Using a machine learning approach, the team analyzed massive databases of thousands of proteins found in both cancer and normal cells. They then combed through millions of possible protein combinations to assemble a catalog of combinations that could be used to precisely target only cancer cells while leaving normal ones alone.
In another paper, published in Science on November 27, 2020, Lim and colleagues then showed how this computationally derived protein data could be put to use to drive the design of effective and highly selective cell therapies for cancer.
"Currently, most cancer treatments, including cell therapies, are told 'block this,' or 'kill this,'" said Lim, also professor and chair of cellular and molecular pharmacology and a member of the UCSF Helen Diller Family Comprehensive Cancer Center. "We want to increase the nuance and sophistication of the decisions that a therapeutic cell makes."
For Lim, cells are akin to molecular computers that can sense their environment and then integrate that information to make decisions. Since solid tumors are more complex than blood cancers, "you have to make a more complex product" to fight them, he said.
In the Cell Systems study - led by Ruth Dannenfelser, PhD, a former graduate student in Troyanskaya's team at Princeton, and Gregory Allen, MD, PhD, a clinical fellow in the Lim lab--the researchers explored public databases to examine the gene expression profile of more than 2,300 genes in normal and tumor cells to see what antigens could help discriminate one from the other. The researchers used machine learning techniques to come up with the possible hits, and to see which antigens clustered together.
Based on this gene expression analysis, Lim, Troyanskaya, and colleagues applied Boolean logic to antigen combinations to determine if they could significantly improve how T cells recognize tumors while ignoring normal tissue. For example, using the Booleans AND, OR, or NOT, tumor cells might be differentiated from normal tissue using markers "A" OR "B," but NOT "C," where "C" is an antigen found only in normal tissue.
To program these instructions into T cells, they used a system known as synNotch, a customizable molecular sensor that allows synthetic biologists to fine-tune the programming of cells. Developed in the Lim lab in 2016, synNotch is a receptor that can be engineered to recognize a myriad of target antigens. The output response of synNotch can also be programmed, so that the cell executes any of a range of responses once an antigen is recognized.
"The field of big data analysis of cancer and the field of cell engineering have both exploded in the last few years, but these advances have not been brought together," said Troyanskaya. "The computing capabilities of therapeutic cells combined with machine learning approaches enable actionable use of the increasingly available rich genomic and proteomic data on cancers."
"You're not just looking for one magic-bullet target. You're trying to use all the data," Lim said. "We need to comb through all of the available cancer data to find unambiguous combinatorial signatures of cancer. If we can do this, then it could launch the use of these smarter cells that really harness the computational sophistication of biology and have real impact on fighting cancer."
MEDICA-tradefair.com; Source: University of California - San Francisco
