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Patterns Of Basal Signaling Heterogeneity Can Distinguish Cellular Populations With Different Drug Sensitivities

D. Singh, Chin-Jen Ku, Chonlarat Wichaidit, R. Steininger, L. Wu, S. Altschuler
Published 2010 · Biology, Medicine

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Phenotypic heterogeneity has been widely observed in cellular populations. However, the extent to which heterogeneity contains biologically or clinically important information is not well understood. Here, we investigated whether patterns of basal signaling heterogeneity, in untreated cancer cell populations, could distinguish cellular populations with different drug sensitivities. We modeled cellular heterogeneity as a mixture of stereotyped signaling states, identified based on colocalization patterns of activated signaling molecules from microscopy images. We found that patterns of heterogeneity could be used to separate the most sensitive and resistant populations to paclitaxel within a set of H460 lung cancer clones and within the NCI‐60 panel of cancer cell lines, but not for a set of less heterogeneous, immortalized noncancer human bronchial epithelial cell (HBEC) clones. Our results suggest that patterns of signaling heterogeneity, characterized as ensembles of a small number of distinct phenotypic states, can reveal functional differences among cellular populations.
This paper references
10.1038/nmeth1032
Image-based multivariate profiling of drug responses from single cells
Lit-Hsin Loo (2007)
10.1038/415530a
Gene expression profiling predicts clinical outcome of breast cancer
L. J. Veer (2002)
10.1016/j.ccr.2008.08.014
Single-cell profiling identifies aberrant STAT5 activation in myeloid malignancies with specific clinical and biologic correlates.
N. Kotecha (2008)
10.1186/1471-2164-10-277
CellMiner: a relational database and query tool for the NCI-60 cancer cell lines
U. Shankavaram (2009)
10.1016/s0169-5002(86)80023-x
Establishment and identification of small cell lung cancer cell lines having classic and variant features.
D. Carney (1985)
10.1038/nature06385
Isolation of rare circulating tumour cells in cancer patients by microchip technology
S. Nagrath (2007)
10.1007/978-94-009-8219-2_4
Tumor heterogeneity.
Gloria H. Heppner (1984)
10.1016/S0169-5002(01)00471-8
The E-cadherin cell-cell adhesion complex and lung cancer invasion, metastasis, and prognosis.
R. Bremnes (2002)
10.1126/SCIENCE.1100709
Multidimensional Drug Profiling By Automated Microscopy
Z. Perlman (2004)
10.1162/jocn.1991.3.1.71
Eigenfaces for Recognition
M. Turk (1991)
10.1016/j.semcdb.2009.07.003
Cancer attractors: a systems view of tumors from a gene network dynamics and developmental perspective.
S. Huang (2009)
10.1126/science.1164382
An Integrated Genomic Analysis of Human Glioblastoma Multiforme
D. Parsons (2008)
A threedimensional model of differentiation of immortalized human bronchial epithelial cells
MB Vaughan (2006)
10.1038/nmeth939
Clonal isolation of hESCs reveals heterogeneity within the pluripotent stem cell compartment
M. Stewart (2006)
10.1038/nature08012
Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis
S. Spencer (2009)
10.1126/science.1158013
Variability and Robustness in T Cell Activation from Regulated Heterogeneity in Protein Levels
O. Feinerman (2008)
10.1073/pnas.0903028106
Automated high-dimensional flow cytometric data analysis
Saumyadipta Pyne (2009)
10.1182/BLOOD-2007-01-067785
Relapse in children with acute lymphoblastic leukemia involving selection of a preexisting drug-resistant subclone.
Seoyeon Choi (2007)
10.1016/J.CELL.2009.06.020
Hematopoietic Stem Cells Reversibly Switch from Dormancy to Self-Renewal during Homeostasis and Repair
A. Wilson (2009)
10.1126/SCIENCE.1099390
Bacterial Persistence as a Phenotypic Switch
N. Balaban (2004)
10.1126/science.1159397
Germline Allele-Specific Expression of TGFBR1 Confers an Increased Risk of Colorectal Cancer
L. Valle (2008)
A (1991) Eigenfaces for recognition
M Turk (1991)
Establishment and characterization of a human lung cancer cell line NCI-H460-LNM35 with consistent lymphogenous metastasis via both subcutaneous and orthotopic propagation.
K. Kozaki (2000)
10.1016/j.cell.2008.10.048
Hematopoietic Stem Cells Reversibly Switch from Dormancy to Self-Renewal during Homeostasis and Repair
A. Wilson (2008)
10.1016/j.cell.2005.05.026
SLAM Family Receptors Distinguish Hematopoietic Stem and Progenitor Cells and Reveal Endothelial Niches for Stem Cells
M. Kiel (2005)
10.1056/NEJMCIBR043143
Breast cancer--loss of PTEN predicts resistance to treatment.
P. Pandolfi (2004)
10.1038/nmeth.1375
An approach for extensibly profiling the molecular states of cellular subpopulations
Lit-Hsin Loo (2009)
10.1096/fj.07-8560rev
Cancer stem cell: target for anti‐cancer therapy
C. Tang (2007)
10.1038/ncb1173
Dual regulation of Snail by GSK-3β-mediated phosphorylation in control of epithelial–mesenchymal transition
B. Zhou (2004)
10.1080/10428190600733325
First among equals: The cancer cell hierarchy
C. Ichim (2006)
10.1126/science.1141478
MET Amplification Leads to Gefitinib Resistance in Lung Cancer by Activating ERBB3 Signaling
J. Engelman (2007)
10.1093/bioinformatics/17.12.1213
A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells
Michael V. Boland (2001)
Signaling heterogeneity can distinguish drug sensitivity DK Singh et al
10.1214/AOS/1176344136
Estimating the Dimension of a Model
G. Schwarz (1978)
10.1007/BF00047585
The significance of biological heterogeneity
H. Rubin (2004)
Mutagenic activity of tumor-associated macrophages in Salmonella typhimurium strains TA98 and TA 100.
A. Fulton (1984)
MUTATIONS OF BACTERIA FROM VIRUS SENSITIVITY TO VIRUS RESISTANCE’-’
LURIAS (2003)
Nongenetic origins of cell-to-cell variability in TRAIL-induced apoptosis
SL Spencer (2009)
10.1083/jcb.200904140
Heterogeneity in the physiological states and pharmacological responses of differentiating 3T3-L1 preadipocytes
Lit-Hsin Loo (2009)
10.1126/science.1160165
Dynamic Proteomics of Individual Cancer Cells in Response to a Drug
A. Cohen (2008)
10.1080/00401706.1992.10485257
Finding Groups in Data: An Introduction to Chster Analysis
A. Hadi (1991)
10.1016/J.MOLCEL.2007.11.009
GSK-3-mediated phosphorylation enhances Maf-transforming activity.
N. Rocques (2007)
10.1073/pnas.0807038105
Characterizing heterogeneous cellular responses to perturbations
M. Slack (2008)
Identification of a cancer stem cell in human brain tumors.
S. Singh (2003)
10.1186/1471-2105-8-32
Context based mixture model for cell phase identification in automated fluorescence microscopy
M. Wang (2006)
10.1126/science.1137455
Tunability and Noise Dependence in Differentiation Dynamics
Gürol M. Süel (2007)
10.1186/1471-2105-9-264
Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens
Z. Yin (2007)
10.1126/science.1164368
Core Signaling Pathways in Human Pancreatic Cancers Revealed by Global Genomic Analyses
S. Jones (2008)
10.1038/nature06965
Transcriptome-wide noise controls lineage choice in mammalian progenitor cells
H. H. Chang (2008)
Hematopoietic stem
A Wilson (2008)
10.1016/j.ccr.2008.07.002
Cancer cells display profound intra- and interline variation following prolonged exposure to antimitotic drugs.
K. Gascoigne (2008)
10.1073/PNAS.81.16.5126
Outline of a theory of cellular heterogeneity.
Walter M. Elsasser (1984)
10.1111/J.1432-0436.2006.00069.X
A three-dimensional model of differentiation of immortalized human bronchial epithelial cells.
Melville B Vaughan (2006)
10.1016/j.cell.2006.09.042
Tumor Morphology and Phenotypic Evolution Driven by Selective Pressure from the Microenvironment
A. Anderson (2006)
10.1002/jcp.20588
The MEK/MAPK pathway is involved in the resistance of breast cancer cells to the EGFR tyrosine kinase inhibitor gefitinib
N. Normanno (2006)
10.1038/nrg2556
Non-genetic heterogeneity — a mutation-independent driving force for the somatic evolution of tumours
A. Brock (2009)
10.1016/J.MOLMED.2006.11.001
The STAT3 oncogene as a predictive marker of drug resistance.
B. Barré (2007)
10.1126/science.1149200
Cancer Proliferation Gene Discovery Through Functional Genomics
M. Schlabach (2008)
10.1038/nature08282
Population context determines cell-to-cell variability in endocytosis and virus infection
B. Snijder (2009)
10.1038/nature03702
MicroRNA expression profiles classify human cancers
J. Lu (2005)
10.1002/path.2382
Heterogeneity of kinase inhibitor resistance mechanisms in GIST
B. Liegl (2008)
10.1126/SCIENCE.280.5365.895
The biochemical basis of an all-or-none cell fate switch in Xenopus oocytes.
J. Ferrell (1998)
invasion , metastasis , and prognosis
A Brock (2009)
Cancer stem cell: target for anticancer therapy
C Tang (2007)
10.1111/J.2517-6161.1977.TB01600.X
Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
A. Dempster (1977)
10.4161/cc.6.19.4914
Breast Tumor Heterogeneity: Cancer Stem Cells or Clonal Evolution?
L. Campbell (2007)
Bacterial persistence as a phenotypic
NQ Balaban (2004)
Molecular Systems Biology is an open-access journal published by European Molecular Biology Organization and
Variability and robustness in Tcell activation from regulated heterogeneity in protein
O Feinerman (2008)
10.1111/J.1745-3984.2003.TB01108.X
Modern Multidimensional Scaling: Theory and Applications
I. Borg (1997)
10.1158/1078-0432.CCR-05-0827
Activated Epidermal Growth Factor Receptor–Stat-3 Signaling Promotes Tumor Survival In vivo in Non–Small Cell Lung Cancer
E. Haura (2005)
This article is licensed under a Creative Commons Attribution- Noncommercial-Share Alike 3.0 Licence
Establishment and identification
DN Carney (1985)



This paper is referenced by
Application of multivariate statistics and machine learning to phenotypic imaging and chemical high-content data
J. Wildenhain (2016)
10.1002/cyto.a.22599
On comparing heterogeneity across biomarkers
R. Steininger (2015)
10.1038/nrm3044
Origins of regulated cell-to-cell variability
B. Snijder (2011)
10.1016/j.bpj.2014.07.025
Stochastic sensitivity analysis and kernel inference via distributional data.
B. Li (2014)
10.1042/BST0381179
GlaxoSmithKline Award Lecture. The O-GlcNAc modification: three-dimensional structure, enzymology and the development of selective inhibitors to probe disease.
G. Davies (2010)
10.1021/bi300846p
Measurement and modeling of signaling at the single-cell level.
S. Kolitz (2012)
10.1021/ac4002029
Microfluidic chemical cytometry of peptide degradation in single drug-treated acute myeloid leukemia cells.
Michelle L Kovarik (2013)
10.3109/10409238.2015.1135868
Computer vision for high content screening
Oren Z. Kraus (2016)
Modeling and parameter estimation for heterogeneous cell populations
J. Hasenauer (2013)
10.1007/s00438-017-1316-2
Mathematical deconvolution uncovers the genetic regulatory signal of cancer cellular heterogeneity on resistance to paclitaxel
I. Morilla (2017)
10.1186/1687-4153-2012-4
A visual analytics approach for models of heterogeneous cell populations
J. Hasenauer (2012)
10.1101/040113
Exploiting single-cell quantitative data to map genetic variants having probabilistic effects
Florent Chuffart (2016)
10.1039/c3ib40249e
Response of single leukemic cells to peptidase inhibitor therapy across time and dose using a microfluidic device.
Michelle L Kovarik (2014)
10.1172/JCI77767
An epigenetically distinct breast cancer cell subpopulation promotes collective invasion.
Jill M. Westcott (2015)
Characterizing Colorectal Cancer Cells and Their Interactions with Polymethoxyflavones by Raman Microscopy
Hua Zhang (2016)
10.1177/2472555216682725
Biologically Relevant Heterogeneity: Metrics and Practical Insights
A. Gough (2017)
10.1016/j.yexcr.2012.06.017
Revealing non-genetic adhesive variations in clonal populations by comparative single-cell force spectroscopy.
L. Dao (2012)
Metabolite profiling of defence-related secondary metabolites in tobacco cells, in response to ergosterol, a steroid from fungal membranes
Fidele Tugizimana (2012)
10.3929/ethz-a-010797620
Single-Cell Mass Spectrometry for High-Throughput Lipid Phenotyping of Chlamydomonas reinhardtii
Jasmin Krismer (2016)
10.1177/1087057115583037
A Multivariate Computational Method to Analyze High-Content RNAi Screening Data
Jonathan Rameseder (2015)
10.1371/journal.pone.0048943
Lentiviral Transduction of CD34+ Cells Induces Genome-Wide Epigenetic Modifications
Y. Yamagata (2012)
10.1016/j.compbiomed.2018.04.008
A hybrid mathematical modeling approach of the metabolic fate of a fluorescent sphingolipid analogue to predict cancer chemosensitivity
J. Molina-Mora (2018)
10.1242/jcs.139733
Fluctuation of Rac1 activity is associated with the phenotypic and transcriptional heterogeneity of glioma cells
H. Yukinaga (2014)
10.1371/journal.pcbi.1002901
Characterizing the Relationship between Steady State and Response Using Analytical Expressions for the Steady States of Mass Action Models
P. Loriaux (2013)
10.2174/157016461103140922164228
Oncogenic Signalling Networks and Polypharmacology as Paradigms to Cope with Cancer Heterogeneity
A. García (2014)
10.15252/msb.20145704
Orthogonal control of expression mean and variance by epigenetic features at different genomic loci
S. S. Dey (2015)
10.1371/journal.pone.0110714
Nonequilibrium Population Dynamics of Phenotype Conversion of Cancer Cells
J. Zhou (2014)
10.1038/aps.2015.92
Intra-tumor heterogeneity of cancer cells and its implications for cancer treatment
Xiao-xiao Sun (2015)
10.1016/bs.mcb.2018.09.010
High-throughput microfluidic single-cell trapping arrays for biomolecular and imaging analysis.
X. Li (2018)
10.1371/journal.pcbi.1003686
ODE Constrained Mixture Modelling: A Method for Unraveling Subpopulation Structures and Dynamics
J. Hasenauer (2014)
High-dimensional single cell analysis : mass cytometry, multi-parametric flow cytometry and bioinformatic techniques
H. Fienberg (2014)
10.1038/srep42383
An end-to-end software solution for the analysis of high-throughput single-cell migration data
Paola Masuzzo (2017)
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