Title: Deconstructing T cell transcriptional heterogeneity and clonal dynamics in response to immune checkpoint blockade
Description: T cells can fight cancer, but an immunosuppressive tumor microenvironment (TME) disallows them from carrying out their function over time. Upregulation of inhibitory checkpoint molecules such as programmed cell death protein 1 (PD1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA4) can lead to such an immunosuppressive TME. Despite their widespread use, immune checkpoint blockade (ICB) antibodies targeting checkpoint molecules remain ineffective in most cancer patients. We do not understand why some patients respond to ICB better than others. To understand the heterogeneity of ICB response, we must understand the heterogeneity of the T cell subsets acted upon by such therapies. Here, we ask how T cell subsets change in the presence and absence of ICB. We track T cell clones through their T cell receptor sequences and link phenotypes with T cell receptor specificities. Through multiplexed single cell TCR sequencing, single cell RNA sequencing, and the use of cell- surface CITE-seq antibodies, coupled with surgical biopsy, we longitudinally tracked the fate of individual T cell clones within tumors at baseline and in response to ICB in an immunogenic mouse tumor model.

Furthermore, computational clustering of T cells solely based on their gene expression profiles may ignore upstream regulatory mechanisms that control T cell gene expression. Hence, we employed Virtual Inference of Protein-activity by Enriched Regulon (VIPER) analysis to cluster CD8+ and CD4+ T cell phenotypes. VIPER leverages inference of gene regulatory networks to allow full quantitative characterization of protein activity for transcription factors, co-factors, and signaling molecules by assessing the enrichment of their transcriptional targets cell-by-cell among expressed genes. This gave us a window into the transcriptional states and their inferred protein activity. We next developed a computational analysis toolkit to study TCR clonality incorporating sub-sampling of TCR clonotypes, forward and back tracing of shared clones between timepoints, and in turn, inferred shared clonal evolution. We employed the above workflow to MC38 tumor-infiltrating and tumor-draining lymph node-derived CD8+ and CD4+ T cells.

We found that T cell phenotypes are highly dynamic within tumors at baseline, in the absence of ICB, particularly within the window that they are responsive to therapy. In the absence of ICB, effector phenotype of CD8+ T cells diminished, while the exhaustion phenotype was enhanced as tumors progressed. Within the CD4+ population, a heterogenous subset of regulatory CD4+ T cells (Tregs) changed phenotype over time, and CD4+ Th1 like effectors, along with stem like progenitor CD4+ showed distinct dynamism. Next, by analyzing responses to therapy within his context, we found that both anti-PD1 and anti-CTLA4 act through distinct mechanisms on CD8+ and CD4+ T cells. Anti-PD1 acted upon intra-tumoral effector CD8+ T cells to slow their progression to terminally differentiated exhausted cells, i.e., increased their persistence within tumors. Anti-CTLA4 therapy increased recruitment of novel effector CD8+ T cell clones to tumors from lymph nodes while diminishing tumor-infiltrating Tregs. ICB also potentiated CD4+ Th1 like phenotype.

These results uncovered a behavior pattern of CD8+ and CD4+ T cells within tumors at baseline tumor progression, and then in the presence of ICB. We believe these findings have added to our understanding of the subtleties of T cell phenotypes in tumors, specifically in response to ICB. This will provide a practical framework for designing and validating novel checkpoint blockade therapies in the future.