Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma

Abstract

Lymphocytes play a key role in immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. Here, we used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of tumor-bearing lung tissues from a Kras/p53 (KP) mouse model and human resections. Networks of directly interacting lymphocytes (lymphonets) emerge as a distinctive feature of the anti-cancer immune response. Lymphonets nucleate from small T-cell clusters and incorporate B cells with increasing size. CXCR3-mediated trafficking modulates lymphonet size and number, but neoantigen expression directs intratumoral localization. Lymphonets preferentially harbor TCF1+/PD1+ progenitor CD8 T cells involved in responses to immune checkpoint blockade (ICB). Upon treatment of mice with ICB therapy or a neoantigen-targeted vaccine, lymphonets retain progenitor and gain cytotoxic CD8 T-cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of CD8 T-cell anti-tumor responses.

Function-associated scRNA-seq on single lung cancer organoids unravels the immune landscape of tumor parenchyma

“…Accurate in vitro models of the patient specific tumor immune microenvironment (TIME) and robust analysis techniques enabling in-depth dissection of the in vitro model are indispensable for the development of next generation immunotherapy. Imaging-based and sequencing-based analysis of the surgically dissected patient tissues has enabled the deconvolution of not only the cell types that constitute the TIME but also their cellstate heterogeneity (1)(2)(3)(4) . However, they can only provide the snapshots rather than the dynamics of TIME, i.e., the subtle changes of TIME in response to treatments.…”

Using random forests to uncover the predictive power of distance–varying cell interactions in tumor microenvironments

“…between tumor cells and specific immune cell populations, and/or individual proteins, often appear predictive of patient outcomes and may guide therapeutic interventions; see for example [9]. Comparisons between cancer subtypes, e.g.…”