Effectively addressing the challenge of cancer is a race against time. Most cancers go undetected until too late because there is no early screening available today for the majority of deadly cancers. We know that the earlier cancer is caught, the better chance of a cure or a successful treatment. But cancer is cunning, and it can spread and grow before any symptoms arise, meaning that many cancers go undetected until they have progressed to later stages.
Survival drops significantly at these later stages, making early detection a critical effort to make a major clinical and economic impact against cancer. For example, people diagnosed with lung cancer when the tumor is still localized to the lungs have a 56 percent chance of surviving five years. Once the cancer has spread to other areas in the body, that number falls drastically to only 5 percent.2
Today, U.S. guideline-recommended screening only exists for four types of cancer, leaving many deadly cancer types undetected until it’s often too late for effective treatment. Further, those screening options often face challenges in uptake due to issues of invasiveness, inconvenience, cost, and access. Take lung cancer again: less than 5 percent of those recommended for screening actually get screened, and many of these cancers detected occur in people who aren’t recommended for screening in the first place.3 There have also been concerns that guideline-recommended screening may lead to the overdiagnosis and overtreatment of cancers that may be slow growing, ones that if went undetected wouldn’t cause serious harm.
We are at an unprecedented time, when the genomic revolution is driving insights into cancer biology, detection, prognosis, and management. Some new treatments are agnostic to tumor type, and instead focus on specific mutations shared by different cancers. Advances in next-generation sequencing (NGS) and machine learning have enabled the ability to detect and sequence cancer-derived DNA signals in blood, which are highly specific. Identification of methylation patterns in the DNA has been shown to be a superior mechanism for cancer signal detection than looking for mutations or copy number variations4.
So, a great challenge for bringing these transformational advancements to patient care is crystal clear: can a single blood test be developed with the ability to identify multiple deadly cancer types at one time while minimizing the risk of false positive results? And can this test identify from where in the body the cancer signal is coming?
It is among the most ambitious undertakings in healthcare, and this is exactly what we’ve set out to do at GRAIL. Through our rigorous scientific and clinical study program, we are developing an early detection blood test that has the ability to find multiple deadly cancers — the majority of which do not have a screening paradigm today. Data presented at ASCO 2019 show the test can also detect cancers with high mortality rates even at early stages, suggesting the test is unlikely to contribute to overdiagnosis5. In fact, 12 of the cancer types our test can detect make up nearly two-thirds of all cancer deaths in the U.S.
A successful test should also minimize the number of false positives (when a test incorrectly reports a positive result) to avoid unnecessary anxiety, emotional distress, and further diagnostic procedures and tests that may cause harm. Validation data from our foundational Circulating Cell-free Genome Atlas (CCGA) study show that the false positive rate of our early detection test is below 1%.
By projecting current screening recommendations onto the United States population aged 50-79 who are screening eligible and average risk at typical compliance rates, almost 10 million positive screening tests would be generated and only 151,000 cancers detected — a true positive result (when a test correctly reports a positive result) to false positive result ratio of 1 to 60. In contrast, our technology has shown the ability to detect multiple deadly cancer types with a very low false positive rate, representing the potential to detect three times more cancers (an additional ~460,000 cancers detected in the same population), and do so much more efficiently, with a true positive result to false positive result ratio of only 1 to 2.46. Adding a multi-cancer early detection test to today’s guideline-recommended screening programs represents a significant opportunity.
There’s also a very practical reason a multi-cancer test makes sense. The occurrence of individual cancers in the general population is low. As such, the number of people needed to screen in order to find cancer is enormous.
However, the number of people needed to screen in order to find cancer goes down when cancer prevalence numbers are combined. David Ahlquist of the Mayo Clinic outlined this issue pointedly in his 2018 NPJ Precision Oncology perspective7. He provided the example that, based on prevalence rates, an estimated 1,000 people would need to be screened to detect one case of esophageal cancer even with perfect test accuracy. By contrast, a test that screens for all major types of gastrointestinal cancers would only have to screen 83 people to find a case. And if one test could detect all cancers, this number would fall to 33. That means that a multi-cancer test would provide greater value by detecting more cancers, with fewer people tested, compared with a single cancer test.
But there is yet another consideration. What good is a multi-cancer test that tells someone that he or she has a cancer signal in their blood, but not where that cancer is located? GRAIL’s proprietary methylation-based technology analyzes DNA methylation patterns from the informative regions of the genome not only to detect the presence of a cancer signal, but also identify where the cancer is located in the body. This knowledge enables providers to direct appropriate follow-up diagnostics and care for their patients.
As GRAIL’s population-scale studies continue, we are learning more about how our early cancer detection test works in more diverse populations — critical for building the foundation of evidence necessary for clinical use and broad adoption. We are conducting what we believe to be the largest clinical study program in genomic medicine, with 165,000 planned participants, to help ensure that our test will be generalizable and useful to as many people as possible.
At GRAIL, our mission is to improve and save lives through early cancer detection, and we are developing our test in a rigorous way to deliver our test to patients safely and effectively. There may be no greater opportunity in healthcare to make a significant impact to public health. We are navigating uncharted territory because no tool like this exists today. The path isn’t easy, but we believe it is the right one — for patients, for providers, and for communities and healthcare systems around the world.
Josh Ofman, MD, MSHS
Chief of Corporate Strategy and External Affairs
 Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2019. CA Cancer J Clin. 2019 Jan;69(1):7-34.
 Five-year Relative Survival Rates by Stage at Diagnosis, US, 2008-2014. Cancer Facts & Figures 2019. American Cancer Society. Available at: https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2019/cancer-facts-and-figures-2019.pdf.
 Jemal A, Fedewa SA. JAMA Oncol 2017.
 Liu MC, et al. Genome-wide cell-free DNA (cfDNA) methylation signatures and effect on tissue of origin (TOO) performance. J Clin Oncol. 2019;37(15_suppl):3049. Poster presented at the 2019 American Society for Clinical Oncology meeting; May 31-June 4, 2019; Chicago, IL.
 Liu MC, et al.
 Ahlquist, D. Universal cancer screening: revolutionary, rational, and realizable. NPJ Precision Oncology 2018.