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Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning

Nature Biomedical Engineering volume 2, pages 761–772 (2018)Cite this article

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Abstract

The risk stratification of prostate cancer and breast cancer tumours from patients relies on histopathology, selective genomic testing, or on other methods employing fixed formalin tissue samples. However, static biomarker measurements from bulk fixed-tissue samples provide limited accuracy and actionability. Here, we report the development of a live-primary-cell phenotypic-biomarker assay with single-cell resolution, and its validation with prostate cancer and breast cancer tissue samples for the prediction of post-surgical adverse pathology. The assay includes a collagen-I/fibronectin extracellular-matrix formulation, dynamic live-cell biomarkers, a microfluidic device, machine-vision analysis and machine-learning algorithms, and generates predictive scores of adverse pathology at the time of surgery. Predictive scores for the risk stratification of 59 prostate cancer patients and 47 breast cancer patients, with values for area under the curve in receiver-operating-characteristic curves surpassing 80%, support the validation of the assay and its potential clinical applicability for the risk stratification of cancer patients.

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Fig. 1: Workflow for the risk stratification of patients via surgical adverse-pathology features using the live-primary-cell phenotypic-biomarker assay (STRAT-AP) and patient-sample characteristics of the clinical study.
Fig. 2: Phenotypic (cellular and molecular) biomarkers measured via sequential live-cell imaging and fixed-cell imaging in a standardized microfluidic environment.
Fig. 3: Quantification of automated machine-vision biomarkers informs random-forest decision trees for the stratification of single cells and the prediction of surgical pathology features.
Fig. 4: Machine-learning statistical algorithms predict specific surgical adverse-pathology features in blinded test-sample sets.
Fig. 5: ROC curve analysis of predictions of surgical adverse-pathology features.
Fig. 6: ROC curve analysis for predicting groups of specific surgical adverse-pathology features generates scores that predict cancer severity and risk-stratify patients with high levels of sensitivity and specificity.

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Data availability

All data supporting the findings of this study are available within the paper and its Supplementary Information. Anonymized biomarker quantifications are available upon request from the corresponding author.

References

  1. Siegel, R., Ma, J., Zou, Z. & Jemal, A. Cancer statistics, 2014. CA Cancer J. Clin. 64, 9–29 (2014).

    Article  PubMed  Google Scholar 

  2. Bell, N.et al. Recommendations on screening for prostate cancer with the prostate-specific antigen test. CMAJ 186, 1225–1234 2014).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Mariotto, A. B. et al. Cancer survival: an overview of measures, uses, and interpretation. J. Natl Cancer Inst. Monogr. 2014, 145–186 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Shieh, Y. et al. Population-based screening for cancer: hope and hype. Nat. Rev. Clin. Oncol. 13, 550–565 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. U.S. Cancer Statistics Working Group United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report http://www.cdc.gov/uscs (U.S. Department of Health and Human Services, 2016).

  6. Canfield, S. E. et al. A guide for clinicians in the evaluation of emerging molecular diagnostics for newly diagnosed prostate cancer. Rev. Urol. 16, 172–180 (2014).

    PubMed  PubMed Central  Google Scholar 

  7. Dahabreh, I. J. et al. Core Needle and Open Surgical Biopsy for Diagnosis of Breast Lesions: An Update to the 2009 Report AHRQ Comparative Effectiveness Reviews (Agency for Healthcare Research and Quality, 2014).

  8. Alvarado, M., Ozanne, E. & Esserman, L. Overdiagnosis and overtreatment of breast cancer. Am. Soc. Clin. Oncol. Educ. Book 2012, e40–e45 (2012).

    Article  Google Scholar 

  9. Evans, A. & Vinnicombe, S. Overdiagnosis in breast imaging. Breast 31, 270–273 (2017).

    Article  PubMed  Google Scholar 

  10. Hugosson, J. & Carlsson, S. Overdetection in screening for prostate cancer. Curr. Opin. Urol. 24, 256–263 (2014).

    Article  PubMed  Google Scholar 

  11. Klotz, L. Cancer overdiagnosis and overtreatment. Curr. Opin. Urol. 22, 203–209 (2012).

    Article  PubMed  Google Scholar 

  12. Lebeau, A. & Kuhn, T. Updates in the treatment of ductal carcinoma in situ of the breast. Curr. Opin. Obstet. Gynecol. 28, 49–58 (2016).

    PubMed  Google Scholar 

  13. Strope, S. A. & Andriole, G. L. Prostate cancer screening: current status and future perspectives. Nat. Rev. Urol. 7, 487–493 (2010).

    Article  PubMed  Google Scholar 

  14. Verma, M., Patel, P. & Verma, M. Biomarkers in prostate cancer epidemiology. Cancers 3, 3773–3798 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Koren, S. & Bentires-Alj, M. Breast tumor heterogeneity: source of fitness, hurdle for therapy. Mol. Cell 60, 537–546 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Marusyk, A., Almendro, V. & Polyak, K. Intra-tumour heterogeneity: a looking glass for cancer? Nat. Rev. Cancer 12, 323–334 (2012).

    Article  CAS  PubMed  Google Scholar 

  17. Zbuk, K. M. & Eng, C. Cancer phenomics: RET and PTEN as illustrative models. Nat. Rev. Cancer 7, 35–45 (2007).

    Article  CAS  PubMed  Google Scholar 

  18. Albala, D., Knopf, K. & Sant, G. Phenotypic cancer biomarkers-future role in precision oncology?. NPJ Precision Oncology 1, 21 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Chander, A. et al. Rapid and short-term extra-cellular matrix-mediated in vitro culturing of tumor and non-tumor human primary prostate cells from fresh radical prostatectomy tissue. Urology 105, 91–100 (2017).

    Article  PubMed  Google Scholar 

  20. Chander, A. C. et al. Systems, devices and methods for microfluidic culturing, manipulation and analysis of tissues and cells. US patent US20130149724A1 (2013).

  21. Chander, A. C. et al. Systems, devices and methods for microfluidic culturing, manipulation and analysis of tissues and cells. US patent US20160272934A1 (2014).

  22. Chander, A. C. Systems, methods and devices for measuring growth/oncogenic and migration/metastatic potential. US patent US20130237453A1 (2011).

  23. Chander, A. C. Integrin-Linked Kinase, ECM Compostion and Substrate Rigidity Regulate Focal Adhesion—Actin Coupling, Modulating Survival, Proliferation andMigration: Towards a Biophysical Cancer Biomarker. PhD thesis, Columbia Univ. (2012).

  24. Abdeen, A. A., Lee, J. & Kilian, K. A. Capturing extracellular matrix properties in vitro: microengineering materials to decipher cell and tissue level processes. Exp. Biol. Med. 241, 930–938 (2016).

    Article  CAS  Google Scholar 

  25. Kacsinta, A. D. et al. Intracellular modifiers of integrin alpha 6p production in aggressive prostate and breast cancer cell lines. Biochem. Biophys. Res. Commun. 454, 335–340 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Chander, A. C., Su, W. R. & Varsanik, J. S. Cell imaging and analysis to differentiate clinically relevant sub-populations of cells. Patent WO2016138041A2 (2015).

  27. Cuzick, J. et al. Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study. Lancet Oncol. 12, 245–255 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Klein, E. A. et al. A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling. Eur. Urol. 66, 550–560 (2014).

    Article  PubMed  Google Scholar 

  29. Little, J. et al. Multigene panels in prostate cancer risk assessment: a systematic review. Genet. Med. 18, 535–544 (2016).

    Article  CAS  PubMed  Google Scholar 

  30. Mendhiratta, N., Meng, X. & Taneja, S. S. Using multiparametric MRI to ‘personalize’ biopsy for men. Curr. Opin. Urol. 25, 498–503 (2015).

    Article  PubMed  Google Scholar 

  31. Ross, A. E. et al. A genomic classifier predicting metastatic disease progression in men with biochemical recurrence after prostatectomy. Prostate Cancer Prostatic. Dis. 17, 64–69 (2014).

    Article  CAS  PubMed  Google Scholar 

  32. Scarpato, K. R. & Barocas, D. A. Use of mpMRI in active surveillance for localized prostate cancer. Urol. Oncol. 34, 320–325 (2016).

    Article  PubMed  Google Scholar 

  33. Sternberg, I. A., Vela, I. & Scardino, P. T. Molecular profiles of prostate cancer: to treat or not to treat. Annu. Rev. Med. 67, 119–135 (2016).

    Article  CAS  PubMed  Google Scholar 

  34. Zhuang, L. & Johnson, M. T. How precisely can prostate cancer be managed? Int. Neurourol. J. 20, S120–S130 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wong, L. M. et al. Evaluation of models predicting insignificant prostate cancer to select men for active surveillance of prostate cancer. Prostate Cancer Prostatic Dis. 18, 137–143 (2015).

    Article  CAS  PubMed  Google Scholar 

  36. Tosoian, J. J., Carter, H. B., Lepor, A. & Loeb, S. Active surveillance for prostate cancer: current evidence and contemporary state of practice. Nat. Rev. Urol. 13, 205–215 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Street, C. A. & Bryan, B. A. Rho kinase proteins—pleiotropic modulators of cell survival and apoptosis. Anticancer Res. 31, 3645–3657 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Wirtz, D., Konstantopoulos, K. & Searson, P. C. The physics of cancer: the role of physical interactions and mechanical forces in metastasis. Nat. Rev. Cancer 11, 512–522 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Kang, J. et al. Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines. Nat. Biotechnol. 34, 70–77 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Almendro, V., Marusyk, A. & Polyak, K. Cellular heterogeneity and molecular evolution in cancer. Annu. Rev. Pathol. 8, 277–302 (2013).

    Article  CAS  PubMed  Google Scholar 

  41. Cross, S. E., Jin, Y. S., Rao, J. & Gimzewski, J. K. Nanomechanical analysis of cells from cancer patients. Nat. Nanotech. 2, 780–783 (2007).

    Article  CAS  Google Scholar 

  42. Akobeng, A. K. Understanding diagnostic tests 1: sensitivity, specificity and predictive values. Acta Paediatr. 96, 338–341 (2007).

    Article  PubMed  Google Scholar 

  43. Akobeng, A. K. Understanding diagnostic tests 3: receiver operating characteristic curves. Acta Paediatr. 96, 644–647 (2007).

    Article  PubMed  Google Scholar 

  44. Akobeng, A. K. Understanding diagnostic tests 2: likelihood ratios, pre- and post-test probabilities and their use in clinical practice. Acta Paediatr. 96, 487–491 (2007).

    Article  PubMed  Google Scholar 

  45. Liu, A. Y. & True, L. D. Characterization of prostate cell types by CD cell surface molecules. Am. J. Pathol. 160, 37–43 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Signoretti, S. & Loda, M. Defining cell lineages in the prostate epithelium. Cell Cycle 5, 138–141 (2006).

    Article  CAS  PubMed  Google Scholar 

  47. Criminisi, A. & Shotton, J. Decision Forests for Computer Vision and Medical Image Analysis (Springer, New York, NY, 2013).

    Chapter  Google Scholar 

  48. Ho, T. K. Proceedings of 3rd International Conference on Document Analysis and Recognition Vol. 2, A1–A7 (IEEE, Montreal, 1995)..

  49. Ho, T. K. The random subspace method for constructing decision forests. IEEE. Trans. Pattern. Anal. Mach. Intell. 20, 832–844 (1998).

    Article  Google Scholar 

Download references

Acknowledgements

Tissue samples were provided by Lahey Hospital and Medical Center, Department of Cancer Research, American Medical Professionals of New York, by Urology Place of San Antonio and by the NCI Cooperative Human Tissue Network (CHTN). The authors thank S.K. Sia for a thoughtful review of the manuscript and P. Chaturvedi, R. Gottileb and M.B. Lisman for discussions, as well as M. Foroohar, S. Zappala, H. Rashid, V. Mouraviev, K. Christ, T.B. Sullivan and N. Kella. M.S.M. thanks J.A. Manak, C.A. Manak, G.H. Manak, P.L. Manak, L.L. Manak and P.W. Manak for support. J.S.V. thanks S.H. Varsanik for support. A.C.C. thanks C.S. Chandersekaran, A.C. Chandersekaran, A.B. Cravens Chander and I.E.A. Chander for support.

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Author notes

  1. These authors contributed equally: Michael S. Manak, Jonathan S. Varsanik.

Authors and Affiliations

  1. Cellanyx Diagnostics, Beverly, MA, USA

    Michael S. Manak, Jonathan S. Varsanik, Brad J. Hogan, Matt J. Whitfield, Wendell R. Su, Nikhil Joshi, Nicolai Steinke, Andrew Min, Delaney Berger, Robert J. Saphirstein, Gauri Dixit, Thiagarajan Meyyappan & Ashok C. Chander

  2. Anoixis Corporation , Natick, MA, USA

    Hui-May Chu

  3. Cancer Commons, Los Altos, CA, USA

    Kevin B. Knopf

  4. Associated Medical Professionals of New York, New York, NY, USA

    David M. Albala

  5. Department of Urology, Tufts University School of Medicine, Boston, MA, USA

    Grannum R. Sant

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Contributions

M.S.M. and J.S.V. carried out technology development, experimental work and data analysis. B.J.H., M.J.W. and W.R.S. performed technology development and experimental work. N.J., R.J.S., G.D. and T.M. carried out experimental work and data analysis. N.S. acquired samples and performed data analysis. A.M. and D.B. carried out sample acquisition. H.M.C. conducted data analysis and contributed to writing the manuscript. K.B.K. and D.M.A. provided clinical guidance and contributed to writing the manuscript. G.R.S. provided clinical guidance, data analysis and contributed to writing the manuscript. A.C.C. provided technology conception, technology development and project planning, and carried out experimental work and data analysis.

Corresponding author

Correspondence to Ashok C. Chander.

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Competing interests

The authors declare the following competing financial interests: M.S.M., J.S.V., M.J.W., W.R.S., N.J., N.S., A.M., D.B., R.J.S., G.D., T.M., H.M.C., K.B.K., G.R.S. and A.C.C.: Cellanyx Diagnostics, stock options. B.J.H.: Cellanyx Diagnostics, consultant stock options. D.M.A.: Genomic Health, speaker; Myriad Genetics, speaker; Cellanyx Diagnostics, stock options; Applied Medical, stock options. The following patents WO2016138041, WO2013075145 and WO201204826921,22,26 cover aspects of the technology presented in this paper.

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Manak, M.S., Varsanik, J.S., Hogan, B.J. et al. Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning. Nat Biomed Eng 2, 761–772 (2018). https://doi.org/10.1038/s41551-018-0285-z

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