Immune cell-based screening assay for response to anticancer agents: applications in pharmacogenomics

Abstract

Background

Interpatient variability in immune and chemotherapeutic cytotoxic responses is likely due to complex genetic differences and is difficult to ascertain in humans. Through the use of a panel of genetically diverse mouse inbred strains, we developed a drug screening platform aimed at examining interstrain differences in viability on normal, noncancerous immune cells following chemotherapeutic cytotoxic insult. Drug effects were investigated by comparing selective chemotherapeutic agents, such as BEZ-235 and selumetinib, against conventional cytotoxic agents targeting multiple pathways, including doxorubicin and idarubicin.

Methods

Splenocytes were isolated from 36 isogenic strains of mice using standard procedures. Of note, the splenocytes were not stimulated to avoid attributing responses to pathways involved with cellular stimulation rather than toxicity. Cells were incubated with compounds on a nine-point logarithmic dosing scale ranging from 15 nM to 100 μM (37°C, 5% CO₂). At 4 hours posttreatment, cells were labeled with antibodies and physiological indicator dyes and fixed with 4% paraformaldehyde. Cellular phenotypes (eg, viability) were collected and analyzed using flow cytometry. Dose-response curves with response normalized to the zero dose as a function of log concentration were generated using GraphPad Prism 6.

Results

Phenotypes were quantified using flow cytometry, yielding interstrain variation for measured endpoints in different immune cells. The flow cytometry assays produced over 16,000 data points that were used to generate dose-response curves. The more targeted agents, BEZ-235 and selumetinib, were less toxic to immune cells than the anthracycline agents. The calculated heritability for the viability of immune cells was higher with anthracyclines than the novel agents, making them better suited for downstream genetic analysis.

Conclusion

Using this approach, we identify cell lines of variable sensitivity to chemotherapeutic agents and aim to identify robust, replicable endpoints of cellular response to drugs that provide the starting point for identifying candidate genes and cellular toxicity pathways for future validation in human studies.

Keywords:

• immunomodulation
• cytotoxicity
• chemotherapy
• precision medicine

Supplementary materials

Figure S1 Cellular subpopulations of freshly isolated splenocytes.

Notes: Splenocytes were isolated from male C57BL/6J (n=4) mice. Our populations were comparable to murine spleen cell composition when available in the Mouse Phenome Database (B-cells, granulocytes, and monocytes P<0.05 using t-tests).

Figure S2 Interstrain variation of viability across immune cell types and anticancer drugs.

Notes: Dose-response curves depict cell populations (columns, ie, T-cells [A, E, I, and M], B-cells [B, F, J, and N], monocytes [C, G, K, and O], and granulocytes [D, H, L, and P]) exposed to anticancer drugs (rows, ie, doxorubicin [A-D], idarubicin [E-H], BEZ-235 [I-L], and selumetinib [M-P]). Thirty-six strains are included: 129S1/SvImJ, 129X1/SvJ, A/J, AKR/J, BALB/cByJ, BTBR T⁺ Itpr3^tf/J, BUB/BnJ, C3H/HeJ, C57BLKS/J, C57BL/6J, C57BR/cdJ, C58/J, CBA/J, CZECHII/EiJ, DBA/2J, FVB/NJ, I/LnJ, KK/HiJ, LG/J, LP/J, MA/MyJ, NOD/LtJ, NON/LtJ, NZB/BINJ, NZO/HiLtJ, NZW/LacJ, PERA/EiJ, PL/J, PWD/PhJ, PWK/PhJ, RIIIS/J, SEA/GnJ, SJL/J, SM/J, SWR/J, and WSB/EiJ.

Abbreviations: AUC, area under the curve; IC₅₀, half maximal inhibitory concentration.

Figure S3 Interstrain phenotype comparisons for area under the curve.

Notes: Area under the curve values across strains, heritability, and correlation to log₁₀ (IC₅₀ [nM]) values, when relevant, are displayed for T-cells exposed to selumetinib (A), T-cells exposed to BEZ-235 (B), T-cells exposed to doxorubicin (C), T-cells exposed to idarubicin (D), B-cells exposed to selumetinib (E), B-cells exposed to BEZ-235 (F), B-cells exposed to doxorubicin (G), B-cells exposed to idarubicin (H), monocytes exposed to selumetinib (I), monocytes exposed to BEZ-235 (J), monocytes exposed to doxorubicin (K), and monocytes exposed to idarubicin (L). Strains are arranged from largest to smallest area under the curve (±SEM) along the X-axis. Abbreviations: AUC, area under the curve; IC₅₀, half maximal inhibitory concentration; SEM, standard error of the mean.

Figure S4 Interstrain phenotype comparisons for slope coefficients.

Notes: Slope coefficients across strains and correlation to log₁₀ (IC₅₀ [nM]) values are displayed for T-cells exposed to idarubicin (A), B-cells exposed to doxorubicin (B), B-cells exposed to idarubicin (C), monocytes exposed to doxorubicin (D), and monocytes exposed to idarubicin (E). Strains are arranged from largest to smallest slope coefficient (±SEM) along the X-axis.

Abbreviations: IC₅₀, half maximal inhibitory concentration; SEM, standard error of the mean.

Table S1 Heritability (%) of viability (%) at specific drug doses (μM) among different phenotypes

Table S2 Phenotype correlations for area under the curve values across strains

Table S3 Phenotype correlations for slope coefficient values across strains

Acknowledgments

The project described was supported by the North Carolina Translational and Clinical Sciences Institute, the National Center for Research Resources, the National Center for Advancing Translational Sciences, and the NIH, through grant award number UL1TR000083. BD is supported by NIH grant CA096500. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This project was also supported by the Pharmaceutical Research and Manufacturers of America Foundation Pre Doctoral Fellowship in Pharmacology/Toxicology. WinNonlin^® software was provided to faculty and trainees in the Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, by Certara as a member of the Pharsight Academic Center of Excellence Program. We thank Novartis for supplying us with the BEZ-235 compound.

Disclosure

KR received an honorarium from Celgene and is on the advisory board for Genentech. The other authors report no conflicts of interest in this work.