How Radiation Therapy Affects Tumors: Glioblastoma vs. Low-Grade Gliomas

“These insights underscore the importance of personalized treatment approaches and the need for further research to improve radiotherapy outcomes in cancer patients.”

Radiation therapy or radiotherapy, is a common treatment for cancer, but its effectiveness differs across patients. A recent study published as the cover for Volume 17, Issue 2 of Aging explored why this happens. The findings provide valuable insights, particularly for brain cancers like glioblastoma (GBM) and low-grade gliomas (LGG).

Understanding Glioblastoma and Low-Grade Gliomas

Glioblastoma and LGG are both brain tumors, but they behave in very different ways. GBM is highly aggressive, with most patients surviving only 12 to 18 months, even with surgery, chemotherapy, and radiation therapy. LGG, on the other hand, grows more slowly, and many patients live for decades with proper care.

Despite their differences, LGG and GBM are biologically linked. Some LGG tumors eventually transform into GBM, making early treatment decisions critical. Given radiation therapy’s effectiveness in GBM, it has often been assumed that LGG patients would also benefit from it. However, a new study titled “Variability in radiotherapy outcomes across cancer types: a comparative study of glioblastoma multiforme and low-grade gliomas” challenges this assumption.

The Study: Investigating Radiation Therapy’s Impact on Cancer Patients Survival

A research team led by first author Alexander Veviorskiy from Insilico Medicine AI Limited, Abu Dhabi, UAE, and corresponding author Morten Scheibye-Knudsen from the Center for Healthy Aging, University of Copenhagen, studied how radiation therapy affects cancer patient survival. They examined data from The Cancer Genome Atlas (TCGA), which includes 32 types of cancer. When they found that GBM and LGG had very different survival outcomes after radiation, they decided to focus on these two types of brain cancer. To learn more about their differences, gene expression and molecular pathways connected to radiation therapy responses were studied.

The Challenge: Why Radiation Therapy Works Only in Certain Tumors

Radiation therapy is an important cancer treatment, but its success is not the same for everyone. Even patients with the same type of cancer can respond differently, making it difficult to predict who will benefit. Understanding why some tumors are sensitive to radiation while others resist it is key to improving treatment and patient survival.

The Results: Radiation Therapy Works for Glioblastoma but Not for Low-Grade Gliomas

Overall, GBM had the highest percentage of patients receiving radiation therapy (82%), followed by LGG (54%). When researchers compared survival outcomes, they found that while radiation improved survival in breast cancer and GBM patients, it had a negative effect on patients with lung adenocarcinoma and LGG. This led researchers to take a closer look at GBM and LGG, especially since LGG can develop into GBM over time.

A key discovery was how GBM and LGG regulate DNA repair differently. GBM tumors have weak DNA repair activity, making them more vulnerable to radiation-induced damage. LGG tumors, however, activate more DNA repair pathways, allowing cancer cells to survive radiation and potentially making treatment less effective.

The immune response to radiation therapy was also different. In GBM, radiation triggered an immune response, which may help fight the tumor. In LGG, however, immune activation was significantly lower, meaning that radiation therapy did not enhance the body’s ability to attack cancer cells. This fact may contribute to worse survival outcomes for LGG patients after treatment.

Further genetic analysis revealed that ATRX gene mutations made GBM and LGG patients more sensitive to radiation. On the other hand, higher EGFR gene activity was linked to lower survival rates after radiation in LGG patients. Similar findings for GBM tumors indicate treatment resistance.

The Breakthrough: Toward Personalized Treatment

This study offers new insights into why radiation therapy benefits certain brain tumors while being less effective, particularly in GBM and LGG. Finding important biological factors, like DNA repair activity, immune response, and genetic changes that may serve as biomarkers, will help radiation therapy be more precisely tailored to each patient’s unique tumor profile. 

The Impact: Rethinking Glioblastoma and Low-Grade Gliomas Treatment

These findings highlight the importance of precision medicine in brain cancer treatment. Instead of automatically recommending radiation therapy for all LGG patients, oncologists should consider genetic testing to determine whether this treatment will be beneficial or not. If not, alternative treatments may be necessary. Immunotherapy and targeted drugs against EGFR could provide better outcomes for patients who do not respond well to radiation therapy.

For GBM, researchers are investigating ways to enhance radiation’s effectiveness by combining it with DNA repair inhibitors, such as PARP inhibitors. These drugs could increase tumor sensitivity to radiation and improve survival rates. 

Conclusion

Advancing cancer treatment requires a personalized approach. Identifying biomarkers that predict how GBM and LGG tumors respond to radiation therapy can help clinicians make more informed treatment decisions, ensuring that patients receive the most effective and least harmful therapies. By uncovering key genetic and molecular insights, this study moves the field closer to individualized brain cancer treatments, improving survival rates while reducing unnecessary risks for patients.

Click here to read the full research paper in Aging.

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High School Students Use AI to Make Aging and Glioblastoma Discoveries

In a breakthrough study, three high school students and Insilico researchers used generative artificial intelligence (AI) to help identify new therapeutic targets for glioblastoma multiforme (GBM) and aging.

High School Students Use AI to Make Aging and Glioblastoma Discoveries
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Glioblastoma multiforme (GBM) is one of the most aggressive and fatal malignant brain tumors. With a median survival time of 15 months, only about 25% of patients survive for one year and less than 5% survive for five years. As people get older, the risk of developing GBM increases. The discovery of new drug targets for GBM is of paramount importance.

The good news here is that high school students, Zachary Harpaz, Andrea Olsen and Christopher Ren, and researchers Anastasia Shneyderman, Alexander Veviorskiy, Maria Dralkina, Simon Konnov, Olga Shcheglova, Frank W. Pun, Geoffrey Ho Duen Leung, Hoi Wing Leung, Ivan V. Ozerov, Alex Aliper, Mikhail Korzinkin, and Alex Zhavoronkov have recently made remarkable strides in the joint field of aging and glioblastoma research. The team used a generative artificial intelligence (AI) engine from Insilico Medicine (founded by Dr. Alex Zhavoronkov) called PandaOmics, to identify new therapeutic targets for both GBM and aging. On April 26, 2023, their research paper was published in Aging’s Volume 15, Issue 8, entitled, “Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics – an AI-enabled biological target discovery platform.”

Their Study

Andrea Olsen, a student at Sevenoaks School in Kent, UK, and CEO/co-founder of The Youth Longevity Association
Photo of Andrea Olsen, courtesy of Insilico Medicine

“[Glioblastoma multiforme] is one of the most horrible cancers because it has such a short survival time,” Andrea Olsen said. “Of course, it affects the brain and so affects the body because the brain is the control center of the entire body.”

Andrea Olsen, a student at Sevenoaks School in Kent, UK, and CEO/co-founder of The Youth Longevity Association, discovered her interest in neurobiology and technology while growing up in Oslo, Norway. In 2021, she started an internship at Insilico Medicine. Through her work with the researchers at Insilico Medicine, Olsen learned how to use AI to uncover new genetic targets that could be used to treat aging and cancer. Zachary Harpaz, a student at Pine Crest School in Fort Lauderdale, Florida, discovered his passion for biology after being introduced to the subject in 2020. He combined his passion for biology with his intrigue for computer science and AI to enter the field of aging research.

Zachary Harpaz, a student at Pine Crest School in Fort Lauderdale, Florida
Photo of Zachary Harpaz, courtesy of Insilico Medicine

“We wanted to find new putative targets for glioblastoma as well as aging—attacking them both at the same time,” explained Harpaz.

The researchers used a comprehensive approach to identify their targets. They split their data into three categories—young, middle-aged and senior—and mapped the importance of gene expression to survival. They analyzed 12 datasets and selected the genes that were overlapped in 11 of the 12 datasets. They also cross-referenced those genes with a recent study conducted by the researchers at Insilico Medicine in 2022 on putative targets for aging and certain diseases. 

PandaOmics

One of the most exciting aspects of their research was the use of PandaOmics. Typically, finding new drugs requires experts to comb through a myriad of data and conduct extensive research. With PandaOmics, AI quickly processes and analyzes the data to identify new therapeutic targets, reducing the time and resources required for drug development. These high school researchers used PandaOmics to screen datasets from the Gene Expression Omnibus repository (maintained by the National Center for Biotechnology Information) and discovered three new potential therapeutic targets for treating both aging and GBM.

The first target for glioblastoma and aging was CNGA3, which they selected after analyzing each gene through PandaOmics. They also analyzed negatively correlated genes and selected GLUD1 as the number one gene produced by PandaOmics. Finally, they cross-referenced genes highly correlated to aging with the previous 2022 study and selected SIRT1 as another potential therapeutic target. The students were excited by the findings. Before she interned with Insilico, Olsen said she didn’t realize that AI could be so helpful in finding completely new therapeutic targets. 

“For me, [working with Insilico] was an incredible opportunity to dive into the field of research, aging, longevity, and neuroscience. It really kick-started my entire career,” Olsen said.

Looking Ahead

Overall, the study conducted by the researchers and these high school students showcases the power of AI in drug discovery and highlights the potential for young researchers to make meaningful contributions to the field. Their findings could lead to the development of new therapies for glioblastoma and aging-related diseases. Other therapeutics identified through Insilico’s Pharma.AI platform—specifically for idiopathic pulmonary fibrosis and COVID-19—have already advanced to human clinical trials.

“I’m excited to continue my research into college, but I’m super grateful for this opportunity at Insilico. It allowed me to get a head start on learning how to conduct research, analyze data and use the coolest and most cutting edge AI in the drug development area,” Harpaz said. “That experience gave me an amazing head start and I’m super excited to continue that into college, and even after college.”

Click here to read the full research paper published by Aging.

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Aging is an open-access, peer-reviewed journal that has been publishing high-impact papers in all fields of aging research since 2009. These papers are available to readers (at no cost and free of subscription barriers) in bi-monthly issues at Aging-US.com.

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