In 1775, Percivall Pott discovered chimney soot exposure increased the likelihood of developing a disease we now have come to understand fairly well in recent years: cancer. The first uses of radiation to cure cancer in 1899 by Stenbeck and Sjorgren, the discovery of hormonal therapy for reproductive-related cancers in 1941, and the discovery of the Philadelphia chromosome, a collection of genes found in 95% of leukemia cells, in 1960, created the foundation for amazing treatments that have saved many people’s lives [5]. Some cancers are more severe though, and have required more study.

Glioblastoma “is a fast-growing and aggressive brain tumor,” according to the article written by the American Association of Neurological Surgeons [8]. In this article they mention that glioblastomas account for 47.7% of malignant brain tumors; with severe symptoms such as vomiting, seizures, mood and personality changes, cognitive changes, etc.; and that usually requires several expensive MRIs to diagnose. Generally, the bigger the glioblastoma, the more severe the tumor. Treatment is usually necessary and requires a mixture of surgery, radiation, and chemotherapy. All of these treatment steps have substantial risk and side effects. The National Brain Tumor Society estimates the five-year survival, or the percentage of people still alive five years after diagnosis, at 6.9% [4]. Overall, having glioblastoma dramatically alters a person’s life, diagnosing and treating glioblastoma is incredibly expensive, and treatment does not dramatically improve the odds of the patient. However, in 2020, a huge breakthrough was made. 

Sri Priya Ponnapalli, a data scientist using AI and mathematical formulas to create computer models, led a team of bioengineers, biological researchers located in Utah, Ohio, and China, to continue researching cancer [7]. Ponnapalli et al. collected all of the genes experimentally proven to be common in glioblastoma cells and added them to a database [6]. For 10 years, they followed glioblastoma patients and their treatment journey, allowing the opportunity for their genes and experience to be compared. They then developed a system that compares the presence of a collection of genes to patient life expectancy. Using the database and this system, the team then went through each possible combination of genes until they determined which genes best predicted the patient’s journey. This model accurately predicted patient outcomes approximately 90% of the time. Now future patients can get a DNA test and use this model to predict what their journey will look like. This is important because doctors will be able to use a patient’s particular genomic sequence to determine which treatments are the most likely to work, doctors and patients will be better prepared for the treatment journey they are most likely to experience, and drugs targeting specific gene mutations could be developed.

Genomic sequencing of individual patients could help determine which currently existing drugs can aid patients. For example, one of the genes incorporated into the model is known as an epidermal growth factor receptor-encoding gene that contributes to kinase. Kinase is an enzyme, or a protein, important in ATP or energy production [1]. This gene is frequently mutated in glioblastoma patients, but patients frequently do not respond well to treatments targeting the mutated gene because a variety of other genes influence the mutation’s behavior. The model created by Ponnapalli accounts for this. With a DNA test, the model could inform a patient and their healthcare provider if the medication that counteracts the growth factor specific gene mutation would be effective since it could determine if the genes that negate the effects of the medication are present. This process could be used for a wide variety of medications, including several medications that could be used to treat the glioblastoma.  

Several treatment options currently exist for glioblastoma and identifying the ones that are working are crucial to effectively treating the disease. The Mayo Clinic lists surgery, radiation, chemotherapy, tumor treating fields therapy, targeted therapy, and clinical trials as categories of available care [3]. Unfortunately, pseudoprogression, a phenomenon where tumors naturally keep increasing in size during the beginning of treatment before shrinking after the treatment starts working, frequently interrupts patient care. Effective treatments that are working can appear to not be working for up to three months [9]. This frequently causes confusion as patients and doctors either continue an ineffective treatment for too long and never see results or, more often, they discontinue a drug too quickly because they are discouraged by the apparent lack of progress. There are a limited number of drug treatments options, and a limited amount of time to try them. Knowing approximately how long before a patient should start seeing results would help doctors and patients make a better timeline that accounts for pseudoprogression while still prioritizing the recovery of the patient [2]. This model could be one of the tools they use to predict how long pseudoprogression will last based on the drug and their specific combination of genes.

Creating more drugs that respond to specific gene mutations within patients would create even more options for patients depending on their specific needs. Addressing these specific gene mutations may be able to prevent cancers from forming, spreading, or coming back. Being able to treat a patient’s cancer with drugs that can target the cause of their specific tumor will likely make cancer treatment more effective, cheaper, and cause less side effects.

In conclusion, cancer research is still continuing even now. This will not only improve the likelihood of effectively treating cancer, but it will improve patient experience during treatment.

Works Cited:

GeneCards. (2023). “EGFR Gene- Epidermal Growth Factor Receptor.”  https://www.genecards.org/cgi-bin/carddisp.pl?gene=EGFR. 

Jia (2019). “The potential mechanism, recognition and clinical significance of tumor pseudoprogression after immunotherapy.” https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6936240/#. 

Mayo Clinic Staff. (2023). “Glioblastoma.” https://www.mayoclinic.org/diseases-conditions/glioblastoma/cdc-20350148. 

National Brain Tumor Society. “About Glioblastoma.” https://braintumor.org/events/glioblastoma-awareness-day/about-glioblastoma/. 

National Cancer Institute. (2020). “Milestones in Cancer Research and Discovery.” https://www.cancer.gov/research/progress/250-years-milestones. 

Ponnapalli, Bradley, Devine, et al. (2020). “Retrospective clinical trial experimentally validates glioblastoma genome-wide pattern of DNA copy-number alterations predictor of survival.” https://doi.org/10.1063/1.5142559. 

Ponnapalli. (2023). “Sri Priya Ponnapalli.” Scientific Computing and Imaging Institute. https://www.sci.utah.edu/alumni-highlight/201-ponnapalli. 

Thakkar, Peruzzi, Prabhu. “Glioblastoma Multiforme.” https://www.aans.org/en/Patients/Neurosurgical-Conditions-and-Treatments/Glioblastoma-Multiforme. 

Yu, Rapalino. (2019). “15 – Treated Gliomas.” https://www.sciencedirect.com/science/article/abs/pii/B9780323445498000158

Thumbnail citation: George, Jill. (2017). “Gene Editing.” https://www.flickr.com/photos/nihgov/31728691073.