Customizing the therapeutic response of signaling networks to promote antitumor responses by drug combinations

Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer

Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.

Advances in computational approaches in identifying synergistic drug combinations

Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.

A novel mechanism of action of HER2 targeted immunotherapy is explained by inhibition of NRF2 function in ovarian cancer cells

Nuclear erythroid related factor-2 (NRF2) is known to promote cancer therapeutic detoxification and crosstalk with growth promoting pathways. HER2 receptor tyrosine kinase is frequently overexpressed in cancers leading to uncontrolled receptor activation and signaling. A combination of HER2 targeting monoclonal antibodies shows greater anticancer efficacy than the single targeting antibodies, however, its mechanism of action is largely unclear. Here we report novel actions of anti-HER2 drugs, Trastuzumab and Pertuzumab, involving NRF2.

HER2 targeting by antibodies inhibited growth in association with persistent generation of reactive oxygen species (ROS), glutathione (GSH) depletion, reduction in NRF2 levels and inhibition of NRF2 function in ovarian cancer cell lines. The combination of antibodies produced more potent effects than single antibody alone; downregulated NRF2 substrates by repressing the Antioxidant Response (AR) pathway with concomitant transcriptional inhibition of NRF2. We showed the antibody combination produced increased methylation at the NRF2 promoter consistent with repression of NRF2 antioxidant function, as HDAC and methylation inhibitors reversed such produced transcriptional effects. These findings demonstrate a novel mechanism and role for NRF2 in mediating the response of cancer cells to the combination of Trastuzumab and Pertuzumab and reinforce the importance of NRF2 in drug resistance and as a key anticancer target.

Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations

Making cancer treatment more effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We urgently need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. The book chapter focuses on mathematical and computational tools to facilitate the discovery of the most promising drug combinations to improve efficacy and prevent resistance. Data integration approaches that leverage drug-target interactions, cancer molecular features, and signaling pathways for predicting, understanding, and testing drug combinations are critically reviewed.

NRF2 Regulates HER2 and HER3 Signaling Pathway to Modulate Sensitivity to Targeted Immunotherapies

NF-E2 related factor-2 (NRF2) is an essential transcription factor for multiple genes encoding antioxidants and detoxification enzymes. NRF2 is implicated in promoting cancer therapeutic resistance by its detoxification function and crosstalk with proproliferative pathways. However, the exact mechanism of this intricate connectivity between NRF2 and growth factor induced proliferative pathway remains elusive. Here, we have demonstrated that pharmacological activation of NRF2 by tert-butylhydroquinone (tBHQ) upregulates the HER family receptors, HER2 and HER3 expression, elevates pAKT levels, and enhances the proliferation of ovarian cancer cells. Preactivation of NRF2 also attenuates the combined growth inhibitory effects of HER2 targeting monoclonal antibodies, Pertuzumab and Trastuzumab. Further, tBHQ caused transcriptional induction of HER2 and HER3, while SiRNA-mediated knockdown of NRF2 prevented this and further caused transcriptional repression and enhanced cytotoxicity of the HER2 inhibitors. Hence, NRF2 regulates both HER2 and HER3 receptors to influence cellular responses to HER2 targeting monoclonal antibodies. This deciphered crosstalk mechanism reinforces the role of NRF2 in drug resistance and as a relevant anticancer target.

Integrative Clinical Sequencing in the Management of Refractory or Relapsed Cancer in Youth

IMPORTANCE

Cancer is caused by a diverse array of somatic and germline genomic aberrations. Advances in genomic sequencing technologies have improved the ability to detect these molecular aberrations with greater sensitivity. However, integrating them into clinical management in an individualized manner has proven challenging.

OBJECTIVE

To evaluate the use of integrative clinical sequencing and genetic counseling in the assessment and treatment of children and young adults with cancer.

DESIGN, SETTING, AND PARTICIPANTS

Single-site, observational, consecutive case series (May 2012-October 2014) involving 102 children and young adults (mean age, 10.6 years; median age, 11.5 years, range, 0-22 years) with relapsed, refractory, or rare cancer.

EXPOSURES

Participants underwent integrative clinical exome (tumor and germline DNA) and transcriptome (tumor RNA) sequencing and genetic counseling. Results were discussed by a precision medicine tumor board, which made recommendations to families and their physicians.

MAIN OUTCOMES AND MEASURES

Proportion of patients with potentially actionable findings, results of clinical actions based on integrative clinical sequencing, and estimated proportion of patients or their families at risk of future cancer.

RESULTS

Of the 104 screened patients, 102 enrolled with 91 (89%) having adequate tumor tissue to complete sequencing. Only the 91 patients were included in all calculations, including 28 (31%) with hematological malignancies and 63 (69%) with solid tumors. Forty-two patients (46%) had actionable findings that changed their cancer management: 15 of 28 (54%) with hematological malignancies and 27 of 63 (43%) with solid tumors. Individualized actions were taken in 23 of the 91 (25%) based on actionable integrative clinical sequencing findings, including change in treatment for 14 patients (15%) and genetic counseling for future risk for 9 patients (10%). Nine of 91 (10%) of the personalized clinical interventions resulted in ongoing partial clinical remission of 8 to 16 months or helped sustain complete clinical remission of 6 to 21 months. All 9 patients and families with actionable incidental genetic findings agreed to genetic counseling and screening.

CONCLUSIONS AND RELEVANCE

In this single-center case series involving young patients with relapsed or refractory cancer, incorporation of integrative clinical sequencing data into clinical management was feasible, revealed potentially actionable findings in 46% of patients, and was associated with change in treatment and family genetic counseling for a small proportion of patients. The lack of a control group limited assessing whether better clinical outcomes resulted from this approach than outcomes that would have occurred with standard care.

Discovery and development of Seliciclib. How systems biology approaches can lead to better drug performance

Seliciclib (R-Roscovitine) was identified as an inhibitor of CDKs and has undergone drug development and clinical testing as an anticancer agent. In this review, the authors describe the discovery of Seliciclib and give a brief summary of the biology of the CDKs Seliciclib inhibits. An overview of the published in vitro and in vivo work supporting the development as an anti-cancer agent, from in vitro experiments to animal model studies ending with a summary of the clinical trial results and trials underway is presented. In addition some potential non-oncology applications are explored and the potential mode of action of Seliciclib in these areas is described. Finally the authors argue that optimisation of the therapeutic effects of kinase inhibitors such as Seliciclib could be enhanced using a systems biology approach involving mathematical modelling of the molecular pathways regulating cell growth and division.

Adaptive stress signaling in targeted cancer therapy resistance

The identification of specific genetic alterations that drive the initiation and progression of cancer and the development of targeted drugs that act against these driver alterations has revolutionized the treatment of many human cancers. While substantial progress has been achieved with the use of such targeted cancer therapies, resistance remains a major challenge that limits the overall clinical impact. Hence, despite progress, new strategies are needed to enhance response and eliminate resistance to targeted cancer therapies in order to achieve durable or curative responses in patients. To date, efforts to characterize mechanisms of resistance have primarily focused on molecular events that mediate primary or secondary resistance in patients. Less is known about the initial molecular response and adaptation that may occur in tumor cells early upon exposure to a targeted agent. Although understudied, emerging evidence indicates that the early adaptive changes by which tumor cells respond to the stress of a targeted therapy may be crucial for tumor cell survival during treatment and the development of resistance. Here, we review recent data illuminating the molecular architecture underlying adaptive stress signaling in tumor cells. We highlight how leveraging this knowledge could catalyze novel strategies to minimize or eliminate targeted therapy resistance, thereby unleashing the full potential of targeted therapies to transform many cancers from lethal to chronic or curable conditions.

Systems analysis of drug-induced receptor tyrosine kinase reprogramming following targeted mono- and combination anti-cancer therapy

The receptor tyrosine kinases (RTKs) are key drivers of cancer progression and targets for drug therapy. A major challenge in anti-RTK treatment is the dependence of drug effectiveness on co-expression of multiple RTKs which defines resistance to single drug therapy. Reprogramming of the RTK network leading to alteration in RTK co-expression in response to drug intervention is a dynamic mechanism of acquired resistance to single drug therapy in many cancers. One route to overcome this resistance is combination therapy. We describe the results of a joint in silico, in vitro, and in vivo investigations on the efficacy of trastuzumab, pertuzumab and their combination to target the HER2 receptors. Computational modelling revealed that these two drugs alone and in combination differentially suppressed RTK network activation depending on RTK co-expression. Analyses of mRNA expression in SKOV3 ovarian tumour xenograft showed up-regulation of HER3 following treatment. Considering this in a computational model revealed that HER3 up-regulation reprograms RTK kinetics from HER2 homodimerisation to HER3/HER2 heterodimerisation. The results showed synergy of the trastuzumab and pertuzumab combination treatment of the HER2 overexpressing tumour can be due to an independence of the combination effect on HER3/HER2 composition when it changes due to drug-induced RTK reprogramming.