Integrative Analysis of Tumor Microenvironment Dynamics in Response to Immune Checkpoint Inhibition
Background. Integration analysis not only offer a panoramic view by making annotation consistent across studies, but also amplify the statistical power restricted by sample size in the individual studies. Commonly used integration methods for scRNA-seq data often struggle with strong batch effects, which can distort or dilute the biological signals. Furthermore, conventional clustering built on improper integration can lead to impure clusters containing multiple cell types, compromising the purity and interpretability of the data.
Methods. To circumvent these issues, we employed a two-step solution that minimizes the need for data integration. In the first step, a combination of cell annotation techniques are used to identify high-level cellular compartments. In the second step we mainly utilize SingleR augmented with curated references from pan-cancer studies in a hierarchical framework. The clustering-free second step, which we term deep-phenotyping, is particularly advantageous for resolving cell states.
Results. We applied this computational framework to annotate 11 scRNA-seq datasets of patients treated with immune checkpoint inhibitors (IBI) with multiple timepoints. Altogether our dataset included longitudinally paired samples from 163 patients. We accurately portrayed the complex landscape of diverse cellular states in the tumor microenvironment (TME) at an individual patient level. Our analysis revealed consistent compositional changes in 19 cell subtypes following ICI treatment. We uncovered co-regulated cell communities within the TME, highlighting the coordinated interplay between adaptive and innate immune cells, as well as immune and non-immune components. Furthermore, we identified two distinct patient groups exhibiting tightly correlated cellular dynamics within the TME post-treatment. The first group, enriched for responders, displayed a marked expansion of naive lymphocytes, while the second group, predominantly composed of non-responders, showed an increased abundance of immune experienced/suppressive cell states. This dichotomy in TME dynamics offers a potential predictive biomarker for patient stratification and personalized therapeutic strategies.
Conclusions. Our study presents a comprehensive landscape of the cellular dynamics within the TME during ICI treatment, enabled by a powerful deep phenotyping approach showcasing the importance of a systems-level understanding of the TME dynamics in improving patient stratification and advancing personalized cancer immunotherapy.
