Extracellular vesicle and particle biomarkers and AI

A very interesting article on extracellular vesicle and particle biomarkers and how they might be used in cancer detection.

 

https://www.sciencedirect.com/science/article/pii/S0092867420308746?via%3Dihub

There are gazillion authors from a bajillion institutions on this paper. Collaboration!

The gold standard to confirm cancer and other aliments is a tissue biopsy, where a small sample of tissue is extracted from the suspicious growth. But extracting a tissue sample isn’t possible in many situations, especially when there are other co-morbidities where the biopsy can introduce more problems than it attempts to solve.

So ‘liquid’ biopsies is another approach: stuff like drawing blood, lymphatic/bile, etc., which is not as difficult. But that stuff isn’t where the tumor is… its stuff floating around the body. Some of the gunk that floats around outside the cell are EVPs...or ‘extracellular vesicles and particles’. Basically they’re goops of stuff that float outside the cell, originating from ‘sorters of things’ in your cells. I (probably mistakenly) think of them as recipe pages floating outside the bookstore that sells recipe books. Except there are gazillion (actually billions of EVPs) recipes, and a gazillion books: trying to figure out what page came from what book would seem an impossible task, right? Well… this is where the story gets interesting!

This team used machine learning techniques to sort through all the EVPs based on sizes and other subcategories (mice/human, cancers). They found that the relationship between +10K EVPs and tumors in mice and humans were not the same (interesting since mouse models are used in so much research). They then sifted through all these possible markers to see if they could be used as a cancer detector.

How do you sort through literally 10s of thousands of markers for trends? Reliably? #Machinelearning, of course. They found the presence/absence of 13 common EVPs could be used to classify both lung and pancreatic cancers. But are those little floaters actually associated with tumors? In other words, is there a relationship between biopsy findings and the floaters?

While their dataset was kinda small, they could verify the biopsy findings with the floaters to +90% sensitivity / specificity (sensitivity is how well you can detect something (like how likely you are to stop at a sign that looks like a stop sign), and specificity is how well you can rule all other possibilities out (like how well you ignore the sign that looks like a stop sign but really isn’t). They then attempted to ensure that what they saw wasn’t just stuff you’d seen normally... not a trivial task.

What does it all mean? Maybe *earlier* cancer detection? Increased precision cancer detection? Dunno… but it is super cool that floaters in the blood could be so precise in detecting disease. These EVPs may be echoes of the body saying ‘something ain’t right’. We didn’t have the tools to be able to appreciate this signal until we developed the technology to detect the echoes.

Super cool.