Abstract
Serum biomarkers are often insufficiently sensitive or specific to facilitate cancer screening or diagnostic testing. In ovarian cancer, the few established serum biomarkers are highly specific, yet insufficiently sensitive to detect early-stage disease and to impact the mortality rates of patients with this cancer. Here we show that a ‘disease fingerprint’ acquired via machine learning from the spectra of near-infrared fluorescence emissions of an array of carbon nanotubes functionalized with quantum defects detects high-grade serous ovarian carcinoma in serum samples from symptomatic individuals with 87% sensitivity at 98% specificity (compared with 84% sensitivity at 98% specificity for the current best clinical screening test, which uses measurements of cancer antigen 125 and transvaginal ultrasonography). We used 269 serum samples to train and validate several machine-learning classifiers for the discrimination of patients with ovarian cancer from those with other diseases and from healthy individuals. The predictive values of the best classifier could not be attained via known protein biomarkers, suggesting that the array of nanotube sensors responds to unidentified serum biomarkers.
| Original language | English |
|---|---|
| Pages (from-to) | 267-275 |
| Number of pages | 9 |
| Journal | Nature Biomedical Engineering |
| Volume | 6 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 2022 |
| Externally published | Yes |
Bibliographical note
Funding Information:We thank B. Kwon, S. Chatterjee, A. Chatterjee, M. Fleisher, B. D. Davison, S. David and N. Osiroff for helpful discussions. This work was supported in part by NIH grants R01-CA215719, U54-CA137788, U54-CA132378 and P30-CA008748; the National Science Foundation CAREER Award (1752506); the Honorable Tina Brozman Foundation for Ovarian Cancer Research; the Tina Brozman Ovarian Cancer Research Consortium 2.0; the Kelly Auletta Fund for Ovarian Cancer Research; the American Cancer Society Research Scholar Grant (GC230452); the Pershing Square Sohn Cancer Research Alliance; the Expect Miracles Foundation – Financial Services Against Cancer; the Experimental Therapeutics Center; W. H. Goodwin and A. Goodwin and the Commonwealth Foundation for Cancer Research. M.K. was supported by the Marie-Josée Kravis Women in Science Endeavor Postdoctoral Fellowship. Y.H.W. gratefully acknowledges support from the National Science Foundation (CHE-1904488) and NIH grant (R01-GM114167). H.-B.L. acknowledges the support provided by the China Scholarships Council (CSC No. 201708320366) during his visit to the University of Maryland. P.W. gratefully acknowledges the Millard and Lee Alexander Fellowship from the University of Maryland. M.Z.’s work was NIST internally funded. Y.Y. was supported by a Dean’s Fellowship at Lehigh University. A.J. acknowledges the NHI initiative at Lehigh University.
Funding Information:
We thank B. Kwon, S. Chatterjee, A. Chatterjee, M. Fleisher, B. D. Davison, S. David and N. Osiroff for helpful discussions. This work was supported in part by NIH grants R01-CA215719, U54-CA137788, U54-CA132378 and P30-CA008748; the National Science Foundation CAREER Award (1752506); the Honorable Tina Brozman Foundation for Ovarian Cancer Research; the Tina Brozman Ovarian Cancer Research Consortium 2.0; the Kelly Auletta Fund for Ovarian Cancer Research; the American Cancer Society Research Scholar Grant (GC230452); the Pershing Square Sohn Cancer Research Alliance; the Expect Miracles Foundation ? Financial Services Against Cancer; the Experimental Therapeutics Center; W. H. Goodwin and A. Goodwin and the Commonwealth Foundation for Cancer Research. M.K. was supported by the Marie-Jos?e Kravis Women in Science Endeavor Postdoctoral Fellowship. Y.H.W. gratefully acknowledges support from the National Science Foundation (CHE-1904488) and NIH grant (R01-GM114167). H.-B.L. acknowledges the support provided by the China Scholarships Council (CSC No. 201708320366) during his visit to the University of Maryland. P.W. gratefully acknowledges the Millard and Lee Alexander Fellowship from the University of Maryland. M.Z.?s work was NIST internally funded. Y.Y. was supported by a Dean?s Fellowship at Lehigh University. A.J. acknowledges the NHI initiative at Lehigh University.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature Limited.
