Q&A: The Value of Clinical and Genomic Data to Accelerate the Discovery and Development of New Cancer Treatments

Todd Johnson Empowered Patient

Q&A With M2GEN’s Chief Growth Officer

M2GEN’s latest blog features an interview Dr. Todd Johnson, Chief Growth Officer at M2GEN, and Karen Jagoda, host of the Empowered Patient Podcast. They discuss the value of using more relevant clinical and genomic data to determine effective cancer treatments. To listen to the podcast click here.

Q: I thought we could start with you telling us a bit about the mission of M2GEN in terms of how you connect patients to a cure.

M2GEN's entire mission is about connecting cancer patients to a cure through our provision of longitudinal clinical and genomic data. We provide a unique data product that is important for discovering new cancer treatments, understanding how patients respond to existing cancer treatments, and helping to elucidate the pathways and the science behind cancer so that new innovations and new precision medicines can be developed.

Q: Can you tell us a little bit more about what makes your data so unique?

We're the only company today that is consenting patients upfront, i.e., asking patients for permission directly to use their data. By data, I mean the data that is captured in their electronic medical records. That might be data that's captured in radiographic images like X-rays or CAT scans, it might be data that's on pathology slides, but we go directly to the patient and ask them for permission to use those data in addition to their specimens taken in the course of, let's say, a biopsy of their tumor or a piece of their tumor that's taken out after surgery. We then take those specimens into genomic sequencing and determine what is called a whole exome sequence, as well as a whole transcriptome sequence. This gives researchers the opportunity to look into all the genes in the human body and how those genes might have been mutated by a cancer and how those genes might be expressed, that is, turned into the body's natural way of turning genes into messenger RNA and then protein that then drives changes in their cancer, changes in the progression of their disease and/or their response to treatment. It is an incredibly unique dataset that, by pulling all of that information together, has allowed research both in academia as well as industry to find out new, really important information about how cancers progress and how they can be better treated and eventually cured.

Q: I would suspect it has also dispelled a lot of myths about cancer, would you say?

Absolutely. Our partners in this journey, the cancer centers that we work with, we call our Oncology Research Information Exchange Network (ORIEN) members. In connection with those ORIEN members, as well as some of the industry partners that we work with, we have made some really dramatic advances in understanding how cancers work and how they can best be treated.

Q: Just to be clear, you are looking at the genome of the patient as well as the growth, the thing that was biopsied. Are they two different things?

Yes, they are, that's exactly right. Let's call the first the natural genome, or the normal genome of a patient, also often called the germline genome. We provide information on germline as well as on the genome of the cancer. The reason that's really important is that cancer is a disease of DNA, and it's a disease of how DNA changes over time or mutates. Those mutations are what cause a cancer to occur in the first place, for it to be able to evade the immune system, which is an important element of cancer progression. Then cancers can continue to mutate during the course of their life as they grow. That access to what we call serial specimens, that access to additional information about the cancer and how it continues to change from the body's normal or the patient's normal DNA, is a really important part of unlocking the mystery of what's happening here.

Q: That's also leading to better understanding of the immune system, correct?

It is. Immuno-oncology is an incredible burgeoning field. By studying the way that these cancers escape the body's natural immune responses, and also the way that oncology science has progressed, we've now been able to design new immuno-oncology drugs that are based on the body's own immune system. In a sense, tricking the body's own immune system to be able to recognize and fight those cancers. This is all part of how current treatments are working to personalize medicine for patients to advance them toward a cure. By understanding that better and by understanding which of those immuno-oncology treatments work and why and how they drive changes, and which types of expressions or expression patterns in the body allow for them to work, we better understand the immune system as well.

Q: It also leads me to think that this would have a path to getting the right treatment at the beginning of the process rather than experimenting with the patient to see if a particular therapy works for them. Is that also part of the mission here, to get the right treatment to the right patient at the right time?

It really is. For example with a dataset like ours - with over 325,000 patients who have given their consent for us to collect their data and then to continue to follow them through their data over their lifetime (Total Cancer Care) - and through the approach that we're taking with access to all of that data, we can see the great diversity of ways in which patients are treated today, and by doing that we can pinpoint what's working best. To your point, we might see that 50% of patients with a certain type of lung cancer that might be treated today first line with chemotherapy. Still, there is another portion of patients with the same lung cancer who are being treated first line with a newer immuno-oncology agent or other types of agents. In some cases, we can see that a portion of them is also treated in combination with chemotherapy and immuno-oncology agents. By studying that and by seeing which of those patient cohorts responds to which treatment or combinations of treatments, we can then define what is the best way for a given patient type to begin their treatment. Hopefully, by doing so, we guide them toward the more effective treatment faster and help them to avoid some of the horrible repercussions and horrible side effects of some of those less effective treatments that they wouldn't have to go through.

Q: And to say nothing of the wasted time since some of these cancers are extremely aggressive, so that's also another factor. Let's go back to what you were saying about the ORIEN group, the Oncology Research Informative Exchange Network. Is all this information being fed into that network and others also adding to it?

It is, yes. All the information that we capture or that we are provided by the ORIEN members is fed back to each of those members who submit it, and it's fed back with a lot of additional data. As I mentioned, the ORIEN members send us kind of an initial set of data and specimens from patients. We then further curate the data that we're sent. We check it against algorithms to make sure that it's sufficient and complete for the research purposes that we're looking for. Then we sequence those specimens, and we perform whole exome and whole transcriptome sequencing on germline and tumors and samples. Then we send all that data back to the ORIEN member for the purpose of their own research. They're interrogating the data for their own research projects to write publications on their new discoveries and make those publications and discoveries known to the broader market, as well as, hopefully, generate new medicines and new cures that are then the intellectual property that they can then turn into a product.

Q: Is this also being used to inform the clinical trials that have to be conducted before they can get to that point? Because it seems to me this could also save a lot of time for those people.

It definitely could. This is going to be a big area of focus for us over the next year. We are supporting a number of clinical trials today, helping  industry partners to find patients that meet the very complex criteria for their trials. Quite frankly, we want to do more of that. We see that there's a real opportunity for the ORIEN network and for the industry partners that we work with. To do that, we need faster access to data, and we need access to more data. So, we're currently working with the ORIEN members to put programs in place that will enable that.

Q: I understand that you've also partnered recently with Microsoft. How does their expertise fit into this long-term goal?

Microsoft has a real commitment to healthcare, and they have a huge focus right now on advancing their technology and the ways that they can work with big health systems to do research in areas like accelerating cures for cancer. They are a terrific partner for us because they have access to incredible computing power, and then also, as we know, they're one of the biggest software companies on the planet. They have incredible access to artificial intelligence and machine learning, and other innovative technologies that can really be leveraged on behalf of driving faster toward a cure, helping researchers on every side and every country to accelerate their research to get us toward a cure. I think that the overall power that Microsoft brings us is the sheer computing capability in addition to their expertise in building new software in addition to their commitment to the science and to healthcare and medicine. It makes them an ideal partner for us.

Q: How are researchers able to use your information to really move their research further ahead more quickly?

I think the best way to answer that would be to say there are several ways that researchers are using our data today. I'll give you three use cases, and then maybe I'll try to provide an example of one of them that really, I think, helps drive it home.

In the first case, researchers look into our dataset today to see are there certain mutations in these cancer patients that they can see correlate to worse outcomes, that they can see correlate to the progression of the cancer and/or to more rapid deterioration of the patient's condition. We call those markers of disease or markers of progression, and oftentimes those markers give us clues into then potential new targets that can be taken forward into the drug discovery and development pathway to lead toward new treatments and cures.

The second use case is ways of looking to the data to see, again, correlations to which patients respond to what types of treatment and to see if are there clues that we can see in their DNA and the way it's mutated or in the way that it's expressed in a tumor that tell us whether or not a given treatment is going to work for a given patient type.

Then the third use case is what we call "indication strategy". By that, I mean oftentimes, a researcher looking at a given type of treatment might be interested in, "Okay, I see that that treatment works for tumor type A, but I don't know yet if that type of treatment or that treatment mechanism can work for tumor types B, C, D and E." Historically, the only way they can really find out was by testing it in a clinical trial which takes, as we all know, years and enormous amounts of money. A great example of this was actually what Merck did with Keytruda, and I can speak openly about this because it's based on published, well-known research that Merck actually looked into our data and was able to see early in the development of Keytruda that the PD-L1 pathway was implicated in many tumor types. By seeing that in our tumor expression data, Merck was able to plot out a series of indications in which PD-L1 and Keytruda, which works against patients whose tumors overexpress PD-L1, was able to say, "Okay. These are the most likely indications of the most likely tumors that will respond to this type of therapy." Through that, Merck laid out their clinical development program.

Really exciting stuff and great ways that these data can be used to accelerate not just the research but also development of new therapies.

Q: I'm just really curious about your background as a researcher and in business and how all that work prepared you to become the chief growth officer there at M2GEN.

I started out my career in cancer research about 25 years ago. I was part of Dr. Peter Nowell's lab at Penn. The reason why Dr. Nowell for me is really important in all this and has been an incredible inspiration. Peter was the scientist who discovered what's called the Philadelphia chromosome, which was the first linkage between DNA and cancer. It's been really neat for me to be able to build on the knowledge that I have from that laboratory and from additional experiences that I had in cancer research and, after that, at Dana-Farber. Then throughout the last 20 years of my career, I have been more focused on drug discovery and development and then on data and how we can use data to drive and accelerate discovery and development, to combine those toward this incredible company and this incredible network, ORIEN, to advance cures for patients and advance the science here has...It's been really exciting, and I'm really happy to be part of it!

Supporting Meaningful Research That Will Shape the Future of Cancer Care

Today, M2GEN’s groundbreaking data platform, ORIEN Avatar, is redefining how data can be used in the fight against cancer. M2GEN will continue to make strides in creating robust longitudinal clinical and genomic datasets while improving the ways in which such data is collected, shared, and analyzed. Contact M2GEN today to request a demonstration and learn more about our custom bioinformatics solutions.