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BMC Cancer ; 19(1): 832, 2019 Aug 23.
Artigo em Inglês | MEDLINE | ID: mdl-31443703

RESUMO

BACKGROUND: Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer. METHODS: Whole-genome sequencing was performed on cfDNA extracted from plasma samples (N = 546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validations to assess generalization performance. RESULTS: In a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance. CONCLUSIONS: A machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.


Assuntos
Biomarcadores Tumorais , DNA Tumoral Circulante , Neoplasias Colorretais/genética , Neoplasias Colorretais/patologia , Genoma Humano , Genômica , Aprendizado de Máquina , Idoso , Idoso de 80 Anos ou mais , Neoplasias Colorretais/sangue , Biologia Computacional/métodos , Feminino , Perfilação da Expressão Gênica , Genômica/métodos , Humanos , Masculino , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Curva ROC , Reprodutibilidade dos Testes , Transcriptoma
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