+ T-cell receptor repertoire was skewed when analyzed by flow cytometry. In bone marrow biopsies, an increased amount of phospho-STAT3 positive cells was discovered. Plasma cytokine profiling showed that ten of the 92 assayed cytokines were elevated both in Felty syndrome and large granular lymphocyte leukemia, and three of these cytokines were also increased in patients with uncomplicated rheumatoid arthritis. In conclusion, somatic STAT3 mutations and STAT3 activation are as frequent in Felty syndrome as they are in large granular lymphocyte leukemia. Considering that the symptoms and treatment modalities are also similar, a unified reclassification of these two syndromes is warranted.

Enteropathy-associated T cell lymphoma subtypes are characterized by loss of function of SETD2.

Enteropathy-associated T cell lymphoma (EATL) is a lethal, and the most common, neoplastic complication of celiac disease. Here, we defined the genetic landscape of EATL through whole-exome sequencing of 69 EATL tumors. SETD2 was the most frequently silenced gene in EATL (32% of cases). The JAK-STAT pathway was the most frequently mutated pathway, with frequent mutations in STAT5B as well as JAK1, JAK3, STAT3, and SOCS1 We also identified mutations in KRAS, TP53, and TERT Type I EATL and type II EATL (monomorphic epitheliotropic intestinal T cell lymphoma) had highly overlapping genetic alterations indicating shared mechanisms underlying their pathogenesis. We modeled the effects of SETD2 loss in vivo by developing a T cell-specific knockout mouse. These mice manifested an expansion of γδ T cells, indicating novel roles for SETD2 in T cell development and lymphomagenesis. Our data render the most comprehensive genetic portrait yet of this uncommon but lethal disease and may inform future classification schemes.

Dasatinib Changes Immune Cell Profiles Concomitant with Reduced Tumor Growth in Several Murine Solid Tumor Models.

Dasatinib, a broad-range tyrosine kinase inhibitor, induces rapid mobilization of lymphocytes and clonal expansion of cytotoxic cells in leukemia patients. Here, we investigated whether dasatinib could induce beneficial immunomodulatory effects in solid tumor models. The effects on tumor growth and on the immune system were studied in four different syngeneic mouse models (B16.OVA melanoma, 1956 sarcoma, MC38 colon, and 4T1 breast carcinoma). Both peripheral blood (PB) and tumor samples were immunophenotyped during treatment. Although in vitro dasatinib displayed no direct cytotoxicity to B16 melanoma cells, a significant decrease in tumor growth was observed in dasatinib-treated mice compared with vehicle-treated group. Further, dasatinib-treated melanoma-bearing mice had an increased proportion of CD8+ T cells in PB, together with a higher amount of tumor-infiltrating CD8+ T cells. Dasatinib-mediated antitumor efficacy was abolished when CD4+ and CD8+ T cells were depleted with antibodies. Results were confirmed in sarcoma, colon, and breast cancer models, and in all cases mice treated daily with dasatinib had a significant decrease in tumor growth. Detailed immunophenotyping of tumor tissues with CyTOF indicated that dasatinib had reduced the number of intratumoral regulatory T cells in all tumor types. To conclude, dasatinib is able to slow down the tumor growth of various solid tumor models, which is associated with the favorable blood/tumor T-cell immunomodulation. The assessment of synergistic combinatorial therapies with other immunomodulatory drugs or targeted small-molecule oncokinase inhibitors is warranted in future clinical trials. Cancer Immunol Res; 5(2); 157-69. ©2017 AACR.

Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples.

BACKGROUND: Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples. METHODS: Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody. The stained slides were digitally scanned, and a training set of immune cell-rich and cell-poor tissue regions was annotated in H&E whole-slide images using the CD45-expression as a guide. In analysis, the images were divided into small homogenous regions, superpixels, from which features were extracted using a pretrained convolutional neural network (CNN) and classified with a support of vector machine. The CNN approach was compared to texture-based classification and to visual assessments performed by two pathologists. RESULTS: In a set of 123,442 labeled superpixels, the CNN approach achieved an F-score of 0.94 (range: 0.92-0.94) in discrimination of immune cell-rich and cell-poor regions, as compared to an F-score of 0.88 (range: 0.87-0.89) obtained with the texture-based classification. When compared to visual assessment of 200 images, an agreement of 90% (κ = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (κ = 0.78). CONCLUSIONS: Our findings indicate that deep learning can be applied to quantify immune cell infiltration in breast cancer samples using a basic morphology staining only. A good discrimination of immune cell-rich areas was achieved, well in concordance with both leukocyte antigen expression and pathologists' visual assessment.

The putative tumor suppressor gene EphA3 fails to demonstrate a crucial role in murine lung tumorigenesis or morphogenesis.

Treatment of non-small cell lung cancer (NSCLC) is based on histological analysis and molecular profiling of targetable driver oncogenes. Therapeutic responses are further defined by the landscape of passenger mutations, or loss of tumor suppressor genes. We report here a thorough study to address the physiological role of the putative lung cancer tumor suppressor EPH receptor A3 (EPHA3), a gene that is frequently mutated in human lung adenocarcinomas. Our data shows that homozygous or heterozygous loss of EphA3 does not alter the progression of murine adenocarcinomas that result from Kras mutation or loss of Trp53, and we detected negligible postnatal expression of EphA3 in adult wild-type lungs. Yet, EphA3 was expressed in the distal mesenchyme of developing mouse lungs, neighboring the epithelial expression of its Efna1 ligand; this is consistent with the known roles of EPH receptors in embryonic development. However, the partial loss of EphA3 leads only to subtle changes in epithelial Nkx2-1, endothelial Cd31 and mesenchymal Fgf10 RNA expression levels, and no macroscopic phenotypic effects on lung epithelial branching, mesenchymal cell proliferation, or abundance and localization of CD31-positive endothelia. The lack of a discernible lung phenotype in EphA3-null mice might indicate lack of an overt role for EPHA3 in the murine lung, or imply functional redundancy between EPHA receptors. Our study shows how biological complexity can challenge in vivo functional validation of mutations identified in sequencing efforts, and provides an incentive for the design of knock-in or conditional models to assign the role of EPHA3 mutation during lung tumorigenesis.

Autoimmunity, hypogammaglobulinemia, lymphoproliferation, and mycobacterial disease in patients with activating mutations in STAT3.

The signal transducer and activator of transcription (STAT) family of transcription factors orchestrate hematopoietic cell differentiation. Recently, mutations in STAT1, STAT5B, and STAT3 have been linked to development of immunodysregulation polyendocrinopathy enteropathy X-linked-like syndrome. Here, we immunologically characterized 3 patients with de novo activating mutations in the DNA binding or dimerization domains of STAT3 (p.K392R, p.M394T, and p.K658N, respectively). The patients displayed multiorgan autoimmunity, lymphoproliferation, and delayed-onset mycobacterial disease. Immunologically, we noted hypogammaglobulinemia with terminal B-cell maturation arrest, dendritic cell deficiency, peripheral eosinopenia, increased double-negative (CD4(-)CD8(-)) T cells, and decreased natural killer, T helper 17, and regulatory T-cell numbers. Notably, the patient harboring the K392R mutation developed T-cell large granular lymphocytic leukemia at age 14 years. Our results broaden the spectrum of phenotypes caused by activating STAT3 mutations, highlight the role of STAT3 in the development and differentiation of multiple immune cell lineages, and strengthen the link between the STAT family of transcription factors and autoimmunity.