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Use of methylation and fragmentation signals in the detection of early-stage colorectal cancer.

2023; Lippincott Williams & Wilkins; Volume: 41; Issue: 4_suppl Linguagem: Inglês

10.1200/jco.2023.41.4_suppl.201

1527-7755

Pol Canal-Noguer, Alejandro Requena Bermejo, Špela Zavodnik, Vivian Erklavec Zajec, Francesco M. Mancuso, Marina Manrique López, Primož Knap, Marko Chersicola, Pablo Pérez‐Martínez, Fernando Trincado Alonso, Carme Nolla Colomer, Kristi Kruusmaa,

Colorectal Cancer Screening and Detection

201 Background: Colorectal cancer (CRC) remains a leading cause of cancer related mortality worldwide. We utilized cell-free DNA (cfDNA) methylation and fragmentation characteristics of selected cancer-related biomarker regions and applied tumor-derived signal deduction and a machine learning algorithm to refine a blood test for the early detection of CRC. Methods: This was a prospective, international (Spain, Ukraine, Germany and USA [part of NCT04792684 study] population), observational cohort study. Plasma samples were collected either prior to a scheduled screening colonoscopy or prior to colonic surgery for primary CRC. 95 cfDNA samples from 48 early stage (I-II), 47 late-stage (III-IV) CRC patients (mean age 65 [48-83], female 45%, distal cancers 51%) and 204 age, gender and country of origin matched colonoscopy-checked controls were analyzed. 79 of the control patients had a negative colonoscopy finding (cNEG), 96 had benign findings of diverticulosis, hemorrhoids and/or hyperplastic polyps (BEN), 29 had non-advanced adenomas (NAA). Samples were analyzed utilizing previously described hybrid-capture based sequencing methodology. Panel of targeted biomarkers was previously identified through tissue- and plasma-based discovery and further narrowed down through cancer-related biological pathways analysis workflow. Individual cfDNA fragments belonging to each biomarker region were scored for cancer-specific signal. Finally, calculated scores were used in prediction model building and testing for establishing panel accuracy for CRC detection. Results: Prediction model utilizing a panel of methylation and fragmentation scores originating from cfDNA biomarkers belonging to relevant cancer development and progression related pathways, such as axonal guidance, ephrin receptor signaling, epithelial-mesenchymal transition and FGF signaling, correctly classified 92% (87/95) of CRC patients. Sensitivity per cancer stage ranged from 91% (21/23) for stage I, 92% (23/25) for stage II, 91% (30/33) for stage III and 93% (13/14) for stage IV. Fragmentation signals contributed most to early-stage cancers (I-II), while methylation signals were more significant for late stage (III-IV) detection. Specificity of the model was 94% (199/204), with 97% (28/29) NAA, 91% (87/96) BEN and 96% (76/79) cNEG patients correctly identified. Lesion location, gender, age and country of origin were not significantly correlated to prediction outcome. Conclusions: Use of methylation and fragmentation characteristics of cancer-related cfDNA regions, combined with a machine-learning algorithm is highly accurate for early-stage (I-II) CRCs (92% sensitivity at 94% specificity). The study is being further expanded on larger cohort for validation of a highly accurate and minimally invasive blood-based CRC screening test.