New EcoTyper Tool May Help Identify Cancer Subtypes

Researchers at the Stanford Institute for Stem Cell Biology and Regenerative Medicine have developed a tool to examine how cells behave and interact in various environments in the body. They used it to better understand how cancer grows and can be treated.

“Now we can look at the building blocks of tissues – how the entire cellular ecosystem is structured – rather than just looking at the types of cells present,” said Aaron Newman, PhD, assistant professor of human science. biomedical data and member of the Institute. “It’s a much more powerful way of looking at tissue organization.”

The tool, called EcoTyper, combines new computer algorithms with those previously developed by researchers to analyze cell types, how they are arranged in relation to each other, and what types of RNA messages cells create. The researchers were able to analyze the interactions of cells in large amounts of loose tissue, using computer analysis to determine where certain cell subtypes live in the tissues and how they interact with their neighbors.

EcoTyper is unique in its ability to decode the cellular architecture of tissues at high definition and at scale in a cost effective manner. This includes the ability to analyze the types of tissue samples stored after biopsies or clinical trials, which would otherwise be difficult and expensive to analyze in this way. “

Aaron Newman, PhD, Assistant Professor of Biomedical Data Science

Another advantage of EcoTyper is that researchers can use large pools of stored tissue and public databases to conduct virtual clinical trials, which they have done to analyze thousands of cancer cases in a very cost effective manner. Newman said.

An article describing the tool was published on September 30 in the journal Cell. Newman, along with Assistant Professor of Biomedical Data Science Andrew Gentles, PhD, are lead co-authors of the article, which presents EcoTyper’s capabilities with tissue architecture analysis in different types of solid cancer tumors. The principal authors are postdoctoral researchers Bogdan Luca, PhD, and Chloé Steen, PhD.

A complementary article, published on September 30 in the journal Cancer cell, describes how EcoTyper was used to identify lymphoma cell subtypes. Newman and Ash Alizadeh, MD, PhD, professor of oncology, are the lead authors of this article. The main authors are Chloé Steen, PhD, and Bogdan Luca, PhD.

“EcoTyping” of cancer cells and their neighbors

While lung cancer may look very different from bladder cancer or other types of cancerous tumors under a microscope, EcoTyper allowed researchers to find 10 distinct multicellular communities, called “ecotypes,” that exist in more than one. dozen of different tumor types. They also found that the presence or absence of certain ecotypes in a tumor was highly predictive of outcomes and often indicated which types of treatment would work best, even for different types of cancer, according to the researchers.

“We found an ecotype predictive of a good response to a particular immunotherapy,” said Luca. “In fact, it was even a better predictor than the other candidate biomarkers we tested, even those that were specifically looked for to predict the response.” What’s more, with EcoTyper, researchers were able to predict whether a precancerous lesion – an abnormal growth that can become cancerous – in the lungs would spontaneously regress or develop into lung cancer.

“EcoTyper can provide a platform for future therapies because you have a better idea of ​​which bad cells in a tumor you want to attack,” Gentles said. This focus on interacting cell populations within a tumor is different from current approaches, which typically target “motor mutations” or genes along a certain pathway. “Many cancer therapies focus on a particular cell type or gene, but there are always other cancer-contributing cells or cells that don’t have that gene mutation,” and these are targets of equally valuable treatment, he added.

Turn to the most common blood cancer

The researchers whose article was published in Cancer cell sought to discern whether there are two different subtypes of a certain type of lymphoma, as has been generally accepted in the art. Using EcoTyper, they analyzed the microenvironment found among and around diffuse large B-cell lymphoma cells. By examining how cancerous and non-cancerous cells organized and interacted, they were able to differentiate not only two sub -types, but nine different subtypes of this lymphoma.

Because the researchers were working on tissue samples from previous cases of lymphoma, they also had a record of how the patients were doing. “We found that not only were there many more subtypes of this B cell lymphoma than previously thought, but we were also able to show that knowing which subtype gave us better ability to make predictions about the likely progression of cancer, ”Steen said.

Investigators were able to find positive results from a clinical trial of a lymphoma drug that appeared to fail. Indeed, the researchers repeated the clinical trial, this time using EcoTyper and including their new understanding of the number of additional types of this B-cell lymphoma.

“What we saw was that there was actually a specific subtype of lymphoma that responded to therapy,” Alizadeh said. “But in the initial trial, they couldn’t identify these other subtypes, so this promising sign of efficacy was lost among the negative results for all other lymphoma subtypes.”

“Being able to find the right medicine and design effective cancer treatments based on particular cancer subtypes is the epitome of precision health and personalized medicine,” Alizadeh added. “EcoTyper helps us do that. “


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