We sat down with Prof. Jacques Cadranel, International Coordinator of the DEEP-Lung-IV study and Head of the Pneumology Department at the Hospital Group University Hospitals of Eastern Paris, who shared his experience in this collaboration with SOPHiA GENETICS, and the importance of the integration of multimodal data in clinical practice to advance personalized medicine in lung cancer.

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Hello Professor Cadranel, thank you for welcoming us today at the Tenon Hospital. Could you explain to us what DEEP-Lung-IV is and the genesis of such a project?

Taking into account the fact that we have what we call artificial intelligence (AI), which allows us to accumulate a lot of data, and ultimately be able to develop signatures that we cannot develop in our heads, or even with, a usual statistical approach. And also to have the impression that the patient cannot be reduced to a sex, a weight, a performance status, an imaging test, or a molecular test.

Could you explain the DEEP-Lung-IV project as it is today?

At the time, and what it has become, was first to say: “Let’s do something from the actual standard of care as it exists today”. That is to say, not to build a project that won't be applicable in real life. That was point number one.

Point number two was to accumulate the usual clinical data we have in our medical records, the usual biological data, blood counts, creatinine levels, liver tests, and imaging data while avoiding focusing solely on what we call the targets that we measure in interventional trials, but rather taking into account the patient as a whole. So, having a radiological phenotype. And then also integrating molecular parameters. Creating an initial profile of the patient, treating them as they were treated, and having their (outcome) information at the first assessment – are they stable or responders? - so that we can subsequently create signatures that would allow us to define their treatment outcomes before exposure.

From that, we can predict not only what treatment the patient received but what they would have been like if they had received another treatment. So that's the first step, that's the basis of DEEP-Lung-IV.

We talk about predictors, we talk about making the signature available so that it can be deployed in the clinical routine. What does such a platform look like to you?

The platform would be similar to what SOPHiA GENETICS was kind enough to show us. It's a platform that is, first and foremost, very tactile, very easy, very pleasant.

That's essential in the Multidisciplinary team meetings (MTBs). We can't get carried away, it musn’t be complex and should be extremely user-friendly. What we envision is a simple interface that provides some kind of a detailed patient profile, including characteristics such as pathology, performance status, and offering clear probabilities for treatment response, progression, overall survival, and for each therapeutic option.

 Why choose SOPHiA GENETICS as a partner for such a project?

I believe that’s not how you find a partner. A partner, you look him in the eye and you say, "I want to see". And then you start exchanging, and you want to see even more. And little by little, that's how you build a bond. I wouldn't have been able to choose SOPHiA GENETICS if I hadn't felt, from the start, the idea that SOPHiA GENETICS was coming towards us with was really important, because that's not what usually happens. Meaning that we have private partners who do not listen to the creativity of the doctors and of their understanding of the complexity of the patients, so that they can provide us with tools that respond to this complexity. Usually, these partners come with their prototypes and expect us to agree with it.

Professor Cadranel, you are the international coordinator of the study and also the scientific committee's chair. Could you tell us what this represents for you, in terms of challenges and opportunities, and why you accepted such an appointment?

Why did I accept? Because I share this baby with SOPHiA GENETICS. It's also my baby, and it's a really meaningful project for me. I believe that conceptually, we can completely change the paradigm and move on from a Newtonian medicine, that is: we observe A, which does better than B in interventional trials. And then we happily apply this, trusting the hazard ratio to double the probability of response, etc. But ultimately, that's not how it works. It's useless, and we should rather lean towards what I call a kind of quantum medicine. That's why I find this project extremely exhilarating and exciting, and I was given some chance to be part of it. That's also unique. That's why I don't want to miss out.

So the challenge is getting others on board. Right now, SOPHiA GENETICS and I, we need to accelerate the project a little more to produce results. And we are close. We hope that this new year will be the one of results and of raising awareness of this project. And SOPHiA GENETICS may not yet be aware that doctors aren't ready to hear what we're going to tell them, and so there will be some challenges ahead of us. We need to educate the doctors currently conducting the investigation because I think they didn't fully understand what they were getting into. They provided data for this study, they trusted SOPHiA GENETICS and also me, but I think they didn't understand what we were going to offer them at the end. And this is an exceptional challenge because it truly represents a complete paradigm shift for precision medicine.

Professor Cadranel, could you explain to me what you expect from the multimodal approach and the use of AI in clinical routine?

When we discussed DEEP-Lung-IV early on, I represented a little slide with my brain, which was composed of the performance status, the burden of the disease, molecular biology, organ pathologies, extent and type of metastases, and so on. And I told you, in fact, I integrate all of this unconsciously, but I also integrate it consciously.

There's still a need for us, first of all, to ultimately try to mimic our approach, without us realizing it, is what I call the black box, to make things more objective and also a little more reproducible, in particular with some “grey” situations where we don't really know what the best strategy is for the patient. If we resubmit the same patient, changing their name, changing their sex, or gaining one kilo, we realize that halve the times, we don't give the same answer. That's still a problem.

What other applications would you see in the context of multimodality and artificial intelligence?

There are. One part that we don't control is the patient's side. Having a perspective from the patient’s and caregiver’s perspectives. Integrating their perspectives into these signatures would be extremely valuable. Even with the greatest desire to do well as a doctor, when you make an announcement to a patient, when you offer them a treatment, you project an extraordinary amount of information on what the patient will receive and how they will experience it.

We did some sociological work to see how they felt about it. The most enthusiastic, compassionate doctor, the one who wants to do good the most, is wrong in 99% of cases by the patient. Patients are an essential element to consider.

We thank Prof. Cadranel for his time and for sharing his experience. To learn more about the DEEP-Lung-IV Study, visit the dedicated page .

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