(664g) Optimization and Data Driven Strategies for Robust Treatment Planning of Cancer Dendritic Cell-Interleukin Therapy

In recent years, cancer immunotherapy (wherein the immune cells are stimulated to combat the cancer cells) has emerged as a promising therapy for treatment. The advantage of immunotherapy over the conventional therapies is its competency to selectively target cancer cells without damaging the normal cells. Thus, it is also known as targeted therapy. There are many forms of cancer immunotherapy - dendritic cell therapy and interleukin therapy are some specific examples. Dendritic cells are the tumor-antigen-presenting cells which sensitize the cytotoxic immune cells whereas interleukin is an essential enzyme to maintain the perpetual activity of cytotoxic immune cells (Adam et al., 2003). In dendritic cell therapy, the key players are dendritic cells, interleukin, cytotoxic T-cells. Clinical trials of dendritic cell therapy are in progress to manifest its targeted ability in enhancing the process of cancer regression.

Like other therapies, the challenge in this therapy is the determination of the optimal timing and dosage of the therapeutic agents and how to personalize the therapeutic intervention plan. In this work, a mathematical model in the form of ODEs explaining the dynamics of tumor cells, immune cells and interleukin is considered. Then, "patients" are generated by varying the parameters of the model within the stipulated bounds. Samples of different parameter set are chosen using sampling methods. The optimum therapeutic interventions are determined for different parameter sets (corresponding to different patients) subject to suitable objectives and constraints. Later, the obtained optimal treatment planning for different patients are analyzed using data-driven techniques to propose personalized optimal treatment protocols for the new patients based only on the parameter values without performing the optimization. The main idea of this work is to mimic a veteran oncologist in suggesting treatment plan for the cancer patients by employing quantitative approaches.

Keywords: Cancer, Immunotherapy, Dendritic cell, Interleukin, Mathematical model, Optimization, Data driven techniques

References: Adam JK, Odhav B, Bhoola KD. 2003. Immune responses in cancer. Pharmacology & Therapeutics 99(1):113-132.

Ballestrero A, Boy D, Moran E, Cirmena G, Brossart P, Nencioni A. 2008. Immunotherapy with dendritic cells for cancer. Advanced Drug Delivery Reviews 60(2):173-183.

Banchereau J, Schuler-Thurner B, Palucka AK, Schuler G. 2001. Dendritic Cells as Vectors for Therapy. Cell 106(3):271-274.

Castiglione F, Piccoli B. 2006. Optimal control in a model of dendritic cell transfection cancer immunotherapy. Bulletin of Mathematical Biology 68(2):255-274.

Castiglione F, Piccoli B. 2007. Cancer immunotherapy, mathematical modeling and optimal control. Journal of Theoretical Biology 247(4):723-732.

Castiglione F, Piccoli B, ieee. Optimal control in a model of dendritic cell transfection cancer immunotherapy; 2004 Dec 14-17; San Diego, CA. p 585-590.

Chen Y, Hu S, Chen D. 2003. Combinative neural network and its applications. Computational Biology and Chemistry 27(3):287-295.

De Boer RJ, Hogeweg P. 1986. Interactions between macrophages and T-lymphocytes: Tumor sneaking through intrinsic to helper T cell dynamics. Journal of Theoretical Biology 120(3):331-351.

de Pillis LG, Gu W, Radunskaya AE. 2006. Mixed immunotherapy and chemotherapy of tumors: modeling, applications and biological interpretations. Journal of Theoretical Biology 238(4):841-862. Goldberg, DE. 1989. Genetic Algorithms in Search, Optimization & Machine Learning: Addison-Wesley.

Hanahan D, Weinberg RA. 2000. The Hallmarks of Cancer. Cell 100(1):57-70.

Kiran, K. L., Lakshminarayanan, S. 2009. Treatment planning of cancer dendritic cell therapy using multi-objective optimization, In Proceedings of ADCHEM 2009, Istanbul, Turkey.

Kirschner D, Panetta JC. 1998. Modeling immunotherapy of the tumor ? immune interaction. Journal of Mathematical Biology 37(3):235-252.

Kleinsmith LJ. 2005. Principles of cancer biology: Addison-Wesley.

Martins ML, Ferreira Jr SC, Vilela MJ. 2007. Multiscale models for the growth of avascular tumors. Physics of Life Reviews 4(2):128-156.