From cellular to high-throughput predictive assays in radiation oncology: challenges and opportunities.
Substantial research efforts into predictive radiation oncology have so far produced very little in terms of clinically applicable assays. This may change with the development of novel high-throughput assays that are of potential interest in a radiation oncology setting. However, it seems that much current research is opportunistic, driven by the available technologies rather than addressing pertinent clinical or biological questions. This review looks at the experience gained from the attempts to develop cellular radiobiology assays. The research process and, in particular, the need for rigorous validation of any promising assay in an independent dataset are stressed. Some common design problems are discussed using examples from radiation oncology. The statistical challenges and some of the key concepts in analyzing dense datasets from high-throughput assays are briefly reviewed. Single nucleotide polymorphisms, immunohistochemical markers, and DNA microarray gene signatures are used as examples of assays that show promise in radiation oncology applications. Some recent studies suggest a differential treatment response between tumor stem cells and other tumor cells. If this is a general pattern, then future predictive assays may have to be performed on stems cells rather than on unselected tumor cells. Advances in radiogenomics or radioproteomics will come from large collaborative research networks, collecting high-quality dosimetric and clinical outcome data and combining state-of-the-art laboratory techniques with appropriate biostatical methods.
Bentzen
Søren M
SM
Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA. bentzen@humonc.wisc.edu
D006801:Humans; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000532:radiotherapy*; D010043:Outcome and Process Assessment, Health Care / Q000379:methods; D011237:Predictive Value of Tests; D011379:Prognosis; D018787:Radiation Oncology / Q000379:methods*; D011853:Radiobiology / Q000379:methods; D013679:Technology, Radiologic