Skip to main content

Advertisement

SpringerLink
Log in

Diagnostic performance improvement with combined use of proteomics biomarker assay and breast ultrasound

  • Clinical trial
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Purpose

To investigate the combined use of blood-based 3-protein signature and breast ultrasound (US) for validating US-detected lesions.

Methods

From July 2011 to April 2020, women who underwent whole-breast US within at least 6 months from sampling period were retrospectively included. Blood-based 3-protein signature (Mastocheck®) value and US findings were evaluated. Following outcome measures were compared between US alone and the combination of Mastocheck® value with US: sensitivity, specificity, positive predictive value (PPV), negative predictive value, area under the receiver operating characteristic curve (AUC), and biopsy rate.

Results

Among the 237 women included, 59 (24.9%) were healthy individuals and 178 (75.1%) cancer patients. Mean size of cancers was 1.2 ± 0.8 cm. Median value of Mastocheck® was significantly different between nonmalignant (− 0.24, interquartile range [IQR] − 0.48, − 0.03) and malignant lesions (0.55, IQR − 0.03, 1.42) (P < .001). Utilizing Mastocheck® value with US increased the AUC from 0.67 (95% confidence interval [CI] 0.61, 0.73) to 0.81 (95% CI 0.75, 0.88; P < .001), and specificity from 35.6 (95% CI 23.4, 47.8) to 64.4% (95% CI 52.2, 76.6; P < .001) without loss in sensitivity. PPV was increased from 82.2 (95% CI 77.1, 87.3) to 89.3% (95% CI 85.0, 93.6; P < .001), and biopsy rate was significantly decreased from 79.3 (188/237) to 72.1% (171/237) (P < .001). Consistent improvements in specificity, PPV, and AUC were observed in asymptomatic women, in women with dense breast, and in those with normal/benign mammographic findings.

Conclusion

Mastocheck® is an effective tool that can be used with US to improve diagnostic specificity and reduce false-positive findings and unnecessary biopsies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Duffy SW, Tabár L, Yen AM, Dean PB, Smith RA, Jonsson H, Törnberg S, Chen SL, Chiu SY, Fann JC, Ku MM, Wu WY, Hsu CY, Chen YC, Svane G, Azavedo E, Grundström H, Sundén P, Leifland K, Frodis E, Ramos J, Epstein B, Åkerlund A, Sundbom A, Bordás P, Wallin H, Starck L, Björkgren A, Carlson S, Fredriksson I, Ahlgren J, Öhman D, Holmberg L, Chen TH (2020) Mammography screening reduces rates of advanced and fatal breast cancers: results in 549,091 women. Cancer 126:2971–2979. https://doi.org/10.1002/cncr.32859

    Article  PubMed  Google Scholar 

  2. Tabár L, Dean PB, Chen TH, Yen AM, Chen SL, Fann JC, Chiu SY, Ku MM, Wu WY, Hsu CY, Chen YC, Beckmann K, Smith RA, Duffy SW (2019) The incidence of fatal breast cancer measures the increased effectiveness of therapy in women participating in mammography screening. Cancer 125:515–523. https://doi.org/10.1002/cncr.31840

    Article  PubMed  Google Scholar 

  3. Mandelson MT, Oestreicher N, Porter PL, White D, Finder CA, Taplin SH, White E (2000) Breast density as a predictor of mammographic detection: comparison of interval- and screen-detected cancers. J Natl Cancer Inst 92:1081–1087. https://doi.org/10.1093/jnci/92.13.1081

    Article  CAS  PubMed  Google Scholar 

  4. Crystal P, Strano SD, Shcharynski S, Koretz MJ (2003) Using sonography to screen women with mammographically dense breasts. AJR Am J Roentgenol 181:177–182. https://doi.org/10.2214/ajr.181.1.1810177

    Article  PubMed  Google Scholar 

  5. Kaplan SS (2001) Clinical utility of bilateral whole-breast US in the evaluation of women with dense breast tissue. Radiology 221:641–649. https://doi.org/10.1148/radiol.2213010364

    Article  CAS  PubMed  Google Scholar 

  6. Kolb TM, Lichy J, Newhouse JH (2002) Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology 225:165–175. https://doi.org/10.1148/radiol.2251011667

    Article  PubMed  Google Scholar 

  7. Berg WA, Blume JD, Cormack JB, Mendelson EB, Lehrer D, Böhm-Vélez M, Pisano ED, Jong RA, Evans WP, Morton MJ, Mahoney MC, Larsen LH, Barr RG, Farria DM, Marques HS, Boparai K (2008) Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA 299:2151–2163. https://doi.org/10.1001/jama.299.18.2151

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Corsetti V, Ferrari A, Ghirardi M, Bergonzini R, Bellarosa S, Angelini O, Bani C, Ciatto S (2006) Role of ultrasonography in detecting mammographically occult breast carcinoma in women with dense breasts. Radiol Med 111:440–448. https://doi.org/10.1007/s11547-006-0040-5

    Article  CAS  PubMed  Google Scholar 

  9. Tohno E, Ueno E, Watanabe H (2009) Ultrasound screening of breast cancer. Breast Cancer 16:18–22. https://doi.org/10.1007/s12282-008-0082-8

    Article  PubMed  Google Scholar 

  10. Bassett LW (2000) Imaging of breast masses. Radiol Clin North Am 38:669–691. https://doi.org/10.1016/s0033-8389(05)70193-7

    Article  CAS  PubMed  Google Scholar 

  11. Moy L, Slanetz PJ, Moore R, Satija S, Yeh ED, McCarthy KA, Hall D, Staffa M, Rafferty EA, Halpern E, Kopans DB (2002) Specificity of mammography and US in the evaluation of a palpable abnormality: retrospective review. Radiology 225:176–181. https://doi.org/10.1148/radiol.2251010999

    Article  PubMed  Google Scholar 

  12. Parker SH, Burbank F, Jackman RJ, Aucreman CJ, Cardenosa G, Cink TM, Coscia JL Jr, Eklund GW, Evans WP 3rd, Garver PR et al (1994) Percutaneous large-core breast biopsy: a multi-institutional study. Radiology 193:359–364. https://doi.org/10.1148/radiology.193.2.7972743

    Article  CAS  PubMed  Google Scholar 

  13. Berg WA (2016) Current status of supplemental screening in dense breasts. J Clin Oncol 34:1840–1843. https://doi.org/10.1200/jco.2015.65.8674

    Article  PubMed  Google Scholar 

  14. Berg WA (2004) Supplemental screening sonography in dense breasts. Radiol Clin North Am 42:845–851. https://doi.org/10.1016/j.rcl.2004.04.003

    Article  PubMed  Google Scholar 

  15. Lee JM, Arao RF, Sprague BL, Kerlikowske K, Lehman CD, Smith RA, Henderson LM, Rauscher GH, Miglioretti DL (2019) Performance of screening ultrasonography as an adjunct to screening mammography in women across the spectrum of breast cancer risk. JAMA Intern Med 179:658–667. https://doi.org/10.1001/jamainternmed.2018.8372

    Article  PubMed  PubMed Central  Google Scholar 

  16. Corsetti V, Houssami N, Ferrari A, Ghirardi M, Bellarosa S, Angelini O, Bani C, Sardo P, Remida G, Galligioni E, Ciatto S (2008) Breast screening with ultrasound in women with mammography-negative dense breasts: evidence on incremental cancer detection and false positives, and associated cost. Eur J Cancer 44:539–544. https://doi.org/10.1016/j.ejca.2008.01.009

    Article  PubMed  Google Scholar 

  17. Sprague BL, Stout NK, Schechter C, van Ravesteyn NT, Cevik M, Alagoz O, Lee CI, van den Broek JJ, Miglioretti DL, Mandelblatt JS, de Koning HJ, Kerlikowske K, Lehman CD, Tosteson AN (2015) Benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts. Ann Intern Med 162:157–166. https://doi.org/10.7326/m14-0692

    Article  PubMed  PubMed Central  Google Scholar 

  18. Weigert JM (2017) The connecticut experiment; the third installment: 4 years of screening women with dense breasts with bilateral ultrasound. Breast J 23:34–39. https://doi.org/10.1111/tbj.12678

    Article  PubMed  Google Scholar 

  19. Underwood JJ, Quadri RS, Kalva SP, Shah H, Sanjeevaiah AR, Beg MS, Sutphin PD (2020) Liquid biopsy for cancer: review and implications for the radiologist. Radiology 294:5–17. https://doi.org/10.1148/radiol.2019182584

    Article  PubMed  Google Scholar 

  20. Ziegler A, Zangemeister-Wittke U, Stahel RA (2002) Circulating DNA: a new diagnostic gold mine? Cancer Treat Rev 28:255–271. https://doi.org/10.1016/s0305-7372(02)00077-4

    Article  CAS  PubMed  Google Scholar 

  21. Tay TKY, Tan PH (2020) Liquid biopsy in breast cancer: a focused review. Arch Pathol Lab Med 145:678–686. https://doi.org/10.5858/arpa.2019-0559-RA

    Article  Google Scholar 

  22. Alimirzaie S, Bagherzadeh M, Akbari MR (2019) Liquid biopsy in breast cancer: a comprehensive review. Clin Genet 95:643–660. https://doi.org/10.1111/cge.13514

    Article  CAS  PubMed  Google Scholar 

  23. Loke SY, Lee ASG (2018) The future of blood-based biomarkers for the early detection of breast cancer. Eur J Cancer 92:54–68. https://doi.org/10.1016/j.ejca.2017.12.025

    Article  CAS  Google Scholar 

  24. Thierry AR, El Messaoudi S, Gahan PB, Anker P, Stroun M (2016) Origins, structures, and functions of circulating DNA in oncology. Cancer Metastasis Rev 35:347–376. https://doi.org/10.1007/s10555-016-9629-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chen L, Bode AM, Dong Z (2017) Circulating tumor cells: moving biological insights into detection. Theranostics 7:2606–2619. https://doi.org/10.7150/thno.18588

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Li J, Guan X, Fan Z, Ching LM, Li Y, Wang X, Cao WM, Liu DX (2020) Non-invasive biomarkers for early detection of breast cancer. Cancers (Basel). https://doi.org/10.3390/cancers12102767

    Article  PubMed  PubMed Central  Google Scholar 

  27. Cohen JD, Li L, Wang Y, Thoburn C, Afsari B, Danilova L, Douville C, Javed AA, Wong F, Mattox A, Hruban RH, Wolfgang CL, Goggins MG, Dal Molin M, Wang TL, Roden R, Klein AP, Ptak J, Dobbyn L, Schaefer J, Silliman N, Popoli M, Vogelstein JT, Browne JD, Schoen RE, Brand RE, Tie J, Gibbs P, Wong HL, Mansfield AS, Jen J, Hanash SM, Falconi M, Allen PJ, Zhou S, Bettegowda C, Diaz LA Jr, Tomasetti C, Kinzler KW, Vogelstein B, Lennon AM, Papadopoulos N (2018) Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359:926–930. https://doi.org/10.1126/science.aar3247

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Duffy MJ (2006) Serum tumor markers in breast cancer: are they of clinical value? Clin Chem 52:345–351. https://doi.org/10.1373/clinchem.2005.059832

    Article  CAS  PubMed  Google Scholar 

  29. Harris L, Fritsche H, Mennel R, Norton L, Ravdin P, Taube S, Somerfield MR, Hayes DF, Bast RC Jr (2007) American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol 25:5287–5312. https://doi.org/10.1200/jco.2007.14.2364

    Article  CAS  Google Scholar 

  30. Kazarian A, Blyuss O, Metodieva G, Gentry-Maharaj A, Ryan A, Kiseleva EM, Prytomanova OM, Jacobs IJ, Widschwendter M, Menon U, Timms JF (2017) Testing breast cancer serum biomarkers for early detection and prognosis in pre-diagnosis samples. Br J Cancer 116:501–508. https://doi.org/10.1038/bjc.2016.433

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kim Y, Kang UB, Kim S, Lee HB, Moon HG, Han W, Noh DY (2019) A validation study of a multiple reaction monitoring-based proteomic assay to diagnose breast cancer. J Breast Cancer 22:579–586. https://doi.org/10.4048/jbc.2019.22.e57

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kim Y, Moon H-G, Lee H-B, Moon WK, Cho N, Chang J-M, Han W, Noh D-Y (2019) Efficacy of Mastocheck for screening of early breast cancer: comparison with screening mammography. J Breast Dis 7:59–64. https://doi.org/10.14449/jbd.2019.7.2.59

    Article  Google Scholar 

  33. Lee HB, Kang UB, Moon HG, Lee J, Lee KM, Yi M, Park YS, Lee JW, Yu JH, Choi SH, Cho SH, Lee C, Han W, Noh DY (2015) Development and validation of a novel plasma protein signature for breast cancer diagnosis by using multiple reaction monitoring-based mass spectrometry. Anticancer Res 35:6271–6279

    CAS  PubMed  Google Scholar 

  34. Mendelson EBB-VM, Berg WA et al (2013) ACR BI-RADS ultrasound. ACR BIRADS atlas, breast imaging reporting and data system. American College of Radiology, Reston, VA

    Google Scholar 

  35. Kang UB, Ahn Y, Lee JW, Kim YH, Kim J, Yu MH, Noh DY, Lee C (2010) Differential profiling of breast cancer plasma proteome by isotope-coded affinity tagging method reveals biotinidase as a breast cancer biomarker. BMC Cancer 10:114. https://doi.org/10.1186/1471-2407-10-114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845

    Article  CAS  Google Scholar 

  37. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174

    Article  CAS  Google Scholar 

  38. Lee SH, Chung J, Choi HY, Choi SH, Ryu EB, Ko KH, Koo HR, Park JS, Yi A, Youk JH, Son EJ, Chu AJ, Chang JM, Cho N, Jang MJ, Kook SH, Cha ES, Moon WK (2017) Evaluation of screening US-detected breast masses by combined use of elastography and color doppler US with B-mode US in women with dense breasts: a multicenter prospective study. Radiology 285:660–669. https://doi.org/10.1148/radiol.2017162424

    Article  PubMed  Google Scholar 

  39. Barr RG (2019) Future of breast elastography. Ultrasonography 38:93–105. https://doi.org/10.14366/usg.18053

    Article  PubMed  PubMed Central  Google Scholar 

  40. Choi JS, Han BK, Ko ES, Bae JM, Ko EY, Song SH, Kwon MR, Shin JH, Hahn SY (2019) Effect of a deep learning framework-based computer-aided diagnosis system on the diagnostic performance of radiologists in differentiating between malignant and benign masses on breast ultrasonography. Korean J Radiol 20:749–758. https://doi.org/10.3348/kjr.2018.0530

    Article  PubMed  PubMed Central  Google Scholar 

  41. Choi JH, Kang BJ, Baek JE, Lee HS, Kim SH (2018) Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience. Ultrasonography 37:217–225. https://doi.org/10.14366/usg.17046

    Article  PubMed  Google Scholar 

  42. Kim S, Choi Y, Kim E, Han BK, Yoon JH, Choi JS, Chang JM (2021) Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses. Sci Rep 11:395. https://doi.org/10.1038/s41598-020-79880-0

    Article  CAS  PubMed Central  Google Scholar 

  43. Altman DG, Bland JM (1994) Diagnostic tests 2: predictive values. BMJ 309:102. https://doi.org/10.1136/bmj.309.6947.102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Hwa Jung Kim, MD, PhD, associate professor of preventive medicine, for helping us with the statistical analysis.

Funding

This study was supported by Seoul National University Hospital Research Fund (Grant No. 04-2021-0510).

Author information

Author notes

  1. Su Min Ha and Hong-Kyu Kim have contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea

    Su Min Ha & Jung Min Chang

  2. Department of Surgery, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea

    Hong-Kyu Kim, Yumi Kim, Dong-Young Noh & Wonshik Han

  3. Department of Surgery, CHA University Gangnam Medical Center, Seoul, Republic of Korea

    Yumi Kim & Dong-Young Noh

  4. Cancer Research Institute, Seoul National University, Seoul, Korea

    Wonshik Han

Authors
  1. Su Min Ha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong-Kyu Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yumi Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dong-Young Noh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wonshik Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jung Min Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jung Min Chang.

Ethics declarations

Conflict of interest

Hong-Kyu Kim and Yumi Kim have unlisted stocks of Berits Inc. Dong-Young Noh report conflict of interest as he became CEO of Berits Inc. since March 2021. Wonshik Han reports being a member on the board of directors of and holding stock and ownership interests at DCGen, Co., Ltd., not relevant to this study. Su Min Ha and Jung Min Chang have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

This retrospective study was approved by the institutional review board, and the informed consent requirement was waived.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

10549_2022_6527_MOESM1_ESM.jpg

Supplementary Fig. 1: Receiver Operating Characteristic curve for Mastocheck®, breast ultrasound, and Mastocheck® combined with breast ultrasound in 63 women with negative/benign mammography. The AUC of breast US (solid line) is 0.70 (95% confidence interval [CI]: 0.62, 0.77) and Mastocheck® (dashed line) is 0.67 (95% CI: 0.54, 0.81). AUC is increased to 0.84 (95% CI: 0.77, 0.91) (P < .001) by addition of Mastocheck® to breast ultrasound (dot-dashed line). (JPG 2309 KB)

10549_2022_6527_MOESM2_ESM.jpg

Supplementary Fig. 2: Receiver Operating Characteristic curve for Mastocheck®, breast ultrasound, and Mastocheck® combined with breast ultrasound in 135 asymptomatic women. The AUC of breast ultrasound (solid line) is 0.66 (95% confidence interval [CI]: 0.60, 0.73) and Mastocheck® (dashed line) is 0.75 (95% CI: 0.68, 0.82). AUC is increased to 0.81 (95% CI: 0.74, 0.88) (P <. 001) by addition of Mastocheck® to breast ultrasound (dot-dashed line). (JPG 2331 KB)

10549_2022_6527_MOESM3_ESM.jpg

Supplementary Fig. 3: Receiver Operating Characteristic curve for Mastocheck®, breast ultrasound, and Mastocheck® combined with breast ultrasound in 57 asymptomatic women with negative/benign mammography. The AUC of breast ultrasound (solid line) is 0.70 (95% confidence interval [CI]: 0.63, 0.78) and Mastocheck® (dashed line) is 0.70 (95% CI: 0.56, 0.85). AUC is increased to 0.85 (95% CI: 0.77, 0.92) (P < .001) by addition of Mastocheck® to breast ultrasound (dot-dashed line). (JPG 2332 KB)

Appendices

Appendix

Material and methods

Preparation of blood samples

Plasma samples were drawn from peripheral veins and were stored in tubes containing ethylene diaminetetra-acetic acid (EDTA) to prevent coagulation. Samples were transferred to the laboratory and underwent centrifugation at 1300×g for 10 min at 4 °C. The supernatant plasma was filtered through a cellulose acetate filter (0.2 μm pore site) and platelet-free plasma was stored at − 80 °C. For mass spectrometry (MS), plasma protein concentration was determined by the Bradford assay. The plasma protein samples were denatured by incubation in 50 mM Tris buffer (pH 8.0) containing 3 M urea at 37 °C for 30 min. Samples were reduced with 10 mM dithiothreitol for 1 h at 56 °C, treated with 60 mM iodoacetamide for 1 h at room temperature in the dark, and then diluted tenfold with 50 mM ammonium bicarbonate. Digestion was performed with sequencing-grade trypsin (Promega, Madison, WI, USA) at 37 °C overnight at protein: trypsin molar ratio of 50:1. Tryptic digests were desalted using a C18 SPE cartridge (Waters, Milford, MA, USA) and dried in vacuo. The dried samples were dissolved in 0.1% formic acid. One hundred femtomoles of a betagalactosidase (β-Gal) peptide (residues 954–962, GDFQFNISR) was added to the desalted peptide mixture.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ha, S.M., Kim, HK., Kim, Y. et al. Diagnostic performance improvement with combined use of proteomics biomarker assay and breast ultrasound. Breast Cancer Res Treat 192, 541–552 (2022). https://doi.org/10.1007/s10549-022-06527-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10549-022-06527-1

Keywords

Search

Navigation