An edited transcript from an interview with Audra Moran, President and CEO of Ovarian Cancer Research Alliance (OCRA).
Golda Arthur
Let’s look to the future in terms of, what are the tools for getting a handle on this cancer that you guys are putting into play now?
Audra Moran
There are many things to be, I think, hopeful for. Certainly, we've seen some drugs, new drugs on the market. PARP inhibitors, which have had a great effect for some people - not everybody - it’s a subset. That's hopeful because if one subset can be helped, perhaps another subset can be helped too. So we're really excited about that. But what we've started to do at OCR A is to look for holes and gaps - where can we serve as a convener or fill those gaps? One of them is that there's not a lot of aggregate large data for people to share, first of all, and second, for AI, which, you know, has so much potential. It's helped in other disease categories. It’s been used in ovarian cancer - we're actually funding a few grants in partnership with Microsoft, which is really exciting. They've offered their AI services to us. But you need large datasets and we've recognized that there really isn't a comprehensive large data set for ovarian cancer. So, we're creating what's called the Data Commons. It's basically linking different institutions, major institutions together so that there will be like a storefront for researchers. And so for us, that's a huge step forward. Certainly, we have the internet, which I think has moved science forward a lot faster over the last 20 years, but you have to be published, in order for people to read about all of these things or, you know, happen upon a conference where someone's speaking about their work. This is going to provide researchers with some actual ability to know what other people are doing, what other people have as a data set, and it kind of democratizes the process, because you know, big cancer centers get a lot of data, compared to random researchers that are in different spots. I mean, we want them to be able to access that same level of data. So anyway, that's one thing that we're doing that we think is going to really help. And as you know, AI is just blowing up everywhere, everyone's talking about it. So we can't wait to see how we can harness the potential of AI.
Golda Arthur
Tell me a little bit more about working with Microsoft on this.
Audra Moran
You know, I'm on LinkedIn, as most people are. And, you get all these random message requests on LinkedIn. And one day, I got a message from someone that said he was the head scientist at Microsoft for the AI For Health lab. And so we reached out, we talked, and it turned out that someone in his lab, actually, her mother had ovarian cancer. And so they're kind of just looking to do good. It's amazing. They actually have this computing power. So they've offered their computing power to us. And so in partnership, we put out our grant proposals, and we offer this as an add-on. So if someone's utilizing AI, they can use that Microsoft grant. We've only had one or two so far because the partnership started just a couple of years ago. We don't really have results yet. And again, there's not a huge amount of data. But it's starting, and it's happening. And they've done it in breast cancer very successfully looking at mammograms. It's very complicated, but it allows them to look at so many things at one time and see the patterns. And I think that's what we'd be looking to do for ovarian cancer.
Golda Arthur
Well, at least we have a screening tool for breast cancer, which doesn't exist for ovarian cancer and probably won't exist for ovarian cancer for a long time. I was going to ask you - this would seem like such an obvious concept, to start to collect a data set, specifically for ovarian cancer, but it doesn't exist. I was going to ask you why doesn't it exist? But I think I know the answer to that already. If we don't have a screening tool for ovarian cancer, we won't have a large dataset for ovarian cancer. Am I right on that?
Audra Moran
You are. There actually was a very large study done that had 200,000 participants - which is a huge trial in the UK that had early detection as its goal. So there is data from that. But this was actually addressing the general public, so not everyone had ovarian cancer. But in terms of large datasets, I just want to just say this: researchers have data. The big institutions probably have a lot more of it, because they keep it in-house, essentially. But that's the thing. It’s siloed. And I think the issue is connecting those silos because that's how we're going to get somewhere.
Golda Arthur
And what kind of timeline are you thinking about for something like that? Several years?
Audra Moran
Yes, we have a roadmap. And it's phased. There are four phases, and we're now in the pre-one phase. Right now we're getting ready. We have a steering committee, and we are starting with rare ovarian cancers. I know that ovarian cancer itself is rare, but there are actually rare ovarian cancers within that group. It just gives us a smaller dataset to work with so that we can get our hands on all of it. And wouldn't that be amazing if we got all of the data that we could on all the rare ovarian cancers in the world? I mean, that's probably a very noble goal. But that's what we'd like to do. So we're going to start with that. And then we'll move to high-grade serous, which is the more common, and so that will expand. But again, we have a huge consortium of rare cancer researchers who have asked to join. And so we're really excited about it.
Golda Arthur
Pockets of hope everywhere.
Audra Moran
A hundred percent. And there are other things like that we're learning every day. Scientists are learning and changing. I think that's important to know, too. You know, clinical trials are so important. We need people to participate in clinical trials. And yet, there are so many myths surrounding them, like, “Oh, I'm not going to get treatment, if I'm in the control arm, I won't receive…” That's not true, you would receive standard of care, of course. So there are things like that, which we're trying to demystify. Now in ovarian cancer, they're starting to use something called a platform clinical trial, which is where they're using a drug. But when they realize it's not working, they can turn to another drug. Whereas usually in a clinical trial you have to do it for a certain period of time. So this is really exciting, because this means they're not going to waste anybody's time, and some patients will still be eligible to continue. So if a drug wasn't working, they can change to a new drug. And to me, that's incredible. That's really hopeful, and will hopefully bring faster progress.