Analysis of radiation-induced changes to human melanoma cultures using a mathematical model

Stat3-targeted therapies overcome the acquired resistance to vemurafenib in melanomas

Vemurafenib (PLX4032), a selective inhibitor of Braf, has been approved by the US Food and Drug Administration for the treatment of unresectable or metastatic melanoma in patients with Braf(V600E) mutations. Many patients treated with vemurafenib initially display dramatic improvement, with decreases in both risk of death and tumor progression. Acquired resistance, however, rapidly arises in previously sensitive cells. We attempted to overcome this resistance by targeting the signal transducer and activator of transcription 3 (STAT3)-paired box homeotic gene 3 (PAX3)-signaling pathway, which is upregulated, owing to fibroblast growth factor 2 (FGF2) secretion or increased kinase activity, with the Braf(V600E) mutation. We found that activation of Stat3 or overexpression of PAX3 induced resistance to vemurafenib in melanoma cells. In addition, PAX3 or Stat3 silencing inhibited the growth of melanoma cells with acquired resistance to vemurafenib. Furthermore, treatment with the Stat3 inhibitor, WP1066, resulted in growth inhibition in both vemurafenib-sensitive and -resistant melanoma cells. Significantly, vemurafenib stimulation induced FGF2 secretion from keratinocytes and fibroblasts, which might uncover, at least in part, the mechanisms underlying targeting Stat3-PAX3 signaling to overcome the acquired resistance to vemurafenib. Our results suggest that Stat3-targeted therapy is a new therapeutic strategy to overcome the acquired resistance to vemurafenib in the treatment of melanoma.

Optimization and preclinical design of genetically engineered viruses for human oncolytic therapy

INTRODUCTION

Oncolytic viruses (OVs) occupy a strategic niche in the dynamic era of biological and gene therapy of human cancers. However, the use of OVs is the subject of close scrutiny due to impediments such as the insufficiency of patient generalizations posed by heterogeneous tumor responses to treatment, inherent or potentially lethal viral pathogenicities, unanticipated host- or immune-related adverse effects, and the emergence of virus-resistant cancer cells. These challenges can be overcome by the design and development of more definitive (optimized, targeted, and individualized) cancer virotherapeutics.

AREAS COVERED

The translation of current knowledge and recent innovations into rational treatment prospects hinges on an iterative loop of variables pertaining to genetically engineered viral oncolytic efficacy and safety profiles, mechanism-of-action data, potencies of synergistic oncolytic viral combinations with conventional tumor, immuno-, chemo-, and radiation treatment modalities, optimization of the probabilities of treatment successes in heterogeneous (virus-sensitive and -resistant) tumor cell populations by mathematical modeling, and lessons learned from preclinical studies and human clinical trials.

EXPERT OPINION

In recent years, it has become increasingly clear that proof-of-principle is critical for the preclinical optimization of oncolytic viruses to target heterogeneous forms of cancer and to prioritize current concerns related to the efficacy and safety of oncolytic virotherapy.

Pharmacodynamic modelling of biomarker data in oncology

The development of pharmacodynamic (PD) biomarkers in oncology has implications for design of clinical protocols from preclinical data and for predicting clinical outcomes from early clinical data. Two classes of biomarkers have received particular attention. Phosphoproteins in biopsy samples are markers of inhibition of signalling pathways, target sites for many novel agents. Biomarkers of apoptosis in plasma can measure tumour cell killing by drugs in phase I clinical trials. The predictive power of PD biomarkers is enhanced by data modelling. With pharmacokinetic models, PD models form PK/PD models that predict the time course both of drug concentration and drug effects. If biomarkers of drug toxicity are also measured, the models can predict drug selectivity as well as efficacy. PK/PD models, in conjunction with disease models, make possible virtual clinical trials, in which multiple trial designs are assessed in silico, so the optimal trial design can be selected for experimental evaluation.