Viji Chelliah (1), Georgia Lazarou (2), Andrzej Kierzek (2), Piet van der Graaf (1)
(1) Certara UK Limited, Unit 43, Canterbury Innovation Centre, University Road, Canterbury, CT2 7FG, United Kingdom, (2) Certara UK Limited, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, United Kingdom
Introduction: Immuno-oncology (IO), where treatments mobilize a patient’s immune system to fight cancer and provide lasting therapeutic benefit, is the fastest developing area of oncology. New therapies are being aimed at targeting different stages of the cancer-immunity cycle [1], which involves a dynamic system of non-linear interactions between cellular and molecular players of the immune system and tumour. The design of an effective cancer immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. Quantitative and mechanistic understanding of this system is crucial to unravel these complexities where the application of the Quantitative Systems Pharmacology (QSP) approach becomes inevitable for diverse clinical decision making in IO drug discovery and development.
This work was carried out as a part of Certara’s QSP IO Simulator Consortium Project [2], which aims to develop a mechanistic model of cancer-immune system dynamics through which combinations of different cancer therapies, different dose regimens and biomarkers in a virtual patient population can be tested.
Objectives: The aim of the present work was to conduct a comprehensive survey of IO literature-based models and to translate this to a QSP map of IO mechanisms. Hence, the study was focused on 1) to survey and integrate the knowledge from IO literature-based, 2) to understand the extent of cancer-immunity system dynamics that are captured by these models, and 3) to objectively compare the gaps where the growing awareness (from experimental/clinical and omics data) on cancer-immune system dynamics have not been well characterized or captured in the models.
Methods: We systematically surveyed 136 published mechanistic models describing various components of cancer-immune system dynamics, and immunotherapies. Information regarding the following topics were captured from the model papers: 1) the purpose for which the model was developed, 2) model variables (cell-types, cytokines, growth-factors, cell-surface receptors and other molecular signatures) and their interactions, 3) treatment types (mono, or in combination), 4) study type (preclinical/clinical data) and 5) the biomarkers used for model validation.
Results and Conclusions: We distill and discuss several example models that have grown in complexity by incorporating the advances in cancer-immune biology. We then integrated the mechanisms described in different models and developed a unified QSP map, which was implemented in the Certara QSP Platform which provided an illustration of the extent of cancer-immune system dynamics that have been mechanistically well characterized and quantitatively studied.
Even though there were several overlapping models that describe the same aspect of cancer-immunity cycle, the level of granularity in describing the underlying mechanisms differed between models, and together they cover a vast majority of cell-types and molecular signatures involved in the cancer-immune system biology. The 136 literature-based models describes the mechanisms involving 15 different cells types (includes cancer cells, immune cells and stromal cells) and 36 molecular signatures (includes cytokines, chemokines, cell-surface receptors, growth factors and intra-cellular molecules), applied to predict different treatment scenarios (with both immunotherapy and non-immunotherapy agents). This comprehensive analysis of literature models of the cancer-immunity cycle allowed us to identify gaps in knowledge incorporation in models and where new interventions are needed to be applied.
References:
[1] Chen D. S. and Mellman I. Oncology meets immunology: the cancer-immunity cycle. Immunity. 2013; 39(1):1-10
[2] https://www.certara.com/pressreleases/certara-launches-industry-first-quantitative-systems-pharmacology-qsp-consortium-on-immuno-oncology-with-leading-pharma-company-members/
Reference: PAGE 28 (2019) Abstr 9189 [www.page-meeting.org/?abstract=9189]
Poster: Drug/Disease Modelling - Oncology