Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model

Simple Summary: We introduced a novel approach to quantitatively validate the performance of a hybrid spatio-temporal method called spatial quantitative systems pharmacology (spQSP). This platform is composed of a compartmental QSP model describing tumor growth dynamics, anti-tumor immune response and immune checkpoint therapy in a whole-patient and a spatial agent-based model, describing the tumor to simulate the effect of anti-PD-1 therapy (an immune checkpoint inhibitor) on simulated intratumoral heterogeneity. Four spatial metrics adopted from computational digital pathology, along with the ratio of cancer cells to immune cells, were used to categorize the tumor microenvironment as cold, mixed and compartmentalized patterns, which were related to the efficacy of the treatment. This study compared the intratumoral heterogeneity description capability of the metrics to facilitate future comprehensive and tangible research on specific cancer types, different therapeutics as single agents or combination therapies, and immunopathological multiplexed samples. Having a better quantitative understanding of intratumoral heterogeneity using numerical simulations can help design more effective treatments. Spatial heterogeneity is a hallmark of cancer. Tumor heterogeneity can vary with time and location. The tumor microenvironment (TME) encompasses various cell types and their interactions that impart response to therapies. Therefore, a quantitative evaluation of tumor heterogeneity is crucial for the development of effective treatments. Different approaches, such as multiregional sequencing, spatial transcriptomics, analysis of autopsy samples, and longitudinal analysis of biopsy samples, can be used to analyze the intratumoral heterogeneity (ITH) and temporal evolution and to reveal the mechanisms of therapeutic response. However, because of the limitations of these data and the uncertainty associated with the time points of sample collection, having a complete understanding of intratumoral heterogeneity role is challenging. Here, we used a hybrid model that integrates a whole-patient compartmental quantitative-systems-pharmacology (QSP) model with a spatial agent-based model (ABM) describing the TME; we applied four spatial metrics to quantify model-simulated intratumoral heterogeneity and classified the TME immunoarchitecture for representative cases of effective and ineffective anti-PD-1 therapy. The four metrics, adopted from computational digital pathology, included mixing score, average neighbor frequency, Shannon's entropy and area under the curve (AUC) of the G-cross function. A fifth non-spatial metric was used to supplement the analysis, which was the ratio of the number of cancer cells to immune cells. These metrics were utilized to classify the TME as cold, compartmentalized and mixed, which were related to treatment efficacy. The trends in these metrics for effective and ineffective treatments are in qualitative agreement with the clinical literature, indicating that compartmentalized immunoarchitecture is likely to result in more efficacious treatment outcomes.

Automated summary

This study introduces a new approach to quantitatively validate the performance of a hybrid spatio-temporal method called spatial quantitative systems pharmacology (spQSP). The study uses a compartmental QSP model and a spatial agent-based model to simulate the effect of anti-PD-1 therapy on tumor growth dynamics and intratumoral heterogeneity. Four spatial metrics and the ratio of cancer cells to immune cells are used to categorize the tumor microenvironment and evaluate the efficacy of the treatment. The results of this study can facilitate future research on specific cancer types, different therapeutics, and immunopathological samples.