HER2

Decision Support & AI

The HER2 gene is an oncogene encoding the “Epidermal Growth Factor Receptor 2” which is a transmembrane glycoprotein. Amplification of the HER2 gene and overexpression of HER2 receptor on the cell membrane is observed in approximately %15 of invasive breast cancer. Patients with HER2 positive breast cancer and gastric cancer can derive more benefit from targeted therapies like Trastuzumab treatment. One of the methods to determine HER2 protein expression is immunohistochemistry (IHC) [1]. IHC stained tissues are evaluated according to American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) HER2 guidelines. According to guidelines, the scoring method for HER2 IHC is semiquantitative and is based on 4 classes (0/ 1+, 2+, 3+) which is as follows; Score 0 (negative): no staining is observed in invasive tumor cells, Score 1+ (negative): weak, incomplete, membrane staining in any proportion of invasive tumor cells, or weak, complete membrane staining in <10% of invasive tumor cells, Score 2+ (equivocal): circumferential membrane staining that is incomplete and/or weak/moderate and in >10% of the invasive tumor cells; or complete and circumferential membrane staining that is intense and in ≤10% of the invasive tumor cells, Score 3+ (positive): circumferential membrane staining that is complete and intense in a homogenous and contiguous population and present in >10% of invasive tumor cells that is readily appreciated using a low-power objective. By IHC, only a score of 3+ is reported as positive for HER2 amplification. All 2+ equivocal cases have to undergo subsequent testing by FISH [2].

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Keywords

Membrane Segmentation, HER2 Assessment, Breast Cancer, Gastric Cancer Digital Pathology, ASCO/CAP

Methods

Automatic HER2 scoring is the ability to give consistent results on similar slides in a short time compared with the manual scoring performed by pathologists. Virasoft HER2 Analyzer is developed based on preprocessing, thresholding and segmentation techniques to score the whole slide images. The technique is comprised of three steps. In the first step, a superpixel-based support vector machine (SVM) feature learning classifier is proposed to classify epithelial and stromal regions from WSI. In the second stage, on classified epithelial regions, a convolutional neural network (CNN) based segmentation method is applied to segment membrane regions. Finally, divided tiles are merged and the overall score of each slide is evaluated.

Quantitative output variables

HER2 IHC Score
Strong Cell (+3) Count and Percentage
Medium Cell (+2) Count and Percentage
Weak Cell (+1) Count and Percentage
Incomplete Cell (0) Count

Workflow

View the HER2 whole slide digital image with ViraPath.
Outline tumor either manually or automatically using Virapath Tissue Segmentation algorithm
Select HER2 analysis and calibrate the parameters.
Run the analysis.

References

[1] Loibl, S., & Gianni, L. (2017). HER2-positive breast cancer. The Lancet, 389(10087), 2415–2429

[2]Wolff, A. C., Hammond, M. E. H., Hicks, D. G., Dowsett, M., McShane, L. M., Allison, K. H., ... & Hanna, W. (2014). Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. Archives of Pathology and Laboratory Medicine, 138(2), 241-256.