Supplementary Issue: Sequencing Platform Modeling and Analysis Prognostic Gene Signature Identification Using Causal Structure Learning: Applications in Kidney Cancer

Identification of molecular-based signatures is one of the critical steps toward finding therapeutic targets in cancer. In this paper, we propose methods to discover prognostic gene signatures under a causal structure learning framework across the whole genome. The causal structures are represented by directed acyclic graphs (DAGs), wherein we construct gene-specific network modules that constitute a gene and its corresponding regulators. The modules are then subsequently used to correlate with survival times, thus, allowing for a network-oriented approach to gene selection to adjust for potential confounders, as opposed to univariate (gene-by-gene) approaches. Our methods are motivated by and applied to a clear cell renal cell carcinoma (ccRCC) study from The Cancer Genome Atlas (TCGA) where we find several prognostic genes associated with cancer progression - some of which are novel while others confirm existing findings.

[1]  S. Arber,et al.  Etv4 and Etv5 are required downstream of GDNF and Ret for kidney branching morphogenesis , 2009, Nature Genetics.

[2]  Peter Bühlmann,et al.  Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm , 2007, J. Mach. Learn. Res..

[3]  Wei Sun,et al.  PenPC: A two‐step approach to estimate the skeletons of high‐dimensional directed acyclic graphs , 2014, Biometrics.

[4]  Rork Kuick,et al.  Convergence of the ZMIZ1 and NOTCH1 pathways at C-MYC in acute T lymphoblastic leukemias. , 2013, Cancer research.

[5]  Tom Burr,et al.  Causation, Prediction, and Search , 2003, Technometrics.

[6]  N. Meinshausen,et al.  High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.

[7]  Y. De Launoit,et al.  The PEA3 group of ETS-related transcription factors. Role in breast cancer metastasis. , 2000, Advances in experimental medicine and biology.

[8]  D. Madigan,et al.  A characterization of Markov equivalence classes for acyclic digraphs , 1997 .

[9]  The PEA3 group of ETS-related transcription factors. Role in breast cancer metastasis. , 2000, Advances in experimental medicine and biology.

[10]  Tian-Li Wang,et al.  The emerging roles of ARID1A in tumor suppression , 2014, Cancer biology & therapy.

[11]  Christopher Meek,et al.  Causal inference and causal explanation with background knowledge , 1995, UAI.

[12]  Steven J. M. Jones,et al.  Comprehensive molecular characterization of clear cell renal cell carcinoma , 2013, Nature.

[13]  S. Varambally,et al.  Characterization of TMPRSS2:ETV5 and SLC45A3:ETV5 gene fusions in prostate cancer. , 2008, Cancer research.

[14]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

[15]  J. Cheville,et al.  Histological subtype is an independent predictor of outcome for patients with renal cell carcinoma. , 2010, The Journal of urology.

[16]  Jiahua Chen,et al.  Extended Bayesian information criteria for model selection with large model spaces , 2008 .

[17]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[18]  Min Chen,et al.  Comparing Statistical Methods for Constructing Large Scale Gene Networks , 2012, PloS one.

[19]  David Warde-Farley,et al.  Dynamic modularity in protein interaction networks predicts breast cancer outcome , 2009, Nature Biotechnology.

[20]  John T. Wei,et al.  The role of SPINK1 in ETS rearrangement-negative prostate cancers. , 2008, Cancer cell.

[21]  M. Gerstein,et al.  The GENCODE pseudogene resource , 2012, Genome Biology.

[22]  Po-Ling Loh,et al.  High-dimensional learning of linear causal networks via inverse covariance estimation , 2013, J. Mach. Learn. Res..

[23]  C. Cole,et al.  COSMIC: High‐Resolution Cancer Genetics Using the Catalogue of Somatic Mutations in Cancer , 2016, Current protocols in human genetics.

[24]  Lan V. Zhang,et al.  Evidence for dynamically organized modularity in the yeast protein–protein interaction network , 2004, Nature.

[25]  M. Takahashi,et al.  The GDNF/RET signaling pathway and human diseases. , 2001, Cytokine & growth factor reviews.

[26]  S. Artavanis-Tsakonas,et al.  Notch signaling: cell fate control and signal integration in development. , 1999, Science.

[27]  Daniel B. Mark,et al.  TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .

[28]  Bernhard Schölkopf,et al.  Kernel-based Conditional Independence Test and Application in Causal Discovery , 2011, UAI.

[29]  A. G. de la Fuente.  From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases. , 2010, Trends in genetics : TIG.

[30]  Steven J. M. Jones,et al.  Comprehensive molecular characterization of clear cell renal cell carcinoma , 2013, Nature.

[31]  Vishal N. Patel,et al.  Network Signatures of Survival in Glioblastoma Multiforme , 2013, PLoS Comput. Biol..

[32]  Leng Han,et al.  Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types , 2014, Nature Communications.

[33]  C. Roberts,et al.  Functional epigenetics approach identifies BRM/SMARCA2 as a critical synthetic lethal target in BRG1-deficient cancers , 2014, Proceedings of the National Academy of Sciences.

[34]  Gyan Bhanot,et al.  Molecular Stratification of Clear Cell Renal Cell Carcinoma by Consensus Clustering Reveals Distinct Subtypes and Survival Patterns. , 2010, Genes & cancer.

[35]  S. Manna,et al.  Suppression of renal cell carcinoma growth by inhibition of Notch signaling in vitro and in vivo. , 2008, The Journal of clinical investigation.

[36]  Diego Colombo,et al.  Order-independent constraint-based causal structure learning , 2012, J. Mach. Learn. Res..

[37]  J. Strominger,et al.  A gene pair from the human major histocompatibility complex encodes large proline-rich proteins with multiple repeated motifs and a single ubiquitin-like domain. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[38]  Hong-zhao Li,et al.  High-Level Expression of Notch1 Increased the Risk of Metastasis in T1 Stage Clear Cell Renal Cell Carcinoma , 2012, PloS one.

[39]  M. Maathuis,et al.  Estimating high-dimensional intervention effects from observational data , 2008, 0810.4214.

[40]  Steffen L. Lauritzen,et al.  Graphical models in R , 1996 .