A mathematical model of immune-system-melanoma competition

TNFa and IL-2 armed adenoviruses enable complete responses by anti-PD-1 checkpoint blockade

Releasing the patient's immune system against their own malignancy by the use of checkpoint inhibitors is delivering promising results. However, only a subset of patients currently benefit from them. One major limitation of these therapies relates to the inability of T cells to detect or penetrate into the tumor resulting in unresponsiveness to checkpoint inhibition. Virotherapy is an attractive tool for enabling checkpoint inhibitors as viruses are naturally recognized by innate defense elements which draws the attention of the immune system. Besides their intrinsic immune stimulating properties, the adenoviruses used here are armed to express tumor necrosis factor alpha (TNFa) and interleukin-2 (IL-2). These cytokines result in immunological danger signaling and multiple appealing T-cell effects, including trafficking, activation and propagation. When these viruses were injected into B16.OVA melanoma tumors in animals concomitantly receiving programmed cell-death protein 1 (PD-1) blocking antibodies both tumor growth control (p < 0.0001) and overall survival (p < 0.01) were improved. In this set-up, the addition of adoptive cell therapy with OT-I lymphocytes did not increase efficacy further. When virus injections were initiated before antibody treatment in a prime-boost approach, 100% of tumors regressed completely and all mice survived. Viral expression of IL2 and TNFa altered the cytokine balance in the tumor microenvironment towards Th1 and increased the intratumoral proportion of CD8+ and conventional CD4+ T cells. These preclinical studies provide the rationale and schedule for a clinical trial where oncolytic adenovirus coding for TNFa and IL-2 (TILT-123) is used in melanoma patients receiving an anti-PD-1 antibody.

Integrating Models to Quantify Environment-Mediated Drug Resistance

Drug resistance is the single most important driver of cancer treatment failure for modern targeted therapies, and the dialog between tumor and stroma has been shown to modulate the response to molecularly targeted therapies through proliferative and survival signaling. In this work, we investigate interactions between a growing tumor and its surrounding stroma and their role in facilitating the emergence of drug resistance. We used mathematical modeling as a theoretical framework to bridge between experimental models and scales, with the aim of separating intrinsic and extrinsic components of resistance in BRAF-mutated melanoma; the model describes tumor-stroma dynamics both with and without treatment. Integration of experimental data into our model revealed significant variation in either the intensity of stromal promotion or intrinsic tissue carrying capacity across animal replicates. Cancer Res; 77(19); 5409-18. ©2017 AACR.

Computational modeling in melanoma for novel drug discovery

INTRODUCTION

There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches.

AREAS COVERED

This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials.

EXPERT OPINION

Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.

Agent-based modeling of the immune system: NetLogo, a promising framework

Several components that interact with each other to evolve a complex, and, in some cases, unexpected behavior, represents one of the main and fascinating features of the mammalian immune system. Agent-based modeling and cellular automata belong to a class of discrete mathematical approaches in which entities (agents) sense local information and undertake actions over time according to predefined rules. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. This behavior is unpredictable, as it does not follow linear rules. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. They have shown the ability to see clearly and intuitively into the nature of immunological processes. NetLogo is a multiagent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. In this paper, we summarize NetLogo applications to immunology and, particularly, how this framework can help in the development and formulation of hypotheses that might drive further experimental investigations of disease mechanisms.

Metastatic melanoma - a review of current and future drugs

Background:

Melanoma is one of the most aggressive cancers, and it is estimated that 76,250 men and women will be diagnosed with melanoma of the skin in the USA in 2012. Over the last few decades many drugs have been developed but only in 2011 have new drugs demonstrated an impact on survival in metastatic melanoma.

Methods:

A systematic search of literature was conducted, and studies providing data on the effectiveness of current and/or future drugs used in the treatment of metastatic melanoma were selected for review. This review discusses the advantages and limitations of these agents, evaluating past, current and future clinical trials designed to overcome such limitations.

Results:

To date, there are four drugs approved by the Food and Drug Administration for melanoma (dacarbazine, interleukin-2, ipilimumab and vemurafenib). Despite efforts to develop new drugs, few of them have demonstrated any clinical benefits. Approved in 1975, dacarbazine remains the gold standard in chemotherapy, although ipilimumab and vemurafenib have raised many hopes in the last few years. Combining dacarbazine or other chemotherapy agents with new pharmacological agents may be a new way to achieve better clinical responses in patients with metastatic melanoma.

Discussion:

Advances in the molecular knowledge of melanoma have led to major improvements in the treatment of patients with metastatic melanoma, providing new targets and insights. However, heterogeneity amongst study populations, different approaches to treatment and the different melanoma types and localisations included in the trials makes their comparison difficult. New studies focusing on drugs developed in recent decades are warranted.