The start-up on a mission to transform metastatic breast cancer management

One in eight women in the US will be diagnosed with breast cancer in their lifetime and one in 39 will die from it. Could this start-up have the solution?

Nine years ago, Hollywood actress Angelina Jolie announced in a New York Times op-ed that she’d decided to go public about her elective double mastectomy to “encourage every woman” to think about their own risk of breast cancer.

The article went viral and increased breast cancer awareness around the globe, leading to the so-called “Jolie effect”.

But while campaigns like this have changed the way we talk about breast cancer, they haven’t translated into better patient outcomes.

The World Health Organisation revealed that in 2022 the “burden of breast cancer” caused 670,000 deaths globally.

In the UK, one woman is diagnosed with breast cancer every 10 minutes, leading to nearly 1,000 deaths each month.

Survival rates have indeed improved in many parts of the world and diagnostic methods have come a long way in identifying a tumour’s molecular profile. However, questions remain when it comes to choosing the most effective treatment combination for a particular tumour or patient.

“Oncologists consider multiple factors to determine the treatment strategy: the patient profile, the clinical tumour stage, the pathological tumour grade, the molecular tumour profile, treatment guidelines and study results, and patient-specific contra- indications,” Wolfgang Hackl, translational oncology scientist and founder and CEO of OncoGenomX, tells Femtech World.

“But there is nothing to assess whether a certain treatment works in a specific tumour. Oncologists can only be certain about that after the entire treatment journey, which can take months, sometimes years.”

Without a tool to support clinical decision-making, doctors turn to risk-adapted approaches, extending moderate therapies for low-risk cancers and intensive therapies for high-risk cancers. But this doesn’t solve the problem, says Hackl.

“Many patients are still over-treated for fear of being under-treated,” the founder explains.

“In breast cancer, about 60 per cent of women receiving treatment are over-treated. These patients end up having side effects which means they have to stop treatment. When they stop treatment, they are at risk of recurrence.

“This applies to under-treatment too. Under-treating and over-treating patients can both lead to sub-optimal treatment.”

The solution, however, could be closer that we might expect. PredictionStar, a decision support technology developed by Hackl and his team at OncoGenomX, could potentially transform breast cancer drug development, diagnosis and outcomes, facilitating individualised therapeutic decisions at any stage of the disease trajectory.

Wolfgang Hackl, founder and CEO of OncoGenomX

The tool, which uses AI and machine learning algorithms, identifies whether a certain therapy works in a particular tumour and finds the treatments proven to be the most effective for each patient and their specific tumour.

“It harnesses four patent-pending core technologies,” says Hackl.

“The first technology is an innovative tumour profiling test, the second one is an individualised prediction model, the third one is a digitalisation tool for information sharing without migrating patient data and the fourth one is a reinforcement technology, which helps the algorithm learn from treatment decisions.”

As far as tumour gene tests are concerned, PredictionStar works with standard, unsupervised, or commercial target gene panels, not only expanding their scope but also raising the applicability of test results from an average 65 per cent to more than 80 per cent.

For the first time, Hackl says, oncologists will be able to understand which treatment combination will work the best for a patient and their tumour.

“PredictionStar takes away the uncertainty around breast cancer treatment decisions and increases the likelihood of opting for the most effective treatment combination without delay and without exposing the patient to the side effects of an ineffective treatment.

“The whole technology is designed as an instrument agnostic which can work with any other existing technologies to allow for seamless interactions, irrespective of the systems that already exist in hospitals.”

The fascinating part is that the tool has a feedback loop which means the technology improves, the more information it gets, refining its therapy guidance.

“This is dramatic if you think about it,” says Hackl. “The US, UK and European countries spend hundreds of billions on cancer treatment every year.

“However, data shows that in cancer R&D, the clinical failure rate is about 30 per cent, which means that one out of three phase three clinical studies fails.

“This is a problem because this is the part of the development which generates 70 per cent of the development cost. Based on the data we have, we believe PredictionStar could reduce treatment expenses by at least 20 per cent.”

At a time when healthcare budgets are being squeezed, this reduction would be significant.

“We tend to believe that healthcare costs are a problem of the poor economies, but richer countries struggle with high healthcare costs too,” Hackl points out.

“This is something that has become very clear in recent years. The way healthcare costs are incrementally increasing over time is not sustainable and needs to be addressed.”

With such impressive capabilities, it seems to be only a matter of time before the availability of robust patient data sets extends the scope of PredictionStar to other cancer types. Hackl doesn’t rule this out.

“Currently, we are focusing on breast cancer, but we are intending to roll this out in other cancer entities,” he says.

If everything goes according to plan, PredictionStar could be ready for use in clinical and translational studies in the next 15-18 months, the founder adds.

“OncoGenomX’s data on file suggest that PredictionStar is leading the game and we have good reasons to believe that it has significant potential to improve clinical and economic outcomes beyond today’s achievements.”