Selecting the right therapeutic disease indication in which to trial new therapies is a notoriously difficult task. Failure to choose the right indication can result in failure in Phase II/III trials resulting in drug discovery programs being halted which, given different indication choices, could have succeeded in the clinic. A critical factor in the choice of indication is ensuring that the target of interest is expressed in the disease tissue in sufficient quantities
Why should you adopt the Tissue Microarray (TMA) Digital Image Analysis approach?
TMAs are microscope slides that can comprise hundreds of tissue specimens from different disease indications, including different cases of the same disease. Consequently, TMAs can be used as a valuable and versatile approach for comparing target expression levels (mRNA, and protein) under identical and standardised conditions across multiple indications. TMAs also permit the use of digital image analysis to quantify IHC marker staining within specific regions of interest, allowing an objective comparison of target expression across different disease tissues.
A Case Study: Quantifying Arginase +ve cells in Multiple Cancer Tissues
An excellent example of the combination of the use of TMA’s to help select the correct therapeutic indication is a study carried out by Calithera Biosciences (Steggerda et al., Journal for ImmunoTherapy of Cancer 5:101 (2017)). In this study, the authors used digital histopathology techniques to profile the number of Arginase positive cells per mm2 of TMA tissue within multiple cancer tissues.
Figure 1: Arginase expression across multiple cancer types
The data highlighted a range of Arginase staining across the different cancer types (see Figure 1) with an abundance of Arginase1+ infiltrating immune cells in multiple tumor types, with especially high numbers in tumors of the lung, gastrointestinal tract, and bladder.
This study features the use of digital histopathology image analysis to quantify biomarker staining across a range of tumour samples, providing quantitative, high throughput data which can help provide more informed decisions regarding the most appropriate therapeutic disease indication selection.
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