Preclinical study of CD19 detection methods post tafasitamab treatment

Introduction

Several CD19 targeted antibody-based therapeutics are currently available for patients with diffuse large B-cell lymphoma (DLBCL), including the Fc-modified antibody immunotherapy tafasitamab. This therapeutic landscape warrants the evaluation of potential sequencing approaches. Prior to a subsequent CD19-targeted therapy, CD19 expression on tafasitamab-treated patient biopsy samples may be assessed. However, no standardized methods for its detection are currently available. In this context, selecting a tafasitamab-competing CD19 detection antibody for immunohistochemistry (IHC) or flow cytometry (FC) may lead to misinterpreting epitope masking by tafasitamab as antigen loss or downregulation.

Methods

We analyzed a comprehensive panel of commercially available CD19 detection antibody clones for IHC and FC using competition assays on tafasitamab pre-treated cell lines. To remove bound tafasitamab from the cell surface, an acidic dissociation protocol was used. Antibody affinities for CD19 were measured using Surface Plasmon Resonance (SPR) or Bio-Layer Interferometry (BLI).

Results

While CD19 was successfully detected on tafasitamab pre-treated samples using all 7 tested IHC antibody clones, all 8 tested FC antibody clones were confirmed to compete with tafasitamab. An acidic dissociation was demonstrated essential to circumvent CD19 masking by tafasitamab and avoid false negative FC results.

Discussion

The current study highlights the importance of selecting appropriate CD19 detection tools and techniques for correct interpretation of CD19 expression. The findings presented herein can serve as a guideline to investigators and may help navigate treatment strategies in the clinical setting.

Pharmacokinetics (PK) and pharmacodynamics (PD) in First-MIND: A phase Ib, open-label, randomized study of tafasitamab (tafa) ± lenalidomide (LEN) in addition to R-CHOP in patients (pts) with newly diagnosed diffuse large B-cell lymphoma (DLBCL)

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Background: The combination of tafa + LEN has accelerated approval in the United States (2020) and conditional approval in Canada and Europe (2021) for R/R DLBCL in ASCT-ineligible adult pts. A tolerable safety profile for R-CHOP + tafa ± LEN in pts with newly diagnosed DLBCL in the Phase Ib First-MIND study (NCT04134936) was previously reported, with numerically superior efficacy for R-CHOP + tafa + LEN (ASH 2021; #3556). We report PK, PD, and immunogenicity of R-CHOP + tafa ± LEN in this first line setting. Methods: Eligible pts ≥18 years with treatment-naïve DLBCL, IPI 2–5, and ECOG PS 0–2 were randomized 1:1 to either six 21-day (D) cycles (C) of R-CHOP (R-CHO, D1; P, D1–5) + tafa (12 mg/kg IV, D1, 8, 15) (Arm A) or R-CHOP + tafa + LEN (25 mg orally, D1–10) (Arm B). Secondary endpoints included tafa serum conc and number and percentage of pts developing anti-tafa Abs. Exploratory endpoints included natural killer (NK) cell, T-cell, and B-cell count in peripheral blood. Results: Data cut-off: 13 March 2021 for safety analysis, incl. ≥1 month follow-up after EoT visit for all pts; 15 September 2021 for efficacy. Tafa serum conc reached steady state by C3 (geometric mean trough conc: Arm A, 186.40–216.55 µg/mL; Arm B, 171.77–201.54 µg/mL) and steadily declined after treatment completion. Anti-tafa Abs were detected in 1/65 (1.5%) pts. This pt showed pre-existing tafa Abs at baseline, which decreased during treatment. Median NK cell count decreased from baseline at C1D8 but was at baseline or higher levels by EoT visit (Arm A) and C1D15 (Arm B; Table). T-cell count decreased from baseline at C1D8 in both arms but was at baseline level or higher by C1D15 (Arm A) and EoT visit (Arm B). Median B-cell count decreased from baseline to 0 cells/μL (Arm A, C1D15; Arm B, C1D8); at 6-month follow-up after EoT visit, B-cell count had recovered to measurable levels in ̃50% of pts. Conclusions: Tafa serum conc reached and maintained a therapeutic dose level in this first-line regimen and declined in line with known tafa half-life (̃16 days) after treatment completion; tafa levels were comparable between the two treatment arms. No pt developed treatment-induced or treatment-boosted anti-tafa Abs. Median NK-cell, T-cell, and B-cell counts were comparable between treatment arms in all cycles. Clinical trial information: NCT04134936. [Table: see text]

A Phase I, Randomized, Double-Blind, Placebo-Controlled, Single-Dose and Multiple-Rising-Dose Study of the BTK Inhibitor TAK-020 in Healthy Subjects

Bruton’s tyrosine kinase (BTK) is a target for treatment of hematologic malignancies and autoimmune diseases. TAK‐020 is a highly selective covalent BTK inhibitor that inhibits both B cell receptor and fragment crystallizable receptor signaling. We assessed the safety/tolerability and pharmacokinetics/pharmacodynamics (PDs) of TAK‐020 in healthy subjects. Each cohort of the single‐rising dose (n = 72; 9 cohorts) and the multiple‐rising dose (n = 48; 6 cohorts) portions of the study comprised six TAK‐020‐treated and two placebo‐treated, subjects aged 18–55 years (inclusive). The PD effects were assessed by measuring BTK occupancy and the inhibition of fragment crystallizable epsilon receptor 1 (FcεRI)‐mediated activation of basophils. Overall, treatment‐emergent adverse events (TEAEs) were similar to placebo; there were no serious TEAEs or no TEAEs leading to discontinuation. TAK‐020 was rapidly absorbed (median time to maximum plasma concentration (T_max) 45–60 minutes) with a half‐life of ~ 3–9 hours at doses ≥ 2.5 mg. TAK‐020 exposure was generally dose proportional for single doses ≤ 70 mg and after multiple doses of ≤ 60 mg once daily. Target occupancy was dose dependent, with doses ≥ 2.5 mg yielding maximum and sustained occupancy > 70% for > 96 hours. Single doses ≥ 4.4 mg reduced FcεRI‐mediated activation of basophils by > 80% and comparable inhibition was observed with daily dosing ≥3.75 mg for 9 days. Inhibition persisted for 24–72 hours postdose and the duration generally increased with dose. TAK‐020 was generally well‐tolerated in healthy subjects after single and multiple doses and demonstrated target engagement and pathway modulation. The PD effects outlasted drug exposures, as expected for covalent inhibition of BTK.

Prospects for Observing and Localizing Gravitational-Wave Transients with Advanced LIGO and Advanced Virgo

We present a possible observing scenario for the Advanced LIGO and Advanced Virgo gravitational-wave detectors over the next decade, with the intention of providing information to the astronomy community to facilitate planning for multi-messenger astronomy with gravitational waves. We determine the expected sensitivity of the network to transient gravitational-wave signals, and study the capability of the network to determine the sky location of the source. We report our findings for gravitational-wave transients, with particular focus on gravitational-wave signals from the inspiral of binary neutron-star systems, which are considered the most promising for multi-messenger astronomy. The ability to localize the sources of the detected signals depends on the geographical distribution of the detectors and their relative sensitivity, and 90% credible regions can be as large as thousands of square degrees when only two sensitive detectors are operational. Determining the sky position of a significant fraction of detected signals to areas of 5 deg2 to 20 deg2 will require at least three detectors of sensitivity within a factor of ∼ 2 of each other and with a broad frequency bandwidth. Should the third LIGO detector be relocated to India as expected, a significant fraction of gravitational-wave signals will be localized to a few square degrees by gravitational-wave observations alone.

Abstract B1-59: Identification of key molecular characteristics for achieving increased anti-tumor activity by combined mTORC1/2 and MEK1/2 investigational agents

The development of combination therapies is important to cancer treatment due to the potential to target multiple oncogenic pathways together, which may lead to enhanced anti-tumor activity and decreased chance for resistance. The RAS/RAF/MEK/ERK (MAP kinase) and PI3K/AKT/mTOR pathways are well-known pathways driving the oncogenic process in many cancer types; therefore, the simultaneous inhibition of these pathways may produce increased anti-tumor activity. It is important to find ideal cancer indications and molecular subtypes that may be most sensitive to drug combination inhibiting these pathways; this requires understanding of the molecular and genetic underpinnings. In this study, an in-vitro cell viability assay on 240 cancer cell lines was used to reveal the synergy mechanisms of the combination of investigational agents MLN0128 (mTORC1/2 inhibitor) with TAK733 (MEK1/2 inhibitor). Cells were administered with MLN0128 and TAK733 in DMSO in ten 2-fold serial dilutions and cell viability was measured using Promega's CellTiter-GLO® assay after a 72 hour incubation period. The 100 data points (10×10) were fit to a 9-parameter model to obtain a response surface for each cell line and the effect of the combined administration was measured using a non-linear blending score. Our previous study found that genes in the MAP kinase pathway such as KRAS, NRAS, or BRAF were mutated in cell lines demonstrating synergy. In this study, a 76-gene expression signature was additionally identified using a regression-based analysis. The 76 mRNAs include some genes within the two oncogenic pathways, as well as those that regulate feedback of the MAP kinase pathway and cross-talk between the pathways. A pathway and protein-protein interaction network-based analysis of the 76-gene signature and mutated genes suggest that the activation of the MAP kinase pathway in cells originally resistant to MLN0128 may be essential for a synergistic outcome from the MLN0128 and TAK733 combination. Cell lines with activating mutations or over-expressed genes in MAP kinase pathway are generally sensitive to TAK733 administration; these cell lines also demonstrated synergy to the combination of MLN0128 and TAK733. On the other hand, cell lines with highly expressed PI3K tend to antagonistically respond to the combination. In conclusion, our study suggests the key molecular conditions of cell lines in order to achieve increased anti-tumor activity by the combination of MLN0128 and TAK733. These findings can be utilized to establish a better-differentiated strategy in terms of patient stratification for the combination therapy using MLN0128 and TAK733.

Citation Format: Hyunjin Shin, Andrew Krueger, Bin Li, Derek Blair, William L. Trepicchio, Jeffrey Ecsedy. Identification of key molecular characteristics for achieving increased anti-tumor activity by combined mTORC1/2 and MEK1/2 investigational agents. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-59.

Improved upper limits on the stochastic gravitational-wave background from 2009-2010 LIGO and Virgo data

Gravitational waves from a variety of sources are predicted to superpose to create a stochastic background. This background is expected to contain unique information from throughout the history of the Universe that is unavailable through standard electromagnetic observations, making its study of fundamental importance to understanding the evolution of the Universe. We carry out a search for the stochastic background with the latest data from the LIGO and Virgo detectors. Consistent with predictions from most stochastic gravitational-wave background models, the data display no evidence of a stochastic gravitational-wave signal. Assuming a gravitational-wave spectrum of Ω_{GW}(f)=Ω_{α}(f/f_{ref})^{α}, we place 95% confidence level upper limits on the energy density of the background in each of four frequency bands spanning 41.5-1726 Hz. In the frequency band of 41.5-169.25 Hz for a spectral index of α=0, we constrain the energy density of the stochastic background to be Ω_{GW}(f)<5.6×10^{-6}. For the 600-1000 Hz band, Ω_{GW}(f)<0.14(f/900  Hz)^{3}, a factor of 2.5 lower than the best previously reported upper limits. We find Ω_{GW}(f)<1.8×10^{-4} using a spectral index of zero for 170-600 Hz and Ω_{GW}(f)<1.0(f/1300  Hz)^{3} for 1000-1726 Hz, bands in which no previous direct limits have been placed. The limits in these four bands are the lowest direct measurements to date on the stochastic background. We discuss the implications of these results in light of the recent claim by the BICEP2 experiment of the possible evidence for inflationary gravitational waves.

Abstract 698: Variability in xenograft growth rates can be explained by intra-tumor evolutionary dynamics

Growing evidence frames cancer as a disease of somatic Darwinian evolution. Recent reports establish both inter- and intra-tumor genetic heterogeneity within patients, and evolution in response to treatment. Cell lines and xenografts also display genetic heterogeneity arising from chromosomal instability, and evolve in response to experimental conditions. Although selection pressures in culture or in a mouse differ from those in a patient, studying the evolutionary process pre-clinically may yield insights useful in the design of experimental systems of cancer.

Using the soft agar colony growth assay, we previously showed that individual colonies in soft agar have widely divergent growth rates that are mostly inherited over experimental timescales. In this work we ask what the impact of that divergence is on xenograft growth kinetics, and examine the implications for the development of resistance.

First, we applied a stochastic, dynamic model of tumor kinetics to simulate clonal heterogeneity in xenograft growth rates. Each simulated “virtual tumor” consisted of a set of independently growing subclone cells, whose growth rates were bootstrapped from a distribution derived from 104 individual HCT-116 colonies in the soft agar colony growth assay. Both the sampling distribution variance and the number of subclones sampled to found each xenograft were varied to test the effect of intercell heterogeneity and starting population size on overall xenograft growth rate. We simulated tumor growth profiles over both a two-week implant and a three-week observation period to reflect experimental conditions. Three stochastic simulation scenarios were considered for possible cellular events: birth only; birth and death; and birth, death, and mutation.

We compared the simulation results to an experimentally determined distribution derived from 308 individual HCT-116 xenograft tumors. Our findings suggest the variability of whole tumors is related to the clonal growth rate diversity within the HCT-116 cell line. In particular, the observed xenograft growth rate heterogeneity can be explained entirely by a scenario where many orders of magnitude fewer cells survive to initiate each tumor than the millions of cells implanted experimentally. This finding is consistent with previously published experimental results that have been interpreted to suggest the existence of cancer stem cells. We demonstrate the small number of founder cells in each xenograft leads to evolutionary drift under treatment conditions, when a resistant mutant may come to dominate stochastically due to the small population size.We then extend this work to ask questions about experimental design - how to develop a xenograft model system that is either more reproducible (less heterogeneity) or more heterogeneous (less reproducible). The latter system may be particularly valuable in studying the emergence of resistance.

Citation Format: Christopher J. Zopf, Andrew Chen, Mayank Patel, Santhosh Palani, Syamala Bandi, Derek Blair, Wen Chyi Shyu, Arijit Chakravarty. Variability in xenograft growth rates can be explained by intra-tumor evolutionary dynamics. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 698. doi:10.1158/1538-7445.AM2014-698

Search for gravitational waves associated with γ-ray bursts detected by the interplanetary network

We present the results of a search for gravitational waves associated with 223 γ-ray bursts (GRBs) detected by the InterPlanetary Network (IPN) in 2005-2010 during LIGO's fifth and sixth science runs and Virgo's first, second, and third science runs. The IPN satellites provide accurate times of the bursts and sky localizations that vary significantly from degree scale to hundreds of square degrees. We search for both a well-modeled binary coalescence signal, the favored progenitor model for short GRBs, and for generic, unmodeled gravitational wave bursts. Both searches use the event time and sky localization to improve the gravitational wave search sensitivity as compared to corresponding all-time, all-sky searches. We find no evidence of a gravitational wave signal associated with any of the IPN GRBs in the sample, nor do we find evidence for a population of weak gravitational wave signals associated with the GRBs. For all IPN-detected GRBs, for which a sufficient duration of quality gravitational wave data are available, we place lower bounds on the distance to the source in accordance with an optimistic assumption of gravitational wave emission energy of 10(-2)M⊙c(2) at 150 Hz, and find a median of 13 Mpc. For the 27 short-hard GRBs we place 90% confidence exclusion distances to two source models: a binary neutron star coalescence, with a median distance of 12 Mpc, or the coalescence of a neutron star and black hole, with a median distance of 22 Mpc. Finally, we combine this search with previously published results to provide a population statement for GRB searches in first-generation LIGO and Virgo gravitational wave detectors and a resulting examination of prospects for the advanced gravitational wave detectors.

Search for gravitational waves associated with γ-ray bursts detected by the interplanetary network

We present the results of a search for gravitational waves associated with 223 γ-ray bursts (GRBs) detected by the InterPlanetary Network (IPN) in 2005-2010 during LIGO's fifth and sixth science runs and Virgo's first, second, and third science runs. The IPN satellites provide accurate times of the bursts and sky localizations that vary significantly from degree scale to hundreds of square degrees. We search for both a well-modeled binary coalescence signal, the favored progenitor model for short GRBs, and for generic, unmodeled gravitational wave bursts. Both searches use the event time and sky localization to improve the gravitational wave search sensitivity as compared to corresponding all-time, all-sky searches. We find no evidence of a gravitational wave signal associated with any of the IPN GRBs in the sample, nor do we find evidence for a population of weak gravitational wave signals associated with the GRBs. For all IPN-detected GRBs, for which a sufficient duration of quality gravitational wave data are available, we place lower bounds on the distance to the source in accordance with an optimistic assumption of gravitational wave emission energy of 10(-2)M⊙c(2) at 150 Hz, and find a median of 13 Mpc. For the 27 short-hard GRBs we place 90% confidence exclusion distances to two source models: a binary neutron star coalescence, with a median distance of 12 Mpc, or the coalescence of a neutron star and black hole, with a median distance of 22 Mpc. Finally, we combine this search with previously published results to provide a population statement for GRB searches in first-generation LIGO and Virgo gravitational wave detectors and a resulting examination of prospects for the advanced gravitational wave detectors.

Constraints on cosmic strings from the LIGO-Virgo gravitational-wave detectors

Cosmic strings can give rise to a large variety of interesting astrophysical phenomena. Among them, powerful bursts of gravitational waves (GWs) produced by cusps are a promising observational signature. In this Letter we present a search for GWs from cosmic string cusps in data collected by the LIGO and Virgo gravitational wave detectors between 2005 and 2010, with over 625 days of live time. We find no evidence of GW signals from cosmic strings. From this result, we derive new constraints on cosmic string parameters, which complement and improve existing limits from previous searches for a stochastic background of GWs from cosmic microwave background measurements and pulsar timing data. In particular, if the size of loops is given by the gravitational backreaction scale, we place upper limits on the string tension Gμ below 10(-8) in some regions of the cosmic string parameter space.

Abstract A282: Antitumor activity of alisertib (MLN8237), an investigational small molecule Aurora A kinase inhibitor, as a single agent and in combination with paclitaxel, in experimental models of small cell lung cancer

Alisertib (MLN8237) is a highly selective, potent small molecule inhibitor of Aurora A kinase that is being developed for the treatment of patients with advanced malignancies, including small cell lung cancer (SCLC). SCLC accounts for approximately 13-15% of all lung cancers diagnosed and represents a major unmet medical need. SCLC is an aggressive disease with a median survival of 2-4 months if left untreated. Despite SCLC often being chemosensitive initially, with response rates between 60 - 80%, relapse is almost universal. Here, we assessed the antitumor effects of alisertib in vitro in various SCLC cell lines and in vivo in primary human SCLC xenograft models. The growth inhibitory effect of alisertib was determined in a number of SCLC cell lines. The effect on growth of these lines was measured by two independent methods, after treating these cells for 96 hours with varying concentrations of alisertib. The viability assays used were CyQuant direct, which measures cell proliferation by quantifying DNA content, and PrestoBlue, which determines cell viability by measuring the ability of cells to reduce resazurin. Cell growth was inhibited by alisertib in all lines tested with concentrations producing a 50% inhibition (IC50) ranging from 9 to 34 nM and 8 to 26 nM in the CyQuant and PrestoBlue assays, respectively. The antitumor activity of alisertib as a single agent was also evaluated in a number of primary human SCLC xenografts. In all tumor models tested, alisertib resulted in significant tumor growth inhibition at 20 mg/kg, administered both QD and BID. Alisertib dosed at 20 mg/kg BID in the CTG-0198 model led to regressions in tumor size during the period of dosing. The antitumor activity of alisertib was also tested in combination with a number of standard of care agents including paclitaxel. The combination of alisertib and paclitaxel provided enhanced antitumor activity in comparison to either agent alone in a number of models. These results demonstrate that alisertib effectively inhibits growth of experimental human SCLC models in cell culture, and displays effective antitumor activity in multiple primary human SCLC xenografts, both alone and in combination with paclitaxel. This work provides preclinical support for the further clinical evaluation of alisertib in SCLC.

Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A282.

Citation Format: Derek Blair, Mengkun Zhang, Michael D. Smith, Carmin Szynal, Jeffrey A. Ecsedy, Huifeng Niu. Antitumor activity of alisertib (MLN8237), an investigational small molecule Aurora A kinase inhibitor, as a single agent and in combination with paclitaxel, in experimental models of small cell lung cancer. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A282.

Abstract B28: Identification of drug-response biomarkers for combined mTORC1/2 and MEK1/2 investigational agents using a large-scale cancer cell line screen

Combination therapy in oncology drug development has the potential to overcome resistance against single agent-based therapy. In many cancers, the RAS/RAF/MEK/ERK and PI3K/AKT/mTOR pathways confer major cell growth and survival mechanisms. Therefore, a greater treatment effect may be achieved by inhibiting both pathways simultaneously through drug combination. In this study, the enhanced anti-proliferative activity of investigational drug MLN0128 (mTORC1/2 investigational agent) when combined with TAK733 (MEK1/2 investigational agent) was assessed using an in-vitro cell viability assay on 240 cancer cell lines. Cells were administered with MLN0128 and TAK-733 in DMSO in ten 2-fold serial dilutions and cell viability was measured using Promega's CellTiter-GLO® assay after a 72 hour incubation period. The data points were fit to a 9-parameter model to obtain a response surface and the effect of the combined administration for each cell line was measured using a non-linear blending score estimated from the response surface. Statistical analysis was conducted to determine if the estimated combination effect was associated with known mutated oncogenes, tumor suppressor genes, or cancer histology reports of the 240 cell lines. Interestingly, the synergy of combined agents appeared to be mainly driven by TAK733, as cell lines sensitive to TAK733 as a single agent demonstrated beneficial combination effect with the addition of MLN0128. Furthermore, cell lines with mutations in KRAS, NRAS, and BRAF showed greater synergy scores of the combination. Likewise, cell lines of colon, pancreatic, and skin origins were more sensitive to the combination. Of particular interest are cell lines with mutated KRAS/NRAS or colon origins since they were generally resistant to single agent MLN0128, but became sensitized to the combination with TAK733. In conclusion, our study of drug combination assays on a large-scale in-vitro cell line panel revealed that mutant KRAS/NRAS or colorectal cancers may be sensitive to combined MLN0128 with TAK733.

Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B28.

Citation Format: Hyunjin Shin, Derek Blair, Bin Li, Greg Hather, William L. Trepicchio, Jeffrey Ecsedy, Esha Gangolli. Identification of drug-response biomarkers for combined mTORC1/2 and MEK1/2 investigational agents using a large-scale cancer cell line screen. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B28.

Abstract A59: Treatment with targeted anticancer agent affects rapidly growing subclones: An integrated in vitro/in silico analysis of clonal heterogeneity in soft agar

A growing body of clinical evidence supports a view that the often observed refractory tumor rebound arises from variation in treatment response among tumor subclones already present in the tumor at the time of diagnosis. Heterogeneity within the tumor provides subpopulations of varying fitness under the selective pressure of treatment. Through evolutionary dynamics, sensitive subpopulations wither under purifying selection, while resistant subclones replenish the population.

In this report, we employ a previously developed in vitro system based on the soft agar clonogenic assay to characterize the degree of heterogeneity in cell growth rate (fitness) between subclones in a population. The system consists of high-throughput incubation and daily imaging of colonies growing in wells of titrated treatment conditions over two to three weeks, followed by automated image analysis, image registration, and colony tracking. Individual colony growth kinetics are well-described by a single-exponential model with fit growth rates uncorrelated to location within the well. Growth rate is likely inherited over the course of the experiment as seen by a strong correlation to final diameter, and further supported by Gillespie stochastic simulations showing the final, untreated population growth rate distribution could only be achieved if heritability was strong.

The methods described here are then applied to HCT-116 colonies allowed to grow for a week and then treated with targeted anticancer agents (in the PI-3K and MAP kinase pathways). This allowed direct comparison of individual colony growth rates pre- and post-treatment. We find growth rate scales down proportionally after treatment as faster growing colonies experience a greater decrease in growth rate. Comparing the post-treatment growth rates of the slowest and fastest growing colonies demonstrates the majority of projected population debulking occurs by inhibiting the fastest growing subclones. Scatter plots of post- vs. pre-treatment growth rate show the effect of treatment is somewhat stochastic: not all fast-growing colonies are affected to the same extent.

Heterogeneity in the population, then, serves to increase the likelihood of the existence of a fast-growing subclone with greater resistance to treatment. This enables the population to more rapidly and assuredly evolve through selection on pre-existing variation in subclones. Thus, natural selection in response to treatment, rather than biochemical or mutational adaptation, appears to drive the emergence of resistance in the population. Because of the high degree of heterogeneity in subclonal growth rates, this resistance can emerge rapidly in soft agar (over several days). The methodology described may be applied to gain insight into heterogeneity in cancer cell line treatment response for drugs with various mechanisms of action.

Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A59.

Citation Format: Christopher J. Zopf, Carmin Szynal, Santhosh Palani, Doug Bowman, John Bradley, Jerome Mettetal, Jeffrey Ecsedy, Andy Dorner, Wen Chyi Shyu, Keisuke Kuida, Derek Blair, Arijit Chakravarty. Treatment with targeted anticancer agent affects rapidly growing subclones: An integrated in vitro/in silico analysis of clonal heterogeneity in soft agar. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A59.

Abstract B214: Application of an evolutionary model of cancer cell response to dose-response viability curves to assess the potential for pre-existing resistance

In recent years, tumor progression in the clinic has been demonstrated to be polyclonal. A single tumor contains diverse subpopulations of cancer cells which under the selective pressure of drug treatment evolve to produce drug resistance. As well, clonal diversity and genomic instability exist in tissue culture cancer cell lines. In this work, we determine the levels of pre-existing resistant subpopulations in cancer cell lines through the application of an evolutionary model of cancer cell response to an in vitro time-course of dose-response viability curves.

Methods: We simulated dose response curves versus exposure time for a range of heterogeneous (varying fractions of sensitive and resistance cells) populations of cancer cells with predefined PD parameters. We added Gaussian noise to our estimates and fitted the time-course dose response data with both a single population model and with a dual population model and compared the goodness of the fit, PD parameter and residual variability levels for each of the heterogeneous populations. Model fitting and simulations were performed using NONMEM. Cancer cells were assumed to grow exponentially and the drug effect was modeled as a sigmoidal Emax function. An analytical solution was derived to estimate the population fractions from the plateau levels of the dose response curves. We validated the model predictions against a previously published dataset (Sci. Transl. Med. 3, 90ra59, 2011) where the baseline fraction of sensitive and resistant cells was varied in a controlled manner. Finally, the model was used to predict the percentage of resistant cells in a pre-existing population in response to treatment with signal transduction inhibitors. These predictions were then validated directly using time-lapse microscopy on cells grown in plastic and on soft agar.

Results: From the simulations, we determined the study design necessary for modeling dose response relationship for heterogeneous populations to be a time course (0, 24, 48, 72h) viability assay with a range of concentrations spanning low to saturable effect. The dual population model accurately fitted the dose response curves simulated from heterogeneous populations, whereas the single population model showed poor predictability and exhibited large unexplained residual variability. The dual population model also predicted well the PD parameters and growth kinetics of the sensitive and resistance populations when applied to the published dataset with single time course (72h) viability measurement performed on heterogeneous populations with varying levels of resistance sub-fractions. When applied to a prospective in vitro study, the dual population model adequately predicted the fraction of resistance populations preexisting in the cell line tested.

Conclusion: Taken together, the modeling approach described here provides a novel evolutionary approach to the assessment of pre-existing resistance to drug treatment that can be rapidly applied for mechanistic investigations of single-agent and combination cancer therapeutics.

Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B214.

Citation Format: Snehal Samant, Derek Blair, Andrew Chen, Jerome Mettetal, Wen Chyi Shyu, Mark Hixon, Jeffrey Ecsedy, Santhosh Palani, Arijit Chakravarty. Application of an evolutionary model of cancer cell response to dose-response viability curves to assess the potential for pre-existing resistance. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B214.

Abstract 5229: An integrated in vitro/in silico platform to study the evolutionary dynamics of resistance to cancer therapy

Emerging clinical evidence suggests that tumor response to treatment occurs along stochastic, evolutionary trajectories, with the amplification of pre-existing clones of resistant cells being shown to be responsible for treatment failure in a number of cases. In this evolutionary view of cancer, pre-existing tumor heterogeneity forms a pool of candidate mutations for selection to act on. Thus, a clearer picture of the heterogeneity in cancer cell lines will facilitate a mechanistic understanding of the dynamics of resistance evolution.

In this report, we describe the development of an in vitro system based on the soft agar clonogenic assay, to characterize the degree of heterogeneity in net growth rate (fitness) and the effects of this variation in fitness on the population-level response to drug treatment.

The in vitro system was developed as a 24-well soft agar clonogenic assay, with sequential images of the growing colonies taken every day, over a period of two weeks. An automated system for plate preparation, imaging, and plate transfer from incubator to imager was developed to increase assay throughput. A modified greedy algorithm was developed in MATLAB to reliably track colonies as they grow and shift in the image frame from day to day. The individual growth rates of colonies in the pre-treatment colonies followed an exponential model, indicative of a clonal origin for the individual colonies, which was confirmed by microscopy.

Next, we developed an in silico platform (based on a Monte Carlo stochastic simulation) to study this heterogeneity in growth rates, and derive insights that are relevant to the planning of therapeutic interventions. This simulation was also used to optimize the design of the experimental system and can provide guidance on the optimal seeding density to avoid merging colonies. We are now using this simulation platform to explore the impact of heterogeneity on population fitness and on drug resistance. This platform can also be extended to examine the individual colonies derived from drug treatment, and provides a rapid and simple means of isolation of resistant subclones for further study.

Taken together, the combined in vitro/in silico platform presented here forms the basis of a Systems Biology approach for assessing the role of cancer cell heterogeneity in the evolution of resistance in response to treatment

Citation Format: Keisuke Kuida, Carl Boand, John Bradley, Jean Courtemanche, John Donovan, Doanh Mai, Jerome Mettetal, Santhosh Palani, Derek Blair, Jeffrey Ecsedy, Andy Dorner, Wen Chyi Shyu, Joe Bolen, Doug Bowman, Arijit Chakravarty. An integrated in vitro/in silico platform to study the evolutionary dynamics of resistance to cancer therapy . [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5229. doi:10.1158/1538-7445.AM2013-5229

Abstract 3411: Mathematical model of the cell cycle to determine mechanism of action and optimize dosing schedule

A mechanistic understanding of a drug's biological effect can have significant implications for selection of clinical dosing schedule, combination partners, as well as biomarker selection. In this context, a question often faced from target validation to the end of Phase I is how much of a drug's antitumor effect is through altering cell-cycle progression or through the induction of cell death. Here we demonstrate the utility of a mathematical model of the cell-cycle for determining the effect of a drug on cell-cycle kinetics, as an objective and quantiative interpretation of DNA flow cytometry and cellular growth rate data. The model uses periodic measurement of DNA content as analyzed by flow cytometry to determine the percentage of cells in G0/G1, S, and G2/M phases of the cell cycle, as well as continuous readouts of cell confluence over the course of treatment to assess growth rates. The model is then fitted to the data to extract effective cell cycle transition rates demonstrating the effect of the drug on cell cycle progression.

To demonstrate of the approach, we applied the model to data acquired during treatment of A375 melanoma cells with the investigational drug TAK-733, an allosteric MEK 1/2 inhibitor hypothesized to cause a G1 arrest. Rather than an arrest at G1, the model predicted that the G1-to-S transition rate was reduced, and that much of the change in cell cycle data was due to an increase in the S-to-G2/M and G2/M-to-G0/G1 transition rates. These model predictions were then tested with video microscopy and both predictions (increased time spent in G0/G1 as well as reduced time spent in mitosis) were supported by experimental data.

The model was then used to test whether the cell cycle activity of the compound has any implications for optimization of dosing schedule. By varying the schedule of drug treatment in the simulation, we find that for the biologically relevant concentration range the compound is schedule independent (i.e. effect is proportional to AUC). This finding suggests that there is no need to maintain constant pathway inhibition to see efficacy in tumor volume reduction. To test this prediction, we compare the results with the outcome of experiments in an A375 xenograft model with different dosing schedules and verify directly that AUC was indeed a better predictor of efficacy than Cmin.

With this example, we demonstrate that mathematical modeling can be used to aid in interpretation of cell cycle data, often generated to describe the cell killing activity of oncology compounds. In addition, the model provides a method of interpreting the mechanism of action for early development decision making around schedule selection. This work demonstrates an approach that can be extended to other compounds to provide an integrated measure of the total contribution of cell death and/or cell cycle arrest, to help provide a mechanistic rationale for schedule selection with single agent and combination therapies.

Citation Format: Jerome Mettetal, Derek Blair, Esha Gangoli, Patrick Vincent, Jeff Ecsedy, Wen Chyi Shyu, Arijit Chakravarty. Mathematical model of the cell cycle to determine mechanism of action and optimize dosing schedule. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3411. doi:10.1158/1538-7445.AM2013-3411

Immune complex-mediated co-ligation of the BCR with FcγRIIB results in homeostatic apoptosis of B cells involving Fas signalling that is defective in the MRL/Lpr model of systemic lupus erythematosus

Negative regulation of B cell activation by cognate immune complexes plays an important homeostatic role in suppressing B cell hyperactivity and preventing consequent autoimmunity. Immune complexes co-ligate the BCR and FcγRIIB resulting in both growth arrest and apoptosis. We now show that such apoptotic signalling involves induction and activation of p53 and its target genes, the pro-apoptotic Bcl-2 family members, Bad and Bid, as well as nuclear export of p53. Collectively, these events result in destabilisation of the mitochondrial and lysosomal compartments with consequent activation and interplay of executioner caspases and endosomal-derived proteases. In addition, the upregulation of Fas and FasL with consequent activation of caspase 8-dependent death receptor signalling is required to facilitate efficient apoptosis of B cells. Consistent with this role for Fas death receptor signalling, apoptosis resulting from co-ligation of the BCR and FcγRIIB is defective in B cells from Fas-deficient MRL/MpJ-Fas(lpr) mice. As these mice develop spontaneous, immune complex-driven lupus-like glomerulonephritis, targeting this FcγRIIB-mediated apoptotic pathway may therefore have novel therapeutic implications for systemic autoimmune disease.