A new strategy for personalized treatment of triple-negative breast cancer (TNBC) is proposed, using patient-specific signaling signatures (PaSSS) and phosphotyrosine proteomics. TNBC is a heterogeneous cancer type that lacks estrogen receptor, progesterone receptor, and HER2 expression, making it difficult to treat with targeted therapies. This study integrated phosphoproteomic analysis of signaling networks with PaSSS analysis to identify individualized targeted combination therapies for TNBC. Using this method on patient-derived xenografts (PDX), they characterized each tumor by computing a set of unbalanced signaling processes and assigned personalized therapies based on these processes. Each tumor was found to have an average of two separate processes, with EGFR being a major core target in at least one of them in half of the tumors analyzed. However, anti-EGFR monotherapies were predicted to be ineffective, so personalized combination treatments based on PaSSS were developed. These were predicted to induce anti-EGFR responses or to be used to develop an alternative therapy if EGFR was not present. In vivo validation of the predicted therapies showed that PaSSS predictions were more accurate than other therapies. This suggests that a detailed identification of molecular imbalances is necessary to tailor therapy for each TNBC. The study found that TNBC tumors are characterized by a patient-specific combination of approximately two unbalanced processes. PaSSS analysis revealed that TNBC tumors displayed 1–4 unbalanced processes each, averaging 2 per tumor. The study also found that only 3 out of 28 tumors may benefit from anti-EGFR monotherapy, while 12 tumors may benefit from combination therapies including EGFR. The other tumors are expected to benefit from non-EGFRi combined therapies. PaSSS provides efficient targeted therapy for TNBC, as validated by in vivo experiments. The study also found that PaSSS drug combinations outperform standard care and non-predicted combined therapies. The results suggest that PaSSS can be used to design personalized treatments for TNBC, which can be applied to various cancer types.A new strategy for personalized treatment of triple-negative breast cancer (TNBC) is proposed, using patient-specific signaling signatures (PaSSS) and phosphotyrosine proteomics. TNBC is a heterogeneous cancer type that lacks estrogen receptor, progesterone receptor, and HER2 expression, making it difficult to treat with targeted therapies. This study integrated phosphoproteomic analysis of signaling networks with PaSSS analysis to identify individualized targeted combination therapies for TNBC. Using this method on patient-derived xenografts (PDX), they characterized each tumor by computing a set of unbalanced signaling processes and assigned personalized therapies based on these processes. Each tumor was found to have an average of two separate processes, with EGFR being a major core target in at least one of them in half of the tumors analyzed. However, anti-EGFR monotherapies were predicted to be ineffective, so personalized combination treatments based on PaSSS were developed. These were predicted to induce anti-EGFR responses or to be used to develop an alternative therapy if EGFR was not present. In vivo validation of the predicted therapies showed that PaSSS predictions were more accurate than other therapies. This suggests that a detailed identification of molecular imbalances is necessary to tailor therapy for each TNBC. The study found that TNBC tumors are characterized by a patient-specific combination of approximately two unbalanced processes. PaSSS analysis revealed that TNBC tumors displayed 1–4 unbalanced processes each, averaging 2 per tumor. The study also found that only 3 out of 28 tumors may benefit from anti-EGFR monotherapy, while 12 tumors may benefit from combination therapies including EGFR. The other tumors are expected to benefit from non-EGFRi combined therapies. PaSSS provides efficient targeted therapy for TNBC, as validated by in vivo experiments. The study also found that PaSSS drug combinations outperform standard care and non-predicted combined therapies. The results suggest that PaSSS can be used to design personalized treatments for TNBC, which can be applied to various cancer types.