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. 2017 May 26;12(5):e0178362.
doi: 10.1371/journal.pone.0178362. eCollection 2017.

Automated analysis of co-localized protein expression in histologic sections of prostate cancer

Thomas A Tennill  1 , Mitchell E Gross  2 , Hermann B Frieboes  1   3
Affiliations

Automated analysis of co-localized protein expression in histologic sections of prostate cancer

Thomas A Tennill et al. PLoS One. .

Abstract

An automated approach based on routinely-processed, whole-slide immunohistochemistry (IHC) was implemented to study co-localized protein expression in tissue samples. Expression of two markers was chosen to represent stromal (CD31) and epithelial (Ki-67) compartments in prostate cancer. IHC was performed on whole-slide sections representing low-, intermediate-, and high-grade disease from 15 patients. The automated workflow was developed using a training set of regions-of-interest in sequential tissue sections. Protein expression was studied on digital representations of IHC images across entire slides representing formalin-fixed paraffin embedded blocks. Using the training-set, the known association between Ki-67 and Gleason grade was confirmed. CD31 expression was more heterogeneous across samples and remained invariant with grade in this cohort. Interestingly, the Ki-67/CD31 ratio was significantly increased in high (Gleason ≥ 8) versus low/intermediate (Gleason ≤7) samples when assessed in the training-set and the whole-tissue block images. Further, the feasibility of the automated approach to process Tissue Microarray (TMA) samples in high throughput was evaluated. This work establishes an initial framework for automated analysis of co-localized protein expression and distribution in high-resolution digital microscopy images based on standard IHC techniques. Applied to a larger sample population, the approach may help to elucidate the biologic basis for the Gleason grade, which is the strongest, single factor distinguishing clinically aggressive from indolent prostate cancer.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Overview of program workflow.
Fig 2
Fig 2. Example of processing of TMA images to identify core samples.
Fig 3
Fig 3
Representative ROI histology images for (A Column) low grade (Gleason score 7) and (B Column) high grade (Gleason score 8) prostate cancer. Staining is shown for CD31 (vascularization), Ki-67 (cell proliferation), and H&E (cell viability) for consecutive slides. Bar, 200 μm.
Fig 4
Fig 4
Representative whole tissue block images for (A Column) low grade (Gleason score 6) and (B Column) high grade (Gleason score 9) prostate cancer. Staining is shown for CD31 (vascularization), Ki-67 (cell proliferation), and H&E (cell morphology) for consecutive slides. Bar, 6 mm.
Fig 5
Fig 5. Comparison of results of the ROI analysis between the manual method and the program in terms of Ki-67 stain.
Fig 6
Fig 6. Comparison of results of the ROI analysis between the manual method and the program in terms of CD31 stain.
Fig 7
Fig 7. Comparison of results of the ROI analysis between the manual method and the program in terms of Ki-67 to CD31 stain ratio.
Fig 8
Fig 8
Comparison between normalized manual and automated results of the ROI analysis for (a) Ki-67 stain and (b) CD31stain data sets.
Fig 9
Fig 9. Epithelial (Ki-67) and stromal (CD31) protein expression in ROI and whole tissue blocks.
Asterisk signifies statistical significance between the two Gleason score categories.
Fig 10
Fig 10. Ratio of epithelial (Ki-67) to stromal (CD31) protein expression in ROI and whole tissue blocks.
Asterisk signifies statistical significance between the two Gleason score categories.
Fig 11
Fig 11. Epithelial (Ki-67) and stromal (CD31) protein expression, and the ratio between these two values in TMAs.
Asterisk signifies statistical significance between the two Gleason score categories. Note that the TMA samples were not used to determine the Gleason categories.
See this image and copyright information in PMC

References

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