ALBERT

Description: HLA associations, T cell receptor (TCR) repertoire bias, and sex bias have independently been shown for many diseases. While some immunological differences between the sexes have been described, they do not fully explain bias in men toward many infections/cancers, and toward women in autoimmunity. Next-generation TCR variable beta chain (TCRBV) immunosequencing of 824 individuals was evaluated in a multiparametric analysis including HLA-A -B/MHC class I background, TCRBV usage, sex, age, ethnicity, and TCRBV selection/expansion dynamics. We found that HLA-associated shaping of TCRBV usage differed between the sexes. Furthermore, certain TCRBVs were selected and expanded in unison. Correlations between these TCRBV relationships and biochemical similarities in HLA-binding positions were different in CD8 T cells of patients with autoimmune diseases (multiple sclerosis and rheumatoid arthritis) compared with healthy controls. Within patients, men showed higher TCRBV relationship Spearman’s rhos in relation to HLA-binding position similarities compared with women. In line with this, CD8 T cells of men with autoimmune diseases also showed higher degrees of TCRBV perturbation compared with women. Concerted selection and expansion of CD8 T cells in patients with autoimmune diseases, but especially in men, appears to be less dependent on high HLA-binding similarity than in CD4 T cells. These findings are consistent with studies attributing autoimmunity to processes of epitope spreading and expansion of low-avidity T cell clones and may have further implications for the interpretation of pathogenic mechanisms of infectious and autoimmune diseases with known HLA associations. Reanalysis of some HLA association studies, separating the data by sex, could be informative.

Description: Introduction The introduction of tyrosine kinase inhibitors (TKIs) has revolutionized the treatment of chronic myeloid leukemia (CML). In addition to blocking their main target kinase, the BCR-ABL oncoprotein, several studies have reported that TKIs could also have secondary effects on the immune system and lymphocyte behavior. The aim of this study was to assess the bone marrow (BM) lymphocyte status at diagnosis and during different first-line TKI therapies and correlate it with treatment responses. Methods Altogether 105 first-line TKI treated patients were included in the study (imatinib n=71, dasatinib n=25 and nilotinib n=9) and samples from 14 healthy bone marrow donors served as controls. BM aspirate samples were taken from patients at the diagnosis and at 3, 6, 12 and 18 months after the TKI therapy start, and MGG-stained BM aspirate slides were examined for cellularity and individual cellular proportions. Treatment responses were evaluated with standard karyotyping and real-time quantitative PCR. Patients were divided in different groups according to ELN criteria based on their therapy response at 12 months. In addition, multi-color flow cytometry was done from both BM and peripheral blood (PB) samples using 5 different antibody panels including markers for T, B, NK and regulatory T cells. Results We found an early (3 months) expansion of BM lymphocytes during all different TKI therapies (imatinib median lymphocyte count 20%; dasatinib 21%; nilotinib 22%; healthy controls 12%, p

Description: Immunotherapy has remarkably changed the treatment paradigm in hematologic malignancies and natural killer (NK) cell therapy represents an attractive option, as it has been feasible and safe in early clinical trials, without graft-versus-host effects. Nevertheless, the molecular markers determining cancer cell sensitivity or resistance to NK cells, especially in the context of tumor cell interaction with the bone marrow (BM) stromal microenvironment remain incompletely understood, but have major translational relevance since these tumor-stromal interactions have been known to attenuate the response of blood cancer cells to diverse classes of pharmacological agents. To address these questions, we performed NK cell treatment of a series of cell lines from hematologic malignancies by applying a pooled "DNA-barcoded" format of these cell lines (PRISM system). We specifically quantified the dose-dependent responses to primary NK cells for 70 molecularly-annotated blood cancer cell lines, including myeloid and lymphoblastic leukemia, diffuse large B cell lymphoma and 15 multiple myeloma (MM) lines, either in presence or in absence of BM stromal cells (BMSCs) and interferon gamma (IFNg), followed by integrated computational analyses to identify candidate molecular markers correlating with tumor cell sensitivity or resistance to NK cells. NK cell cytotoxicity, quantified by the relative abundance of barcodes in treated cells compared to controls, was correlated with the transcriptional, mutational and other molecular features of each of the 70 cell lines from publicly available databases. Furthermore, data from MM cell lines were compared to our genome-wide loss of function (LOF) and gain of function (GOF) CRISPR screen data in the MM cell line MM.1S. Two distinct clusters of cell lines, sensitive and resistant to NK cell treatment, were identified. Such clusters retained distinct pattern of in vitro resistance in the presence of stroma, while showing an overall markedly decreased NK cell responsiveness, which underscores the protective effect of stromal microenvironment in blood malignancies, regardless of the addition of IFNg. RNA-seq data showed no differences in dependencies between the two clusters and no distinct gene expression patterns at baseline that clearly allows to predict NK cell response, which underscores the heterogeneity of resistance patterns at single gene level, across different hematologic malignancies. However, when comparing baseline RNAseq data to data obtained from previous GOF and LOF CRISPR screens in MM.1S, surface antigens such as PVR, ULBP1, ULBP3 were more frequently downregulated, whereas MUC1 was upregulated in resistant cells clusters. An important observation is that gene lesions such as TP53, PTEN, MMSET, commonly associated with high-risk diseases, do not affect NK cell responses in the cell lines tested. Interestingly, a gene set enrichment analysis (GSEA) showed that the cluster of resistant cells displays upregulation of class I MHC complex, class II MHC complex binding, IL7 pathway and a downregulation of transmembrane receptor protein serine/threonine kinase signaling pathway. GSEA also showed that baseline state of IFN-JAK-STAT signaling correlates with BMSCs-induced NK cell resistance, a result further confirmed by addition of IFNgto tumor-NK cocultures in the absence of BMSCs. No significant differences in NK cell response were observed when comparing cell lines of different hematologic neoplasms, suggesting that candidate markers from these studies may be relevant across different hematologic malignancies. In conclusion, this is the first study of this size correlating the molecular annotation of different concurrently-treated hematologic cell lines with their response to a NK-based treatment in the context of BMSC interaction. This study of a large panel of pooled "DNA-barcoded" cell lines provided complementary and orthogonal information to our LOF and GOF screens, expanding our potential to identify and validate molecular markers for individualized use of NK cell-based therapies in hematologic malignancies. Disclosures Mustjoki: BMS: Honoraria, Research Funding; Novartis: Research Funding; Pfizer: Research Funding. Mitsiades:EMD Serono: Research Funding; Abbvie: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Arch Oncology: Research Funding; Fate Therapeutics: Honoraria; Ionis Pharmaceuticals: Honoraria; Takeda: Other: employment of a relative ; Janssen/Johnson & Johnson: Research Funding; TEVA: Research Funding.

Description: Immune aplastic anemia (AA) is a life-threatening bone marrow failure syndrome in which the hematopoietic stem cells are destroyed, leading to pancytopenia. Although the exact biological process leading to AA remains largely unknown, bone marrow destruction is thought to be mediated by an autologous T cell response. We hypothesized that the autoimmune process in AA would create a T cell fingerprint unique to aplastic anemia. To decipher this signature, we collected an international, multi-centre cohort of 245 AA-samples from bone marrow and peripheral blood profiled with T-cell receptor beta (TCRβ) -sequencing. CD8+ T cell- and MNC-sorted samples from 153 clinically annotated AA patients were obtained from diagnosis, during remission and at relapse. To compare AA to similar diseases, we gathered 116 samples from other bone marrow failure syndromes, including MDS, PNH and hypoplastic LGL, and 45 samples from other autoimmune disorders. As healthy controls, we profiled 60 CD4+ and CD8+ T cells and utilized 786 MNC samples from public data repositories. To gain insight into T cell phenotypes, we also profiled 6 longitudinal samples with scRNA+TCRαβ-sequencing. As there are 1x1012-16 different TCRs and most of them are exclusive to individuals (private), we reasoned that by studying the most biologically interesting clonotypes from each individual, we could explain differences in disease severities, variation in treatment responses and pathogenesis. From all subjects, we selected private response clonotypes: highly expanded clonotypes (at least 1% of the total repertoire), convergent clonotypes (in which multiple nucleotide sequences converge to encode the same amino acid sequence) and from patients with AA, treatment-responding clonotypes (clonotypes that were suppressed/expanded after immune therapy). To analyse epitope-specificities of these clonotypes we leveraged TCRGP, our recently described Gaussian process method that can predict if TCRs recognize previously known epitopes. Clonotypes recognising viral epitopes (CMV, EBV and Influenza A) were enriched among private response clonotypes in comparison to the total repertoire (Fisher's exact test, p=2e-16), indicating that our filtering strategy indeed enriched for epitope-specific clonotypes. Of interest, the healthy donors' private response clonotypes showed more anti-viral clonotypes than did AA-patients (p=0.003), suggesting that in AA the epitope-specifities of private response clonotypes are not driven by common viral antigens. To identify specifities against unknown epitopes of the private response AA clones, we used an unsupervised learning strategy, GLIPH,that groups TCRs recognising the same epitope based on amino acid level similarities. Clonotypes in AA showed high convergence in their epitope-targets, as 1709 of 5744 (29.75%) clonotypes formed a single, potentially epitope-specific cluster that was not viral-specific. Similar analysis of control samples resulted in fewer clones clustering to the most prominent cluster (23.20%, p=1.63e-10), suggesting for a more homogenous target population within AA patients' clones. After showing sequence-level similarity of the private response clonotypes in AA, we aimed to link these pathological clonotypes to transcriptomes at the single-cell level using scRNA+TCRαβ-sequenced samples. The cells of the private response clonotypes showed multiple T cell phenotypes, but most cells (47.13%) in the bone marrow environment were recently activated CD8+ effector phenotype, marked by expression of GZMH, GNLY and PRF1. In comparison, the anti-viral clonotypes were mostly (37.3%) central memory phenotype (CCR7, TCF7). In serially sampled patients, anti-thymocyte globulin treatment suppressed private response clonotypes in a responding patient (55.03% of T cells to 12.79%), while the amount of these clonotypes increased in a non-responding patient (18.65% to 37.86%), where treatment mostly affected the viral-specific clonotypes. In summary, our data suggest that the private response clonotypes in immune AA patients may recognise a common antigen, which was not predicted to be viral. Further, at the single-cell level AA signature clonotypes are of effector phenotype and fluctuate following immunosuppressive treatment. Monitoring of these clonotypes throughout treatment may provide insight into disease biology and variation in treatment responses. Figure Disclosures Blombery: Janssen: Honoraria; Novartis: Consultancy; Invivoscribe: Honoraria. Maciejewski:Novartis: Consultancy; Alexion: Consultancy. Mustjoki:BMS: Honoraria, Research Funding; Novartis: Research Funding; Pfizer: Research Funding.

Description: Background. Acquired aplastic anemia (AA) is a bone marrow failure syndrome, in which patients' hematopoietic stem cells are destroyed, resulting in pancytopenia. The exact mechanism and biological process leading to AA remain largely unknown. Bone marrow destruction is perceived as an immune-mediated process, which is supported by elevated cytotoxic T lymphocyte (CTL) counts, responsiveness to immunosuppressive therapy and skewed CTL T cell receptor (TCR) repertoire. Although there is a well-established role of T cells in the pathology of AA, the putative antigen behind the autoimmune response, and thus, the TCRs recognising the antigen are still unrevealed. Methods. Our cohort comprised of 130 samples, consisting of bone marrow and/or peripheral blood samples from AA patients (n=52) from diagnosis and/or follow-up phases and from healthy controls (n=27). We performed TCRβ sequencing (immunoSEQ, Adaptive Biotechnologies) on sorted AA and healthy CTLs (n=25 and n=27) or AA mononuclear cells (MNCs, n=27). To gain in-depth understanding of the TCR-repertoire, we built two novel analysis methods: 1) an unsupervised clustering method to characterize epitope-specific T cells based on the amino acid -level similarities of TCRs and 2) a probability based classifier to identify AA based on TCR data in the CTL (discovery) and MNC (validation) cohorts. Results. To fully appreciate the complex nature of AA pathology, we divided the cytotoxic T cell response in two distinct categories, private and public response. Private response comprises T cell clones found only in individual patients, and this compartment could explain the variation in treatment responses and disease severity across patients. In the CTL TCR repertoire, we identified patient exclusive expanded T cell clones (〉1% frequency in TCR repertoire, n = 317) and treatment responding clones (n = 364). Clustering analysis revealed that there is significant amino acid -level similarity between the TCRs of the private clones. We found several epitope-specific TCR clusters that were associated with different treatment responses, disease severities and HLA-DR15 risk-allele positivity. Interestingly, we discovered that the public response (CTL clones that are statistically enriched in AA patients compared to healthy controls) could discriminate AA from healthy TCR repertoire. Based on these publicly enriched TCRs, we built a classifier which could identify AA CTL TCR repertoire from healthy controls with 97% accuracy (F-score, revieved from leave-one-out cross-validation). We tested our classifier with the validation cohort. The accuracy to diagnose AA based on the TCR repertoire data in the MNC cohort was 0.72. Furthermore, the public clones that differentiated best the AA cases from healthy controls showed statistically significant peptide similarity. These public TCRs occurred also in healthy controls, but with smaller frequencies. When combining the private and public response TCRs, we discovered that some of AA patients' most expanded and treatment responding clones clustered in the same putative epitope-specific clusters with the public TCRs, showing an interesting intersection between public and private signatures. In addition, the comparison of the interesting private and public TCRs against databases of TCR sequences of known antigen specificities hinted that some of these clones may originally target known viral species (CMV, EBV and Influenza A), suggesting a role of these common pathogens in the development of AA. Discussion. CTLTCR repertoire analysis of AA patients revealed a TCR signature that was typical of AA patients, but varied between different patients, and it was validated with an independent dataset. Thus, we could design a TCR-based framework which could identify AA patients based on their TCR repertoire, independently of sample type. A future application for our classifier could be distinguishing AA from other AA-like diseases, like hypoplastic myelodysplastic syndrome. Furthermore, we found groups of TCRs that look similar on amino acid level, and hence these clones may target the same epitope. These TCR clusters were associated with clinical features. Amino acid similarity -based TCR signatures or TCR classifiers have not previously been published for AA or any other autoimmune diseases, and thus our pioneering tools could be utilized to study pathogenesis of other T cell mediated diseases as well. Figure 1. Figure 1. Disclosures Ebeling: Boehringer Ingelheim: Consultancy; Celgene: Speakers Bureau; Otsuka Pharma Scandinavia AB: Consultancy. Maciejewski:Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Ra Pharmaceuticals, Inc: Consultancy; Apellis Pharmaceuticals: Consultancy; Apellis Pharmaceuticals: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Ra Pharmaceuticals, Inc: Consultancy. Mustjoki:Novartis: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria; Ariad: Research Funding; Pfizer: Honoraria, Research Funding.

Description: Background Clonal proliferation of T/NK cells has been noted after the treatment of CML patients with dasatinib. Previous reports have suggested that persistent expansion of clonal cytotoxic T cells or NK cells in dasatinib-treated patients may be associated with higher response rates and increased occurrence of pleural effusions. This retrospective study analyzed the incidence of lymphocytosis and its association with response, progression-free survival (PFS) and overall survival (OS), and pleural effusion in a large sample of dasatinib-treated patients. Methods Analyses were conducted using dasatinib-treated patients from three large studies with ≥3 years of follow-up: CA180-056 (DASISION), which included 258 dasatinib-treated patients with newly diagnosed CML in chronic phase (CML-CP); CA180-034, which included 662 dasatinib-treated patients with CML-CP who were previously treated with imatinib; and CA180-035, which included 316 dasatinib-treated patients with CML in accelerated phase (CML-AP) and 148 dasatinib-treated patients with CML in myeloid blast phase (CML-MBP) who were previously treated with imatinib. Results Lymphocytosis, as defined by ≥2 consecutive lymphocyte counts 〉 3600/µl after 28 days of treatment, was present in 33% of patients (85/258) with newly diagnosed CML-CP in DASISION (median time to onset, 4.6 months) and 31% of patients (206/662) with imatinib-resistant or -intolerant CML-CP in CA180-034 (median time to onset, 3.0 months). The median on-treatment follow-up times were 36.8 months and 29.3 months for DASISION and CA180-034, respectively. For CA180-035, the median on-treatment follow-up time was 6.1 months, and lymphocytosis developed in 35% of patients (110/316) with CML-AP and 34% of patients (51/148) with CML-MBP. Lymphocytosis persisted for 〉12 months in 64% of patients (54/85) with newly diagnosed CML-CP, in 52% of patients (107/206) with imatinib-resistant or -intolerant CML-CP, in 42% (46/110) with CML-AP, and in 18% (9/51) with CML-MBP. The proportion of newly diagnosed patients with complete cytogenetic response (CCyR) or major molecular response (MMR) at any time was higher among those with vs. without lymphocytosis: 89% (76/85) vs. 80% (138/173) for confirmed CCyR and 74% (63/85) vs. 67% (116/173) for MMR. Patients who developed lymphocytosis during treatment with second-line dasatinib were more likely to achieve CCyR, regardless of disease phase; the proportion of patients who achieved CCyR with vs. without lymphocytosis was 62% (127/206) vs. 49% (222/456) for CML-CP, 46% (51/110) vs. 27% (55/206) for CML-AP, and 31% (16/51) vs. 14% (14/97) for CML-MBP. In landmark analyses of patients with CML-CP in DASISION who were still on first-line dasatinib at 3 or 8 months, lymphocytosis status did not significantly affect PFS or OS. Similar results were found in the second-line studies, when considering patients with CML-CP, -AP, or -MBP who were still on study treatment (second-line dasatinib) at 3 months. Pleural effusions (all grades) developed more often in newly diagnosed patients with lymphocytosis (28% [24/85] vs. 16% [27/173] without lymphocytosis) and in imatinib-resistant or -intolerant patients with CML-CP (38% [79/206] vs. 30% [136/456]) or CML-AP (53% [58/110] vs. 31% [64/206]). The proportion of patients with CML-MBP developing pleural effusions was 27%, regardless of the presence of lymphocytosis (14/51 with lymphocytosis and 26/97 without lymphocytosis). Conclusions Lymphocytosis develops very commonly after treatment with dasatinib and persists for 〉1 year in an appreciable fraction of patients. Immunophenotyping was not done, but it can be presumed that this represents a large granular lymphocyte proliferation in most patients, based on other studies. Lymphocytosis was associated with higher CCyR rates in all stages of CML, as well as higher rates of pleural effusions in CML-CP and -AP. Lymphocytosis was also associated with higher MMR rates in patients with CML-CP receiving first-line dasatinib. There appears to be no significant association, however, between lymphocytosis and PFS or OS in this analysis. Prospective studies are warranted to more carefully characterize the functional activity of these cells and to help assess whether an immunologic effect against CML is detectable in some patients, particularly advanced phase patients with unexpected long responses to treatment with dasatinib alone. Disclosures: Schiffer: Novartis: Consultancy, Research Funding; BMS: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Research Funding; Teva: Consultancy; Eisai: Consultancy; Ariad: Research Funding; Pfizer: Research Funding. Cortes:Ariad: Consultancy, Grant to institution Other, Honoraria; BMS: Grant to institution, Grant to institution Other; Novartis: Grant to institution, Grant to institution Other; Pfizer: Consultancy, Grant to institution, Grant to institution Other, Honoraria; Teva: Consultancy, Grant to institution Other, Honoraria; Tragara: Membership on an entity’s Board of Directors or advisory committees; Ambit: Grants/grants pending for institution Other; Astellas: Grants/grants pending for institution, Grants/grants pending for institution Other; Incyte: Grants/grants pending for institution, Grants/grants pending for institution Other; Arog: Grants/grants pending for institution Other; Celgene: Grants/grants pending for institution, Grants/grants pending for institution Other; sanofi: Grants/grants pending for institution, Grants/grants pending for institution Other. Saglio:Novartis: Consultancy, Honoraria; BMS: Consultancy, Honoraria; ARIAD: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. le Coutre:Novartis: Honoraria, Research Funding; BMS: Honoraria; Pfizer: Honoraria. Porkka:BMS: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Mustjoki:BMS: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Mohamed:BMS: Employment, Stock/stock options; travel/accommodations/meeting expenses unrelated to activities listed Other. Shah:BMS: Consultancy, Grants/grants pending to institution for costs related to clinical research Other; Ariad: Consultancy, Grants/grants pending to institution for costs related to clinical research, Grants/grants pending to institution for costs related to clinical research Other.

Description: Key Points Germline activating STAT3 mutations were detected in 3 patients with autoimmunity, hypogammaglobulinemia, and mycobacterial disease. T-cell lymphoproliferation, deficiency of regulatory and helper 17 T cells, natural killer cells, dendritic cells, and eosinophils were common.