A Predictor of Early Disease Recurrence in Patients With Breast Cancer Using a Cell-free RNA and Protein Liquid Biopsy

被引:11
作者
Lasham, Annette [1 ,2 ]
Fitzgerald, Sandra J. [1 ]
Knowlton, Nicholas [1 ,2 ]
Robb, Tamsin [1 ]
Tsai, Peter [1 ,2 ]
Black, Michael A. [2 ,3 ]
Williams, Liam [4 ]
Mehta, Sunali Y. [2 ,3 ]
Harris, Gavin [1 ,5 ]
Shelling, Andrew N. [6 ]
Blenkiron, Cherie [1 ,2 ,6 ]
Print, Cristin G. [1 ,2 ]
机构
[1] Univ Auckland, Sch Med Sci, Dept Mol Med & Pathol, POB 50, Auckland 1142, New Zealand
[2] Univ Auckland, Maurice Wilkins Ctr, Auckland, New Zealand
[3] Univ Otago, Dunedin, New Zealand
[4] Univ Auckland, Auckland Genom, Auckland, New Zealand
[5] Canterbury Hlth Labs, Christchurch, New Zealand
[6] Univ Auckland, Fac Med & Hlth Sci, Dept Obstet & Gynaecol, Auckland, New Zealand
关键词
Breast Cancer; CA; 15-3; miR-923; Plasma Biomarkers; Prognosis; DROPLET-DIGITAL PCR; CIRCULATING MIRNAS; PROGNOSTIC VALUE; SURVIVAL; DECISIONS; THERAPY; MARKERS; CA-15.3; WOMEN; CEA;
D O I
10.1016/j.clbc.2019.07.003
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
To assist clinicians with treatment decisions for patients with breast cancer, we investigated whether the preoperative plasma levels of RNAs and the protein cancer antigen 15-3 were associated with prognosis. We identified a novel biomarker, microRNA-923, and combined this with cancer antigen 15-3 and clinicopathological features to build a multivariable model predictive of prognosis, irrespective of treatment, in 253 patients with breast cancer. Introduction: Circulating biomarkers have been increasingly used in the clinical management of breast cancer. The present study evaluated whether RNAs and a protein present in the plasma of patients with breast cancer might have utility as prognostic biomarkers complementary to existing clinical tests. Patients and Methods: We performed microarray profiling of small noncoding RNAs in plasma samples from 30 patients with breast cancer and 10 control individuals. Two small noncoding RNAs, including microRNA (miR)-923, were selected and quantified in plasma samples from an evaluation cohort of 253 patients with breast cancer, using droplet digital polymerase chain reaction. We also measured cancer antigen (CA) 15-3 protein levels in these samples. Cox regression survival analysis was used to determine which markers were associated with patient prognosis. Results: As independent markers of prognosis, the plasma levels of miR-923 and CA 15-3 at the time of surgery for breast cancer were significantly associated with prognosis, irrespective of treatment (Cox proportional hazards, P = 3.9 x 10(-3) and 1.9 x 10(-9), respectively). After building a multivariable model with standard clinical and pathological features, the addition of miR923 and CA 15-3 information into the model resulted in a significantly better predictor of disease recurrence in patients, irrespective of treatment, compared with the use of clinicopathological data alone (area under the curve at 3 years, 0.858 vs. 0.770 with clinicopathological markers only; P = .017). Conclusion: We propose that the plasma levels of miR-923 and CA 15-3, combined with standard clinicopathological predictors, could be used as a preoperative, noninvasive estimate of patient prognosis to identify which women might need more aggressive treatment or closer surveillance after surgery for breast cancer. (C) 2019 Elsevier Inc. All rights reserved.
引用
收藏
页码:108 / 116
页数:9
相关论文
共 57 条
[41]   Prognostic and predictive biomarkers in breast cancer: Past, present and future [J].
Nicolini, Andrea ;
Ferrari, Paola ;
Duffy, Michael J. .
SEMINARS IN CANCER BIOLOGY, 2018, 52 :56-73
[42]   Circulating microRNAs in the early prediction of disease recurrence in primary breast cancer [J].
Papadaki, Chara ;
Stratigos, Michalis ;
Markakis, Georgios ;
Spiliotaki, Maria ;
Mastrostamatis, Georgios ;
Nikolaou, Christoforos ;
Mavroudis, Dimitrios ;
Agelaki, Sofia .
BREAST CANCER RESEARCH, 2018, 20
[43]   Preoperative CA 15-3 and CEA serum levels as predictor for breast cancer outcomes [J].
Park, B. -W. ;
Oh, J. -W. ;
Kim, J. -H. ;
Park, S. H. ;
Kim, K. -S. ;
Kim, J. H. ;
Lee, K. S. .
ANNALS OF ONCOLOGY, 2008, 19 (04) :675-681
[44]  
R Core Team, 2019, R: A language and environment for statistical computing
[45]  
Rasmy A, 2016, Asian Pac J Cancer Prev, V17, P3595
[46]   Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer [J].
Ravdin, PM ;
Siminoff, LA ;
Davis, GJ ;
Mercer, MB ;
Hewlett, J ;
Gerson, N ;
Parker, HL .
JOURNAL OF CLINICAL ONCOLOGY, 2001, 19 (04) :980-991
[47]   ESTIMATION OF REGRESSION-COEFFICIENTS WHEN SOME REGRESSORS ARE NOT ALWAYS OBSERVED [J].
ROBINS, JM ;
ROTNITZKY, A ;
ZHAO, LP .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1994, 89 (427) :846-866
[48]   A Serum MicroRNA Signature Predicts Tumor Relapse and Survival in Triple-Negative Breast Cancer Patients [J].
Sahlberg, Kristine Kleivi ;
Bottai, Giulia ;
Naume, Bjorn ;
Burwinkel, Barbara ;
Calin, George A. ;
Borresen-Dale, Anne-Lise ;
Santarpia, Libero .
CLINICAL CANCER RESEARCH, 2015, 21 (05) :1207-1214
[49]   Prognostic role of CA15.3 in 7942 patients with operable breast cancer [J].
Sandri, M. T. ;
Salvatici, M. ;
Botteri, E. ;
Passerini, R. ;
Zorzino, L. ;
Rotmensz, N. ;
Luini, A. ;
Mauro, C. ;
Bagnardi, V. ;
Cassatella, M. C. ;
Bottari, F. ;
Casadio, C. ;
Colleoni, M. .
BREAST CANCER RESEARCH AND TREATMENT, 2012, 132 (01) :317-326
[50]   On the validity of time-dependent AUC estimators [J].
Schmid, Matthias ;
Kestler, Hans A. ;
Potapov, Sergej .
BRIEFINGS IN BIOINFORMATICS, 2015, 16 (01) :153-168