Expectations, validity, and reality in omics.

Diverse methods of large-scale measurements of biological processes have emerged in the last 15 years and their list is growing rapidly. Almost invariably, these advances in omics have been associated with major expectations of transforming not only biological knowledge but also medicine and health. However, practical applications of omics in biomedicine have often suffered from poor attention to issues of validity. As a consequence, major promises of personalized medicine have not yet materialized in improving patient or population outcomes. Several omics fields increasingly realize the need to safeguard the validity of their efforts, make reporting more transparent, and improve the translational potential of their studies. Many discoveries point indeed toward a highly individualized profile of health and disease, where each case is different, but this is currently difficult to translate into more effective personalized treatment or prevention. Given the exponential growth of collected data, understanding is often drowning in the sea of measurements.

[1]  J. V. Moran,et al.  Initial sequencing and analysis of the human genome. , 2001, Nature.

[2]  John P A Ioannidis,et al.  Overinterpretation of clinical applicability in molecular diagnostic research. , 2009, Clinical chemistry.

[3]  David B Allison,et al.  Expectations, validity, and reality in gene expression profiling. , 2010, Journal of clinical epidemiology.

[4]  Renee F Wilson,et al.  Systematic Review: Gene Expression Profiling Assays in Early-Stage Breast Cancer , 2008, Annals of Internal Medicine.

[5]  John P A Ioannidis,et al.  Researching Genetic Versus Nongenetic Determinants of Disease: A Comparison and Proposed Unification , 2009, Science Translational Medicine.

[6]  Jason E. Stewart,et al.  Minimum information about a microarray experiment (MIAME)—toward standards for microarray data , 2001, Nature Genetics.

[7]  J. Witte,et al.  The Scientific Foundation for Personal Genomics: Recommendations from a National Institutes of Health–Centers for Disease Control and Prevention Multidisciplinary Workshop , 2009, Genetics in Medicine.

[8]  J. Ioannidis.  Personalized Genetic Prediction: Too Limited, Too Expensive, or Too Soon? , 2009, Annals of Internal Medicine.

[9]  M. Relling,et al.  Pharmacogenomics: translating functional genomics into rational therapeutics. , 1999, Science.

[10]  Russ Altman,et al.  Pharmacogenomics: Challenges and Opportunities , 2006, Annals of Internal Medicine.

[11]  J. Lindon,et al.  'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. , 1999, Xenobiotica; the fate of foreign compounds in biological systems.

[12]  M. Khoury,et al.  Personal genomics: information can be harmful , 2010, European journal of clinical investigation.

[13]  Jeffrey S. Morris,et al.  Signal in noise: evaluating reported reproducibility of serum proteomic tests for ovarian cancer. , 2005, Journal of the National Cancer Institute.

[14]  John P. A. Ioannidis,et al.  Limits to forecasting in personalized medicine: An overview , 2009 .

[15]  S. Batalov,et al.  A gene atlas of the mouse and human protein-encoding transcriptomes. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[16]  A. Dupuy,et al.  Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. , 2007, Journal of the National Cancer Institute.

[17]  Muin J. Khoury,et al.  Challenges of translating genetic tests into clinical and public health practice , 2009, Nature Reviews Genetics.

[18]  J. Gulcher,et al.  Genetic risk information for common diseases may indeed be already useful for prevention and early detection , 2010, European journal of clinical investigation.

[19]  M. McCarthy,et al.  Genome-wide association studies for complex traits: consensus, uncertainty and challenges , 2008, Nature Reviews Genetics.

[20]  John P A Ioannidis,et al.  Beyond genome-wide association studies: genetic heterogeneity and individual predisposition to cancer. , 2010, Trends in genetics : TIG.

[21]  S. Teutsch,et al.  Recommendations from the EGAPP Working Group: can tumor gene expression profiling improve outcomes in patients with breast cancer? , 2009, Genetics in Medicine.

[22]  H. Kitano.  Systems Biology: A Brief Overview , 2002, Science.

[23]  D. Moher,et al.  A catalogue of reporting guidelines for health research , 2010, European journal of clinical investigation.

[24]  Barry S. Coller,et al.  Traversing the Valley of Death: A Guide to Assessing Prospects for Translational Success , 2009, Science Translational Medicine.

[25]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[26]  E. Petricoin,et al.  Use of proteomic patterns in serum to identify ovarian Cancer , 2002 .

[27]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[28]  David F Ransohoff,et al.  How to improve reliability and efficiency of research about molecular markers: roles of phases, guidelines, and study design. , 2007, Journal of clinical epidemiology.

[29]  J. Uhm.  Comprehensive genomic characterization defines human glioblastoma genes and core pathways , 2009 .

[30]  R. Aebersold,et al.  Mass spectrometry-based proteomics , 2003, Nature.

[31]  J. Uhm.  An Integrated Genomic Analysis of Human Glioblastoma Multiforme , 2009 .

[32]  M. Socinski,et al.  Gefitinib in advanced non-small cell lung cancer: does it deserve a second chance? , 2008, The oncologist.

[33]  Nigel W. Hardy,et al.  Meeting Report from the Second “Minimum Information for Biological and Biomedical Investigations” (MIBBI) workshop , 2010, Standards in genomic sciences.

[34]  T. Ideker,et al.  A new approach to decoding life: systems biology. , 2001, Annual review of genomics and human genetics.

[35]  J. Brooks.  Why most published research findings are false: Ioannidis JP, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece , 2008 .

[36]  E. Balasse,et al.  Operation of the “Glucose‐Fatty Acid Cycle” during Experimental Elevations of Plasma Free Fatty Acid Levels in Man , 1974, European journal of clinical investigation.

[37]  Jeremiah Stamler,et al.  Opening up the "Black Box": metabolic phenotyping and metabolome-wide association studies in epidemiology. , 2010, Journal of clinical epidemiology.

[38]  Ping Wang,et al.  Generally detected proteins in comparative proteomics – A matter of cellular stress response? , 2009, Proteomics.

[39]  Muin J Khoury,et al.  A critical appraisal of the scientific basis of commercial genomic profiles used to assess health risks and personalize health interventions. , 2008, American journal of human genetics.

[40]  David L Veenstra,et al.  Expectations, validity, and reality in pharmacogenetics. , 2010, Journal of clinical epidemiology.

[41]  John P A Ioannidis,et al.  Is molecular profiling ready for use in clinical decision making? , 2007, The oncologist.

[42]  C. Vulpe,et al.  Déjà vu in proteomics. A hit parade of repeatedly identified differentially expressed proteins , 2008, Proteomics.

[43]  J. Ioannidis,et al.  STrengthening the REporting of Genetic Association Studies (STREGA)— An Extension of the STROBE Statement , 2009, PLoS medicine.

[44]  David B. Goldstein,et al.  Rare Variants Create Synthetic Genome-Wide Associations , 2010, PLoS biology.

[45]  C. Ball,et al.  Repeatability of published microarray gene expression analyses , 2009, Nature Genetics.

[46]  J. Ioannidis,et al.  Evolution and Translation of Research Findings: From Bench to Where? , 2006, PLoS clinical trials.

[47]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.