BIOCHEMICAL MARKERS AND HISTOPATHOLOGICAL PATTERNS IN BREAST CARCINOMA: A COMPARATIVE STUDY
Main Article Content
Keywords
Breast carcinoma, ER, PR, HER2/neu, immunohistochemistry, histopathology, tumor grade, triple-negative
Abstract
Background: Breast carcinoma is the most common malignancy among women worldwide and a leading cause of cancer-related deaths. Early diagnosis and characterization are crucial for effective treatment. Biochemical markers such as estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2/neu) play a vital role in the molecular classification and prognosis of breast cancer. Histopathological patterns, on the other hand, provide insight into tumor behavior and grade.
Objective: To compare the expression of biochemical markers (ER, PR, HER2/neu) with various histopathological patterns in diagnosed cases of breast carcinoma, and evaluate their prognostic relevance.
Methodology: A retrospective study was conducted at Sughra Shafi Medical Complex Narowal in two-year tenure from January 2023 to December 2024. A total of 100 breast carcinoma cases confirmed by histopathology were included. Tissue samples were analyzed for histological type and grade using H&E staining. Immunohistochemical (IHC) staining was performed to detect the presence of ER, PR, and HER2/neu. The results were correlated with tumor type, grade, and other clinical parameters.
Results: Invasive ductal carcinoma (IDC) was the most common histopathological type observed (78%), followed by invasive lobular carcinoma (12%) and others (10%). ER positivity was seen in 65% of cases, PR in 58%, and HER2/neu overexpression in 25%. ER and PR positivity were significantly associated with lower tumor grade and better differentiation (p<0.05). HER2/neu overexpression was more common in high-grade tumors and was negatively associated with ER/PR expression. Triple-negative breast cancer (TNBC) accounted for 12% of the cases, showing aggressive features histologically.
Conclusion: This study highlights the importance of correlating biochemical markers with histopathological findings in breast carcinoma. ER and PR positivity are associated with favorable histological features, while HER2/neu overexpression and triple-negative status correspond to higher grade and aggressive behavior. Routine evaluation of these markers is essential for guiding therapeutic decisions and predicting patient outcomes.
References
2. Verras G-I, Tchabashvili L, Mulita F, Grypari IM, Sourouni S, Panagodimou E, et al. Micropapillary breast carcinoma: from molecular pathogenesis to prognosis. Breast Cancer: Targets and Therapy. 2023:41-61.
3. Monjo T, Koido M, Nagasawa S, Suzuki Y, Kamatani Y. Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation. Scientific reports. 2022;12(1):4133.
4. Routray N, Rout SK, Sahu B, Panda SK, Godavarthi D. Ensemble learning with symbiotic organism search optimization algorithm for breast cancer classification and risk identification of other organs on histopathological images. IEEE Access. 2023;11:110544-57.
5. Mandair D, Reis-Filho JS, Ashworth A. Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology. NPJ breast cancer. 2023;9(1):21.
6. Wang J, Li B, Luo M, Huang J, Zhang K, Zheng S, et al. Progression from ductal carcinoma in situ to invasive breast cancer: molecular features and clinical significance. Signal transduction and targeted therapy. 2024;9(1):83.
7. Botlagunta M, Botlagunta MD, Myneni MB, Lakshmi D, Nayyar A, Gullapalli JS, et al. Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms. Scientific Reports. 2023;13(1):485.
8. Aswathy M, Jagannath M. An SVM approach towards breast cancer classification from H&E-stained histopathology images based on integrated features. Medical & biological engineering & computing. 2021;59(9):1773-83.
9. Ektefaie Y, Yuan W, Dillon DA, Lin NU, Golden JA, Kohane IS, et al. Integrative multiomics-histopathology analysis for breast cancer classification. NPJ Breast Cancer. 2021;7(1):147.
10. Thakur A, Gupta M, Sinha DK, Mishra KK, Venkatesan VK, Guluwadi S. Transformative breast Cancer diagnosis using CNNs with optimized ReduceLROnPlateau and Early stopping Enhancements. International Journal of Computational Intelligence Systems. 2024;17(1):1-18.
11. Park NJ-Y, Jeong JY, Park JY, Kim HJ, Park CS, Lee J, et al. Peritumoral edema in breast cancer at preoperative MRI: an interpretative study with histopathological review toward understanding tumor microenvironment. Scientific reports. 2021;11(1):12992.
12. Beňačka R, Szabóová D, Guľašová Z, Hertelyová Z, Radoňák J. Classic and new markers in diagnostics and classification of breast cancer. Cancers. 2022;14(21):5444.
13. Szymiczek A, Lone A, Akbari MR. Molecular intrinsic versus clinical subtyping in breast cancer: A comprehensive review. Clinical genetics. 2021;99(5):613-37.
14. Romeo V, Accardo G, Perillo T, Basso L, Garbino N, Nicolai E, et al. Assessment and prediction of response to neoadjuvant chemotherapy in breast cancer: A comparison of imaging modalities and future perspectives. Cancers. 2021;13(14):3521.
15. Rashmi R, Prasad K, Udupa CBK. Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review. Journal of Medical Systems. 2022;46(1):7.
16. Zhang X. Molecular classification of breast cancer: Relevance and challenges. Archives of Pathology & Laboratory Medicine. 2023;147(1):46-51.