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Astronomy, Biology Come Together for Cancer Biomarker Discovery

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NEW YORK – Highlighting the power of interdisciplinary scientific research, investigators at Johns Hopkins University and Yale University have combined biology and astronomy to develop a platform capable of performing multispectral imaging of whole-tumor sections with high-fidelity single-cell resolution.

The platform, called AstroPath, uses multispectral imaging approaches and a data management infrastructure that were originally developed to study stars and galaxies. By substituting cells for those astronomical objects, the researchers were able to scale down the original astronomical tools to use them in the pathologic analysis of cancer specimens.

The project began in January 2020 after the Mark Foundation for Cancer Research created the Mark Foundation Center for Advanced Genomics and Imaging at Johns Hopkins University, bringing together researchers and technology from the fields of pathology, computer science, cancer genomics, and immunogenomics to study oncology. Specifically, they set out to expand the number of cancer patients who could benefit from immunotherapy.

"There was a new technology that we thought would be great for analyzing multiple cells in the tumor microenvironment at the same time. And yet I didn't have the computing capacity or the know-how to be able to organize or even query the data that we were generating," Johns Hopkins dermatology professor Janis Taube said. She called on the expertise of Johns Hopkins astronomy professor Alexander Szalay, who used his experience to advise not only on the management of the data, but also on issues with the group's microscope lenses and imaging protocols.

When it comes to comparing the gigantic scope of the heavens to the relative tininess of a cancer cell, there may be more similarities than one may think.

"When we look at the sky, we see distinct celestial objects," Szalay said. "We see stars and we see the bigger blurry galaxies. But essentially the sky is covered with these objects in a somewhat random pattern, and these celestial objects are the atoms of the sky."

When examining cellular images, "it's a very different pattern in the way that the cells actually touch each other, but still [similar in] the way they interact with each other, whether the immune cells attack the cancer or two galaxies collide and one of them is cannibalizing the other," he further explained. "So the same statistical techniques are really very well applied, [as are] the way we analyze the images and segment them into these individual objects."

Because of these apparent similarities, and his decades of experience with telescopes, Szalay was able to make optical corrections to the biologists' microscopes, and to configure the platform's imaging protocol more precisely, Taube added.

By applying high-quality imaging and the establishment of relational databases to multiplex immunofluorescence labeling of pathology specimens, AstroPath can analyze the spatial relationships and immuno-architecture of the host-tumor interface.

In a paper recently published in Science, on which Taube was the corresponding author, the researchers described how they used the platform to develop a multiplex immunofluorescent assay that's highly predictive of responses and outcomes for melanoma patients receiving immunotherapy.

Despite the success of immunotherapies, such as drugs that target PD-L1, some patients don't respond to them, the researchers noted, making biomarkers that could aid in therapeutic decision-making highly desirable. The only US Food and Drug Administration-approved histopathology biomarker tests for anti-PD-1 or anti-PD-L1 therapy, however, assess PD-L1 protein expression through immunohistochemistry, which is a limited unidimensional approach that doesn't take factors such as the tumor microenvironment into account.

AstroPath's multispectral imaging capabilities overcome these limitations. It can characterize the co-expression of key molecules in cells and can help pathologists analyze the spatial relationships between tumor cells and multiple immune elements. In the new study, the investigators curated and mapped six markers, both individually and in combination in tumor tissue from 98 patients with melanoma receiving anti-PD-1 therapy. This dataset comprised about 127,400 image mosaics composed of more than 100 million single cells, with data outputs linked to patient outcomes.

These six markers (PD-1, PD-L1, CD8, FoxP3, CD163, and Sox10/S100) were then used to develop 41 combinations of expression patterns in melanoma cells, and the researchers were able to map relatively rare cells such as CD8-positive, FoxP3-positive cells to the tumor stromal boundary. Moreover, a high density of CD8-positive, FoxP3-positive cells with low- to mid-level expression of PD-1 was closely associated with response to PD-1 blockade.

They also associated certain cell types with a lack of response to therapy, such as CD163-positive macrophages that were PD-L1-negative. This phenotype was also found to have a negative effect on long-term survival. Combining these and other cell phenotype densities, the researchers were able to develop an assay that was highly predictive of objective response and that stratified long-term patient outcomes after anti-PD-1-based therapies in both a discovery cohort and an independent validation cohort.

The researchers noted that while AstroPath is compatible with pathology data generated by a variety of systems and imaging technologies, the Science study relied on data from Akoya Biosciences' Phenoptics multispectral imaging platform.

"Akoya were the ones [with the staining reagents] that were closest to the clinic in terms of true clinical utility, and that's really what I was looking for," Taube said. "Their approach to combining six markers together was the equivalent of if you had done each marker independently, and there are a lot of other multiplexing technologies that don't pass that test. Also, in terms of the imaging microscopy equipment, they're closest to clinical implementation."

Szalay also noted that Akoya's imaging platform offers the unique capability of a tunable narrow-band filter that scans in sequential steps through the whole visible spectrum to compensate for temperature variations.

"No matter what happens, the optical signal that comes through is consistent and stable. And that is the key for reproducibility," he said.

Now that AstroPath is capable of generating tumor immune maps, the researchers can use it to develop individual assays, Taube said. They will then partner with commercial entities to take those assays forward. Akoya will be the commercial partner for the first assay, she added.

The investigators also believe that AstroPath has the potential to aid in the development of additional assays that could help clinicians make treatment decisions for cancer patients.

"We adopted different strategies from astronomy to different parts of an imaging pipeline," Taube explained. "It's probably best described as a platform for discovery. Now we have the potential to compare across data generated at different institutions and different trials, rather than a little bit of the Wild West that was out there before."

She also noted that AstroPath could be combined with more traditional technologies like next-generation sequencing for the development of even more precise assays for cancer patients.

"We imagine that in the end, we're not just going to have multiplex immunohistochemistry, but multiplexed multimodality biomarkers where we're pulling in some genomic components, along with some of the spatial imaging data," Taube said.

The researchers are already planning their next set of studies. While they looked at pre-treatment biomarkers for the Science study, they're next working on studying tissues after the patient has been on treatment to look at a therapy's mechanism of action and efficacy. They're also going to continue research in the pre-treatment setting, looking at different cancer types as well as a possible pan-cancer assay.

"In doing each of this pre- and on-treatment [research], we are also doing [treatment resistance research], because as you identify the patients who are most likely to respond, you are identifying those who are unlikely to respond," Taube added. "And we are exploring why that is and how to get them to new clinical trials and look for additional immune checkpoints that could also be potentially targeted."

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