Industry leader interviews: Jana Fischer

Proteins are typically the target of cancer drugs and measuring them on a cellular level allows us to identify different types of tumour cells, as well as immune cells that are present and how the two interact. This data is highly relevant to inform clinicians of the best form of (immuno-) oncology and combinatorial treatment for individual patients. Also, this information is highly relevant to pharma companies in order to accelerate their oncology drug development, by providing insight on drug mode of action, and signatures to identify responders to novel drugs.

The kind of data that we are able to extract from different types of tumours are monumentally valuable, so the work doesn’t stop there. All of the data we harness from these tumours is stored centrally, and we plan on utilising this data by building it into a system we refer to as the Digital Tumour, that will continuously allow us to improve the recommendations we can make to our clinical and pharma partners. Our journey has been rapid, though it is built on years of research and preparation: we founded the business in 2022, as a spin-off from the Bodenmiller Lab at the University of Zurich.

The dream became a reality for us in November 2022, when we secured a seed investment of 7.5m CHF. This seed funding will allow us to pursue our initial goals of establishing the company, achieving certification for our first diagnostic product and developing our Digital Tumour. By extension, collaborating with pharma and biotech partners in oncology drug development. It has also given us the resource we need to move to our own premises. We are due to move off university campus in May 2023. This offers us great opportunity to push forward with the certification processes for our new lab, and it gives us to the chance to grow our team and expand our operation. We will be located in a start-up campus for life science organisations in the region of Zurich, so we’ll be surrounded by companies operating in a similar field and at a similar capacity.

Tell us more about the Digital Tumour – how does it work?

The Digital Tumour will be the accumulation of all the molecular data we have extracted from every tumour that we have analysed to date, and ongoing. Connected to that, we store information on the clinical parameters and patient response to treatment. Over time, our aim is to utilize this central data repository to identify new tumour signatures, and build a self-learning system that will provide fully automated treatment suggestions for new patients, based on how their molecular properties compare to previously analysed patients that have been successfully treated.

Sounds interesting – are there any challenges to working with a database of this size?

Our data storage is quite advanced, so volume isn’t really a challenge for us. Our main focus is standardising the input of data itself. The technology is based on years of research and the data analysis requires a great deal of experience and in-depth expertise. In order to extract the full value from this data, it must be completely standardised. Data integrity is therefore vital to our work, and allows us to get the maximum value from past analyses. Our past experience in the Bodenmiller Lab allowed us to develop standardised processes to ensure that all of our data is fully comparable, which means that we can learn more and more from our past data, and apply this to new cases that we analyse.

It is also important to report on our complex data in a comprehensive but easily interpretable manner to the clinician/tumour board who needs to organise a treatment plan. We’re currently working with our clinical collaborators to develop readily understandable and concise reporting outputs. Unlike genomics analysis, our reports focus on proteins in tissue, which is the same information that clinicians are used to working with. So, there is a common language there that offers us the unique opportunity to provide clinicians with data they can easily interpret and work with.

What does this kind of research and data mean for oncology, both in terms of pharmaceuticals, biologics, and healthcare?

It’s important to note that personalised treatment approaches and precision medicine are not new concepts in the diagnostics space. However, our technology and algorithms allow us to extract novel types of biomarkers which were previously inaccessible or unknown, so we’re helping to level up the playing field and give clinicians and drug developers’ comprehensive information to individualize therapies.

Comprehensive tumour data is truly at the heart of what we do, and one key benefit of our technology is that we’re able to analyse very small amounts of sample – such as fine needle biopsies – to provide therapy suggestions. We can also analyse bio banked tumour material, so if there is any old material that has been stored, we have the ability to analyse those samples retrospectively. Not only does this help us to fuel our Digital Tumour with more data, but it also allows us to examine new fields such as long-term survival rates of patients with these tumours. This is of huge value to fuel our product development pipeline because it allows us to identify different molecular properties between individuals that may not have been considered on a clinical level, but may have played a role in patient responses to treatments and survival outcomes in the long-term.

This kind of retrospective data also plays a key role in the evolution of healthcare and drug development, as having the technologies available to acquire this sort of data and mine it to our advantage will provide enormous benefits. These include improving individual treatment courses for patients, as well as expediting the development of novel cancer drugs so pharma companies can get more effective treatments to market sooner.

For example, one commonly cited statistic is that 90% of clinical drug development fails during phase I, II, III trials and drug approval. Often, this may arise from a lack of available information to identify the subset of patients most likely to benefit from a novel drug. Having access to Navignostics’ technology and algorithms and a database such as the Digital Tumour will offer the potential to pre-select the right patients to enroll in clinical trials, and more easily identify the patients that do respond to the novel treatment, which could substantially expedite the speed of drug development in the trial stage, and help bring more effective drugs to the market.

Even unsuccessful trials offer valuable opportunities: it is possible to repurpose and reanalyse material from previous failed trials. Such high rates of failure in clinical development means that there are a large number of companies that have invested $millions in developing drugs that have not come to fruition, so if companies want to re-mine their data, our team can reinterpret the existing work into identifying more successful strategies, so we can give those drugs another chance and offer a better chance of Return on Investment.

A failure no longer needs to be a failure. Navignostics and its offerings can bring value to our pharma and biotech partners, and will also bring direct benefit to patients and clinicians once we launch our diagnostics product. So, data from every facet of the oncology industry, from curing a patient to halting the development of a drug, can offer us valuable insight that both we and the Digital Tumour could learn from when developing treatments.

What does 2023 and beyond have in store for Navignostics?

The next three years will be critical for our work, and we have projected timelines and key milestones for our diagnostics developments that we will achieve until our next funding round. Along the way, we are actively speaking to biotech and pharmaceutical organisations to identify projects and build the foundation for long lasting collaborations. We are looking forward to a successful continuation of the Navignostics development in 2023!