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Oliver Bathe

Qualisure Diagnostics develops state-of-the-art diagnostic tests that help to personalize cancer care. Our first test, Thyroid GuidePx, was recently launched. Thyroid GuidePx is a test that determines the risk of recurrence after treatment of thyroid cancer. If the thyroid cancer has an aggressive biology and has a high risk of recurrence, then clinicians can treat the cancer more aggressively (by removal of the entire thyroid gland and administration of radioactive iodine, for example). On the other hand, if the tumour is more slow growing and has a low risk of recurrence, then more conservative treatments can be offered, avoiding the complications and side effects of aggressive treatments. We also have tests under development for lung cancer, colorectal cancer, and head and neck cancer.

Tell us about yourself?

I am a professor of surgery and oncology at the University of Calgary and a surgical oncologist with more than 25 years of clinical experience. In that time, I have seen countless examples where patients have been given very toxic treatments without clear knowledge of whether they will benefit. This experience has spurred me to develop diagnostic tools that will refine treatment decisions for cancer patients. My research at the University has focused on using cutting-edge analytical technologies to gain an understanding of what biological features discriminate aggressive cancers and cancers that grow more slowly. The molecular patterns that discriminate those kinds of cancers form the basis of diagnostic tests that could be potentially used to personalize cancer care.

Because of the molecular complexity of cancers, to develop a precision oncology test (a test that helps to refine clinical decision making for cancer care), there is a tremendous amount of work required to focus on molecular features that are most impactful to the patient. About five years ago, in my lab, we developed a machine learning algorithm that was capable of scouring large genomic databases to identify the molecular features of a cancer that are most closely associated with survival and recurrence. This algorithm has dramatically accelerated the discovery process, facilitating the design of precision oncology tests.

If you could go back in time a year or two, what piece of advice would you give yourself?

  1. Make sure there is a solid clinical use case and an unmet clinical need.
  2. Form a diverse and mission-focused team, including clinicians, scientists and business experts. Constantly reach out for extramural advice in all of these domains.
  3. Make sure that the team understands that the journey to adoption is not short. But the journey is achievable if number one is operative.
  4. While introspection is essential, you need to continue believing in yourself and the team to get to the finish line.
  5. Securing investment is tough. Non-dilutive funding is essential.

What problem does your business solve?

Cancer treatments are generally very toxic. It is very difficult for doctors to determine with any certainty whether an individual is likely to benefit from any particular treatment. We need better tools to identify the right treatments for the right patients.

What is the inspiration behind your business?

During my years of practice, I have seen countless cases where cancer treatments have caused harm or loss of quality of life, and the benefit was very difficult to measure. I am sure that anyone treating cancer patients has had the same experience. We need better tools to determine whether a patient needs aggressive treatments and what treatments would be most likely to produce a benefit. Personally, I encountered the same situation when my own mother developed lung cancer. We had to make important decisions, such as whether to start chemotherapy, when to stop chemotherapy, and whether to radiate the brain to prevent brain metastases. I found that medical knowledge and the current state of clinical evidence were insufficient to make those decisions. We needed better tools.

What is your magic sauce?

  1. The machine learning algorithm we developed makes diagnostic test design faster and easier.
  2. We have built a cloud-based engine that houses our diagnostic algorithms, automatically processes data from next generation sequencers in minutes (as opposed to days by a bioinformatician), and delivers a test report wherever our test is performed (anywhere in the world). This cloud-based engine is integral to our global growth strategy, as it facilitates testing in numerous labs in different countries.

What is the plan for the next 5 years? What do you want to achieve?

We intend to apply what we have learned as we brought Thyroid GuidePx to market to the development of our follow-on tests. We intend to have five tests out by the end of 2025. We are building our cloud-based engine to have the capacity to process tests produced by other precision oncology test developers. Our ultimate goal is to use that cloud-based engine to enhance access to precision oncology testing globally.

What is the biggest challenge you’ve faced so far?

Our biggest challenge has been attracting investment. While life sciences companies and medical technology companies are potentially very profitable, there appears to be significant risk aversion. It is my impression that the one factor that will sufficiently de-risk our company and convince investors that we have a great company is sales. But to get to the point of producing sales, there are numerous hurdles, including regulatory hurdles, producing sufficient evidence to support the clinical value proposition, and health economics studies. It’s expensive to overcome these challenges. I see a need for investment firms that have a superior knowledge in the space that are willing to take risks with earlier stage companies.

How can people get involved?

Unfortunately, we do not currently have the resources for recruitment at this time. We are always interested in people who are willing to consider investment.