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Abstract 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.

Abstract 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.

Abstract Heparanase promotes tumor growth in breast tumors. We now evaluated heparanase protein and gene-expression status and investigated its impact on disease-free survival in order to gain better insight into the role of heparanase in ER-positive (ER+) breast cancer prognosis and to clarify its role in cell survival following chemotherapy. Using pooled analysis of gene-expression data, we found that heparanase was associated with a worse prognosis in estrogen receptor-positive (ER+) tumors (log-rank p

Agents used to reduce adverse effects common in cancer treatment modalities do not typically possess tumor-suppressing properties. We report that heparanase, an extracellular matrix-degrading enzyme, is a promising candidate for preventing radiation nephropathy. Heparanase promotes tumor development and progression and is upregulated in tumors found in the abdominal/pelvic cavity, whose radiation treatment may result in radiation nephropathy. Additionally, heparan sulfate degradation by heparanase has been linked to glomerular and tubular/interstitial injury in several kidney disorders. In this study, heparanase mRNA levels were measured in HK-2- and HEK-293-irradiated kidney cells and in a murine radiation nephropathy model by qRT-PCR. Roneparstat (specific heparanase inhibitor) was administered to irradiated mice, and 24 h urinary albumin was measured. Kidneys were harvested and weighed 30 weeks post-irradiation. Clinically relevant doses of ionizing radiation upregulated heparanase expression in both renal cells and mice kidneys. A murine model of abdominal radiation therapy revealed that Roneparstat abolished radiation-induced albuminuria—the hallmark of radiation nephropathy. Given the well-documented anti-cancer effects of heparanase inhibition, our findings attest this enzyme to be a unique target in cancer therapy due to its dual action. Targeting heparanase exerts not only direct anti-tumor effects but protects against radiation-induced kidney damage—the backbone of cancer therapy across a range of malignancies.

Breast cancer (BC) and obesity are two heterogeneous conditions with a tremendous impact on health. BC is the most commonly diagnosed neoplasm and the leading cause of cancer-related mortality among women, and the prevalence of obesity in women worldwide reaches pandemic proportions. Obesity is a significant risk factor for both incidence and worse prognosis in estrogen receptor positive (ER+) BC. Yet, the mechanisms underlying the association between excess adiposity and increased risk/therapy resistance/poorer outcome of ER+, but not ER−negative (ER−), BC are not fully understood. Tumor-promoting action of obesity, predominantly in ER + BC patients, is often attributed to the augmented production of estrogen in ‘obese’ adipose tissue. However, in addition to the estrogen production, expression levels of ER represent a key determinant in hormone-driven breast tumorigenesis and therapy response. Here, utilizing in vitro and in vivo models of BC, we show that macrophages, whose adverse activation by obesogenic substances is fueled by heparanase (extracellular matrix-degrading enzyme), are capable of upregulating ER expression in tumor cells, in the setting of obesity-associated BC. These findings underscore a previously unknown mechanism through which interplay between cellular/extracellular elements of obesity-associated BC microenvironment influences estrogen sensitivity—a critical component in hormone-related cancer progression and resistance to therapy.

While at least six types of cancer have been associated with diabetes, pancreatic ductal adenocarcinoma (PDAC) and diabetes exhibit a unique bidirectional relationship. Recent reports indicate that majority of PDAC patients display hyperglycemia, and ~50% have concurrent diabetes. In turn, hyperglycemic/diabetic state in PDAC patients fosters enhanced growth and dissemination of the tumor. Heparanase enzyme (the sole mammalian endoglycosidase degrading glycosaminoglycan heparan sulfate) is tightly implicated in PDAC progression, aggressiveness, and therapy resistance. Overexpression of heparanase is a characteristic feature of PDAC, correlating with poor prognosis. However, given the lack of heparanase expression in normal pancreatic tissue, the regulatory mechanisms responsible for induction of the enzyme in PDAC have remained largely unknown. Previously reported inducibility of heparanase gene by diabetic milieu components in several non-cancerous cell types prompted us to hypothesize that in the setting of diabetes-associated PDAC, hyperglycemic state may induce heparanase overexpression. Here, utilizing a mouse model of diet-induced metabolic syndrome/diabetes, we found accelerated PDAC progression in hyperglycemic mice, occurring along with induction of heparanase in PDAC. In vitro, we demonstrated that advanced glycation end-products (AGE), which are largely thought as oxidative derivatives resulting from chronic hyperglycemia, and the receptor for AGE (RAGE) are responsible for heparanase induction in PDAC cells. These findings underscore the new mechanism underlying preferential expression of heparanase in pancreatic cancer. Moreover, taken together with the well-established causal role of the enzyme in PDAC progression, our findings indicate that heparanase may sustain (at least in part) reciprocal causality between diabetes and pancreatic tumorigenesis.