A microfluidic platform for systems pathology: multiparameter single-cell signaling measurements of clinical brain tumor specimens.

The clinical practice of oncology is being transformed by molecular diagnostics that will enable predictive and personalized medicine. Current technologies for quantitation of the cancer proteome are either qualitative (e.g., immunohistochemistry) or require large sample sizes (e.g., flow cytometry). Here, we report a microfluidic platform-microfluidic image cytometry (MIC)-capable of quantitative, single-cell proteomic analysis of multiple signaling molecules using only 1,000 to 2,800 cells. Using cultured cell lines, we show simultaneous measurement of four critical signaling proteins (EGFR, PTEN, phospho-Akt, and phospho-S6) within the oncogenic phosphoinositide 3-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR) signaling pathway. To show the clinical application of the MIC platform to solid tumors, we analyzed a panel of 19 human brain tumor biopsies, including glioblastomas. Our MIC measurements were validated by clinical immunohistochemistry and confirmed the striking intertumoral and intratumoral heterogeneity characteristic of glioblastoma. To interpret the multiparameter, single-cell MIC measurements, we adapted bioinformatic methods including self-organizing maps that stratify patients into clusters that predict tumor progression and patient survival. Together with bioinformatic analysis, the MIC platform represents a robust, enabling in vitro molecular diagnostic technology for systems pathology analysis and personalized medicine.

[1]  Bert Vogelstein,et al.  The role of companion diagnostics in the development and use of mutation-targeted cancer therapies , 2006, Nature Biotechnology.

[2]  C. James,et al.  PTEN mutation, EGFR amplification, and outcome in patients with anaplastic astrocytoma and glioblastoma multiforme. , 2001, Journal of the National Cancer Institute.

[3]  Paul S Mischel,et al.  Analysis of the phosphatidylinositol 3'-kinase signaling pathway in glioblastoma patients in vivo. , 2003, Cancer research.

[4]  Yasodha Natkunam,et al.  Nanofluidic proteomic assay for serial analysis of oncoprotein activation in clinical specimens , 2009, Nature Medicine.

[5]  Peter O. Krutzik,et al.  Intracellular phospho‐protein staining techniques for flow cytometry: Monitoring single cell signaling events , 2003, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[6]  Junbai Wang,et al.  Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study , 2002, BMC Bioinformatics.

[7]  S. Quake,et al.  Discovery of a hepatitis C target and its pharmacological inhibitors by microfluidic affinity analysis , 2008, Nature Biotechnology.

[8]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[9]  L. Chin,et al.  Malignant astrocytic glioma: genetics, biology, and paths to treatment. , 2007, Genes & development.

[10]  Sophia Adamia,et al.  Microfluidic chips for detecting the t(4;14) translocation and monitoring disease during treatment using reverse transcriptase-polymerase chain reaction analysis of IgH-MMSET hybrid transcripts. , 2007, Journal of Molecular Diagnostics.

[11]  Jonathan M Irish,et al.  Single Cell Profiling of Potentiated Phospho-Protein Networks in Cancer Cells , 2004, Cell.

[12]  C. García-echeverría,et al.  PI3K and mTOR inhibitors: a new generation of targeted anticancer agents. , 2009, Current opinion in cell biology.

[13]  S. Horvath,et al.  Neurosphere Formation Is an Independent Predictor of Clinical Outcome in Malignant Glioma , 2009, Stem cells.

[14]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[15]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[16]  D. Rimm,et al.  Immunohistochemistry and quantitative analysis of protein expression. , 2009, Archives of pathology & laboratory medicine.

[17]  Daniel H. Geschwind,et al.  Cancerous stem cells can arise from pediatric brain tumors , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[18]  K. Hunter,et al.  Mapping normal and cancer cell signalling networks: towards single-cell proteomics , 2006 .

[19]  D. Louis,et al.  Influence of unrecognized molecular heterogeneity on randomized clinical trials. , 2002, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[20]  E. Domany,et al.  Stem cell-related "self-renewal" signature and high epidermal growth factor receptor expression associated with resistance to concomitant chemoradiotherapy in glioblastoma. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[21]  R. Yu,et al.  Single-cell quantification of molecules and rates using open-source microscope-based cytometry , 2007, Nature Methods.

[22]  Nader Sanai,et al.  Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma. , 2010, World neurosurgery.

[23]  Garry P. Nolan,et al.  Simultaneous measurement of multiple active kinase states using polychromatic flow cytometry , 2002, Nature Biotechnology.

[24]  Hong Wu,et al.  PTEN tumor suppressor regulates p53 protein levels and activity through phosphatase-dependent and -independent mechanisms. , 2003, Cancer cell.

[25]  Hsian-Rong Tseng,et al.  An integrated microfluidic culture device for quantitative analysis of human embryonic stem cells. , 2009, Lab on a chip.

[26]  Zhifei Wang,et al.  Mammalian target of rapamycin signaling pathway contributes to glioma progression and patients' prognosis. , 2011, The Journal of surgical research.

[27]  Raymond R Tubbs,et al.  Assessment of the HER2 status in breast cancer by fluorescence in situ hybridization: a technical review with interpretive guidelines. , 2005, Human pathology.

[28]  Garry P Nolan,et al.  Fluorescent cell barcoding in flow cytometry allows high-throughput drug screening and signaling profiling , 2006, Nature Methods.

[29]  M. Berger,et al.  Dysregulation of PTEN and protein kinase B is associated with glioma histology and patient survival. , 2002, Clinical cancer research : an official journal of the American Association for Cancer Research.

[30]  Jesse V Jokerst,et al.  Nano-bio-chips for high performance multiplexed protein detection: determinations of cancer biomarkers in serum and saliva using quantum dot bioconjugate labels. , 2009, Biosensors & bioelectronics.

[31]  P. Kleihues,et al.  Predominant Expression of Mutant EGFR (EGFRvIII) is Rare in Primary Glioblastomas , 2004, Brain pathology.

[32]  C. Brennan,et al.  Glioblastoma Subclasses Can Be Defined by Activity among Signal Transduction Pathways and Associated Genomic Alterations , 2009, PloS one.

[33]  K. Jensen,et al.  Cells on chips , 2006, Nature.

[34]  S. Nelson,et al.  DNA-microarray analysis of brain cancer: molecular classification for therapy , 2004, Nature Reviews Neuroscience.

[35]  L. Hood,et al.  Integrated barcode chips for rapid, multiplexed analysis of proteins in microliter quantities of blood , 2008, Nature Biotechnology.

[36]  James R Heath,et al.  Nanotechnology and cancer. , 2008, Annual review of medicine.

[37]  Koji Yoshimoto,et al.  Molecular determinants of the response of glioblastomas to EGFR kinase inhibitors. , 2005, The New England journal of medicine.

[38]  Bo Huang,et al.  Counting Low-Copy Number Proteins in a Single Cell , 2007, Science.

[39]  Forest M White,et al.  Quantitative analysis of EGFRvIII cellular signaling networks reveals a combinatorial therapeutic strategy for glioblastoma , 2007, Proceedings of the National Academy of Sciences.

[40]  Elisa Lee,et al.  Statistical Methods for Survival Data Analysis: Lee/Survival Data Analysis , 2003 .

[41]  S. Horvath,et al.  Antitumor Activity of Rapamycin in a Phase I Trial for Patients with Recurrent PTEN-Deficient Glioblastoma , 2008, PLoS medicine.

[42]  Jonathan M Irish,et al.  Single-cell profiling identifies aberrant STAT5 activation in myeloid malignancies with specific clinical and biologic correlates. , 2008, Cancer cell.

[43]  T. Cloughesy,et al.  PTEN-Mediated Resistance to Epidermal Growth Factor Receptor Kinase Inhibitors , 2007, Clinical Cancer Research.

[44]  Lutgarde M. C. Buydens,et al.  Self- and Super-organizing Maps in R: The kohonen Package , 2007 .

[45]  C. James,et al.  Amplified and rearranged epidermal growth factor receptor genes in human glioblastomas reveal deletions of sequences encoding portions of the N- and/or C-terminal tails. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[46]  Andre Levchenko,et al.  High Content Cell Screening in a Microfluidic Device*S , 2009, Molecular & Cellular Proteomics.

[47]  Alok J. Saldanha,et al.  Java Treeview - extensible visualization of microarray data , 2004, Bioinform..

[48]  T. Cloughesy,et al.  Mammalian target of rapamycin inhibition promotes response to epidermal growth factor receptor kinase inhibitors in PTEN-deficient and PTEN-intact glioblastoma cells. , 2006, Cancer research.