Microarrays outcomes-based

Due to the issues surrounding the analysis of archived samples, most microarray-based oncology studies have relied on prospective analyses—running samples as they become available, rather than retrospective analyses—running archived samples. While the prospective analysis tends to provide excellent-quality data, the associated clinical data from archived samples tends to be of greater value. In particular, in obtaining archived samples, it is possible to concentrate on those patients for which outcome information is known (e g., survival rates, recurrence, treatment success, and so on). 

It is the goal of array-based prognostics to utilize a panel of markers that are more accurately able to stratify patients into prognostic groupings. There are several examples in the literature of biomarker panels identified by microarray analysis that may have some relation to prognostic outcome (4, 77, 78, 79,80). However, the issue remains that many of these biomarker panels do not afford sufficient specificity or sensitivity to warrant clinical application. Microarrays offer the potential to profile tumors at a finer level of detail than what can be obtained by the pathologist however, it is the reliance on classification from traditional pathology that has confounded many studies. 

Our results demonstrated that the identified subsets of the activated protein kinases significantly increased the accuracy of clinical outcome predictions. Most notably in the study, we evaluated protein phosphorylation levels instead of total protein expression levels. Protein phosphorylation and dephosphorylation are well-characterized biochemical processes for protein kinases to conduct cellular signal transduction. Phosphorylation at certain tyrosine, serine, or threonine residues in kinases is a key step for their activation, and the measurement of these phosphorylations reflects their functional status in vivo. Thus, the protein kinase phosphorylation-based tissue microarray more accurately reveals the molecular mechanisms of breast cancers, and more accurately predicts the individualized survival and treatment response. 

Tolgay Ocal I, Doiled-Filhart M, D Aquila TG, et al. Tissue microarray-based studies of patients with lymph node negative breast carcinoma show that met expression is associated with worse outcome but is not correlated with epidermal growth factor family receptors. Cancer 2003 97 1841-48. 

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