Comparison of Automated and Conventional IHC Visual Scoring Analysis for MHC Class I and Tapasin Expression in
Background:
Cervical cancer (CXCA) is the second most common cancer among women in Thailand and worldwide. Immune evasion caused by down-regulation of host immune responsive genes, such as MHC class I and loss of antigen processing machinery (APM), presents a capability leading to cancer development. Immunohistochemical staining (HC) is regarded as a common technique for protein marker detection in clinical laboratories. At present, IHC automation has been launched to facilitate the speed and feasibility to replace conventional IHC. However, evaluation of its use is still limited.
Objective:
This study aimed to evaluate IHC scoring by automated visual analysis compared to conventional IHC analysis.
Material And Method:
The paraffin-embedded tissues of 96 invasive CXCA were processed using a tissue microarray (TMA) platform followed by automated IHC staining of the anti-MHC class I (heavy chain, β2M) and an APM-Tapasin expression. Conventional IHC and automated slide scanning with scoring visual analysis were compared.
Results:
The results showed significant association between conventional and automated IHC evaluation (p-value > 0.05, Chi-square) for MHC class I and Tapasin stated in percentage of positive cancer cells, whereas intensity was found (p-value < 0.05, Chi-square) with moderate agreement (p-value < 0.001, kappa) 0.434-0.615 and 0.353-0.554, respectively. After calculated values, the results showed significant association between conventional and automated IHC evaluation (p-value > 0.05, Chi-square) for MHC class I and Tapasin with the highest agreement level (p-value < 0.001, kappa) of summation 0.595-0.755 and multiply scoring 0.633-0.689, respectively.
Conclusion And Discussion:
The automation softwarefor IHC scoring and interpretation can be used for the determination of MHC class I and Tapasin in CXCA. In addition, an antigen presentation pattern must be included to allow an accurate result for MHC class I in clinical use. An appropriate sample size and design of staging coverage as well as clinical prognosis outcomes of progression should be used infurther investigation.
Insights
Automated immunohistochemical staining (IHC) scoring shows significant agreement with conventional IHC for evaluating MHC class I and Tapasin in cervical cancer (CXCA). This automated method offers a reliable alternative for clinical assessment of immune evasion markers in CXCA.
Area of Science:
- Oncology
- Immunology
- Pathology
Background:
- Cervical cancer (CXCA) is a prevalent malignancy globally, particularly in Thailand.
- Immune evasion, through down-regulation of MHC class I and antigen processing machinery (APM), is crucial for cancer development.
- Immunohistochemical staining (IHC) is a standard diagnostic technique, with automation emerging to improve efficiency.
Purpose of the Study:
- To evaluate the efficacy of automated IHC scoring compared to conventional IHC analysis for CXCA.
- To assess the agreement between automated and conventional methods for MHC class I and Tapasin expression.
Main Methods:
- Utilized a tissue microarray (TMA) platform with paraffin-embedded tissues from 96 invasive CXCA cases.
- Performed automated IHC staining for MHC class I (heavy chain, β2M) and APM-Tapasin.
- Compared scoring results from conventional IHC with automated slide scanning and visual analysis.
Main Results:
- Significant associations were found between conventional and automated IHC for both MHC class I and Tapasin (p > 0.05).
- Moderate agreement was observed for intensity scoring (kappa: 0.434-0.615 for MHC class I, 0.353-0.554 for Tapasin).
- High agreement levels were achieved after calculated values, with summation and multiply scoring (kappa: 0.595-0.755 and 0.633-0.689, respectively).
Conclusions:
- Automated IHC scoring software is a viable tool for determining MHC class I and Tapasin in CXCA.
- Incorporating antigen presentation patterns is essential for accurate MHC class I clinical interpretation.
- Future research should focus on sample size, staging, and clinical prognosis for further validation.
