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Cytobank Aims to Improve Cytometry Informatics, Starting With Immunotherapy, Single-Cell Analysis


CHICAGO (GenomeWeb) — A relatively small National Institutes of Health grant may turn out to have a big impact on cytometry by modernizing an established field to support immunotherapies and single-cell analysis in the genomics age.

Cytobank, a maker of cytometry data analysis technology, this month announced that it has received a two-year, $1.3 million Phase II Small Business Innovation Research grant from the US National Institutes of Health. Actually awarded in April with work beginning in May, the funding is helping the Mountain View, California-based company scale up and add machine-learning algorithms to its informatics platform, with an eye toward supporting clinical trials in immunotherapy.

"There is a paucity of tools to deal with the amount of data being generated" in immunotherapy, noted Cytobank President and CEO David Craford. The cloud-based Cytobank platform collects, stores, and analyzes single-cell flow and mass cytometry data.

"In many cases, people are collecting a lot of data, but they don't have the capability to do the analysis," Craford said. Instead of picking through that data "in a low-dimensional, two-by-two way,", Cytobank's pipelines will allow users to "deposit all the data into a pipeline and get a bird's-eye view of what's going on with these different datasets," he added.

In its grant abstract, NIH said that the Cytobank project will offer a "knowledge management framework" to scientists worldwide, with the goal of advancing biomarker discovery in immunotherapy. This will provide researchers with scalable and secure access to new single-cell data analysis tools that will result in new automated workflows, and enable more efficient "cross-platform knowledge generation with increased meta-analysis capabilities across experiments and data types," the abstract said.

This contract is part of NIH's effort to develop methods and resources for the biomedical community to deal with the proliferation of data, according to Craford. In June, NIH issued a five-year strategic plan for data science, with five broad goals: to support an efficient biomedical research data infrastructure; to promote the modernization of the data ecosystem; to promote advanced data management, analytics, and visualization technologies; to support workforce development; and to create policies that lead to sustainability of the other steps.

"They recognize that they need to catalyze these activities," Craford said. The Cytobank contract actually comes out of the National Institute of General Medical Sciences, which funds many informatics infrastructure-type projects within NIH.

Cytobank's scope of work includes scaling its platform, adding normalization tools to handle multiple data types, and creating new algorithms to increase the number of pipelines that are available to end users.

"With immunotherapy, there are a lot of companies that have built a lot of value doing genomic measurements, but the protein is kind of where it's at. As these technologies enable you to capture bigger protein pictures, they are going to be a bigger piece of the puzzle for discovering biomarkers and monitoring patients on an ongoing basis," Craford said.

"Cytometry has arguably been in the clinic 10 years before genomics was even around, so I think you will see a rebirth of the use of cytometry with immunotherapy," he added.

While the NIH request for proposal was for enhancing single-cell analysis tools, Craford said that his company applied with an eye toward adapting the Cytobank platform for genomics and imaging data as well.

"Some of the same technologies that are being used ... to increase the number of parameters that people can measure in cytometry are also being used in imaging now, where you can look at 30 to 50 or more features per image," Craford said. He reported seeing significant interest from customers in processing genomes and images in recent months.

"We would really like to have a platform that enables people to look at multiple of these data types that they are collecting in the clinical trials," Craford said.

Cytobank was founded in 2007 in the laboratory of Garry Nolan at Stanford, with three PhD students who were unable to work with phospho-flow data. One of them, current Cytobank CSO Jonathan Irish, wrote the initial code for the software platform, with support, in part, from the National Heart, Lung, and Blood Institute's Proteomics Initiative last decade.

The company was mostly grant-funded for its first five years, Craford said, and offered the software for free or at very low cost to its initial user base of academic researchers, with a small amount of corporate support. The firm has had angel investments since about 2012.

"On the commercial side, we have really focused more and more on biotech [since then] and pharma than we had historically," Craford said. "With angel investment about six years ago, there was a larger commitment to investing in getting some true software developers in, putting [quality assurance] in place …  and also moving into a formal cloud-based approach."

Craford said that the 10 largest drug companies all use Cytobank technology, as do many biotech firms that are focused on immunotherapies. This would not have happened, he said, without Cytobank moving to Amazon Web Services, which offers the kind of security such companies require.

A clinical immunologist for a major US-based pharma company — and a regular Cytobank user — said that this kind of technology gives investigators new opportunities to understand their data better.

"Many people talk about the need to analyze, visualize, collaborate, and archive their data," said the immunologist, who requested anonymity due to the sensitive nature of pharma R&D. The Cytobank platform handles all of these activities.

"The one thing about flow cytometry that becomes frustrating to somebody like me if you really cling to traditional legacy-type methods of analyzing data is that you do these data-rich analyses and then use procedures like gating and cursor placement on two-dimensional plots to extract what you're predisposed to be looking for. But, in essence, what you've done is left 99 percent of the information on the table," the pharma professional said.

The analytics technology provides researchers with a holistic look at data. After a while, "the data starts to inform you," this immunologist said. "You always have the option to go in with very specific questions. That is not taken away from you. But you are immediately afforded the option to take the big-picture look."

The platform also facilitates collaboration in a secure environment and provides better organization than traditional flow cytometry systems, according to this user.

"With respect to data integrity, it's not unusual for investigators to put data on a thumb drive or to go to their desktop, or even just to go to their own server. If you go to Cytobank, it's locked and loaded and it's put somewhere in a very organized way. You just don't have to worry," he said. This is particularly helpful in continuing research after an investigator leaves the company.

Since this pharma company started using Cytobank, researchers have been able to focus hypotheses in immuno-oncology and autoimmunity, among other fields.

"I can pull two levers. One is that I can work in this cloud-based space and I can see the cells in a traditional way," the researcher said. "The other thing that I can do is see those cells in the context of all of the other cells that are present in the blood or the disaggregated tissue of an individual," regardless of the type of cytometry, he explained.

"I'm showing this to our clinical development teams. It immediately then becomes a conversation of what other targets might exist in these settings of pathology that you may not be fully appreciative of," he said.

"Why wouldn't you want to do that with the data that you've already collected? Without these tools, the odds of you finding it alone are very low."