Detection and localization of surgically resectable cancers with a multi-analyte blood test

See allHide authors and affiliations

Science  23 Feb 2018:
Vol. 359, Issue 6378, pp. 926-930
DOI: 10.1126/science.aar3247
  • Fig. 1 Development of a PCR-based assay to identify tumor-specific mutations in plasma samples.

    Colored curves indicate the proportion of cancers of the eight types evaluated in this study that can be detected with an increasing number of short (<40 bp) amplicons. The sensitivity of detection increases with the number of amplicons but plateaus at ~60 amplicons. Colored dots indicate the fraction of cancers detected by using the 61-amplicon panel used in 805 cancers evaluated in our study, which averaged 82%. Publicly available sequencing data were obtained from the COSMIC repository.

  • Fig. 2 Performance of CancerSEEK.

    (A) ROC curve for CancerSEEK. The red point on the curve indicates the test’s average performance (62%) at >99% specificity. Error bars represent 95% confidence intervals for sensitivity and specificity at this particular point. The median performance among the eight cancer types assessed was 70%. (B) Sensitivity of CancerSEEK by stage. Bars represent the median sensitivity of the eight cancer types, and error bars represent standard errors of the median. (C) Sensitivity of CancerSEEK by tumor type. Error bars represent 95% confidence intervals.

  • Fig. 3 Identification of cancer type by supervised machine learning for patients classified as positive by CancerSEEK.

    Percentages correspond to the proportion of patients correctly classified by one of the two most likely types (sum of light and dark blue bars) or the most likely type (light blue bar). Predictions for all patients for all cancer types are provided in table S8. Error bars represent 95% confidence intervals.

Supplementary Materials

  • Detection and localization of surgically resectable cancers with a multianalyte blood test

    Joshua D. Cohen, Lu Li, Yuxuan Wang, Christopher Thoburn, Bahman Afsari, Ludmila Danilova, Christopher Douville, Ammar A. Javed, Fay Wong, Austin Mattox, Ralph. H. Hruban, Christopher L. Wolfgang, Michael G. Goggins, Marco Dal Molin, Tian-Li Wang, Richard Roden, Alison P. Klein, Janine Ptak, Lisa Dobbyn, Joy Schaefer, Natalie Silliman, Maria Popoli, Joshua T. Vogelstein, James D. Browne, Robert E. Schoen, Randall E. Brand, Jeanne Tie, Peter Gibbs, Hui-Li Wong, Aaron S. Mansfield, Jin Jen, Samir M. Hanash, Massimo Falconi, Peter J. Allen, Shibin Zhou, Chetan Bettegowda, Luis Diaz, Cristian Tomasetti, Kenneth W. Kinzler, Bert Vogelstein, Anne Marie Lennon, Nickolas Papadopoulos

    Materials/Methods, Supplementary Text, Tables, Figures, and/or References

    Download Supplement
    • Material and Methods
    • Figs. S1 to S4
    • References
    Tables S1 to S11
    Table S1: Primer sequences for multiplex PCR assays.
    Table S2: Mutations identified in primary tumors.
    Table S3: Protein biomarkers analyzed and included in CancerSEEK test.
    Table S4: Histopathological and clinical characteristics of the cancer patients and healthy controls.
    Table S5: Mutations identified in plasma samples from cancer patients and healthy controls.
    Table S6: Concentrations of assayed protein biomarker in plasma samples from cancer patients and healthy controls.
    Table S7: Concordance between mutations identified in the plasma with those identified in primary tumors.
    Table S8: Cancer type localization results for the 617 cancer patients identified by CancerSEEK.
    Table S9: Logistic regression model coefficients and importance scores.
    Table S10: Confusion matrix of top predictions from cancer type localization results.
    Table S11: Cancer patients evaluated in this study by tumor type and stage.

Navigate This Article