An Integrative MuSiCO Algorithm: From the Patient-Specific Transcriptional Profiles to Novel Checkpoints in Disease Pathobiology

Strong efforts are invested in the field of cancer and other multifactorial diseases to evaluate the applicability of gene expression patterns for identification of novel disease-relevant checkpoints and nomination of promising biomarkers for disease and/or targets. Deciphering the disease complexity demands the implementation of a holistic approach, which covers the levels of the biological hierarchy from molecules to functional gene network(s) and biological pathways and further to disease (patho)mechanisms and clinical relevance. In this chapter we describe the systems biology-based integrative algorithm, named by us as MuSiCO/fromMultigeneSignature to Patient-OrientatedClinicalOutcome, and discuss its applicability for translational research. This innovative approach is based on the implementation of consecutive analytical modules integrating advanced gene expression profiling of clinical patient specimens, prognostic/predictive modeling, digital pathology, and systems biology. It consolidates in-depth expertise from diverse scientific and medical disciplines and hereby bridges systems biology and systems medicine to maximize the benefit of the patient.

[1]  Charles E McCulloch,et al.  Relaxing the rule of ten events per variable in logistic and Cox regression. , 2007, American journal of epidemiology.

[2]  J. Baak.  Manual of quantitative pathology in cancer diagnosis and prognosis , 1991 .

[3]  R. Tibshirani,et al.  Improvements on Cross-Validation: The 632+ Bootstrap Method , 1997 .

[4]  Stefan Michiels,et al.  Gene expression profiling: does it add predictive accuracy to clinical characteristics in cancer prognosis? , 2007, European journal of cancer.

[5]  R. Tibshirani.  The lasso method for variable selection in the Cox model. , 1997, Statistics in medicine.

[6]  Frank E. Harrell,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2001 .

[7]  S. Conticello.  Creative deaminases, self‐inflicted damage, and genome evolution , 2012, Annals of the New York Academy of Sciences.

[8]  Patrick Royston,et al.  Correcting for Optimistic Prediction in Small Data Sets , 2014, American journal of epidemiology.

[9]  T. Honjo,et al.  Class Switch Recombination and Hypermutation Require Activation-Induced Cytidine Deaminase (AID), a Potential RNA Editing Enzyme , 2000, Cell.

[10]  J. Concato,et al.  Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. , 1995, Journal of clinical epidemiology.

[11]  G. Heinze,et al.  Exploring the role of sphingolipid machinery during the epithelial to mesenchymal transition program using an integrative approach , 2016, Oncotarget.

[12]  G. Heinze,et al.  An unjustified benefit: immortal time bias in the analysis of time‐dependent events , 2018, Transplant international : official journal of the European Society for Organ Transplantation.

[13]  S. Shen-Orr,et al.  Computational deconvolution: extracting cell type-specific information from heterogeneous samples. , 2013, Current opinion in immunology.

[14]  Z. Trajanoski,et al.  Type, Density, and Location of Immune Cells Within Human Colorectal Tumors Predict Clinical Outcome , 2006, Science.

[15]  Etienne Becht,et al.  Cancer immune contexture and immunotherapy. , 2016, Current opinion in immunology.

[16]  N. Kakazu,et al.  Constitutive Expression of AID Leads to Tumorigenesis , 2003, The Journal of experimental medicine.

[17]  J. C. van Houwelingen,et al.  Predictive value of statistical models , 1990 .

[18]  T. Lumley,et al.  Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker , 2000, Biometrics.

[19]  C. Bachert,et al.  Activation-Induced Cytidine Deaminase (AID)-Associated Multigene Signature to Assess Impact of AID in Etiology of Diseases with Inflammatory Component , 2011, PloS one.

[20]  M. Schemper.  Predictive accuracy and explained variation , 2003, Statistics in medicine.

[21]  M. Svoboda,et al.  Activation-induced cytidine deaminase (AID) linking immunity, chronic inflammation, and cancer , 2012, Cancer Immunology, Immunotherapy.

[22]  Anne-Laure Boulesteix,et al.  Added predictive value of omics data: specific issues related to validation illustrated by two case studies , 2014, BMC Medical Research Methodology.

[23]  Thomas Agoritsas,et al.  Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. , 2011, Journal of clinical epidemiology.

[24]  P. J. Verweij,et al.  Penalized likelihood in Cox regression. , 1994, Statistics in medicine.

[25]  P. Zimmermann,et al.  AID/APOBEC-network reconstruction identifies pathways associated with survival in ovarian cancer , 2016, BMC Genomics.

[26]  Jean-Pierre Gillet,et al.  Multidrug Resistance–Linked Gene Signature Predicts Overall Survival of Patients with Primary Ovarian Serous Carcinoma , 2012, Clinical Cancer Research.

[27]  Anne-Laure Boulesteix,et al.  Subsampling versus bootstrapping in resampling‐based model selection for multivariable regression , 2016, Biometrics.

[28]  M. Svoboda,et al.  B Cells and Ectopic Follicular Structures: Novel Players in Anti-Tumor Programming with Prognostic Power for Patients with Metastatic Colorectal Cancer , 2014, PloS one.

[29]  Georg Heinze,et al.  Variable selection – A review and recommendations for the practicing statistician , 2018, Biometrical journal. Biometrische Zeitschrift.

[30]  C. Sautès-Fridman,et al.  The immune contexture in human tumours: impact on clinical outcome , 2012, Nature Reviews Cancer.

[31]  M. Schemper,et al.  Statistical controversies in clinical research: the importance of importance. , 2016, Annals of oncology : official journal of the European Society for Medical Oncology.

[32]  M. Pencina,et al.  On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival data , 2011, Statistics in medicine.