Learning The Language Cells Use To Communicate With One Another

Cells are the building blocks of life. The way cells recognize other cells and external signals can lead to different biological fates, including cell growth, death and mobility. Researchers are trying to understand cell-cell communication, reverse engineer it and ultimately shape cell interactions that exceed natural possibilities. While cell therapies already exist, the future of such cell therapies will likely involve deeper modification of patient cells to treat a range of diseases and repair tissues.

In a previous article, we discussed a study that modularly replaces the extracellular portion of a protein to recognize different ligands; this “reassembled” protein transduces the same signaling pathway as long as the transmembrane and intracellular parts remain intact. Here we discuss an article that instead focuses on the intracellular part of the cell. The University of California, San Francisco researchers are theoretically recomposing the signaling domains of CAR T cells and investigating possible effects on cell-cell communication.

Making a chimeric antigen receptor

Chimeric Atigen Rreceptors (CAR) require genetic modification to express new, synthetic components. Figure 1 illustrates the three main regions of a CAR-T cell: the antigen-binding domain, the transmembrane domain, and the signaling domain. Scientists often fixate on the binding domain and modify it for a specific therapeutic target (e.g., proteins found on cancer cells). However, the researchers here focus on the composition of the signal domain and its influence on the performance of CAR T cells.

Costimulatory molecules

The signaling domain of a CAR T cell usually contains a CD3ζ T cell receptor (TCR) molecule and any combination of costimulatory molecules. Costimulatory molecules contain multiple signaling motifs, or short peptides that bind to specific downstream signaling molecules. These molecules influence T cell signal transduction with different effects. Two examples include 4-1BB, which can increase T cell memory and persistence, and CD28, which is associated with effective T cell killing but reduced T cell persistence.

Extend capabilities with machine learning

The researchers at the Wendell Lim lab sought to find unspoken rules for co-stimulatory signaling and thereby optimize CAR T cell characteristics. They used a library of synthetic signal motifs, machine learning and a unique conceptual approach to discover combinations beyond what occurs naturally.

From words, to sentences, to language

The researchers looked at natural signaling proteins, extracted signaling motifs and synthetically assembled combinations of signaling motifs to form unique signaling programs. This approach can be conceptualized as an exploration of sentence structure.

Figure 2 illustrates this rearrangement of various ‘words’ – signal motives – into separate ‘sentences’ or signal programs. To understand and predict the “language” of these combinations, the team then used machine learning algorithms called neural networks to detect the underlying “grammar” of the datasets. This revealed the importance of word order, word meaning and word combinations in the final product – otherwise reformulated as the impact of the identity, function and arrangement of the signaling motif on the T cell phenotype.

The team assembled a library of anti-CD19 CAR T cells with diverse costimulatory domains. Each cell contained one, two or three signaling motifs derived from natural signaling proteins (see Figure 2). The team randomly placed 12 native signal motifs next to one spacer motif in positions i, j and k to yield a total of 2,379 different motif configurations, as shown in Figure 3.

Next, researchers screened random subsets from the library to classify the T cells’ cytotoxicity and ability to proliferate (stemness). This process produced unique and unusual combinations, including combinations similar to costimulatory molecule 4-1BB (e.g., M10-M1-M1-ζ).

Decoding “language” using predictive neural networks

The signal motif sequences possessed different levels of cytotoxicity and stemness, according to experimental analysis. The team then used this data to understand the invisible rules surrounding the design of costimulatory molecules.

An artificial neural network turned out to be crucial for this research. As shown in Figure 4, the data was split to train or test the algorithm to predict the cytotoxicity or stemness of a chimeric antigen receptor. This process elucidated several associations, such as the ability of 4-1BB-like costimulatory domains to enhance cytotoxicity and stemness.

Successful prediction with M1 Co-stimulatory molecule

Could the neural network accurately predict the fate of a T cell with a given costimulatory combination? The team tested the waters by adding costimulatory molecule M1 to 4-1BB-like versus CD28-like signal domains. The neural network predicted that adding M1 motifs would show enhanced cytotoxicity and stemness in 4-1BB-like domains, while having no effect in the CD28-like counterpart.

In an in vitro model, the CAR T cells with 4-1BB-like domains and M1 motifs effectively killed tumor cells and preserved the T cell lineage; on the other hand, the addition of M1 motifs caused no change for the CD28-like derivatives. This correct prediction also translated into mouse model results. The 4-1BB/M1 CAR T cells slowed the growth of tumor cells for two weeks longer than 4-1BB CAR T cells alone. These observations show how a neural network can be used to accurately predict T cell characteristics depending on the synthetic signaling motifs involved.

Options for CAR T Therapy

It can be difficult to predict how a synthetic receptor component will affect the characteristics of the resulting cell. This study unravels part of this mystery using signal motif libraries and machine learning. By analyzing CAR T cell costimulatory domain combinations, the team created a neural network that successfully predicts T cell phenotype based on the costimulatory molecules present. This in turn revealed rules of CART costimulatory signaling that can be used to design better synthetic signaling domains. Similar libraries and subsequent analyzes could be used to improve other modular regions of a CAR T cell.

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