Characterization of Streptomyces coelicolor ParH in development-associated chromosome segregation

S. coelicolor uses an active chromosome partitioning system for developmentally-regulated genome segregation, which is associated with spore formation. There are four known trans-acting segregation proteins (ParA, ParB, ParJ and Scy) and cis-acting centromere-like sites (parS). parA encodes a Walker-type ATPase that is required for efficient DNA segregation and proper placement of the ParB-parS nucleoprotein complexes. A paralogue of ParA is encoded by the S. coelicolor genome, SCO1772 (named ParH), that has 45% identical residues to ParA. In S. coelicolor aerial hyphae, a ∆parH mutant produces 5% of anucleate spores. In this study, ParH was identified as a novel interaction partner of S. coelicolor ParB. However, a Walker A motif K99E substitution in ParH and removal an N-terminal extension in ParH impaired interaction between ParH and ParB, as judged by bacterial two-hybrid analyses. ParH-EGFP localization resembles the evenly-spaced localization pattern of ParH-EGFP in aerial hyphae, which might suggest that ParH colocalizes with ParB. A parH-null mutant appears to be unable to properly organize the oriC regions within a subset of prespores, as judged by ParB-EGFP foci. In this study, through a random chromosomal library screening, a novel protein that interacts with ParA and ParH was also identified. HaaA (ParH and ParA Associated protein A) is required for proper chromosome segregation and is one of the 24 signature proteins of the Actinomycetes that are not found in other bacterial lineages. A bacterial two-hybrid analysis showed that HaaA interacts with itself and interaction between ParH and ParA was through the C-terminal unstructured region. Interaction between HaaA and ParA and ParA-like proteins was conserved in other Actinomycetes, such as S. venezuelae, C. glutamicum and M. smegmatis. There was no evidence for interaction with other tested segregation proteins. In addition, a haaA insertion-deletion mutant strain revealed that loss of HaaA affected chromosome segregation (6% anucleate spores) and HaaA-EGFP localizes within spores of the mature spore chains. Together these data revealed new information to further understand chromosome segregation in S. coelicolor

A Machine Learning Model of Response to Hypomethylating Agents in Myelodysplastic Syndromes

Hypomethylating agents (HMA) prolong survival and improve cytopenias in individuals with higher-risk myelodysplastic syndrome (MDS). Only 30-40% of patients, however, respond to HMAs, and responses may not occur for more than 6 months after HMA initiation. We developed a model to more rapidly assess HMA response by analyzing early changes in patients’ blood counts. Three institutions’ data were used to develop a model that assessed patients’ response to therapy 90 days after the initiation using serial blood counts. The model was developed with a training cohort of 424 patients from2 institutions and validated on an independent cohort of 90 patients. The final model achieved an area under the receiver operating characteristic curve (AUROC) of 0.79 in the train/test group and 0.84 in the validation group. The model provides cohort-wide and individual- level explanations for model predictions, and model certainty can be interrogated to gauge the reliability of a given prediction

Multiple Myeloma Therapy: Emerging Trends and Challenges

Multiple myeloma (MM) is a complex hematologic malignancy characterized by the uncontrolled proliferation of clonal plasma cells in the bone marrow that secrete large amounts of immunoglobulins and other non-functional proteins. Despite decades of progress and several landmark therapeutic advancements, MM remains incurable in most cases. Standard of care frontline therapies have limited durable efficacy, with the majority of patients eventually relapsing, either early or later. Induced drug resistance via up-modulations of signaling cascades that circumvent the effect of drugs and the emergence of genetically heterogeneous sub-clones are the major causes of the relapsed-refractory state of MM. Cytopenias from cumulative treatment toxicity and disease refractoriness limit therapeutic options, hence creating an urgent need for innovative approaches effective against highly heterogeneous myeloma cell populations. Here, we present a comprehensive overview of the current and future treatment paradigm of MM, and highlight the gaps in therapeutic translations of recent advances in targeted therapy and immunotherapy. We also discuss the therapeutic potential of emerging preclinical research in multiple myeloma

Clinical and Molecular Heterogeneity of Moderate Aplastic Anemia

Abstract Based on clinical Camitta criteria, acquired aplastic anemia is categorized according to the degree of blood count depression as severe (sAA) or moderate (mAA). In some cases mAA is a precursor of sAA; in others it is a pathophysiologically distinct chronic non-progressive entity (chronic mAA (cmAA)). It is also possible that some seemingly mAA cases represent entities ranging from congenital bone marrow failure syndromes or hypocellular MDS to misdiagnosed systemic conditions with secondary aplasias. Discriminating true cmAA from these may be important for clinical management and requires prolonged observation. We have been able to accurately dx and treat patients (pts) with acute sAA. In contrast, pts with cmAA can have mildly depressed counts, which remain relatively stable for yrs without the need for treatment (tx).Tx delay, though, may lead to unopposed destruction of stem cells. At our institution we have evaluated and managed 308 AA pts from 1998-2018. Of these pts, we identified 88 who met the Camitta criteria for mAA and of these, 2 progressed to sAA within 3 mos, 1 had clinically significant PNH at dx, and 85 were truly cmAA. Focusing on this true cmAA cohort our goals were to identify its distinct clinical features and response to therapies. The median f/u for the cohort was 45 mos, with median Hgb 10.1 g/dl, ANC 1.36 k/uL, Plts 47 k/uL, and absolute retic count of 0.053 M/uL at dx. The median age for cmAA was 43 yrs (range 6-88), with 55% (47/85) females (vs 48% (108/223) for sAA p=.31). By ultra-sensitive flow cytometry, PNH clones were found in 26% (22/85) of pts at presentation of cmAA vs 22% (50/223) for sAA. From initial dx to last f/u 72% (61/85) remained mAA, of whom 57% (35/61) of pts did not require any tx. Those pts who required tx received at least one line of therapy, in most instances immunosuppression with cyclosporine (ORR~67%) and supportive care. Of those who required tx, 46% achieved a CR, 35% PR and 19% NR to 1st line tx. As no obvious distinction for cmAA was identified based on clinical presentation, we next classified pts based on the number of cell lineages involved, transfusion requirements, and severity of the counts (characterized into mild, moderate and severe based on severity of deviation from normal). At the time of dx 29 pts were transfusion dependent, of which one pt had a single cytopenia, 8 pts had bicytopenia, and 20 had pancytopenia. Based on our findings 34% (10/29) of pts who were transfusion dependent with borderline severe pancytopenia received ≥ 1 therapy and eventually progressed to sAA, in contrast to those mAA that were transfusion independent with similar counts. The overall median time to transformation was 14 yrs (CI=95%, 5.9-21.7). Average time to progression to sAA was 31 mos, with a progression rate of 21% (CI=95%; 10-30). Across the entire cohort, 16% (14/85) progressed to sAA, 11% (9/85) to full-blown PNH, and 1% (1/85) to AML. At progression 3 pts acquired -7 and another acquired additional chromosome six. One pt did receive allo-HSCT for mAA. In contrast, in sAA 8.5% (19/223) pts progressed to MDS/AML and 8.5% (19/223) to PNH, where the median time to transformation was 24 yrs. We also performed a sub-cohort analysis of somatic events present in these pts: at dx 79% (67/85) had normal karyotype. NGS for somatic mutations revealed the presence of at least 1 mutation in cmAA and sAA (p:0.47). Serial NGS was available for 11/85 for assessment of clonal dynamics. At the time of dx 8 out of 11 pts had no mutations at presentations and the three other pts had a single mutation in RUNX1, ASXL1, and PIGA, of which the RUNX1 was found to be a transient clone (VAF: 30%) at the time of last f/u. The pt with ASXL1 eventually progressed to MDS with -7 with expansion of the clone from 3 to 19% and the pt with a PIGA clone was never treated for mAA progressed to full-blown PNH requiring tx with eculizumab. NGS from the last follow up revealed 6/11 pts acquired single mutation ETV6 (VUS), ASXL1, and PIGA, of which 4 pts went on to receiving tx for PNH. In sum, our results suggest that cmAA does not have, unlike sAA, a relentless clinical course. The rate of evolution from mAA to sAA was 30% in 10 yrs with median time to progression 2.6 yrs, with a similar rate of evolution to PNH with median time to progression of 4yrs and only one pt progressed to AML. In relation to sAA, there was not a significant difference in progression to MDS or PNH and the survival and response to therapy of these pts was excellent as expected from Camitta's criteria. Disclosures Thota: Incyte: Speakers Bureau. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Carraway:Balaxa: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; FibroGen: Consultancy; Jazz: Speakers Bureau; Amgen: Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy, Speakers Bureau; Novartis: Speakers Bureau. Maciejewski:Ra Pharmaceuticals, Inc: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Apellis Pharmaceuticals: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Apellis Pharmaceuticals: Consultancy

TET Dioxygenase Inhibition As a Therapeutic Strategy in TET2 Mutant Myeloid Neoplasia

TET2 is one of the most commonly mutated genes in myeloid neoplasia. Somatic TET2 mutations (TET2MT) cause complete or partial loss of enzymatic activity. TET2 (along with TET1/3) are Fe2+ and αKG-dependent DNA-dioxygenases that catalyze the oxidation of 5mC→5hmC→5fC→5caC. Ultimately, 5hmC generated by TET2-dioxygenase passively prevents maintenance methylation due to DNA methyltransferase's inability to recognize 5hmC. Alternatively, demethylation may also be a result of base excision repair of fC and caC. TET2MT can serve as therapeutic targets because they are often initiating lesions and present in a large fraction of patients. In this study, a comprehensive analysis of the configurations of TET2MT in myeloid neoplasia including MDS (n=1809) and AML (n=808), showed a remarkable exclusivity with 2-HG producing neomorphic IDH1/2MT (Fig.A). TET2 expression in 97 healthy and 909 MDS/MPN or AML patients from two independent studies showed that IDH1/2MT cases have significantly higher TET2 expression and were also mutually exclusive in cases with lower TET2 expression. Doxycycline inducible expression of IDH1MT led to profound growth inhibition of both a natural TET2MT cell line SIG-M5 and engineered TET2-/- K562, while the effect to parental K562 was mild (Fig.B-E). These observations suggest that mutual exclusivity of TET2MT and IDH1/2MT is due to synthetic lethality of TET2MT cell caused by 2-HG production, rather than redundancy of the consequences of IDH1/2MT and TET2MT. In TET2MT cell 2-HG further inhibit the residual TET-activity (TET1/3) and may cause synthetic lethality to cells with affected TET2 function. SIG-M5 cells expresses significant amount of TET3 while negligible levels of TET1. The reliance on relative compensation through residual TET3 activity has been confirmed in cells by inducible TET3 knockdown. We hypothesized that transient suppression of the residual DNA dioxygenase activity with inhibitors may selectively eliminate TET2-deficient clones. The known TET inhibitors 2-HG, N-oxalylglycine (NOG) and dimethyl methyl fumarate (DMF) lack specificity, pharmacologic properties and potency. Based on the results of in silico docking simulations, we designed and synthesized 16 aKG derivatives. Among them, TETi76 showed best inhibition effect in both TET activity and cell growth of TET2 low expressing cell. TETi76 binds to the α-KG co-factor site of TET2 that principally involves H1801, H1381 and S1898. These amino acids are conserved in all three TET enzymes. To test the in vitro efficacy and specificity of TETi, we used several human myeloid cell lines that harbor loss of function TET2 mutations or constitutively express low TET2 levels as well as bone marrow derived from Tet2+/+, Tet2+/- and Tet2-/- mice (Fig.F-G). Results showed that cells with low 5hmC level were more sensitive to TETi76 treatment. Specificity of TETi76 was further confirmed by RNAseq analyses of TETi76 treated K562, TET2-/- K562 and parental control cells. Moreover, TETi treatment did not appear to affect the function of α-KG-dependent histone dioxygenases. Mechanistically, treatment of SIG-M5 cells with TETi76 induced early and late stages of apoptotic cell death, a finding further confirmed by PARP1 and caspase-3 cleavage. RNAseq analyses of SIGM5 cells after treatment with TETi demonstrated a significant down-regulation of genes involved in transcription and peptide elongation, consistent with the consequences of TET inhibition. Interestingly, we also observed significant up-modulation of oxidative stress response pathway genes consistent with the inhibition of dioxygenases. In particular, TETi76 treatment induces 8-fold increase of oxidative stress sensor NQO1 a NRF2 target gene. To further probe the effects of TETi76 on TET2 deficient cells, Tet2MT/Tet2WT BM cells were co-cultured at fixed ratios to mimic the evolving Tet2MT clones. TETi76 effectively eliminated otherwise dominating Tet2MT cells (Fig.H). To determine the in vivo effects of TETi e.g., on elimination of Tet2MT clones, we performed bone marrow competitive reconstitution assays in PEP mice. TETi treatment selectively restricted the proliferative advantage of Tet2MT HSC compared to vehicle control where, as expected, TET2 mutant clones took over the WT cells. In clinical applications, TET inhibitors may constitute a new class of agents to be used in a targeted fashion in TET2 mutant neoplasia. Figure. Disclosures Meggendorfer: MLL Munich Leukemia Laboratory: Employment. Abazeed:Bayer AG: Honoraria, Other: Travel Support, Research Funding; Siemens: Research Funding. Sekeres:Millenium: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski:Novartis: Consultancy; Alexion: Consultancy

Genomic Biomarkers Predict Response/Resistance to Lenalidomide in Non-Del(5q) Myelodysplastic Syndromes

Abstract Lenalidomide (Len) is FDA approved for the treatment of patients (pts) with lower-risk, transfusion-dependent myelodysplastic syndromes (MDS) with deletion(5q). It is frequently used in lower-risk pts with non-del(5q) MDS, with a transfusion independence response rate of 27%. Identification of pts who may or may not respond to Len can prevent prolonged exposure to ineffective therapy, avoid toxicities, and decrease unnecessary costs. Clinical or genomic data have limited utility in predicting response/resistance to Len. We developed an unbiased framework to study the association of several mutations/cytogenetic abnormalities in predicting response/resistance to Len in non-del(5q) pts, analogous to Netflix or Amazon's recommender system, in which customers who bought products A and B are likely to buy C: pts who have a molecular/cytogenetic abnormalities in gene A, and B are likely to respond or not respond to Len. Clinical and genomic data from pts with MDS or other myeloid malignancies diagnosed according to 2008 WHO criteria between 02/2004 and 06/2015 were analyzed. Next generation targeted deep sequencing panel of 50 genes that are commonly mutated in MDS and myeloid malignancies was included. Association rules using an apriori algorithm were used to study the relationships among multiple genes/cytogenetic abnormalities and response/resistance to Len. Responses included complete and partial remission and hematologic improvement (CR, PR, HI) per IWG 2006 criteria. Pts with stable disease or progressive disease were considered resistant. Association rules are a machine learning algorithm used to identify the association of variables based on their relationships. Rules with the highest confidence (that an association exists) and highest lift (measuring the strength of the association) were chosen. Of 139 pts treated with Len as monotherapy or in combination for at least 2 cycles included, 118 (85%) had MDS and 21 (15%) had other myeloid malignancies. Median age at diagnosis was 69 years (range 20-90 yrs) and 45% were female. Risk stratification by IPSS-R for MDS pts; 51.5 % had very low/low risk, 19.5% intermediate, and 29% high and very high risk disease. Most pts 100 (73%) had non-del(5q) abnormalities, others (39) had del(5q). Cytogenetic abnormalities for the non-del(5q) cohort included 58 pts with normal karyotype (NK), 19 pts with complex karyotype (CK), 4 pts with trisomy 8, 3 pts with del(7q) abnormalities, and 15 pts with other abnormalities. A total of 108 (79%) pts were treated with Len monotherapy. The median duration of treatment was 6 months (range 2- 66 m). Response rates were 46% (n=46) in the non-del(5q) cohort and 74% (n=29) in del(5q). Association rules identified the following combinations of genomic/cytogenetic abnormalities to predict response to Len in non-del(5q): (DDX41, NK) and (MECOM, KDM6A/KDM6B). The combination of the following abnormalities predicted resistance (ASXL1, TET2, NK), (DNMT3A, SF3B1), (TP53, del(5q)+CK), (STAG2, IDH 1/2, NK), (EZH2, NK), (BCOR/ BCORL1, NK), (JAK2, TET2, NK), (U2AF1, +/- ETV6, NK). [Table 1] Only TP53/CK mutations predicted resistance to Len in del(5q) pts. These associations are present in 39% of pts with non-del(5q), and have a specificity of 77%, with a negative predictive value and sensitivity=100%. The algorithm predicted response/resistance to Len with 82% accuracy. The median OS for non-del(5q) pts was 33.2m [95% CI: 19.9, 40.5]. The median OS for responders was 54.8 compared to 24.7 m for non-responders p=.017. The median OS for rules that predicted response was 70.3 m (95% CI: 70.3-NA). The median OS for pts with del(5q) + CK with a TP53 mutation was 9.8m. Several genomic combinations predicted very poor overall survival, including: (ETV6, U2AF1, NK), (BCOR/ BCORL1, NK), (EZH2, NK) , (JAK2, TET2, NK), with median OS of 10.7 m, 7.6 m, 10.8 m and 7.6 m, respectively. [Figure 1] Genomic biomarkers can identify 39% of non-del(5q) MDS pts who may or may not respond to treatment with very high accuracy. Although these abnormalities are only present in a subset of pts, treatment options for these pts can be tailored, by offering alternative therapies to pts with lower-risk disease who may not respond to Len, and preferentially offering Len to those who are more likely to respond. This study highlights how advanced analytic technologies such as machine learning can translate genomic/clinic data into useful clinical tools. Disclosures Sekeres: Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Gerds:Celgene: Consultancy; Apexx Oncology: Consultancy; CTI Biopharma: Consultancy; Incyte: Consultancy. Carraway:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Membership on an entity's Board of Directors or advisory committees; Novartis: Speakers Bureau; Balaxa: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; FibroGen: Consultancy; Jazz: Speakers Bureau; Agios: Consultancy, Speakers Bureau. Santini:Novartis: Honoraria; Amgen: Membership on an entity's Board of Directors or advisory committees; Otsuka: Consultancy; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees. Maciejewski:Ra Pharmaceuticals, Inc: Consultancy; 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; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Nazha:MEI: Consultancy

Multicenter Validation of a Personalized Model to Predict Hypomethylating Agent Response in Myelodysplastic Syndromes (MDS)

Background While hypomethylating agents (HMAs) can improve cytopenias and even survival for MDS patients (pts), only 30-40% of pts respond to HMAs. Predicting response or resistance to therapy can improve pt outcomes, decrease cost and toxicities, and suggest alternative therapies when response is unlikely. No clinical or molecular model can reliability predict response or resistance to HMAs. We developed and validated a model to provide personalized predictions of response or resistance to HMAs during 12 weeks of treatment by monitoring changes in blood counts during therapy. Methods MDS pts treated with HMAs (azacitidine or decitabine) at Cleveland Clinic (314 pts) and the Moffit Cancer Center (100) and had their CBCs with differential monitored every 1-2 weeks in the first 12 weeks of therapy compromised the training cohort. The final model was externally validated in 80 MDS pts treated with HMAs at Sunnybrook hospital. Responses were defined per 2006 IWG criteria and pts with complete response (CR), marrow CR, partial response (PR), or hematologic improvement (HI) were considered responders. Time series analysis (analysis of serial changes in blood count parameters) using machine learning technology was used to develop the model, analogous to voice recognition algorithms such as Apple's Siri and Alexa, in which the sequence of words allows these algorithms to understand sentences. Changes in blood counts and monitoring the patterns of these changes during HMA therapy similarly can predict response/resistance to treatment. The area under the curve (AUC) was used to evaluate the performance of the final model. A feature importance algorithm was used to define the variables that most impacted the algorithm's decision for a given pt. Results For 494 included pts from all cohorts, the median age was 72 years (range: 40-94), 145 (29%) were female. Pts' IPSS-R scores at the time of treatment were: very low 4%; low 21%; intermediate 24%; high 21%; and very high 22%. Responses included: 56 (11%) complete remission (CR), 17 (3%) marrow CR, 6 (3%) partial remission (PR), and 143 (29%) hematologic improvement (HI). When trained exclusively on serial CBC values (adding other clinical or molecular values did not improve the model's performance), the model achieved an AUC of 0.82 in a cross-validated train/test schema and a similar AUC of 0.78 when it was applied to the Sunnybrook cohort. Feature importance algorithms identified improvements in hemoglobin from baseline between days 21-30 of therapy, improvement in platelets between days 51 and 60, changes in monocyte % between days 41 and 50, and changes in MCV and RDW between days 31 and 60 as predictors of response, Figure 1a. The model also can provide a personalized heatmap that summarizes the variables that impacted the response or resistance to HMAs and are specific for a given pt, Figure 1b, 1c. Conclusions We developed and externally validated a personalized prediction model that uses changes in blood counts during the initial 3 cycles of HMA therapy and can predict response or resistance to treatment with high accuracy. The model can provide personalized explanations of the variables that inform a given outcome. It can be used to develop novel clinical trial designs in which pts who are predicted not to respond within 3 cycles of HMA therapy can receive an investigational agent in addition to continuing HMA or change treatment entirely, whereas patients who are predicted to respond continue to receive HMA monotherapy. Disclosures Sallman: Agios, Bristol Myers Squibb, Celyad Oncology, Incyte, Intellia Therapeutics, Kite Pharma, Novartis, Syndax: Consultancy; Celgene, Jazz Pharma: Research Funding. Buckstein:Celgene: Research Funding; Takeda: Research Funding; Celgene: Honoraria; Astex: Honoraria; Novartis: Honoraria. Brunner:Forty Seven, Inc: Consultancy; Biogen: Consultancy; Acceleron Pharma Inc.: Consultancy; Jazz Pharma: Consultancy; Novartis: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Xcenda: Consultancy; GSK: Research Funding; Janssen: Research Funding; Astra Zeneca: Research Funding; Celgene/BMS: Consultancy, Research Funding. Mukherjee:Celgene/Acceleron: Membership on an entity's Board of Directors or advisory committees; Aplastic Anemia and MDS International Foundation: Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Bristol Myers Squib: Honoraria; Partnership for Health Analytic Research, LLC (PHAR, LLC): Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; EUSA Pharma: Consultancy. Komrokji:Abbvie: Honoraria; Agios: Speakers Bureau; BMS: Honoraria, Speakers Bureau; Jazz: Honoraria, Speakers Bureau; Incyte: Honoraria; Acceleron: Honoraria; Geron: Honoraria; Novartis: Honoraria. Maciejewski:Novartis, Roche: Consultancy, Honoraria; Alexion, BMS: Speakers Bureau. Sekeres:BMS: Consultancy; Pfizer: Consultancy; Takeda/Millenium: Consultancy. Nazha:Jazz: Research Funding; Incyte: Speakers Bureau; Novartis: Speakers Bureau; MEI: Other: Data monitoring Committee

Predicting Response to Hypomethylating Agents in Patients with Myelodysplastic Syndromes (MDS) Using Artificial Intelligence (AI)

Introduction While the hypomethylating agents (HMAs) azacitidine (AZA) and decitabine (DAC) improve cytopenias and prolong survival in MDS patients (pts), response is not guaranteed. Timely identification of non-responders could prevent prolonged exposure to ineffective therapy, thereby reducing toxicities and costs. Currently no widely accepted clinical or genomic models exist to predict response or resistance to HMAs. We developed a clinical model to predict response or resistance to HMA after 90 days of initiating therapy based on changes in blood counts using time series analysis technology similar to the kind used in Apple's Siri or Google Assistant. In the setting of voice recognition, the sequence and context of words determines the meaning of a sentence; similarly, we hypothesized that the pattern of changes in MDS pts' blood counts would predict response or resistance early during treatment. Methods We screened a cohort of 107 pts with MDS (per 2016 WHO criteria) who received HMAs at our institution between February 2005 and July 2013 and had regular CBCs drawn during treatment. Mutations from a panel of 60 genes commonly mutated in myeloid malignancy were included. Responses were assessed after 6 months of therapy per International Working Group (IWG) 2006 criteria. Pts were divided randomly into training (80%) and validation (20%) cohorts. To address the potential for bias due to a small sample size, an oversampling algorithm was used to cluster similar pts based on their CBC data, Revised International Prognostic Scoring System (IPSS-R) score, and % bone marrow blasts at the time of diagnosis. CBC data from the first 90 days of treatment were fed into deep neural network (recurrent neural network) and decision tree algorithms, which were trained to predict whether pts would achieve a response (defined as complete remission (CR), partial remission (PR), or hematologic Improvement (HI)). Area under the curve (AUC) was used to assess model performance. Important features that impact the algorithm's predictions were extracted and plotted. Results 20747 unique data points were used, including CBC, clinical and genomic data. Among 107 pts, 61 (57.0%) received AZA only, 19 (17.8%) DAC only, 4 (3.7%) received both DAC and AZA, and 23 (21.5%) received HMA with an additional agent. Median age was 69 years (range: 37-100 years), and 27 (26.4%) were female. Forty pts (37.4%) were very low/low risk, 32 (29.9%) intermediate, 19 (17.8%) high, and 16 (14.9%) very high risk per IPSS-R. Responses included 23 (22.5%) CR, 2 (1.9%) marrow CR, 4 (3.9%) PR, and 20 (19.6%) HI. The most commonly mutated genes were ASXL1 (17.6%), TET2 (16.7%), SRSF2 (15.7%), SF3B1 (11.8%), RUNX1 (10.8%), STAG2(10.8%), and DNMT3A (10.8%). The median number of mutations per sample was 1 (range, 0-11), and 40 pts (39.2%) had > 3 mutations per sample. When trained using absolute values and changes in CBC values, the model's AUC was 0.95 in the training cohort and 0.83 in the validation cohort. When the cohort was oversampled to 1000 pts, the validation cohort AUC increased to 0.89. Feature extraction algorithms identified increases in MCV and RDW during weeks 2-8 of treatment, increased proportion of lymphocytes, decreased proportion of monocytes, and increased platelet counts during weeks 6-8 as factors favoring response to HMA. The model provides personalized, patient-specific predictions that correlate with blood counts (Figure 1). Conclusions We describe a machine learning model that monitors changes in blood counts during therapy with HMA to predict response or resistance to HMA in MDS pts. Such a model can be used to develop novel trial designs wherein pts predicted to not respond after 90 days of HMA treatment could be assigned to an investigational agent. Conversely, it would help inform the decision to continue HMA therapy in pts predicted to respond. Increasing sample size with oversampling dramatically increased model accuracy; a larger cohort of pts treated at different institutions is currently under development. Disclosures Sekeres: Millenium: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Mukherjee:Partnership for Health Analytic Research, LLC (PHAR, LLC): Consultancy; Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Projects in Knowledge: Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria; McGraw Hill Hematology Oncology Board Review: Other: Editor; Bristol-Myers Squibb: Speakers Bureau. Advani:Glycomimetics: Consultancy, Research Funding; Kite Pharmaceuticals: Consultancy; Amgen: Research Funding; Pfizer: Honoraria, Research Funding; Macrogenics: Research Funding; Abbvie: Research Funding. Maciejewski:Alexion: Consultancy; Novartis: Consultancy. Nazha:Novartis: Speakers Bureau; Tolero, Karyopharma: Honoraria; Abbvie: Consultancy; Jazz Pharmacutical: Research Funding; Incyte: Speakers Bureau; Daiichi Sankyo: Consultancy; MEI: Other: Data monitoring Committee

Eltrombopag inhibits TET dioxygenase to contribute to hematopoietic stem cell expansion in aplastic anemia

Eltrombopag, an FDA-approved non-peptidyl thrombopoietin receptor agonist, is clinically used for the treatment of aplastic anemia, a disease characterized by hematopoietic stem cell failure and pancytopenia, to improve platelet counts and stem cell function. Eltrombopag treatment results in a durable trilineage hematopoietic expansion in patients. Some of the eltrombopag hematopoietic activity has been attributed to its off-target effects, including iron chelation properties. However, the mechanism of action for its full spectrum of clinical effects is still poorly understood. Here, we report that eltrombopag bound to the TET2 catalytic domain and inhibited its dioxygenase activity, which was independent of its role as an iron chelator. The DNA demethylating enzyme TET2, essential for hematopoietic stem cell differentiation and lineage commitment, is frequently mutated in myeloid malignancies. Eltrombopag treatment expanded TET2-proficient normal hematopoietic stem and progenitor cells, in part because of its ability to mimic loss of TET2 with simultaneous thrombopoietin receptor activation. On the contrary, TET inhibition in TET2 mutant malignant myeloid cells prevented neoplastic clonal evolution in vitro and in vivo. This mechanism of action may offer a restorative therapeutic index and provide a scientific rationale to treat selected patients with TET2 mutant-associated or TET deficiency-associated myeloid malignancies

Molecular dissection of normal karyotype acute myeloid leukemia

7534 Background: Conventional cytogenetics remain one of the most important prognostic factors in acute myeloid leukemia (AML), though 50-60% of patients (pts) have normal karyotype (NK), conventionally classified as intermediate-risk, and have very heterogeneous outcomes. A fraction of mutations such as NPM1, FLT3-ITD, and CEBPa can improve risk stratification for some pts but underestimate the molecular complexity and interactions between these genes and others. Methods: Genomic and clinical data of 2,793 primary AML (pAML) pts were analyzed. A panel of 35 genes that are commonly mutated in AML and myeloid malignancies and have shown to impact OS was included. Correlation of each mutation with others and their impact on OS were evaluated. OS was calculated from the date of diagnosis to date of death or last follow-up. Results: Of 2,793 pts with pAML, 1,352 (48%) had NK and were included in the final analysis. The median age was 55 years (range, 18-93). The median number of mutations/sample was 3 (range, 0-7). The most commonly mutated genes were: NPM1 (49%), DNMT3A (37%), FLT3-ITD (24%), CEBPa (19%), TET2 (17%), IDH2 (17%), and RUNX1 (15%). In univariate Cox regression analysis, mutations in NPM1 (HR 0.81, p =0.008), and CEBPa (single mutant, HR 0.8, double mutant, HR 0.69, p< 0.001, respectively) were associated with longer OS, while mutations in DNMT3a (HR 1.26, p =0.003), FLT3-ITD (HR 1.49, p< 0.001), TET2 (HR 1.26, p =0.02), RUNX1 (HR 1.36, p =0.003), SRSF2 (HR 1.58, p <0.001), IDH1 (HR 1.29, p <0.001), and ASXL1 (HR 1.89, p <0.001) were associated with shorter OS. A total of 67% of pts had NPM1, DNMT3A, and FLT3-ITD mutated alone or in combination with each other. The median OS for pts with NMP1 Mut / DNMT3A WT /FLT3-ITD WT was 99.1 months(m), NMP1 Mut /DNMT3A Mut /FLT3-ITD WT 54.8m, NMP1 Mu t /DNMT3A WT /FLT3-ITD Mut 42.3m, NMP1 Mut /DNMT3A Mut /FLT3-ITD Mut 13.4m, NMP1 WT /DNMT3A Mut /FLT3-ITD Mut 13.1m, and NMP1 WT /DNMT3A WT /FLT3-ITD WT (triple negative) 32.7m. The median OS for pts with 0-2 mutations/sample was 59.3m, compared to 34.1m for pts with 3-4 mutations, and 16.1m for pts with > 5 mutations ( p< 0.001). Conclusions: We propose a simplified and robust approach to risk stratify AML pts with NK based on the mutational status of NPM1, DNMT3A, FLT3-ITD (alone or in combination with each other), CEBPa, and the number of mutations/sample