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Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set.
Klein, E A; Richards, D; Cohn, A; Tummala, M; Lapham, R; Cosgrove, D; Chung, G; Clement, J; Gao, J; Hunkapiller, N; Jamshidi, A; Kurtzman, K N; Seiden, M V; Swanton, C; Liu, M C.
Afiliación
  • Klein EA; Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, USA. Electronic address: kleine@ccf.org.
  • Richards D; The US Oncology Network, Tyler, USA.
  • Cohn A; The US Oncology Network, Denver, USA.
  • Tummala M; Mercy Clinic Cancer Center, Springfield, USA.
  • Lapham R; Spartanburg Regional Healthcare System, Spartanburg, USA.
  • Cosgrove D; Compass Oncology, Vancouver, Canada.
  • Chung G; The Christ Hospital Health Network, Cincinnati, USA.
  • Clement J; Hartford HealthCare Cancer Institute, Hartford, USA.
  • Gao J; GRAIL, Inc., Menlo Park, USA.
  • Hunkapiller N; GRAIL, Inc., Menlo Park, USA.
  • Jamshidi A; GRAIL, Inc., Menlo Park, USA.
  • Kurtzman KN; GRAIL, Inc., Menlo Park, USA.
  • Seiden MV; US Oncology Research, The Woodlands, USA.
  • Swanton C; The Francis Crick Institute, London, UK; University College London Cancer Institute, London, UK.
  • Liu MC; Mayo Clinic, Rochester, USA.
Ann Oncol ; 32(9): 1167-1177, 2021 09.
Article en En | MEDLINE | ID: mdl-34176681
BACKGROUND: A multi-cancer early detection (MCED) test used to complement existing screening could increase the number of cancers detected through population screening, potentially improving clinical outcomes. The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) was a prospective, case-controlled, observational study and demonstrated that a blood-based MCED test utilizing cell-free DNA (cfDNA) sequencing in combination with machine learning could detect cancer signals across multiple cancer types and predict cancer signal origin (CSO) with high accuracy. The objective of this third and final CCGA substudy was to validate an MCED test version further refined for use as a screening tool. PATIENTS AND METHODS: This pre-specified substudy included 4077 participants in an independent validation set (cancer: n = 2823; non-cancer: n = 1254, non-cancer status confirmed at year-one follow-up). Specificity, sensitivity, and CSO prediction accuracy were measured. RESULTS: Specificity for cancer signal detection was 99.5% [95% confidence interval (CI): 99.0% to 99.8%]. Overall sensitivity for cancer signal detection was 51.5% (49.6% to 53.3%); sensitivity increased with stage [stage I: 16.8% (14.5% to 19.5%), stage II: 40.4% (36.8% to 44.1%), stage III: 77.0% (73.4% to 80.3%), stage IV: 90.1% (87.5% to 92.2%)]. Stage I-III sensitivity was 67.6% (64.4% to 70.6%) in 12 pre-specified cancers that account for approximately two-thirds of annual USA cancer deaths and was 40.7% (38.7% to 42.9%) in all cancers. Cancer signals were detected across >50 cancer types. Overall accuracy of CSO prediction in true positives was 88.7% (87.0% to 90.2%). CONCLUSION: In this pre-specified, large-scale, clinical validation substudy, the MCED test demonstrated high specificity and accuracy of CSO prediction and detected cancer signals across a wide diversity of cancers. These results support the feasibility of this blood-based MCED test as a complement to existing single-cancer screening tests. CLINICAL TRIAL NUMBER: NCT02889978.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Detección Precoz del Cáncer / Neoplasias Tipo de estudio: Clinical_trials / Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Humans Idioma: En Revista: Ann Oncol Asunto de la revista: NEOPLASIAS Año: 2021 Tipo del documento: Article Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Detección Precoz del Cáncer / Neoplasias Tipo de estudio: Clinical_trials / Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Humans Idioma: En Revista: Ann Oncol Asunto de la revista: NEOPLASIAS Año: 2021 Tipo del documento: Article Pais de publicación: Reino Unido