Biomolecular Events in Cancer Revealed by Attractor Metagenes

Mining gene expression profiles has proven valuable for identifying signatures serving as surrogates of cancer phenotypes. However, the similarities of such signatures across different cancer types have not been strong enough to conclude that they represent a universal biological mechanism shared among multiple cancer types. Here we present a computational method for generating signatures using an iterative process that converges to one of several precise attractors defining signatures representing biomolecular events, such as cell transdifferentiation or the presence of an amplicon. By analyzing rich gene expression datasets from different cancer types, we identified several such biomolecular events, some of which are universally present in all tested cancer types in nearly identical form. Although the method is unsupervised, we show that it often leads to attractors with strong phenotypic associations. We present several such multi-cancer attractors, focusing on three that are prominent and sharply defined in all cases: a mesenchymal transition attractor strongly associated with tumor stage, a mitotic chromosomal instability attractor strongly associated with tumor grade, and a lymphocyte-specific attractor.

[1]  M. Cronin,et al.  A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. , 2004, The New England journal of medicine.

[2]  Gyan Bhanot,et al.  Amplified Loci on Chromosomes 8 and 17 Predict Early Relapse in ER-Positive Breast Cancers , 2012, PloS one.

[3]  Benjamin M. Bolstad,et al.  affy - analysis of Affymetrix GeneChip data at the probe level , 2004, Bioinform..

[4]  Robert A. Weinberg,et al.  A checkpoint on the road to cancer , 1998, Nature.

[5]  Carsten O. Daub,et al.  Estimating mutual information using B-spline functions – an improved similarity measure for analysing gene expression data , 2004, BMC Bioinformatics.

[6]  S. Lindquist,et al.  Heat Shock Factor 1 Is a Powerful Multifaceted Modifier of Carcinogenesis , 2007, Cell.

[7]  S. Gabriel,et al.  Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. , 2010, Cancer cell.

[8]  Alan Mackay,et al.  Genomic analysis of the HER2/TOP2A amplicon in breast cancer and breast cancer cell lines , 2008, Laboratory Investigation.

[9]  Friedrich Buck,et al.  repp86: A human protein associated in the progression of mitosis. , 2003, Molecular cancer research : MCR.

[10]  J. Thiery.  Epithelial–mesenchymal transitions in tumour progression , 2002, Nature Reviews Cancer.

[11]  Zoltan Dezso,et al.  Genome-wide functional synergy between amplified and mutated genes in human breast cancer. , 2008, Cancer research.

[12]  D. Koller,et al.  From signatures to models: understanding cancer using microarrays , 2005, Nature Genetics.

[13]  Rui Chen,et al.  TWISTing Stemness, Inflammation, and Proliferation of Epithelial Ovarian Cancer Cells through MIR199A2/214 , 2010, Oncogene.

[14]  P. Hieter,et al.  The kinetochore and cancer: what's the connection? , 2005, Current opinion in cell biology.

[15]  Gavin D. Grant,et al.  Common markers of proliferation , 2006, Nature Reviews Cancer.

[16]  T. Eberlein.  A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer , 2006 .

[17]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..

[18]  F. Markowetz,et al.  The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups , 2012, Nature.

[19]  David Cameron,et al.  A stroma-related gene signature predicts resistance to neoadjuvant chemotherapy in breast cancer , 2009, Nature Medicine.

[20]  A. McCullough,et al.  Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing , 2013 .

[21]  Fumio Nomura,et al.  An essential role of alternative splicing of c-myc suppressor FUSE-binding protein-interacting repressor in carcinogenesis. , 2006, Cancer research.

[22]  M. Pencina,et al.  Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation , 2004, Statistics in medicine.

[23]  Dimitris Anastassiou,et al.  A Multi-Cancer Mesenchymal Transition Gene Expression Signature Is Associated with Prolonged Time to Recurrence in Glioblastoma , 2012, PloS one.

[24]  H. Lee,et al.  A novel function for HSF1-induced mitotic exit failure and genomic instability through direct interaction between HSF1 and Cdc20 , 2008, Oncogene.

[25]  Charles Theillet,et al.  What do we learn from HER2-positive breast cancer genomic profiles? , 2010, Breast Cancer Research.

[26]  Wenjun Guo,et al.  The Epithelial-Mesenchymal Transition Generates Cells with Properties of Stem Cells , 2008, Cell.

[27]  P. Spellman,et al.  Subtypes of Pancreatic Ductal Adenocarcinoma and Their Differing Responses to Therapy , 2011, Nature Medicine.

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

[29]  N. Dyson,et al.  RB: mitotic implications of a tumour suppressor , 2012, Nature Reviews Cancer.

[30]  Laura Lentini,et al.  CENPA overexpression promotes genome instability in pRb-depleted human cells , 2009, Molecular Cancer.

[31]  Dimitris Anastassiou,et al.  Human cancer cells express Slug-based epithelial-mesenchymal transition gene expression signature obtained in vivo , 2011, BMC Cancer.

[32]  Eytan Domany,et al.  Identification of a proliferation gene cluster associated with HPV E6/E7 expression level and viral DNA load in invasive cervical carcinoma , 2005, Oncogene.

[33]  J. Nevins,et al.  Mining gene expression profiles: expression signatures as cancer phenotypes , 2007, Nature Reviews Genetics.

[34]  J. Watkinson,et al.  Multi-cancer computational analysis reveals invasion-associated variant of desmoplastic reaction involving INHBA, THBS2 and COL11A1 , 2010, BMC Medical Genomics.

[35]  J. Vishwanatha,et al.  Novel gene C17orf37 in 17q12 amplicon promotes migration and invasion of prostate cancer cells , 2009, Oncogene.

[36]  Yung-Jen Chuang,et al.  A Novel Molecular Signature Identified by Systems Genetics Approach Predicts Prognosis in Oral Squamous Cell Carcinoma , 2011, PloS one.

[37]  Georges Bismuth,et al.  SLP-76-ADAP adaptor module regulates LFA-1 mediated costimulation and T cell motility , 2009, Proceedings of the National Academy of Sciences.

[38]  Jill P. Mesirov,et al.  Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data , 2003, Machine Learning.

[39]  Rocio Sotillo,et al.  Mitotic chromosomal instability and cancer: mouse modelling of the human disease , 2010, Nature Reviews Cancer.

[40]  Benjamin J. Raphael,et al.  Integrated Genomic Analyses of Ovarian Carcinoma , 2011, Nature.

[41]  Pablo Tamayo,et al.  Metagenes and molecular pattern discovery using matrix factorization , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[42]  Zoltan Szallasi,et al.  Jetset: selecting the optimal microarray probe set to represent a gene , 2011, BMC Bioinformatics.

[43]  J. Decaprio,et al.  The MuvB complex sequentially recruits B-Myb and FoxM1 to promote mitotic gene expression. , 2012, Genes & development.

[44]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[45]  Michael Mitzenmacher,et al.  Detecting Novel Associations in Large Data Sets , 2011, Science.

[46]  Dimitris Anastassiou,et al.  Integrated Analysis Reveals hsa-miR-142 as a Representative of a Lymphocyte-Specific Gene Expression and Methylation Signature , 2012, Cancer informatics.

[47]  P. A. Futreal,et al.  Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. , 2012, The New England journal of medicine.

[48]  E. Hay.  An overview of epithelio-mesenchymal transformation. , 1995, Acta anatomica.

[49]  Zoltan Szallasi,et al.  Paradoxical relationship between chromosomal instability and survival outcome in cancer. , 2011, Cancer research.

[50]  Nicholas D. Socci,et al.  Mad2-induced chromosome instability leads to lung tumor relapse after oncogene withdrawal , 2010, Nature.

[51]  Kristina Hanspers,et al.  Gene expression profiles during human CD4+ T cell differentiation. , 2004, International immunology.

[52]  Z. Szallasi,et al.  A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers , 2006, Nature Genetics.

[53]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[54]  M. J. van de Vijver,et al.  Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. , 2006, Journal of the National Cancer Institute.

[55]  Raghu Kalluri,et al.  The basics of epithelial-mesenchymal transition. , 2009, The Journal of clinical investigation.