On October 7, 2019 GRAIL, Inc., a healthcare company whose mission is to detect cancer early, reported new data validating the performance of its investigational multi-cancer early detection blood test for the first time in an independent cohort of participants. The results from this validation analysis demonstrate the ability of GRAIL’s technology to detect more than 20 cancer types across all stages with a single, very low false positive rate. When cancer was detected, the test also identified where the cancer was located in the body (the tissue of origin) with high accuracy. In a pre-specified group of 12 deadly cancer types, which are responsible for approximately two-thirds of all cancer deaths, there was an even greater detection rate.
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GRAIL’s large-scale clinical study program is designed to train and develop a multi-cancer early detection test, as well as validate its performance. This new analysis, comprising data from the second pre-planned sub-study of its foundational Circulating Cell-free Genome Atlas (CCGA) study, evaluated performance of the test in an independent group of participants. Performance in the validation analysis was consistent with previously reported results from data used to train GRAIL’s cancer detection algorithm to classify cancer and non-cancer signals.
"Today, cancer remains the second leading cause of death worldwide, and we believe an effective multi-cancer early detection technology has the potential to transform the cancer care landscape," said Hans Bishop, Chief Executive Officer at GRAIL. "We are excited to present these new validation data that support the potential clinical applicability of our targeted methylation technology, and reinforce our ongoing efforts to bring our multi-cancer early detection test into the clinic to begin returning test results to patients."
The independent set used to validate GRAIL’s test included participants from the CCGA (n=927; 654 cancer, 273 non-cancer) and STRIVE (n=337 non-cancer) studies, spanning more than 20 cancer types across all stages. These new data show strong performance at high specificity (99.3%), representing a very low false positive rate. A low false positive rate is important for tests designed for use at population-scale in order to minimize unnecessary testing and associated harms.
The overall detection rate for the group of 12 pre-specified deadly cancer types at 99.3 percent specificity was 76 percent (n=273/359; 95% confidence interval: 72-81%), and a combined analysis of this group of cancers showed robust detection across all stages with detection rates of 39 percent (27-52%), 69 percent (56-80%), 83 percent (75-90%), and 92 percent (86-96%) at stages I (n=62), II (n=62), III (n=102), and IV (n=130), respectively.
In this group of 12 high mortality cancers, a tissue of origin result was provided for 97 percent (265/272), and of these, the test correctly identified the tissue of origin in 93 percent of cases (246/265). Accurately identifying where the cancer originated in the body is critical to inform the diagnostic work-up and next steps.
The 12 pre-specified deadly cancers in the independent validation set include anal, bladder, colorectal, esophageal, head and neck, liver/bile duct, lung, lymphoma, ovary, pancreatic, plasma cell neoplasm, and stomach.
"Performance validation with an independent group of participants is critical prior to returning results of a multi-cancer early detection test to patients," said Minetta Liu, MD, Research Chair and Professor, Department of Oncology, Mayo Clinic. "The promising results from this independent validation demonstrate the robustness of the test’s performance, its ability to detect more than 20 cancer types across all stages, and its generalizability to broader populations due to a low false positive rate."
In the group of more than 20 cancer types, the overall detection rate at 99.3 percent specificity was 55 percent (n=361/659; 95% confidence interval: 51-59%), with detection rates of 18 percent (13-25%), 43 percent (35-51%), 81 percent (73-87%), and 93 percent (87-96%) at stages I (n=185), II (n=166), III (n=134), and IV (n=148), respectively. A tissue of origin result was provided for 96 percent (344/359), and of these, the test correctly identified the tissue of origin in 93 percent of cases (321/344).
These new data will be presented at the inaugural ASCO (Free ASCO Whitepaper) 2019 Breakthrough in Bangkok, Thailand, taking place October 11-13.
ASCO Breakthrough Presentation Details
Abstract 44
Minetta Liu, et al. Simultaneous multi-cancer detection and tissue of origin (TOO) localization using targeted bisulfite sequencing plasma cell-free DNA (cfDNA)
Poster Session: October 11, 2019: 1:45 – 2:15PM ICT, Centara Grand at CentralWorld
About CCGA
The Circulating Cell-free Genome Atlas (CCGA) study is a prospective, observational, longitudinal, case-control study that has completed enrollment of approximately 15,000 participants with and without cancer across 142 sites in the United States and Canada. CCGA is designed to characterize the landscape of genomic cancer signals in the blood and to discover, train, and validate GRAIL’s multi-cancer early detection blood test through three pre-planned sub-studies.
About GRAIL’s Investigational Multi-Cancer Early Detection Test
GRAIL is developing a next-generation sequencing (NGS) blood test for the early detection of multiple deadly cancer types. GRAIL’s high efficiency methylation-based technology preferentially targets the most informative regions of the genome and is designed to use its proprietary database and machine-learning algorithms to both detect the presence of cancer and identify the tumor’s tissue of origin. GRAIL’s sequencing database of cancer and non-cancer methylation signatures, which GRAIL believes to be the largest of its kind, covers approximately 30 million methylation sites across the genome. More than 20 cancer types across stages are represented within the database.
DNA methylation is a natural process used by cells to regulate gene expression. It is a chemical modification to DNA and a well-studied epigenomic feature of the genome. In cancer, abnormal methylation patterns and the resulting changes in gene expression can contribute to tumor growth. For example, hypermethylation can cause tumor-suppressor genes to be inactivated.