How ‘real world’ data can advance precision oncology: Q&A with Ray Bergan

There is a big gap between the narrowly focused data gathered in clinical trials and the lower-quality but more plentiful “real world” data routinely collected in clinical records, insurance claims and disease registries.

Photo: Raymond Bergan, M.D. (OHSU/Joe Rojas-Burke)The OHSU Knight Cancer Institute recently joined an effort to bridge this gap with a new framework for collecting and sharing standardized, real world data from consenting patients: the Master Registry of Oncology Outcomes Associated to Testing and Treatment, or ROOT. The long-term goal is to build a national oncology database for guiding patient care and developing new personalized diagnostics and therapies. The Knight Cancer Institute’s Raymond Bergan, M.D., a professor in the OHSU School of Medicine, is a principal investigator for ROOT. He talked with Cancer Translated about the endeavor.

Cancer Translated: When the drug Gleevec was ushered to clinical success by Brian Druker in 2001, it transformed the outlook for patients with chronic myeloid leukemia. And it affirmed the premise that understanding the earliest drivers of cancer formation can lead to less toxic and more effective treatments. Why hasn’t precision oncology delivered more successes like this?

Ray Bergan: The short answer to your question is, the biology of chronic myeloid leukemia is somewhat unique in that it only takes one genetic mutation to cause the disease. In that sense CML is the exception. With other cancers, we typically have multiple genetic abnormalities driving the disease. We can find one and we can target it, and it can have an effect for a while. But eventually, since it’s not the only thing driving the cancer, the other abnormalities come to the forefront, and that’s why the treatments are so limited.

CT: You and your colleagues have proposed a new clinical trial structure, the master observational trial, or MOT, as a way forward. In a recent commentary in the journal Cell, Dane Dickson, M.D., and you and other co-authors describe it as an amalgamation of master interventional trials, prospective observational trials, and a precise method of cataloging molecular data. How will this advance precision oncology?

RB: It will allow us to follow multiple biological characteristics of each cancer at the same time – multiple molecular abnormalities, be it at the DNA level,  the RNA level or the protein level. This type of trial will allow us to collect the information on a large scale. The way I look at this is not like it’s the answer, I look at it as a new tool that will augment existing approaches.

CT: Can you give an example of how this will work?

RB: Let’s say you and I both have metastatic prostate cancers, the same stage when diagnosed, the same metastases to the bones – I’m just making this up, so I hope you don’t mind I’ve given you cancer. We both start hormone therapy and you respond for four years but I only respond to it for three months and mine comes rip roaring  back when everything looked the same clinically. Clearly my cancer is different from yours even though we have prostate cancers that looked the same on biopsy. We can profile me and profile you and we’ll find differences at the molecular level. If we get a whole bunch of people like you who responded for a long time and a whole bunch like me who didn’t, we can then start looking for what makes the difference. It might not be the genetic mutations. It might be some of the other factors, some other characteristics of the tumor. Genes are important, but they aren’t everything.

CT: What are the biggest challenges to implementing master observational trials?

RB: The biggest challenges will be ensuring physician engagement and resource support for time, and that is not unique to this type of trial – it’s true of any clinical trial. People in the clinic are way overstretched and everyone wants them to do just this one extra thing. So, I think the biggest challenge will be finding people who are truly, passionately committed to this, and then making sure we are able to get enough resources that institutions don’t lose money by doing this.

CT: How easily will a master observational trial fit into the work flow of an oncology clinic?

RB: We’ve put a lot of thought into the design. This could be implemented in parallel with any clinical trial, and they could benefit each other. Let’s say you’ve talked to a patient about a new drug and you tell them: since each cancer is so complex, we have this new trial that we’ll do in parallel, and we can help track a broader range of things, take a broader look at your cancer. You don’t have to do anything in addition in terms of treatment. The consent process will explain how we will handle and keep confidential your clinical samples and medical information.

CT: In the Cell commentary, you describe a shared business model. Data providers are asked to receive less than fair market value for collecting the elements that are required by the master observational trial. Then, as data are collected, if any commercial group wants access to the provided data for research purposes, revenue will be shared by the institutions using a transparent and equitable model based on quantity and quality of provided data to allow continued participation.

RB: The idea is not to make money off of patients or add more costs to the health care system. The idea is recognizing that implementing these trials takes vast amounts of resources. We want to return some of that money to health care institutions so they can continue their mission to improve patient care.

CT: Cancer clinical trials typically exclude many people, which can be a problem if the study is not representative of the true patient population. Can this new structure have an impact on that kind of a problem?

RB: That particular issue is exactly where it will have the biggest impact. Clinical trial enrollment criteria are often so strict that most of the patients get excluded. If your trial excludes 90% of people with the disease, it means you are only analyzing 10 percent of the data. Gathering standardized, real world data gives you a window on the other 90 percent of the patient population.

Further reading:

The Master Observational Trial: A New Class of Master Protocol to Advance Precision Medicine by Dane Dickson, Jennifer Johnson, Raymond Bergan, Rebecca Owens, Vivek Subbiah and Razelle Kurzrock. Cell (Jan. 9, 2020)


2 responses to “How ‘real world’ data can advance precision oncology: Q&A with Ray Bergan

  1. don’t forget that principal, as it applies to ‘main’ is spelled ‘al’, versus principle, which is something one ‘stands on’…

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