Dataset Information

Digital pattern recognition-based image analysis quantifies immune infiltrates in distinct tissue regions of colorectal cancer and identifies a metastatic phenotype.

ABSTRACT:

Background

Several studies in colorectal cancer (CRC) indicate a relationship between tumour immune infiltrates and clinical outcome. We tested the utility of a digital pattern recognition-based image analysis (DPRIA) system to segregate tissue regions and facilitate automated quantification of immune infiltrates in CRC.

Methods

Primary CRC with matched hepatic metastatic (n=7), primary CRC alone (n=18) and primary CRC with matched normal (n=40) tissue were analysed immunohistochemically. Genie pattern recognition software was used to segregate distinct tissue regions in combination with image analysis algorithms to quantify immune cells.

Results

Immune infiltrates were observed predominately at the invasive margin. Quantitative image analysis revealed a significant increase in the prevalence of Foxp3 (P<0.0001), CD8 (P<0.0001), CD68 (<0.0001) and CD31 (<0.0001) positive cells in the stroma of primary and metastatic CRC, compared with tumour cell mass. A direct comparison between non-metastatic primary CRC (MET-) and primary CRC that resulted in metastasis (MET+) showed an immunosuppressive phenotype, with elevated Foxp3 (P<0.05) and reduced numbers of CD8 (P<0.05) cells in the stroma of MET+ compared with MET- samples.

Conclusion

By combining immunohistochemistry with DPRIA, we demonstrate a potential metastatic phenotype in CRC. Our study accelerates wider acceptance and use of automated systems as an adjunct to traditional histopathological techniques.

SUBMITTER: Angell HK

PROVIDER: S-EPMC3776996 | biostudies-literature |

REPOSITORIES: biostudies-literature

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