Abstract B64: Development and analytical validation of a fully-automated platform for quantification of MetaSites to predict systemic metastasis
Authors: Peterson Michael , Graves Lonnie , Donovan Michael J. , Hamilton Douglas , Poxdnyakova Olga , Gustavson Mark 
Published: May 2019

Abstract
Background: Tumor metastasis is responsible for the majority of solid tumor related deaths. Diagnostic assays that accurately predict risk of cancer dissemination provide useful information for optimizing personalized treatment strategies. As previously demonstrated, using manual methods, the number and distribution of MetaSites in the tumor microenvironment significantly predicts metastatic disease in patients with ER-positive early stage invasive breast cancer. A MetaSite is the juxtaposed multicellular structure comprised of a blood vessel, the macrophage immune cell, and an invasive tumor cell. To reduce pathologist counting variability, an objective and reproducible automated laboratory process workflow to identify and quantify MetaSites for a clinical grade assay was developed.
Methods: Digital pathology imaging coupled with image analysis tools was employed to develop a fully-automated, objective method and workflow for quantification of MetaSites in formalin-fixed paraffin embedded tumor samples. Using this method, areas for analysis and quantification of MetaSites were automated by integrating high resolution automated microscopy with multiple image analysis algorithms. A pathologist ensured overall diagnostic quality of the sample in addition to approving individual images for MetaSite scoring.
Results: In this analytical validation study, the platform was demonstrated to be greater than 97% reproducible with a mean coefficient of variation of 6.6% (n=35) for 3 independent measurements of the same slide. Further, MetaSite scores showed correlation coefficients (Pearson's R) greater than 0.98 between measurements with no significant difference in absolute values by repeated measures analysis. Importantly, MetaSite scores on independently stained tumor sections showed greater than 90% reproducibility, indicating minimal heterogeneity within the tumor with respect to MetaSite score, section to section. Additionally, day-to-day MetaSite scores showed correlation coefficients (Pearson's R) greater than 0.90 between staining runs with no significant difference in mean MetaSite scores.
Conclusion: Taken together, these data demonstrate the successful development and analytical validation of a fully-automated, highly reproducible MetaSite quantification platform. With development and analytical validation of this test, it is now possible to provide physicians with information regarding the aggressiveness of patient tumors and accurate prediction of cancer metastasis. This method is being further validated in a large (n=481) case control clinical study.
Citation Format: Michael Peterson, Lonnie Graves, Michael J. Donovan, Douglas Hamilton, Olga Poxdnyakova, Mark Gustavson. Development and analytical validation of a fully-automated platform for quantification of MetaSites to predict systemic metastasis. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Metastasis; 2015 Nov 30-Dec 3; Austin, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(7 Suppl):Abstract nr B64.