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Plasma cell-free DNA as a sensitive biomarker for multi-cancer detection and immunotherapy outcomes prediction.
Xu, Juqing; Chen, Haiming; Fan, Weifei; Qiu, Mantang; Feng, Jifeng.
Afiliación
  • Xu J; Department of Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China.
  • Chen H; Department of Hematology and Oncology, Department of Geriatric Lung Cancer Laboratory, The Affiliated Geriatric Hospital of Nanjing Medical University, Nanjing, China.
  • Fan W; Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
  • Qiu M; Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China.
  • Feng J; Department of Hematology and Oncology, Department of Geriatric Lung Cancer Laboratory, The Affiliated Geriatric Hospital of Nanjing Medical University, Nanjing, China.
J Cancer Res Clin Oncol ; 150(1): 7, 2024 Jan 09.
Article en En | MEDLINE | ID: mdl-38196018
ABSTRACT

BACKGROUND:

Cell-free DNA (cfDNA) has shown promise in detecting various cancers, but the diagnostic performance of cfDNA end motifs for multiple cancer types requires verification. This study aimed to assess the utility of cfDNA end motifs for multi-cancer detection.

METHODS:

This study included 206

participants:

106 individuals with cancer, representing 20 cancer types, and 100 healthy individuals. The participants were divided into training and testing cohorts. All plasma cfDNA samples were profiled by whole-genome sequencing. A random forest model was constructed using cfDNA 4 bp-end-motif profiles to predict cancer in the training cohort, and its performance was evaluated in the testing cohort. Additionally, a separate random forest model was developed to predict immunotherapy responses.

RESULTS:

In the training cohort, the model based on 4 bp-end-motif profiles achieved an AUC of 0.962 (95% CI 0.936-0.987). The AUC in the testing cohort was 0.983 (95% CI 0.960-1.000). The model also maintained excellent predictive ability in different tumor sub-cohorts, including lung cancer (AUC 0.918, 95% CI 0.862-0.974), gastrointestinal cancer (AUC 0.966, 95% CI 0.938-0.993), and other cancer cohort (AUC 0.859, 95% CI 0.776-0.942). Moreover, the model utilizing 4 bp-end-motif profiles exhibited sensitivity in identifying responders to immunotherapy (AUC 0.784, 95% CI 0.609-0.960).

CONCLUSION:

The model based on 4 bp-end-motif profiles demonstrates superior sensitivity in multi-cancer detection. Detection of 4 bp-end-motif profiles may serve as potential predictive biomarkers for cancer immunotherapy.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Ácidos Nucleicos Libres de Células / Neoplasias Pulmonares Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: J Cancer Res Clin Oncol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Ácidos Nucleicos Libres de Células / Neoplasias Pulmonares Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: J Cancer Res Clin Oncol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania