Proteomics based MCEDs are coming in hot and will probably hit the market sooner than anyone thinks

One of the early promises that was made during the pitch for funding the human genome project was that once we had figured out the code of life, we'd be able to understand and cure all diseases.

In retrospect, (and even at the time) scientists knew this was hyperbole and that the genome was really just the bottom of the molecular biology pyramid.

Knowing the sequence of the bases is important, but it tells you very little about what is actually expressed by the genome.

For that you need other tools to look at the products of the genome like mRNA, proteins, and metabolites from cellular processes but also modifications to the genome itself that control which parts of it are accessible.

Together we refer to the genome plus all of these other things as the 'multi-ome.'

One of the hardest of these other 'omes' to measure is the proteome.

It represents all of the proteins that make up the little machines that allow our cells to function.

Each of our cells expresses different proteins, and these work together, ultimately creating all of the tissues that make up our bodies.

However, in diseases like cancer, these cellular functions are disrupted due to mutations in the genome that change what is expressed or alter how those proteins function.

We can pick up some of these signals by looking at the genome, but we can get an actual read out of the biology of these cells by looking at the composition of the other 'omes' in the bloodstream!

Up until about 10 years ago, looking at proteins was a very tedious task requiring gels and antibodies or highly complex purification schemes paired with tandem mass spectrometry.

Now we have new techniques for quantifying thousands of proteins at once which gives us a much more comprehensive look at the underlying biology of cancers.

The paper I'm showing a figure from today was written by the group behind the Human Disease Blood Atlas and they characterized 1,463 proteins in more than 1,400 cancer patients.

They then took the data from those results and used machine learning to develop algorithms for predicting AML, ALL, DLCBL, Myeloma, Lung, Colorectal, Glioma, Prostate, Breast, Cervical, Endometrial, and Ovarian cancer.

They followed up by detecting those cancers with relatively high sensitivity and specificity including AUCs for 6 out of 12 above 0.95 (and above 0.8 for the rest!).

While not perfect, this is pretty freakin' good for a first crack, and this work highlights the future potential for proteomics in multi-cancer early detection (MCED).

With some optimization and a more comprehensive method validation, proteomic approaches could make the current players in the MCED space sweat!