The study addresses the challenge of developing effective targeted therapies for triple-negative breast cancer (TNBC), a heterogeneous group of tumors lacking estrogen receptor, progesterone receptor, and HER2 expression. The authors integrated phosphoproteomic analysis of altered signaling networks with patient-specific signaling signature (PaSSS) analysis, using an information-theoretic, thermodynamic-based approach. This method was applied to a large number of patient-derived xenografts (PDX) to characterize each tumor by computing a personalized set of unbalanced signaling processes and assigning a tailored therapy based on these signatures. The results showed that each tumor had an average of two separate processes, with EGFR being a major core target in at least half of the analyzed tumors. However, anti-EGFR monotherapies were predicted to be ineffective, leading to the development of personalized combination treatments. In vivo validation of the predicted therapies demonstrated that PaSSS predictions were more accurate than other therapies, highlighting the need for detailed identification of molecular imbalances to tailor therapy for each TNBC patient. The study proposes a new strategy to design personalized therapy for TNBC using pY proteomics and PaSSS analysis, which can be applied to various cancer types to improve response to biomarker-based treatments.The study addresses the challenge of developing effective targeted therapies for triple-negative breast cancer (TNBC), a heterogeneous group of tumors lacking estrogen receptor, progesterone receptor, and HER2 expression. The authors integrated phosphoproteomic analysis of altered signaling networks with patient-specific signaling signature (PaSSS) analysis, using an information-theoretic, thermodynamic-based approach. This method was applied to a large number of patient-derived xenografts (PDX) to characterize each tumor by computing a personalized set of unbalanced signaling processes and assigning a tailored therapy based on these signatures. The results showed that each tumor had an average of two separate processes, with EGFR being a major core target in at least half of the analyzed tumors. However, anti-EGFR monotherapies were predicted to be ineffective, leading to the development of personalized combination treatments. In vivo validation of the predicted therapies demonstrated that PaSSS predictions were more accurate than other therapies, highlighting the need for detailed identification of molecular imbalances to tailor therapy for each TNBC patient. The study proposes a new strategy to design personalized therapy for TNBC using pY proteomics and PaSSS analysis, which can be applied to various cancer types to improve response to biomarker-based treatments.