Immune microenvironment of basal cell carcinoma and tumor regression following combined PD-1/LAG-3 blockade.

Systemic treatment options for patients with locally advanced or metastatic basal cell carcinoma (BCC) are limited, particularly when tumors are refractory to anti-programmed cell death protein-1 (PD-1). A better understanding of immune checkpoint expression within the BCC tumor microenvironment may inform combinatorial treatment strategies to optimize response rates. CD3, PD-1, programmed death ligand-1 (PD-L1), lymphocyte activation gene 3 (LAG-3), and T-cell immunoglobulin domain and mucin domain 3 (TIM-3)+ cell densities within the tumor microenvironment of 34 archival, histologically aggressive BCCs were assessed. Tumor infiltrating lymphocyte (TIL) expression of PD-1, PD-L1, and LAG-3, and to a lesser degree TIM-3, correlated with increasing CD3+ T-cell densities (Pearson's r=0.89, 0.72, 0.87, and 0.63, respectively). 100% of BCCs (34/34) demonstrated LAG-3 and PD-1 expression in >1% TIL; and the correlation between PD-1 and LAG-3 densities was high (Pearson's r=0.89). LAG-3 was expressed at ~50% of the level of PD-1. Additionally, we present a patient with locally-advanced BCC who experienced stable disease during and after 45 weeks of first-line anti-PD-1 (nivolumab), followed by a partial response after the addition of anti-LAG-3 (relatlimab). Longitudinal biopsies throughout the treatment course showed a graduated increase in LAG-3 expression after anti-PD-1 therapy, lending support for coordinated immunosuppression and suggesting LAG-3 as a co-target for combination therapy to augment the clinical impact of anti-PD-(L)1.

Comparing and Correcting Spectral Sensitivities between Multispectral Microscopes: A Prerequisite to Clinical Implementation.

Multispectral, multiplex immunofluorescence (mIF) microscopy has been used to great effect in research to identify cellular co-expression profiles and spatial relationships within tissue, providing a myriad of diagnostic advantages. As these technologies mature, it is essential that image data from mIF microscopes is reproducible and standardizable across devices. We sought to characterize and correct differences in illumination intensity and spectral sensitivity between three multispectral microscopes. We scanned eight melanoma tissue samples twice on each microscope and calculated their average tissue region flux intensities. We found a baseline average standard deviation of 29.9% across all microscopes, scans, and samples, which was reduced to 13.9% after applying sample-specific corrections accounting for differences in the tissue shown on each slide. We used a basic calibration model to correct sample- and microscope-specific effects on overall brightness and relative brightness as a function of the image layer. We tested the generalizability of the calibration procedure and found that applying corrections to independent validation subsets of the samples reduced the variation to 2.9 ± 0.03%. Variations in the unmixed marker expressions were reduced from 15.8% to 4.4% by correcting the raw images to a single reference microscope. Our findings show that mIF microscopes can be standardized for use in clinical pathology laboratories using a relatively simple correction model.

Whole-Slide Imaging, Mutual Information Registration for Multiplex Immunohistochemistry and Immunofluorescence.

Multiplex immunohistochemistry/immunofluorescence (mIHC/mIF) is a developing technology that facilitates the evaluation of multiple, simultaneous protein expressions at single-cell resolution while preserving tissue architecture. These approaches have shown great potential for biomarker discovery, yet many challenges remain. Importantly, streamlined cross-registration of multiplex immunofluorescence images with additional imaging modalities and immunohistochemistry (IHC) can help increase the plex and/or improve the quality of the data generated by potentiating downstream processes such as cell segmentation. To address this problem, a fully automated process was designed to perform a hierarchical, parallelizable, and deformable registration of multiplexed digital whole-slide images (WSIs). We generalized the calculation of mutual information as a registration criterion to an arbitrary number of dimensions, making it well suited for multiplexed imaging. We also used the self-information of a given IF channel as a criterion to select the optimal channels to use for registration. Additionally, as precise labeling of cellular membranes in situ is essential for robust cell segmentation, a pan-membrane immunohistochemical staining method was developed for incorporation into mIF panels or for use as an IHC followed by cross-registration. In this study, we demonstrate this process by registering whole-slide 6-plex/7-color mIF images with whole-slide brightfield mIHC images, including a CD3 and a pan-membrane stain. Our algorithm, WSI, mutual information registration (WSIMIR), performed highly accurate registration allowing the retrospective generation of an 8-plex/9-color, WSI, and outperformed 2 alternative automated methods for cross-registration by Jaccard index and Dice similarity coefficient (WSIMIR vs automated WARPY, P < .01 and P < .01, respectively, vs HALO + transformix, P = .083 and P = .049, respectively). Furthermore, the addition of a pan-membrane IHC stain cross-registered to an mIF panel facilitated improved automated cell segmentation across mIF WSIs, as measured by significantly increased correct detections, Jaccard index (0.78 vs 0.65), and Dice similarity coefficient (0.88 vs 0.79).

Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade.

Next-generation tissue-based biomarkers for immunotherapy will likely include the simultaneous analysis of multiple cell types and their spatial interactions, as well as distinct expression patterns of immunoregulatory molecules. Here, we introduce a comprehensive platform for multispectral imaging and mapping of multiple parameters in tumor tissue sections with high-fidelity single-cell resolution. Image analysis and data handling components were drawn from the field of astronomy. Using this "AstroPath" whole-slide platform and only six markers, we identified key features in pretreatment melanoma specimens that predicted response to anti-programmed cell death-1 (PD-1)-based therapy, including CD163+PD-L1- myeloid cells and CD8+FoxP3+PD-1low/mid T cells. These features were combined to stratify long-term survival after anti-PD-1 blockade. This signature was validated in an independent cohort of patients with melanoma from a different institution.