Cellworks Group Research Articles

Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Identifies Novel Biomarkers to Inform Mitoxantrone, Etoposide, and Cytarabine (MEC)-Based Combination Therapy in Refractory & Relapsed Acute Myeloid Leukemia (AML) Patients

Background: The optimal treatment strategy for managing Acute Myeloid Leukemia (AML) and the use of reliable and predictive biomarkers to guide selection of cytotoxic chemotherapy regimens among patients with diverse genomic profiles remain unmet needs in the clinic. The combination of MEC [mitoxantrone (MIT), etoposide (VP16), and cytarabine (ARA-C)] is a commonly used regimen for relapsed or refractory AML patients. Unfortunately, many patients do not respond to MEC, and which of the three drug agents matters most for each individual patient is not known. Predictors of response are needed urgently.Methods: The Computational Omics Biology Model (CBM) is a computational multi-omic biology software model created using artificial intelligence heuristics and literature sourced from PubMed to generate a patient-specific protein network map. The aberration and copy number variations from individual cases served as input into the CBM. Disease-biomarkers unique to each patient were identified within patient-specific protein network maps. Biosimulations were conducted on the Cellworks Biosimulation Platform by measuring the effect of chemotherapy on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Biosimulation of drugs was conducted by mapping the interaction of various drug combinations with the patient's genomic and pathway alterations based on signaling pathway mechanisms and their phenotypic consequences.The Cellworks Biosimulation Platform identified unique chromosomal signatures that permit a stratification of patients that are most likely to respond to MIT, VP16, or ARA-C as well as their combinations.65 AML patients were selected for this study largely based on genomic data published in TCGA and PubMed: •ARA-C [N=12, 7 responders (R) & 5 non-responders (NR)]•ARA-C + MIT [N=30, 29 R & 1 NR]•ARA-C + MIT + VP16 [N=23, 12 R & 11 NR]Results: Of the12 patients treated with ARA-C alone, 5 were predicted to be NR and 7 were predicted to be R. Of the 5 NR, 4 had 5q del which resulted in loss of APC, CSNK1A1 and SLC22A4 (nucleoside carrier) forming the non-response biomarkers for ARA-C. Notably, the biosimulation predicted lenalidomide to be beneficial for these patients. Out of 7 R, 4 patients also had 5q del, but were predicted to be R because of co-occurring aberrations involving CLSPN del, DHODH del, MSH2 del, EP300 del, CREBBP del, MSH6 Del, and RRM2 del. These genes were exclusively present in ARA-C responders. Of 53 patients who received ARA-C + VP16 + MIT or ARA-C + MIT, 41 patients were predicted to be R and 12 patients were predicted to be NR. The genomic aberrations predicted by biosimulation to be associated with response to this regimen include: NPM1-mut, TET2-mut, IDH1-mut, IDH2-mut, RAD17-del, NRAS-mut (Table 1). Notably, CBM predicted 19 of the 41 R had no genomic biomarkers of response to VP16 or MIT, suggesting these patients might have benefited equally from ARA-C alone with less toxicity and cost. Finally, 11/65 patients were predicted NR to MEC treatment. In the biosimulation, treatment failure was associated with high aberration frequencies of KMT2C-mut/del, FLT3 mut, TWIST1 del, LIMK1 del, SNAI2 amp, FNTA amp, and KAT6A amp. Of note, these genomic markers suggested a likelihood of benefit from other therapies, including vincristine, JQ1 and rigosertib.Conclusions: The Cellworks Biosimulation Platform identified novel polygenic biomarkers of response that can be employed to determine the optimal therapy for relapsed AML patients. Biosimulation permits avoidance of cytotoxic drugs with little chance of efficacy and reveals vulnerabilities in each patient's cancer that can be exploited to improve disease control. In AML, biosimulation promises to improve intensive therapy regimens by tailoring chemotherapy to optimize disease control and minimize toxicity. [Display omitted] DisclosuresMarcucci: Novartis: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings; Abbvie: Other: Speaker and advisory scientific board meetings. Kumar: Cellworks Group Inc.: Current Employment. Castro: Cellworks Group Inc.: Current Employment; Omicure Inc: Consultancy; Caris Life Sciences Inc.: Consultancy; Exact sciences Inc.: Consultancy; Bugworks: Consultancy; Guardant Health Inc.: Speakers Bureau. Grover: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Alam: Cellworks Group Inc.: Current Employment. Azam: Cellworks Group Inc.: Current Employment. Mohapatra: Cellworks Group Inc.: Current Employment. Tyagi: Cellworks Group Inc.: Current Employment. Kumari: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Lunkad: Cellworks Group Inc.: Current Employment. Joseph: Cellworks Group Inc.: Current Employment. G: Cellworks Group Inc.: Current Employment. Chauhan: Cellworks Group Inc.: Current Employment. Basu: Cellworks Group Inc.: Current Employment. Behura: Cellworks Group Inc.: Current Employment. Ghosh: Cellworks Group Inc.: Current Employment. Husain: Cellworks Group Inc.: Current Employment. Mandal: Cellworks Group Inc.: Current Employment. Raman: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Howard: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees.

Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Identifies Genomic and Molecular Markers for Decitabine (DAC) Plus Valproic-Acid (VPA) Treatment Response in Patients with Myelodysplastic Syndromes (MDS)

Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Identifies Genomic and Molecular Markers for Decitabine (DAC) Plus Valproic-Acid (VPA) Treatment Response in Patients with Myelodysplastic Syndromes (MDS)

Therapy Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Is Predictive of Individual Acute Myeloid Leukemia (AML) Patient Probability of Clinical Response (CR) and Overall Survival (OS): Mycare-023

Therapy Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Is Predictive of Individual Acute Myeloid Leukemia (AML) Patient Probability of Clinical Response (CR) and Overall Survival (OS): Mycare-023

Biosimulation Using the Cellworks Computational Omics Biology Model (CBM)-Based Assessment of Cytarabine (ARA-C) and Anthracycline Response and Novel Biomarker Response Criteria for the Addition of Etoposide (VP16) in Acute Myeloid Leukemia (AML)

Biosimulation Using the Cellworks Computational Omics Biology Model (CBM)-Based Assessment of Cytarabine (ARA-C) and Anthracycline Response and Novel Biomarker Response Criteria for the Addition of Etoposide (VP16) in Acute Myeloid Leukemia (AML)

Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Identifies Immune Modulation As a Key Pathway for Predicting Azacitidine (AZA) Response in Myelodysplastic Syndromes (MDS)

Background: DNA methyltransferase inhibition (DNMTi) with the hypomethylating agents (HMA) azacitidine (AZA) or decitabine, remains the mainstay of therapy for the majority of high-risk Myelodysplastic Syndromes (MDS) patients. Nevertheless, only 40-50% of MDS patients achieve clinical improvement with DNMTi. There is a need for a predictive clinical approach that can stratify MDS patients according to their chance of benefit from current therapies and that can identify and predict responses to new treatment options. Ideally, patients predicted to be non-responders (NR) could be offered alternative strategies while being spared protracted treatment with HMA alone that has a low likelihood of efficacy. Recently, an intriguing discovery of immune modulation by HMA has emerged. In addition to the benefits of unsilencing differentiation genes and tumor suppressor genes, HMA's reactivate human endogenous retroviral (HERV) genes leading to viral mimicry and upregulation of the immune response as a major mechanism of HMA efficacy. Although the PD-L1/PD1 blockade plus HMA has been recognized as a beneficial combination, there are no established markers to guide decision-making. We report here the utility of immunomic profiling of chromosome 9 copy number status as a significant mechanism of immune evasion and HMA resistance.Methods: 119 patients with known clinical responses to AZA were selected for this study. Publicly available data largely from TCGA and PubMed was utilized for this study. The aberration and copy number variations from individual cases served as input into the Cellworks Computation Omics Biology Model (CBM), a computational biology multi-omic software model, created using artificial intelligence heuristics and literature sourced from PubMed, to generate a patient-specific protein network map. Disease-biomarkers unique to each patient were identified within protein network maps. The Cellworks Biosimulation Platform has the capacity to biosimulate disease phenotypic behavior and was used to create a disease model and then conduct biosimulations to measure the effect of AZA on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Biosimulation of drug response was conducted to identify and predict therapeutic efficacy.Results: Although AZA treatment increased tumor associated antigens and interferon signaling, it also increased PD-L1 expression to inactivate cytotoxic CD8(+) T cells. Copy number alterations of the chromosome 9p region were found to significantly drive PD-L1 expression with multiple genes such as CD274, IFNA1, IFNA2, JAK2, PDCD1LG and KDM4C playing a role in PD-L1 regulation further increasing immune suppression (Figure 1). Among 6 cases of chromosome 9p aberration in this dataset, 9p amp (n=2) were clinical non-responders (NR) while 9p del (n=4) were responders (R) to AZA. In principle, checkpoint immunotherapy could improve outcomes for patients with 9p abnormalities. Additionally, copy number variation loss of key genes located on chromosome 16 involved in antigen processing and presentation such as CIITA, CTCF, IRF8, PSMB10, NLRC5, and SOCS1 were found to negatively impact AZA sensitivity (NR=4; R=0); these patients would also be unlikely to respond to checkpoint immunotherapy. Also, aberrations in melanoma antigen gene (MAGE) family proteins (NR=2; R=O), and STT3A (NR=1; R=5) were found to impact AZA efficacy by decreasing antigen processing on tumor cells.Conclusion: Based on the results from the Cellworks Biosimulation Platform applied to the CBM, copy number variants of chromosome 9p and 16 can be converted into CBM-derived biomarkers for response to checkpoint immunotherapy in combination with HMA. Our results support a future prospective evaluation in larger cohorts of MDS patients. [Display omitted] DisclosuresHoward: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees. Kumar: Cellworks Group Inc.: Current Employment. Castro: Bugworks: Consultancy; Exact sciences Inc.: Consultancy; Guardant Health Inc.: Speakers Bureau; Cellworks Group Inc.: Current Employment; Caris Life Sciences Inc.: Consultancy; Omicure Inc: Consultancy. Grover: Cellworks Group Inc.: Current Employment. Mohapatra: Cellworks Group Inc.: Current Employment. Kapoor: Cellworks Group Inc.: Current Employment. Tyagi: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Suseela: Cellworks Group Inc.: Current Employment. Pampana: Cellworks Group Inc.: Current Employment. Lala: Cellworks Group Inc.: Current Employment. Singh: Cellworks Group Inc.: Current Employment. Shyamasundar: Cellworks Group Inc.: Current Employment. Kulkarni: Cellworks Group Inc.: Current Employment. Narvekar: Cellworks Group Inc.: Current Employment. Sahni: Cellworks Group Inc.: Current Employment. Raman: Cellworks Group Inc.: Current Employment. Balakrishnan: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Palaniyeppa: Cellworks Group Inc.: Current Employment. Balla: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Marcucci: Abbvie: Other: Speaker and advisory scientific board meetings; Novartis: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings.

Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Predicted Novel Biomarkers for Hyper-CVAD (CVAD) Treatment Response and Combination of Rituximab and Cladribine (RC) in CVAD Resistant Cases of Mantle Cell Lymphoma (MCL)

Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Predicted Novel Biomarkers for Hyper-CVAD (CVAD) Treatment Response and Combination of Rituximab and Cladribine (RC) in CVAD Resistant Cases of Mantle Cell Lymphoma (MCL)

Research and Care Networks in Acute Leukemia As a Model for Global Healthcare Collaboration

Introduction: Clinicians in academia face four major career challenges:1.Gaining clinical advice from colleagues experienced in a particular disease or treatment2.Experiencing Life-long coaching and mentoring from senior experienced clinicians3.Accessing high quality continuing medical education relevant for patient care4.Support to carry out medical researchAll four challenges have been adversely impacted during the Covid-19 pandemic as traditional face-to-face networks have become harder to access. This is especially pertinent when treating complex or rare diseases like acute lymphoblastic leukemia (ALL). Atypical or refractory patients, and those who experience toxicities often benefit from timely input from experts with considerable experience managing ALL. Online networks offer a robust pandemic-proof source of health and care support and advice. Until recently there have been few dedicated professional networks that provide a regular online forum dedicated to research and care on specific diseases across countries and none related to ALL.Methods: We describe the Resonance ALL Research and Care Network (ALL RCaN; https://resonancehealth.org/networks/all-rcan ) which includes a network of colleagues and a weekly, multidisciplinary online forum that brings together pediatric and adult hematologists and oncologists from around the world to share data, discuss cases and support patient care. In addition, there is a monthly ‘Fellows Fourth Friday’ to help fellows build their own professional network and gain scientific and clinical advice. The network was born out of a monthly meeting of study chairs (the “Study Chair Affinity Group”) for ALL research protocols which had been running for 10 years.Results: The network launched formally in June 2020 with 30 founding members but has expanded rapidly through word of mouth. The Acute Leukemia network grew by 850% by Dec 2020 and 1460% by March 2021 and as of July 2021 has 579 members across 18 time zones. It has succeeded in 'Building ALL Bridges“ between physicians that treat adults and pediatricians for joint discussions in acute leukemia. This collaboration has been severely lacking in the past. The network also presents selected abstracts from major national and international conferences every 4 weeks. This model has been replicated for other cancers including the Global Neuroblastoma Network (resonancehealth.org/networks/gnn) and High-dose Methotrexate Quality Improvement Network (resonancehealth.org/networks/hdmtx). Network software development, video conferencing, meeting coordination, and content hosting have been funded by volunteer network leaders, many volunteer presenters, philanthropic contributions, and unrestricted educational grants. Now that the Resonance infrastructure is fully developed (and available to all at no cost), most Networks function well without funding.Conclusions: Providing free video conferencing, content hosting, and network management tools combined with dedicated leadership and clinically relevant discussions and presentations has led to massive growth of the ALL Research and Care Network, which continues to grow. Networks for other cancers are in various stages of development since the tool set and methodology easily scales to new groups of colleagues and new diseases. [Display omitted] DisclosuresGuscott: EUSA Pharma UK Ltd: Ended employment in the past 24 months. Douer: Amgen: Consultancy, Speakers Bureau; Servier: Consultancy, Speakers Bureau; Jazz: Consultancy; Adaptive: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company, Speakers Bureau. Howard: Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees; Servier: Consultancy.

Personalized Therapy Design for Early T-Cell Precursor Acute Lymphoblastic Leukemia (ETP-ALL) Using Computational Biology Modeling with in Vitro Validation

ETP-ALL is a high-risk subtype of T-ALL with poor prognosis and high risk of relapse in patients treated with standard of care (SOC). ETP-ALL patients have more complex cytogenetic profiles and a unique immunophenotype compared to other T-ALL cases. ETP-ALL is characterized by the expression of myeloid and hematopoietic stem cell (HSC) surface proteins, as well as gene expression profiles similar to myeloid progenitors and HSCs, including frequent mutations in myeloid oncogenes. Targeted therapies for ETP-ALL are limited because identified gene mutations are not actionable by currently available drugs. Recently we developed a predictive simulation modeling method that projects cancer patient-specific protein network maps based on the patient's unique cancer genomics (PMID 27855285). Here, we present a computational biology approach to analyze the genomic profile of ETP-ALL and identify novel treatment strategies using available FDA approved drugs. In this study, we used the LOUCY cell line as a model of ETP-ALL due to its high expression of characteristic ETP-ALL markers HHEX, LYL1, MEF2C and ZEB2.Aim: To identify and validate novel drug or drug combinations for ETP-ALL using an integrated genomics and computational biology modeling (CBM) approach.Methods: The LOUCY cell line was used as a model of ETP-ALL. Publicly available genomic profiling data (cBioPortal, Sanger) were collected. All available genomic data for the LOUCY cell line were entered into a CBM program (Cellworks Group) that generates a disease-specific protein network map using PubMed and other online resources. Disease-specific biomarkers unique to the LOUCY cell line were identified within the protein network maps. A digital drug library of FDA approved therapies and drugs in clinical development was developed and digitally simulated in the computational model at varying doses. Drug impact was quantitatively assessed by a cell growth score, which is a composite of cell proliferation, viability, apoptosis, along with the impact on disease-specific biomarkers. This CBM's predictions for drug response were compared with publicly available in vitro experimental data. Novel drug options and combinations to treat ETP-ALL were also identified by CBM. The novel drugs and drug combinations identified by CBM were then tested on the LOUCY cells at varying concentrations to calculate IC50 values. Drug activity was measured by XTT assay, using the Jurkat cell line as a non-ETP-ALL control and the KG-1 AML cell line as a non-ALL control.Results: CBM predicted extensive dysregulation of multiple protein signaling pathways in the LOUCY cell line: Cell cycle upregulation was predicted due to copy number loss of CDKN1B, CDKN2B and CDKN2A genes. BCL2 expression was predicted to be increased due to the presence of a NUP214 - SET fusion protein, activating the BCL2 transcription factor HOXA9. Knockdown of PTEN and TSC2 was predicted to activate the AKT-mTOR pathway, while knockdown of ETV6 and SOCS1 was predicted to activate the JAK-STAT pathway (Fig 1). The response predictions of 31 drugs were compared with data from in vitro experiments. LOUCY cells were predicted to respond to 9 of the drugs, and in vitro testing showed sensitivity to 8 out of 9 drugs (88.8%). LOUCY cells were predicted to show no response to 22 of the drugs, and in vitro testing showed resistance to all 22 drugs (100%) (Table 1). Additionally, CBM predicted everolimus, nelfinavir, and palbociclib as novel therapies for the ETP-ALL representative LOUCY cell line, and were validated (100%) by additional in vitro XTT assays (Fig 2). Key statistical correlations for drug efficacy were observed (Table 2).Conclusions: Predictive simulation modeling of the unique genomic abnormalities found in the ETP-ALL LOUCY cell line led to the creation of an ETP-ALL protein network map that shows multiple interacting and cooperative protein network disturbances including cell cycle, anti-apoptosis, AKT-mTOR and JAK-STAT pathways. Digital drug screening using this distinctive protein network map led to the discovery of new drugs with predicted efficacy. In vitro studies validated the computational method with high accuracy. Results from this work inform future clinical trials for patients with ETP-ALL. This CBM technology may be a useful tool for clinicians to personalize treatment for patients with relapsed/refractory ETP-ALL. [Display omitted] DisclosuresKumar:Cellworks: Employment. K:Cellworks: Employment. Kamath:cellworks: Employment. Nair:cellworks: Employment. Raju:cellworks: Employment. Abbasi:Cellworks Group Inc.: Employment. Vali:Cellworks Group Inc.: Employment. Cogle:Celgene: Other: Membership on Steering Committee for Connect MDS/AML Registry.

Predicting Response to IDH1/IDH2 Inhibitors Beyond IDH Mutations Using a Computational Model and Its Validation: A Beat AML Project Study

Predicting Response to IDH1/IDH2 Inhibitors Beyond IDH Mutations Using a Computational Model and Its Validation: A Beat AML Project Study

Computational Modeling of Protein Networks Predicts Treatment Outcomes in Multiple Myeloma (MM)

Background: MM is a highly refractory disease that is rarely cured. While introduction of novel therapies has improved survival and overall response rates (ORR), complete remission (CR) rates remain low (30%) and relapse is still frequent. Although nearly all newly diagnosed MM patients respond to standard of care (SOC) therapy, the depth of response varies. Triplet regimens, such as RVd (lenalidomide [R], bortezomib [V] and dexamethasone [d]), are accepted as SOC, but four-drug regimens are being investigated in an effort to improve CR rates. Doublet regimens are commonly initiated in older, transplant ineligible patients. Limitations of combination therapies include treatment-related adverse events, resulting in patients receiving suboptimal therapy. Unfortunately, no precise method exists to predict MM response to SOC, or to predict which patients will respond with two-drug combinations, making MM management challenging. Computational biological modeling (CBM) is a genomics-based tool to identify aberrant protein signaling networks within disease cells, and predict how each MMD case will respond to FDA-approved drugs and combinations. Predicting disease response would improve MM patient management and potentially reduce unnecessary treatment-related adverse events by identifying drug regimens with high potential for therapeutic activity against patient-specific, MM protein networks.Aim: To test the application of a genomics-informed CBM in MM patients treated with RVd.Methods: Forty newly-diagnosed patient profiles were identified from the publicly available MMRF CoMMpass dataset and divided into training (n=15) and test (n=25) cohorts. For each patient, all available genomic information was entered into computational biology program (Cellworks Group) that uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated protein networks. Digital drug simulations with standard of care (SOC) drugs were conducted by quantitatively measuring drug effect on a composite MM disease inhibition score (i.e. cell proliferation, viability, and apoptosis) to predict patient clinical outcomes. Additionally, the dynamics of antibody (heavy and light chains) transcription, translation, folding, and secretion were modeled to measure the extent that SOC therapy reduced paraprotein expression. Clinically, patients received SOC treatment and clinical responses were recorded. CR, VGPR, and PR were considered as responsive, while stable disease (SD) was considered non-response. Predictive values were calculated based on comparisons of the computer predictions and actual clinical outcomes.Results: The computational modeling correctly predicted 36 out of 40 clinical outcomes resulting in 94.12% PPV, 66.67% NPV (p=0.02), 94.12% sensitivity, and 66.67% specificity, and 90% accuracy. Additionally, 33 of 34 simulated paraprotein reductions were matched to actual clinical reductions in paraprotein levels, resulting in 97% accuracy. CBM was used to identify the minimum number of drugs each responder needed to achieve a simulated response, and selected the drug within the doublet or triplet regimen that had no therapeutic effect due to absence of the drug's target within the disease profiles.Conclusions: Computational modeling and digital drug simulations using MM patient genomic data resulted in highly accurate matching of clinical response to SOC treatment. CBM also accurately predicted the extent by which each patient's therapy reduced paraprotein levels, which can precede improved clinical outcomes. The computational approach identified each patient's anchor drug in their combination therapy that was most likely responsible for achieving improved clinical outcome. For patients who were clinical non-responders, the CBM identified probable protein networks responsible for drug resistance and rapidly screened for alternative drug combinations with predicted efficacy in light of the patients' drug-resistant pathways. Thus, CBM may be a useful tool for clinicians and translational scientists in search of personalized treatment or newer therapies for patients with MM. DisclosuresGrover:Cellworks: Employment. Narvekar:Cellworks: Employment. Kumari:Cellworks: Employment. Narayanabhatia:Cellworks: Employment. Bhowmick:Cellworks: Employment. Abbasi:Cellworks Group Inc.: Employment. Vali:Cellworks Group Inc.: Employment. Usmani:Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Speakers Bureau; Bristol-Myers Squibb: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Onyx: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Speakers Bureau; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Honoraria, Research Funding; Array BioPharma: Honoraria, Research Funding. Cogle:Celgene: Other: Membership on Steering Committee for Connect MDS/AML Registry.

Predicting Mechanisms of Ibrutinib-Resistance in Chronic Lymphocytic Leukemia (CLL) Using Computational-Based Modeling for Personalized Therapy with Clinical Validation

Background: Bruton's tyrosine kinase (BTK), a mediator of B cell receptor signaling, has been effectively exploited as a therapeutic target in Chronic lymphocytic leukemia (CLL). Ibrutinib is a BTK inhibitor that has shown excellent efficacy in treatment-naïve, relapsed/refractory, and high-risk CLL. However, concerns regarding ibrutinib resistance have emerged and characterizing the underlying mechanisms may aid in identifying patients with increased risk of resistance/disease progression. Moreover, understanding the interplay between determinants of ibrutinib resistance may reveal alternate therapeutic targets. Using a computational biology modeling (CBM) simulation-based method, we reverse-engineered the salient molecular architecture of a CLL patient progressing on ibrutinib to identify potential drug combinations to overcome drug resistance and induce remission.Methods: For validation of the computational biological modeling (CBM) software (Cellworks group), we selected a cohort of 15 ibrutinib resistant CLL patients from previously published studies (Table 1). Genomic data derived from whole exome sequencing (WES), targeted next-generation sequencing (NGS), copy number variation (CNV), and/or karyotype data were used in this study. All available genomic data for each profile was entered into the CBM, which generates a disease-specific protein network map using PubMed and other online resources. Disease-biomarkers that are unique to each patient were identified within the protein network maps by using CBM technology. Ibrutinib was selected from a digital drug library and simulated on every relapsed CLL patient case. Drug impact was assessed by quantitatively measuring the drug's effect on a cell growth score, which is a composite of cell proliferation, viability, and apoptosis, along with the simulated impact on each disease-specific biomarker score. Each patient-specific map was digitally screened to identify any Ibrutinib resistance mechanism(s).Results: In our modeled patient set, 4 different resistance mechanisms were identified across 14/15 ibrutinib- refractory CLL cases. 1) BTK C481S mutation (n=4) impacts ibrutinib binding affinity; 2) PLCG2 mutations (n=4) lead to activation of PRKCB and AKT1 signaling; 3) CARD11 mutation (n=2) results in constitutive activation of BTK downstream effectors; and 4) decreased G-protein coupled receptor (GPCR) pathway signaling, an upstream activator of BTK (n=5), was found to be a novel resistance mechanism identified by CBM. Knockdown (KD) of ALOX5AP, ARRB1 and CYSLTR2 reduced GPCR pathway signaling, resulting in reduced BTK activity (Table 1). After validating the CBM algorithm, we rendered a digital avatar for a relapsed-refractory CLL patient (Del17p+); progressing on ibrutinib. The patient had rapidly escalating ALC and lymphadenopathy; with the largest node measuring ~16cm. In addition to p53 loss, simulation modeling accounted for BTK C481S mutation (BAF 0.69) expressed in the patients tumor cells. CBM predicted venetoclax would be the most effective therapeutic for the patient due to several upstream signaling pathways converging on Bcl-2. Indeed, addition of venetoclax to the ongoing ibrutinib monotherapy dramatically reduced the patients ALC (from 113 to 3.3 x 109/L) by day 4 post-treatment, accompanied by reduction in lymphadenopathy (left axillary node decreased to ~3cm). Ibrutinib was discontinued within 1 month of starting the combination, with venetoclax monotherapy continued per dosing guidelines thereafter. Notably, the patient maintained remission status on venetoclax monotherapy alone.Conclusions: Computational modeling was used to identify ibrutinib resistance mechanisms in CLL patients. Using this novel approach, we identified an effective therapy (venetoclax) for a high-risk CLL patient who was progressing on ibrutinib. CBM-based approaches can be used to predict alternative therapeutic regimens for drug-refractory patients, targeting the disease-specific biomarkers unique to each patient. Altogether, our study demonstrates proof-of-principle on the use of computational predictive algorithms to simulate multiple protein-interaction networks operational in a tumor cell to exploit individualized therapeutic vulnerabilities. [Display omitted] DisclosuresYsebaert:Janssen: Consultancy, Research Funding, Speakers Bureau. Kumar:Cellworks: Employment. Singh:Cellworks Research India Pvt. Ltd: Employment. Ullal:Cellworks Research India: Employment. Bhargav:Cellworks: Employment. Azam:cellworks: Employment. Abbasi:Cellworks Group Inc.: Employment. Vali:Cellworks Group Inc.: Employment. Cogle:Celgene: Other: Membership on Steering Committee for Connect MDS/AML Registry.

Predictive Simulation Modeling of Early T-Cell Precursor Acute Lymphoblastic Leukemia (ETP-ALL) Patient Genomics Identifies Targeted Combination Treatment Options

ETP-ALL, an orphan disease, is a subtype of T-ALL with poor prognosis and high risk of relapse after standard of care (SOC) chemotherapy. ETP-ALL is a genomically heterogeneous disease with many cases harboring complex karyotype and genetic mutations typically seen in myeloid neoplasias, such as acute myeloid leukemia (AML). Targeted therapies for ETP-ALL are limited because identified gene mutations are not actionable by currently available drugs. Unfortunately, empirical chemotherapy trials have yet to show significant improvements in clinical outcomes of ETP-ALL patients (pts), which makes treating ETP-ALL challenging. Ideally, selecting treatment for ETP-ALL is rationally designed from the panoply of unique molecular abnormalities observed in ETP-ALL cases. Recently, we developed a predictive simulation modeling method that projects protein network maps from the hundreds of leukemia genomic mutations observed in individual pts (PMID 27855285). Our computational modeling enables multi-gene/multi-drug simulations. We hypothesized that ETP-ALL is comprised of protein network disturbances distinct from other T-ALL cases. We furthermore hypothesized these unique ETP-ALL protein networks would lead to the discovery of new treatment combinations for pts with ETP-ALL.Aim: Identify novel combinations of drugs for ETP-ALL pts who have failed SOC therapy by using an integrated genomics and computational approach.Methods: 66 ETP-ALL pts genomic data (whole exome sequencing, copy number variations, cytogenetics) were collected from various sources including NCBI-GEO dataset (GSE28703) (N=12), Moffitt Cancer Center (N=6) (IRB201600284) and published literature (PMID 22237106, 19147408) (N=48). The genomic data for each profile were entered into a computational biology modeling (CBM) software (Cellworks Group), which generated a disease-specific protein network map using PubMed and other online resources. Disease-specific biomarkers were identified within the protein network maps using CBM technology. A digital library of FDA-approved drugs, along with investigational agents, was simulated on every pt model both as single agent and combinations at varying doses. Drug impact was assessed by quantitatively measuring drug effect on a cell growth score (CGS), which is a composite of cell proliferation, viability and apoptosis. Unique combinations that reduced the CGS by interacting with the pt specific disease-biomarkers were selected. Synergy of drug combinations was calculated using the Co-efficient of Drug Interaction (CDI) formula. Each novel therapeutic agent or drug combination was mapped to the pts genomics, and supported by a PubMed referenced scientific mechanism of action based on disease biology.Results: Of the 66 ETP-ALL profiles, 22/66 (33%) harbored actionable mutations, including NRAS, TET2, FLT3 and various deletions such as del(5q) or deletions in tumor suppressor genes including CDKN2A / 2B, TSC1 and others. 44/66 (67%) had many deleterious mutations that were not directly actionable by currently available drugs. The integrated genomics and CBM workflow identified targeted combination therapies for 59/66 (89.4%) of cases. Eighty-seven unique targeted combinations were identified across the 66 ETP-ALL pts in this study (Fig 1). Eleven of the identified combinations were synergistic, as calculated by CDI, and 5 of the combinations included idarubicin as one of the agents, while the most synergistic combination was lenalidomide + nelfinavir (CDI = 3.34). Analysis of CBM identified pt-specific biomarkers for 66/66 (100%) pts. Dysregulated protein networks unique to ETP-ALL pt cases included pathways involving TP53, MYC, NFKB1, FOXM1, RUNX3, STAT3, CTNNB1, E2F1 and CEBPA. Some of the key kinase biomarkers identified in the ETP-ALL profiles were AURKB, CSNK2A1, MAP3K14, RAF1, ERK and mTOR (Fig 2).Conclusions: Computational biology analysis of complex ETP-ALL genomics found a unique protein network that may be amenable to several new treatment combinations. This CBM approach identified personalized drug regimens in all cases, including those who had no actionable genetic mutations present. This predictive technology improves the utility of cancer genetic profiling and may enhance clinical decision-making to identify treatments for ETP-ALL pts who would otherwise have no options based on limited SOC alternatives. [Display omitted] DisclosuresKumar:Cellworks: Employment. K:Cellworks: Employment. Bhargav:Cellworks: Employment. Choudhary:Cellworks: Employment. L:Cellworks: Employment. Birajdar:Cellworks: Employment. Roy:Cellworks: Employment. Abbasi:Cellworks Group Inc.: Employment. Vali:Cellworks Group Inc.: Employment. Cogle:Celgene: Other: Membership on Steering Committee for Connect MDS/AML Registry.

Icare 1: A Prospective Clinical Trial to Predict Treatment Response Based on Mutanome-Informed Computational Biology in Patients with Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS)

Refractory disease remains a challenge in treating patients (pts) with MDS and AML. Despite their widespread use, cytotoxic agents (7+3, HiDAC) and hypomethylating agents (HMAs) fail in the majority of pts, and lenalidomide (LEN) fails in 75% of non-del(5q) MDS. 90% of MDS and AML pts harbor at least one somatic mutation, further contributing to disease complexity. Currently, no comprehensive method exists to predict disease response based on the entire mutational burden detected in malignant samples. Predicting response to treatment would improve effectiveness, limit treatment-related adverse events, and reduce health care costs. Hence, there is need to predict pt response based on disease biology.Aim: Determine the biological and clinical predictive values of a genomics-informed computational biology method (CBM) in pts who are treated with standard of care (SOC) therapy.Methods: Pts with AML or MDS were recruited in a prospective clinical trial (NCT02435550) designed to assess predictive values by comparing computer predictions of treatment response to actual clinical response. Genomic profiling was conducted by cytogenetics, whole exome sequencing (WES), and copy number variation (CNV) analysis. These genomic results were entered into a CBM software (Cellworks Group), which generates disease-specific protein network maps using PubMed and other online resources. Digital drug simulations were conducted by quantitatively measuring drug effect on a cell growth score, which is a composite of cell proliferation, viability and apoptosis. Each pt-specific protein network map was digitally screened for the extent to which each pt's therapy reduced simulated disease growth in a dose-respondent manner. Treatment was physician's choice of SOC. Before initiating treatment, treating physicians were masked to the results of WES and CBM predictions. Clinical outcomes were prospectively recorded by the physicians. To be eligible for efficacy assessment, MDS pts must have had at least 4 cycles of HMA treatment or 2 cycles of LEN treatment. For AML, CR+PR was used to define response (IWG 2003). For MDS, CR+PR+HI was used to define response (IWG 2006). AML pt response was determined at time of recovery bone marrow biopsy, post induction therapy and/or consolidation therapy. Western blot assays were performed to validate the predicted protein network perturbations. Comparisons of CBM versus actual responses were performed using 2x2 tables. Fisher's exact tests were used to compare prediction values of the genomics-informed computer method versus empiric drug administration.Results: Between June 2015 and July 2017, 120 pts were recruited, and 81 have had all genetic tests performed. At the time of this report, 42 pts were eligible for clinical response evaluation. 25/42 (60%) had AML, 15/42 had MDS (36%), and 2 pts had myelofibrosis. The median age was 65 (range 47-90). 24/42 (57%) were treatment-naïve and 18/42 (43%) were treatment-refractory. Laboratory validation study of computer-predicted, activated protein networks in 19 samples from 11 different pts showed correct prediction of 4 activated networks (Akt2, Akt3, PIK3CA, Erk1/2) in 17 samples, exhibiting 89% accuracy. At the time of this report, 42/81 pts received 52 treatments and were eligible for efficacy evaluation. 28/52 treatments resulted in clinical response to SOC therapy, while 24/52 did not achieve clinical response. 48 drug outcome predictions (92%) were correctly matched to their actual clinical outcomes, and 4/52 were incorrectly matched, resulting in 93% PPV, 92% NPV, 93% sensitivity, and 92% specificity (Table 1). Of note, CBM performed accurately in both treatment-naïve and relapsed/refractory pts.The accuracy of the CBM was significantly greater than empiric drug administration (p=6.034 E-08). Additionally, new genomic signature rules were discovered to correlate with clinical response after HMA or len.Conclusions: CBM that models multiple genomic abnormalities simultaneously showed high predictive value of protein network perturbations and clinical outcomes after SOC treatments. The network method uncovered molecular reasons for drug failure and highlighted resistance pathways that could be targeted to recover chemosensitivity. This technology could also be used to establish eligibility criteria for precision enrollment in drug development trials, and will be used in upcoming precision medicine trials. [Display omitted] DisclosuresSingh:Cellworks Research India Pvt. Ltd: Employment. Radhakrishnan:Cellworks: Employment. Ullal:Cellworks Research India: Employment. Talawdekar:Cellworks: Employment. Sikora:Cellworks: Employment. Nair:Cellworks: Employment. Bhowmick:Cellworks: Employment. Abbasi:Cellworks Group Inc.: Employment. Vali:Cellworks Group Inc.: Employment. Norkin:Celgene: Honoraria, Research Funding. Cogle:Celgene: Other: Membership on Steering Committee for Connect MDS/AML Registry.

Predicting Response to BET Inhibitors Using a Computational Model and Its Validation: A Beat AML Project Study

Predicting Response to BET Inhibitors Using a Computational Model and Its Validation: A Beat AML Project Study

Predicting Response to CDK4/6 Inhibitors and Combinations Using a Computational Biology Model and Its Validation: A Beat AML Project Study

Predicting Response to CDK4/6 Inhibitors and Combinations Using a Computational Biology Model and Its Validation: A Beat AML Project Study

Digital Drug Screening and Computational Modeling Identifies Treatment Opportunities Despite Lack of Directly Actionable Mutations in Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS)

Background: Although virtually all patients (pts) with AML or MDS harbor at least one somatic mutation, a minority of pts possess genetic mutations considered directly targetable by a drug. Moreover, in pts with multiple gene mutations, single-gene/single-drug matching often produces conflicting drug recommendations. This lack of clinically relevant mutation-targeted therapy represents an unmet need in treating AML and MDS, and is a major limitation in personalized medicine. We hypothesized that genomic mutations from pts with AML or MDS could be used to generate pt-specific protein network maps for use in digital drug screening (DDS) even in cases when the gene mutations per se are not directly actionable. This would dramatically increase the percent of pts with actionable findings. Our primary goal was to establish a genomics and computational biology workflow that identified disease-relevant treatment options for every pt.Methods: We prospectively enrolled AML and MDS pts and collected bone marrow (BM), peripheral blood and clinical data relevant to their diagnosis and treatment. BM specimens were examined by conventional cytogenetics (Giemsa), whole exome sequencing (WES, Agilent), and copy number variation (CNV). Directly actionable mutations were defined as IDH1/2, JAK2, NRAS, KRAS, FLT3, TET2 or del(5q), based on currently available drugs that directly act on these mutations. Genomic data from each pt were used as inputs into a computational biology modeling (CBM) software (Cellworks Group), that generated pt-specific protein network maps based on software written from PubMed references and other online databases. DDS was conducted by mathematically quantifying drug effects on a cell growth score (CGS), which is a composite of modeled cell proliferation, viability and apoptosis. 77 drug combinations, comprised of 10 standard of care (SOC) and 16 non-SOC drugs were digitally screened on each pt's case to identify drug or drug combinations with significant reductions in CGS. Evaluation for drug-drug synergy was calculated by coefficient of drug interaction (CDI) and the extent to which each drug reduced the CGS.Results: 80 pts with either AML (n=41, 51%) or MDS (n=31, 39%), or myeloproliferative neoplasms (n=8, 10%) were successfully analyzed by the genomics and computational biology analysis plan. 23/80 (29%) were treatment-naive, while 58/80 (71%) were treatment-refractory. The median age was 65 years (range 36-90). While 28/80 (34%) pts had a directly actionable mutation, 53/80 (66%) patients did not. Disease-relevant biomarkers were identified for all (100%) of the pts using the CBM technology, involving pathways related to kinase activity (BTK, MAPK3, SRC, AURKA/B, AKT, mTOR, etc.) and transcription factors (TP53, MYC_MAX, CTNNB1, NFkB1, RUNX3, STAT3, etc.). DDS identified 77 unique drug combinations with potential therapeutic activity in these 81 patients, where cytarabine + dexamethasone (n=10), cytarabine + ruxolitinib (n=8), cytarabine + everolimus (n=6) were the most commonly identified combinations. A majority of pts (63/80, 79%) were predicted to respond to a combination of SOC + non-SOC, while 18/80 (21%) patient's cases were predicted to respond to a combination of non-SOC + non-SOC. In addition to selecting therapeutic regimens, computational modeling identified 9 pt cases whose simulated drug combinations resulted in synergistic activity, more frequently including cytarabine + ruxolitinib or cytarabine + dexamethasone.Conclusions: In contrast to a widely used practices of next generation sequencing techniques to identify single-gene/single-drug matching, this strategy of using a protein networking mapping and DDS identified therapeutic options for all pts with AML or MDS, even when there was no directly actionable mutation. In clinical situations of no established standard of care, such as refractory AML or MDS, the use of computational modeling and digital drug simulations may increase therapeutic options. CBM is currently being validated in a prospective clinical trial (NCT02435550). These results serve as the basis for a new, non-basketized clinical trial testing the effectiveness of computer-informed treatment based on the pt's malignant mutanome. DisclosuresNorkin:Celgene: Honoraria, Research Funding. Vasista:Cellworks Research India: Employment. Basu:Cellworks: Employment. Usmani:Cellworks: Employment. Roy:Cellworks: Employment. Lala:Cellworks: Employment. Radhakrishnan:Cellworks: Employment. Kumar:Cellworks: Employment. Rajagopalan:Cellworks: Employment. Abbasi:Cellworks Group Inc.: Employment. Vali:Cellworks Group Inc.: Employment. Cogle:Celgene: Other: Membership on Steering Committee for Connect MDS/AML Registry.

A Genomic Signature Predicting Venetoclax Treatment Response in Acute Myeloid Leukemia (AML) Identified By Protein Network Mapping and Validated By Ex Vivo Ddrug Sensitivity Testing

A Genomic Signature Predicting Venetoclax Treatment Response in Acute Myeloid Leukemia (AML) Identified By Protein Network Mapping and Validated By Ex Vivo Ddrug Sensitivity Testing

Computational Biology Model of ETP-ALL Used to Accurately Classify ETP-ALL Vs Non ETP-ALL Patients with Genomic Input

Background: Early T-cell precursor acute lymphoblastic leukemia (ETP-ALL) is an aggressive hematological malignancy with poor prognosis after chemotherapy compared to other ALLs. ETP-ALL cases typically display a high burden of chromosomal copy number variations (CNVs) and myeloid gene mutations. To enhance the understanding of this disease, a computational biological modeling approach was developed to classify ETP-ALL vs. non-ETP-ALL patient based on genomic abnormalities and protein biomarkers associated with ETP-ALL and non ETP-ALL profiles.Methods: Genomic profiling data for whole exome sequencing (WES), array CGH (Agilent) and karyotype data of 69 ETP- ALL and Non-ETP-ALL patient were used for this study. The genomic data for each patient were entered into a computational biology model (CBM) (Cellworks Group), which generates a disease-specific protein network map using PubMed and other online resources. CBM was used for a) Classification between ETP-ALL and Non-ETP ALL model, b) Identification of unique patient characteristics and c) Drug response.For the CBM approach, we divided the data into training and validation data sets. 40 patients were selected for training data and 39 patients selected for the validation data set. In a training dataset, genomic data from 22 ETP-ALL patients were used from a GEO database (GSE28703) (N=11) and a prior genomics report (PMID:22237106) (N=11). The training dataset also included non-ETP-ALL patients from a GEO database (GSE28703) (N=18). Each patient's data was analyzed by CBM, to translates genomic variants into protein networks.To validate these findings, genomic data from 15 ETP-ALL patients, different than training set patients, were used from a GEO database (GSE28703) (N=1), Moffitt Cancer Center (IRB201600284) (N=6), a prior genomics report (PMID: 22237106) (N=7), and an ETP-ALL LOUCY cell line with known genomic aberrations. The validation dataset also included Non-ETP-ALL patients different than training set patients, from a GEO database (GSE28703) (N=14).Results: Prediction accuracy for classification between ETP-ALL and Non ETP ALL patient was 89.65%. PPV value was 86.67% and NPV value was observed 92.86%. Sensitivity and Specificity of prediction outcome was observed as 92.86% and 86.67% respectively.The simulation model identified six protein biomarkers which predicted increased activity in ETP-ALL profiles compared to non-ETP-ALL cases: STAT3, RUNX1, HIF1A, FOXM1, CEBPA, mTORC1.Key genomic differences were also identified between ETP-ALL and non ETP-ALL patients (Table1).Conclusions: ETP-ALL is distinct from other T-ALL with respect to genomic and protein network abnormalities. Computational biology model can accurately classify between ETP-ALL and Non ETP-ALL patients. CBM also predicted unique biomarkers between ETP-ALL and Non-ETP-ALL patients. [Display omitted] DisclosuresKumar:Cellworks: Employment. K:Cellworks: Employment. Vasista:Cellworks Research India: Employment. Tyagi:Cellworks: Employment. Rajagopalan:Cellworks: Employment. Alam:Cellworks: Employment. Abbasi:Cellworks Group Inc.: Employment. Vali:Cellworks Group Inc.: Employment. Cogle:Celgene: Other: Membership on Steering Committee for Connect MDS/AML Registry.

Sequential Azacitidine (Aza) and Lenalidomide (Len) for Patients (Pts) with Relapsed and Refractory (R/R) Acute Myeloid Leukemia (AML): Clinical Results and Predictive Modeling Using Computational Analysis and Serial Genomics

▪Introduction: Pts with R/R AML have few effective treatment options. Len has activity in myeloid malignancies including AML, and may have additive properties with aza. Based on encouraging data using sequential aza with high dose len in untreated elderly AML pts, we designed a phase 2 pilot study exploring this therapy (tx) in pts with R/R AML.Methods: Adult pts with R/R AML or myelodysplastic syndromes (MDS) and preserved organ function with a WBC<10,000/μL (hydroxyurea permitted) were eligible. The tx consisted of aza 75 mg/m2 d1-7, a bone marrow biopsy on cycle (C) 1 day 8, len 50 mg d8-28, and 2 weeks off tx, constituting a 42-day cycle. Response assessments were made per International Working Group (IWG) criteria. Cycles were repeated if tolerated and in the setting of response. Next generation sequencing (NGS) was performed with Illuimina MiSeq at repeated time points serially for each pt (baseline, C1 day 8, C1 day 42) through multiplex PCR amplification of 548 amplicons targeting mutational hot spots in 49 recurrently mutated genes, and analyzed with SoftGenetics NextGENe software. The UCSC hg19 and GRCh 37 reference genome were used as the reference sequence. Genomic information was entered into computational biology modeling (CBM) software (Cellworks), which generates disease specific protein network maps using PubMed and other online resources. Digital drug simulations of aza and len were conducted by quantitatively measuring drug effect on a cell growth score. Each pt specific protein network map was screened for the extent to which tx reduced simulated disease growth.Results: 37 pts were eligible and received at least one day of tx. Median age was 73 (18-86); 34 had AML, 3 had high-risk MDS (Table 1). The most common possibly related/related > grade 2 adverse events (AEs) were thrombocytopenia (44%), neutropenia (44%), leukopenia (43%), fatigue (32%), anemia (30%) and febrile neutropenia (16%). During C1, 2 pts required len dose decreases and 11 pts did not receive the entire course of len, for infection, rash, neuropathy, renal insufficiency, GI toxicity or progression.9 pts did not live to complete cycle 1; all died of AML. The overall response rate (ORR, complete remission [CR] + CR with incomplete count recovery [CRi] + partial remission [PR] + morphologic leukemia free state [MLFS]) was 14/37 (38%); 4 CR/CRi and 10 MLFS. 6 additional pts had blast reductions that did not meet IWG criteria for response. Median number of cycles was 1 (0-5); median time to achieve best response was 1 cycle (1-3). 16 pts were identified at baseline as potential candidates for a 1st/2nd allogeneic transplant or donor lymphocyte infusion; 4/16 (25%) achieved this endpoint and 1 remains alive. Median follow up time is 3 years. 36/37 patients have died; 33 from progression and 3 from treatment related causes. 5/36 (14%) died within 30 days of study initiation. Median progression free survival (PFS) was 112 days; 132 for responders and 98 for non responders (p=0.7). 1 year PFS was 7%. Median overall survival was 150 days, 216 for responders and 124 for non responders (P=0.5); 1 year OS was 25%. (Fig 1). No clinical features predicted response (Table 2).CBM analysis has been performed on 5 pts (2 non responders and 3 responders). None were predicted to respond to len, 4/5 were predicted to respond to aza. 4/5 (80%) were predicted correctly; 3/3 responders and 1/2 non responder. Analysis of the entire cohort is ongoing.Serial quantitative NGS was performed at multiple time points for most patients; results have been analyzed for 7 (3 responders, 4 non responders). These preliminary results do not reveal a correlation between response and specific mutations. However, there is a trend toward a correlation between increased total number of mutations at baseline and lack of response (p=0.099) (Fig 2). This is being further explored with clonal analysis to measure the heterogenous responses of individual pts over time.Conclusion: Len has activity in a subpopulation of pts with AML, perhaps with additive value from sequential use with aza; responses are transient but can provide sufficient reduction in disease burden to allow for a transplant, which can result in long term remissions. However, the toxicity of this regimen is significant and typical clinical features are not predictive for response, limiting its widespread use. Therefore other tools, such as CBM or serial NGS, must be employed to select pts most likely to respond. [Display omitted] DisclosuresAbbasi:Cellworks Group Inc.: Employment. Vali:Cellworks Group Inc.: Employment. Singh:Cellworks Research India Pvt. Ltd: Employment. Pollyea:Takeda, Ariad, Alexion, Celgene, Pfizer, Pharmacyclics, Gilead, Jazz, Servier, Curis: Membership on an entity's Board of Directors or advisory committees; Agios, Pfizer: Research Funding.

The Oral Proteasome Inhibitor Ixazomib, Alone and in Combination with Ibrutinib, Induces Lethality in Waldenstrom Macroglobulinemia Cells That Are Resistant to Ibrutinib

Introduction: Dysregulated B-cell receptor (BCR) signaling drives proliferation of Waldenstrom macroglobulinemia (WM) cells, which characteristically secrete excessive IgM. To maintain IgM production and preserve cellular homeostasis, WM cells rely on optimally functioning proteasomes to degrade and recycle proteins. This biological dependence has been clinically exploited using the proteasome inhibitor (PI), bortezomib (Btz), which is capable of inducing remission in 85% of WM patients (Treon et al Clin Canc Res, 2007) as a single agent. Despite, its activity, Btz use is associated with a high incidence of sensory and autonomic neuropathies as well as its cumbersome dosing schedule and non-oral route of administration. Thus, new agents that 1.) build on the activity of Btz, 2.) have a more favorable toxicity profile and 3.) can be combined with standard of care agents such as, ibrutinib (Ibr), are highly attractive and should be prioritized for investigation. Here we describe the preclinical activity of ixazomib (Ixa), a PI that is administered orally and is associated with significantly less peripheral neuropathy; in combination with Ibr, in WM cells.Methods: WM cell lines (BCWM.1 and RPCI.WM1) and their drug-resistant subclones were used. Genomic data including whole exome sequencing (WES) and copy number variations (CNV) was used in creation of digital simulation avatars of RPCI-WM1 and RPCI-WM1/IR (Ibr-resistant, Ibr-R variant), where the salient and prominently dysregulated cellular pathways were identified. Ixa +/- Ibr was tested in digital avatars and dose-dependent impact on simulated disease growth was assessed (Fig 1A). The predictive results were experimentally validated. Cell viability was measured using trypan blue or CellTiter Glo. Apoptosis was examined by annexin-V/PI staining of drug-treated cells followed by flow cytometry analysis. Mitochondrial membrane permeability (MOMP) was determined by TMRM staining of drug-treated cells followed by flow cytometry.Results: We first profiled the growth inhibitory action of Ixa in WM cell lines (n=9); all of which were sensitive to Ixa irrespective of acquired resistance to Btz, Ibr or carfilzomib with a median IC50 of 95.59nM. We then tested whether Ixa could cooperate with Ibr to enhance WM cell kill. Indeed, the Cellworks modeling tool, predicted a significant loss of viability and induction of apoptosis in both wildtype and Ibr-R cells treated with Ibr + Ixa, as compared to either single agent (Fig 1B-D). We confirmed this in vitro, where viability of wildtype WM cells treated with Ixa (20nM), Ibr (5uM) or Ibr + Ixa for 6h significantly decreased; most notable with Ixa + Ibr (25.5% viable cells). And this effect was similar in Ibr-R cell lines (30.3% viable cells) after combination treatment. Further confirming in silico predictive modeling results, we noted a significant increase in annexin-V/PI positivity in Ixa treated wildtype (24.5%) and Ibr-R WM cell lines (33.1%). Whereas Ibr alone exhibited minimal apoptosis in WM cell lines (~11 - 15%), the combination of with Ixa + Ibr enhanced programmed cell death in wildtype (40.2%) and Ibr-R cells (50.5%). As intrinsic apoptosis is triggered through mitochondrial disruption, we also examined mitochondrial transmembrane permeability (MOMP) in cells treated with Ibr, Ixa or Ixa + Ibr. In wildtype cells, mean MOMP induced by single agent Ixa was 23.5% and in Ibr-R cells 33%; increasing to 39% and 50.4%, respectively, after treatment with Ibr + Ixa. Mechanistic studies revealed changes in proteins regulated by the ubiquitin proteasome system and those associated with BTK signaling. While Ixa alone downregulated Bcl-2 and Mcl-1 levels, this effect was more marked in cells (both wildtype and Ibr-resistant) treated with Ixa + Ibr combination. Similar effects were noted in pPLCg2, albeit with some variability between the cell lines.Conclusion: Using a combination of in silico modeling and experimental approaches, we provide first preclinical evidence on the antitumor effects of Ibr combined with the oral PI, Ixa, in WM cells, including those resistant to Ibr. Whereas further investigation on the precise molecular mechanisms of how Ibr + Ixa cooperate to induce apoptosis in WM cell is ongoing, our studies provide the basis for clinical testing of the Ibr + Ixa regimen; with a phase II clinical study in development. [Display omitted] DisclosuresSingh:Cellworks Research India Pvt. Ltd: Employment. Kumar:Cellworks: Employment. Basu:Cellworks: Employment. Trivedi:Cellworks: Employment. Majumdar:Cellworks: Employment. Abbasi:Cellworks Group Inc.: Employment. Vali:Cellworks Group Inc.: Employment. Martin:Janssen: Consultancy, Honoraria, Other: travel expenses; Genentech: Consultancy; Celgene: Consultancy; Teva: Research Funding; Novartis: Consultancy; Gilead: Consultancy, Other: travel expenses. Ansell:Bristol-Myers Squibb: Research Funding; Merck: Research Funding; Celldex: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding. Ailawadhi:Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Pharmacyclics: Research Funding; Takeda: Consultancy, Honoraria.