Computational methods involving virtual screening could potentially be employed to discover new biomolecular targets for an individual molecule of interest (MOI). However, existing scoring functions may not accurately differentiate proteins to which the MOI binds from a larger set of macromolecules in a protein structural database. An MOI will most likely have varying degrees of predicted binding affinities to many protein targets. However, correctly interpreting a docking score as a hit for the MOI docked to any individual protein can be problematic. In our method, which we term “Virtual Target Screening (VTS)”, a set of small drug-like molecules are docked against each structure in the protein library to produce benchmark statistics. This calibration provides a reference for each protein so that hits can be identified for an MOI. VTS can then be used as tool for: drug repositioning (repurposing), specificity and toxicity testing, identifying potential metabolites, probing protein structures for allosteric sites, and testing focused libraries (collection of MOIs with similar chemotypes) for selectivity. To validate our VTS method, twenty kinase inhibitors were docked to a collection of calibrated protein structures. Here we report our results where VTS predicted protein kinases as hits in preference to other proteins in our database. Concurrently, a graphical interface for VTS was developed.

Most patients with multiple myeloma (MM) respond well to initial therapy, but invariably relapse due to evolution of resistant phenotypes. Here we examine the evolutionary dynamics of proliferation of resistant MM phenotypes during therapy. By applying computational models to data from three clinical trials for newly diagnosed MM patients, we have quantified the size and level of chemoresistance of subpopulations within the tumor burden in 124 patients, prior to and during therapy. Subsequently, we used the computational models to explore an alternative strategy of "adaptive therapy" (AT), which includes defined treatment holidays, to improve the duration of "controlled disease" (CD). Simulations showed that AT could prolong CD in all three trials: 50.0% vs. 11.1% 50-month CD for a single agent approach in older adults (P = 0.0123), 80.4% vs. 58.8% 60-month CD for a multi-agent bortezomib based therapy (P = 0.0082), and 54.0% vs. 24.0% 60-month CD for a multi-agent lenalidomide based therapy (P < 0.0001). Increases in duration of CD resulted from the stabilization of tumor burden, which in turn would delay the growth of chemoresistant sub-populations in patients with partial (PR), or very good partial response (VGPR). These computational algorithms suggest that AT may provide an alternative and feasible therapeutic management strategy in MM.

2960 Multiple myeloma (MM) is currently treatable but incurable. Many patients have a good initial response, but become refractory to therapy, and relapse. We propose that these patients, prior to therapy, harbor sub-populations of chemoresistant clones, which rely on intrinsic or environment-mediated drug resistance (EMDR). These sub-populations, initially a small fraction of the total tumor burden, are allowed to proliferate after aggressive therapy eradicates the sensitive clones. We have previously proposed an approach, Adaptive Therapy (AT), which employs evolutionary principles to use the minimum amount and number of chemotherapeutic agents needed to reduce the tumor burden to a level that permits satisfactory quality of life, instead of high dose therapy aimed at eradication of the disease. In this work we studied a cohort of 21 MM patients enrolled in an ongoing clinical trial using a single agent lenalidomide, combined with prednisone or dexamethasone in case of stable or progressive disease. We use a computational evolutionary model to determine the size, growth rate and drug resistance of sub-clones in each patient, and how these sub-populations evolve during therapy. We used these models to estimate progression of these patients for 48 months, as well as their hypothetical response to AT (Fig. 1). Simulations using the model demonstrate that 9 patients would have benefited from AT, with lower accumulated dose and longer time to progression: 42.7mo vs. 15.4mo, P<0.001. These results indicate the need to develop a method to determine which patients benefit from this technique. We propose an ex vivo approach to identify, prior to therapy, patients candidate for AT, and personalized agent combinations. This assay estimates the number, size, and drug resistance of sub-clones from live cells from MM patients' biopsies. We recreate within a 60uL microfluidic chamber, a stable drug gradient equivalent to a serial dilution in a 96-well plate. Each assay requires as few as 10,000 cells, which can be assayed in single or co-culture with stromal cells, to evaluate intrinsic and EMDR drug resistance levels. This assay was capable of determining the size and drug resistance properties of mixed cell line sub-populations at sizes as small as 10% (Fig. 2). We will next test this proof of principle with primary cells from relapsed patients, in order to determine the size and drug resistance properties of the MM clones present. This preliminary work suggests that the standard aggressive therapy paradigm may not benefit all MM patients, but indeed accelerate relapse in patients with a pre-existing small sub-population of drug resistant clones. We also describe an approach that may be able to identify these patients. Fig. 1 Standard dose (MTD) versus AT for an IgD MM patient with initial serum level of 1,400mg/dl. Patient was treated with lenalidomide until month 6, when dexamethasone was added. (A) Serum m-protein level (black squares) was used to fit a computational model of the tumor burden. 50 possible solutions are shown as curves, and a validation data point is shown as a green square. (B) Each of the models was simulated under AT, the actual m-protein levels are shown for reference. (C) The simulation that best fit the validation data points shows that the majority of the burden was composed of lenalidomide-sensitive cells, which are eradicated after 4 months of treatment (red), allowing growth of a multidrug-resistant clone (orange). (D) AT prevents the growth of the MDR clones by maintaining the sensitive clone at a manageable level. (E) The models predict that under AT this patient would have a 95% of chance of being under the initial burden, compared to 5% under MTD, after 48 months. Fig. 1. Standard dose (MTD) versus AT for an IgD MM patient with initial serum level of 1,400mg/dl. Patient was treated with lenalidomide until month 6, when dexamethasone was added. (A) Serum m-protein level (black squares) was used to fit a computational model of the tumor burden. 50 possible solutions are shown as curves, and a validation data point is shown as a green square. (B) Each of the models was simulated under AT, the actual m-protein levels are shown for reference. (C) The simulation that best fit the validation data points shows that the majority of the burden was composed of lenalidomide-sensitive cells, which are eradicated after 4 months of treatment (red), allowing growth of a multidrug-resistant clone (orange). (D) AT prevents the growth of the MDR clones by maintaining the sensitive clone at a manageable level. (E) The models predict that under AT this patient would have a 95% of chance of being under the initial burden, compared to 5% under MTD, after 48 months. Fig. 2 Drug resistance heterogeneity in MM. The human MM cell lines H929 and H929/60, resistant to HYD-1 were suspended in matrigel and loaded in a microfluidic chamber, across which a stable drug (HYD-1) gradient (from 0 to 50uM) was established. The media contained a fluorescent dye to mark the nuclei of dead cells (A). The fluorescent channel was converted to a black and white mask (B), which was superimposed to the brightfield channel (C), removing the dead cells (D). The number of live cells for each region of interest (ROI), or drug concentration, was quantified (E). After 24h of drug exposure the number of live cells is significantly reduced (F). Through non-linear regression (G), we determined the parameters of dose response of each population (H). Fig. 2. Drug resistance heterogeneity in MM. The human MM cell lines H929 and H929/60, resistant to HYD-1 were suspended in matrigel and loaded in a microfluidic chamber, across which a stable drug (HYD-1) gradient (from 0 to 50uM) was established. The media contained a fluorescent dye to mark the nuclei of dead cells (A). The fluorescent channel was converted to a black and white mask (B), which was superimposed to the brightfield channel (C), removing the dead cells (D). The number of live cells for each region of interest (ROI), or drug concentration, was quantified (E). After 24h of drug exposure the number of live cells is significantly reduced (F). Through non-linear regression (G), we determined the parameters of dose response of each population (H). Disclosures: No relevant conflicts of interest to declare.

4002 The lack of specific molecules to define malignant B progenitor cells in multiple myeloma (MM) has hampered the evaluation of minimal residual disease (MRD). We have identified a bone marrow (BM) CD138- subset that co-express CD19+ with identical κ or λ light chain (LC) restriction as the abnormal plasma cell (PC), as previously shown by others. The majority of LC restricted (LCR) B progenitors are CD19+/CD34- and <0.5% of whole BM (WBM) cells exhibit an immature phenotype: CD19+/CD34+ with aberrant CD27 expression. Immature B cell precursors are undetectable in peripheral blood (PB). LCR CD138-/CD19+ cells represent only 0.72± 0.5% of WBM in newly diagnosed patients (n=23) and do not increase (0.47± 0.51%) in patients with relapsed disease (n=21). The κ/λ LC ratio is 1.46±0.6 regardless of disease stage suggesting that conventional LC ratios for PCs (> 4 or <0.5) may not apply in B progenitors. LCR B progenitors (CD19+/34+ or CD19+/34-) are CD117+, Notch+ and Survivin+ as MM patient's hematopoietic stem cells (HSC). ALDH enzymatic activity is 3.1% (0.1-–7.26%) in LCR B cells. Flow sorted CD138+ did not grow in a colony formation assay (methylcellulose with PHA-LCM), whereas CD19+/CD34- or CD19+/CD34+ grew colonies with efficiency of 1:25,000 or 1:10000 respectively. Cells harvested from colonies have a lympho-plasmacytoid appearance and LCR B progenitors differentiated into CD138+ PC (80±5%) compared to HSC (10±5%). Colony efficiency was optimized (3 fold) using conditioned medium (CM) from HS5-stroma. Isolated CD138-/CD19+ cells were relatively bortezomib and melphalan resistant compared to CD138+ PC. We hypothesize that CD138-/CD19+/CD34+ cells contains earlier progenitor B cells that differentiate into the malignant PC. Surrogate assays for stem cell activity and xenotransplant models should determine cancer stem cell activity of immature B cell precursors. Research studies of MM putative progenitor cells will allow developing novel treatments to eradicate potential MM MRD reservoir. Disclosures: Baz: Millenium: Research Funding, Speakers Bureau; Celgene: Research Funding.

127 Background: Evidence-based clinical pathways have been shown to improve quality and cost effectiveness. Moffitt Cancer Center (MCC) has developed clinical pathways for more than 50 cancer sites. Using data derived from multiple sources, we performed a series of measurements of adherence to breast cancer (ca) pathways, provided clinician feedback and evaluated changes over time. Methods: We developed an automated method to evaluate pathway adherence for 1st line systemic treatment (tx) recommendations using data from Stages I-IV analytic breast ca cases presenting to MCC for tx during calendar years (CY) 2013-2015. As a baseline, cases were manually audited for adherence in CY 2012. Data sources included Cancer Registry and electronic health record (EHR). A patient (pt) was considered “On” pathway if the correct tx, including relevant clinical trials, was administered based on various prognostic and clinical factors. Only pts who received all 1st line systemic tx at MCC were included. Pts who refused txor had contraindications, non-applicable histologies or multiple primary cancers were excluded. The EHR was reviewed to assure the accuracy of “Off” pathway determinations. Results: A total of 3727 analytic breast ca cases were seen at MCC between CY 2012-2015; 873 met all criteria and were eligible for analysis. 80% was set as an institutional target for adherence. The following table displays yearly trends for pts “On Pathway” for all applicable chemotherapy, immunotherapy and hormonal tx. Conclusions: Cancer Registry and EHR data can be used to automate surveillance of pathway adherence creating a feedback loop to clinicians who can, in turn, improve adherence over time. [Table: see text]