Abstract
Accurate and effective early detection of colorectal cancer and advanced precancerous lesions (APL) is still a challenge. The purpose of this study was to evaluate the clinical performance of a novel, noninvasive multimodal stool RNA (mm-stRNA) test that combines five human messenger RNA (mRNA) biomarkers and a fecal immunochemical test (FIT) in a machine learning (ML)-generated algorithm for the sensitive detection of APLs and colorectal cancers. For this purpose, stool samples from 265 subjects (34 colorectal cancers, 68 APLs, and 163 controls) were evaluated as part of the eAArly DETECT study, a US multisite study with subjects suspected to have at least one APL or colorectal cancer, as well as average-risk individuals. FIT was evaluated with clinical positivity thresholds of 5 µg hemoglobin (Hb)/g of stool and 17 µg Hb/g. RNA was isolated from stabilized stool and analyzed for the expression levels of five mRNA biomarkers. Lab data were analyzed using an ML-generated algorithm that was developed in a stratified split-sample design and then applied as a locked model to the full 265-subject cohort. The mm-stRNA test achieved 97.1% sensitivity for colorectal cancer and 83.8% sensitivity for APLs, with 95.7% specificity. When applying the cutoff levels of 5 µg Hb/g versus 17 µg Hb/g, FIT sensitivity was 76.5% versus 70.6% for colorectal cancer and 45.6% versus 36.8% for APL, with a specificity of 84.0% versus 90.8%, respectively. The mm-stRNA approach seemed to have substantially improved performance compared with existing tests, but results need to be replicated in an independent prospective cohort. PREVENTION RELEVANCE: This study evaluates a noninvasive stRNA test-FIT that markedly improves the detection of APLs and early colorectal cancers compared with FIT alone. By enabling the removal of high-risk lesions before malignant transformation, this approach could substantially reduce colorectal cancer incidence and mortality in population-based screening programs.