Development of a microRNA Panel for Classification of Abnormal Mammograms for Breast Cancer

被引:14
|
作者
Zou, Ruiyang [1 ]
Loke, Sau Yeen [2 ,3 ]
Tan, Veronique Kiak-Mien [4 ,5 ,6 ]
Quek, Swee Tian [7 ,8 ]
Jagmohan, Pooja [7 ,8 ]
Tang, Yew Chung [1 ]
Madhukumar, Preetha [3 ,4 ,5 ]
Tan, Benita Kiat-Tee [3 ,4 ,5 ,6 ,9 ]
Yong, Wei Sean [3 ,4 ,5 ,6 ]
Sim, Yirong [3 ,4 ,6 ]
Lim, Sue Zann [3 ,4 ,5 ,6 ]
Png, Eunice [10 ]
Lee, Shu Yun Sherylyn [11 ]
Chan, Mun Yew Patrick [11 ]
Ho, Teng Swan Juliana [3 ,12 ]
Khoo, Boon Kheng James [3 ,12 ]
Wong, Su Lin Jill [12 ]
Thng, Choon Hua [3 ,12 ]
Chong, Bee Kiang [13 ]
Teo, Yik Ying [14 ,15 ]
Too, Heng-Phon [16 ]
Hartman, Mikael [14 ,15 ,17 ]
Tan, Ngiap Chuan [10 ,18 ]
Tan, Ern Yu [11 ]
Lee, Soo Chin [15 ,19 ]
Zhou, Lihan [1 ]
Lee, Ann Siew Gek [2 ,3 ,20 ]
机构
[1] MiRXES Lab, Dept Res & Dev, Singapore 138623, Singapore
[2] Natl Canc Ctr Singapore, Humphrey Oei Inst Canc Res, Cellular & Mol Res, Singapore 169610, Singapore
[3] Duke NUS Med Sch, SingHlth Duke NUS Oncol Acad Clin Programme, Singapore 169857, Singapore
[4] Natl Canc Ctr Singapore, Div Surg & Surg Oncol, Singapore 169610, Singapore
[5] Singapore Gen Hosp, Dept Breast Surg, Singapore 169608, Singapore
[6] SingHlth Duke NUS Breast Ctr, Singapore 169610, Singapore
[7] Natl Univ Singapore, Natl Univ Hosp, Dept Diagnost Imaging, Singapore 119228, Singapore
[8] Natl Univ Singapore, Yong Loo Lin Sch Med, Singapore 119228, Singapore
[9] Sengkang Gen Hosp, Dept Gen Surg, Singapore 544886, Singapore
[10] SingHlth Polyclin, Singapore 150167, Singapore
[11] Tan Tock Seng Hosp, Dept Gen Surg, Singapore 308433, Singapore
[12] Natl Canc Ctr Singapore, Div Oncol Imaging, Singapore 169610, Singapore
[13] Tan Tock Seng Hosp, Dept Diagnost Radiol, Singapore 308433, Singapore
[14] Natl Univ Singapore, Saw Swee Hock Sch Publ Hlth, Singapore 117549, Singapore
[15] Natl Univ Hlth Syst, Singapore 117549, Singapore
[16] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Biochem, Singapore 119077, Singapore
[17] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Surg, Singapore 119228, Singapore
[18] Duke NUS Med Sch, SingHlth Duke NUS Family Med Acad Clin Programme, Singapore 169857, Singapore
[19] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Hematol Oncol, Singapore 119228, Singapore
[20] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Physiol, Singapore 117593, Singapore
基金
英国医学研究理事会;
关键词
breast cancer; abnormal mammograms; circulating microRNAs; biomarkers; qRT-PCR; CIRCULATING MICRORNAS; SERUM MICRORNA; IDENTIFICATION; BIOMARKER; BLOOD; SIGNATURES; MIRNAS;
D O I
10.3390/cancers13092130
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Simple Summary Breast cancer screening by mammography suffers from high rates of false positivity, resulting in unnecessary investigative imaging and biopsies. There is an unmet need for biomarkers that can distinguish between malignant and benign breast lesions. We performed miRNA profiling on 638 patients with abnormal mammograms and 100 healthy controls. A six-miRNA panel was identified and validated in an independent cohort that had an AUC of 0.881 when differentiating between cases versus those with benign lesions or healthy individuals with normal mammograms. In addition, biomarker panel scores increased with tumor size, stage and number of lymph nodes involved. This study demonstrates that circulating miRNAs can potentially be used in conjunction with mammography to differentiate between patients with malignant and benign breast lesions. Mammography is extensively used for breast cancer screening but has high false-positive rates. Here, prospectively collected blood samples were used to identify circulating microRNA (miRNA) biomarkers to discriminate between malignant and benign breast lesions among women with abnormal mammograms. The Discovery cohort comprised 72 patients with breast cancer and 197 patients with benign breast lesions, while the Validation cohort had 73 and 196 cancer and benign cases, respectively. Absolute expression levels of 324 miRNAs were determined using RT-qPCR. miRNA biomarker panels were identified by: (1) determining differential expression between malignant and benign breast lesions, (2) focusing on top differentially expressed miRNAs, and (3) building panels from an unbiased search among all expressed miRNAs. Two-fold cross-validation incorporating a feature selection algorithm and logistic regression was performed. A six-miRNA biomarker panel identified by the third strategy, had an area under the curve (AUC) of 0.785 and 0.774 in the Discovery and Validation cohorts, respectively, and an AUC of 0.881 when differentiating between cases versus those with benign lesions or healthy individuals with normal mammograms. Biomarker panel scores increased with tumor size, stage and number of lymph nodes involved. Our work demonstrates that circulating miRNA signatures can potentially be used with mammography to differentiate between patients with malignant and benign breast lesions.
引用
收藏
页数:12
相关论文
共 50 条
  • [21] Study on cascade classification in abnormal shadow detection for mammograms
    Nemoto, Mitsutaka
    Shimizu, Akinobu
    Kobatake, Hidefumi
    Takeo, Hideya
    Nawano, Shigeru
    DIGITAL MAMMOGRAPHY, PROCEEDINGS, 2006, 4046 : 324 - 331
  • [22] Plasma MicroRNA Panel for Minimally Invasive Detection of Breast Cancer
    Cuk, Katarina
    Zucknick, Manuela
    Madhavan, Dharanija
    Schott, Sarah
    Golatta, Michael
    Heil, Joerg
    Marme, Frederik
    Turchinovich, Andrey
    Sinn, Peter
    Sohn, Christof
    Junkermann, Hans
    Schneeweiss, Andreas
    Burwinkel, Barbara
    PLOS ONE, 2013, 8 (10):
  • [23] Abnormal axillary lymph nodes on negative mammograms: causes other than breast cancer
    Gorkem, Sureyya Burcu
    O'Connell, Avice M.
    DIAGNOSTIC AND INTERVENTIONAL RADIOLOGY, 2012, 18 (05) : 473 - 479
  • [24] Rapid Point-Of-Care Breath Test for Biomarkers of Breast Cancer and Abnormal Mammograms
    Phillips, Michael
    Beatty, J. David
    Cataneo, Renee N.
    Huston, Jan
    Kaplan, Peter D.
    Lalisang, Roy I.
    Lambin, Philippe
    Lobbes, Marc B. I.
    Mundada, Mayur
    Pappas, Nadine
    Patel, Urvish
    PLOS ONE, 2014, 9 (03):
  • [25] Breast cancer: Classification of suspicious regions in digital mammograms based on capsule network
    Soulami, Khaoula Belhaj
    Kaabouch, Naima
    Saidi, Mohamed Nabil
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2022, 76
  • [26] Automated classification of breast lesions on digital mammograms
    Nishikawa, RM
    Giger, ML
    Jiang, Y
    Huo, Z
    Doi, K
    Schmidt, RA
    Wolverton, DE
    Vyborny, CJ
    CAR '97 - COMPUTER ASSISTED RADIOLOGY AND SURGERY, 1997, 1134 : 347 - 351
  • [27] Quantitative classification of breast tumors in digitized mammograms
    Pohlman, S
    Powell, KA
    Obuchowski, NA
    Chilcote, WA
    GrundfestBroniatowski, S
    MEDICAL PHYSICS, 1996, 23 (08) : 1337 - 1345
  • [28] CLASSIFICATION OF BREAST TISSUE DENSITY IN DIGITAL MAMMOGRAMS
    Devi, S. Sathiya
    Vidivelli, S.
    2017 INTERNATIONAL CONFERENCE ON INNOVATIONS IN INFORMATION, EMBEDDED AND COMMUNICATION SYSTEMS (ICIIECS), 2017,
  • [29] Classification of benign and malignant masses in breast mammograms
    Serifovic-Trbalic, A.
    Trbalic, A.
    Demirovic, D.
    Prljaca, N.
    Cattin, P. C.
    2014 37TH INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO), 2014, : 228 - 233
  • [30] A neural network method for normal/abnormal classification of digitized mammograms
    Mini, MG
    Thomas, T
    PROCEEDINGS OF THE SECOND IASTED INTERNATIONAL CONFERENCE ON NEURAL NETWORKS AND COMPUTATIONAL INTELLIGENCE, 2004, : 238 - 243