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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.

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.

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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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