Tumor heterogeneity in research and diagnostics
Diagnostic pathologists and scientists often rely on tissue data for important diagnostic-, research-, or even business- decisions. One of the major concerns relate to tumor heterogeneity in the context of quantifying tissue biomarker response. Questions revolve around how to reliably visualize, quantify, and in practical terms deal with heterogeneity.
The impact of heterogeneity on diagnostic accuracy
Concerns are apparently well founded. In a recent publication, Stålhammer et. al. [1] demonstrated that manual stratification of breast cancer patients into Luminal A vs. Luminal B based on Ki67 was associated with an error rate of 31%. Using image analysis with computer identified hot-spots reduced the error rate to 19%, when using PAM50 as a pseudo gold-standard.
Heterogeneity and hot-spots
What really made the difference in reducing error rates, was automated identification of hot-spots, using image analysis. Gudlagsson et. al [2] showed that as much as 50% of pathologists were unable to correctly identify the hottest hot-spot which, in some cases, can represent a major cognitive challenge. This challenge was effectively mitigated using image analysis.
Diagnostic applications
As a first practical diagnostic application, we considered automated hot-spot identification for Ki67 expression in breast cancer. By creating heat maps of biomarker expression, the intended use of the APP is to support pathologists in both visualizing heterogeneity and locate the hottest hot-spot (s). This can be used as input to APPs that quantify biomarker expression in the hot-spot(s). CE-marking for In-Vitro Diagnostic purposes, required special attention to the study designs for validating clinical performance.
Research applications
The challenges related to heterogeneity may well be further amplified when interrogating far more complex, and sometimes multiplexed, biomarkers across the entire tumor micro-environment. With Oncotopix® Author, the ability to visualize and quantify heterogeneity wrt. biomarker response has been generalized for tissue-based cancer research.
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References