Abstract 1082: Linking tumor microenvironment properties in murine syngeneic tumors with resistance to immune checkpoint inhibitors: Insights from a quantitative systems approach
Authors: Gabriel Helmlinger, Ivan Azarov, Yuri Kosinsky, Veronika Voronova, Lulu Chu, Suzanne Mosely, Simon Dovedi, Kirill Peskov
Journal: Cancer Research (2019)

Abstract Objectives: Studies in murine syngeneic tumors are critical in the development of immune-based therapies, yet there still are knowledge gaps in the functional meaning (vs. response and resistance to treatments) of baseline molecular and immunological features in these tumors. We developed a quantitative systems model of immuno-oncology (IO), to (i) understand factors within the TME which may underlie anti PD-(L)1 and CTLA-4 efficacy in 6 syngeneic tumors (4T1, LLC, CT-26, MC-38, B16, RENCA); (ii) identify potential baseline factors which relate to treatment resistance. Methods: Our IO model [1] was used, firstly to incorporate rich datasets from the 6 syngeneic tumor types [2] and to characterize differences in baseline TME conditions. The model was then used to perform mechanistic population simulations of the initiation and development of anti-tumor T cell immune responses, linked to observed individual animal- and cohort-level tumor size dynamics (TSD) under anti PD-(L)1 and CTLA-4 treatments. Variability in individual tumor size dynamics was taken into account using a mixed-effects technique, implemented in the model at the level of tumor-infiltrating T cell influx. Results: The model adequately described individual- and cohort-level TSD patterns, for all treatment regimens in all 6 tumor types. The model incorporated in one quantitative framework immune cell count data measured in these tumors, by capturing empirical dependencies between TME properties and model parameters. Anti PD-L1 therapy was incorporated into the model via a direct increase in an immune activation rate (IAR) function in TME, validating our previous results [1]. Interestingly, an optimal model incorporating anti CTLA-4 mechanism of action was one considering an indirect effect on IAR through the decrease of immuno-suppressive cell (ISC) function, which supports the hypothesis that the driving force of anti CTLA-4 effects in syngeneic tumors would go through ISC deactivation, e.g., via regulatory T cell (Tregs) depletion. Also, higher counts of Tregs at baseline (e.g., CT26, RENCA) correlated well with responses to anti CTLA-4 treatment. Higher levels of macrophages and/or MDSC infiltration in lesser “immunologically hot” tumors (e.g., 4T1, MC38, LLC) were shown to be the main immuno-suppressive factors limiting tumor responsiveness to checkpoint inhibitor treatments. Conclusions: This quantitative model may be used as a platform to analyze immune-based treatment data from various tumor types, while providing mechanistic insights on the contributions of baseline TME conditions to response or resistance to treatment. The model may be further used to perform predictive tumor response simulations (monotherapies and combinations), of untested anti CTLA-4, PD-(L)1 dose schedules and of other novel IO agents beyond these two checkpoint inhibitors. Citation Format: Gabriel Helmlinger, Ivan Azarov, Yuri Kosinsky, Veronika Voronova, Lulu Chu, Suzanne Mosely, Simon Dovedi, Kirill Peskov. Linking tumor microenvironment properties in murine syngeneic tumors with resistance to immune checkpoint inhibitors: Insights from a quantitative systems approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1082.