Why some tumors are so persistent

Health and medicine

Radiation resistance in focus: Why some tumors are so stubborn

The Kemp laboratory uses genome-scale modeling to understand tumor metabolism and predict tumor responses to radiation therapy

May 11, 2021

• Atlanta, GA

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Melissa Kemp and Josh Lewis used genomic-scale models to predict tumor responses to radiation therapy.

Radiation therapy was and is a cornerstone of cancer treatment for good reason: it works.

Most of time.

Currently, more than half of cancer patients receive radiation as part of their treatment. But 20 percent of them, give or take, will find that they need different options because their tumors are resistant to radiation therapy. It’s a bad place: you can still be exposed to the potential side effects without the therapeutic benefit, and you’ve lost valuable time.

What if doctors could predict and potentially improve the radiation sensitivity of individual patients? A team of researchers at the Georgia Institute of Technology and Emory University are working on something that has that ultimate goal in mind.

“There is still a poor understanding of why some tumors do not respond well to radiation, and it is a significant barrier to long-term survival for many patients,” he noted Joshua Lewiswho, as a doctoral student in the laboratory of, was looking for answers to the question of radiation resistance Melissa Kemp in the Wallace H. Coulter Department of Biomedical Engineering.

Together, they took steps to understand the underlying metabolism and develop a tool to predict whether certain tumors are among the stubborn ones that are unresponsive. In back-to-back articles with Lewis as lead author, Kemp said they created a new pipeline “where you can automatically collect data, incorporate it into our whole-cell modeling of metabolism, and actually predict the way certain tumors of different types of cancers will actually predict the way” different patients will respond to it. “

She added, “This is the first example of how, in relation to radiation resistance, to really ask why there are differences that manifest in tumor metabolism.”

In January, they published the first study with staff from Wake Forest and Indiana University in Cell Systems magazine. The latest research appears in Nature Communications this month. Lewis based the studies on his Ph.D. Thesis, “Genome-scale modeling of the redox metabolism and the therapeutic response in radiation-resistant tumors”, which he defended a year ago.

Shortly after Lewis started school, Wake Forest researcher Cristina Furdui reached out to the Kemp lab with the issue of radiation resistance and asked us to apply our expertise in systems biology, he said. “It’s a more holistic approach than working with individual molecules or proteins, looking at many different factors and how they interact, to see if this leads to a particular response to radiation therapy.”

Lewis used an established type of cellular modeling called “flow balance analysis,” where “you’re trying to model the entire metabolism of a cell – all the different chemical reactions. We model them with various biochemical equations. “The researchers then connect these equations to a computer. They can precisely analyze around 13,000 different metabolic reactions within seconds.

“We developed our own approach to building more accurate models for flow balance analysis by incorporating various types of omics data,” said Lewis, who is now studying medicine in the Emory MD / PhD program. “Omics” measure properties of molecules such as genes, proteins or metabolites that make up the cells of an organism.

By integrating genomics, transcriptomics and metabolomics data, the researchers could model redox metabolism – the process of oxidation and reduction reactions or the loss and gain of electrons – in cancer cells and thus accurately predict how certain tumors react to radiation Therapy, ”said Lewis, who digested data from the cancer genomatlas (TCGA).

“We were looking for metabolic enzymes, or metabolic targets, that if you optimize them, can compromise a tumor’s radiation sensitivity,” said Lewis. “So imagine you are giving a patient radiation therapy and at the same time you can give them a chemotherapy drug that blocks the action of a certain enzyme to make a tumor more sensitive to radiation.”

This is essentially the first paper. In the second – written only by Lewis and Kemp – the researchers incorporated machine learning into genomic-scale metabolic modeling “to see if we could better predict what biological traits are associated with a patient’s response to radiation”.

A major challenge for the researchers in both papers, Lewis said, was the datasets they used from TCGA. They had good genomics and transcriptomics data, but their metabolomics data were incomplete. The computational models they developed for the Cell Systems paper helped fill in the gaps and make metabolic predictions. They fed the data from these models into a machine learning model to better identify biomarkers for radiation resistance.

“We can clinically think of a patient giving a blood sample and from that blood you can measure the levels of various metabolites and determine whether the patient would be a good candidate for radiation therapy or whether we should consider other therapies,” said Lewis .

That speaks directly to what Lewis is currently training in his MD program. He wants to become a pathologist and better understand how patients respond to different therapies.

“I want to help bridge the gap between research and clinical practice,” he said.

He’s got off to a good start and it’s exciting for Kemp.

“Josh’s computing platform turns easy-to-collect data into a model representation of hard-to-collect attributes like metabolic flows and metabolite changes that are otherwise very difficult to measure when it comes to covering many different patients,” she said.

This research was supported by a grant from the National Institutes of Health / the National Cancer Institute (F30 CA224968), an NIH / NCI U01 grant (CA215848), and the Wake Forest Baptist Comprehensive Cancer Center (NIH / NCI P30 CA12197).

QUOTES: Joshua Lewis, Tom E. Forsha, David A. Boothman, Cristina Furdui and Melissa Kemp, “Personalized metabolic models on a genomic scale identify targets of redox metabolism in radiation-resistant tumors” (Cell Systems, 2021)

Joshua Lewis, Melissa Kemp, “Integrating Machine Learning and Metabolic Modeling at Genomic Scale Identifies Multi-Omics Biomarkers for Radiation Resistance” (Nature Communications, 2021).

Related links:

“Personalized metabolic models on a genome scale identify targets of redox metabolism in radiation-resistant tumors” (Cell Systems, 2021)

“The integration of machine learning and metabolic modeling at the genome scale identifies multi-omics biomarkers for radiation resistance” (Nature Communications, 2021).

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