Precision Medicine for Ovarian Cancer: directing treatment options 

Ovarian cancer has high rates of recurrence. Patients treated initially respond well, before their cancer returns or ‘recurs’. When it recurs it often becomes resistant to currently available therapies including chemotherapy, contributing to low ovarian cancer survival rates. Dr Maree Bilandzic and her team are investigating a precision medicine approach to overcome these challenges that leave many ovarian cancer patients without treatment options. 

What is precision medicine?  

Currently, most ovarian cancers are treated in a standard way. However, there are over 30 subtypes of ovarian cancer, so a ‘one-size-fits’-all’ approach often isn’t effective. It is also difficult for clinicians to anticipate how an ovarian cancer patient will respond to standard therapy. Precision medicine is an approach to treatment that tailors treatment according to a person’s specific cancer subtype and characteristics and can predict whether the patient is likely to respond well to that treatment. This avoids patients experiencing side effects, and losing valuable time and money, if the treatment is unlikely to help them.  

Dr Bilandzic’s team seek to help clinicians identify the most effective treatment for an individual. In their previous studies the team were supplied a sample from someone diagnosed with ovarian cancer and tested it against a panel of currently available and emerging treatment approaches and drugs. They could then identify if specific treatments worked on that sample and inform the patient’s clinician. 

Although this approach has worked in the lab, to scale it and benefit many patients, the team need to determine the best process to feasibly carry this out long-term on a larger scale. 

With OCRF-funding the team aim to: 

• Collect samples from patients who have not had chemotherapy and from patients who have had interval debulking surgery, which is conducted in later stages of cancer when initial surgery hasn’t removed all of the tumour. 
• Create patient avatars called 3D spheroid models to test treatments on. 
• Where patients have failed to respond to standard treatment, test their samples using a drug screening panel and then test how these samples respond to new drugs in combination with chemotherapy.  
• Evaluate the data collected and correlate which samples responded to which treatments. The team will then document any treatment markers that predict treatment response and compare a patient’s treatment response alongside their genetic profiling information to get a fuller picture of which treatments work for certain patients and why. 

Models and samples:  

In previous studies, Dr Maree Bilandzic and her team demonstrated that ovarian cancer spheroids constructed from the ascites fluid of ovarian cancer patients can provide accessible samples for testing treatments, especially because ascites fluid from the abdomen often occurs.  

Trialing their ovarian cancer precision medicine screening process:  

The team will test the below process to identify any challenges or opportunities for improvement.  

Step 1: Review of the patient’s clinical data and treatments from initial diagnosis to relapse to understand the treatment pathway that the patient has already been on and any genetic data, such as whether they carry a BRCA variant or have a familial history.  

Step 2: The team will conduct molecular profiling of the sample by extracting RNA and sequencing it to identify the genes and pathways contributing to the patient’s specific cancer and why it is resisting current treatments. Molecular profiling could help find indicators to predict how a specific patient will respond to a treatment. This could not just indicate which drug is most effective for which type of ovarian cancer, but also consider the individuals genetics and therefore their individual responses to treatment, increasing their chance of survival. If, following molecular profiling, a drug is identified as likely to be effective for a patient’s tumour then it will be tested directly on the patient’s sample to determine if the patient will respond well, and therefore indicate whether that drug could be used as a tailored treatment for them.  

Step 3: Conduct functional genomics screening on the sample which allows them to identify new genetic targets to match to emerging treatments or that are likely to be able to be targeted by chemotherapy. 

Step 4: If the team detects that a patient is likely to have a poor response to standard therapies like chemotherapy, then they will test the sample with chemotherapy and other standard of care treatments, in combination with FDA-approved therapies that complement standard treatments. 

Step 5: Dr Bilandzic will compile the clinical data of patients with the new molecular data that the team uncover within a central hub. Compiling the data may reveal associations between genetics, response to treatments and ovarian cancer subtypes, offering new insights.