Cellworks Group Research Articles

A Study to Predict Response to Azacitidine or Lenalidomide/Azacitidine Combination Therapy in MDS and AML Patients Using a Computational Biological Modeling (CBM) Approach

Background: Several treatment options are available for patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML); however, the majority of patients will fail treatment or relapse. Predicting response is imperative in determining the right treatment regimen for each patient but currently no validated method exists.Aim: In this study, we aimed to predict treatment response in patients with MDS or AML using Cellworks' computational biology modeling (CBM) approach.Methods: In total, 16 patients were included in this analysis. 11 patients were treated on a single-institution phase II study of 5-day intravenous azacitidine (AZA) in patients with previously untreated MDS (Martin et al, AM J Hematol 2009 [NCT00384956]). An additional 5 patients were treated on a single-institution phase I study of daily high-dose lenalidomide (LEN) and 5-day intravenous AZA induction followed by low dose LEN and AZA maintenance in patients with newly diagnosed or relapsed AML (Ramsingh et al, Leukemia 2013 [NCT01016600]). Both treatments were generally well tolerated and showed promising response rates.For this analysis, whole exome sequencing (SureSelectXT Clinical Research Exome) and CGH microarrays were performed on previously collected samples associated with the respective clinical trials. The genomic data and non-genomic clinical data (age, diagnosis, white blood cell count, hemoglobin, platelet count, and bone marrow blast percentage) were used for CBM and response prediction. Digital drug simulations were conducted by quantitatively measuring drug effect on a cell growth score (proliferation + viability + apoptosis) along with impact on a identified disease specific biomarker unique to each patient. Each patient-specific protein network was screened for the extent by which AZA or LEN+AZA, respectively, reduced disease growth. The CBM tool used simulation prediction and clinical data through weighted decision tree analysis for the final response call. The predicted response was compared with the clinical response to assess for accuracy and predictive value. Patients who had a partial response or better per standard IMW criteria were classified as responders, all others were classified as non-responders.Results: 16 drug predictions were made by the CBM with 81% accuracy, 86% positive predictive value, 78% negative predictive value, 75% sensitivity and 88% specificity.In the subset of MDS patients treated with AZA, 4/11(36%) were classified as responders and 7/11 (64%) non-responders per IMW criteria. The median age at study entry was 73 and 6/11 were male. The median number of cycles of AZA administered was 6 (range 1-6). The CBM accurately predicted the clinical outcomes from 9/11 patients including 2/4 responders and 7/7 non-responders.In the subset of AML patients treated with LEN + AZA, 4/5 (80%) were classified as responders and 1/5 (20%) non-responders per IMW criteria. The median age at study entry was 71 and 1/5 were male. 3 of the patients had newly diagnosed AML and 2 had relapsed disease. The median number of cycles of LEN + AZA administered was 5 (range 1-17). The CBM accurately predicted the clinical outcomes from 4/5 patients including 4/4 responders.Conclusions: The CBM tested herein, which included genomic abnormalities and clinical data, showed high predictive value of protein network aberrations and clinical outcomes. The study validates the approach to a priori predict response and identify the right therapy option for the patient and could be used to establish eligibility criteria for precision enrollment in drug development trials. In addition, the network method uncovered possible molecular reasons for drug failure and resistance pathways that could be targeted to induce chemo sensitivity. This clinical decision support technology could enable improved effectiveness of therapy and help avoid unneeded toxicity and reduce health care expenses by eliminating unnecessary treatments. DisclosuresVij:Bristol-Meyers-Squibb: Honoraria; Takeda: Honoraria, Research Funding; Celgene: Honoraria; Abbvie: Honoraria; Amgen: Honoraria, Research Funding; Janssen: Honoraria; Konypharma: Honoraria; Jazz: Honoraria. Singh:Cellworks Research India Pvt. Ltd: Employment. Sahu:Cellworks Research India Pvt. Ltd: Employment. Kumari:Cellworks Research India Pvt. Ltd: Employment. Narayan:Cellworks Research India Pvt. Ltd: Employment. G:Cellworks Research India Pvt. Ltd: Employment. Vali:Cellworks Group Inc.: Employment. Abbasi:Cellworks Group Inc.: Employment.

Computational Biology Approach to Predict Hypomethylating Agent (HMA) Response Using Genomic and Clinical Characteristics in Myelodsyplastic Syndromes (MDS)

Background: HMAs (e.g., azacitidine (AZA), decitabine (DAC)) are approved agents for the treatment of patients with MDS. Despite their widespread use, only 50% of patients respond to HMA without reliable biomarkers of response despite knowing the patient's genomic and clinical (non-genomic) characteristics prior to treatment. Unfortunately, no comprehensive method exists to predict HMA response in a patient using both genomic and non-genomic parameters, though we recently developed a genomics-informed computational biology method (CBM) for MDS (PMID 27855285). Predicting treatment response would improve management of MDS patients by empowering the clinician to restrict treatment-related adverse events to those who would benefit and also reduce health care costs.Aim: To predict response to HMA in MDS patients using a CBM approach, which analyzes genomic and non-genomic parameters at the time of treatment.Methods: Seventy-seven HMA treated higher-risk MDS, CMML and oligoblastic AML (20-30% blasts) patients with genomic and clinical data were identified from the Moffitt Cancer Center MDS database. 46 out of 77 had sufficient data for this analysis. The other 31 patients had insufficient genomic data to create a CBM profile (n=28), were missing clinical outcomes (n=1), or were treated with an agent not supported by CBM (n=2). The patient's genomic aberrations were detected on a targeted amplicon based next-generation sequencing (NGS) panel of 24 genes. Genomic (cytogenetic and NGS data) and non-genomic clinical data (age, WBC, ANC, Hb, platelets, bone marrow blast percentage) were used for CBM and drug response prediction. Digital drug simulations were conducted on each patient's computational MDS model by quantitatively measuring the drug effect on a cell growth score, which is a composite of cell proliferation, viability and apoptosis. Each patient-specific protein network map was digitally screened for the extent by which AZA or DAC reduced simulated MDS cell growth in a dose-respondent manner. The CBM tool combined the simulated drug response prediction and clinical data via a weighted decision tree analysis for final response call. Composite outcome of CR (complete response) + PR (partial response) + HI (hematological improvement) was used to define positive response (IWG 2006).Results: Clinically, 18/46 (39%) MDS patients treated with HMA achieved CR, PR, or HI, (Table 1). The CBM tool correctly predicted response by combining clinical and genomic data in 37/46 (80.43%) patients, resulting in 76.47% PPV, 82.76% NPV, 72.22% sensitivity and 85.71% specificity. 8 patients achieved a CR, and CBM correctly predicted 5 out of 8 responders (62.5%). This analysis found that MDS patients who harbored wild-t ype TET2, which historically are less likely to respond to HMA treatment, but possessed -7/7q and -20 chromosomal aberrations achieved a PR or HI response (N=3) and begs further investigation. CBM identified differential expression of protein kinases and transcription factors between responders and non-responders (Figure 1), suggesting potential HMA sensitivity and resistance mechanisms. Of note, non-responders were found to have higher activity in HIF1A, IRF7, SP1, MAPK3, PAK1, and RAF1.Conclusions: These results demonstrate a computational biological modelingtool to predict MDS response to standard of care HMA therapy based on genomic and non-genomic parameters. Predicting non-response could spare patients from ineffective therapies, avoid treatment-related adverse events, and decrease cost of care. This technology also gives novel insights into the molecular mechanisms by which each drug impacts the MDS disease-specific biomarkers and characteristics. [Display omitted] DisclosuresSingh:Cellworks Research India Pvt. Ltd: Employment. Kumar:Cellworks Research India: Employment. Vasista:Cellworks Research India: Employment. Sahu:Cellworks Research India Pvt. Ltd: Employment. Naga:Cellworks: Employment. Dattatraya:cellwoks: Employment. Abbasi:Cellworks Group Inc.: Employment. Vali:Cellworks Group Inc.: Employment. Sallman:Celgene: Research Funding. Komrokji:Celgene: Honoraria; Novartis: Honoraria, Speakers Bureau. Cogle:Celgene: Other: Membership on Steering Committee for Connect MDS/AML Registry.

Superior Therapy Response Predictions for Patients with Myelodysplastic Syndrome (MDS) Using Cellworks Singula™: Mycare-020-02

Background: Therapy selection for MDS patients is often based on information considering only cytogenetics and single molecular aberrations and ignoring other patient-specific omics data that could potentially enable more effective treatments. In turn, despite using cytogenetic and molecular-risk stratification and precision medicine, the current overall outcome of MDS patients remains relatively poor. The Cellworks Singula™ report predicts response for physician prescribed treatments using the novel Cellworks Omics Biology Model (CBM) to simulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. Methods: The performance of Singula™ was evaluated in an independent, randomly selected, retrospective cohort of 144 MDS patients aged 28 to 89 years (median 69). The pre-defined Singula™ Classifier utilizes an individual's genomics profile to provide a dichotomous prediction of response or non-responses to a given physician prescribed treatment (PPT). Outcome data for these subjects, including measurement of complete response (CR), were obtained from 42 PubMed publications, each including patient genomics data of either karyotyping, targeted gene panels, and/or whole exome sequencing. Blinded to clinical outcomes, Cellworks utilized these data to generate a Singula™ classifier of responder vs non-responder in this MDS cohort. Statistical analyses, including assessments of accuracy, sensitivity, specificity, negative (NPV) and positive predictive (PPV) values were performed on the merged data to compare the Singula™ predicted response with the actual observed CR. Multivariate logistic regression models of complete response were performed incorporating covariates for patient age, PPT, and the Singula™ Classifier. Results: Table 1 reveals that the pre-defined Singula™ classifier had 90.3% (Exact 95% CI: 84.2%, 94.6%) accuracy in predicting observed patient response from the physician prescribed treatment. In this study, Singula™ was able to accurately identify responders with 90.0% (81.2%, 95.6%) sensitivity. Importantly, Singula™ had 90.6% (80.7%, 96.5%) specificity for the subset of 64 patients (44.4%) that had a non-response. For 32% (17/54) of the non-responders patients, Singula™ provided an alternative Standard of Care treatment therapy, as shown in Table 2. The remaining 37 patients were predicted to be non-responders to all remaining Standard of Care options, so did not have alternate treatment predictions. Assuming at least 4% of these non-responding patients would have responded to the alternative Singula™ prescribed therapy, then these data support that Singula™ improves prediction of CR compared to the original PPT (McNemar's p-value < 0.05). In multivariate logistic regression models of CR that included patient age and prescribed drug therapy, the Singula™ Classifier remained an independent, significant predictor of CR (OR > 100, p-value < 0.0001), while both patient age (p = 0.372) and drug therapy (p = 0.720) fell off the model. Conclusions: Cellworks Singula™ has high accuracy and sensitivity in predicting CR for MDS patient response to physician prescribed therapies. Singula™ also has high specificity in identifying patients who are unlikely to respond to physician prescribed therapies and provides alternative treatment recommendations for these patients. The Singula™ Classifier is an independent and superior predictor of CR compared with other clinical (age) or therapeutic (PPT) factors. Figure Disclosures Stein: Amgen: Consultancy, Speakers Bureau; Stemline: Consultancy, Speakers Bureau. Watson:BioAI Health Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mercy Bioanalytics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; SEER Biosciences, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellworks Group Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellmax Life Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees. Nair:Cellworks Research India Private Limited: Current Employment. Basu:Cellworks Research India Private Limited: Current Employment. Ullal:Cellworks Research India Private Limited: Current Employment. Ghosh:Cellworks Research India Private Limited: Current Employment. Narvekar:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Sahu:Cellworks Research India Private Limited: Current Employment. Prakash:Cellworks Research India Private Limited: Current Employment. Behura:Cellworks Research India Private Limited: Current Employment. Balakrishnan:Cellworks Research India Private Limited: Current Employment. Roy:Cellworks Research India Private Limited: Current Employment. Rajagopalan:Cellworks Research India Private Limited: Current Employment. Alam:Cellworks Research India Private Limited: Current Employment. Parashar:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Christie:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Marcucci:Abbvie: Speakers Bureau; Novartis: Speakers Bureau; Pfizer: Other: Research Support (Investigation Initiated Clinical Trial); Merck: Other: Research Support (Investigation Initiated Clinical Trial); Takeda: Other: Research Support (Investigation Initiated Clinical Trial); Iaso Bio: Membership on an entity's Board of Directors or advisory committees.

Monosomy 7 and Co-Occurrent Genomic Aberrations Determine Chemotherapy Response in Acute Myeloid Leukemia (AML) Patients Using the Cellworks Omics Biology Model (CBM): Mycare-021-04

Background: Monosomy 7/Del 7 (-7) or its long arm (del(7q)) is one of the most common cytogenetic abnormalities in pediatric and adult myeloid malignancies, particularly in adverse-risk acute myeloid leukemias (AMLs). In general, (-7) is associated with poor response to induction chemotherapy (PMID 12393746). At the same time, not all patients fare poorly so the ability to identify responders and non-responders remains a high priority. Aim: To predict the response for induction chemotherapy in AML patients with (-7) and identify novel genomic signatures of response and resistance. Methods: Genomic data from 13 consecutive patients with (-7) were analyzed using the Cellworks Omics Biology Model (CBM) to generate patient-specific protein network models. All data was anonymized, de-identified and exempt from IRB review. For each model, disease simulations were performed and patients were segregated into HOXA-upregulated and HOXA-downregulated cohorts based on the simulation levels of HOXA5 and HOXA9. Digital drug simulations for induction chemotherapy were accomplished by measuring the impact of drug effect on a cell growth score, a composite of cell proliferation, viability and apoptosis indices. Each patient-specific model was analyzed to identify mechanisms underlying treatment outcomes. Results: 7/13 (54%) of (-7) patients failed to achieve remission after induction chemotherapy (Table 1) which highlighted that (-7) alone does not confer resistance to chemotherapy. CBM identified other genomic alterations that determine chemotherapy response, including DNA repair deficiency genes, mismatch repair (MMR), and homologous recombination repair (HRR) genes. DNA methylation and histone methylation (H3K27me) impacting HOXA gene expression, mainly HOXA5 and HOXA9, were identified as upstream regulators of DNA repair genes. Loss or reduced levels of EZH2 is associated with lower H3K27 methylation and thereby higher expression of HOXA5 and HOXA9 gene targets. High active levels of HOXA correlated with low rates of successful remission induction (22%, n=7) and lower activity levels of HOXA correlated with successful induction chemotherapy (100%, n=4) (Table 1). CBM identified that (-7) results in a decreased expression of EZH2, CARD11, EIF3, PMS2, HUS1, KMT2C (MLL3), CDK5 and IKZF1 genes. Since EZH2 abnormalities alone are not implicated in poor prognosis for AML patients, we sought other aberrations that prevent HOXA upregulation. Using CBM, we identified multiple accompanying aberrations which regulate HOXA genes. Deletions of KAT6A, ASXL1, DNMT3B, DNMT3L genes and high KMT2A-partial tandem duplication correspond to HOXA-upregulation whereas EED amplification, gain of function mutations in DNMT3A, mutations in IDH1/2, MYC and KAT6A amplification and KDM4A deletion result in HOXA-downregulation. Conclusion: Alterations of chromatin regulation have consequences for transcription factors that regulate expression of DNA repair genes. Under conditions where DNA repair is enhanced, induction chemotherapy was 78% less likely to effect remission in (-7) AML patients undergoing induction chemotherapy. Loss of H3K27 methylation associated with loss of PRC2 function by any means resulted in HOXA-upregulation and upregulation of DNA repair genes induced resistance to induction therapy. On the other hand, CBM analysis identified genetic signatures associated with a 100% remission rate from AML induction therapy despite the presence of (-7). Generation of H3K27me caused by PRC2 activation resulting from numerous mechanisms led to HOXA-downregulation and 100% response to induction therapy. Stratification of patients harboring (-7) by HOXA biomarker analysis could inform treatment planning, avoid drug-related adverse events and reduce treatment costs after validation with a larger prospective dataset. Disclosures Castro: Cellworks Group Inc: Consultancy. Watson:Cellworks Group Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellmax Life Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mercy Bioanalytics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; SEER Biosciences, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; BioAI Health Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees. Kumar:Cellworks Research India Private Limited: Current Employment. Nair:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Sahu:Cellworks Research India Private Limited: Current Employment. Mohapatra:Cellworks Research India Private Limited: Current Employment. G:Cellworks Research India Private Limited: Current Employment. Agarwal:Cellworks Research India Private Limited: Current Employment. Suseela:Cellworks Research India Private Limited: Current Employment. Ganesh:Cellworks Research India Private Limited: Current Employment. Sauban:Cellworks Research India Private Limited: Current Employment. Kumar:Cellworks Research India Private Limited: Current Employment. Raman:Cellworks Research India Private Limited: Current Employment. Singh:Cellworks Research India Private Limited: Current Employment. Basu:Cellworks Research India Private Limited: Current Employment. Lunkad:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Howard:Servier: Consultancy, Other: Speaker; Boston Scientific: Consultancy; Sanofi: Consultancy, Other: Speaker; EUSA Pharma: Consultancy; Cellworks: Consultancy.

Assessment of Cellworks Omics Biosimulation Therapy Response Predictions for Patients with Acute Myeloid Leukemia (AML) Using Cellworks Singula™: Mycare-020-01

Background. In addition to clinical considerations (e.g., age, de novo vs secondary disease, comorbidities), therapy selection for AML patients is often based on information considering only cytogenetics and/or molecular aberrations and ignoring other patient-specific omics information that could potentially enable selection of more effective treatments. In turn, despite using cytogenetic and molecular-risk stratification, the current overall outcome of AML patients remains relatively poor. The Cellworks Singula™ report predicts clinical response to physician-prescribed treatments using the novel Cellworks Omics Biology Model (CBM) that simulate in silico downstream molecular effects on cell signaling and survival of drug treatments in patient-specific diseased cells. Methods. The performance of Singula™ was evaluated in a cohort of 474 AML patients aged 2 to 85. The pre-defined Singula™ Classifier utilizes individual patients' next-generation sequencing (NGS) profiles to provide a dichotomous prediction of response or non-response to the physician prescribed treatments. The clinical outcome data for these subjects, i.e., complete response (CR) and overall survival (OS), were obtained from the TCGA and other 144 PubMed publications, each including also information on patients' cytogenetics, targeted gene mutations, and/or whole exome sequencing. Blinded to clinical outcomes, Cellworks utilized the cytogenetic and molecular data to generate a Singula™ predicted response (i.e., CR vs non-response) classification for each patient. Statistical analyses, including assessments of accuracy, sensitivity, specificity, and negative (NPV) and positive predictive (PPV) values were performed to compare the Singula™ predicted clinical response to the actual observed clinical response. Kaplan-Meier curves, associated log rank tests and median OS are provided for patients stratified by Singula™ predicted response. Multivariate Cox proportional hazards regression was used to further test the hypothesis that Singula™ is an independent predictor for OS once adjusted for patient age and actual prescribed treatment. Results. Data are summarized in Table 1. The Singula™ classifier had 92.3% (90.6%, 95.3%) accuracy in predicting correctly observed patient complete response to the prescribed treatment. with 97.3% (95.0%, 98.8%) sensitivity. Singula™ had 83.3% (76.1%, 89.1%) specificity for the non-responder patients (n=138; 29.1%). For each of the non-responders, Singula™ provided an alternative treatment therapy predicted to produce clinical response. Assuming at least 2% of the non-responders would have responded to the alternative Singula™ prescribed treatment, Singula™ improves CR rates compared to the original physician prescribed treatment (McNemar's p-value < 0.05). Figure 1 provides the Kaplan-Meier curves of Singula-predicted responders vs non-responders for a subset of 292 subjects that had OS data available. In multivariate Cox proportional hazards models, the Singula Classifier remained a significant predictor of overall survival (HR = 2.171, p-value < 0.0001) once adjusted for patient age and physician prescribed treatment. Conclusions. Cellworks Singula™ has high accuracy and sensitivity in predicting CR for AML patient. Singula also has high specificity in identifying patients who are unlikely to respond physician and may prescribed potentially effective therapies. The Singula™ predicted responders have a significantly longer OS than the predicted non responders. Thus, Cellworks Singula™ can accurately predict AML response, be used to validate or reject a physician's therapy selection decision and, eventually, provide alternative treatment recommendations. Disclosures Marcucci: Novartis: Speakers Bureau; Abbvie: Speakers Bureau; Iaso Bio: Membership on an entity's Board of Directors or advisory committees; Takeda: Other: Research Support (Investigation Initiated Clinical Trial); Pfizer: Other: Research Support (Investigation Initiated Clinical Trial); Merck: Other: Research Support (Investigation Initiated Clinical Trial). Watson:Mercy Bioanalytics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; SEER Biosciences, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; BioAI Health Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellmax Life Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellworks Group Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees. Nair:Cellworks Research India Private Limited: Current Employment. Basu:Cellworks Research India Private Limited: Current Employment. Ullal:Cellworks Research India Private Limited: Current Employment. Ghosh:Cellworks Research India Private Limited: Current Employment. Narvekar:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Sahu:Cellworks Research India Private Limited: Current Employment. Amara:Cellworks Research India Private Limited: Current Employment. Pampana:Cellworks Research India Private Limited: Current Employment. Roy:Cellworks Research India Private Limited: Current Employment. Rajagopalan:Cellworks Research India Private Limited: Current Employment. Alam:Cellworks Research India Private Limited: Current Employment. Parashar:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Christie:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Stein:Stemline: Consultancy, Speakers Bureau; Amgen: Consultancy, Speakers Bureau.

Prediction of Clinical Response for Frontline Treatment of Acute Myeloid Leukemia (AML) Patients Using the Cellworks Omics Biology Model (CBM): Mycare-021-02

Background: AML is a heterogeneous hematological cancer, characterized by the clonal expansion of myeloid blasts in the peripheral blood, bone marrow and other tissues. Among adults, AML is the leading cause of leukemia-associated death. Patients are often elderly and have comorbid conditions. Response to remission induction therapy varies by biologic subtype and by the drugs used for induction, but responses to each therapy are not predictable, even within specific biologic subgroups. To improve prognostication and identify new therapeutic options, Cellworks developed the Cellworks Omics Biology Model (CBM) that relies on multi-omics inputs from malignant cells to predict response and identify potential therapies tailored to the unique mechanisms of each patient's disease. Aim: To predict the response to induction chemotherapy regimens in AML patients and identify predictive genomic signatures, pathways, and personalized treatment options for refractory patients. Method: 57 AML patients with known therapy response were selected from 8 PubMed publications (Table 1). The cohort was split into 3 groups with CBFB-MYH11 fusion (n=19), RUNX1-RUNX1T1 fusion (n=10) and CEBPα del (n=28). All data was anonymized, de-identified and exempt from IRB review. NCCN offers specific recommendations for AML patients with these mutations and they are associated with high rates of remission after induction therapy. The available genomic data for each profile was processed using Cellworks' CBM to generate an AML subtype-specific protein network map using published information from PubMed and other online resources permitting patient-specific biomarkers to be mapped to sensitivity and resistance pathways. Drugs selected from a digital drug library were simulated for each patient across the three cohorts. Benefit was assessed by measuring each drug's effect on a cell growth score, a composite of proliferation, viability, and apoptosis indices. Results: Of 57 patients with favorable-risk AML, 6 (11%) experienced induction failure. 1/19 patients harboring CBFB-MYH11 fusion failed to respond to induction therapy (Table 2) and CBM identified co-occurring gene aberrations responsible for resistance in the patient with resistant disease (ID:757637), who harbored trisomy 6 with amplification of GSTA1, GSTA2, GSTA4, DEK, TFAP2A, NFYA and EHMT2 causing dysregulation of pathways involved in treatment failure. CBM also identified 3 prospective therapies that target this patient's specific biomarkers (Table 3). Similarly, 2/10 patients with RUNX1-RUNX1T1 fusion failed to respond to induction therapy. CBM identified a loss of function mutation in EZH2 causing increased levels of HOXA5 and HOXA9 as key orchestrators of treatment failure (Patient ID: 757805). Furthermore, CBM also identified 3 prospective therapies for this patient, targeting patient-specific resistance mechanisms (Table 3). In the CEBPα cohort, 3/28 patients did not respond to induction therapy (Table 2). For Patient ID:755125, CBM identified a DNMT3A loss of function mutation and consequential gain in the levels of HOXA5 and HOXA9 as key orchestrators of treatment failure. Again, CBM identified 3 novel therapies for this patient targeting patient-specific biomarkers (Table 3). Conclusion: Even in biological subgroups of AML expected to have sensitive disease, induction failure is not rare. Fortunately, the CBM could identify causes of failure and suggest alternative therapies based on co-occurring genomic abnormalities to mitigate the ineffectiveness of standard induction regimens in patients with resistant disease despite their favorable biology. The identification of patient-specific resistance mechanisms characterizes a new therapeutic imperative founded on deep molecular diagnosis that promises to enhance disease outcomes, inform treatment planning, avoid adverse events from ineffective therapies, and reduce costs. Also, computational modeling identifies alternate therapy regimens for refractory patients by incorporating patient-specific disease biomarkers. For all the non-responders, CBM identified 3 potential therapies across each fusion subgroup. Although these combinations need further validation in the clinic, the Cellworks CBM biosimulation platform allows for precision medicine approaches that target patient-specific disease biomarkers and treatment resistance mechanisms. Disclosures Castro: Cellworks Group Inc: Consultancy. Nair:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Kumar:Cellworks Research India Private Limited: Current Employment. Agarwal:Cellworks Research India Private Limited: Current Employment. Suseela:Cellworks Research India Private Limited: Current Employment. Gopi:Cellworks Research India Private Limited: Current Employment. Ganesh:Cellworks Research India Private Limited: Current Employment. Shyamasundar:Cellworks Research India Private Limited: Current Employment. Kulkarni:Cellworks Research India Private Limited: Current Employment. Sauban:Cellworks Research India Private Limited: Current Employment. Balla:Cellworks Research India Private Limited: Current Employment. Chafekar:Cellworks Research India Private Limited: Current Employment. Choudhury:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Howard:Boston Scientific: Consultancy; Cellworks: Consultancy; EUSA Pharma: Consultancy; Sanofi: Consultancy, Other: Speaker; Servier: Consultancy, Other: Speaker.

Predicting Resistance to the Combination of ATO and ATRA in APL Patients with PML-Rara Fusions, Using a Computational Biology Modeling Approach: Mycare-021-01

Background: Acute promyelocytic leukemia (APL) is a biologically and clinically distinct subtype of acute myeloid leukemia (AML) with unique molecular pathogenesis, clinical manifestations, and treatment. APL is cytogenetically characterized by a balanced translocation t(15;17) (q24;q21), which involves the retinoic acid receptor alpha (RARA) gene on chromosome 17 and the promyelocytic leukemia (PML) gene on chromosome 15 that results in a PML-RARA fusion gene (PMID: 30575821). The PML-RARA fusion gene is the most critical event involved in the pathogenesis of APL, reported in 99% of APL patients (PMID: 32182684). The fusion confers a selective sensitivity to the targeted drugs, arsenic trioxide (ATO) and all-trans-retinoic acid (ATRA), with response rates over 90% (PMID: 31635329). However, the mechanism of resistance in the minority of non-responders is not well understood. This study used the Cellworks Omics Biology Model (CBM) to predict response to the combination of ATO-ATRA in patients harboring the PML-RARA fusion and identify mechanisms of resistance. Methods: Outcomes of 30 APL patients treated with ATRA or ATRA plus ATO were compared with outcomes predicted by CBM (Table 1). Genomic data from 6 publications (Table 2) derived from whole exome sequencing (WES), targeted next-generation sequencing (NGS), copy number variation (CNV) and/or karyotype data were used. All data was anonymized, de-identified and exempt from IRB review. The available genomic data for each profile was entered into the CBM which generates a patient-specific disease protein network model using PubMed and other online resources. The CBM predicts the patient-specific biomarker and phenotype response of a personalized diseased cell to drug agents, radiation and cell signaling. Disease biomarkers that are unique to each patient were identified within the protein network models. ATO and ATRA were simulated on all 30 patient cases. The treatment impact was assessed by quantitatively measuring the drug's effect on a cell growth score which is a composite of the quantified values for cell proliferation, survival, and apoptosis, along with the simulated impact on each patient-specific disease biomarker score. Each patient-specific model was also digitally screened to identify response to ATO and ATRA. Results: The CBM correctly predicted the response to ATO and ATRA in 28 of 30 cases. The overall prediction accuracy was 93% with a PPV of 100%, NPV of 60%, sensitivity of 93%, and specificity of 100%. In 2 of 30 patients who did not respond to ATO and ATRA, the CBM identified clinically relevant deletions to EZH2, KMT2E, and HIPK2 genes. All three genes are located on chromosome 7 and these non-responders had monosomy 7. Conclusions: The Cellworks Omics Biology Model predicted response to ATO and ATRA in APL patients harboring PML-RARA fusions. Predicting non-response to ATO and ATRA in patients with PML-RARA fusion up-front could prevent ineffective treatment, avoid unnecessary adverse events and reduce treatment costs. Additionally, computational modeling can identify new mechanisms of resistance and suggest alternative regimens for non-responding patients by targeting the patient-specific disease biomarkers unique to each. Disclosures Howard: Servier: Consultancy, Other: Speaker; Boston Scientific: Consultancy; Sanofi: Consultancy, Other: Speaker; EUSA Pharma: Consultancy; Cellworks: Consultancy. Nair:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Tyagi:Cellworks Research India Private Limited: Current Employment. Kumari:Cellworks Research India Private Limited: Current Employment. Prasad:Cellworks Research India Private Limited: Current Employment. Mitra:Cellworks Research India Private Limited: Current Employment. Lala:Cellworks Research India Private Limited: Current Employment. Azam:Cellworks Research India Private Limited: Current Employment. Gupta:Cellworks Research India Private Limited: Current Employment. Mohapatra:Cellworks Research India Private Limited: Current Employment. G:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment.

Increased Early Mortality after Fludarabine and Melphalan Conditioning with Peripheral Blood Grafts in Haploidentical SCT with Post-Transplant Cyclophosphamide

The use of post-transplant cyclophosphamide (PTCy) in the context of haploidentical stem cell transplant (haplo-SCT) has led to drastically reduced rates of Graft-vs-Host (GvH) disease through selective depletion of highly allo-reactive donor T-cells. Early trials utilized a reduced-intensity Flu/Cy/TBI preparative regimen and bone marrow grafts; however, relapse rates remained relatively high (Luznik et al. BBMT. 2008). This led to the increased use of myeloablative (MA) regimens for haplo-SCT, which have been associated with decreased relapse rates (Bashey et al. J Clin Oncol. 2013). Most studies have used a MA total body irradiation (TBI) based regimen for haplo-SCT. Preparative regimens using fludarabine and melphalan (FluMel), with or without thiotepa, ATG, and/or low dose TBI have also been reported using bone marrow grafts. Reports on the safety and toxicity of FluMel in the haplo-SCT setting with PTCy and peripheral blood stem cell (PBSC) grafts are lacking. In this two-center retrospective analysis, the safety/toxicity of FluMel as conditioning for haplo-SCT was evaluated. We report increased early mortality and toxicity using standard FluMel conditioning and PBSC grafts for patients undergoing haplo-SCT with PTCy. 38 patients at the University of Rochester Medical Center and the Washington University School of Medicine underwent haplo-SCT with FluMel conditioning and PBSC grafts between 2015-2019. Outcomes were measured by retrospective chart review through July 2019. 34 patients (89.5%) received FluMel(140 mg/m2). Two patients received FluMel(100 mg/m2) and two patients received FluMel(140 mg/m2) + ATG. The median age at time of haplo-SCT was 60 years (range 21-73). 20 patients were transplanted for AML, eight for MDS, two for PMF, two for NHL, and five for other malignancies. The median Hematopoietic Cell Transplantation-specific Comorbidity Index (HCT-CI) score was 4 (≥3 indicates high risk). 11 patients had a history of prior stem cell transplant, and 16 patients had active disease prior to their haplo-SCT. Seven patients had sex mismatch with their stem cell donor. Median donor age was 42 (range 21-71). 20 patient deaths occurred by July 2019 with a median follow up of 244 days for surviving patients. Nine patients died before day +100 (D100, "early mortality"), with a D100 non-relapse mortality (NRM) rate of 24%. Median overall and relapse free survival (OS and RFS, respectively) were 197 days (95% CI 142-not reached) and 180 days (95% CI 141-not reached), respectively, for the entire cohort. The 1 year OS and NRM were 29% and 50%. The incidence of grades 2-4cytokine release syndrome (CRS) was 66%, and 52% of these patients were treated with tocilizumab. CRS was strongly associated with early mortality, with D100 NRM of 36% in patients with grade 2-4 CRS compared to 0% in those with grade 0-1. The incidence of acute kidney injury (AKI) was 64% in patients with grade 2-4 CRS, and 8% in those without (p < 0.001). 28% of patients with AKI required dialysis. Grade 2-4 CRS was seen in 54% of patients in remission prior to haplo-SCT and in 92% of those with active disease (p = 0.02). Of the 9 patients with early mortality, 89% had AKI, 44% needed dialysis, and 100% had grade 2-4 CRS, compared to 31%, 10%, and 55% in those without early mortality (p = 0.002, p = 0.02, p = 0.01). Early mortality was not significantly associated with age, HCT-CI score, second transplant, disease status at transplant, total dose of melphalan, volume overload/diuretic use, or post-transplant infection. In conclusion, we observed a very high rate of NRM with FluMel conditioning and PBSC grafts for haplo-SCT with PTCy. The pattern of toxicity was strongly associated with grade 2-4 CRS, AKI, and need for dialysis. These complications may be mediated by excessive inflammation in the context of allo-reactive donor T-cell over-activation. Consistent with this, multiple groups have shown that FluMel conditioning in haplo-SCT is safe when using bone marrow or T-cell depleted grafts. Based on our institutional experiences, we would discourage the use of FluMel as conditioning for haplo-SCT with PTCy with T-cell replete PBSC grafts. Alternative regimens or variations on melphalan-based regimens, such as fractionated melphalan dosing or inclusion of TBI may improve outcomes but further study and randomized controlled trials are needed. This study is limited in its retrospective design and sample size. Figure Disclosures DiPersio: WUGEN: Equity Ownership, Patents & Royalties, Research Funding; Karyopharm Therapeutics: Consultancy; Magenta Therapeutics: Equity Ownership; Celgene: Consultancy; Cellworks Group, Inc.: Membership on an entity's Board of Directors or advisory committees; NeoImmune Tech: Research Funding; Amphivena Therapeutics: Consultancy, Research Funding; Bioline Rx: Research Funding, Speakers Bureau; Macrogenics: Research Funding, Speakers Bureau; Incyte: Consultancy, Research Funding; RiverVest Venture Partners Arch Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees. Liesveld:Onconova: Other: Data safety monitoring board; Abbvie: Membership on an entity's Board of Directors or advisory committees.

Improvement in Cytokine Release Syndrome Management for the Treatment of AML Patients with Flotetuzumab, a CD123 x CD3 Bispecific Dart® Molecule for T-Cell Redirected Therapy

Improvement in Cytokine Release Syndrome Management for the Treatment of AML Patients with Flotetuzumab, a CD123 x CD3 Bispecific Dart® Molecule for T-Cell Redirected Therapy

Flotetuzumab, an Investigational CD123 x CD3 Bispecific Dart® Protein, in Salvage Therapy for Primary Refractory and Early Relapsed Acute Myeloid Leukemia (AML) Patients

Flotetuzumab, an Investigational CD123 x CD3 Bispecific Dart® Protein, in Salvage Therapy for Primary Refractory and Early Relapsed Acute Myeloid Leukemia (AML) Patients

Targeting CXCR4, VLA4, and CXCR2 for Hematopoietic Stem Cell Mobilization

Background: Hematopoietic stem cell (HSC) transplant is an essential treatment for a variety of blood disorders and malignancies. A key step in this procedure is the mobilization of donor stem cells. The most commonly used regimen for donor mobilization is a 5-day course of G-CSF. The length of this regimen coupled with the associated side effects emphasizes a need for superior alternatives. In recent years, there has been a growing understanding of mechanisms governing stem cell retention within the bone marrow niche. This has led to the development of new mobilization drugs that specifically target these processes. Two examples of previously described drugs that target mechanisms of stem cell retention are Plerixafor (a CXCR4 inhibitor already in clinical use), and truncated Gro-Beta (tGroβ; a CXCR2 agonist). Another potential target for inducing mobilization is disruption of the interaction between the VLA-4 integrin and its ligand VCAM-1. In this study, we evaluate the efficacy of novel VLA-4 inhibitors (VLA4i) alone and in combination with Plerixafor and/or tGroβ for the purposes of hematopoietic stem cell mobilization. Methods: We synthesized over 15 novel VLA-4 inhibitor molecules and tested their potency using soluble VCAM-1 binding assays. The 5 inhibitors determined to be most potent were then tested in vivo in DBA mice for their ability to mobilize HSCs alone and in combination with tGroβ and/or Plerixafor (n=5). HSC mobilization was measured in wild-type and splenectomized mice via flow cytometry to quantify the proportion of LSK (Lineage- Sca+ cKit+) cells as well as via Colony Forming Unit (CFU) assays. For competitive transplant, mobilized CD45.1+ BALB/c mouse blood (10 uL) was injected into lethally irradiated CD45.2+ BALB/c recipients alongside 2.5x105 CD45.2+ BALB/c bone marrow cells (n=10 / cohort). HSC engraftment was monitored monthly via flow cytometry for ratio of 45.1+ vs. 45.2+ cells in peripheral blood. Results: Firetagrast and BIO5192 are previously characterized VLA4i that have been administered to humans for indications unrelated to HSC mobilization. Our best VLA4i to date, LGB-2019, exhibited similar potency as BIO5192 in preventing the binding of sVCAM-1 to VLA-4 (IC50: 1.7nM) and was >200-fold more potent than firategrast. LGB-2019 showed increased aqueous solubility and mobilized 1.5-fold more murine LSK cells for a longer time period (peak HSC mobilization maintained for 4 hours) than BIO5192 when administered alone. Simultaneous injection of C57BL/6 mice with LGB-2019 (VLA4i), Plerixafor (CXCR4i) and tGro-β (CXCR2a) resulted in a synergistic increase in circulating CFUs (Fig. 1A; 9.8 x 103 CFUs/mL) and LSKs (Fig. 1B; 12.8 LSKs/uL) at 4 hours post-injection. In contrast, 5 days of G-CSF treatment mobilized approximately 3-fold and 8-fold less CFUs and LSKs, respectively (Fig. 1A-B). We saw no significant difference in mobilization for splenectomized vs. wildtype mice (23.4 x 103 CFUs/mL vs. 23.0 x 103 CFUs/mL) when mobilizing DBA/2 mice via VLA4i+CXCR4i+CXCR2a. Three months after competitive transplantation, blood obtained from BALB/c mice mobilized with the triple combination engrafted significantly better than blood obtained from mice treated with G-CSF or the dual combinations (Fig. 1C). Summary: New insights about the stem cell niche have allowed for the development of targeted drugs for the purposes of mobilization. Here, we show that a novel VLA-4 receptor inhibitor in combination with two other known mobilizers induces mobilization of hematopoietic stem and progenitor cells (CFU/LSK) at levels superior to the standard of care G-CSF and in a dramatically shortened time frame. Mouse transplant data also show superior engraftment in lethally irradiated recipients when using the triple cocktail regimen compared to the G-CSF mobilized graft. Secondary transplants are ongoing and will provide a more complete picture of primitive HSC mobilization and serial engraftment properties of the cells. Disclosures Rettig: WashU: Patents & Royalties: Patent Application 16/401,950. Karpova:WashU: Patents & Royalties: Patent Application 16/401,950. Ruminski:WahU: Patents & Royalties: Patent Application 16/401,950. Morrow:Magenta Therapeutics: Employment, Equity Ownership, Patents & Royalties. DiPersio:Cellworks Group, Inc.: Membership on an entity's Board of Directors or advisory committees; RiverVest Venture Partners Arch Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Magenta Therapeutics: Equity Ownership; Incyte: Consultancy, Research Funding; Bioline Rx: Research Funding, Speakers Bureau; Macrogenics: Research Funding, Speakers Bureau; Karyopharm Therapeutics: Consultancy; Celgene: Consultancy; Amphivena Therapeutics: Consultancy, Research Funding; WUGEN: Equity Ownership, Patents & Royalties, Research Funding; NeoImmune Tech: Research Funding.

Blocking JAK1/JAK2 While Sparing JAK3 Not Only Prevents GvHD but Also Promotes Damaged Tissue Repair

The therapeutic benefits of allogeneic hematopoietic stem cell transplantation (allo-HSCT) are primarily derived from graft-versus-leukemia (GvL) that is mediated by mature T cells in donor grafts. Unfortunately, graft-versus-host disease (GvHD) results from immune reactions of these same donor T cells against host tissues and organs after allo-HSCT. Due to this strong association between GvHD and the beneficial GvL, our lab and others have been working on developing effective therapeutic strategies to selectively prevent and treat GvHD without abrogating GvL. Recently we have reported that pharmacologic inhibition of Janus kinases 1/2 (JAK1/JAK2) prevents GvHD by blocking IFNGR and IL6R signaling. Although baricitinib (BARI) and ruxolitinib (RUX) are both JAK1/JAK2 inhibitors with similar potency, BARI was found to be superior to RUX in prevention of GvHD in our mouse models of allo-HSCT. One of our proposed mechanistic pathways by which BARI prevents GvHD is via a robust increase in regulatory T cells (Tregs) which play a pivotal role in controlling GvHD. Since the proliferation and survival of Tregs are mediated through the IL2-JAK1/JAK3-STAT5 signaling pathway and that RUX is a more JAK3 inhibitor, we hypothesized that sparing JAK3 by BARI is critical for this increase of Tregs. To test this hypothesis, we administered BARI to allo-HSCT recipient mice with or without JAK3-specific inhibitor and monitored them for GvHD signs. The mice treated with BARI and iJAK3 had significantly lower Tregs and overall survival rates compared to the mice treated with BARI alone. This data suggests that BARI prevents GvHD by retaining JAK3, thereby increasing Tregs. We have also previously reported that BARI treats established GvHD. However, the mechanisms by which BARI reverses ongoing GvHD remain unclear. Consequently, we hypothesized that BARI not only modulates immune cell functions as we previously reported, but also promotes restoration of GvHD damaged tissues. To test our hypothesis, we delayed the administration of BARI until mice developed clinically apparent GvHD and treated them starting on days 10, 17, or 24 after allo-HSCT. Despite this latency, the day 10 group showed significantly better overall survival (p<0.001, n=14) and lower histopathological grades (p<0.001, n=5) in comparison with all other groups (Fig. A). Since the gastrointestinal (GI) tract is the one of most frequently diagnosed and severely damaged GvHD target organs, we tested the possible mechanistic pathways by which BARI reverses GvHD in the GI tract. We found that Paneth cells and intestinal stem cells which are both involved in maintaining the intestinal barrier integrity are substantially increased in day 10 (p<0.0001, n=13) and 17 (p<0.001, n=29) groups compared to the vehicle control (Fig. B). To understand the molecular mechanisms underlying BARI-mediated reversal of established GvHD, we treated human intestinal organoids with BARI and RUX. Upon observing an increased proliferation in organoids treated with BARI and RUX, we then performed RNA-seq analyses on them to determine the specific genes that are differentially regulated by the treatment of BARI and RUX. Thirteen genes were found to be significantly upregulated in the RUX and BARI treated samples and three of these genes (TFF1, ABCB1, and IGFBP3) have been shown to be positively associated with EGFR signaling while negatively associated with TNFR signaling. Once we confirmed the expression levels of these genes in the human organoids using real-time PCR, we then generated an in vitro established GvHD model by treating mouse intestinal organoids with inflammatory cytokines (IFNγ, TNFα, and IL-1β). Two days later, we treated these organoids with BARI and RUX to evaluate expressions of IGFBP3, TFF1 and ABCB1 (Fig. C). We found that BARI significantly upregulates expression of TFF1, ABCB1, and IGFBP3 RNA levels in those cytokine damaged intestinal organoids compared to RUX. Collectively, these data suggest that BARI possibly reverses established GvHD by promoting damaged tissue repair by activating EGFR signaling while inhibiting inflammation and crypt apoptosis by TNFR1 signaling through TFF1, IGFBP3, and ABCB1. Lastly, the increase of Tregs by sparing JAK3 and the upregulation of these three genes in BARI-treated groups may explain the superiority of BARI over RUX in both prevention and treatment of GvHD. Figure Disclosures DiPersio: Karyopharm Therapeutics: Consultancy; RiverVest Venture Partners Arch Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amphivena Therapeutics: Consultancy, Research Funding; Celgene: Consultancy; Incyte: Consultancy, Research Funding; Macrogenics: Research Funding, Speakers Bureau; WUGEN: Equity Ownership, Patents & Royalties, Research Funding; Magenta Therapeutics: Equity Ownership; Cellworks Group, Inc.: Membership on an entity's Board of Directors or advisory committees; Bioline Rx: Research Funding, Speakers Bureau; NeoImmune Tech: Research Funding. Choi:Daewoong Pharmaceuticals Co., Ltd: Consultancy.

Single-Cell Transcriptomic and Proteomic Diversity in Multiple Myeloma

Multiple myeloma (MM) is a disease defined by clonal proliferation of abnormal plasma cells from B-cells. Improved treatments for MM have led to improving overall lifespan, but still remains incurable due to acquired resistance to therapy and tumor heterogeneity. Single-cell RNA sequencing studies (scRNA-seq) of MM patients have highlighted the significant inter-individual heterogeneity and subclonal architecture of the malignant plasma cell populations, emphasizing the importance of developing personalized therapies specific to a patients molecular pathogenesis. In this study, we have integrated scRNA-seq with single-cell proteomics (sc-Prot) for 10 plasma cells and CD4+ T cells to validate and prioritize driver events in malignant cells and evaluate the tumor microenvironment. This effort will be expanded to another 10 cases to further integrate scRNA-seq, snATAC-seq, whole exome sequencing and bulk RNA-sequencing on a fraction of the cells isolated from bone marrow. The remaining cells will be sorted using FACS to select for specific malignant and immune cells including 40 plasma cells, 15 CD4+ T and 15 CD8+ T cells. These sorted cells will be profiled with a scProt technology (BASIL nanoPOTS) to illuminate their cell-to-cell heterogeneity. In our pilot study comparing bulk and single-cell proteomic data of a single patient's plasma cells (CD138+) for 400 representative proteins, while a majority of expression signatures are concurrent between the two methods, some signaling pathways including translation and apoptotic cleavage are discordant. Our findings stress the importance of interrogating subpopulations of immune and malignant cells at the single-cell level to further refine the transcriptomic and proteomic heterogeneity of MM in a cell type specific manner. With the aid of single-cell technology, we have assessed the heterogeneity of malignant and immune cell types to evaluate transcriptomic and proteomic changes contributing to altering the interplay between the immune environment and tumor cells. Disclosures Fiala: Incyte: Research Funding. Rettig:WashU: Patents & Royalties: Patent Application 16/401,950. O'Neal:Wugen: Patents & Royalties: Patent Pending; WashU: Patents & Royalties: Patent Pending. DiPersio:WUGEN: Equity Ownership, Patents & Royalties, Research Funding; Macrogenics: Research Funding, Speakers Bureau; Cellworks Group, Inc.: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Magenta Therapeutics: Equity Ownership; RiverVest Venture Partners Arch Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; NeoImmune Tech: Research Funding; Karyopharm Therapeutics: Consultancy; Incyte: Consultancy, Research Funding; Amphivena Therapeutics: Consultancy, Research Funding; Bioline Rx: Research Funding, Speakers Bureau. Vij:Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Genentech: Honoraria; Janssen: Honoraria; Karyopharm: Honoraria; Sanofi: Honoraria; Takeda: Honoraria, Research Funding.

Dramatic Resolution of HLH after Treatment with the JAK 1/2 Inhibitor, Ruxolitinib

Introduction: Preclincal murine models of primary and secondary hemophagocytic lymphohistiocytosis (HLH) have demonstrated the role of JAK/STAT signaling in propagating the cytokine-mediated hyperinflammatory state. Das et al. (Blood 2016) demonstrated that ruxolitinib (rux) effectively attenuates murine HLH, and Albeituni et al. (Blood 2019) found it superior to IFN-γ inhibition (the target of emapalumab-lzsg). A few single case reports exist in which rux was used for salvage therapy, however they report only limited clinical success and/or were used as a bridge to allogeneic stem cell transplantation. Here we present a series of three consecutive cases in which rux was employed as either salvage therapy for relapsed/refractory secondary HLH or front-line for moderate HLH manifestations. Importantly, each patient was maintained on a taper of rux which prevented recurrence and obviated the need for allogeneic transplantation. Two of the three patients have been tapered completely off rux without recurrence of either laboratory or clinical manifestations of HLH at the time of this submission. Case 1: Patient 1 is a 24 yo woman who had initially presented to an outside hospital with jaundice and was found to have a warm AIHA. She was initially started on steroids, but developed worsening anemia, fever, splenomegaly, and encephalopathy. On transfer to us she had ferritin of 58,505 ng/ml. Her bone marrow biopsy demonstrated abundant hemophagocytosis (Fig. 1B,C). She met HLH criteria and was started on HLH-94 protocol; however, her total and direct bilirubin climbed dramatically, peaking at 95.2 and 82.0 mg/dl, respectively. Rux 5mg BID was initiated as salvage therapy, and within 48 hours, her labs significantly improved, her fevers resolved, and she became responsive to blood transfusions (Fig. 1A). After two weeks on rux, she developed recurrent hemolysis. Her dose was increased to 20mg BID, resulting in a complete resolution of her hemolysis, fevers, transaminitis and hyperbilirubinemia. After 3 months of clinical stability, rux was tapered. She has been off rux now for three months. Case 2: Patient 2 is a 26 yo woman who presented with unremitting fevers and myalgias, and was found to have an acute hepatitis C infection. Her ferritin was 24,023ng/ml, and a bone marrow biopsy demonstrated abundant hemophagocytosis. She initially responded to the HLH-94 protocol; however, attempts to hold treatment following induction were met with recurrent fevers, encephalopathy, and hyperferritinemia. She transferred to us, and a repeat bone marrow biopsy demonstrated persistent hemophagocytosis (Fig. 2B,C), while her ferritin was 15,073 ng/ml. We initiated salvage treatment with rux 10mg BID. Her fevers, encephalopathy, and lab abnormalities quickly improved; her ferritin fell to 2,973ng/ml within 3 days (Fig. 2A). A small subsequent elevation in her ferritin prompted an increase of the rux to 20mg BID. She improved and was discharged on rux maintenance at 20mg BID. Since discharge, she remains asymptomatic with mildly elevated ferritin levels, mild pancytopenia, and is tolerating a slow wean of rux without incident. Case 3: Patient 3 is a 40 yo man with a diagnosis of hairy cell leukemia who received 5 days of cladribine. He developed febrile neutropenia 2 days following the end of chemotherapy. His infectious workup was negative with the exception of a positive peripheral blood EBV DNA PCR. He developed a mononucleosis-like syndrome, with persistent high fevers, splenomegaly, and hyperferritinemia to 6684 ng/ml. He also developed hepatic dysfunction with a direct hyperbilirubinemia peaking at 14.1 mg/dl and coagulopathy. He was treated initially with dexamethasone without resolution. A bone marrow biopsy demonstrated abundant hemophagocytosis (Fig. 3B,C). Given modest manifestations and concern that chemotherapy would exacerbate his immunosuppression, rux was initiated at 10mg BID. The liver dysfunction rapidly improved, and fevers resolved within 3 days (Fig. 3A). All other manifestations resolved within 1 week, and he was able to taper and discontinue rux within 2 months. Conclusions: The preclinical data by Albeituni et al. coupled with our clinical observations suggest that ruxolitinib may be a viable alternative to aggressive chemotherapy regimens such as HLH-94 or anti-IFN-γ (emapalumab-lzsg) therapy for HLH, and clinical trials should be considered. Disclosures DiPersio: Karyopharm Therapeutics: Consultancy; Cellworks Group, Inc.: Membership on an entity's Board of Directors or advisory committees; NeoImmune Tech: Research Funding; Macrogenics: Research Funding, Speakers Bureau; Bioline Rx: Research Funding, Speakers Bureau; RiverVest Venture Partners Arch Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Magenta Therapeutics: Equity Ownership; WUGEN: Equity Ownership, Patents & Royalties, Research Funding; Amphivena Therapeutics: Consultancy, Research Funding; Incyte: Consultancy, Research Funding; Celgene: Consultancy. OffLabel Disclosure: Ruxolitinib was used off-label for the treatment of Hemophagocytic Lymphohistiocytosis (HLH) in the relapsed/refractory setting and in a patient who was unable to tolerate standard therapy. This was not in a clinical trial, but reported as a case series

CD45-ADC Plus Janus Kinase (JAK) Inhibitors As Conditioning for MHC-Mismatched Murine Hematopoietic Stem Cell Transplantation Is Associated with Minimal Toxicity and Graft Versus Host Disease

INTRODUCTION: Allogeneic hematopoietic stem cell transplantation (alloHSCT) provides the best chance for disease control in acute myeloid leukemia (AML). The known anti-leukemia benefit of alloHSCT is attributable to pre-transplant conditioning with chemotherapy and/or radiation and the graft-versus-leukemia (GvL) effect. However, the utility of alloHSCT is limited by significant treatment-related morbidity and mortality resulting from conditioning regimen-related toxicities and graft versus host disease (GvHD). AML is primarily diagnosed in elderly patients, for whom alloHSCT may be contraindicated due to an inability to tolerate these adverse effects. Therefore, an unmet clinical need exists for novel alloHSCT conditioning regimens that minimize toxicity without sacrificing therapeutic efficacy. Previously, we and others (Palchaudhuri et al. (2016), Nat Biotech; Persaud et al. (2018), Blood) have shown that antibody-drug conjugates (ADCs) composed of a CD45.2-specific antibody linked to the ribosome-inactivating protein saporin (CD45-SAP) permitted stable, high-level engraftment in murine syngeneic HSCT. We hypothesize that such targeted, immunotherapeutic strategies can be applied to the setting of alloHSCT, enhancing its safety and applicability to the treatment of hematologic diseases. METHODS: For engraftment studies, recipient mice were conditioned with 3 mg/kg CD45-SAP 7 days prior to infusion of 10-20 x 106 whole bone marrow cells. The following alloHSCT models were used: MHC-matched (BALB/c→DBA/2), F1-to-parent (CB6F1→B6), and F1-to-F1 (B6CBAF1→CB6F1). In some experiments, CD45-SAP treatment was combined with CD4+ and CD8+ T cell depletion and/or the Janus kinase (JAK) inhibitor, baricitinib. For GvHD studies, a parent-to-F1 (B6→CB6F1) model was used in which recipients were infused with 25 x 106 donor splenocytes after CD45-SAP treatment or sublethal irradiation. For studies with human CD45-SAP, MOLM13 AML cell line viability was measured with XTT assays, and cord-blood CD34+cell colony formation was assessed using methylcellulose assays. RESULTS: CD45-SAP combined with CD4+ and CD8+ T cell depletion allowed high-level engraftment in MHC-matched and F1-to-parent alloHSCT models (Figure 1A-B). Baricitinib, previously shown by our lab to prevent GvHD while preserving the GvL effect (Choi et al. (2018), Leukemia), permitted short-term HSC engraftment in F1-to-parent alloHSCT when mice were dosed daily with the drug during the first 21 days post-HSCT (Figure 1C). Graft stability in baricitinib-treated mice was improved by either combining daily baricitinib treatment with pre-transplant T cell depletion (Figure 1D), or by delivering baricitinib via continuous infusion (Figure 1E). In parent-to-F1 GvHD models, CD45-SAP conditioned mice showed minimal morbidity or mortality when infused with allogeneic splenocytes, whereas mice conditioned with sublethal irradiation succumbed within 3 weeks to severe GvHD (Figure 1F). That alloreactive T cells did not cause GvHD in CD45-SAP treated mice prompted us to use donor lymphocyte infusions to enhance engraftment in a fully haploidentical (F1-to-F1) HSCT model. Our results suggest that addition of donor T cells to the bone marrow graft promotes donor engraftment without eliciting clinically apparent GvHD (Figure 1G). Finally, anti-human CD45 (clone BC8), when conjugated to saporin, inhibited MOLM13 cell growth and cord blood CD34+ cell colony formation in vitro (Figure 1H-I), consistent with both an anti-stem cell and anti-leukemic effect. CONCLUSIONS: Combining CD45-SAP with T cell depletion and/or pharmacologic immunomodulation with the JAK inhibitor baricitinib effectively conditions mice for engraftment of MHC-matched and haploidentical HSCs without causing significant toxicity. Strikingly, mice conditioned with CD45-SAP, unlike mice conditioned with irradiation, did not develop GvHD when infused with allogeneic splenocytes. Finally, we have demonstrated that human CD45-SAP is cytotoxic to both the MOLM13 AML cell line and cord-blood derived stem cells. Taken together, these studies provide key proof-of-principle evidence that targeted immunotherapeutics (CD45-SAP plus JAK inhibitors) can accomplish the goals of alloHSCT - creating marrow space for donor HSCs, overcoming immune barriers to engraftment, and eliminating leukemia cells - with limited toxicity or GvHD. Disclosures Cooper: Wugen: Consultancy, Equity Ownership, Patents & Royalties. Rettig:WashU: Patents & Royalties: Patent Application 16/401,950. DiPersio:Incyte: Consultancy, Research Funding; Celgene: Consultancy; Amphivena Therapeutics: Consultancy, Research Funding; Cellworks Group, Inc.: Membership on an entity's Board of Directors or advisory committees; RiverVest Venture Partners Arch Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; NeoImmune Tech: Research Funding; Macrogenics: Research Funding, Speakers Bureau; WUGEN: Equity Ownership, Patents & Royalties, Research Funding; Magenta Therapeutics: Equity Ownership; Karyopharm Therapeutics: Consultancy; Bioline Rx: Research Funding, Speakers Bureau.

Immune Landscapes Predict Chemotherapy Resistance and Anti-Leukemic Activity of Flotetuzumab, an Investigational CD123×CD3 Bispecific Dart® Molecule, in Patients with Relapsed/Refractory Acute Myeloid Leukemia

Immune Landscapes Predict Chemotherapy Resistance and Anti-Leukemic Activity of Flotetuzumab, an Investigational CD123×CD3 Bispecific Dart® Molecule, in Patients with Relapsed/Refractory Acute Myeloid Leukemia

Identification of Small Molecule Kinase Inhibitors That Potently and Reversibly Block Chimeric Antigen Receptor T Cell Proliferation and Cytotoxicity

Introduction: Despite remarkable clinical efficacy, CAR-T therapy has been limited by life threatening toxicities in over 30% of patients.1, 2 Toxicities primarily manifest as cytokine release syndrome (CRS) characterized by an early phase with fever, hypotension and elevations of cytokines including IFNγ, GM-CSF, TNF, IL-10, and IL-6 and a later phase associated with life-threatening or life-ending neurologic events. We hypothesized that reversible inhibition of CAR mediated signaling will enable controllable regulation of CAR-T cell activity in vivo and mitigate CAR-T mediated toxicities. There are specific protein kinases such as the SRC kinases, LCK, and ZAP-70 that are known to be involved in various cellular signaling pathways, especially T cell receptor mediated signaling and may also be appropriate targets for modulating (both enhancing and inhibiting) CAR-T function in a rapid and reversible fashion in vivo. Our hypothesis is that small molecule inhibitors of TCR signaling and downstream pathways could be identified using specific high throughput screens. Methods: To identify novel inhibitors of CAR-T cell proliferation, we developed a high throughput kinase inhibitor screen to identify compounds that reversibly inhibit CAR-T function. T cells containing a third generation CAR targeting CD19 cells (CAR19) and CD19+ tumor cells (Ramos cells expressing both GFP and luciferase) were incubated at an effector to target ratio of 1:1 in 96 well plates in the presence of 1µM of each inhibitor. After 24 hours, tumor cell death induced by CAR-T was measured using bioluminescence (BLI) imaging. Small molecules that inhibited CAR-T proliferation and cytotoxicity were determined by assessing the BLI signal in each well. Results: A protein kinase inhibitor library (Selleckchem, Texas) containing 644 independent compounds was tested (Figure 1). Of the 644 kinase inhibitors tested, 32 were found to be potent inhibitors of CART19 cell activation and cytotoxic killing of CD19+ target tumor cells, reducing anti-tumor viability in 24 hours by >50% compared to vehicle control. Compounds such as Nintedanib (C3), Dasatinib (C9), and Saracatinib (C12), all SRC kinase inhibitors, were able to inhibit cell killing by 99%, 84%, and 76% respectively. Next we assessed the reversibility of CAR-T cell mediated killing upon removal of inhibitors from the cultures. Reinitiation of potent, anti-tumor activity was observed within 24 hours after inhibitor removal, confirming reversible nature of CAR-T cell inhibition by the three most potent compounds. Conclusions: Recent publications (Weber et al Blood Adv, 2018, Westermann et al. Sci Transl Med, 2019) have also shown that dasatinib can reversibly suppresses CAR-T cell cytotoxicity, cytokine secretion, and proliferation in vitro and in vivo. 3, 4 Here we confirm the reports of others regarding dasatinib and that show for the first time that reversible inhibition of CAR-T activity by kinase inhibitors is not limited solely to dasatanib, but is observed with other small molecules targeting many different kinases. This work further demonstrates the potential applications of tyrosine kinase inhibitors as a safety switch to modulate CAR-T cell toxicity. 1. Maude, NEJM 2014 2. Davila, SciTransMed 2014 3. Mestermann SciTransMed 2019 4. Weber. Blood Adv 2019 Disclosures Cooper: Wugen: Consultancy, Equity Ownership, Patents & Royalties. DiPersio:WUGEN: Equity Ownership, Patents & Royalties, Research Funding; Magenta Therapeutics: Equity Ownership; Celgene: Consultancy; Karyopharm Therapeutics: Consultancy; RiverVest Venture Partners Arch Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellworks Group, Inc.: Membership on an entity's Board of Directors or advisory committees; NeoImmune Tech: Research Funding; Bioline Rx: Research Funding, Speakers Bureau; Macrogenics: Research Funding, Speakers Bureau; Incyte: Consultancy, Research Funding; Amphivena Therapeutics: Consultancy, Research Funding.

Single-Cell Pathway Enrichment and Regulatory Profiling of Multiple Myeloma across Disease Stages

Multiple myeloma (MM) is a hematological malignancy, defined by aberrant monoclonal proliferation of plasma cells in the bone marrow, that to date remains an incurable disease despite advances in treatment. Key genetic and epigenetic alterations that drive MM pathogenesis have been identified, but a comprehensive profile of affected cellular pathways has yet to be fully characterized. In this study, we integrate whole-genome and whole-exome sequencing data with single-cell RNA sequencing (scRNA-seq) data from 13 patients across multiple treatment stages to 1) assess differential pathway enrichment between tumor subpopulations, 2) trace the clonal evolution of dominant disease mechanisms, and 3) investigate signaling interactions between surrounding cell types. We also analyzed bulk genomic and transcriptomic data from 662 additional Multiple Myeloma Research Foundation (MMRF) tumor samples as a large reference cohort for highly prevalent pathway disturbances. To assess whether tumor subpopulations rely on different oncogenic programs for proliferation, we analyzed the differential expression of key genes (FDR-adjusted p-value <0.05) in 12 canonical oncogenic pathways. Cell cycle, Hippo, RTK/RAS, and NFkB pathways contain the highest numbers of differentially expressed genes, with certain subclusters upregulating as many as 25% of annotated cell cycle genes and over 90% of annotated Hippo genes, whereas p53, Notch, Nrf2, and DNA repair genes tend to be uniformly expressed across subpopulations. Next, we evaluated changes in pathway enrichment across different disease timepoints, with the goal of capturing the reorganization of functional profiles through successive treatment and relapse cycles. We assessed statistical enrichment of pathways containing differentially expressed genes (DEGs) unique to Smoldering Multiple Myeloma (SMM), primary, and relapse stages using the KEGG pathway database (n = 2, 17, and 7 pathways, respectively; FDR-adjusted p-value of enrichment < 0.05). SMM is the only stage where hematopoietic differentiation and the PI3K-Akt pathways are variably expressed between plasma cell subpopulations, suggesting that these pathways may play a role in initiating events. Only primary tumor samples show significant intra-tumor variability of p53 regulation, which is lost in the relapsed tumor and may reflect selection due to treatment. Relative to SMM, primary and relapse samples are enriched for changes in the MAPK, NFkB, RAP1, and cell cycle pathways, indicating potential sources of tumor resistance. We then analyzed pathway enrichment within the tumor microenvironment to enhance our understanding of tumor development in the context of surrounding tissues. We see frequent changes in many immune cell types in TLR signaling as the disease progresses, driven by differential expression of NFkB1A, JUN, and FOS, all of which are key upstream regulators of the AP-1 pathway and responders to the MAPK and PI3K signaling cascades. These microenvironment changes may be complementary to the PI3K and MAPK dysregulation observed in tumor plasma cells. Proteasome and ubiquitin genes, which affect secretion, autophagy, and apoptosis pathways that may be relevant to MM pathogenesis are also frequently differentially expressed in immune cells between disease stages. Finally, we integrate bulk whole-exome and whole-genome sequencing analysis (from both the 13-patient cohort and MMRF) to obtain a more complete understanding of how pathways become dysregulated in MM. Our findings advance the understanding of how MM tumor subpopulations differentially regulate cellular pathways and interact within the tumor microenvironment. Disclosures O'Neal: Wugen: Patents & Royalties: Patent Pending; WashU: Patents & Royalties: Patent Pending. Rettig:WashU: Patents & Royalties: Patent Application 16/401,950. Oh:Incyte: Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy. Vij:Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Genentech: Honoraria; Janssen: Honoraria; Karyopharm: Honoraria; Sanofi: Honoraria; Takeda: Honoraria, Research Funding. DiPersio:Amphivena Therapeutics: Consultancy, Research Funding; Magenta Therapeutics: Equity Ownership; Karyopharm Therapeutics: Consultancy; Incyte: Consultancy, Research Funding; RiverVest Venture Partners Arch Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Bioline Rx: Research Funding, Speakers Bureau; Macrogenics: Research Funding, Speakers Bureau; WUGEN: Equity Ownership, Patents & Royalties, Research Funding; NeoImmune Tech: Research Funding; Cellworks Group, Inc.: Membership on an entity's Board of Directors or advisory committees.

Rapid and Robust Mobilization of CD34+ HSCs without G-CSF Following Administration of Mgta-145 Alone or in Combination with Plerixafor

Rapid and Robust Mobilization of CD34+ HSCs without G-CSF Following Administration of Mgta-145 Alone or in Combination with Plerixafor

CXCR4 Inhibition with BL-8040 in Combination with Nelarabine in Patients with Relapsed or Refractory T-Cell Acute Lymphoblastic Leukemia / Lymphoblastic Lymphoma

Background: The chemokine receptor, CXCR4, mediates the trafficking and retention of hematopoietic stem cells to the marrow through its interaction with the chemokine, CXCL12. CXCR4 is the most highly expressed chemokine receptor on T-ALL cells, and preclinical data demonstrate that CXCR4 is critical for the growth and survival of T-cell acute lymphoblastic lymphoma (T-ALL). Genetic loss or pharmacologic blockade of CXCR4 signaling suppresses T-ALL cell growth and leukemia initiating activity in murine models. BL-8040 is a novel synthetic peptide antagonist of the chemokine receptor, CXCR4. Compared to other CXCR4 inhibitors, BL-8040 is a more potent mobilizer of normal HSCs with sustained occupancy and inhibition of CXCR4. We previously reported that BL-8040 potently suppresses T-ALL cell growth in xenotransplantation models of T-ALL. To test the efficacy of BL-8040 in T-ALL by conducting a multicenter, open label pilot study of BL-8040 in combination with nelarabine in patients with relapsed or refractory disease (ClinicalTrials.gov: NCT0276338). Methods: Adults with relapsed or refractory T-ALL/LBL, age ≥18 years and ECOG PS ≤2, were eligible for the study. A peripheral blood lymphoblast count ≤50,000/mm3 was required prior to starting treatment. During cycle 1, subjects received daily subcutaneous administration of BL-8040 1.5 mg/kg from Day 1 to Day 6 with nelarabine 1,500 mg/m2 intravenously over 2 hours on Days 2, 4 and 6. In subsequent 21-day cycles, BL-8040 1.5 mg/kg was administered daily on days 1-5 with nelarabine 1,500 mg/m2 given on days 1, 3 and 5. Up to 4 cycles of treatment was allowed. Results: Nine patients with median age of 29 years (range 20-75) have been enrolled in the study out of which including fout patients were in untreated 1st relapse, one in 2nd relapse, two with relapsed and refractory disease, and two primary refractory patient. Two patients had failed prior allogeneic hematopoietic cell transplant. The rate of complete remission is 56% (5/9) with all responses occurring after 1 cycle of treatment. Adverse events occurred in 2 subjects requiring dose modification. Consistent with the known mechanism of BL-8040, one subject developed G3 leukocytosis after BL-8040 administration with the peripheral WBC increasing from 5.3 to 273 h/mm3 with evidence of spontaneous tumor lysis requiring interruption of BL-8040 and leukapheresis. A second patient developed injection site pain requiring omission of the final dose of BL-8040. Peripheral blood analyzed at baseline and 24 hours after the first dose of BL-8040 (prior to nelarabine) showed the following: 1) sustained blockade of CXCR4 on leukemic blasts; 2) mobilization of leukemic blasts into the blood; and 3) no induction of leukemia blast apoptosis as measured by activated caspase 3 expression. Conclusions: BL-8040 can be combined with nelarabine in subjects with relapsed or refractory T-ALL with encouraging CR rates. Evidence of on target effects on CXCR4 has been observed in this study with sustained CXCR4 receptor occupancy at 24 hours after administration along with tumor mobilization of ALL blasts into the peripheral circulation. Disclosures Uy: Astellas: Consultancy; Pfizer: Consultancy; Curis: Consultancy; GlycoMimetics: Consultancy. Kadia:Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Bioline RX: Research Funding; BMS: Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Research Funding. Stock:Astellas: Membership on an entity's Board of Directors or advisory committees; Daiichi: Membership on an entity's Board of Directors or advisory committees; Research to Practice: Honoraria; UpToDate: Honoraria; Agios: Membership on an entity's Board of Directors or advisory committees; Kite, a Gilead Company: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees. Brammer:Verastem, Inc: Research Funding; Viracta Therapeutics, Inc.: Research Funding; Bioniz Therapeutics, Inc.: Research Funding. Bohana-Kashtan:BiolineRx: Employment, Equity Ownership. Vainstein:BiolineRx: Employment, Equity Ownership. Sorani:BiolineRx: Employment, Equity Ownership. Chen:BiolineRx: Employment. DiPersio:Bioline Rx: Research Funding, Speakers Bureau; Macrogenics: Research Funding, Speakers Bureau; Celgene: Consultancy; Amphivena Therapeutics: Consultancy, Research Funding; Magenta Therapeutics: Equity Ownership; NeoImmune Tech: Research Funding; RiverVest Venture Partners Arch Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellworks Group, Inc.: Membership on an entity's Board of Directors or advisory committees; WUGEN: Equity Ownership, Patents & Royalties, Research Funding; Incyte: Consultancy, Research Funding; Karyopharm Therapeutics: Consultancy. OffLabel Disclosure: BL-8040 for ALL