Authors: Berry, Sneha; Giraldo, Nicolas A.; Green, Benjamin F.; Cottrell, Tricia R.; Stein, Julie E.; Engle, Elizabeth L.; Xu, Haiying; Ogurtsova, Aleksandra; Roberts, Charles; Wang, Daphne; Nguyen, Peter; Zhu, Qingfeng; Soto-Diaz, Sigfredo; Loyola, Jose; Sander, Inbal B.; Wong, Pok Fai; Jessel, Shlomit; Doyle, Joshua; Signer, Danielle; Wilton, Richard; Roskes, Jeffrey S.; Eminizer, Margaret; Park, Seyoun; Sunshine, Joel C.; Jaffee, Elizabeth M.; Baras, Alexander; Marzo, Angelo M. De; Topalian, Suzanne L.; Kluger, Harriet; Cope, Leslie; Lipson, Evan J.; Danilova, Ludmila; Anders, Robert A.; Rimm, David L.; Pardoll, Drew M.; Szalay, Alexander S.; Taube, Janis M.
Online: https://science.sciencemag.org/content/372/6547/eaba2609
Issue: Science. 2021 Jun 11;372(6547):eaba2609.
Abstract
Astronomy accelerates tumor imaging Immunohistochemical stains for individual markers revolutionized diagnostic pathology decades ago but cannot capture enough information to accurately predict response to immunotherapy. Newer multiplex immunofluorescent technologies provide the potential to visualize the expression patterns of many functionally relevant molecules but present numerous challenges in accurate image analysis and data handling, particularly over large tumor areas. Drawing from the field of astronomy, in which petabytes of imaging data are routinely analyzed across a wide spectral range, Berry et al. developed a platform for multispectral imaging of whole-tumor sections with high-fidelity single-cell resolution. The resultant AstroPath platform was used to develop a multiplex immunofluorescent assay highly predictive of responses and outcomes for melanoma patients receiving immunotherapy. Science, aba2609, this issue p. eaba2609 Structured Abstract INTRODUCTIONNew therapies have been designed to stimulate the host’s immune system to fight cancer. Despite these exciting, recent successes, a large proportion of patients still do not respond to anti–programmed cell death-1 (PD-1) or anti–programmed death ligand-1 (PD-L1) therapies, and thus, biomarkers for patient selection are highly desirable. The only U.S. Food and Drug Administration–approved histopathology biomarker tests for anti–PD-1 or anti–PD-L1 therapy is assessment of PD-L1 protein expression by means of immunohistochemistry. This approach is unidimensional and has limitations. Innovative characterization of the tumor microenvironment (TME) with a focus on multidimensional, spatially resolved interactions at the single-cell level will provide critical mechanistic insights into therapeutic responses and potentially identify improved biomarkers for patient selection. Using multispectral approaches to image the TME and substituting cells for stars and galaxies, we applied the methodology and infrastructure developed for astronomy to pathologic analysis of specimens from patients with melanoma. RATIONALEThe next generation of pathologic analyses will require platforms that can characterize the coexpression of key molecules on specific cellular subsets in situ and spatial relationships between tumor cells and multiple immune elements. To that aim, we applied astronomical algorithms for high-quality imaging and the establishment of relational databases to multiplex immunofluorescence (mIF) labeling of pathology specimens, facilitating spatial analyses and immunoarchitectural characterization of the host-tumor interface. In all, we curated and coordinately mapped six markers, both individually and in combination in tumor tissue from 98 patients with melanoma receiving anti–PD-1 therapy. This dataset comprised ~127,400 image mosaics composed of more than 100 million single cells. The data outputs were linked to patient outcomes, informing in a clinically relevant way how cancer evades the immune system and potentiating biomarker assay development for precision immunotherapy. RESULTSThe imaging protocols used in this study were used to address outstanding questions regarding the impact of high-power field sampling strategies on biomarker performance. This information was then used to develop an approach for operator-independent field selection. The image handling strategies also facilitated the robust assessment of the intensity of PD-1 and PD-L1 expression in situ (negative, low, mid, and high levels) on different cell types. Thus, with only six markers (PD-1, PD-L1, CD8, FoxP3, CD163, and Sox10/S100), we were able to develop 41 combinations of expression patterns for these molecules and map relatively rare cells such as CD8+FoxP3+ cells to the tumor stromal boundary. Moreover, a high density of CD8+FoxP3+PD-1low/mid cells was closely associated with response to PD-1 blockade. Cell types associated with a lack of response to therapy were also identified—for example, CD163+ macrophages that were PD-L1–. This latter phenotype was also found to have a negative effect on long-term survival. When these and other key cell phenotype densities were combined, they were highly predictive of objective response and stratified long-term patient outcomes after anti–PD-1–based therapies in both a discovery cohort and an independent validation cohort. CONCLUSIONHere, we present the AstroPath platform, an end-to-end pathology workflow with rigorous quality control for creating quantitative, spatially resolved mIF datasets. Although the current effort focused on a six-plex mIF assay, the principles described here provide a general framework for the development of any multiplex assay with single-cell image resolution. Such approaches will vastly improve the standardization and scalability of these technologies, enabling cross-site and cross-study comparisons. This will be essential for multiplex imaging technologies to realize their potential as biomarker discovery platforms and ultimately as standard diagnostic tests for clinical therapeutic decision-making. <img class=”fragment-image” aria-describedby=”F1-caption” src=”https://science.sciencemag.org/content/sci/372/6547/eaba2609/F1.medium.gif”/> Download high-res image Open in new tab Download Powerpoint Strong parallels between multispectral analyses in astronomy and emerging multiplexing platforms for pathology.The next generation of tissue-based biomarkers are likely to be identified by use of large, well-curated datasets. To that end, image analysis approaches originally developed for astronomy were applied to pathology specimens to produce trillions of pixels of robust tissue imaging data and facilitate assay and atlas development.IMAGES: (BENEATH “ASTRONOMY”) SLOAN DIGITAL SKY SURVEY; (MICROSCOPE) AKOYA BIOSCIENCE 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. Image analysis methods from astronomy improve tumor characterization and prediction of immunotherapy response. Image analysis methods from astronomy improve tumor characterization and prediction of immunotherapy response.
