BRCA1/2 analysis: an interview with Jurgi Camblong, CEO of Sophia Genetics

insights from industryJurgi CamblongCEO of Sophia Genetics

Please can you give a brief introduction to the BRCA1 and BRCA2 genes?

BRCA1 and BRCA2 genes are two of the most well studied genes in the cancer field. They  are tumor suppressors -  mutations in these genes can lead to breast and/or ovarian cancer. Predispositions can be detected in women before they develop cancer.

Family members can also be tested for the mutations and receive the necessary care to help prevent the cancer developing if they return positive results. People may have the breast surgically removed and replaced with an implant, for example, or they may choose to accept the risk and undergo frequent mammographies so the cancer can be detected at the earliest possible stage if it does develop.

By how much can mutations in BRCA1 and BRCA2 genes increase breast cancer and ovarian cancer risk?

By a great deal. The presence of mutations in these genes can increase the risk of breast cancer by about 70% and the risk for ovarian cancer by about 50%.

Up until now, how have people accessed information about their possible cancer risk based on mutations in BRCA1 and BRCA2 genes?

Traditionally, the best way to perform genetic testing was using a technique called Sanger sequencing. A few years ago, however, many labs started using next generation sequencing (NGS) to analyze mutations related to BRCA1 and BRCA2.

Our technology allows clinicians to obtain extremely accurate results compared with those obtained purely by using NGS data, which tends to produce  quite ‘dirty’, or messy results.  

With NGS, the sequencer does not release the data in order, everything is disorganized and a lot of bias is introduced. Our algorithm enables this bias to be eliminated.

Small substitutions, insertions or deletions can be identified, as well as copy number variations. For copy number variants, many laboratories use an alternative technique, but with our algorithm, you get a lot more out of the data and even complex variants can be detected.

Could you please outline the recent upgrade to Sophia Genetics’ Data Driven Medicine Platform (Sophia DDM)?

The upgrade contains a CNV detection module for identifying BRCA1 and BRCA2 mutations. In addition, we have added other upgrades to the front end of the platform that will improve interpretation. For instance, users can perform a more in depth analysis of the variants, as well as being able to share the information with other members of their community.

We believe it to be a significant  and very important upgrade because now, thousands of sequences are produced every day, and more and more geneticists are available to detect variance of significance, even for genes like BRCA1 and BRCA2.

The only way we as a community can help better diagnose patients is by flagging those variants and improving our knowledge as we come across them. Any user can flag the variants, assign a degree of pathogenicity and share it with other community members.

The idea is therefore very simple – the information obtained for one patient now could be used to help another patient in the future.

If today, I detected some variants that are not yet classified and I spent one hour classifying and using the database’s system, I would be able to quickly diagnose any patients with the same variant.

In addition, I could  help my colleagues in other hospitals by sharing the information. This integration of knowledge is extremely well peer-received and is the only way we will be able to constantly improve the interpretation of genetic-based data.

How does this compare to Sanger Sequencing and MLPA techniques?

Sanger sequencing is 100% equivalent. The big difference is that NGS is much faster and cheaper. Sanger sequencing was a good technology for analyzing germ-line based genetic disorders where you expect that 50% of the DNA molecules will have a variant or 100% in the case of dominant disease.

Using this process, you have to go to each position one after the other, meaning the turnaround time is very slow, which is the main problem for hospitals now. Some patients wait a year to be diagnosed, but thanks to NGS, up to 96 patients can be analyzed in a single run.

With NGS, we see a dramatic decrease in turnaround time and with the adoption of our platform, many hospitals will be able to process six or seven hundred patients that have been waiting as long as six months. This will enable us to give patients a diagnosis just two weeks after they have come to the hospital to be diagnosed.

The platform requires very clever algorithms that go into great detail, so that all the little problems related to sample progression can be eliminated.

We have now analyzed over 3,000 samples and so far we have been able to detect all copy number variants that have previously been reported with MLPA techniques.

In some cases, we even managed to discard false negatives that were reported earlier. We systematically asked labs to compare our results with the MLPA results and since there are some false positives or samples where the algorithm cannot resolve the problem, they have to perform MLPA for around 10% of their samples. However, there is still a huge saving in  time and cost for the hospital.

What impact will the Sophia DDM upgrade have on diagnostics in oncology?

I think, in general, we will start seeing the impact as the upgrade is adopted in the clinical community. Today, we have 37 institutions working with us across Europe and about five new institutions per month have started using the platform since June.

We have now reached a peak where the communal  effects of knowledge sharing start to be very powerful and we believe this will have a huge impact in terms of improving patient diagnostics.

In addition, it will help to communicate  the message about standardization to physicians, because interpretation can still vary slightly between labs and our platform standardizes how data is interpreted.

There really is a double benefit. As well as allowing a more comprehensive analysis of variants, we simplify and standardize data so that physicians can interpret reports in the same way across institutions.

How do sites using the existing software install the update?

It's automatic and very simple. The old platform is made much like a modern web-based application that simplifies everything for the user. The user does not need to deal with the IT department and the IT department does not have to worry about it.

In house, a lot of tests are done automatically before we release any upgrade and upgrades are being built and commissioned around every two weeks.

Each upgrade is an important one, releasing new features and improved algorithms every time. This is mainly due to the fact that we have been able to collect thousands of reference data that we know are right because we analyze them with NGS in parallel with techniques such as Sanger sequencing.

Also, each time we make upgrades and updates, we make sure there is no regression and this is done fully automatically.

What do you think the future holds for BRCA1/2 analysis and how do Sophia Genetics plan to add to this?

I think there are two points to consider. More and more laboratories will start analyzing these  two genes in countries such as Greece, Turkey and the Czech Republic where they are just now acquiring NGS. The knowledge is becoming affordable, thanks to platforms like ours. However, in the meantime, there will be changes.

In the most advanced countries, we predict that  labs will start adding additional genes to BRCA1 and BRCA2 and we have heard that about 26 genes are being linked to breast cancer. These will  start being frequently analyzed in about 6 months’ time.

Also, BRCA1 and BRCA2 will be analyzed for cancer tissues and not just predispositions, so that the drugs currently being prescribed can be adapted. AstraZeneca, for example, has received approval from European authorities to market an inhibitor that is associated with diagnostics based on the BRCA1 and BRCA2 genes.

I think the community effect is very important in terms of making the information more comprehensive. Our ambition is to progress quickly in terms of extending the market and we think that by the end of next year, we will have over 100 institutions in Europe using our system.
For those institutions, it will be a fantastic opportunity for them to work together, share knowledge and keep improving the diagnostics of their patients.

In terms of evolution, we are taking forward a lot of applications in the cancer field that will support precision medicine, as well some other fields such as cardiomyopathies.

To date, our algorithm already has over 100 different paths that account each time for the strategy used in a lab, such as the sequencer that has been chosen, the sample type that is analyzed, the gene panel, and the chemistry used to enrich the genes analyzed.

Where can readers find more information?

http://www.sophiagenetics.com/

About Jurgi Camblong

Jurgi Camblong is a young entrepreneur whose training was in biochemistry and molecular biology. He holds a PhD in Life Sciences (University of Geneva) and an EMBA in Management of Technology (EPFL-HEC Lausanne).

In 2010, he entered the start-up scene, becoming the CEO of Gene Predictis SA. In 2011, he founded Sophia Genetics SA with Dr. Pierre Hutter and Prof. Lars Steinmetz. Since then he has been successfully leading the development of the company.

April Cashin-Garbutt

Written by

April Cashin-Garbutt

April graduated with a first-class honours degree in Natural Sciences from Pembroke College, University of Cambridge. During her time as Editor-in-Chief, News-Medical (2012-2017), she kickstarted the content production process and helped to grow the website readership to over 60 million visitors per year. Through interviewing global thought leaders in medicine and life sciences, including Nobel laureates, April developed a passion for neuroscience and now works at the Sainsbury Wellcome Centre for Neural Circuits and Behaviour, located within UCL.

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