As a medical oncologist, I’ve spent my career working to understand the genomic basis of cancer in order to develop molecular diagnostics that can help us detect and treat it. My ultimate mission has been to help detect cancer as early as possible – when it can be more successfully treated. Fueled by the latest advances in genomic technology, and a rigorous clinical research program, our team at GRAIL is now making real, exciting progress toward achieving this mission.

We recently reached an important milestone in this journey – we successfully completed enrollment of approximately 100,000 participants in the STRIVE study. As I will explain later, this is a change from our original target (120,000 participants), following promising initial results from our Circulating Cell-free Genome Atlas (CCGA) study. I am deeply proud of the work we’ve done to get us to this point and tremendously grateful for the participation of the people who’ve enrolled in our studies. Without them, our work wouldn’t be possible.

How we got here

The central premise that drives our work at GRAIL – day in and day out – is that early cancer detection saves lives. For example, ninety to 100 percent of people diagnosed at the earliest stage of breast, colon, and skin cancers will survive for at least five years. What if we could make early detection and longer survival a reality for all people with cancer?

Thanks to decades of pioneering cancer and genomics research, we know that tumors release DNA into the bloodstream. Tumor DNA has a unique, identifiable genomic signature that can be detected through sequencing and attributed to a specific organ in the body. This understanding has informed our approach to developing a blood test that can detect cancer signals in the blood at early stages of disease.

While the principle may sound simple, putting it into action is a challenge. Cancer is really hundreds or even thousands of different diseases – we have to take into account all the different cancer types and the many different genomic alterations that can drive each cancer. On top of that, non-cancer cells also undergo genomic changes of their own and release DNA into the blood. Most of their DNA is indistinguishable from DNA released by cancer cells. We have to be able to differentiate cancer DNA from other DNA. This makes our work incredibly complex – but it is crucial that we get it right.

Our team is realistic about these complexities. To be successful, we must address two important challenges. First, and most importantly, our test needs to be as accurate as possible. We do not want to tell someone who does not have cancer that they might (this is known as a false positive result). While it is not possible to achieve 100 percent accuracy with a test like this, we are aiming for 99 percent or greater. Second, our test should be sensitive enough to find signals for cancers that are likely to be deadly when detected at later stages, but may be treated successfully if detected at earlier stages. We believe both are possible.

The need for large amounts of data

The tests we are developing combine high-intensity genomic sequencing and complex computational algorithms that assess vast amounts of data to find cancer-specific patterns. The more data we generate, the more sophisticated our machine-learning algorithms should become in pinpointing rare cancer signals.

Every year, around 1,300 of every 100,000 people aged 50 or older in the United States are diagnosed with new cancers. Therefore, in a research study of only 1,000 participants of this age group, 13 would be expected to be diagnosed with new cancers – too few for any meaningful analyses. This is why we’ve put so much emphasis on our large, rigorous clinical research program. In order to ensure we have enough data to develop and evaluate our blood tests and algorithms, we are conducting population-scale clinical studies, which include more than 100,000 participants to date.

Optimizing our approach – in real-time

Promising results from our CCGA study earlier this year gave us the opportunity to focus our efforts on developing a blood test for early detection of multiple cancers. We believe a multi-cancer early detection test could have a significant impact for many people – particularly those with cancers that are not typically screened for, are diagnosed at later stages, and are associated with high mortality.

Since our first CCGA results, we have been optimizing our clinical studies to rapidly advance our multi-cancer test, including changing the enrollment target of the STRIVE study. STRIVE is a prospective, observational, longitudinal cohort study of women without a known cancer diagnosis who enrolled at the time of their screening mammogram. We originally designed the study to develop and evaluate a breast cancer detection test. However, STRIVE was also elegantly designed to evaluate a test for multiple cancer types.

Now that we are focusing our efforts on a multi-cancer test and are making real progress towards this goal, we determined we would need fewer participants overall in STRIVE to evaluate a multi-cancer test, compared with the original plan to develop and evaluate a breast cancer test. The volunteers participating in the STRIVE study represent one of the populations in which the multi-cancer test is intended to be used. With a study size of 100,000, we expect approximately 1,000 women will unfortunately be diagnosed with cancer within a year of enrollment.

The future

I believe now more than ever that our data will support the development of a test that could help detect cancers earlier than is currently possible in medical practice today.

We’ve made tremendous progress in just two-and-a-half years, and we’re on our way to fulfilling our mission of making early cancer detection through a blood test a reality. I’m excited to continue this journey with all of our partners and study participants as we move forward with the same level of rigor, dedication and tenacity that has brought us one step closer to achieving our mission.