Patient-specific signaling signatures predict optimal therapeutic combinations for triple negative breast cancer.

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.

Metabolomic profiling of triple negative breast cancer cells suggests that valproic acid can enhance the anticancer effect of cisplatin.

Cisplatin is an effective chemotherapeutic agent for treating triple negative breast cancer (TNBC). Nevertheless, cisplatin-resistance might develop during the course of treatment, allegedly by metabolic reprograming, which might influence epigenetic regulation. We hypothesized that the histone deacetylase inhibitor (HDACi) valproic acid (VPA) can counter the cisplatin-induced metabolic changes leading to its resistance. We performed targeted metabolomic and real time PCR analyses on MDA-MB-231 TNBC cells treated with cisplatin, VPA or their combination. 22 (88%) out of the 25 metabolites most significantly modified by the treatments, were acylcarnitines (AC) and three (12%) were phosphatidylcholines (PCs). The most discernible effects were up-modulation of AC by cisplatin and, contrarily, their down-modulation by VPA, which was partial in the VPA-cisplatin combination. Furthermore, the VPA-cisplatin combination increased PCs, sphingomyelins (SM) and hexose levels, as compared to the other treatments. These changes predicted modulation of different metabolic pathways, notably fatty acid degradation, by VPA. Lastly, we also show that the VPA-cisplatin combination increased mRNA levels of the fatty acid oxidation (FAO) promoting enzymes acyl-CoA synthetase long chain family member 1 (ACSL1) and decreased mRNA levels of fatty acid synthase (FASN), which is the rate limiting enzyme of long-chain fatty acid synthesis. In conclusion, VPA supplementation altered lipid metabolism, especially fatty acid oxidation and lipid synthesis, in cisplatin-treated MDA-MB-231 TNBC cells. This metabolic reprogramming might reduce cisplatin resistance. This finding may lead to the discovery of new therapeutic targets, which might reduce side effects and counter drug tolerance in TNBC patients.

Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance.

BACKGROUND: Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance. METHODS: In this research, an information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach. Briefly, this single-cell quantification strategy computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell. RESULTS: Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy. The strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT). CONCLUSIONS: We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use.