A Genome-Wide Systematic Analysis Reveals Different and Predictive Proliferation Expression Signatures of Cancerous vs. Non-Cancerous Cells

Understanding cell proliferation mechanisms has been a long-lasting goal of the scientific community and specifically of cancer researchers. Previous genome-scale studies of cancer proliferation determinants have mainly relied on knockdown screens aimed to gauge their effects on cancer growth. This powerful approach has several limitations such as off-target effects, partial knockdown, and masking effects due to functional backups. Here we employ a complementary approach and assign each gene a cancer Proliferation Index (cPI) that quantifies the association between its expression levels and growth rate measurements across 60 cancer cell lines. Reassuringly, genes found essential in cancer gene knockdown screens exhibit significant positive cPI values, while tumor suppressors exhibit significant negative cPI values. Cell cycle, DNA replication, splicing and protein production related processes are positively associated with cancer proliferation, while cellular migration is negatively associated with it – in accordance with the well known “go or grow” dichotomy. A parallel analysis of genes' non-cancerous proliferation indices (nPI) across 224 lymphoblastoid cell lines reveals surprisingly marked differences between cancerous and non-cancerous proliferation. These differences highlight genes in the translation and spliceosome machineries as selective cancer proliferation-associated proteins. A cross species comparison reveals that cancer proliferation resembles that of microorganisms while non-cancerous proliferation does not. Furthermore, combining cancerous and non-cancerous proliferation signatures leads to enhanced prediction of patient outcome and gene essentiality in cancer. Overall, these results point to an inherent difference between cancerous and non-cancerous proliferation determinants, whose understanding may contribute to the future development of novel cancer-specific anti-proliferative drugs.

[1]  Anastasia Khvorova,et al.  Identification of genes that regulate epithelial cell migration using an siRNA screening approach , 2008, Nature Cell Biology.

[2]  Thomas D. Wu,et al.  Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. , 2006, Cancer cell.

[3]  Michael C. Ostrowski,et al.  MicroRNA-451 regulates LKB1/AMPK signaling and allows adaptation to metabolic stress in glioma cells. , 2010, Molecular cell.

[4]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[5]  D. Hanahan,et al.  Hallmarks of Cancer: The Next Generation , 2011, Cell.

[6]  Joshy George,et al.  Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. , 2006, Cancer research.

[7]  S. Coons,et al.  Dichotomy of astrocytoma migration and proliferation , 1996, International journal of cancer.

[8]  R. Gartenhaus,et al.  Targeting the translational machinery as a novel treatment strategy for hematologic malignancies. , 2010, Blood.

[9]  F. Clavel-Chapelon,et al.  Blood lipid and lipoprotein concentrations and colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition , 2011, Gut.

[10]  Erik L. L. Sonnhammer,et al.  InParanoid 7: new algorithms and tools for eukaryotic orthology analysis , 2009, Nucleic Acids Res..

[11]  Ji Luo,et al.  Cancer Proliferation Gene Discovery Through Functional Genomics , 2008, Science.

[12]  E. Hattingen,et al.  The “go or grow” potential of gliomas is linked to the neuropeptide processing enzyme carboxypeptidase E and mediated by metabolic stress , 2012, Acta Neuropathologica.

[13]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..

[14]  T. Ideker,et al.  Subnetwork-based analysis of chronic lymphocytic leukemia identifies pathways that associate with disease progression. , 2011, Blood.

[15]  C. Bakal,et al.  Genomic screening with RNAi: results and challenges. , 2010, Annual review of biochemistry.

[16]  Mats Lambe,et al.  Biomarker Discovery in Non–Small Cell Lung Cancer: Integrating Gene Expression Profiling, Meta-analysis, and Tissue Microarray Validation , 2012, Clinical Cancer Research.

[17]  L. Staudt,et al.  The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma. , 2003, Cancer cell.

[18]  C. Mathers,et al.  Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008 , 2010, International journal of cancer.

[19]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[20]  Douglas G Altman,et al.  The logrank test , 2004, BMJ : British Medical Journal.

[21]  Monica L. Mo,et al.  Global reconstruction of the human metabolic network based on genomic and bibliomic data , 2007, Proceedings of the National Academy of Sciences.

[22]  Christian A. Rees,et al.  Systematic variation in gene expression patterns in human cancer cell lines , 2000, Nature Genetics.

[23]  Dongsup Kim,et al.  Analysis of a genome-wide set of gene deletions in the fission yeast Schizosaccharomyces pombe , 2010, Nature Biotechnology.

[24]  P. Hall,et al.  An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Zhongming Zhao,et al.  TSGene: a web resource for tumor suppressor genes , 2012, Nucleic Acids Res..

[26]  Peter Bühlmann.  Regression shrinkage and selection via the Lasso: a retrospective (Robert Tibshirani): Comments on the presentation , 2011 .

[27]  Clémentine Dressaire,et al.  Growth rate regulated genes and their wide involvement in the Lactococcus lactis stress responses , 2008, BMC Genomics.

[28]  R. Shoemaker.  The NCI60 human tumour cell line anticancer drug screen , 2006, Nature Reviews Cancer.

[29]  S. Lowe,et al.  Altering Chemosensitivity by Modulating Translation Elongation , 2009, PloS one.

[30]  M. Meyerson,et al.  Recurrent Hemizygous Deletions in Cancers May Optimize Proliferative Potential , 2012, Science.

[31]  S. Itzkovitz,et al.  Functional atlas of the integrin adhesome , 2007, Nature Cell Biology.

[32]  M. Westphal,et al.  Cost of migration: invasion of malignant gliomas and implications for treatment. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[33]  E. Wiemer,et al.  The spliceosome as target for anticancer treatment , 2008, British Journal of Cancer.

[34]  A. Kornblihtt,et al.  The connection between splicing and cancer , 2006, Journal of Cell Science.

[35]  Yudong D. He,et al.  A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients. , 2005, Cancer research.

[36]  Ritsert C. Jansen,et al.  MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects , 2011, Bioinform..

[37]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[38]  F. Meric,et al.  Translation initiation in cancer: a novel target for therapy. , 2002, Molecular cancer therapeutics.

[39]  Matthew J. Brauer,et al.  Coordination of growth rate, cell cycle, stress response, and metabolic activity in yeast. , 2008, Molecular biology of the cell.

[40]  Optimization of Energy-Consuming Pathways towards Rapid Growth in HPV-Transformed Cells , 2007, PloS one.

[41]  Kevin R Brown,et al.  Essential gene profiles in breast, pancreatic, and ovarian cancer cells. , 2012, Cancer discovery.

[42]  D. Botstein,et al.  Diversity of gene expression in adenocarcinoma of the lung , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[43]  Chris Sander,et al.  CancerGenes: a gene selection resource for cancer genome projects , 2006, Nucleic Acids Res..

[44]  Carleen Cullinane,et al.  Inhibition of RNA polymerase I as a therapeutic strategy to promote cancer-specific activation of p53. , 2012, Cancer cell.

[45]  A. Hyman,et al.  Genome-scale RNAi profiling of cell division in human tissue culture cells , 2007, Nature Cell Biology.

[46]  Jason H. Moore,et al.  Characterization of microRNA expression levels and their biological correlates in human cancer cell lines. , 2007, Cancer research.

[47]  David Venet,et al.  Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome , 2011, PLoS Comput. Biol..

[48]  Van,et al.  A gene-expression signature as a predictor of survival in breast cancer. , 2002, The New England journal of medicine.

[49]  Norbert Perrimon,et al.  Applications of High-Throughput RNA Interference Screens to Problems in Cell and Developmental Biology , 2007, Genetics.

[50]  Matej Oresic,et al.  Integrated Systems and Technologies Metabolic Associations of Reduced Proliferation and Oxidative Stress in Advanced Breast Cancer , 2012 .

[51]  G. Evan,et al.  Proliferation, cell cycle and apoptosis in cancer , 2001, Nature.

[52]  Elliott Kieff,et al.  Genetic Analysis of Human Traits In Vitro: Drug Response and Gene Expression in Lymphoblastoid Cell Lines , 2008, PLoS genetics.

[53]  Ronald W. Davis,et al.  Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. , 1999, Science.

[54]  Eytan Domany,et al.  Classification of human astrocytic gliomas on the basis of gene expression: a correlated group of genes with angiogenic activity emerges as a strong predictor of subtypes. , 2003, Cancer research.

[55]  Jae K. Lee,et al.  A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery , 2007, Proceedings of the National Academy of Sciences.

[56]  E. Ben-Jacob,et al.  Bacterial survival strategies suggest rethinking cancer cooperativity. , 2012, Trends in microbiology.

[57]  J. Venables.  Aberrant and Alternative Splicing in Cancer , 2004, Cancer Research.

[58]  N. Hacohen,et al.  Highly parallel identification of essential genes in cancer cells , 2008, Proceedings of the National Academy of Sciences.

[59]  D. Ruggero.  Revisiting the Nucleolus: From Marker to Dynamic Integrator of Cancer Signaling , 2012, Science Signaling.

[60]  Gregory D. Schuler,et al.  Database resources of the National Center for Biotechnology Information: update , 2004, Nucleic acids research.

[61]  Anjana Rao,et al.  RNAi screening: tips and techniques , 2009, Nature Immunology.

[62]  J. Bergh,et al.  Interactions between immunity, proliferation and molecular subtype in breast cancer prognosis , 2013, Genome Biology.

[63]  Yudong D. He,et al.  A Gene-Expression Signature as a Predictor of Survival in Breast Cancer , 2002 .

[64]  Roded Sharan,et al.  Translation efficiency in humans: tissue specificity, global optimization and differences between developmental stages , 2010, Nucleic acids research.

[65]  A. Levine,et al.  Proliferation and tissue remodeling in cancer: the hallmarks revisited , 2012, Cell Death and Disease.

[66]  R. Tibshirani,et al.  Regression shrinkage and selection via the lasso: a retrospective , 2011 .

[67]  M. Eileen Dolan,et al.  Mixed Effects Modeling of Proliferation Rates in Cell-Based Models: Consequence for Pharmacogenomics and Cancer , 2012, PLoS genetics.