Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Nature Biomedical Engineering
  • Article
  • Published:

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

Nature Biomedical Engineeringvolume 6, pages267–275 (2022)Cite this article

Subjects

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.

This is a preview of subscription content,access via your institution

Access options

Access through your institution

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

9,800 Yen / 30 days

cancel any time

Subscribe to this journal

Receive 12 digital issues and online access to articles

￥14,900 per year

only ￥1,242 per issue

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

Data availability

The main data supporting the results in this study are available within the paper and its SupplementaryInformation. 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.

References

  1. Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin.68, 394–424 (2018).

    Article PubMed  Google Scholar 

  2. Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2020.CA Cancer J. Clin.70, 7–30 (2020).

    Article  Google Scholar 

  3. Blyuss, O. et al. Comparison of longitudinal CA125 algorithms as a first-line screen for ovarian cancer in the general population.Clin. Cancer Res.24, 4726 (2018).

    Article CAS PubMed PubMed Central  Google Scholar 

  4. Cramer, D. W. et al. Ovarian cancer biomarker performance in prostate, lung, colorectal, and ovarian cancer screening trial specimens.Cancer Prev. Res.4, 365 (2011).

    Article  Google Scholar 

  5. Dupont, J. et al. Early detection and prognosis of ovarian cancer using serum YKL-40.J. Clin. Oncol.22, 3330–3339 (2004).

    Article CAS PubMed  Google Scholar 

  6. Han, C. et al. A novel multiple biomarker panel for the early detection of high-grade serous ovarian carcinoma.Gynecol. Oncol.149, 585–591 (2018).

    Article CAS PubMed PubMed Central  Google Scholar 

  7. Hertlein, L. et al. Human epididymis protein 4 (HE4) in benign and malignant diseases.Clin. Chem. Lab. Med.50, 2181–2188 (2012).

    Article CAS PubMed  Google Scholar 

  8. Pinsky, P. F. et al. Extended mortality results for ovarian cancer screening in the PLCO trial with median 15 years follow-up.Gynecol. Oncol.143, 270–275 (2016).

    Article PubMed PubMed Central  Google Scholar 

  9. Jacobs, I. J. et al. Ovarian cancer screening and mortality in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial.Lancet387, 945–956 (2016).

    Article PubMed PubMed Central  Google Scholar 

  10. Menon, U. et al. Ovarian cancer population screening and mortality after long-term follow-up in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial.Lancet397, 2182–2193 (2021).

    Article PubMed PubMed Central  Google Scholar 

  11. Diamandis, E. P. The failure of protein cancer biomarkers to reach the clinic: why, and what can be done to address the problem?BMC Med.10, 87 (2012).

    Article PubMed PubMed Central  Google Scholar 

  12. Su, C.-Y., Menuz, K. & Carlson, J. R. Olfactory perception: receptors, cells, and circuits.Cell139, 45–59 (2009).

    Article CAS PubMed PubMed Central  Google Scholar 

  13. Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA.Ann. Oncol.31, 745–759 (2020).

    Article CAS PubMed  Google Scholar 

  14. Hao, Y. et al. Detection of volatile organic compounds in breath as markers of lung cancer using a novel electronic nose.Proc. IEEE Sens.2, 1333–1337 (2003).

  15. Zhang, J. et al. Nondestructive tissue analysis for ex vivo and in vivo cancer diagnosis using a handheld mass spectrometry system.Sci. Transl. Med.9, eaan3968 (2017).

    Article PubMed PubMed Central  Google Scholar 

  16. Yu, K.-H. et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features.Nat. Commun.7, 12474 (2016).

    Article CAS PubMed PubMed Central  Google Scholar 

  17. Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V. & Madabhushi, A. Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology.Nat. Rev. Clin. Oncol.16, 703–715 (2019).

    Article PubMed PubMed Central  Google Scholar 

  18. Rajkomar, A. et al. Scalable and accurate deep learning with electronic health records.NPJ Digit. Med.1, 18 (2018).

    Article PubMed PubMed Central  Google Scholar 

  19. Bachilo, S. M. et al. Structure-assigned optical spectra of single-walled carbon nanotubes.Science298, 2361 (2002).

    Article CAS PubMed  Google Scholar 

  20. Cognet, L. et al. Stepwise quenching of exciton fluorescence in carbon nanotubes by single-molecule reactions.Science316, 1465 (2007).

    Article CAS PubMed  Google Scholar 

  21. Heller, D. A. et al. Optical detection of DNA conformational polymorphism on single-walled carbon nanotubes.Science311, 508 (2006).

    Article CAS PubMed  Google Scholar 

  22. Jena, P. V. et al. A carbon nanotube optical reporter maps endolysosomal lipid flux.ACS Nano11, 10689–10703 (2017).

    Article CAS PubMed PubMed Central  Google Scholar 

  23. Heller, D. A. et al. Multimodal optical sensing and analyte specificity using single-walled carbon nanotubes.Nat. Nanotech.4, 114–120 (2009).

    Article CAS  Google Scholar 

  24. Roxbury, D., Jena, P. V., Shamay, Y., Horoszko, C. P. & Heller, D. A. Cell membrane proteins modulate the carbon nanotube optical bandgap via surface charge accumulation.ACS Nano10, 499–506 (2016).

    Article CAS PubMed  Google Scholar 

  25. Williams, R. M. et al. Noninvasive ovarian cancer biomarker detection via an optical nanosensor implant.Sci. Adv.4, eaaq1090 (2018).

    Article PubMed PubMed Central  Google Scholar 

  26. Roxbury, D., Mittal, J. & Jagota, A. Molecular-basis of single-walled carbon nanotube recognition by single-stranded DNA.Nano Lett.12, 1464–1469 (2012).

    Article CAS PubMed  Google Scholar 

  27. Roxbury, D., Jagota, A. & Mittal, J. Structural characteristics of oligomeric DNA strands adsorbed onto single-walled carbon nanotubes.J. Phys. Chem. B117, 132–140 (2013).

    Article CAS PubMed  Google Scholar 

  28. Roxbury, D., Tu, X., Zheng, M. & Jagota, A. Recognition ability of DNA for carbon nanotubes correlates with their binding affinity.Langmuir27, 8282–8293 (2011).

    Article CAS PubMed  Google Scholar 

  29. Horoszko, C. P., Jena, P. V., Roxbury, D., Rotkin, S. V. & Heller, D. A. Optical voltammetry of polymer-encapsulated single-walled carbon nanotubes.J. Phys. Chem. C123, 24200–24208 (2019).

    Article CAS  Google Scholar 

  30. Brozena, A. H., Kim, M., Powell, L. R. & Wang, Y. Controlling the optical properties of carbon nanotubes with organic colour-centre quantum defects.Nat. Rev. Chem.3, 375–392 (2019).

    Article CAS PubMed PubMed Central  Google Scholar 

  31. Piao, Y. M. et al. Brightening of carbon nanotube photoluminescence through the incorporation of sp3 defects.Nat. Chem.5, 840–845 (2013).

    Article CAS PubMed  Google Scholar 

  32. Kwon, H. et al. Optical probing of local pH and temperature in complex fluids with covalently functionalized, semiconducting carbon nanotubes.J. Phys. Chem. C119, 3733–3739 (2015).

    Article CAS  Google Scholar 

  33. Luo, H.-B. et al. One-pot, large-scale synthesis of organic color center-tailored semiconducting carbon nanotubes.ACS Nano13, 8417–8424 (2019).

    Article CAS PubMed  Google Scholar 

  34. Ao, G., Streit, J. K., Fagan, J. A. & Zheng, M. Differentiating left- and right-handed carbon nanotubes by DNA.J. Am. Chem. Soc.138, 16677–16685 (2016).

    Article CAS PubMed  Google Scholar 

  35. Shahriari, B., Swersky, K., Wang, Z., Adams, R. P. & de Freitas, N. Taking the human out of the loop: a review of Bayesian optimization.Proc. IEEE104, 148–175 (2016).

    Article  Google Scholar 

  36. Wolff, R. F. et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies.Ann. Intern. Med.170, 51–58 (2019).

    Article PubMed  Google Scholar 

  37. Moore, L. E. et al. Proteomic biomarkers in combination with CA 125 for detection of epithelial ovarian cancer using prediagnostic serum samples from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial.Cancer118, 91–100 (2012).

    Article CAS PubMed  Google Scholar 

  38. Pinals, R. L., Yang, D., Lui, A., Cao, W. & Landry, M. P. Corona exchange dynamics on carbon nanotubes by multiplexed fluorescence monitoring.J. Am. Chem. Soc.142, 1254–1264 (2020).

    Article CAS PubMed  Google Scholar 

  39. Tenzer, S. et al. Rapid formation of plasma protein corona critically affects nanoparticle pathophysiology.Nat. Nanotech.8, 772–781 (2013).

    Article CAS  Google Scholar 

  40. Heller, D. A. et al. Peptide secondary structure modulates single-walled carbon nanotube fluorescence as a chaperone sensor for nitroaromatics.Proc. Natl Acad. Sci. USA108, 8544 (2011).

    Article CAS PubMed PubMed Central  Google Scholar 

  41. Wu, X., Kim, M., Qu, H. & Wang, Y. Single-defect spectroscopy in the shortwave infrared.Nat. Commun.10, 2672 (2019).

    Article PubMed PubMed Central  Google Scholar 

  42. Lee, M. A. et al. Can fish and cell phones teach us about our health?ACS Sens.4, 2566–2570 (2019).

    Article CAS PubMed  Google Scholar 

  43. Zednik, C. Solving the Black Box Problem: a normative framework for explainable artificial intelligence.Philos. Technol.34, 265–288 (2021).

    Article  Google Scholar 

  44. Docter, D. et al. Quantitative profiling of the protein coronas that form around nanoparticles.Nat. Protoc.9, 2030–2044 (2014).

    Article CAS PubMed  Google Scholar 

  45. Pinals, R. L. et al. Quantitative protein corona composition and dynamics on carbon nanotubes in biological environments.Angew. Chem. Int. Ed.59, 23668–23677 (2020).

    Article CAS  Google Scholar 

  46. Lai, Z. W., Yan, Y., Caruso, F. & Nice, E. C. Emerging techniques in proteomics for probing nano–bio interactions.ACS Nano6, 10438–10448 (2012).

    Article CAS PubMed  Google Scholar 

  47. Hadjidemetriou, M. et al. Nano-scavengers for blood biomarker discovery in ovarian carcinoma.Nano Today34, 100901 (2020).

    Article CAS  Google Scholar 

  48. Zheng, M. & Diner, B. A. Solution redox chemistry of carbon nanotubes.J. Am. Chem. Soc.126, 15490–15494 (2004).

    Article CAS PubMed  Google Scholar 

  49. Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, W. P. SMOTE: synthetic minority over-sampling technique.J. Artif. Intell. Res.16, 321–357 (2002).

    Article  Google Scholar 

  50. Pedregosa, F. et al. Scikit-learn: machine learning in Python.J. Mach. Learn. Res.12, 2825–2830 (2011).

    Google Scholar 

Download references

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

  1. Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Mijin Kim, Chen Chen, Merav Antman-Passig, Sun Cho, Kara Long-Roche, Lakshmi V. Ramanathan & Daniel A. Heller

  2. Weill Cornell Medicine, Cornell University, New York, NY, USA

    Chen Chen & Daniel A. Heller

  3. Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Chen Chen

  4. Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, USA

    Peng Wang, Hong-Bin Luo & YuHuang Wang

  5. Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA

    Joseph J. Mulvey

  6. Departments of Bioengineering, and Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA, USA

    Yoona Yang & Anand Jagota

  7. Hunter College High School, New York, NY, USA

    Christopher Wun

  8. Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD, USA

    Ming Zheng

Authors
  1. Mijin Kim
  2. Chen Chen
  3. Peng Wang
  4. Joseph J. Mulvey
  5. Yoona Yang
  6. Christopher Wun
  7. Merav Antman-Passig
  8. Hong-Bin Luo
  9. Sun Cho
  10. Kara Long-Roche
  11. Lakshmi V. Ramanathan
  12. Anand Jagota
  13. Ming Zheng
  14. YuHuang Wang
  15. Daniel A. Heller

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 toDaniel A. Heller.

Ethics declarations

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.

Peer review

Peer review information

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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, andd,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, anddwl*) 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, anddwl*) 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.

Supplementary Table

Supplementary Tables 2–6.

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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. Eng6, 267–275 (2022). https://doi.org/10.1038/s41551-022-00860-y

Download citation

This article is cited by

Access through your institution
Buy or subscribe

Associated content

Collection

Machine learning in healthcare

Advertisement

Search

Advanced search

Quick links

Nature Briefing

Sign up for theNature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox.Sign up for Nature Briefing
[8]ページ先頭
©2009-2025 Movatter.jp