Han-Byoel Lee1, Un-Beom Kang2, Hyeong-Gon Moon3, Jiwoo Lee4, Kyung-Min Lee4, Minju Yi4, Yong Sun Park2, Jong Won Lee5, Jong-Han Yu5, Seung Ho Choi6, Sang Heon Cho6, Cheolju Lee7, Wonshik Han3, Dong-Young Noh8. 1. Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea. 2. R&D Center, Biomedieng Co. Ltd., Gyeonggi-do, Republic of Korea. 3. Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea. 4. Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea. 5. Department of Surgery, Asan Medical Center, Ulsan University College of Medicine, Seoul, Republic of Korea. 6. Healthcare System Gangnam Center, Healthcare Research Institute, Seoul National University Hospital, Seoul, Republic of Korea. 7. Life Sciences Division, Korea Institute of Science and Technology, Seoul, Republic of Korea. 8. Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea dynoh@snu.ac.kr.
Abstract
AIM: We aimed to develop a plasma protein signature for breast cancer diagnosis by using multiple reaction monitoring (MRM)-based mass spectrometry. MATERIALS AND METHODS: Based on our previous studies, we selected 124 proteins for MRM. Plasma samples from 80 patients with breast cancer and 80 healthy women were used to develop a plasma proteomic signature by an MRM approach. The proteomic signature was then validated in plasma samples from 100 patients with breast cancer and 100 healthy women. RESULTS: A total of 56 proteins were optimized for MRM. In the verification cohort, 11 proteins exhibited significantly differential expression in plasma from patients with breast cancer. Three proteins (neural cell adhesion molecule L1-like protein, apolipoprotein C-1 and carbonic anhydrase-1) with highest statistical significance which gave consistent results for patients of stage I and II breast cancer were selected and a 3-protein signature was developed using binary logistic regression analysis [area under the curve (AUC)=0.851, sensitivity=80.6%]. The 3-protein signature showed similar performance in an independent validation cohort with an AUC of 0.797 and sensitivity of 77.2% for detection of stage I and II breast cancer. CONCLUSION: We developed a distinct plasma protein signature for breast cancer diagnosis based on an MRM-based approach, and the clinical value of the 3-protein signature was validated in an independent cohort. Copyright
AIM: We aimed to develop a plasma protein signature for breast cancer diagnosis by using multiple reaction monitoring (MRM)-based mass spectrometry. MATERIALS AND METHODS: Based on our previous studies, we selected 124 proteins for MRM. Plasma samples from 80 patients with breast cancer and 80 healthy women were used to develop a plasma proteomic signature by an MRM approach. The proteomic signature was then validated in plasma samples from 100 patients with breast cancer and 100 healthy women. RESULTS: A total of 56 proteins were optimized for MRM. In the verification cohort, 11 proteins exhibited significantly differential expression in plasma from patients with breast cancer. Three proteins (neural cell adhesion molecule L1-like protein, apolipoprotein C-1 and carbonic anhydrase-1) with highest statistical significance which gave consistent results for patients of stage I and II breast cancer were selected and a 3-protein signature was developed using binary logistic regression analysis [area under the curve (AUC)=0.851, sensitivity=80.6%]. The 3-protein signature showed similar performance in an independent validation cohort with an AUC of 0.797 and sensitivity of 77.2% for detection of stage I and II breast cancer. CONCLUSION: We developed a distinct plasma protein signature for breast cancer diagnosis based on an MRM-based approach, and the clinical value of the 3-protein signature was validated in an independent cohort. Copyright
Authors: Su Min Ha; Hong-Kyu Kim; Yumi Kim; Dong-Young Noh; Wonshik Han; Jung Min Chang Journal: Breast Cancer Res Treat Date: 2022-01-27 Impact factor: 4.872
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Authors: Anna Kazarian; Oleg Blyuss; Gergana Metodieva; Aleksandra Gentry-Maharaj; Andy Ryan; Elena M Kiseleva; Olga M Prytomanova; Ian J Jacobs; Martin Widschwendter; Usha Menon; John F Timms Journal: Br J Cancer Date: 2017-01-12 Impact factor: 7.640