P53

Decision Support & AI

Tumor protein 53 is a one of transcription factor regulate the cell cycle. TP53 has a novel task to suppress the cancer. TP53 mutations are shown with high incidence many different cancer types. P53 mutations lead to lost mutant p53 tumor suppressor activity. Moreover mutant p53 may contribute to malignant progression. p53 immunohistochemistry is used to detect pathological presence of the TP53 gene product.

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Keywords

p53, Immunohistochemistry, Mutant p53, Tumor Suppressor

Methods

The techniques of nuclear classification algorithm is used to detect the nuclei in morphological form and size and classify them as positive or negative according to pixel colorization and intensity. Also, tile extraction, color deconvolution, morphological filters, threshold value method,and cell segmentation algorithms are used to develop p53 analysis.

The analysis algorithm contains H-DAB colour deconvolution method. To detect negative nuclei H channel, and to detect and classify brown nuclei DAB channel are used. The positive nuclei are classified into three groups according to given threshold values by the user before running the analysis.

Quantitative output variables

Dark Brown Nuclei Number
Medium Brown Nuclei Number
Light Brown Nuclei Number
Blue Nuclei Number
Stroma Nuclei Number
Positivity Index

Workflow

View the P53 stained whole slide digital image with ViraPath.
Outline tumor either manually or automatically using Virapath Tissue Segmentation algorithm
Select P53 analysis and calibrate the parameters.
Run the analysis

References

Muller PA, Vousden KH. Mutant p53 in cancer: new functions and therapeutic opportunities. Cancer Cell. 2014;25(3):304‐317. doi:10.1016/j.ccr.2014.01.021.