How Artificial Intelligence Found the Words To Kill Cancer Cells


Cancer Cell Immune Cell Illustration

Cancer is a disease characterized by the abnormal growth and division of cells in the body. Tumors can affect any part of the body and can be benign (non-cancerous) or malignant (cancerous), spreading to other parts of the body through the bloodstream or lymph system.

A predictive model has been developed that enables researchers to encode instructions for cells to execute.

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 “sentences” are based on combinations of “words” that direct engineered immune cells to find and continuously eliminate cancer cells.

This research, which was recently published in the journal Science, is the first time that advanced computational techniques have been applied to a field that has traditionally progressed through trial-and-error experimentation and the use of pre-existing molecules rather than synthetic ones to engineer cells.

The advance allows scientists to predict which elements – natural or synthesized – they should include in a cell to give it the precise behaviors required to respond effectively to complex diseases.

“This is a vital shift for the field,” said Wendell Lim, Ph.D., the Byers Distinguished Professor of Cellular and Molecular Pharmacology, who directs the UCSF Cell Design Institute and led the study. “Only by having that power of prediction can we get to a place where we can rapidly design new cellular therapies that carry out the desired activities.”

Meet the Molecular Words That Make Cellular Command Sentences

Much of therapeutic cell engineering involves choosing or creating receptors that, when added to the cell, will enable it to carry out a new function. Receptors are molecules that bridge the cell membrane to sense the outside environment and provide the cell with instructions on how to respond to environmental conditions.

Putting 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) have been effective against some cancers but not others.

Lim and lead author Kyle Daniels, Ph.D., a researcher in Lim’s lab, focused on the part of a receptor located inside the cell, containing strings of

What the Grammar of Cellular Commands Can Reveal About Treating Disease

Next, Daniels partnered with computational biologist Simone Bianco, Ph.D., 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, Ph.D., also at IBM Almeden, and Shangying Wang, Ph.D., 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 tumor 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…



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