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Supplemental Figures and Tables to augment (1) the mathematical modeling (providing details on specific therapeutics); (2) patient characteristics (treatment history, clinical and predicted response), (3) synergistic activities identified by EMMA; and (4) data processing guides

Multiple myeloma remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system, Ex vivo Mathematical Myeloma Advisor (EMMA), consisting of patient-specific mathematical models parameterized by an ex vivo assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from multiple myeloma patients, allowing us to predict clinical response to up to 31 drugs within 5 days after bone marrow biopsy. From a cohort of 52 multiple myeloma patients, EMMA correctly classified 96% as responders/nonresponders and correctly classified 79% according to International Myeloma Working Group stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson r = 0.5658, P < 0.0001). Preliminary estimates indicate that, among the patients enrolled in this study, 60% were treated with at least one ineffective agent from their therapy combination regimen, whereas 30% would have responded better if treated with another available drug or combination. Two in silico clinical trials with experimental agents ricolinostat and venetoclax, in a cohort of 19 multiple myeloma patient samples, yielded consistent results with recent phase I/II trials, suggesting that EMMA is a feasible platform for estimating clinical efficacy of drugs and inclusion criteria screening. This unique platform, specifically designed to predict therapeutic response in multiple myeloma patients within a clinically actionable time frame, has shown high predictive accuracy in patients treated with combinations of different classes of drugs. The accuracy, reproducibility, short turnaround time, and high-throughput potential of this platform demonstrate EMMA's promise as a decision support system for therapeutic management of multiple myeloma. Cancer Res; 77(12); 3336–51. ©2017 AACR.

Individual patient EMMA predicitons of actual responses to therapy (companion to Figure 3i).

Multiple myeloma remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system, Ex vivo Mathematical Myeloma Advisor (EMMA), consisting of patient-specific mathematical models parameterized by an ex vivo assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from multiple myeloma patients, allowing us to predict clinical response to up to 31 drugs within 5 days after bone marrow biopsy. From a cohort of 52 multiple myeloma patients, EMMA correctly classified 96% as responders/nonresponders and correctly classified 79% according to International Myeloma Working Group stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson r = 0.5658, P < 0.0001). Preliminary estimates indicate that, among the patients enrolled in this study, 60% were treated with at least one ineffective agent from their therapy combination regimen, whereas 30% would have responded better if treated with another available drug or combination. Two in silico clinical trials with experimental agents ricolinostat and venetoclax, in a cohort of 19 multiple myeloma patient samples, yielded consistent results with recent phase I/II trials, suggesting that EMMA is a feasible platform for estimating clinical efficacy of drugs and inclusion criteria screening. This unique platform, specifically designed to predict therapeutic response in multiple myeloma patients within a clinically actionable time frame, has shown high predictive accuracy in patients treated with combinations of different classes of drugs. The accuracy, reproducibility, short turnaround time, and high-throughput potential of this platform demonstrate EMMA's promise as a decision support system for therapeutic management of multiple myeloma. Cancer Res; 77(12); 3336–51. ©2017 AACR.

The novel Bruton's tyrosine kinase inhibitor ibrutinib has demonstrated high response rates in B-cell lymphomas; however, a growing number of ibrutinib-treated patients relapse with resistance and fulminant progression. Using chemical proteomics and an organotypic cell-based drug screening assay, we determine the functional role of the tumour microenvironment (TME) in ibrutinib activity and acquired ibrutinib resistance. We demonstrate that MCL cells develop ibrutinib resistance through evolutionary processes driven by dynamic feedback between MCL cells and TME, leading to kinome adaptive reprogramming, bypassing the effect of ibrutinib and reciprocal activation of PI3K-AKT-mTOR and integrin-β1 signalling. Combinatorial disruption of B-cell receptor signalling and PI3K-AKT-mTOR axis leads to release of MCL cells from TME, reversal of drug resistance and enhanced anti-MCL activity in MCL patient samples and patient-derived xenograft models. This study unifies TME-mediated de novo and acquired drug resistance mechanisms and provides a novel combination therapeutic strategy against MCL and other B-cell malignancies., Ibrutinib has demonstrated high response rates in B-cell lymphomas but a lot of ibrutinib-treated patients relapse with resistance. This study unified TME-mediated de novo and acquired drug resistance through B-cell receptor signalling and PI3K-AKT-mTOR axis and provides a combination therapeutic strategy against B-cell malignancies.

Multiple myeloma (MM) remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system, EMMA (Ex vivo Mathematical Myeloma Advisor), consisting of patient-specific mathematical models parameterized by an ex vivo assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from MM patients, allowing us to predict clinical response to up to 31 drugs within 5 days post-bone marrow biopsy. From a cohort of 52 MM patients, EMMA correctly classified 96% as responders/non-responders and correctly classified 79% according to IMWG stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson r=0.5658, P

Molecular analysis of complex modular structures, such as promoter regions or multi-domain proteins, often requires the creation of families of experimental DNA constructs having altered composition, order, or spacing of individual modules. Generally, creation of every individual construct of such a family uses a specific combination of restriction sites. However, convenient sites are not always available and the alternatives, such as chemical resynthesis of the experimental constructs or engineering of different restriction sites onto the ends of DNA fragments, are costly and time consuming. A general cloning strategy (nucleic acid ordered assembly with directionality, NOMAD; WWW resource locator http:@Lmb1.bios.uic.edu/NOMAD/NOMAD.htm l) is proposed that overcomes these limitations. Use of NOMAD ensures that the production of experimental constructs is no longer the rate-limiting step in applications that require combinatorial rearrangement of DNA fragments. NOMAD manipulates DNA fragments in the form of "modules" having a standardized cohesive end structure. Specially designed "assembly vectors" allow for sequential and directional insertion of any number of modules in an arbitrary predetermined order, using the ability of type IIS restriction enzymes to cut DNA outside of their recognition sequences. Studies of regulatory regions in DNA, such as promoters, replication origins, and RNA processing signals, construction of chimeric proteins, and creation of new cloning vehicles, are among the applications that will benefit from using NOMAD.

Introduction: Multiple myeloma is a heterogeneous plasma cell neoplasm that remains all but incurable despite significant advances in treatment. We anticipate that the ability to overcome this hurdle resides in personalized strategies designed to specifically recognize, target, and anticipate dynamic tumor subpopulations with variable drug response profiles within an individual. To this end, we have developed a novel multi-disciplinary approach using organotypic drug screening and mathematical modeling to assess drug sensitivity of the different subpopulations within the tumor burden of individual patients and, in turn, provide accurate predictions of clinical outcome to anti-myeloma therapy. Material and methods: We have used a novel combination of ex vivo drug sensitivity assay and mathematical models to predict clinical response of 48 MM patients (11 newly diagnosed and 37 relapsed, 18 females and 30 males, median age 64.5, range 45-77) treated with a combination of proteasome inhibitors and IMIDs (37), nuclear export and topo2 isomerase inhibitors (10), and high dose melphalan (1). MM cells (CD138+) were extracted from fresh bone marrow aspirates and seeded in an ex vivo co-culture model with human stroma in 384-well plates. These cells were exposed to a number of chemotherapeutic and experimental agents (up to 31) for a period of 4 days, during which viability was assessed continuously using bright field imaging and digital image analysis. A mathematical model was used to interpolate the dose response dynamics to each drug, and combined with drug and regimen-specific pharmacokinetic data, generate predictions of clinical response to each individual drug. We have then validated ex vivo-based predictions with actual outcome 90 days post-biopsy. In patients treated with combinations, the mathematical model combined the effect of each single drug assuming additivity. Results: To examine the accuracy of the predicted in silico responses, we have assessed the model according to three increasingly strict standards of accuracy: (A) The model correctly predicted 32 out of 32 responders (100%) and 14 out of 16 non-responders (88%), with an overall accuracy of 96%; (B) According to IMWG stratification, the model correctly stratified 14 out of 16 patients as stable or progressive disease (PD/SD, 88%, the remaining 2 incorrectly predicted as MR/PR), 15 our of 18 as minimal or partial response (MR/PR, 83%, the remaining 3 incorrectly predicted as VGPR/CR), and 10 out of 14 patients as very good partial response or complete response (71%, the remaining 4 incorrectly classified as MR/PR), with an overall accuracy of 81%; (C) The 48 patients from this study provided a total of 120 measures of tumor burden (M-spike or SFLC) within the 90-day post-biopsy period. The direct correlation between tumor burden measures and model predictions led to a Pearson r=0.5547 (P Conclusion: We observed an excellent correlation between in silicopredicted and clinically observed responses in 48 MM patient specimens. Our data suggest that this model may provide critical insight in the selection the appropriate therapeutic agents and number of agents to combine for a given individual. Further validation is required to better define the role of this approach as a clinical decision support tool. Figure Figure. Disclosures Baz: Novartis: Research Funding; Signal Genetics: Research Funding; Karyopharm: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda/Millennium: Research Funding; Bristol-Myers Squibb: Research Funding; Merck: Research Funding. Shain:Signal Genetics: Research Funding; Takeda/Millennium: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen/Onyx: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Speakers Bureau.

Multiple myeloma (MM) is a heterogeneous plasma cell neoplasm that remains all but incurable despite recent advances in treatment. Indeed, nearly all patients eventually experience disease progression or relapse due to a reservoir of residual myeloma cells that appear to persist through pro-survival signaling from interactions with the tumor microenvironment (TME), leading to eventual clonal expansion. Thus, identifying targets that are induced in MM by the TME may reveal new and important targets amenable to therapeutic intervention. To develop a non-biased method to screen bone marrow specimens from myeloma patients for activated targets throughout the course of disease, we used a combination of activity-based protein profiling (ABPP) and a high-throughput protein kinase inhibitor (PKI) screen using a platform that recapitulates the TME. Target validation was then performed using ex vivo functional screens of pathways using MM patient specimens. The MM cell lines MM1.S, H929, and OPM2 were grown in mono-culture or co-culture with HS5 bone marrow stroma cells for 24h and lysates were enriched for ATP binding proteins by affinity purification versus a chemical probe (ActivX, Thermo). Tryptic peptides were measured using discovery proteomics (nano-UPLC and QExactive Plus mass spectrometer). Using this method, 176, 136, and 85 kinases out of a total of 1511, 1409, and 1281 proteins were preferentially enriched by 2-fold change from MM1.S, H929, and OPM2 myeloma cells grown in co-culture conditions with HS5 bone marrow stroma, respectively. Of these, 42 kinases were common to all three and 87 were common to two of three MM cell lines. Kinases were chosen for target validation after pathway analysis using the Kyoto Encyclopedia of Genes and Genomes database to identify signaling networks. To identify functionally relevant signaling networks identified via ABPP experiments, the same MM cell lines were simultaneously screened with 30 protein kinase inhibitors (PKIs) in a novel high throughput viability assay. This label-free method measures the viability of MM cells grown in a collagen matrix with bone marrow stroma cells in 384-well plates to simulate the TME by capturing brightfield images every 30 minutes for 96h using a motorized microscope equipped with an incubation chamber. Digital image analysis software measures live cell numbers by detecting membrane motion and generates viability curves as a function of drug concentration and exposure time (Khin et al. Cancer Research 2014). This functional screen confirming known MM survival networks, validated 12 kinases/PKIs in the context of the TME and highlighted novel targetable pathways. To provide an additional level of screening, the same PKIs were tested in CD138-MACS-selected cells from 15 MM patient specimens in a high-throughput viability assay. Eight PKIs targeting IGFR, PLK1, Abl, mTOR, FAK/Pyk2, ALK, Akt, and Casein Kinase-1δ (CK1δ)/CK1ε also showed significant activity in the 15 primary MM specimens. Our three-tiered pharmaco-proteomic screen identified eight kinases critical to MM survival in the context of the TME. Notably, a highly specific in-house inhibitor of Casein Kinase 1δ/CK1ε, SR-3029, which targets the Wnt/β-catnenin pathway, was identified as the most effective compound assessed as a single agent in our ex vivo viability assay in all patients with an average 36h LD50 of 290nM. This compound is under further investigation in MM (Submitted Abstract: Burger, et al, ASH 2016). Additional studies are underway to functionally interrogate the pathways identified in this screen, including ErbB1/EGFR, EphA1 and AMPK. Future work will optimize this method for evaluation of primary bone marrow specimens with ABPP followed by functional validation to better predict potential clinical response at different disease stages. We anticipate that this iterative "at the moment of care" approach is critical because drug resistant tumor phenotypes fluctuate with therapy, and this strategy can track and define clinically relevant changes in tumor cells in situ after the selection pressures applied by exposure to therapy. Disclosures Shain: Novartis: Speakers Bureau; Amgen/Onyx: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda/Millennium: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Signal Genetics: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau.

The future of cancer treatment lies in personalized strategies designed to specifically recognize, target, and anticipate dynamic tumor subpopulations within an individual in response to drug. Multiple myeloma (MM) is at present an incurable malignancy of bone marrow resident plasma cells with highly variable survival as a consequence of both disease- and host-specific factors. 20% of MM patients, deemed high-risk (HRMM), have shown little benefit in the era of novel agents, with an OS of less than 2 years. Intuitive treatment strategies fail to account for the complexities and evolutionary dynamics of human tumors in the face of drugs. Intuitive treatment fails to adequately account for MM evolutionary dynamics and remains a critical barrier to successful cure or, at least, long-term disease control. Reasons for therapy failure include, but are not limited to, alternation of dominant clones with each line of therapy as a consequence of Darwinian dynamics, genomic instability leading to of tumor heterogeneity, and tumor microenvironment(TME)- mediated drug resistance. We have developed an integrated computational method accounting for phenotypic tumor heterogeneity. This novel ex vivo drug screen approach, termed EMMA (evolutionary mathematical myeloma advisor), predicts patient-specific drug response in silico from fresh bone marrow biopsies within 5 days. This method utilizes longitudinal non-destructive quantification of rate and dose responses of patient-derived MM cells to drugs in an ex vivo 3D reconstruction of the bone marrow microenvironment to provide real-time personalized predictions of treatment success (percent decrease in disease burden at 90 days). The current automated 384-well plate format allows testing of 31 different drugs or combinations against a single patient sample in 5 days. An evolutionary-based computational model uses the drug sensitivity profile obtained ex vivo to detect sub-populations and their contribution to overall clinical drug response. Each drug dose is imaged once every 30 minutes for 96h. This generates 1,920 data points per drug (or combination). From these data we characterize clonal architecture as it relates to drug sensitivity as phenotypic/functional biomarker for each drug or drug combination in each MM patient sample simultaneously. We have examined the predictive accuracy of EMMA in 26 patients to date. The Pearson correlation between ex vivo model predictions and actual tumor burden changes for the 26 patients examined generated the correlation coefficient r=0.87 (P Disclosures Baz: Karyopharm: Research Funding; Celgene Corporation: Research Funding; Millennium: Research Funding; Sanofi: Research Funding.