Shannon Entropy as a Predictive Biomarker for Targeted Therapy Resistance in Pan-Cancer Cell Lines

Overview

Drug resistance remains a critical challenge in oncology, necessitating the identification of quantitative biomarkers for therapeutic evasion. This study investigated the extent to which Shannon transcriptomic entropy correlates with resistance to targeted anticancer therapies across breast, melanoma, and hematologic cancer cell lines. It was hypothesized that higher transcriptomic entropy would lead to greater resistance because it corresponds to a broader distribution of gene expression states, enabling functional redundancy, pathway bypass, and the adaptive survival of cell subpopulations under anticancer therapies. To test this, a Python-based bioinformatics pipeline was developed using VS code to analyze transcriptomic and drug sensitivity data sourced from the Broad Institute’s DepMap Portal and Genomics of Drug Sensitivity in Cancer (GDSC) Project. To refine the analysis and filter out the noise of regular gene variance, entropy was calculated specifically for oncogenic signaling pathways. Statistical significance was evaluated using Spearman correlation tests to map the relationship between calculated entropy scores and pharmacological resistance. The analysis indicated no significant correlation between Shannon transcriptomic entropy and drug resistance (DepMap Correlations – Breast Cancer: r = -0.013, Melanoma: r = 0.005, Hematologic: r = -0.017; GDSC Correlations – Breast Cancer: r = -0.069, Melanoma: r = 0.005, Hematologic: r = -0.020). Consequently, the study failed to disprove the null hypothesis, suggesting that transcriptomic stochasticity within these specific pathways is not a universal driver of resistance in the lineages examined. This model provides a novel framework for predicting treatment failure before clinical intervention, potentially saving patients from the side effects of ineffective drugs.

Competition history

• CSEF 2026 Biochemistry/ Molecular Biology (Senior Division) · Entry S-04-07

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