ALBERT

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.

Description: Targeted tyrosine kinase inhibitors (TKIs) efficiently induce rapid hematologic and cytogenetic remission in most chronic myeloid leukemia (CML) patients. However, in vitro experiments have suggested that the most primitive CML stem cells residing in the CD34posCD38neg fraction are relatively resistant to TKIs. The prevalence of these stem cells in vivo in patients under TKI therapy is unclear. The aim of this project was to analyze the effect of TKI therapy on Ph+ leukemia stem cell pool in patients and to analyze the proportion of Ph+ cells in different stem cell fractions. A total of 26 chronic phase CML patients were included in the study. 18 patients were treated with imatinib, 5 with dasatinib, and 3 with bosutinib. The median time of TKI treatment was 20 months (range 3–72 months). Large volume (median 30 ml, range 5–55 ml) of bone marrow (BM) aspirate was collected and mononuclear cells (MNC) were isolated. CD34pos cells were separated with paramagnetic beads and further sorted into CD34posCD38pos and CD34posCD38neg cell populations with multicolor flow cytometry in order to analyze progenitor cell fractions of different maturation stage. Proportion of Ph+ cells was determined with interphase FISH by counting 1000 cells in each fraction. The median yield of MNCs from 30 ml of BM aspirate was 280x106 cells resulting in a median of 32 000 CD34posCD38neg cells (range 1000–91000). High-sensitivity counting of the proportion of Ph+ cells was feasible with a median number of counted interphase nuclei of 1005. During TKI therapy the CD34pos cells expressing highest CD38 antigen level were already mostly differentiated into B-cell lineage (CD19 positive). The CD34pos cells expressing low CD38 antigen levels expressed markers of more primitive cells such as C-kit (CD117) and CD133. Of 26 patients with CML, 19 were in complete cytogenetic remission (CCyR) when assessed by metaphase FISH of non-fractionated BM cells (1000 cells analyzed). Only 3 patients had single Ph+ cells in CD34pos cell fractions (less than 1%). In remainder of patients, all progenitor cell fractions, including the most primitive CD34posCD38neg cells, were negative for Ph+ cells. 3 patients had 0–1% of Ph+ cells in non-fractionated BM sample. One of them had 0.2% of Ph+ cells in CD34posCD38neg fraction, but the other 2 patients had 0/1000 Ph+ stem cells. 4 patients had a partial cytogenetic response (5–20% of Ph+ cells in non-fractionated BM sample). Again, the proportion of Ph+ cells was not increased in the most primitive CD34posCD38neg cell fraction. Interestingly, patients who had discontinued imatinib treatment had lower level of Ph+ cells in different CD34pos fractions (median 0.1%) when compared to non-fractionated BM (median 9.3%). Based on our data, in chronic phase CML patients, TKI therapy eradicates most Ph+ CD34pos progenitor cells. Unexpectedly, leukemic stem cells were not enriched in the most primitive CD34posCD38neg cell fraction in vivo. These results differ from the in vitro studies, where CD34posCD38neg cells have been shown to be resistant to TKIs. This could be due to non-physiological conditions (growth factor sensitivity, other cytokines) in cell culture assays. In addition, leukemic stem cells in vivo may be located in the subcortical hypoxic stem cell niche in the BM and are less likely to be aspirated. Our data underline the tremendous proliferative potential of very rare stem cells in CML patients in CCyR, as is evident after discontinuation of TKI therapy. Future studies evaluating the kinetics of disappearance of Ph+ cells from stem cell fractions during TKI therapy and the location of residual Ph+ stem cells in the BM are warranted and may give important information on the depth of the therapy response. Furthermore, this knowledge may aid in targeting therapy to these cells and finding curative treatment strategies in CML.

Description: Aberrant cytokine and growth factor signaling is the hallmark of CML and results from constitutive oligomerization of the oncogenic BCR-ABL tyrosine kinase (TK). Inhibition of BCR-ABL by imatinib mesylate is the current standard of care of CML and results in durable responses in majority of patients. However, a proportion of patients shows primary or secondary resistance to imatinib, which can be attributed either to selection of clones harboring mutations in the kinase domain of BCR-ABL or activation of a BCR-ABL independent pathway. Dasatinib, a potent multikinase inhibitor, can rescue some imatinib-resistant patients, but carries an increased risk of adverse effects due to inhibition of off-target wild-type kinases, particularly in immune effector cells. In concord, recent in vitro data indicate a profound immunosuppressive effect of dasatinib. The aim of this study was to analyze and predict TK inhibitor (TKI) resistance and off-target effects using single-cell profiling of aberrant phosphoprotein networks upon cytokine stimulus by multiparameter flow cytometry. The study cohort consisted of 5 healthy controls, 4 non-treated CML patients at diagnosis and 5 CML patients on dasatinib therapy and in cytogenetic remission. Stimuli included GM-CSF, IL-4+IL-6+IFNγ and IL-2+IL-10+IFNα and they were added to freshly drawn whole blood or bone marrow. The readout phosphoproteins were pERK1/2, pSTAT1, pSTAT3, pSTAT5a and pSTAT6 (with isotype controls), and were analyzed separately from granulocytes, monocytes, CD3+, CD4+ and CD8+ lymphocytes and regulatory T-cells. In unstimulated blood samples from healthy controls the phosphoproteins were essentially unphosphorylated. The responses to cytokines were consistent among individuals resulting in phosphorylation of ERK1/2, STAT3 and STAT5a on GM-CSF stimulus, STAT-1, STAT-3 and STAT-5a on IL-2+IL10+IFNα and STAT-1, STAT-3 and STAT-6 on IL4+IL6+IFN-γ. Compared to healthy controls, increased baseline phosphorylation of STAT-1, STAT-3 and STAT5a, but not ERK1/2 was seen in CML patients at diagnosis, especially in myeloid cell lineages (neutrophils/monocytes), but also in lymphocyte subgroups. The responses to cytokine stimulation were modest overall, in particular the ERK1/2 responses to GM-CSF were absent. This indicated the inactivation of the Ras/MEK/MAPK pathway and saturation of other BCR-ABL downstream pathways. Already at diagnosis, the phosphorylation pattern of a TKI primary resistant patient differed profoundly from the responding patients. Marked activation of STAT-1 and STAT-3 was seen in granulocytes and monocytes stimulated either by GM-CSF or by combination of IL2+IL10+IFN-α, suggesting activation by a pathway circumventing BCR-ABL. In dasatinib treated patients, the baseline activation status was similar in granulocytes and monocytes and slightly diminished in lymphocytes when compared to healthy controls. Similarly, the responses to cytokines resembled those seen in healthy controls, in contrast to published in vitro data. Remarkably, in some of the dasatinib treated patients, STAT1 and STAT3 responses were even more pronounced than in healthy controls. This underlines the importance of studying the in vivo/ex vivo effects of TKIs on off-target kinases, in particular of drugs with a short half-life such as dasatinib. In conclusion, inter-individual differences in TKI response and immunomodulatory effects of pan-TKI dasatinib can readily be discerned by analyzing key intracellular phosphoprotein responses to cytokine and growth factor stimuli ex vivo. The method allows profiling of aberrant signaling pathways in different subsets of leukocytes in CML patients and can be used to predict TKI resistance and spectrum of potential adverse effects due to inhibition of wildtype targets. Similar analyses of signaling pathways at the stem cell level are ongoing and may aid in understanding TKI resistance of CML stem cells.

Description: Background T-cell acute lymphoblastic leukemia (T-ALL) is caused by the cooperation of multiple oncogenic lesions. Recent evidence supports that IL-7 and its receptor IL-7R contribute to T-ALL development (Zenatti et al, 2011). The two main pathways induced by IL-7R are JAK/STAT5 and PI3K/Akt/mTOR. Activating mutations to IL7R, JAK1, JAK2 or JAK3 are estimated to occur in 20-30% of all T-ALL patients (Cools 2013). STAT5 plays an important role in many hematologic malignancies but constitutive STAT5 activation often is a secondary event. Mutations in STAT5B (N642H) were recently described in LGL-leukemia in patients with an unusually aggressive and fatal form of the disease (Rajala et al, 2013). In other cancers, including ALL, patients with mutations in STAT5B have not been described. Here we report novel activating STAT5B mutations as drivers of T-ALL. Methods We performed exome sequencing of bone marrow (BM) samples from an 18-year-old female with relapsed T-ALL. Targeted next-generation amplicon sequencing and Sanger sequencing was used to analyze the region encoding the STAT5B SRC homology 2 (SH2) domain including the N642, T648 and I704 codons in a cohort of 17 adult and pediatric T-ALL patients treated at HUCH 2008-2013. For functional studies STAT5B expression vectors with the N642H, T648S or I704L mutation and an expression vector with both N642H and T648S mutations were used to transiently transfect HEK293 cells. To investigate the effect on transcriptional activity we co-transfected the mutant constructs with a STAT5 luciferase reporter plasmid and used Western blot analysis to study the phosphorylation status of the generated constructs. For drug sensitivity of STAT5B mutated cells we performed ex vivo drug testing on primary blasts from the index patient using a comprehensive set of 202 oncology drugs (approved and in clinical development). Each drug was tested over a 10,000-fold concentration range. Results Sequencing of the index patient revealed 3 different somatic missense mutations in STAT5B (T648S, N642H, I704L) and mutations in KRAS, WT1 and SUZ12. No mutations affecting the JAK genes or IL7R were detected. All STAT5B mutations were located in the SH2 domain, which mediates dimerization and activation by trans-phosphotyrosine binding. The same three STAT5B mutations were also found in the diagnostic sample and most likely represent founding events in leukemogenesis. The N642H and T648S mutations occurred on the same allele with tumor mutation frequencies of approximately 40% while the I704L mutation occurred on a different allele with a similar tumor mutation frequency. To investigate the prevalence of STAT5B mutations in T-ALL we sequenced 17 BM samples from T-ALL patients. In this cohort we could not detect any other patients carrying mutations in the STAT5B SH2 domain. Western blot analysis made with mutant constructs showed that the N642H and I704L mutations induced constitutive phosphorylation of STAT5B. Compared to wild type STAT5B the N642H and I704L mutants induced 47- and 6-fold increases in transcriptional activity, respectively, while T648S mutation had no effect in the assays. The construct with both the N642H and T648S mutations showed the highest amount of constitutive phosphorylation and induced a 56-fold increase in transcriptional activity compared to wild type STAT5B. Using ex vivo drug testing the STAT5B mutated blasts were resistant (EC50≥1 uM) to inhibitors of PI3K (e.g. idelalisib, XL147), dual inhibitors of PI3K/mTOR (PF-04691502, dactolisib) and mTOR inhibitors (temsirolimus, everolimus). Furthermore the blasts showed no response to AKT1 inhibitors (MK-2220) or JAK inhibitors (ruxolitinb, tofacitinib). In contrast, the cells were most sensitive to the BCL-2/BCL-XL inhibitor navitoclax (EC50 83 nM). Summary STAT5B mutations are uncommon in T-ALL but their occurrence underlines the significance of the IL7R-JAK-STAT5 pathway in the pathogenesis of T-ALL. While STAT5B mutant blasts were not sensitive to inhibitors targeting JAK kinases, the cells were unusually sensitive to inhibitors of target molecules of STAT5B, including anti-apoptotic BCL-2 proteins. These results suggest that BCL-2/BCL-XL inhibitors such as navitoclax are novel candidate therapies for T-ALL patients. Disclosures: Mustjoki: Novartis: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau. Porkka:BMS: Consultancy, Research Funding, Speakers Bureau; Novartis: Consultancy, Research Funding, Speakers Bureau.

Description: Earlier evaluation of therapy effect in patients with CML would assist in optimal use of available tyrosine kinase inhibitors (TKI). Single cell analysis by mass cytometry has enabled the quantification of up to 46 antibody epitopes, making it ideally suited for exhaustive immunophenotyping of the haematological hierarchy, and evaluation of associated dynamic signal transduction events, in a clinical setting. By integrating time resolved single cell signalling data with clinical parameters, we searched for prognostic and efficacy-response mass cytometry biomarkers within a month of TKI therapy. We report data from experiments used to validate the custom panels of antibodies, highlighting the power of mass cytometry in the analysis of primary patient material obtained on clinical studies. Peripheral Blood (PB) samples were collected before, 3 hours, 7 days and 28 days, after start of nilotinib (300 mg BID) treatment in a subset of patients (n=55) enrolled in the ENEST1st trial. PB cells were stained with two panels of antibodies, allowing a comprehensive immunophenotyping of numerous cellular subsets, and also the evaluation of intracellular phosphorylation status of several epitopes. Moreover, using a straightforward barcoding scheme, the time-resolved samples from each individual patient were pooled after barcoding and stained with the antibody panels to minimize sample variation. In a pilot study, 7 and 10 cell subsets were identified in PB samples from 4 untreated healthy donors and 2 complete sets of 4 patients enrolled in this sub study, respectively. Furthermore, a robust signal was measured for pCrkL, pStat5, pStat3, pCreb, pAbl Y412 and pAbl Y245. The two sets of samples from study patients showed substantial changes in activation status over the course of therapy. Some changes, such as pStat3 alterations are only detectable in neutrophils and monocytes, while the activity of others i.e. pCreb was found to be ubiquitous. CD34+ cells indicated decreased phosphorylation of CrkL, Stat5, and Abl Y412/245. To increase the immunophenotyping resolution of the myeloid lineage, 3 additional cell surface markers were incorporated into the cell surface panel. In 1 healthy donor, and in diagnostic samples from three patients enrolled in this sub study, this allowed the identification of 13 cell subsets: CD3+, CD4+, and CD8+ T cells, regulatory T cells (Tregs), monocytes, dendritic cells (DCs), plasmacytoid dendritic cells (pDC's), neutrophils, basophils, B cells, hematopoietic stem cells (Lin- CD34+ CD38-) and progenitor cells (Lin- CD34+ CD38-) (Figure 1 A,B). With respect to the relative number of cells identified for each cell type, the three diagnosis samples differed from the single healthy control. In the patients, we observed an expansion of the granulocytic compartment, as well as the emergence of CD34+ progenitor and stem cells in the peripheral blood. In conclusion, the here presented developed assay is able to resolve most of the cell subpopulations found in the hematopoietic tree, and also robustly measure the activity of central signalling substrates known to be involved in CML pathogenesis. With the addition of new phospho-specific antibodies, the methodology may facilitate the detailed characterization of CML in an immunological context, and may shed new light on both the disease and therapeutic mechanism. Analysis of variation in signal responses and immune profile are now in progress in the subset of patients (n=55) in the ENEST1st trial. Figure 1. Manually annotated SPADE tree from healthy donor and patient (3581_0002). With the incorporation of additional cell surface markers, the protocol was able to identify 13 cellular subsets in healthy donors (A) and a typical CML patient (B): CD3+, CD4+, and CD8+ T cells, regulatory T cells (Tregs), monocytes, dendritic cells (DCs), plasmacytoid dendritic cells (pDC's), neutrophils, basophils, B cells, hematopoietic stem cells (Lin- CD34+ CD38-) and progenitor cells (Lin- CD34+ CD38-). Figure 1. Manually annotated SPADE tree from healthy donor and patient (3581_0002). With the incorporation of additional cell surface markers, the protocol was able to identify 13 cellular subsets in healthy donors (A) and a typical CML patient (B): CD3+, CD4+, and CD8+ T cells, regulatory T cells (Tregs), monocytes, dendritic cells (DCs), plasmacytoid dendritic cells (pDC's), neutrophils, basophils, B cells, hematopoietic stem cells (Lin- CD34+ CD38-) and progenitor cells (Lin- CD34+ CD38-). Disclosures Thaler: AOP Orphan: Research Funding. Lang:Celgene: Consultancy. Hjorth-Hansen:Bristol-Myers Squibb: Research Funding; Ariad: Honoraria; Novartis: Honoraria; Pfizer: Honoraria, Research Funding. Hellmann:Novartis: Consultancy, Other: funding of travel, accomodations or expenses, Research Funding, Speakers Bureau; BMS: Consultancy, Other: funding of travel, accomodations or expenses, Speakers Bureau. Giles:Novartis: Consultancy, Honoraria, Research Funding. Hochhaus:Novartis: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; ARIAD: Honoraria, Research Funding. Janssen:ARIAD: Consultancy; Bristol Myers Squibb: Consultancy; Pfizer: Consultancy; Novartis: Research Funding. Porkka:Bristol-Myers Squibb: Honoraria; Celgene: Honoraria; Novartis: Honoraria; Pfizer: Honoraria. Ossenkoppele:Novartis: Honoraria, Research Funding; BMS: Honoraria, Research Funding; ARIAD: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding. Mustjoki:Signe and Ane Gyllenberg Foundation: Research Funding; Finnish Cancer Institute: Research Funding; Sigrid Juselius Foundation: Research Funding; Pfizer: Honoraria, Research Funding; the Finnish Cancer Societies: Research Funding; Academy of Finland: Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Gjertsen:Bergen University Hospital: Research Funding.