Research ArticleCancer

Circulating tumor DNA methylation profiles enable early diagnosis, prognosis prediction, and screening for colorectal cancer

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Science Translational Medicine  01 Jan 2020:
Vol. 12, Issue 524, eaax7533
DOI: 10.1126/scitranslmed.aax7533
  • Fig. 1 Workflow of model generation and subject enrolment.

    (A) Workflow for building the diagnostic and prognostic models with cfDNA methylation markers. (B) Enrollment and outcomes of the prospective screening cohort study.

  • Fig. 2 cfDNA methylation analysis for CRC diagnosis.

    (A) Workflow for building the diagnostic model. (B and C) Unsupervised hierarchical clustering of methylation markers differentially methylated between CRC and normal subject DNA in the training (B) and validation (C) testing cohorts. Each row represents an individual patient, and each column is a CpG marker. (D and E) Receiver operating characteristic (ROC) curves and the associated areas under curves (AUCs) of the diagnostic prediction model (cd-score) using cfDNA methylation analysis in the training (D) and validation (E) testing cohorts. (F) ROC curves and corresponding AUCs of cd-score and CEA for CRC diagnosis in the validation dataset. (G and H) Confusion matrices built from the diagnostic model prediction in the training (G) and validation (H) testing cohorts.

  • Fig. 3 Prognostic prediction of CRC survival based on cfDNA methylation profiling.

    (A) Workflow for building the prognostic models. (B) Overall survival curves of patients with CRC with low or high risk of death according to the combined prognosis score (cp-score) in the training testing cohort. (C) Overall survival curves of patients with CRC with low or high risk of death according to the combined prognosis score (cp-score) in the validation testing cohort. (D to E) ROC and corresponding AUCs for 6-month survival predicted by cp-score, primary tumor location, TNM stage, CEA status, and all combined in the training (D) and validation (E) testing cohorts. **P < 0.001.

  • Fig. 4 cfDNA methylation subtyping analysis in 801 patients with CRC.

    (A) Schematic diagram of the core algorithm used in the sample clustering. (B) Iteratively unsupervised clustering of cfDNA methylation markers identified two subtypes/clusters in training data. Clinical and molecular features are indicated by the annotation bars above the heatmap. Patients without such information were colored in white. Mutation status was defined by the mutation detected in one or more of the following genes: BRAF, KRAS, NRAS, and PIK3CA. (C) Silhouette analysis of the clusters in the last iteration. (D) Predicted subtypes/clusters of validation using the 45 markers. (E) Upper panel: Overall survival for each of the cfDNA methylation patterns in each subtype (log-rank test, P < 0.05). Lower panel: Proportion of patients with stage III to IV CRC in two clusters (χ2 test, **P < 0.01; left, training cohort; right, validation cohort).

  • Table 1 Sensitivity and specificity of the cfDNA methylation test for colonoscopy findings.

    Colonoscopy findingPatients (n = 1493)ctDNA methylation test (n = 1493)
    Positive resultsNegative resultsSensitivitySpecificity
    CRCNo.No.No.% (95% CI)% (95% CI)
    Stage I–III CRC2119290.5 (72.7–97.8)
    High-grade dysplasia87187.5 (47.4–99.7)
    All CRC89.7 (72.7–97.8)
    Advanced
    precancerous lesion*
    78265233.3 (23.1–44.9)66.7 (55.1–76.9)
    Nonadvanced
    adenoma
    114258921.9 (14.7–30.7)78.1 (69.4–85.3)
    Other benign lesions250202308.0 (4.9–12.1)92.0 (87.9–95.1)
    Negative on
    colonoscopy
    101212388987.9 (85.7–89.8)
    All nonadvanced
    adenoma and
    non-neoplastic findings
    and negative results on
    colonoscopy
    1386168121887.9 (86.0–89.6)
    All noncancer1464194127086.8 (84.9–88.4)

    *Advanced precancerous lesion includes the following: villous adenoma, adenoma >1.0 cm in size, and sessile serrated polyps >1.0 cm in size.

    Supplementary Materials

    • stm.sciencemag.org/cgi/content/full/12/524/eaax7533/DC1

      Materials and Methods

      Fig. S1. List of methylation correlated blocks used for cd-score generation.

      Fig. S2. ctDNA methylation analysis for predicting tumor burden, staging, and treatment response using a cd-score in patients with CRC.

      Fig. S3. The diagnosis efficiency of each marker among the nine markers in the diagnostic model.

      Fig. S4. Patient treatment response monitoring with methylation rate of cg10673833.

      Fig. S5. Methylation values correlated with treatment outcomes in patients with CRC with serial plasma samples.

      Fig. S6. Nomogram for predicting overall survival of patients with CRC.

      Fig. S7. Comparison of subtype markers, diagnosis markers, and prognosis markers.

      Fig. S8. Unsupervised hierarchical clustering of the top 1000 methylation markers differentially methylated between CRC tumor DNA and normal blood.

      Table S1. Clinical characteristics of the entire study cohort.

      Table S2. Clinical characteristics of the screening study cohort.

      Table S3. Characteristics of the nine methylation markers and their coefficients in diagnosis.

      Table S4. Characteristics of the five methylation markers and their coefficients in prognosis.

      Table S5. Multivariable Cox regression analysis with covariates including cp-score, gender, age, tumor location, TNM stage, and CEA for overall survival.

      Table S6. Characteristics of the 45 methylation markers in ctDNA methylation–based subtyping of CRC.

      Table S7. Clinicopathological and molecular associations of subtype groups.

      Table S8. Association between ctDNA methylation–based CRC subtypes and CRC prognosis in both the training and validation sets (the same cohort as the prognosis model analysis).

      Table S9. Methylation value of cg10673833 in different categories reported by colonoscopy.

      Table S10. Positive and negative predictive values of ctDNA methylation test.

      Data file S1. Questionnaire for screening patients at high risk of CRC.

      References (4157)

    • The PDF file includes:

      • Materials and Methods
      • Fig. S1. List of methylation correlated blocks used for cd-score generation.
      • Fig. S2. ctDNA methylation analysis for predicting tumor burden, staging, and treatment response using a cd-score in patients with CRC.
      • Fig. S3. The diagnosis efficiency of each marker among the nine markers in the diagnostic model.
      • Fig. S4. Patient treatment response monitoring with methylation rate of cg10673833.
      • Fig. S5. Methylation values correlated with treatment outcomes in patients with CRC with serial plasma samples.
      • Fig. S6. Nomogram for predicting overall survival of patients with CRC.
      • Fig. S7. Comparison of subtype markers, diagnosis markers, and prognosis markers.
      • Fig. S8. Unsupervised hierarchical clustering of the top 1000 methylation markers differentially methylated between CRC tumor DNA and normal blood.
      • Table S1. Clinical characteristics of the entire study cohort.
      • Table S2. Clinical characteristics of the screening study cohort.
      • Table S3. Characteristics of the nine methylation markers and their coefficients in diagnosis.
      • Table S4. Characteristics of the five methylation markers and their coefficients in prognosis.
      • Table S5. Multivariable Cox regression analysis with covariates including cp-score, gender, age, tumor location, TNM stage, and CEA for overall survival.
      • Table S6. Characteristics of the 45 methylation markers in ctDNA methylation–based subtyping of CRC.
      • Table S7. Clinicopathological and molecular associations of subtype groups.
      • Table S8. Association between ctDNA methylation–based CRC subtypes and CRC prognosis in both the training and validation sets (the same cohort as the prognosis model analysis).
      • Table S9. Methylation value of cg10673833 in different categories reported by colonoscopy.
      • Table S10. Positive and negative predictive values of ctDNA methylation test.
      • References (4157)

      [Download PDF]

      Other Supplementary Material for this manuscript includes the following:

      • Data file S1 (Microsoft Word format). Questionnaire for screening patients at high risk of CRC.

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