"Stemness" genomics law governs clinical behavior of human cancer: implications for decision making in disease management.

Abstract

One of the most significant accomplishments of translational oncogenomics is a realistic promise of efficient diagnostic tests that would facilitate implementation of the concept of individualized cancer therapies. Recent discovery of the BMI1 pathway rule indicates that gene expression signatures (GESs) associated with the "stemness" state of a cell might be informative as molecular predictors of cancer therapy outcome. We illustrate a potential clinical utility of this concept using GESs derived from genomic analysis of embryonic stem cells (ESCs) during transition from self-renewing, pluripotent state to differentiated phenotypes. Signatures of multiple stemness pathways (signatures of BMI1, Nanog/Sox2/Oct4, EED, and Suz12 pathways; transposon exclusion zones and ESC pattern 3 signatures; signatures of Polycomb-bound and bivalent chromatin domain transcription factors) seem informative in stratification of cancer patients into low- and high-intensity treatment groups on the basis of prediction of the long-term therapy outcome. A stemness cancer therapy outcome predictor (CTOP) algorithm combining scores of nine stemness signatures outperforms individual signatures and demonstrates a superior prognostic accuracy in retrospective supervised analysis of large cohorts of breast, prostate, lung, and ovarian cancer patients. Our analysis suggests that stemness genomics law governs clinical behavior of human malignancies and defines epigenetic boundaries of therapy-resistant and -sensitive tumors within distinct stemness/differentiation programs. One of the main conclusions of our analysis is that near-term progress in practical implementation of the concept of personalized cancer therapies would depend on timely delivery to practicing physicians of relevant scientific information regarding the outcome of prospective trials validating prognostic performance of CTOP tests in a clinical setting.

• Authors: Gennadi V Glinsky
• Journal: Journal of clinical oncology : official journal of the American Society of Clinical Oncology (J Clin Oncol) Vol. 26 Issue 17 Pg. 2846-53 (Jun 10 2008) ISSN: 1527-7755 [Electronic] United States
• PMID: 18539963 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Review)

Chemical References

• Polycomb-Group Proteins
• Repressor Proteins

Topics

• Algorithms
• Breast Neoplasms (genetics, pathology, therapy)
• Decision Support Techniques
• Drug Resistance, Neoplasm (genetics)
• Female
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Gene Silencing
• Genetic Testing
• Genotype
• Humans
• Lung Neoplasms (genetics, pathology, therapy)
• Male
• Models, Genetic
• Neoplasms (genetics, metabolism, pathology, therapy)
• Neoplastic Stem Cells (metabolism, pathology)
• Ovarian Neoplasms (genetics, pathology, therapy)
• Patient Selection
• Pharmacogenetics (methods)
• Phenotype
• Polycomb-Group Proteins
• Prostatic Neoplasms (genetics, pathology, therapy)
• Repressor Proteins (metabolism)
• Reproducibility of Results