With large amounts of sections available, all of them can be analyzed simultaneously. Tissues from multiple points of origin can be tested under the exact circumstance in a microarray, such as experiments that require precise uniformity in factors such as temperature, incubation time, chemical exposure, etc. Considering that each individual piece of tissue must be stained independently, variances are minimized in a control tissue microarray. Furthermore, due to entire cohorts being analyzed together, the amount of resource volume and time required is decreased, resulting in increased cost-efficiency. As a result, control tissue microarrays contribute to increased statistical quality control by providing extra samples to minimize experimental drift, a drastic improvement from using only a single tissue sample to control for quality.
As the original usage of arrays describes, predictive tissue microarrays are used in the testing of drugs, such as antibodies, to calculate responses. Predictive microarrays are invaluable in identifying drug-resistant and other therapeutic markers in genes. Other possibilities include examining how to obtain optimal staining and providing a spectrum of tissue for testing. Validation tissue microarrays corroborate biomarkers found in cDNA, such as in a study by Bubendorf et al. , checking that a certain gene expression was correlated with hormone refractory prostate cancer. The results found a 100% match.
TMAs have been irreplaceable in oncology. Through the identification of cancer-associated genetic markers and molecular profiling of tumors, microarrays assist in the diagnosis and prognosis of cancer. Using multiple tissues in one block, called multitumor arrays, can help identify novel markers and hone in on specific treatment options. In the treatment process itself, sections can be presented in tumor progression tissue microarrays, which track the expression of certain genes or proteins and determine the evolution of a cancer. Finally, prognostic tissue microarrays use staining techniques to predict the ultimate conclusion of a tumor.