Gleason Scores Explained
I heard of this term on Wednesday during a presentation by my PI on Multi Omics Image Integration with Dimensional Reduction for Tissue State Mapping. The context was that the lab had been trying to build a logistic regression model to predict the severity of prostate cancer in patients and they had divided outcomes into 3 cases: benign(2-4), moderate(5-7), and extreme >7.
I have a statistics background, so I thought it'd be helpful to explain this in a way accessible to people who do not have an oncology background.
Gleason Scores are values assigned by doctors to evaluate the progression of prostate cancer in a patient. They get these samples by conducting a prostate biopsy and by analyzing tissue samples. The manner in which the three classes are defined is on the basis of how 'different' do the cells look from 'normal' cells. This is done by referring to how 'differentiated' the tumor/sample cells are in comparison to a healthy tissue. A benign diagnosis means the sample looks a lot like a normal healthy cell. A moderate score refers to the sample cells looking markedly different than normal cells, and an extreme rating refers to the sample being highly abnormal.
Generally, doctors take two samples and sum up the scores to get a range from 2-10. It is important to note that previous medical literature suggests the sum is not commutative. Stark et al. 2009 found that 4 + 3 cancers were associated with a three-fold increase in lethal PCa compared with 3 + 4 cancers.
The gleason score has also been acknowledged as a strong predictor of survival among men with prostate cancer. However, it is important to note it is not the only factor to be taken into consideration. There are other factors such as blood PSA level, findings on imaging tests etc.
P.S. I should hopefully be writing more posts on biological topics from a perspective of someone who doesn't have a classical background in the field. The reason for this is to help further my understanding of the field and also help other computational biology researchers coming from a more mathematically oriented background.