A Model for Cancer Tissue Heterogeneity

An important problem in the study of cancer is the understanding of the heterogeneous nature of the cell population. The clonal evolution of the tumor cells results in the tumors being composed of multiple subpopulations. Each subpopulation reacts differently to any given therapy. This calls for the development of novel (regulatory network) models, which can accommodate heterogeneity in cancerous tissues. In this paper, we present a new approach to model heterogeneity in cancer. We model heterogeneity as an ensemble of deterministic Boolean networks based on prior pathway knowledge. We develop the model considering the use of qPCR data. By observing gene expressions when the tissue is subjected to various stimuli, the compositional breakup of the tissue under study can be determined. We demonstrate the viability of this approach by using our model on synthetic data, and real-world data collected from fibroblasts.

[1]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[2]  Aniruddha Datta,et al.  From biological pathways to regulatory networks , 2010, CDC.

[3]  Thomas D. Schmittgen,et al.  Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. , 2001, Methods.

[4]  F Verrecchia,et al.  Blocking sp1 transcription factor broadly inhibits extracellular matrix gene expression in vitro and in vivo: implications for the treatment of tissue fibrosis. , 2001, The Journal of investigative dermatology.

[5]  S. Samson,et al.  Role of Sp1 in insulin regulation of gene expression. , 2002, Journal of molecular endocrinology.

[6]  Aniruddha Datta,et al.  Introduction to Genomic Signal Processing with Control , 2006 .

[7]  Nikolaus Rajewsky,et al.  Modeling the Initiation and Progression of Human Acute Leukemia in Mice , 2022 .

[8]  G. Pfeifer,et al.  In vivo protein-DNA interactions at the c-jun promoter: preformed complexes mediate the UV response , 1993, Molecular and cellular biology.

[9]  P. Nowell.  The clonal evolution of tumor cell populations. , 1976, Science.

[10]  A. A. Buzdin,et al.  Functional significance of a putative Sp1 transcription factor binding site in the survivin gene promoter , 2008, Biochemistry (Moscow).

[11]  Michael L. Bittner,et al.  Ratio statistics of gene expression levels and applications to microarray data analysis , 2002, Bioinform..

[12]  Kornelia Polyak,et al.  Breast Tumor Heterogeneity: Cancer Stem Cells or Clonal Evolution? , 2007, Cell cycle.

[13]  Peter D. Hoff,et al.  A First Course in Bayesian Statistical Methods , 2009 .

[14]  Zang Ai-hua,et al.  Stem Cells,Cancer and Cancer Stem Cells , 2005 .

[15]  M. Waters,et al.  Ternary complex factors Elk-1 and Sap-1a mediate growth hormone-induced transcription of egr-1 (early growth response factor-1) in 3T3-F442A preadipocytes. , 1999, Molecular endocrinology.

[16]  Sara Salinas,et al.  Immediate-early gene induction by the stresses anisomycin and arsenite in human osteosarcoma cells involves MAPK cascade signaling to Elk-1, CREB and SRF , 2003, Oncogene.

[17]  Edward R. Dougherty,et al.  Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks , 2002, Bioinform..

[18]  Tapabrata Maiti,et al.  Bayesian Data Analysis (2nd ed.) (Book) , 2004 .

[19]  D. W. Scott,et al.  Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .

[20]  Aniruddha Datta,et al.  Systems biology Advance Access publication December 30, 2010 Cancer therapy design based on pathway logic , 2022 .

[21]  Min Zou,et al.  A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data , 2005, Bioinform..

[22]  D. Levens,et al.  How the c-myc promoter works and why it sometimes does not. , 2008, Journal of the National Cancer Institute. Monographs.

[23]  James M. Bower,et al.  Computational modeling of genetic and biochemical networks , 2001 .

[24]  David W. Scott,et al.  Multivariate Density Estimation: Theory, Practice, and Visualization , 1992, Wiley Series in Probability and Statistics.

[25]  A Kumar,et al.  Critical role of p42/44(MAPK) activation in anisomycin and hepatocyte growth factor-induced LDL receptor expression: activation of Raf-1/Mek-1/p42/44(MAPK) cascade alone is sufficient to induce LDL receptor expression. , 1999, Journal of lipid research.

[26]  Andrew Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2006 .

[27]  J. Dick,et al.  Cancer stem cells: lessons from leukemia. , 2005, Trends in cell biology.

[28]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[29]  Yi Wang,et al.  Transcription factors Sp1 and Sp3 regulate basal transcription of the human IRF-3 gene. , 2012, Biochimie.

[30]  S Fuhrman,et al.  Reveal, a general reverse engineering algorithm for inference of genetic network architectures. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[31]  Y. Chen,et al.  Ratio-based decisions and the quantitative analysis of cDNA microarray images. , 1997, Journal of biomedical optics.