TY - JOUR
T1 - Patient-specific signaling signatures predict optimal therapeutic combinations for triple negative breast cancer
AU - Alkhatib, Heba
AU - Conage-Pough, Jason
AU - Roy Chowdhury, Sangita
AU - Shian, Denen
AU - Zaid, Deema
AU - Rubinstein, Ariel M.
AU - Sonnenblick, Amir
AU - Peretz-Yablonsky, Tamar
AU - Granit, Avital
AU - Carmon, Einat
AU - Kohale, Ishwar N.
AU - Boughey, Judy C.
AU - Goetz, Matthew P.
AU - Wang, Liewei
AU - White, Forest M.
AU - Kravchenko-Balasha, Nataly
N1 - Publisher Copyright:
© 2024, The Author(s).
PY - 2024/1/16
Y1 - 2024/1/16
N2 - Triple negative breast cancer (TNBC) is a heterogeneous group of tumors which lack estrogen receptor, progesterone receptor, and HER2 expression. Targeted therapies have limited success in treating TNBC, thus a strategy enabling effective targeted combinations is an unmet need. To tackle these challenges and discover individualized targeted combination therapies for TNBC, we integrated phosphoproteomic analysis of altered signaling networks with patient-specific signaling signature (PaSSS) analysis using an information-theoretic, thermodynamic-based approach. Using this method on a large number of TNBC patient-derived tumors (PDX), we were able to thoroughly characterize each PDX by computing a patient-specific set of unbalanced signaling processes and assigning a personalized therapy based on them. We discovered that each tumor has an average of two separate processes, and that, consistent with prior research, EGFR is 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, thus we developed personalized combination treatments based on PaSSS. These were predicted to induce anti-EGFR responses or to be used to develop an alternative therapy if EGFR was not present. In-vivo experimental validation of the predicted therapy showed that PaSSS predictions were more accurate than other therapies. Thus, we suggest that a detailed identification of molecular imbalances is necessary to tailor therapy for each TNBC. In summary, we propose a new strategy to design personalized therapy for TNBC using pY proteomics and PaSSS analysis. This method can be applied to different cancer types to improve response to the biomarker-based treatment.
AB - Triple negative breast cancer (TNBC) is a heterogeneous group of tumors which lack estrogen receptor, progesterone receptor, and HER2 expression. Targeted therapies have limited success in treating TNBC, thus a strategy enabling effective targeted combinations is an unmet need. To tackle these challenges and discover individualized targeted combination therapies for TNBC, we integrated phosphoproteomic analysis of altered signaling networks with patient-specific signaling signature (PaSSS) analysis using an information-theoretic, thermodynamic-based approach. Using this method on a large number of TNBC patient-derived tumors (PDX), we were able to thoroughly characterize each PDX by computing a patient-specific set of unbalanced signaling processes and assigning a personalized therapy based on them. We discovered that each tumor has an average of two separate processes, and that, consistent with prior research, EGFR is 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, thus we developed personalized combination treatments based on PaSSS. These were predicted to induce anti-EGFR responses or to be used to develop an alternative therapy if EGFR was not present. In-vivo experimental validation of the predicted therapy showed that PaSSS predictions were more accurate than other therapies. Thus, we suggest that a detailed identification of molecular imbalances is necessary to tailor therapy for each TNBC. In summary, we propose a new strategy to design personalized therapy for TNBC using pY proteomics and PaSSS analysis. This method can be applied to different cancer types to improve response to the biomarker-based treatment.
KW - EGFR
KW - Information theory
KW - Patient-specific signaling signatures
KW - Personalized combined treatment
KW - Phosphotyrosine proteomics
KW - Targeted therapy
KW - Triple-negative breast cancer
UR - http://www.scopus.com/inward/record.url?scp=85182477148&partnerID=8YFLogxK
U2 - 10.1186/s12943-023-01921-9
DO - 10.1186/s12943-023-01921-9
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C2 - 38229082
AN - SCOPUS:85182477148
SN - 1476-4598
VL - 23
JO - Molecular Cancer
JF - Molecular Cancer
IS - 1
M1 - 17
ER -