School of Biological Sciences’ research tests widely-held medical hypothesis

John McDonald, professor in the School of Biological Sciences and director of the Integrated Cancer Research Center.

Cancer associated mutations were identified in the 1000 genomes population (1KGP.)

Oncotarget (January 28th, 2020)

A new study by researchers in the School of Biological Sciences raises new questions about a decades-old, award-winning theory regarding how many genetic mutations are necessary for cancer to develop in human cells.

That theory, called the Knudson Hypothesis, argued that two mutations in the type of genes that suppress tumors are needed to lead to changes that could cause cancer. However, John McDonald, a School of Biological Sciences professor and the director of Georgia Tech’s Integrated Cancer Research Center, says the research, published in Oncotarget, “shows, for the first time, that nearly all normal healthy individuals carry at least one potentially cancer-causing tumor suppressor gene mutation. The implication is that a majority of the human population is, to a greater or lesser extent, predisposed to develop cancer.”

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Computational Oncology: Predicting Best Cancer Responses Using Computer Algorithms

Targeted Oncology (January, 10th, 2019)

The use of big data is revolutionizing many industries, allowing greater insights by drilling into evidence with more detail. Pair that with personalized medicine, where clinicians can tailor a patient’s cancer treatment based on biomarkers, genetic aberrations, or other individual characteristics, and oncologists gain powerful insight that can help predict the best course of treatment for each patient that is based on individual disease characteristics, not just cancer type and stage. Medical centers are also taking unique approaches, but they all have 2 things in common: technology and data. (full story..)

 

Focus on Ovarian Cancer – Ask the Expert

MedPage Today (June, 2018)

Machine learning, which helps find correlations in large data sets, is gaining ground in cancer treatment. John F. McDonald, PhD, explains why and shares his vision for the future of this technology in oncology. (full story..)

 

Georgia Tech Releases Machine Learning Software to Further Cancer Drug Research

HealthTech (November 30th, 2017)

New cancer-fighting drugs are sorely needed, but getting effective drugs to market takes years of clinical trials. Researchers at the Georgia Institute of Technology are hoping to change that, however, and speed up the process via a machine learning algorithm that has successfully used raw genetic data to predict when cancer drugs will be effective. (full story..)