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.

Overexpressed kinetochore genes are used by cancer cells as genome destabilizers and transformation catalysts.

Cancer cells have an altered transcriptome, which contributes to their abnormal behavior. Many tumors have high levels of kinetochore genes, which play important roles in genome stability. This overexpression could be utilized to destabilize cancer cell genomes, however this has not been proven specifically. We investigated the link between kinetochore gene overexpression, chromosomal number variations (CNVs) and genomic instability. Data on RNA expression and CNV from 12 different cancer types were evaluated using information theory. In all cancer types, we looked at the relationship between RNA expression and CNVs. Kinetochore gene expression was found to be substantially linked with CNV levels. In all cancer types, with the exception of thyroid cancer, highly expressed kinetochore genes were enriched in the most dominant cancer-specific co-expression subnetworks characterizing the largest patient subgroups. Except for thyroid cancer, kinetochore inner protein CENPA was among the transcripts most strongly associated with CNV values in all cancer types studied, with significantly higher expression levels in patients with high CNVs than in patients with low CNVs. CENPA function was investigated further in cell models by transfecting genomically stable (HCT116) and unstable (MCF7 and HT29) cancer cell lines using CENPA overexpression vectors. This overexpression increased the number of abnormal cell divisions in the stable cancer cell line HCT116 and, to a lesser extent, in the unstable cell lines MCF7 and HT29. Overexpression improved anchorage-independent growth properties of all cell lines. Our findings suggest that overexpression of kinetochore genes in general, and CENPA in particular, can cause genomic instability and cancer progression.