Visualization of Precision Medicine: A Case Study of PD-1 Therapy Efficacy Evaluation through Analytical Algorithm in Triple Negative Breast Cancer

Triple Negative Breast Cancer (TNBC) is an aggressive breast cancer subtype that lacks estrogen

receptor, progesterone receptor, and human epidermal growth factor receptor 2, accompanied by

a heightened recurrence risk. While chemotherapy remains the prevailing standard of care, high

molecular heterogeneity and a strong tendency to develop chemotherapy resistance pose

treatment challenges. Precision medicine emerges as a promising paradigm shift, maximizing the

therapeutic effect by individualizing treatment to account for characteristic differences. In this

study, we explored PD-1 therapies as a novel approach to TNBC treatments with better efficacy

than chemotherapy. The interaction between PD-L1 expressed on cancerous cells and PD-1

assists in escaping anti-tumor immune responses. Thus, it employs monoclonal antibodies to

target the PD-1 receptor, blocking the ligands that inhibit the cell's cytokine secretion and induce

apoptosis. The aim of our work is to implement the concept of precision medicine to investigate

the drug efficacy of PD-1 therapy based on individual TNBC profiles. An analytical algorithm

that evaluates the correlation between influential factors and the effectiveness extent of PD-1

therapy is developed using Python. After refining possible input/output factors, we generated

mock data from a clinical data resource and simulated via our analytical algorithm to conclude

similar results as the literature. Our research demonstrates the feasibility of the analytical

platform by applying precision medicine through a case study of PD-1 treatment for TNBC.

Future work will focus on extending the framework to precision medicine for other diseases,

unlocking insights to optimize medical therapeutic strategies across diverse individuals.