A Preclinical Assay for Chemosensitivity in Multiple Myeloma

The purposes of preclinical systems range from early identification of compounds with anticancer activity, estimation of patient-specific clinical response, or the discovery of novel targetable cellular mechanisms (6, 7). All available systems have strengths and limitations: in vitro assays using cell lines are scalable, reproducible, and inexpensive, but cell lines are significantly different from their originating tumors (8), and the tumor microenvironment's effects are often absent in these assays. Animal models include more realistic elements such as drug pharmacokinetics and influence of the tumor microenvironment, but they often rely on cell lines, require long-term experiments, and carry significant financial cost. Irrespective of the preclinical model used, the data generated cannot be directly ported into clinical estimations without the help of an adequate computational framework.

Computational modeling has long been used to study the dynamics of tumor response to therapy, as well as emergence of drug resistance (9–11). These theoretical models are powerful tools for analyzing complex interactions like the tumor-host-therapy system, and could, in a near future, become decision-support systems for oncologists, making personalized oncology a possibility (12). The Achilles' heel of such models, however, is the reliability of the experimental data used to parameterize them. More often than not, these computational models are parameterized by data from literature, in many cases from experiments that have been performed at incompatible conditions.

We propose that preclinical assays, specifically designed to generate data to parameterize such computational models, would significantly advance the field. Such assays, however, should comply with minimum requirements: (i) compatibility with patient primary cancer cells; (ii) recapitulate the tumor microenvironment, namely extra-cellular matrix and stroma; (iii) be nondestructive, so longitudinal studies can be performed, incorporating the temporal dimension; (iv) require as few cells per experimental condition as possible, so each patient sample could be tested against a panel of chemotherapeutic agents, in different environmental conditions; and (v) the data generated should result in testable clinical predictions, such as the depth of response and/or progression-free survival (PFS).

Four decades ago, Salmon and colleagues (13) proposed an in vitro method for estimation of clinical response of patients with cancer based on the capacity of primary cancer cells to form colonies at physiologically reachable chemotherapy concentrations. The main limitation of these early assays, however, was the small number of patient samples that were capable of forming colonies under control conditions. With a cloning efficiency between 0.001% and 0.1%, the growth of colonies in vitro was a challenge comparable to surviving the chemotherapeutic insult itself. Consequently, these restrictions limited the number of drugs, concentrations, and time points that could be studied for a single patient (14), even in more recent models (15). Finally, the outcome of these assays were often dichotomized, in other words, either a patient was “sensitive” or “resistant” to the drug, but no information was provided regarding duration of response and time to relapse. Given that in many cancers the overall survival is more dependent on the duration of the response than on its depth (16, 17), the application of these early assays as predictive biomarkers was somewhat limited.

Similar to Leonardo Da Vinci's “aerial screw,” designed in the 15th century, which only came to fruition as the helicopter 5 centuries later, we propose that Salmon's drug sensitivity assay was hindered by technological limitations of its time, not by an inherent design fault. Building on this pioneering work, we here describe a novel approach for preclinical assessment of drug efficacy. We apply this method to multiple myeloma, an incurable plasma cell malignancy in which cancer cells uncontrollably proliferate in the bone marrow. Adhesion to bone marrow's extracellular matrix or stroma has been shown to confer de novo multidrug resistance [environment-mediated drug resistance (EMDR); refs. 18, 19].

The here described system combines a microfluidic dose–response platform, for in vitro screening of drugs, and a computational model of clinical response. The in vitro component consists of a 3-dimensional (3D) reconstruction of the bone marrow microenvironment, including primary multiple myeloma cells, extracellular matrix, and patient-derived stroma and growth factors. Live microscopy and digital image analysis are used to detect cell death events in different drug concentrations, which are used to generate dose–response surfaces. The in silico component is an evolutionary computational model designed to simulate how a heterogeneous population of cancer cells responds to therapy. From the in vitro data, the model identifies the size and chemosensitivity of subpopulations within the patient's tumor burden, and simulates how the tumor would respond to the drug(s) in physiological conditions in a clinical regimen.

The main innovations of this platform are: (i) small number of cancer cells required (1,000–10,000 per experiment); (ii) assessment of drug efficacy in different environmental conditions (collagen ± patient stroma ± patient-derived growth factors/cytokines), allowing quantification of innate and environmental drug resistance; (iii) only bright field imaging is used, thus no toxicity from viability markers; (iv) continuous imaging provides drug effect as a function of concentration and exposure time; and (v) the integration between in vitro and computational evolutionary models, to estimate clinical outcome: not only the initial response, but also PFS, a more relevant clinical endpoint for assessment of drug efficacy (16).

In this article, we describe the experimental and computational platforms, the results with human myeloma cells lines, and how they compare with literature data. We also describe preliminary experiments with patient primary cells, and how these results could be used in the estimation of clinical efficacy of experimental drugs, or personalized medicine (the right drugs and the right regimen for each patient).

The human myeloma cell lines RPMI-8226, HS-5/GFP-labeled, NCI-H929, and 8226/LR-5 were kindly provided by Dr. William Dalton's laboratory. The 8226/dsRed2 cell line was stably transfected with the fluorescent protein dsRed2. All cells were maintained in culture with RPMI 1640 (Gibco) media supplemented with 10% heat inactivated FBS (Life Technologies) and 1% penicillin–streptomycin solution (Invitrogen), in incubators at 5% CO₂, 37°C. Melphalan-resistant 8226/LR-5 cells were maintained in 5 μmol/L melphalan in medium, and cultured in drug-free medium for 2 weeks before the experiments.

We investigated the in vitro response of cancer cells from 7 patients with multiple myeloma in the clinical trial MCC# 14745 conducted at the H. Lee Moffitt Cancer Center and Research Institute, as approved by the Institutional Review Board. The medical records were deidentified and only the following clinical-relevant information was reviewed: (i) treatment administered (chemotherapeutic agents, doses, and schedule) before biopsy; (ii) cytogenetics; and (iii) blood and urine electrophoresis results. Patients in trial MCC# 14745 received standard-of-care treatment, and consented to provide an extra sample of bone marrow aspirate during a routine biopsy. These aspirates were used in the in vitro assays further described. After informed written consent, bone marrow aspirates were obtained from patients with multiple myeloma either newly diagnosed or with refractory disease. Processing of bone marrow aspirate and selection of multiple myeloma cells is described in Supplementary Material. Multiple myeloma cells were seeded into the Ibidi μ-slide Chemotaxis 3D device under experimental culture conditions (described below) within 4 hours of each patient biopsy.

In this work, the following chemotherapeutic agents were tested: bortezomib (acquired from Selleckchem), melphalan (from Sigma), and fluorescent molecule fluorescein (FAM)-HYD-1 (kindly provided by Dr. Hazlehurst).

Commercially available 3D cell-culture slides (μ-slide Chemotaxis 3D Ibitreat from Ibidi, LLC) were gas and temperature equilibrated at 37°C, 5% CO₂ overnight before cell seeding. Each slide is composed of 3 separate chambers each with a 1-mm wide, 50-μm high cell-viewing chamber that holds a volume of 6 μL. It is connected to two 65 μL reservoirs along both sides. Linear chemical gradients form across the cell chamber via passive diffusion. Aliquots consisting of 6.67 μL 10× MEM (Life Technologies), 6.67 μL deionized H₂O, 3.33 μL 7.5% sodium bicarbonate solution (Life Technologies), and 16.67 μL 1× RPMI 1640 (Life Technologies) were premixed and stored at 4°C before experiments, as per manufacturer (Ibidi) instructions. Fifty microliters of 3.1 mg/mL Bovine collagen type I (Advanced BioMatrix) was added at time of seeding. A total of 16.67 μL of cells suspended in RPMI 1640 were mixed into the collagen/media mix to a final volume of 100 μL in 1.5 mg/mL bovine collagen I (6-fold dilution of RPMI 1640 cell suspension). Six microliters of this cell/matrix mix were used to load each viewing chamber. For cell lines in single culture or mixed culture, the final concentration of cells was 3 × 10⁶ myeloma cells/mL. For patient primary cells, the densities were 7 × 10⁶ cells/mL for multiple myeloma (CD138⁺) and 1 × 10⁶ cells/mL for mesenchymal cells. These cell densities were optimized to better reflect physiological cell density and maximize the number of cells in the observation chamber, while still maintaining enough separation to allow the individual identification of cells. Cell lines were seeded at lower density to account for their larger size and faster replication. The interval between mixing collagen with cells and media and seeding the chambers was kept below 5 minutes at ambient temperature to minimize collagen polymerization. After seeding, an additional 15 minutes at room temperature allowed adherent cells (HS-5 or patient stroma) to sink to the bottom of the 3D chamber and keep the same focal plane for subsequent live imaging. Slides were then incubated at 37°C, 5% CO₂ for 1 hour. Collagen polymerization was checked by visual inspection of fiber formation on an inverted phase contrast microscope with a ×20 objective lens. After gelation, reservoirs on each side of the slide were filled with 65 μL culture media. A total of 16.25 μL of 4× drug in culture media was dropped onto a filling port on the left reservoir and then an equal volume was immediately drawn out of the other filling port. Slides were then placed into incubator for live imaging. For each experiment, there was a control with no drug added, which was used to detect spontaneous cell death. For single culture experiments, chemotherapy was added 2 to 4 hours after cell seeding. For coculture experiments with adherent stroma (HS-5 or patient stroma), drugs were added 24 hours later to ensure stroma adhesion.

In experiments with continuous exposure, the drug was maintained in media for the duration of the experiment. If this duration exceeded 48 hours, the media on both reservoirs was completely removed, and replaced by fresh media, to which drug was added as previously described (16.25 μL at 4× concentration). In pulsed exposure experiments, the media on both reservoirs was completely removed and replaced by fresh media at the end of the pulsed exposure.

Two different models of fluorescence microscopes were used for the experiments here described: the first, JULI (Digital Bio), is a portable fluorescence microscope with bright field and red fluorescence capacities (ex/em 630 nm/660 nm), which was maintained inside a standard incubator for the duration of the experiments. The second platform was the EVOS FL (AMG), a bench top fluorescence microscope (red channel ex/em 531 nm/593 nm), which required the use of a stage-top heating stage/incubator (Ibidi), which maintained the cells at 37°C, 5% CO₂, and 80% humidity. For the experiments here described, images were acquired every 5-minute intervals. In experiments where the red fluorescent 8226/dsRed2 cell line was tested, or the cell-death molecular probe ethidium homodimer-1 (EthD-1) was used, both bright field and red fluorescent channels were imaged, the first for changes in cell morphology and membrane motion, and the second for loss of innate fluorescence or activity of EthD-1, respectively.

To quantify the shape and stability of the drug gradient in the microfluidic device, we used a fluorophore-conjugated of the peptide HYD-1 (1.5 kDa) within the dose–response assay, a 3D gel matrix consisting of 1.5 mg/mL bovine collagen I with RPMI1640/MEM media was placed into the culture chamber of the Ibidi microfluidics device. After 45 minutes incubation at 37°C, reservoirs were filled with RPMI1640 media 10% heat inactivated FBS, 1% penicillin–streptomycin. FAM-HYD1 was diluted into media before replacing one fourth of the volume in the left reservoir with fluorescent drug solution (1/10 of stock). Fluorescence within the culture chamber was imaged in an EVOS FL microscope using the GFP filter (ex/em 470 nm/525 nm, ×5 objective) with heated stage and gas incubation (37°C, 5% CO₂). Images were acquired at 1-minute intervals for 24 hours.

With a stable drug gradient established across the main channel of the microfluidic slide, we arbitrarily divided the observation channel into 5 sections, or ROIs, each with an average drug concentration of 100%, 80%, 60%, 40%, and 20% of the concentration in the drug reservoir, respectively. Sectioning the channel into 5 areas was a compromise between a minimum number of cells in each area, and the rounding because of the averaging of the drug concentrations across each section. Dose–response was quantified with a macro developed for the software ImageJ (http://rsbweb.nih.gov/ij), further described. As discussed in the Results section, membrane-impermeable probes for detection of cell death, such as EthD-1, present a significant variation in the time for fluorescence acquisition after death in cell lines or patient samples. To avoid this confounding effect, we have developed a novel approach that identifies cell death based of motion of the membrane, described later.

We have observed that, although it was not possible to clearly discern a dead from a live cell based on the morphology in the bright field of a single image, all live cells suspended within the collagen matrix had observable membrane motion or shape changes, between 2 images taken in a 5-min interval. These morphological changes abruptly stopped before cell death, indicating that this feature could be exploited as a marker for cell death. We created a macro for the open source software ImageJ using the plugins TurboReg (20) and RunningZProjector (http://valelab.ucsf.edu/index.html). The macro quantifies the amount of cell membrane motion in the different regions of interest, and writes a file with this information for each frame, or time point. The source code of the macro is provided in the Supplementary Material. Briefly, the macro loads the stack of bright field images taken at 5-minute intervals, and aligns them using the plugin TurboReg. This action removes translational motion, such as sliding of the microfluidic chamber, as well as vibration. Next, the native ImageJ “background subtraction” function was used with parameters “rolling ball radius = 1 pixel” and “sliding parabolic” (21). Background subtraction served to normalize image sequences across different experiments and/or microscopes used to image the chambers, making cells appear as bright spots against a uniform dark background. Motion and small variations in cell membranes were detected using the plugin RunningZProjector. It detects the maximum pixel intensity across a 6-frame/slice interval, corresponding to 30 minutes. The original image was then subtracted from the maximum pixel intensity projection, resulting in an image where actively moving membranes appear as bright rings. ImageJ's Gaussian blur filter was used to convert these bright rings into spots that cover the entire cell, and produce the overlaid images shown in this article.

Different fluorescent-labeling agents for cell viability were tested as live-imaging approaches for response to chemotherapy. However, cytotoxicity, photo bleaching, intercellular variability of delay between cell death and signal detection, and incomplete representation of viable/apoptotic/necrotic cell states added noise to the assay. A multiple myeloma cell line was stably transfected with dsRed2 (8226/dsRed2), and used as a reference to visually detect the cytotoxic effect of drugs through loss of red fluorescence. dsRed2 expression is an intrinsic marker for these cells: live cells will quickly loose fluorescence upon cell death because of membrane burst accompanied by release of cytoplasmic components, including the fluorescent protein.

NCI-H929 cells were seeded in 96-well plates in culture media or in a 3D collagen matrix with culture media added on top of the cell/collagen layer. In wells without collagen, 1.5 × 10⁵ cells were resuspended in 50 μL of media for a final density of 3 × 10⁶ cells/mL. To more closely resemble microfluidic assay conditions, 1.5 × 10⁵ cells were suspended in 30 μL of 1.5 mg/mL collagen matrix and were left to polymerize at 37°C for 1 hour. Twenty microliters of media was then added as a separate phase on top of the cell/collagen layer. Melphalan was serially diluted in 2-fold steps to a final concentration range of 100 to 1.56 μmol/L in 7 rows. The same procedure was performed for bortezomib to final concentrations of 20 to 0.31 nmol/L. All conditions and controls were performed in triplicate. After 24 hours of continuous drug exposure at 37°C and 5% CO₂, 50 μL CellTiterGlo was added to each well and the plates were placed on an orbital shaker at room temperature for 10 minutes. Twenty minutes later, bioluminescence was measured at ambient temperature on a microplate reader. Percent cell viability was defined as luminescence normalized to controls at 24 hours.

The quantification of the dose–response of the cells in the experiment used Matlab's (MathWorks) function lsqcurvefit, which finds the coefficients that minimize the distance between a function and a set of data points. Details on the fitting procedure and equations used are presented in the Quick Guide to Equations and Assumptions.

As a proof of principle, to exemplify the application of these in vitro chemosensitivity assays in estimating patient response to therapy, we have used a computational model, previously described (22), to simulate a hypothetical single-agent bortezomib regimen in an animal model (s.c. NCI-H929 in SCID mouse), and for 4 patients whose multiple myeloma cells' sensitivity to bortezomib were tested in vitro. Implementation of the computational model of therapy is presented in the Quick Guide to Equations and Assumptions.

The first step of validating the in vitro platform was to determine the stability, and also the duration of any transients during the formation of the drug gradient across the observation chamber. For this purpose, we used a conjugate of the FAM and the 1.5 kDa peptide HYD-1, an experimental drug with direct toxicity to multiple myeloma cells (23). We used live imaging to quantify the fluorescence in images taken at 1-minute intervals during 18 hours (Fig. 1). The fluorescent signal gradient was stable for the interval of the experiment, and the transient time for its formation was shorter than the period between drug injection in the slide and start of imaging (∼5–10′).

A novel algorithm for detection of cell membrane motion was necessary to detect cell death in patient primary cells, because of the significant variation of the delay between cell death and membrane permeabilization, and acquisition of fluorescence from molecular probes. Supplementary Fig. S1 depicts the delay between the detection of cell death using the motion-detection algorithm, and loss of fluorescence in the stably transfected cell line 8226/dsRed2. Supplementary Fig. S2 exemplifies the delay of acquisition of the molecular probe EthD-1 red fluorescence in NCI-H929 cells.

The cell line NCI-H929 was exposed to a stable gradient of bortezomib (maximum concentration 10 nmol/L) for 24 hours, and a dose–response surface was created (Fig. 2A). According to these results, the bortezomib concentration that would lead to a 50% reduction in the number of live cells after 24 hours, compared with the initial time point, was ∼2.5 nmol/L. The concentration that would lead to a 50% reduction in the number of live cells, compared with the control at 24 hours was ∼1.9 nmol/L. The same cell line was seeded in a 96-well plate, in suspension or in collagen, and cell viability was measured using the ATP-based assay CellTiter-Glo (Fig. 2C). The Pearson test produced r values of 0.8905 and 0.8704 (P values 0.003 and 0.0049) for the correlation between the model and suspension, and collagen results, respectively.

The melphalan sensitive and resistant cell lines NCI-H929 and 8226/LR5 were exposed to stable gradients of melphalan for 24 hours (highest concentrations of 50 and 100 μmol/L, respectively) and chemosensitivity was quantified. The analysis of 8226/LR5 detected a subpopulation of sensitive cells (∼30%; Fig 3A), indicating that this cell line is actually heterogeneous, a possible explanation for the loss of resistance commonly observed when these cells are maintained in melphalan-free medium for many weeks (24). Melphalan concentration that induced 50% of death in cells (EC50) for 24 hours continuous exposure was ∼50 μmol/L for 8226/LR5 and ∼12 μmol/L for H929. Long-term exposure to lower, more physiological doses (10–20 μmol/L) of melphalan, however, indicated that, although all melphalan had been hydrolyzed in the first 24 hours in media, cell death continued to occur after 6 days of drug exposure (Supplementary Fig. S3).

Cell adhesion–mediated drug resistance is believed to be a major cause of minimal residual disease in multiple myeloma (18). This mechanism is caused by direct multiple myeloma–stroma cell adhesion, by paracrine loops of soluble factor secretion, or by multiple myeloma–extracellular matrix adhesion. To quantify the importance of multiple myeloma–stroma adhesion under physiological conditions (high density, in presence of ECM), the multiple myeloma cell line NCI-H929 was cocultured with the bone marrow–derived stromal cell line HS-5/GFP. A significant shift toward resistance was observed at later time points (∼24 hours) and was most expressive around the concentration of 20 to 30 μmol/L (Fig. 4).

To exemplify the study of continuous versus pulsed exposure to drugs, 2 chambers with NCI-H929 cells were exposed to bortezomib for 24 hours. In one, the medium was replaced by drug-free medium, whereas in the other fresh medium with bortezomib was added. Being a reversible proteasome inhibitor, the results suggest that bortezomib-induced death stops upon drug withdrawal (Supplementary Fig. S4), unlike melphalan (Supplementary Fig. S3).

From the 17 patient samples obtained so far in this protocol, the first 10 were used for development and optimization of the platform. The results of the 7 others are described here. We have exposed CD138⁺-sorted primary multiple myeloma cells from patient 14, a newly diagnosed patient, for 48 hours to a stable gradient of 25 μmol/L melphalan in single and coculture, with patient-derived stroma. As shown in Fig. 5, adhesion to stroma significantly increased the survival of multiple myeloma cells, shifting the 48 hours EC₅₀ from 2 μmol/L in single culture to 12 μmol/L in coculture. This effect could be circumvented by combination of a proteasome inhibitor at sublethal levels (Fig. 6 and Supplementary Video S1; ref. 25).

Supplementary Fig. S5 depicts the in vitro chemosensitivity of 3 patients with multiple myeloma to melphalan in single culture: patient 14, patient 11 (smoldering myeloma), and patient 12 (relapsed after bone marrow transplantation). The EC₅₀s at 24-hour exposure were 4 μmol/L for patients 14 and 12 and 1 μmol/L for patient 11. However, the percentage surviving cells at 20 μmol/L, a more physiological concentration of high-dose melphalan treatment, was 30% for patient 12, 11% for patient 14, and 4% for patient 11. Fig. 7 represents bortezomib chemosensitivity of patients 11, 12, 13 (newly diagnosed), and 17 (smoldering myeloma). For patients 11 and 12, EC₅₀ after 24 hours continuous exposure was below 2 nmol/L; however, at higher concentrations, multiple myeloma cells from patient 11 were significantly more resistant: 30% live cells at 50 nmol/L bortezomib for patient 11 and ∼8% for patient 12. Twenty-four-hour EC₅₀ for patient 13 was ∼10 nmol/L, whereas EC₅₀ was not reached with the sample from patient 17.

By parameterizing equation C with values obtained from fitting equation A to the in vitro dose–response data, it is possible to simulate how a tumor mass would respond to a therapeutic regimen. As an example, the subcutaneous mouse model severe combined immunodeficient (SCID), when implanted with the cell line NCI-H929, develops a tumor that grows 45-fold in 20 days (26). When treated with 1 mg/kg bortezomib twice a week, the tumor growth is reduced, and tumors are 20-fold bigger at day 20 than at implantation (27). From the bortezomib in vitro chemosensitivity assay with the cell line NCI-H929 (Fig. 2), the parameters from equation C were: IC_50Rx = 10.35 nmol/L, IC_50ΔT = 10.38 hours, exp Rx = 2.7, and exp T = 7.1. Supplementary Fig. 6 depicts the computational simulation of the tumor growth under control conditions, under a bi-weekly treatment with 1 mg/kg of bortezomib (which leads to a stable blood concentration of 0.4 nmol/L; ref. 28), and a hypothetical regimen where mice received a pulsed therapy with the same AUC, with bi-weekly injections of bortezomib every other week (therapy holidays). The simulated tumor would have increased 53.4-fold in control conditions (Pearson r = 0.9762), 18-fold in standard bortezomib treatment (Pearson r = 0.9869), and 5-fold in the hypothetical pulsed regimen. The same approach was used to simulate the response of patients 11, 12, 13, and 17 to a single-agent regimen of bortezomib (1.3 mg/m²; Supplementary Fig. 7). In this regimen, plasma concentration stabilizes at ∼1 nmol/L (4), and according to simulations, would achieve complete response in patients 11 an 12, relapse in patient 17, and no response in patient 13.

In this work, we have described an interdisciplinary platform to study preclinical drug activity in primary multiple myeloma cells. First, multiple myeloma cells are embedded in a microfluidic chamber that recapitulates the bone marrow microenvironment, including high-cell density, extracellular matrix, and patient-derived stromal cells. A linear and stable drug gradient is established across the chamber, which is then imaged sequentially in bright field. A digital image analysis algorithm detects live multiple myeloma cells by the motion of cell membrane: upon death this activity ceases. The measurements of viability, at different concentrations and time points, are fit to mathematical models of chemosensitivity. These models can represent one or multiple subpopulations, and can be empirical or mechanistic. The data from these experiments can thus be used to parameterize mathematical models to simulate clinical outcome.

This platform overcomes some major limitations of preclinical assays using primary cancer cells. It has long been known that extracellular matrix and stroma are major components of chemoresistance in many tumors. However, the inclusion of these elements significantly increases the complexity of dose–response assays, often requiring the separation between cancer and stromal cells, by matrix digestion and/or flow sorting (29). Also, viability assays are often destructive or cytotoxic, if carried for long periods of time, limiting the information acquired in the temporal dimension. In the here described assay, multiple myeloma cells, stroma, and matrix are never separated, and no cytotoxic agents are used to determine cell viability, thus allowing longitudinal studies of drug activity without interfering with the microenvironment.

In cancers such as multiple myeloma, where a few million cells are obtainable per patient biopsy, it is important to minimize the number of cells per experimental condition, which is in the order of 1,000 to 10,000 cells in this assay. The poor clonal efficiency of multiple myeloma cells, as well as their spontaneous death in vitro (6), suggest that experiments with these samples be performed in the first few days after the biopsy. By studying the effect of long-term exposure and drug withdrawal in human multiple myeloma cell lines, we have created mechanistic theoretical models of the drug activity (2). Once a model is generated for a particular drug, the data from patient samples are used to parameterize and extrapolate the response for longer periods of time.

As shown for bortezomib-induced melphalan sensitization in coculture (Fig. 6), this system can be used to study drug interactions (30). The addition of the time dimension, instead of fixed time points, would allow the study of time-shifted drug combinations, such as, for instance, nuclear export agents and doxorubicin (31). Combination indices (30) may be obtained by adding the 2 drugs being studied on the same reservoir, which will induce 2 superimposed drug gradients.

This assay allows the observation of individual cells. Thus, it is possible to assess the heterogeneity of drug–response by plotting in a histogram the AUC at the moment of death of each individual cell. Further improvements in the digital image analysis algorithm could identify and track individual cells from their original replication until their death. By combining this information with the dose–response surfaces, it would be possible to determine if particular drugs and concentrations are capable of maintaining a tumor burden quiescent, or in a balance between proliferation and death (32, 33).

These preliminary results describe a framework to better understand the dynamics of interactions between tumor and stroma in response to therapeutic agents in vitro. These assays can be performed in a middle- to high-throughput manner, and significantly reduce the complexity of working with patient primary cells in reconstructions of the tumor microenvironment. Ultimately, this may become a platform for personalized preclinical estimation of drug efficacy in cancer.

Future directions are to standardize methods to extrapolate in vitro predictions into clinical outcome. We will also explore the application of alternative therapeutic regimens of drug combinations, proposed by data extrapolated from this in vitro system, and simulated using evolutionary computational models (34, 35). We hypothesize that, patient-specific computational models, parameterized by in vitro platforms as the here described, could be combined with genomic (36) datasets to better understand the dynamics that underlie evolution of drug resistance in patients with multiple myeloma. Understanding these dynamics would not only allow accurate predictions of response, but also suggest the best therapeutic strategies for each patient, and continue adjusting these strategies as needed.

No potential conflicts of interest were disclosed.

Conception and design: Z.P. Khin, M.L.C. Ribeiro, K. Shain, A.S. Silva

Development of methodology: Z.P. Khin, M.L.C. Ribeiro, T. Jacobson, K. Shain, A.S. Silva

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): Z.P. Khin, M.L.C. Ribeiro, T. Jacobson, A.S. Silva

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): Z.P. Khin, T. Jacobson, K. Shain, A.S. Silva

Writing, review, and/or revision of the manuscript: Z.P. Khin, T. Jacobson, L. Hazlehurst, L. Perez, R. Baz, K. Shain, A.S. Silva

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): K. Shain

Study supervision: A.S. Silva

The authors thank the patients at H. Lee Moffitt Cancer Center who provided clinical samples for our in vitro assays as well as consented access to the their clinical data through the Total Cancer Care database.

This research was funded by the State of Florida's Bankhead-Coley Team Science Grant (2BT03), the National Institutes of Health/National Cancer Institute (1R21CA164322-01), and the H. Lee Moffitt Cancer Center Physical Sciences in Oncology (PSOC) Transnetwork Grant (U54CA143803).

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