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Background: Successful digital image analysis (DIA) of cancer tissue is accurate and reproducible. These points of emphasis have brought procedures like the tissue microarray (TMA) and hotspot regions of interest (ROI) under scrutiny. The nature in which a pathologist selects TMAs and ROIs is conducive to bias. Whole Slide Imaging (WSI) offers a solution in its unbiased region selection and consideration of a larger tissue sample. However, options for softwares that can handle such large throughput are scarce. Additionally, while multiplex immunohistochemistry (mIHC) is becoming popular [1], documentation of its digital analysis tools remains minimal [2]. The combination of these procedures potentiates a deeper understanding of the tumor microenvironment. This study presents the whole-slide mIHC analysis capabilities of QuPath, an open-source application developed at Queen’s University Belfast [3].

Methods: A multiplex fluorescent stain panel was performed on patient samples. The slides were imaged and cells were detected and segmented in QuPath. QuPath parallelizes its workload to manage whole-slide throughput efficiently. Custom scripts were written that exhibit machine-learning and thresholding techniques to aggregate cell phenotype totals. Additionally, cell detection numbers were generated for specific ROIs and compared to a commercial DIA software. All scripts and protocols in this study are made public for replication and improvement by the community.

Results: QuPath’s automated cell segmentation and classification were demonstrated as a proof-of-concept for whole-slide multiplex immunohistochemistry analysis. Across an entire slide, cells positive for multiple markers were effectively segmented and properly phenotyped.

Conclusions: Open-source applications have become a driving force for innovation and collaboration in the field of digital image analysis. In litigating the strengths and weaknesses of QuPath for whole-slide mIHC analysis, we aim to advance the field’s knowledge of available software tools and bring attention to necessary points of growth in this rapidly changing industry.

References: 1. Feng Z, Jensen SM, Messenheimer DJ, Farhad M, Neuberger M, Bifulco CB, Fox BA. Multispectral imaging of T and B cells in murine spleen and tumor. J Immunol. 2016;196:3943-3950.

2. Blom S, Paavolainen L, Bychkov D, Turkki R, Mäki-Teeri P, Hemmes A, Välimäki K, Lundin J, Kallioniemi O, Pellinen T. Systems pathology by multiplexed immunohistochemistry and whole-slide digital image analysis. Sci Rep. 2017; 7:1-13.

3. Bankhead P, Loughrey MB, Fernández JA, Dombrowski Y, McArt DG, Dunne PD, McQuaid S, Gray RT, Murray LJ, Coleman HG, James JA, Salto-Tellez M, Hamilton PW. Qupath: open source software for digital pathology image analysis. Sci Rep. 2017; 7:1-7.

Publication Date

11-2018

Clinical Institute

Cancer

Department

Oncology

Keywords

Cancer, immunotherapy, multispectral imaging, histology, tumor microenvironment, open source, machine learning

Disciplines

Oncology

Comments

Poster presented at Society for Immunotherapy of Cancer Annual Meeting, Washington, D.C., November 7 – 11, 2018.

Open-source digital image analysis of whole-slide multiplex immunohistochemistry

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Oncology Commons

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