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Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning

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.

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Fig. 1: OCC-DNA nanosensor array.
Fig. 2: Spectroscopic responses of OCC-DNA sensors to patient serum samples.
Fig. 3: Optimization of machine learning algorithms for HGSOC classification.
Fig. 4: Known serum biomarkers make up part of the disease fingerprint in the nanosensor array response.

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. Source data for the figures are provided with this paper. The raw datasets generated during the study are too large to be publicly shared, yet they are available for research purposes from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

The custom Python and MATLAB codes for the machine learning and the data analyses reported in this study are not yet publicly available owing to intellectual-property-filing issues, yet they are available for research purposes from the corresponding author on reasonable request.

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Acknowledgements

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.

Author information

Authors and Affiliations

Authors

Contributions

M.K. and D.A.H. designed experiments and analysed the data. M.K., D.A.H., Y.H.W., M.Z. and A.J. conceived and supervised the research. M.K., P.W. and H.-B.L. synthesized the sensor materials. M.K., C.C. and M.A.-P. performed the screening experiments. M.K., Y.Y. and C.W. performed machine learning. S.C. and L.V.R. obtained and processed the patient samples. J.J.M. reviewed the patient charts. J.J.M., L.V.R. and K.L.-R. provided clinical direction to the study. M.K. and D.A.H. wrote the manuscript. Y.H.W., M.Z., A.J. and J.J.M. edited the manuscript.

Corresponding author

Correspondence to Daniel A. Heller.

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Competing interests

D.A.H. is a co-founder and officer, with an equity interest, of Goldilocks Therapeutics Inc., Lime Therapeutics Inc. and Resident Diagnostics Inc., and is a member of the scientific advisory board of Concarlo Holdings LLC, Nanorobotics Inc. and Mediphage Bioceuticals Inc. The other authors declare no competing interests.

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Nature Biomedical Engineering thanks Kanyi Pu, Steven Skates and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Spectral responses of OCC-DNAs to a small set of HGSOC and benign serum samples.

Four spectral parameters –intensity and wavelength changes of the E11 and E11- peaks– were extracted from fluorescence spectra of four serum samples in each group. Each sample was measured in triplicate. Horizontal lines denote the median. Six OCC-DNA nanosensors, with p-values of the spectroscopic features lower than 0.10, were selected for the sensor array.

Source data

Extended Data Fig. 2 Spectral responses of the nanosensor array to training and validation sets of patient serum samples (Nsa = 215).

Four spectral parameters, a, dint, b, dint*, c, dwl, and d, dwl*, were extracted from fluorescence spectra of the sensor array after 2-hour serum incubation. Each sample was measured in triplicate.

Source data

Extended Data Fig. 3 Averaged F-scores of optimized machine learning models with 10-fold validation.

The classification was divided as HGSOC versus other gynecologic diseases and benign groups. The blue line is the logarithmic regression of the median F-score.

Source data

Extended Data Fig. 4 Assessment of medications as potential interferents to nanosensor prediction.

a, Fraction of medication dose for HGSOC and other disease patients. b, Chronic conditions, and prevalence thereof, in patients measured in this study. Comorbidity was identified based on the patients’ medication information. c, Anti-cancer drugs or prescription drugs whose occurrence differed by 0.1 or higher between HGSOC and other disease groups.

Source data

Extended Data Fig. 5 Serum levels of known ovarian cancer biomarkers in the model study population.

a, CA125, b, HE4, and c, YKL40. The serum protein levels were quantified by automated immunoassay. Dotted lines indicate the clinical reference of each biomarker for HGSOC diagnosis. The error bars denote median ± 95% CI.

Source data

Extended Data Fig. 6 Response of OCC-DNA nanosensors to protein HGSOC biomarkers, creatinine, and bilirubin in 20% fetal bovine serum.

The fluorescence spectra were obtained 2 hours after the incubation. Vertical dashed lines indicate the clinical reference of each serum biomarker for HGSOC screening.

Source data

Extended Data Fig. 7 Relative feature importance of each spectroscopic variable in the HGSOC binary classification models.

a, Feature importance of each spectral parameter, used to train the SVM models, of all OCC-DNA sensors in the arrays tested in this work. Solid lines indicate the median feature importance. b, Correlation of averaged F-score with the averaged feature importance of each spectroscopic variable. Vertical dashed lines indicate F-score when all four spectroscopic variables (dint, dint*, dwl, and dwl*) of the OCC-DNA were included as feature vectors in the model development.

Source data

Extended Data Fig. 8 Correlation of F-score and r2 of the biomarker prediction models with the relative feature importance of each spectroscopic variable.

For the binary classification models (top rows), samples were divided into two groups–abnormal vs. normal levels of serum biomarkers–based on the clinical references (CA125: 50 U/mL, HE4: 150 pM, YKL40: 1650 pM) and assessed the prediction accuracy of abnormal levels of each biomarker. Feature importance of the prediction models shows which spectral parameters most impacted the model performance using an ablation study. Biomarker dependent variables that were identified in Extended Data Fig. 4 are highlighted in bold. Vertical dashed lines indicate F-score when all four spectroscopic variables (dint, dint*, dwl, and dwl*) of the OCC-DNA were included as feature vectors in the model development.

Source data

Supplementary information

Supplementary Information

Supplementary figures, tables, captions for Tables 2–6, and Appendix 1.

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Supplementary Table

Supplementary Tables 2–6.

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Kim, M., Chen, C., Wang, P. et al. Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning. Nat. Biomed. Eng 6, 267–275 (2022). https://doi.org/10.1038/s41551-022-00860-y

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