Frontiers in Cancer Immunology

Our immune system is a dynamic environment that is orchestrated by a network of immune cells and signalling molecules and is differentially expressed and regulated at different stages of life. Interestingly, components of the immune system function differently under diseased or non-diseased state, healthy or immune-deficient state. Cancer is generally regarded as a genetic disease and caused from inflammation. Due to the complex nature of the disease, it is necessary to understand how the immune system responses in a tumor environment. A detailed understanding on the cancer immuno-biology will enable formulation of appropriate treatment strategies for cancer.

Cancer cells show excessive need of nutrients and energy. Not only this, activated lymphocytes, in particular the T lymphocytes and myeloid derived cells also show differential expression in the metabolic pathway genes in tumor microenvironment. Therefore, understanding the metabolic changes in the context of cancer development will help to identify new therapeutic targets to treat cancer.

Signal transduction pathways and associated molecules play important role in maintaining cell death and cell survival. However, in cancer cells, some of these molecules are mutated and lead to cancer progression. These molecules also interfere with the components of the humoral and the cell mediated immune systems in tumor environment. Therefore, identifying these can be potential targets for cancer treatment.

Treatment of cancer does not depend on a single drug and the existing strategies are challenged by the the frequent development of drug resistance properties of cancer cells. Although chemotherapy drugs are still used as the first line of treatment modalities for cancer, immunotherapyis also used as a targeted treatment modality nowadays. However, major challenge with the treatment is that, patient recovery is very low. Therefore, combination therapies are being investigated and some of them are at the clinical stage with FDA approval. In this chapter, we will discuss currently available immunotherapy drugs and their synergistic effects in combination with chemotherapy drugs or two different immunotherapy drugs.

Systems biology is relatively a new field of study in cancer research. However, this approach has gained much attention as it can be used to understand the molecular level of a system under diseased or healthy condition and under dynamic or static condition. The approach allows understanding the interaction of DNA, RNA, protein and metabolite levels of a cell. Since, the cancer micro-environment consists of highly heterogeneous population of cells, systems biology is the robust tool that can be applied to understand this complex environment in the presence of perturbed condition using small molecules or targeted drugs like immunotherapy. Systems biology is already applied by drug design and discovery companies as well as by drug regulatory agencies to monitor safety and toxicity of the drug. The high throughput (HT) technological platforms generate un-biased datasets. However, data–mining is a problem for this approach. Despite this drawback, systems biology has been used in cancer immunotherapy to some extent. In this chapter, we discuss the known application of systems biology in cancer immunotherapy in particular to its application in biomarker identification, vaccine development, application in combination therapy, use in the development of validation models and future application in personalized medicine.

Both system biology and cancer immunotherapy are emerging fields in life sciences research. Immunotherapy is considered as targeted treatment modalities for cancer. However, the complex nature of the tumor environment affects the efficacy and clinical outcome of this type of therapy. Combination therapy can be a solution. But this requires investigation at the cellular and molecular level of effect of the drugs. Systems biology is such a powerful tool, which allows the cellular level understanding and has been used in target discovery and validation in different fields of cancer research. Despite this advantage, challenges exist in large dataset mining. In this concluding chapter, we discuss some of the challenges and possible future directions that may help to overcome this problem.