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Drug sensitivity of single cancer cells is predicted by changes in mass accumulation rate

Nature Biotechnology volume 34, pages 1161–1167 (2016)Cite this article

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Abstract

Assays that can determine the response of tumor cells to cancer therapeutics could greatly aid the selection of drug regimens for individual patients. However, the utility of current functional assays is limited, and predictive genetic biomarkers are available for only a small fraction of cancer therapies. We found that the single-cell mass accumulation rate (MAR), profiled over many hours with a suspended microchannel resonator, accurately defined the drug sensitivity or resistance of glioblastoma and B-cell acute lymphocytic leukemia cells. MAR revealed heterogeneity in drug sensitivity not only between different tumors, but also within individual tumors and tumor-derived cell lines. MAR measurement predicted drug response using samples as small as 25 μl of peripheral blood while maintaining cell viability and compatibility with downstream characterization. MAR measurement is a promising approach for directly assaying single-cell therapeutic responses and for identifying cellular subpopulations with phenotypic resistance in heterogeneous tumors.

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Figure 1: MAR measurements characterize single-cell heterogeneity in growth across GBM-PDCLs and conventional cell lines.
Figure 2: Murine BaF3 lymphoblastoid cells rapidly reduce MAR following exposure to active kinase inhibitors.
Figure 3: MAR predicts sensitivity of human GBM-PDCLs to targeted therapy.
Figure 4: MAR distributions predict drug sensitivity of primary murine ALL cells to targeted therapy.
Figure 5: Patient samples treated in vivo or ex vivo show consistent reduction in MAR.

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Acknowledgements

These studies were supported by R01 CA170592 (S.R.M., K.L.L., P.Y.W.), P50 CA165962 (K.L.L., P.Y.W.), P01 CA142536 (K.L.L.) and R33 CA191143 (S.R.M., D.M.W.) from the US National Institutes of Health, U54 CA143874 from the National Cancer Institute (S.R.M.), and partially by Cancer Center Support (core) Grant P30 CA14051 from the National Cancer Institute, The Bridge Project, a partnership between the Koch Institute for Integrative Cancer Research at MIT and the Dana-Farber/Harvard Cancer Center (DF/HCC) (S.R.M., D.M.W.), and the Dana-Farber Cancer Institute Brain Tumor Therapeutics Accelerator Program (P.Y.W., K.L.L.). A.I. acknowledges support from Fondation ARC pour la Recherche sur le Cancer, The Institut Universitaire de Cancérologie, OncoNeuroThèque and the program Investissements d'avenir” ANR-10-IAIHU-06. M.M.S. acknowledges support from the NIH/NIGMS T32 GM008334, Interdepartmental Biotechnology Training Program grant. N. Chou acknowledges support from the National Science Scholarship, Agency for Science, Technology and Research (STAR), Singapore. D.M.W. is a Leukemia and Lymphoma Scholar. M.A.M. gratefully acknowledges support from the institutional research training grant T32 CA009172, from the National Cancer Institute.

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Author notes

  1. Mark M Stevens, Cecile L Maire, Nigel Chou and Mark A Murakami: These authors contributed equally to this work.

Authors and Affiliations

  1. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Mark M Stevens, Nigel Chou, Yuki Kikuchi, Robert J Kimmerling, Nicholas L Calistri, Selim Olcum & Scott R Manalis

  2. Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Mark M Stevens

  3. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA

    Cecile L Maire, Mark A Murakami, David S Knoff, Huiyun Liu, Samer Haidar, Nicolas A Cordero, David M Weinstock & Keith L Ligon

  4. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Nigel Chou, Robert J Kimmerling & Scott R Manalis

  5. Hitachi High-Technologies Corporation, Ibaraki-ken, Japan

    Yuki Kikuchi

  6. Program in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Nathan Cermak

  7. Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France

    Ahmed Idbaih

  8. AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Service de Neurologie 2-Mazarin, Paris, France

    Ahmed Idbaih

  9. Center For Neuro-Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA

    Patrick Y Wen

  10. Broad Institute, Cambridge, Massachusetts, USA

    David M Weinstock & Keith L Ligon

  11. Department of Pathology, Boston Children's Hospital, Boston, Massachusetts, USA

    Keith L Ligon

  12. Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, USA

    Keith L Ligon

  13. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Scott R Manalis

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  1. Mark M Stevens
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Contributions

N. Cermak, S.O. and S.R.M. designed devices. M.M.S., N. Chou, N. Cermak and S.O. designed and constructed the experimental setup. C.L.M., D.S.K., S.H., A.I., P.Y.W. and K.L.L. managed and created BT GBM-PDCLs. M.A.M. and H.L. managed and processed murine models of B-cell acute lymphocytic leukemia. M.A.M., H.L. and N.A.C. procured and processed patient samples. M.M.S., C.L.M., N. Chou, M.A.M., D.S.K., Y.K., N.L.C., N.A.C., N. Cermak, D.M.W., K.L.L. and S.R.M. designed the experiments. M.M.S., C.L.M., N. Chou, M.A.M., D.S.K., Y.K., R.J.K., H.L., S.H., N.L.C. and N.A.C. performed the experiments. M.M.S., C.L.M., N. Chou., M.A.M., D.S.K., Y.K., R.J.K., N.L.C., N. Cermak, N.A.C. analyzed the data. M.M.S., C.L.M., N. Chou, D.M.W., K.L.L. and S.R.M. wrote the paper with input from all of the other authors.

Corresponding authors

Correspondence to David M Weinstock, Keith L Ligon or Scott R Manalis.

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Competing interests

S.R.M. is a cofounder of Affinity Biosensors, which develops techniques relevant to the research presented. S.O. and M.M.S. anticipate employment at Affinity Biosensors. D.M.W. is a consultant for and receives research support from Novartis.

Integrated supplementary information

Supplementary Figure 1 Sphere forming assay of BT145 GBM PDCL

(a) Sphere forming potential of single BT145 GBM cells isolated from bulk culture or SMR post-MAR measurement. p-values reflect output of Pearson’s chi-squared test. (b) Representative single-cell trajectories paired with images of sphere forming potential 2-weeks after measurement. Note that even cells with minimal or negative growth over the 15-minute period may retain tumorsphere-forming potential (bottom right).

Supplementary Figure 2 Growth heterogeneity maintained across multiple passages of PCDLs

Representative staining results from immunohistochemistry for Ki67 on BT145, BT159, BT179, BT240, BT320, and BT333 cell lines. Results quantified as percentage of total cells stained positive.

Supplementary Figure 3 Growth heterogeneity maintained across multiple passages of PCDLs

Box plot comparison of MAR normalized to mass for the same cell lines as in Figure 1c with three passages of GBM-PDCLs shown separately. Boxes represent the inter-quartile range and white squares the average of all measurements. From left to right, n = 84; 46; 13, 14, 17; 12, 21, 18; 21, 18, 13; 19, 21, 21; 16, 16, 16; 14, 18, 14; 18, 25, 21; 19, 22, 18.

Supplementary Figure 4 PDCLs following treatment with MDM2 inhibitor RG7112

Scatter plots of MAR versus mass for BT484, BT3731, BT159, BT333 cells following treatment with 1 μM RG7112.

Supplementary Figure 5 Dose-response curves for GBM PDCLs treated with RG7112

Curves from PDCLs were generated using CellTiter-Glo at 72 hrs, following treatment with 1 μM RG7112. IC50 values embedded in each graph reflect the output of a four parameter nonlinear regression model +/- the range of the 95% confidence interval.

Supplementary Figure 6 Viability and purity of primary murine B-ALL by flow cytometry

(a) Representative dot plots of cells stained with DAPI and Annexin V, as markers of viability. (b) Representative histogram of GFP expression after cell sorting. Leukemia cells in this model uniquely express GFP. FACS analysis was performed on all primary murine splenocyte samples.

Supplementary Figure 7 In vivo clonal dynamics in a transgenic murine model of BCR-ABL B-ALL

(a) The allelic frequency of BCR-ABL T315I in two mice treated with nilotinib, a surrogate for imatinib. Allelic frequencies were calculated by visual measurement of the relative heights of the electropherogram peaks; values from the paired forward and reverse sequencing phases were averaged to produce the allelic frequencies shown. Mouse 1 (closed circles) was sacrificed on day 14 for routine pharmacodynamic assessment (open circle). Mouse 2 (closed squares) was sacrificed after developing clinical signs of advanced leukemia on day 33 (open square). (b) Representative electropherograms showing ABL codon 315 (in gray; ACT indicates wild type T315, and ATT indicates the point mutation T315I). As demonstrated, the mutant subclone expands in relation to WT during treatment with nilotinib, to which WT but not T315I BCR-ABL is sensitive.

Supplementary Figure 8 MAR or mass can be used individually as a classifier for drug susceptibility

Primary murine BCR-ABL ALL and BCR-ABL T315I ALL cells treated with 1 μM imatinib, or 100 nM ponatinib, respectively. (a) MAR versus mass plot with overlay of an orthogonal vector (dotted line) designating the threshold resulting from LDA. Cells treated with drug are in red, and DMSO control cells are blue (b) ROC curves from same paired control and treatment data following LDA of MAR per mass plot. (c) Overlaid ROC curves of paired control and treatment data for all treatment replicates using only mass or MAR parameter. Cells treated with therapy to which they are sensitive or resistant are shown with blue solid lines or red dotted lines, respectively.

Supplementary Figure 9 Predictive power of MAR for cells isolated from circulation

Primary murine BCR-ABL T315I cells isolated from circulation, treated with DMSO or 100 nM ponatinib. ROC curves of paired control and treatment data for each replicate following LDA.

Supplementary Figure 10 Patient sample treated in vivo shows consistent reduction in MAR

MAR versus Mass plot for blasts from peripheral blood samples of AML. Pre-treatment sample shown in black (n=86), and sample obtained after the patient received 48 hrs of of treatment with an experimental MDM2 inhibitor shown in red (n=95).

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Stevens, M., Maire, C., Chou, N. et al. Drug sensitivity of single cancer cells is predicted by changes in mass accumulation rate. Nat Biotechnol 34, 1161–1167 (2016). https://doi.org/10.1038/nbt.3697

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