Role of Molecular-Genetic Profiling and Artificial Intelligence in Breast Cancer Diagnosis and insight into Targeted Pharmacotherapeutics

Breast cancer is a complex and diverse disease with varying responses to therapeutics.  To address this diversity and offer personalized treatment plans, molecular and genetic analysis of breast tumors is crucial. The World Health Organization classifies breast cancer into different subtypes, including precursor lesions like Ductal carcinoma in-situ (DCIS), lobular carcinoma in-situ (LCIS), and Pleomorphic LCIS, which have the potential to develop into cancer. Invasive breast carcinomas infiltrate nearby tissues and can metastasize. These subtypes are categorized based on their microscopic appearances, such as Invasive Ductal Carcinoma (NOS), Invasive Lobular Carcinoma, Triple-Negative Breast Cancer (Estrogen receptor-, Progesterone receptor-, and, HER2-), HER2-Positive Breast Cancer (HER2 overexpression), and less common types like Mucinous, Metaplastic, and Papillary carcinomas. Molecular and genetic profiling are powerful tools to aid in treatment decisions. Understanding the underlying biology of the disease helps physicians develop personalized treatment plans that consider the unique characteristics of each patient's tumor. Ongoing advancements in technology and research are improving our ability to diagnose and treat breast cancer effectively.  This review presents an insight into molecular genetic profile, the role of artificial intelligence in breast cancer, and a concise overview of targeted pharmacotherapeutics for treating hormone receptor-based breast cancer.

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The authors declare no conflict of interest.

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The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.