A microfluidic assay for the quantification of the metastatic propensity of breast cancer specimens


The challenge of predicting which patients with breast cancer will develop metastases leads to the overtreatment of patients with benign disease and to the inadequate treatment of aggressive cancers. Here, we report the development and testing of a microfluidic assay that quantifies the abundance and proliferative index of migratory cells in breast cancer specimens, for the assessment of their metastatic propensity and for the rapid screening of potential antimetastatic therapeutics. On the basis of the key roles of cell motility and proliferation in cancer metastasis, the device accurately predicts the metastatic potential of breast cancer cell lines and of patient-derived xenografts. Compared with unsorted cancer cells, highly motile cells isolated by the device exhibited similar tumourigenic potential but markedly increased metastatic propensity in vivo. RNA sequencing of the highly motile cells revealed an enrichment of motility-related and survival-related genes. The approach might be developed into a companion assay for the prediction of metastasis in patients and for the selection of effective therapeutic regimens.

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Fig. 1: Use of MAqCI for prediction of metastatic potential of breast epithelial and breast cancer cell lines with high accuracy, sensitivity and specificity.
Fig. 2: Migratory cells have similar tumourigenic but markedly enhanced metastatic potential in vivo than unsorted breast cancer cells.
Fig. 3: Characterization of phenotype and genotype of migratory cells.
Fig. 4: MAqCI predicts metastatic potential conferred by activation of PI3K and RAS/MAPK pathways in breast epithelial cells.
Fig. 5: MAqCI accurately predicts the metastatic potential of cells obtained from PDXs.
Fig. 6: MAqCI testing of therapeutic agents from ongoing clinical trials.

Data availability

The main data supporting the results of this study are available within the paper and its Supplementary Information files. Source data for the figures in this study are available from the corresponding author upon reasonable request. RNA sequencing data are available at the National Center for Biotechnology Information Gene Expression Omnibus, under accession number GSE128313.


  1. 1.

    Steeg, P. S. Targeting metastasis. Nat. Rev. Cancer 16, 201–218 (2016).

    CAS  Article  Google Scholar 

  2. 2.

    Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2018. CA Cancer J. Clin. 68, 7–30 (2018).

    Article  Google Scholar 

  3. 3.

    Harms, W. et al. DEGRO practical guidelines for radiotherapy of breast cancer VI: therapy of locoregional breast cancer recurrences. Strahl. Onkol. 192, 199–208 (2016).

    Article  Google Scholar 

  4. 4.

    Paik, S. et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med. 351, 2817–2826 (2004).

    CAS  Article  Google Scholar 

  5. 5.

    Nagrath, S. et al. Isolation of rare circulating tumour cells in cancer patients by microchip technology. Nature 450, 1235–1239 (2007).

    CAS  Article  Google Scholar 

  6. 6.

    Lippman, M. & Osborne, C. K. Circulating tumor DNA—ready for prime time? N. Engl. J. Med 368, 1249–1250 (2013).

    CAS  Article  Google Scholar 

  7. 7.

    Chandler, Y. et al. Cost effectiveness of gene expression profile testing in community practice. J. Clin. Oncol. 36, 554–562 (2018).

    Article  Google Scholar 

  8. 8.

    Alix-Panabières, C. & Pantel, K. Clinical applications of circulating tumor cells and circulating tumor DNA as liquid biopsy. Cancer Discov. 6, 479–491 (2016).

    Article  Google Scholar 

  9. 9.

    Garcia-Murillas, I. et al. Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer. Sci. Transl. Med. 7, 302ra133 (2015).

    Article  Google Scholar 

  10. 10.

    Riggi, N., Aguet, M. & Stamenkovic, I. Cancer metastasis: a reappraisal of its underlying mechanisms and their relevance to treatment. Annu. Rev. Pathol. 13, 117–140 (2018).

    CAS  Article  Google Scholar 

  11. 11.

    Paul, C. D., Mistriotis, P. & Konstantopoulos, K. Cancer cell motility: lessons from migration in confined spaces. Nat. Rev. Cancer 17, 131–140 (2017).

    CAS  Article  Google Scholar 

  12. 12.

    Wolf, K. et al. Collagen-based cell migration models in vitro and in vivo. Semin. Cell Dev. Biol. 20, 931–941 (2009).

    CAS  Article  Google Scholar 

  13. 13.

    Fidler, I. J. The pathogenesis of cancer metastasis: the ‘seed and soil’ hypothesis revisited. Nat. Rev. Cancer 3, 453–458 (2003).

    CAS  Article  Google Scholar 

  14. 14.

    Irianto, J. et al. Nuclear constriction segregates mobile nuclear proteins away from chromatin. Mol. Biol. Cell 27, 4011–4020 (2016).

    CAS  Article  Google Scholar 

  15. 15.

    Irianto, J. et al. DNA damage follows repair factor depletion and portends genome variation in cancer cells after pore migration. Curr. Biol. 27, 210–223 (2017).

    CAS  Article  Google Scholar 

  16. 16.

    Abubakar, M. et al. Prognostic value of automated KI67 scoring in breast cancer: a centralised evaluation of 8088 patients from 10 study groups. Breast Cancer Res. 18, 104 (2016).

    Article  Google Scholar 

  17. 17.

    Cidado, J. et al. Ki-67 is required for maintenance of cancer stem cells but not cell proliferation. Oncotarget 7, 6281–6293 (2016).

    Article  Google Scholar 

  18. 18.

    Duval, K. et al. Modeling physiological events in 2D vs. 3D cell culture. Physiology (Bethesda) 32, 266–277 (2017).

    CAS  Google Scholar 

  19. 19.

    Dallas, M. R. et al. Divergent roles of CD44 and carcinoembryonic antigen in colon cancer metastasis. FASEB J. 26, 2648–2656 (2012).

    CAS  Article  Google Scholar 

  20. 20.

    López-Knowles, E. et al. PI3K pathway activation in breast cancer is associated with the basal-like phenotype and cancer-specific mortality. Int J. Cancer 126, 1121–1131 (2010).

    Article  Google Scholar 

  21. 21.

    McLaughlin, S. K. et al. The RasGAP gene, RASAL2, is a tumor and metastasis suppressor. Cancer Cell 24, 365–378 (2013).

    CAS  Article  Google Scholar 

  22. 22.

    Giltnane, J. M. & Balko, J. M. Rationale for targeting the Ras/MAPK pathway in triple-negative breast cancer. Discov. Med 17, 275–283 (2014).

    PubMed  Google Scholar 

  23. 23.

    Thompson, K. N. et al. The combinatorial activation of the PI3K and Ras/MAPK pathways is sufficient for aggressive tumor formation, while individual pathway activation supports cell persistence. Oncotarget 6, 35231–35246 (2015).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    DeRose, Y. S. et al. Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes. Nat. Med. 17, 1514–1520 (2011).

    CAS  Article  Google Scholar 

  25. 25.

    Dobrolecki, L. E. et al. Patient-derived xenograft (PDX) models in basic and translational breast cancer research. Cancer Metastasis Rev. 35, 547–573 (2016).

    CAS  Article  Google Scholar 

  26. 26.

    Rouzier, R. et al. Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin. Cancer Res. 11, 5678–5685 (2005).

    CAS  Article  Google Scholar 

  27. 27.

    Prat, A. et al. Research-based PAM50 subtype predictor identifies higher responses and improved survival outcomes in HER2-positive breast cancer in the NOAH study. Clin. Cancer Res. 20, 511–521 (2014).

    CAS  Article  Google Scholar 

  28. 28.

    Leonowens, C. et al. Concomitant oral and intravenous pharmacokinetics of trametinib, a MEK inhibitor, in subjects with solid tumours. Br. J. Clin. Pharm. 78, 524–532 (2014).

    CAS  Article  Google Scholar 

  29. 29.

    Csonka, D. et al. A phase-1, open-label, single-dose study of the pharmacokinetics of buparlisib in subjects with mild to severe hepatic impairment. J. Clin. Pharm. 56, 316–323 (2016).

    CAS  Article  Google Scholar 

  30. 30.

    Hollestelle, A., Elstrodt, F., Nagel, J. H., Kallemeijn, W. W. & Schutte, M. Phosphatidylinositol-3-OH kinase or RAS pathway mutations in human breast cancer cell lines. Mol. Cancer Res. 5, 195–201 (2007).

    CAS  Article  Google Scholar 

  31. 31.

    Zimmermann, S. & Moelling, K. Phosphorylation and regulation of Raf by AKT (protein kinase B). Science 286, 1741–1744 (1999).

    CAS  Article  Google Scholar 

  32. 32.

    Tong, Z. et al. Chemotaxis of cell populations through confined spaces at single-cell resolution. PLoS ONE 7, e29211 (2012).

    CAS  Article  Google Scholar 

  33. 33.

    Mathieu, E. et al. Time-lapse lens-free imaging of cell migration in diverse physical microenvironments. Lab Chip 16, 3304–3316 (2016).

    CAS  Article  Google Scholar 

  34. 34.

    Chen, Y. C. et al. Functional isolation of tumor-initiating cells using microfluidic-based migration identifies phosphatidylserine decarboxylase as a key regulator. Sci. Rep. 8, 244 (2018).

    Article  Google Scholar 

  35. 35.

    Song, W. et al. Targeting EphA2 impairs cell cycle progression and growth of basal-like/triple-negative breast cancers. Oncogene 36, 5620–5630 (2017).

    CAS  Article  Google Scholar 

  36. 36.

    Camarda, R. et al. Inhibition of fatty acid oxidation as a therapy for MYC-overexpressing triple-negative breast cancer. Nat. Med. 22, 427–432 (2016).

    CAS  Article  Google Scholar 

  37. 37.

    Mulholland, D. J. et al. Pten loss and RAS/MAPK activation cooperate to promote EMT and metastasis initiated from prostate cancer stem/progenitor cells. Cancer Res. 72, 1878–1889 (2012).

    CAS  Article  Google Scholar 

  38. 38.

    Mendoza, M. C., Er, E. E. & Blenis, J. The Ras-ERK and PI3K-mTOR pathways: cross-talk and compensation. Trends Biochem. Sci. 36, 320–328 (2011).

    CAS  Article  Google Scholar 

  39. 39.

    Bedard, P. L. et al. A phase Ib dose-escalation study of the oral pan-PI3K inhibitor buparlisib (BKM120) in combination with the oral MEK1/2 inhibitor trametinib (GSK1120212) in patients with selected advanced solid tumors. Clin. Cancer Res. 21, 730–738 (2015).

    CAS  Article  Google Scholar 

  40. 40.

    Ridley, A. J. et al. Cell migration: integrating signals from front to back. Science 302, 1704–1709 (2003).

    CAS  Article  Google Scholar 

  41. 41.

    Toker, A. & Yoeli-Lerner, M. AKT signaling and cancer: surviving but not moving on. Cancer Res. 66, 3963–3966 (2006).

    CAS  Article  Google Scholar 

  42. 42.

    Huang, C., Jacobson, K. & Schaller, M. D. MAP kinases and cell migration. J. Cell Sci. 117, 4619–4628 (2004).

    CAS  Article  Google Scholar 

  43. 43.

    Cheng, H. et al. PIK3CAH1047R and Her2 initiated mammary tumors escape PI3K dependency by compensatory activation of MEK-ERK signaling. Oncogene 35, 2961–2970 (2016).

    CAS  Article  Google Scholar 

  44. 44.

    Hoeflich, K. P. et al. In vivo antitumor activity of MEK and phosphatidylinositol 3-kinase inhibitors in basal-like breast cancer models. Clin. Cancer Res. 15, 4649–4664 (2009).

    CAS  Article  Google Scholar 

  45. 45.

    Butler, D. E. et al. Inhibition of the PI3K/AKT/mTOR pathway activates autophagy and compensatory Ras/Raf/MEK/ERK signalling in prostate cancer. Oncotarget 8, 56698–56713 (2017).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Ebi, H. et al. PI3K regulates MEK/ERK signaling in breast cancer via the Rac-GEF, P-Rex1. Proc. Natl Acad. Sci. USA 110, 21124–21129 (2013).

    CAS  Article  Google Scholar 

  47. 47.

    Paul, C. D. et al. Interplay of the physical microenvironment, contact guidance, and intracellular signaling in cell decision making. FASEB J. 30, 2161–2170 (2016).

    CAS  Article  Google Scholar 

  48. 48.

    Zabransky, D. J. et al. HER2 missense mutations have distinct effects on oncogenic signaling and migration. Proc. Natl Acad. Sci. USA 112, E6205–E6214 (2015).

    CAS  Article  Google Scholar 

  49. 49.

    Sflomos, G. et al. A preclinical model for ERα-positive breast cancer points to the epithelial microenvironment as determinant of luminal phenotype and hormone response. Cancer Cell 29, 407–422 (2016).

    CAS  Article  Google Scholar 

  50. 50.

    Jiang, Y., Woosley, A. N., Sivalingam, N., Natarajan, S. & Howe, P. H. Cathepsin-B-mediated cleavage of Disabled-2 regulates TGF-β-induced autophagy. Nat. Cell Biol. 18, 851–863 (2016).

    CAS  Article  Google Scholar 

  51. 51.

    Rizwan, A. et al. Breast cancer cell adhesome and degradome interact to drive metastasis. NPJ Breast Cancer 1, 15017 (2015).

    CAS  Article  Google Scholar 

  52. 52.

    Wiegmans, A. P. et al. Rad51 supports triple negative breast cancer metastasis. Oncotarget 5, 3261–3272 (2014).

    Article  Google Scholar 

  53. 53.

    Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    CAS  Article  Google Scholar 

  54. 54.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  Article  Google Scholar 

  55. 55.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

  56. 56.

    Huang, dW., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37, 1–13 (2009).

    Article  Google Scholar 

  57. 57.

    Huang, dW., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    CAS  Article  Google Scholar 

  58. 58.

    DeRose, Y. S. et al. Patient-derived models of human breast cancer: protocols for in vitro and in vivo applications in tumor biology and translational medicine. Curr. Protoc. Pharmacol. 60, 14.23.1–14.23.43 (2013).

    Google Scholar 

  59. 59.

    Shea, D. J., Li, Y. W., Stebe, K. J. & Konstantopoulos, K. E-selectin-mediated rolling facilitates pancreatic cancer cell adhesion to hyaluronic acid. FASEB J. 31, 5078–5086 (2017).

    CAS  Article  Google Scholar 

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This line of research was supported by the National Cancer Institute through grants R01-CA183804 (K.K., A.K.-K., S.S.M.), R01-CA216855 (K.K.), R01-CA154624 (S.S.M.), R01-CA174385 (N.V.) and K01-CA166576 (M.I.V.), as well as by CPRIT RP180466 (N.V.), MRA Award 509800 (N.V.), CDMRP CA160591 (N.V.) and Department of Defense grant W81XWH-17-1-0246 (V.K.B.). M.I.V. was also supported by a Research Scholar Grant, RSG-18-028-01-CSM, from the American Cancer Society.

Author information




C.L.Y., C.D.P. and K.K. designed the study. C.L.Y. performed experiments, interpreted the data and wrote the manuscript. K.N.T., C.D.P., M.I.V. and P.M. contributed to design the study, performed experiments and interpreted the data. A.M. and V.K.B. helped to design, perform and analyse the RNA sequencing experiments. D.J.S. and K.M.M. performed select experiments. A.C.C. wrote code and used it to analyse data. N.V., A.K.-K. and S.S.M. interpreted data, provided critical insights and edited the manuscript. K.K. designed and supervised the study, and wrote the manuscript.

Corresponding author

Correspondence to Konstantinos Konstantopoulos.

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Competing interests

The PTEN−/− cells are licensed to Horizon Discovery Ltd (Cambridge, UK). M.I.V receives compensation for the sale of these cells. MAqCI is the subject of US Utility Patent applications 15/780,768 and 14/906,055.

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Supplementary information

Supplementary Information

Supplementary figures, tables and video legends.

Reporting Summary

Supplementary Dataset 1

Genes upregulated by migratory compared with unsorted MDA-MB-231 cells.

Supplementary Dataset 2

Genes downregulated by migratory compared with unsorted MDA-MB-231 cells.

Supplementary Dataset 3

Statistical tests.

Supplementary Video 1

Definition of migratory and non-migratory cells in MAqCI.

Supplementary Video 2

Non-migratory MCF7 breast cancer cells in MAqCI.

Supplementary Video 3

Migration of breast cancer cells obtained from patient-derived xenografts in MAqCI.

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Yankaskas, C.L., Thompson, K.N., Paul, C.D. et al. A microfluidic assay for the quantification of the metastatic propensity of breast cancer specimens. Nat Biomed Eng 3, 452–465 (2019). https://doi.org/10.1038/s41551-019-0400-9

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