We sat down with Prof. Jean-Christophe Bernhard, UroCCR Coordinator, and Dr. Gaëlle Margue, Urology Fellow, at University Hospital Bordeaux, to discuss the collaboration between UroCCR – the French Kidney Cancer Research Network - and SOPHiA GENETICS, and get their insights on the use of AI-powered multimodal approaches to improve patient care.

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Gaëlle, Jean-Christophe, hello. It's a real pleasure to be with you today to discuss the collaboration between SOPHiA GENETICS and UroCCR.

Before we go into more details about this collaboration, I would like you to tell us a little about your background, your life as a surgeon at the hospital, and also introduce the UroCCR network and the role you play within this network.

Gaëlle Margue, I am a junior doctor in urology at the Bordeaux University Hospital. I arrived in 2018 to start my internship and now, I have been a PhD student for two years and my science thesis focuses on kidney cancer, surgical and oncological themes, within the framework of the UroCCR network, the French Kidney Cancer Research Network, and the I.CaRe team, a Kidney Cancer Research team in Bordeaux.

Jean-Christophe Bernard, I am a professor of urology at the Bordeaux University Hospital, and I coordinate the I.CaRe team, the Kidney Cancer Research and Innovation Program in Bordeaux. I am also the national coordinator of the French Kidney Cancer Research Network, UroCCR.

Gaëlle, we worked together on your medical thesis around UroPredict. Could you describe UroPredict, and what it does?

Yes, absolutely. That was the subject of my medical thesis. The initial idea was to better characterize the risk of recurrence after kidney cancer surgery. Therefore, for patients with localized kidney tumors that are operated and are considered cured or in remission after surgery, we want to better determine what factors lead to patient relapse or not. We have prognostic scores to try to determine that, but they are not very effective, and so we wanted to better characterize that, to propose a follow-up schedule tailored to the risk of recurrence, or additional treatments for patients who have a high risk of recurrence.

In UroCCR, we have a lot of data that relates to these kidney cancer surgeries - clinical, biological, imaging, surgical, and monitoring data -, which we were not able to leverage, with traditional statistics, to better characterize the risk of recurrence. So the goal was to create a new machine learning score, a tool to predict recurrence in these patients, based on all this data that we have in UroCCR.

It's quite fascinating, from an outside perspective, to see surgeons saying that we need Artificial Intelligence and Machine Learning, to be able to advance our care. Jean-Christophe, from a strategic point of view, how do you see the collaboration with SOPHiA GENETICS, in particular? And then more broadly, the role these precision medicine tools are expected to play in the future?

UroCCR, to go back a little into the history, is a project that dates back to 2006, which was certified by the National Cancer Institute in 2011. It was initially deployed as a multicenter project in 2013 across 11 centers. And today, we’ve grown from 11 centers to 54 and soon 58. And so, the positive excitement of the system means that we are constantly collecting highly qualified data on the pathology of patients who are diagnosed with a urological tumor. We collect this data regardless of the treatment method, whether the patient is in active surveillance, whether he is operated on, whether he is treated systemically with medication, or whether he is treated with interventional radiology.

In doing so, coupled with the increase in the number of centers, and the increase in the number of patients, we reached the 20,000 patients included in the UroCCR, this dataset became even more considerable since we are linked to the SNDS, the National Health Data System, and therefore we can also do medico-economic evaluation, representing nearly 10 million data points available on patients treated contemporaneously by French teams for kidney cancer.

This is, I believe, the foundation of our collaboration with you and your team at SOPHiA GENETICS, to explore how we can take advantage of all the data patients have entrusted us with, regarding their illness, since everything is done with the patients’ consent - and I think it’s also important to point this out. Being able to produce new tools, and what Gaëlle said is that her thesis work, which initially is a scientific and fairly general work, I would say, has nevertheless led to the online publication of a calculator, which can now be used in a pragmatic way to answer a question that we may ask ourselves for a given patient.

Ok. So, beyond the research aspect that we were able to carry out together, which I think we are all very happy with, there is this role that an industrial company can play afterward, which will be the deployment of the tools, their validation, their improvement, the entire life cycle of the software.

Are these the things that are beginning to resonate in the minds of the medical profession, or are we still at the beginning and still at a time when everyone needs to find their role?

Yes, it is... It is very timely, because the UroCCR network has just been certified by the ANR, the National Research Agency, as a clinical investigation network on medical devices. These are also themes that we are addressing within the framework of the I.CaRe program, within the framework of the RHU (University Hospital Research in Health) where there is this desire for collaboration between academics and manufacturers for carrying out projects and arriving at outlets that are tools, products that can be used in everyday life and in the routine of patient care.

The UroPredict tool that we co-developed during your thesis, Gaëlle, it is now deployed and accessible. How is it being used by you?

It is a tool that is not yet a certified medical device, so it cannot yet be used to change patient care strategies.

Do you see among your colleagues or among the pharmaceutical companies, a little reluctance to apply them, to trust them, at the level of clinical routine?

So, reluctance… I don’t think it’s really a reluctance. I think that at each time there is something new, that potentially can bring a change in practices. There is always an observation phase and we have to assimilate what this novelty can be and what it can bring. We experienced the same thing, for example, for the introduction of robotic assistance in surgery, which today everyone is convinced on its benefit, both for health professionals, for surgeons, but also for patients. There was a whole phase where the community asked itself the question of whether introducing robotics would actually make it possible to do better than conventional techniques that were, I would say, well known, and had been validated for many years.

So there is always this moment where we ask the question of whether it is a real innovation, whether it will really bring added value. There is always this observation phase. Afterwards, I think that more and more, the medical and surgical community is convinced that with technological progress, we are finally able to improve what our daily routine is, what our practice is and obtain additional information that will enrich our practice and patient care.

What do you think the next algorithm we should develop is?

We have several ideas. We could further enrich this one by perhaps incorporating radiomics or pathomics, as we discussed, adding imaging data to increase the precision of the current tool. Then, we could explore many other algorithms with different objectives, such as predicting kidney function loss after kidney cancer surgery, or predicting the risk of morbidity, and intra or post-operative complications.

Objectives and things that we are trying to better characterize and there are several that we could look into and probably we have all the necessary data from UroCCR, we need to be able to exploit it.

And I think if I can complete.

There is also a question that we should address. Every time, we evaluate outcomes that are very objective, focused rather on the practitioner, the surgeon, the quality of his surgery, the outcome, the evolution of the disease, etc.

Quality of life, satisfaction, anxiety, which are things that we capture in UroCCR and in particular thanks to the UroConnect application. Today, in UroCCR, we have what we call PROMS and PREMS, which are patient-reported outcomes, and I believe this is another field worth investigating, moving beyond purely scientific outcome predictions, but also to take into consideration what the patient's experience is, and to judge what we do or to predict how we can improve what we do and propose to patients, based on their feedback about the quality of their care. I think this is one of the objectives we should collectively aim for.

We thank Prof. Bernhard and Dr. Margue for their time and for sharing their experience. Visit the UroPredict page to learn more about this machine learning model on real-world data for the prediction of kidney cancer recurrence.

SOPHiA GENETICS products are for Research Use Only, not for use in diagnostic procedures unless otherwise specified.