In Cofactor Genomics, Investor

This is an amazing time to be in biology.

In fact, I like to say that right now we’re in a place that’s a lot like where personal computers were in 1987. In those days, PCs were pretty limited in what they could do. They still ran off of floppy disks, they really weren’t connected together and they were only capable of running a few basic software programs. But, even with those limitations, they were still useful tools that made all of our lives easier. And look how far we’ve come; today we all carry the equivalent of a 1987 supercomputer—aka a smartphone—in our pocket, capable of doing things we couldn’t have even imagined 30 years ago.

But that pre-Internet PC is right about where we are with genomic approaches to medicine right now. We have these amazing tools at our disposal, and the exciting part is that we’re just beginning to get to the point where we can see how it’s going to change all our lives and what it’s going to be in 10, 20, or even 30 years. Thomas Friedman refers to this acceleration of technologies post 2007 (including genomic technologies) as “the supernova”, where we are just now getting a grasp on how we might harness the power of these technologies 1.

Before the Human Genome Project completed its work in 2003, before we had that map, the way we analyzed DNA samples was pretty rough. We essentially had signposts mapped across all the different chromosomes and, in order to find shared markers, researchers had to sit down and simply look for them. That’s all we could do at the time and it was extremely time consuming.

It wasn’t even very precise. The whole human genome is three billion-plus letters long, so even if you’re looking at a tiny sliver of one chromosome, that still can be a million letters — a huge region and a huge margin for error. There might be 50 different genes in a segment that size.

But the Human Genome Project changed all that. It showed the value of biological big data. It created a demand for high-throughput sequencing. Today, a process that used to take years and years of tedious, hands-on work can be completed in a matter of hours, and a job that used to cost over $1 billion now comes in at around $1000 2.

We are now working at a completely different scale.

 

Beyond DNA

And this has changed everything about genetic medicine. Today we know exactly where each gene is located in the code and we can go in and read each one directly. That means we are able to identify and isolate exact mutations, allowing us to much more closely target specific diseases at the genetic level.

This has also allowed us to look at RNA in a much higher resolution. RNA is different from DNA in that RNA changes when disease is present. We can detect that change and see the indicators of disease — even when someone isn’t showing symptoms yet. At a high level, all DNA can really tell you are the hereditary risk factors that a given individual might face, or their chances for developing a certain disease. But that’s just a risk — it can’t say if you will or won’t become sick. Real symptoms and today’s conditions are what really matter, so if all you’re looking at is DNA you can end up with a lot of false positives and misdiagnoses.

In the case of serious diseases like cancer, this can mean spending thousands of dollars on the wrong therapy — that’s what many of these drugs can cost. More than just the lost money, patients can be on the incorrect treatment for a month or two. If they’re really sick, they may not get a second chance at finding the right therapy. They may run out of time.

Whereas when we look at somebody’s RNA we’re able to essentially see a snapshot of what’s happening in their body right now. This allows us to see beyond the DNA and get much more information about what’s going on with a patient, or how they are responding to a certain treatment, than we could if we just looked at their DNA.

 

Multiple Applications

 In addition to clinical, front line treatment like above, this could be very valuable in immune-oncology and the development of cancer vaccines. Every major pharmaceutical company today has an immunotherapy program that is trying to develop drugs in this area, but one of the challenges they all face is understanding how the immune system responds to the treatment, what’s going on in what they call the microenvironment of the tumor. It is very difficult to get a good sense of what immune cells are up to inside a tumor, particularly with solid tumor cancers, ones like lung cancer which is the deadliest.

But by isolating the pure immune cell subtypes and analyzing their RNA individually we’ve been able to develop a set of fingerprints, a signature for what each immune cell’s RNA looks like. With this information we can now take a patient sample, match their RNA up with our database of fingerprints and accurately pick out and report exactly what types of immune cells are present in their tumors.

We can use this information to help predict a patient’s response to immunotherapy.  Drug developers who are working on new drugs want to learn as much about their patient populations as possible. Now we can see what the immune system is doing before a drug has been administered, watch as it’s administered over time, and then see how it’s responding.

Everything in medicine today is based on the statistical average. How most people respond most of the time. With tools like RNA fingerprints, we won’t have to rely on an average that doesn’t describe us as an individual and what is going on within our body right now. Physicians and drug developers will be able to make decisions on a person-by-person basis, using that information to precisely tailor their work to focus on what is best for each individual at any given point in time.

And that is the true power of personalized medicine.

 

  1. Thomas Friedman. Thank You For Being Late: An Optimist’s Guide To Thriving in the Age of Accelerations. First Edition, Farrar, Straus and Giroux, 2016
  2. Kevin Davies eloquently covers this technological jump and price plummet in The $1,000 Genome: The Revolution in DNA Sequencing and the New Era of Personalized Medicine. First Edition. Free Press, 2010
Dave Messina
Dr. David Messina serves as Cofactor's Chief Operations Officer. He has spent the last 19 years in computational biology and genetics. He worked on the Human Genome Project at Washington University in Saint Louis, trained in molecular biology and human genetics at the University of Chicago, and and earned his PhD in computational biology in Stockholm, Sweden.
Recent Posts
Showing 2 comments
  • Dale Yuzuki
    Reply

    Thank you Dave for an excellent and informative piece!

    One question – does your RNA fingerprinting technique require then single-cell RNA-Seq? It seems to be implied when you referred to bulk vs. individual cell signals.

  • Dave Messina
    Reply

    Hi Dale, great question!

    Our approach does not require single-cell RNA-Seq. We have invested significant resources in building a large database of RNA fingerprints comprising each cell type. With that resource, we can now use those fingerprints to computationally determine the cell types present in the bulk RNA from the tumor sample. And the best part is it even works on FFPE samples, which as you know is the typical means of storing solid tumor specimens.

Leave a Comment

9 + = 17

Start typing and press Enter to search