BRAFV600E melanoma patients, despite initially responding to the clinically prescribed anti-BRAFV600E therapy, often relapse, and their tumors develop drug resistance. While it is widely accepted that these tumors are originally driven by the BRAFV600E mutation, they often eventually diverge and become supported by various signaling networks. Therefore, patient-specific altered signaling signatures should be deciphered and treated individually. In this study, we design individualized melanoma combination treatments based on personalized network alterations. Using an information-theoretic approach, we compute high-resolution patient-specific altered signaling signatures. These altered signaling signatures each consist of several co-expressed subnetworks, which should all be targeted to optimally inhibit the entire altered signaling flux. Based on these data, we design smart, personalized drug combinations, often consisting of FDA-approved drugs. We validate our approach in vitro and in vivo showing that individualized drug combinations that are rationally based on patient-specific altered signaling signatures are more efficient than the clinically used anti-BRAFV600E or BRAFV600E/MEK targeted therapy. Furthermore, these drug combinations are highly selective, as a drug combination efficient for one BRAFV600E tumor is significantly less efficient for another, and vice versa. The approach presented herein can be broadly applicable to aid clinicians to rationally design patient-specific anti-melanoma drug combinations.

Triple-negative breast cancer (TNBC) is an aggressive subgroup of breast cancers which is treated mainly with chemotherapy and radiotherapy. Epidermal growth factor receptor (EGFR) was considered to be frequently expressed in TNBC, and therefore was suggested as a therapeutic target. However, clinical trials of EGFR inhibitors have failed. In this study, we examine the relationship between the patient-specific TNBC network structures and possible mechanisms of resistance to anti-EGFR therapy. Using an information-theoretical analysis of 747 breast tumors from the TCGA dataset, we resolved individualized protein network structures, namely patient-specific signaling signatures (PaSSS) for each tumor. Each PaSSS was characterized by a set of 1–4 altered protein–protein subnetworks. Thirty-one percent of TNBC PaSSSs were found to harbor EGFR as a part of the network and were predicted to benefit from anti-EGFR therapy as long as it is combined with anti-estrogen receptor (ER) therapy. Using a series of single-cell experiments, followed by in vivo support, we show that drug combinations which are not tailored accurately to each PaSSS may generate evolutionary pressure in malignancies leading to an expansion of the previously undetected or untargeted subpopulations, such as ER+ populations. This corresponds to the PaSSS-based predictions suggesting to incorporate anti-ER drugs in certain anti-TNBC treatments. These findings highlight the need to tailor anti-TNBC targeted therapy to each PaSSS to prevent diverse evolutions of TNBC tumors and drug resistance development.

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.

SummaryTriple negative breast cancer (TNBC) is an aggressive type of cancer that is known to be resistant to radiotherapy (RT). Evidence is accumulating that is indicative of the plasticity of TNBC, where one cancer subtype switches to another in response to various treatments, including RT. In this study we aim to overcome tumor resistance by designing TNBC-sensitizing targeted therapies that exploit the plasticity occurring due to radiation exposure. Using single cell analysis of molecular changes occurring in irradiated TNBC tumors, we identified two initially undetected distinct subpopulations, represented by overexpressed Her2 and cMet, expanding post-RT and persisting in surviving tumors. Using murine cancer models and patient-derived TNBC tumors, we showed that only simultaneous targeting of Her2 and cMet was successful in sensitizing TNBC to RT and preventing its regrowth. The strategy presented herein holds the potential to be broadly applicable in clinical use.HighlightsSensitization of TNBC to radiotherapy (RT) is a clinically unmet needSingle cell strategy creates a precise map of subpopulations expanding post-RTEvolution of intra-tumor heterogeneity is turned into a therapeutic advantageSimultaneous targeting of expanding subpopulations sensitizes TNBC to radiotherapy

Background: Prolonged patient stay in ER is an issue frequently raised with regards to patient safety. In addition to patient complains and dissatisfaction, it increases the risk of healthcare associated infections, increases pressure on ER staff, increases waiting time and eventually impacts bed utilization. Oncology patients frequently visits ER due to their disease nature, progression and treatment protocols (radiotherapy, chemotherapy, and hormonal therapy), in which they come in with multiple serious medical complains that need early and immediate interventions. Septic shock, neutropenic fever and electrolyte imbalance are some of these serious conditions. Aim: To decrease the length of stay of ER patients at an oncology center. Methods: Lean improvement methodology was adopted to eliminate the unnecessary waste during ER workflow. Lean improvement team was trained on lean concepts and methodology by an expert staff. ER value stream map was drawn and an initial data were collected by outside volunteers to eliminate data collection bias, then lean interactions were deployed on multidisciplinary dimensions, followed by quarterly data collection to measure the success of the interventions. It was a cycle of training, collecting data, meeting ER physicians, pharmacy, laboratory, radiology, support services, and nursing. Then implementing the proposed interventions and finally collecting data. Results: ER patients' length of stay gradually decreased by 42% from 377 minutes to 221 minutes. There were remarkable deductions in radiology procedures turn-around time by 62%, and pharmacy by 57%. Improvement in patient flow, decreasing waiting time and ultimately improved patient and family satisfaction were measured outcomes to lean project. Conclusion: Lean improvement methodology is an excellent tool to reduce the nonvalue added time and ultimately improves the patient's safety.