A cancer cell's shape offers clues on how it will act, how dangerous it is and what treatments should be used against it.
Doctors can sometimes use a cancer cell's genetics to predict the same.
The researchers from Colorado State University whose paper is published in the journal Integrative Biologyhope that their measurements of cell shape could be combined with genomic data to offer a more precise prognosis and guide strategies for treating a patient's disease.
He points out that when a pathologist examines a tumor sample, he or she may see physical abnormalities in a cell's growth or nucleus or chromosomes. However, "Our hypothesis is that there are subtler changes in shape at a statistical level, early in the process of carcinogenesis, that a computer could pick up," Prasad says.
To determine what these changes may be, Prasad and colleagues including first author Elaheh Alizadeh, PhD candidate in the Prasad lab, first had to quantify cell shape.
Rather than trying to categorize cells as "a little oblong" or "somewhat spherical", the group used what are called Zernike moments to precisely capture cell dimensions. Basically, a Zernike moment, named after physicist Frits Zernike who won the 1953 Nobel Prize in Physics, is a way of representing shapes as data.
This requires choosing a finite number of measurements (the researchers chose 256), which then became a sort of numerical cell-shape signature. The researchers also found a way to represent differences in Zernike moments between cell lines as vectors. The question was whether a vector describing differences in Zernike moments could help identify which cell line was likely to be the most invasive.
"Machine learning is a tool that you have to give some examples - you have to say 'these are oranges and these are apples' and then a neural network can learn what distinguishes one from the other. Then you give the system an apple and see if it can correctly tell you which one it is," Alizadeh says.
In this case, the "apples" and "oranges" were more and less invasive osteosarcoma cell lines. "The four cell lines we used, the model can predict them with good accuracy," Alizadeh says.
"What we found is that if we first allowed our neural network to evaluate new cells - to recalibrate its categorization based on the characteristics of these new cells - it does predict the classes pretty well," Alizadeh added.
However, Prasad and Alizadeh point out that significant questions remain. For example, how a cell is prepared for imaging can affect its shape - "What you put these cells on when you look at them may affect what you see," Prasad says. Likewise, there are myriad ways to quantify cell shape, of which Zernike moment is only one possibility. Future research will explore additional ways to capture the data of a cell's dimensions.
Still, "If we could say that this set of shape changes represents these genetic changes - if we could do this, I think it would be amazing," Prasad says. "It would let us add shape as one more variable in determining a cancer's prognosis from biopsy."