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Background Despite the introduction of targeted therapies, most patients with myeloid malignancies will not be cured and progress. Genomics is useful to elucidate the mutational landscape but remains limited in the prediction of therapeutic outcome and identification of targets for resistance. Dysregulation of phosphorylation-based signaling pathways is a hallmark of cancer, and therefore, kinase-inhibitors are playing an increasingly important role as targeted treatments. Untargeted phosphoproteomics analysis pipelines have been published but show limitations in inferring kinase-activities and identifying potential biomarkers of response and resistance. Methods We developed a phosphoproteomics workflow based on titanium dioxide phosphopeptide enrichment with subsequent analysis by liquid chromatography tandem mass spectrometry (LC-MS). We applied a novel Kinase-Activity Enrichment Analysis (KAEA) pipeline on differential phosphoproteomics profiles, which is based on the recently published SetRank enrichment algorithm with reduced false positive rates. Kinase activities were inferred by this algorithm using an extensive reference database comprising five experimentally validated kinase-substrate meta-databases complemented with the NetworKIN in-silico prediction tool. For the proof of concept, we used human myeloid cell lines (K562, NB4, THP1, OCI-AML3, MOLM13 and MV4–11) with known oncogenic drivers and exposed them to clinically established kinase-inhibitors. Results Biologically meaningful over- and under-active kinases were identified by KAEA in the unperturbed human myeloid cell lines (K562, NB4, THP1, OCI-AML3 and MOLM13). To increase the inhibition signal of the driving oncogenic kinases, we exposed the K562 (BCR-ABL1) and MOLM13/MV4–11 (FLT3-ITD) cell lines to either Nilotinib or Midostaurin kinase inhibitors, respectively. We observed correct detection of expected direct (ABL, KIT, SRC) and indirect (MAPK) targets of Nilotinib in K562 as well as indirect (PRKC, MAPK, AKT, RPS6K) targets of Midostaurin in MOLM13/MV4–11, respectively. Moreover, our pipeline was able to characterize unexplored kinase-activities within the corresponding signaling networks. Conclusions We developed and validated a novel KAEA pipeline for the analysis of differential phosphoproteomics MS profiling data. We provide translational researchers with an improved instrument to characterize the biological behavior of kinases in response or resistance to targeted treatment. Further investigations are warranted to determine the utility of KAEA to characterize mechanisms of disease progression and treatment failure using primary patient samples. Graphical abstract

Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein–protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography–sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein–protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography–MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics. ISSN:1548-7105 ISSN:1548-7091

Anemic patients with lower risk myelodysplastic syndromes are frequently treated with erythropoiesis stimulating agents (ESA), eventually in combination with granulocyte colony stimulating factor (G-CSF). However, the evidence for the efficacy of a combined treatment remains controversial. The goal of our analysis was to assess the available evidence for a combined treatment. We performed a systematic review and identified only nine eligible studies. In two randomized controlled trials (n = 98), erythroid response rates were 33% and 40% after low-/standard-doses of ESA alone (10,000-30,000 rHuEPO equivalents/week) versus 65% and 73% after combination treatment. In seven trials with sequential drug administration (n = 393), erythroid response rates ranged from 12% to 71% after full-doses of ESA alone (60,000-80,000 rHuEPO equivalents/week) and from 35% to 74% after combination therapy. Our analysis supports an additional efficacy of G-CSF added to low-/standard-dose ESA, but the available data remains controversial, if G-CSF is added to full-dose ESA.

Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein-protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography-sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein-protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography-MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics.

Myelodysplastic syndromes (MDS) and associated diseases, like chronic myelomonocytic leukemias (CMML), are heterogeneous, clonal disorders affecting the hematopoietic stem cells. They are characterized by dysplasia and a propensity to evolve toward acute myeloid leukemia. Systemic inflammatory and autoimmune manifestations (SIAMs) occur with a prevalence of 10% to 20% in myeloid malignancies, but the underlying pathogenetic mechanisms remain obscure. In this study, we aimed to characterize patient- and disease-based differences in MDS and CMML patients with and without SIAMs and explore the impact of SIAMs on progression and survival. We performed a retrospective, single-centre, and case-control study in a cohort of 93 patients diagnosed with MDS and CMML between 01/2008 and 12/2015. Thirty patients (32%) were identified with SIAMs: musculoskeletal and connective tissue (26.8%), vascular (19.5%), systemic autoinflammation (17%), skin (12.2%), gastrointestinal (9.8%), and others (14.6%). SIAMs were treated with glucocorticoids (60%), methotrexate (16.7%), biologicals (13.3%), and cyclosporine (3.3%). No significant differences between the SIAM and non-SIAM patients were observed in age, gender, or previous exposure to cancer treatment. Cardiovascular comorbidities were significantly more frequent in patients with SIAMs (63.1% vs 90%; OR 5.5; P < .01), but no differences were observed for other comorbidities or IPSS and IPSS-R risk scores. CMML and refractory anemia with excess blasts 1/2 subtypes were by tendency more frequent in patients with and refractory cytopenia with multilineage dysplasia (RCMD) in those without SIAMs. Finally, time to progression, leukemia free survival and overall survival were similar for both groups. Despite patient heterogeneity and small cohort size, we were able to identify a significant association of SIAMs with cardiovascular comorbidities but without influence on progression or survival.

Background: Chronic myeloid neoplasms are heterogeneous malignancies caused by sequential accumulation of genetic lesions in hematopoietic stem cells with a tendency to evolve towards acute myeloid leukemia. Genomic approaches can stratify patients according to their mutational landscape but are limited in predicting response to therapeutic agents. Reliable biomarkers to identify patients' chances for response to targeting compounds remain a crucial necessity in the current post-genomic era of precision medicine. We hypothesize that differential phosphoproteomic (PP) profiles might represent a suitable functional biological layer that allows to identify relevant determinants of oncogenic phenotypes. Aim: The aim of the project was to build a bioinformatics pipeline using PP data to i) identify differentially phosphorylated sites, ii) infer targetable kinases and iii) characterize involved oncogenic pathways. Here, we present results from further exploratory experiments of a previously established PP analysis pipeline that enables to infer sensitive/bypassing kinase activities in a Midostaurin/PKC412 (MIDO) resistant myeloid cell line model. Methods: For the validation of our analysis pipeline, we have previously used the human myeloid cell lines K562 and MOLM13, driven by the oncogenic BCR-ABL1 and FLT3 kinases, and exposed to the kinase inhibitors Nilotinib (NILO) and MIDO, respectively. For the current biological study, we applied our pipeline to explore MIDO resistance mechanisms in published MIDO-resistant (rMOLM13) and sensitive (sMOLM13) cell lines (kindly provided by E. Weisberg, Dana Farber Cancer Institute, USA). r/sMOLM13 were cultured in triplicates for 24hrs and subsequently exposed for 1h to 25nM MIDO or DMSO (CTRL). PPs were enriched with titanium-dioxide and analyzed by mass spectrometry (nanoLC-MS2). Our previously validated Kinase Activity Enrichment Analysis (KAEA) bioinformatics pipeline was further applied to infer kinase activities based on the SetRank enrichment algorithm. KAEA integrates substrate-kinase datasets from five experimentally validated databases complemented with NetworKIN in-silico predictions. The pipeline is supported by a Shiny web-app interface to allow interactive visualization and interrogation of the data. Results: In our previous validation experiments, K562 treated with NILO showed expected inhibition of ABL1 and KIT, whereas MOLM13 treated with MIDO showed expected inhibition of PRKC and downstream kinases of FLT3 (AKT1 and MAPK). In the current biological exploration, we compared rMOLM13 vs sMOLM13 exposed to MIDO and CTRL. rMOLM13 showed slower proliferation and vacuolization compared to sMOLM13. 13,682 phosphorylation sites were identified and the three replicates clustered adequately in the corresponding four conditions sMOLM13_CTRL, sMOLM13_MIDO, rMOLM13_CTRL and rMOLM13_MIDO (fig. 1A). Clusters of over- or underexpressed sites associated with distinct conditions emerged in the heatmap. We performed a KAEA comparison of rMOLM13_MIDO vs sMOLM13_MIDO (fig. 1B) and rMOLM13_CTRL vs sMOLM13_CTRL (fig. 1C). CDK1 and other CDKs enriched as underactive in rMOLM13, in line with the observed reduced proliferation. Most interestingly, kinases PDHK1, PDHK2 and PKM, all pyruvate kinases involved in regulation of mitochondrial metabolism, enriched as overactive, as well as MAPK1/3 and CK2. Reduced proliferation, vacuolization and patterns of enriched kinases pointed towards autophagy as potential resistance mechanism in rMOLM13. We performed preliminary experiments with autophagy inhibitors in combination with MIDO, which supported a synergistic activity (fig. 1D). More results of combined inhibition of the identified kinases will be shown at the meeting. Conclusions: With our PP analysis pipeline, we were able to characterize, in unanticipated detail, dynamic changes of PP profiles, kinase activities and potentially involved oncogenic pathways in a MIDO resistant myeloid cell line model. We identified biologically relevant mechanisms of resistance that will be explored as potential targets for combination therapy. Our experiments in myeloid cell lines provide a proof of concept for our analysis pipeline. They allow us to move forward to the analysis of primary patient samples and promote precision medicine based on functional biomarkers identified by PP in patients with myeloid neoplasms. Disclosures Jankovic: Celgene: Other: financial support for travel. Bonadies:Roche: Other: financial support for travel, Research Funding; Novartis: Other: financial support for travel, Research Funding; Celgene: Other: financial support for travel, Research Funding; Janssen: Other: financial support for travel; Amgen: Other: financial support for travel; Sanofi Genzyme: Other: financial support for travel.

Hematopoietic stem cells (HSC) in higher vertebrate species, especially in mammals, maintain hematopoiesis throughout adult life and require critical cell cycle regulation for their self-renewal and cell fate decisions. Although cell cycle pathways are quite conserved across animal species, it is unknown whether a higher vertebrate specific cell cycle regulation exists in adult mammalian HSCs. Recently, we have published that Ribonuclease inhibitor (RNH1) regulates erythropoiesis by controlling GATA1 mRNA translation. Here, we report that RNH1, which is present only in higher vertebrates regulates HSC cell cycle and HSC function. To study the role of RNH1 in hematopoiesis, we generated hematopoietic-specific knockout mice by backcrossing Rnh1FL/FL mice with Vav1-iCre and Mx1-Cre mice, respectively. Rnh1-deficiency (Rnh1FL/FLVav1-iCre mice) resulted in hematopoietic alterations resembling emergency myelopoiesis. At 15 weeks of age Rnh1-deficient mice had reduced hemoglobin levels (144.4 ± 2.6 vs 165.0 ± 4.2 g/L, p = 0.005), decreased lymphocytes (4.1 ± 0.8 vs 9.6 ± 1.6 K/µL, p = 0.023), increased neutrophils (3.2 ± 0.6 vs 1.5 ± 0.2 K/µL, p = 0.046) and monocytes (0.65 ± 0.05 vs 0.09 ± 0.02 K/µL, p = 0.0001) in the peripheral blood. Total bone-marrow (BM) cellularity was similar in wild type andRnh1-deficient mice, however the number of erythroid cells and lymphoid cells (T and B cells) was significantly decreased, whereas myeloid cells were significantly increased. Rnh1-deficient spleens were significantly larger than wild type controls and showed extramedullary hematopoiesis. Surprisingly, although Rnh1-deficient mice showed myeloproliferation they survived normally and did not show progression to leukemia. However, they did not tolerate even little stress, such as 35 µg LPS administration, which lead to early mortality. We analysed the progenitor populations in the BM. In line with the myelopoiesis dominant phenotype granulocyte-monocyte progenitor (GMP) cell numbers were increased but common lymphoid progenitor (CLP) and megakaryocyte-erythrocyte progenitor (MEP) cell numbers were decreased. Cell extrinsic factors such as growth factors and the bone marrow niche play a critical role in shaping lineage choice. To exclude this, we performed bone marrow transplantation experiments (BMT) by transplanting wild type (Rnh1FL/FL) and Rnh1-deficient (Rnh1FL/FLMx1-Cre+) bone marrow into lethally irradiated CD45.1 congenic mice. After reconstitution Rnh1 was deleted by administration of polyinosinic:polycytidylic acid (polyI:C). We observed a similar myelopoiesis dominant phenotype in Rnh1-deleted mice. Interestingly, we found increased numbers of long term HSCs (LT-HSCs) and short term HSCs (ST-HSCs) in Rnh1-deficient mouse BM, suggesting that RNH1 could affect HSC function. Supporting this Rnh1-deficient HSCs failed to engraft lethally irradiated mice in competitive BMT experiments. Furthermore, Rnh1-deficient HSCs produced significantly less and smaller colonies in in-vitro colony forming cell (CFC) assays. Transcriptome analysis showed increased expression of genes related to cell cycle, kinetochore, DNA damage and decreased expression of genes related to stem cell function in Rnh1-deficient LT-HSCs and ST-HSCs. Corroborating this, Rnh1-deficient LT-HSCs and ST-HSCs showed increased S/G2/M phase in cell cycle analysis. In line with this, at the molecular level, we found that RNH1 directly binds to cell-cycle related proteins such as cyclin-dependent kinase 1 (CDK1), cell-division cycle protein 20 (CDC20) and mitotic checkpoint protein BUB3, suggesting direct involvement of RNH1 in cell cycle regulation. Confirming this, pharmacological inhibition of CDK1 (RO-3306, 10 µM) in Rnh1-deficinet ST-HSCs restored colony size in CFC assays, suggesting that RNH1 and CDK1 inhibition have a synergistic effect in ST-HSCs. In summary, our results demonstrate that RNH1, which is present only in higher vertebrates, is essential for HSC cell cycle regulation and steady state hematopoiesis. Disclosures No relevant conflicts of interest to declare.