Next Generation Imaging Techniques to Define Immune Topographies in Solid Tumors

Abstract

In recent years, cancer immunotherapy experienced remarkable developments and it is nowadays considered a promising therapeutic frontier against many types of cancer, especially hematological malignancies. However, in most types of solid tumors, immunotherapy efficacy is modest, partly because of the limited accessibility of lymphocytes to the tumor core. This immune exclusion is mediated by a variety of physical, functional and dynamic barriers, which play a role in shaping the immune infiltrate in the tumor microenvironment. At present there is no unified and integrated understanding about the role played by different postulated models of immune exclusion in human solid tumors. Systematically mapping immune landscapes or "topographies" in cancers of different histology is of pivotal importance to characterize spatial and temporal distribution of lymphocytes in the tumor microenvironment, providing insights into mechanisms of immune exclusion. Spatially mapping immune cells also provides quantitative information, which could be informative in clinical settings, for example for the discovery of new biomarkers that could guide the design of patient-specific immunotherapies. In this review, we aim to summarize current standard and next generation approaches to define Cancer Immune Topographies based on published studies and propose future perspectives.

Keywords: deep learning; imaging techniques; immune exclusion; immune topography; single-cell analysis; solid tumors.

Figure 1

Immune topographies of cancer. (A–C) First-order immune topographies. (D–F) Second-order immune topographies.

Figure 2

Spatial transcriptomics workflow including the downstream analysis. (A) Histological tumor sections are annotated by a pathologist and sections of interest are stained with hematoxylin and eosin before permeabilization. (B) The sections are placed on glass slides containing RT-primers arrayed as spots that correspond to tissues domains. The RT-primers at each spot have a unique spatial ID barcode, which is sequenced along with the transcript to enable trace-back to a specific tissue domain. (C, D) After sequencing, gene expression profiles and factor activity maps are created.

Figure 3

Example of spatial transcriptomic analysis on three prostate cancer biopsies: histology and gene expression factors (257). (A) Annotated brightfield images of tissue sections of interest, stained with hematoxylin and eosin. (B) Factor activity maps for morphological features (normal glands, PIN glands, stroma and cancer cells) and for inflamed regions (reactive stroma, immune profile).

Figure 4

Example of NICHE-seq, assessing the cellular composition of defined niches (215). (A) Two-photon laser scanning microscopy (TPLSM) images of naïve inguinal lymph nodes from PA-GFP host mice before and after photoactivation (green). In red, adoptively transferred cells and cyan marks the T-cell area and the B follicles, respectively. (B) Expression profile from photoactivated B follicles (cyan) or T-cell areas (red). (C) Relative enrichment of different T-cell types [(B), CD4, CD8^low, CD8^high and CD8 Activated] in each subregion. *p < 0.05.

Figure 5

Intravital two photon imaging of naïve T-cells in lymph nodes (302). (A) 3D reconstruction representing 85 x 120 x 75 μm of the T-cell area. Fluorescently labeled T-cells (green) are observed in the proximity of presumptive high endothelial venules (red), identified by i.v. injection of tetramethylrhodamine dextran. Scale bar 30 μm in all axes. (B) Video-rate imaging of a T-cell flowing in a small vessel within a T-cell region of the node. Image is a superposition of nine consecutive video frames and shows progression of a single labeled T-cell traveling at about 0.03 cm/s within a blood vessel. Scale bar 25 μm. Copyright (2003) National Academy of Sciences, U.S.A.

Figure 6

Protein-based scaffolds for targeting cell antigens in vivo.