Visualization of Precision Medicine: A Case Study of PD-1 Therapy Efficacy Evaluation through Analytical Algorithm in Triple Negative Breast Cancer Alice Jiang Havergal College, Toronto, Ontario, Canada Introduction & Background Results I would like to acknowledge the help that I received from the Rapidamic Lab and offer special thanks to my parents and advisors for their direct and indirect support and guidance throughout the project. • Future work will focus on the optimization of our algorithm and incorporating more factors to assess the correlations with the outcome to meet the large database analytics requirements of precision medicine. • The system of the analytical platform is quite versatile It can be applied and extended to other different precision medicines for diseases, especially cancer therapy. Figure 5. Correlation matrix. Values range from -1 to 1. Figure 3. Boxplots in comparison of CPS and Placebo/Pembrolizumab to survival length. An increase in combined positive score (CPS) values corresponds to a larger range and median, indicative of a longer survival length. The Pembrolizumab + chemotherapy (2) group exhibits a significantly wider range, higher median, and a more extensive IQR in contrast with the placebo + chemotherapy group (1), highlighting the salient distinctions between the two cohorts. An increased presence of the CPS positively correlates to a higher survival length of individuals for PD-1 therapy + chemotherapy. Other input factors did not display a significant influence or correlation to survival length. The analytical algorithm tool proved to be promising in processing vast datasets and was capable of generating comprehensive visualizations. Utilizing the created linear regression model, approximate survival length (y value) could be predicted from input variables. We can efficiently and precisely identify underlying factors, understanding the strength and general trend of influence. Recognizing key interactions can enable professionals to prioritize these factors when making a medical prognosis, ensuring that treatments are tailored to maximize the therapeutic efficacy and outcome for patients. This guides personalized medicine, moving away from generalized treatment protocols, by identifying influential factors but also quantifying the impact of drug efficacy on a specific individual. The importance of precise data-enhanced decision-making is emphasized. Conclusions [1] Sporikova Z, Koudelakova V, Trojanec R, Hajduch M. Genetic markers in triple-negative breast cancer. Clinical breast cancer. 2018 Oct 1;18(5):e841-50. [2] Jaber N. SACITUZUMAB earns regular FDA approval for TNBC. National Cancer Institute. Available from: https://www.cancer.gov/news-events/cancer-currents- blog/2021/sacituzumab-govitecan-tnbc-regular-approval Figure 1. Visual representation of TNBC’s missing receptors [2] Triple-Negative Breast Cancer (TNBC) is an aggressive breast cancer subtype that lacks the estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2, accompanied by a heightened metastasis and recurrence risk [1]. Treatments lack specificity and are limited by TNBC’s various molecular subtypes and chemoresistance. In this study, we explored programmed cell death protein 1 (PD-1) therapies as a novel TNBC treatment and implemented the concept of precision medicine, developed an analytical algorithm using Python to investigate the drug efficacy of PD-1 therapy based on individual TNBC profiles. There is a positive, moderately strong correlation determined between CPS (r=0.51) and Placebo/Pembrolizumab (r=0.65) with the survival length. Correlations among other variables are relatively weak. Most Breast Cancer TNBC Methodology Future Work Acknowledgements References 93% 5-year survival rate 77% 5-year survival rate Figure 2. Analytical algorithm process. In this study, a mock database was randomized and generated using Pembrolizumab + chemotherapy clinical trial summary statistics on