How artificial intelligence found words to destroy cancer cells

Cancer is a disease characterized by abnormal growth and division of cells in the body. Tumours can affect any part of the body and can be benign (not cancerous) or malignant (cancerous), spreading to other parts of the body through the bloodstream or lymphatic system.

A predictive model has been developed that allows researchers to code instructions for the cells to carry out.

Scientists at the University of California San Francisco (UCSF) and IBM Research have created a virtual library of thousands of “command sentences” for cells using machine learning. These “suggestions” are based on combinations of “words” that direct engineered immune cells to seek out and permanently destroy cancer cells.

This study was recently published in the journal The science this is the first time that advanced computational techniques have been applied to a field that has traditionally evolved through trial and error experimentation and the use of pre-existing molecules rather than synthetic ones to create cells.

Advances allow scientists to predict which elements—natural or synthesized—they should include in a cell to give it the precise behaviour it needs to respond effectively to complex diseases.

“This is a vital shift in this field,” said Wendell Lim, PhD, Byers Professor Emeritus of Cellular and Molecular Pharmacology, who directs the UCSF Institute for Cell Design and led the study. “Only with this predictive ability can we get to the point where we can quickly develop new cellular therapies that perform the desired actions.”

Get to know the molecular words that make up cellular command sentences

Much of therapeutic cell engineering involves selecting or designing receptors that, when added to a cell, will allow it to perform a new function. Receptors are molecules that connect the cell membrane to sense the external environment and provide the cell with instructions on how to respond to environmental conditions.

Inserting the right receptor into a type of immune cell called a T cell can reprogram it to recognize and kill cancer cells. These so-called chimeric antigen receptors (CARs) are effective against some cancers but not others.

Lim and lead author Kyle Daniels, PhD, a researcher in Lim’s lab, focused on the part of the receptor located inside the cell containing the chains

amino acids
<класс деления="текстовый контейнер ячейки большой-6 малый-порядок-0 большой-порядок-1"> <класс деления="текстовая оболочка">
Amino acids are a set of organic compounds used to build proteins. About 500 naturally occurring amino acids are known, although only 20 are present in the genetic code. Proteins are made up of one or more chains of amino acids called polypeptides. The sequence of the amino acid chain causes the polypeptide to fold into its biologically active form. The amino acid sequences of proteins are encoded in genes. The nine proteinogenic amino acids are called “essential” for humans because they cannot be produced by the human body from other compounds and therefore must be supplied with food.

“data-gt-translate-attributes=”[{” attribute=””>amino acids

, referred to as motifs. Each motif acts as a command “word,” directing an action inside the cell. How these words are strung together into a “sentence” determines what commands the cell will execute.

Many of today’s CAR-T cells are engineered with receptors instructing them to kill cancer, but also to take a break after a short time, akin to saying, “Knock out some rogue cells and then take a breather.” As a result, the cancers can continue growing.

The team believed that by combining these “words” in different ways, they could generate a receptor that would enable the CAR-T cells to finish the job without taking a break. They made a library of nearly 2,400 randomly combined command sentences and tested hundreds of them in T cells to see how effective they were at striking leukaemia.

What the Grammar of Cellular Commands Can Reveal About Treating Disease

Next, Daniels partnered with computational biologist Simone Bianco, PhD, a research manager at IBM Almaden Research Center at the time of the study and now Director of Computational Biology at Altos Labs. Bianco and his team, researchers Sara Capponi, PhD, also at IBM Almaden, and Shangying Wang, PhD, who was then a postdoc at IBM and is now at Altos Labs, applied novel machine learning methods to the data to generate entirely new receptor sentences that they predicted would be more effective.

“We changed some of the words of the sentence and gave it a new meaning,” said Daniels. “We predictively designed T cells that killed cancer without taking a break because the new sentence told them, ‘Knock those rogue tumour cells out, and keep at it.’”

Pairing machine learning with cellular engineering creates a synergistic new research paradigm.

“The whole is definitely greater than the sum of its parts,” Bianco said. “It allows us to get a clearer picture of not only how to design cell therapies, but to better understand the rules underlying life itself and how living things do what they do.

Given the success of the work, added Capponi, “We will extend this approach to a diverse set of experimental data and hopefully redefine T-cell design.”

The researchers believe this approach will yield cell therapies for autoimmunity, regenerative medicine, and other applications. Daniels is interested in designing self-renewing stem cells to eliminate the need for donated blood.

He said the real power of the computational approach extends beyond making command sentences, to understanding the grammar of the molecular instructions.

“That is the key to making cell therapies that do exactly what we want them to do,” Daniels said. “This approach facilitates the leap from understanding the science to engineering its real-life application.”

Reference: “Decoding CAR T cell phenotype using combinatorial signalling motif libraries and machine learning” by Kyle G. Daniels, Shangying Wang, Milos S. Simic, Hersh K. Bhargava, Sara Capponi, Yurie Tonai, Wei Yu, Simone Bianco and Wendell A. Lim, 8 December 2022, Science.
DOI: 10.1126/science.abq0225

The study was funded by the National Institutes of Health. 


Font Size
lines height