- Research
- Open Access
A two-step strategy for identification of plasma protein biomarkers for endometrial and ovarian cancer
- Stefan Enroth†1,
- Malin Berggrund†1,
- Maria Lycke4,
- Martin Lundberg2,
- Erika Assarsson2,
- Matts Olovsson3,
- Karin Stålberg3,
- Karin Sundfeldt†4 and
- Ulf Gyllensten†1Email authorView ORCID ID profile
- Received: 24 May 2018
- Accepted: 22 November 2018
- Published: 1 December 2018
Abstract
Background
Over 500,000 women worldwide are diagnosed with ovarian or endometrial cancer each year. We have used a two-step strategy to identify plasma proteins that could be used to improve the diagnosis of women with an indication of gynecologic tumor and in population screening.
Methods
In the discovery step we screened 441 proteins in plasma using the proximity extension assay (PEA) and five Olink Multiplex assays (CVD II, CVD III, INF I, ONC II, NEU I) in women with ovarian cancer (n = 106), endometrial cancer (n = 74), benign ovarian tumors (n = 150) and healthy population controls (n = 399). Based on the discovery analyses a set of 27 proteins were selected and two focused multiplex PEA assays were developed. In a replication step the focused assays were used to study an independent set of cases with ovarian cancer (n = 280), endometrial cancer (n = 228), women with benign ovarian tumors (n = 76) and healthy controls (n = 57).
Results
In the discovery step, 27 proteins that showed an association to cancer status were identified. In the replication analyses, the focused assays distinguished benign tumors from ovarian cancer stage III–IV with a sensitivity of 0.88 and specificity of 0.92 (AUC = 0.92). The assays had a significantly higher AUC for distinguishing benign tumors from late stage ovarian cancer than using CA125 and HE4 (p = 9.56e−22). Also, population controls could be distinguished from ovarian cancer stage III–IV with a sensitivity of 0.85 and a specificity of 0.92 (AUC = 0.89).
Conclusion
The PEA assays represent useful tools for identification of new biomarkers for gynecologic cancers. The selected protein assays could be used to distinguish benign tumors from ovarian and endometrial cancer in women diagnosed with an unknown suspicious pelvic mass. The panels could also be used in population screening, for identification of women in need of specialized gynecologic transvaginal ultrasound examination.
Funding
The Swedish Cancer Foundation, Vinnova (SWELIFE), The Foundation for Strategic Research (SSF), Assar Gabrielsson Foundation.
Keywords
- Ovarian cancer
- Endometrial cancer
- Proximity extension assay (PEA)
- Sensitivity
- Specificity
- Diagnostics
Introduction
In 2012 more than 500,000 women worldwide were diagnosed with epithelial ovarian cancer (OC) or endometrial cancer (EC) [1]. OC is the most lethal gynecologic malignancy, with 238,719 cases reported worldwide in 2012, corresponding to 3.4% of all cancer [1].
OC is an heterogenous disease with at least five sub-types. The biomarker CA125 can detect the most common late stage high-grade serous cancer, but lack diagnostic power for early stage and the less common ovarian adenocarcinomas, especially mucinous cancer. Also, CA125 often result in false positive results in inflammatory diseases such as endometriosis and is not regarded appropriate for fertile women, i.e. those aged 50 or below. The risk of ovarian malignancy algorithm (ROMA) is based on CA125, HE4 and menopausal status to assign women with adnexal ovarian mass into high-risk and low-risk groups. Cut-off for ROMA was estimated at a set specificity of 0.75 and has a sensitivity of 0.94 [2]. In the hands of specialists, transvaginal ultrasound (TVU) assessment can out-perform ROMA, but these specialized units are very scarce, while a serum test can be easily performed. The multivariate index assay Overa®, based on five plasma proteins, can distinguish between benign tumors and OC with a sensitivity of 0.69 and specificity of 0.91 [3]. Additional plasma biomarkers have been described but not yet clinically evaluated [4, 5]. The Risk of Ovarian Cancer Algorithm (ROCA) estimates the changes in annual CA125 measurements to identify women with high-risk scores in a screening population and refer these to specialized units for TVU examination. Use of ROCA followed by TVU has been shown to result in an increase in the number of women with OCs detected than using of a fixed cutoff for CA125, with half of these women detected by ROCA prior to CA125 > 35 and the other half at the same time as CA125 > 35 [6]. The United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) has reported a reduction in ovarian cancer deaths, excluding prevalent cases, using annual screening using ROCA followed by TVU for ROCA positive subjects [7].
EC is the most frequent gynecologic malignancy in the developed world and in 2012 affected 319,605 women, corresponding to 7.1% of all cancer [1]. EC is diagnosed in early stage and have high 5-year survival due to early typical symptoms, such as post-menopause bleedings. Women with EC go through hysterectomy, bilateral salpingoophorectomy and after risk stratification, pelvic lymph node resection. Imaging techniques like CT, MRI and PET have shown variable performance in predicting the depth of EC, myometrial growth, cervical invasion and lymph node metastases [8]. The histopathology does not reliably reflect the underlying molecular nature of the tumors and more than 20% of tumors that were first classified as non-aggressive later develop into metastatic cancer [9]. Candidate plasma protein biomarkers have been described for EC, but in clinical practice preoperative biomarkers are lacking to stratify EC patients for lymph node resection, and to high-risk and low-risk groups for recurrence [10, 11].
For OC there is a strong need to identify proteins for differential diagnosis of suspicious ovarian tumors, early stage detection and sub-type specific diagnosis as complements to HE4 and CA125, in order to refer women to specific imaging techniques, reduce overtreatment and identify women with ovarian malignancy. For EC, biomarkers are needed for stratification of patients to different surgical interventions. In this study, we have used the proximity extension assay (PEA) to identify plasma proteins with a higher sensitivity and specificity that could be used to address the diagnostic needs for the two cancer types.
Materials and methods
Clinical material and ethics approval
Baseline information for women included in the discovery and replication steps
Discovery | Replication | ||||||||
---|---|---|---|---|---|---|---|---|---|
Benign tumors cohort I | Ovarian cancer cohort I | Endometrial cancer cohort I | Population controls I | Benign tumors cohort II | Ovarian cancer cohort II | Ovarian cancer cohort III | Endometrial cancer cohort | Population controls II | |
Number of women | 150 (SAL1) | 106 (SAL1) | 74 (SU1) | 399 (NSPHS) | 76 (SU2) | 160 (SU2) | 120 (UU) | 228 (UU) | 57 (UU) |
Age | |||||||||
Mean | 59.8 | 61.2 | 59.84 | 49.3 | 60.8 | 61.8 | 60.9 | 66.8 | 57.3 |
Median | 60 | 60 | 60 | 49 | 63 | 63 | 62 | 68 | 58 |
Range | 16–88 | 28–88 | 29–86 | 14–94 | 22–88 | 19–87 | 21–86 | 29–90 | 18–86 |
SD | 15.8 | 12.9 | 10.75974 | 20.3 | 14.5 | 11.9 | 12.9 | 10.5 | 14.2 |
P-value versus benign | NA | 0.870 | 0.003665 | 2.22E−08 | NA | 0.868 | 0.937 | 0.001 | 0.163 |
Ethnicity | Caucasian | Caucasian | Caucasian | Caucasian | Caucasian | Caucasian | Caucasian | Caucasian | Caucasian |
Stage | |||||||||
I (I/IA/IB) | 46 | 50 (0/41/9) | 35 | 10 | 109 (3/83/23) | ||||
II (II/IIA/IIB) | 8 | 8 (8/0/0) | 9 | 3 | 13 (12/0/1) | ||||
III (III/IIIA/IIIB/IIIC/IIIC1/IIIC2) | 47 | 10 (0/2/2/0/2/4) | 96 | 40 | 22 (1/4/4/5/1/7) | ||||
IV (IV/IVA/IVB) | 5 | 6 (2/0/4) | 19 | 24 | 11 (5/0/6) | ||||
Classification unvailable | 1 | 43 | 73 |
Plasma protein measurements
The abundance of 441 unique protein in plasma were analyzed using the Olink Multiplex assays CVD II, CVD III, INF I, ONC II and NEU I (http://www.olink.com) and quantified by real-time PCR using the Fluidigm BioMark™ HD real-time PCR platform as described earlier [12]. Briefly, for each protein a unique pair of oligonucleotide-labeled antibody probes bind to the targeted protein, and if the two probes are in close proximity a PCR target sequence is formed by a proximity-dependent DNA polymerization event and the resulting sequence is subsequently detected and quantified using standard real-time PCR. Data is then normalized and transformed using internal extension controls and inter-plate controls, to adjust for intra- and inter-run variation as described earlier [12]. The final assay read-out is given in Normalized Protein eXpression (NPX), which is an arbitrary unit on log2-scale where a high value corresponds to a higher protein expression. Each PEA measurement has a lower detection limit (LOD) calculated based on negative controls that are included in each run, and measurements below LOD were removed from further analysis. All assay characteristics including detection limits and measurements of assay performance and validations are available from the manufacturers webpage (http://www.olink.com). The analyses were based on 1 μL of plasma for each panel of 92 assays. To avoid batch effects, samples from the different disease entities and cohorts, including benign tumors, were randomized across assay plates. Each plate included internal controls, as described previously used to adjust for technical variation and/or sample irregularities.
Development of focused multiplex PEA panels
The focused PEA assays were designed to be compatible with the Fluidigm 192 × 24 Integrated Fluidic Circuit (IFC) which can measure up to 24 assays on 192 samples simultaneously. Hence, 21 protein marker assays and three internal controls for run QC (Incubation Control, Extension Control and Detection control) can be analyzed per IFC. The main difference between the focused assays and the discovery assays is that the focused assays allow higher PEA probe concentration, which in turn means that higher levels of antigen can be measured without reaching the hook in the measuring range (i.e. too much antigen will decrease signals in a homogenous immunoassay format). This modification allowed inclusion of PEA assays from the CVD III panel, where samples are normally diluted 1:100 before analysis, into the focused assays used to analyze undiluted samples, making the differences in protein concentration to be measured more than 7 logs.
Statistical methods
All calculations were carried out in R version 3.2.3 (R core team) [13]. Individual protein levels were also normalized by plate and sampling round using the MDimNormn-package [14]. This was done separately for the discovery and replication cohorts.
Significance levels for comparison of protein levels between cases and controls in the discovery cohort were calculated using the two-sided rank-based Spearman test (Wilcoxon). From the entire discovery-cohort, the top-ranking proteins for each of the two cancers was identified based on the p value in the comparison of benign tumors to cancer samples. 15 proteins were selected for association with OC and 16 with EC out of which 4 overlapped. Lasso and Elastic-Net Regularized Generalized Linear Models (’glmnet’ package in R) was used to fit multivariate models for each cancer using the selected proteins. These models were then evaluated based on the best point and their specificity at a fixed sensitivity of 0.95. This was repeated for each of the tumor/control combination investigated.
A few proteins overlapped between the two cancers, and a total of 27 proteins were selected to be characterized in the replication cohort using focused PEA-panels.
Model performance was evaluated by randomly splitting the observations in the replication cohort into a training set (75%) and a test set (25%). A model was then built using the training set and with the same proteins as selected from the discovery-cohort. The multivariate models were retrained in the replication cohorts using the ‘lm’ function in R. The reason for re-building the model in the replication data set is that the scaling of the NPX-values can differ between the multiplex PEA panels. This model was then used to predict the response variable in the test set. The random-split into training and test set will often result in different performance due to the limited sample-size. To accommodate for this, a cross-validation schema was applied and the process was repeated 100 times and model prediction errors on the training and test set were recorded. Sensitivity and specificity from the replication were reported as mean ± 1 SD of the 100 runs.
Results
Discovery step analyses
The study design for identification of protein biomarkers using PEA
Distribution of differences in NPX values in the discovery step between benign tumors and OC (a–c) and between OC and EC (d). Only protein labels for the significant differences are shown
p-values for the individual proteins in the discovery and replication steps in the comparison of ovarian cancer to benign tumors, population controls
Uniprot no | Uniprot protein name | Proseek Protein ID | Cancer type | Ovarian cancer versus benign tumors | Ovarian cancer versus populations control | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Discovery | Replication | Discovery | Replication | ||||||||||
Stage I–IV | Stage I–II | Stage III–IV | Stage I–IV | Stage I–II | Stage III–IV | Versus ovarian cancer stage I–IV | Versus ovarian cancer stage I–II | Versus ovarian cancer stage III–IV | Versus ovarian cancer stage I–IV | ||||
Q16651 | Prostasin | CVD2_173_PRSS8 | OE | 1.08E−08 | 0.437395062 | 9.05E−11 | 3.95E−14 | 0.001859491 | 4.83E−16 | 0.028270178 | 1 | 1.29E−06 | 1.91E−05 |
P21741 | Midkine | ONC2_161_MK | OE | 2.85E−08 | 1 | 2.73E−13 | 2.60E−08 | 0.020068076 | 4.57E−09 | 0.007863784 | 1 | 6.50E−11 | 1.06E−06 |
Q14508 | Major epididymis-specific protein E4 | ONC2_182_WFDC2 | OE | 4.38E−14 | 0.012381315 | 5.28E−17 | 1.49E−21 | 7.55E−07 | 2.68E−23 | 2.11E−07 | 1 | 7.00E−14 | 2.35E−13 |
P22301 | Interleukin-10 | INF_162_IL-10 | OE | 1.64E−06 | 1 | 5.23E−10 | 0.479046977 | 1 | 0.097292101 | 1 | 0.00410586 | 0.529576635 | 1 |
P04179 | Superoxide dismutase [Mn], mitochondrial | CVD2_137_SOD2 | O | 0.00066448 | 1 | 0.000688683 | 2.07E−09 | 0.042029761 | 8.56E−11 | 1 | 1 | 0.25750062 | 1.00E−05 |
P09874 | Poly [ADP-ribose] polymerase 1 | CVD2_195_PARP-1 | O | 1.78E−07 | 1 | 2.19E−11 | 3.81E−14 | 0.00192284 | 3.50E−16 | 0.210991816 | 1 | 2.04E−07 | 0.001644308 |
Q9UBX7 | Kallikrein-11 | ONC2_121_hK11 | O | 3.83E−05 | 1 | 1.20E−07 | 3.72E−09 | 0.001466544 | 2.15E−09 | 0.011603143 | 1 | 3.43E−06 | 0.1190456 |
Q96NY8 | Nectin-4 | ONC2_141_PVRL4 | O | 1.50E−06 | 1 | 1.57E−09 | 2.23E−07 | 0.096598347 | 2.39E−08 | 0.258520796 | 1 | 1.18E−07 | 0.242568821 |
Q8WXI7 | Mucin-16 | ONC2_191_MUC-16 | O | 9.15E−23 | 2.37E−08 | 4.49E−21 | 1.20E−16 | 0.001302092 | 1.45E−19 | 6.59E−18 | 0.752014614 | 9.16E−25 | 3.73E−11 |
P15328 | Folate receptor alpha | ONC2_196_FR-alpha | O | 3.91E−10 | 0.115430697 | 3.90E−12 | 2.50E−18 | 0.004482048 | 3.56E−22 | 0.000243851 | 1 | 5.22E−09 | 1.14E−05 |
P22223 | Catenin beta-1 | NEU_150_CDH3 | O | 3.91E−05 | 0.484466735 | 7.72E−05 | 1 | 0.055334855 | 1 | 0.118136808 | 1 | 0.010350682 | 1 |
Q16288 | NT-3 growth factor receptor | NEU_190_NTRK3 | O | 0.030055578 | 1 | 0.039744561 | 2.31E−11 | 0.003961683 | 1.35E−12 | 1 | 1 | 1 | 0.015043749 |
P10145 | Interleukin-8 | INF_101_IL-8 | O | 8.17E−07 | 1 | 1.32E−09 | 9.27E−09 | 0.027613554 | 9.11E−10 | 0.027906542 | 1 | 3.41E−06 | 0.001205718 |
Q9P0M4 | Interleukin-17C | INF_114_IL-17C | O | 2.51E−05 | 0.043046188 | 0.000689734 | 1.07E−06 | 0.000623164 | 4.22E−06 | 1 | 1 | 1 | 0.044912595 |
P80511 | Protein S100-A12 | INF_175_EN-RAGE | O | 4.87E−08 | 0.000153701 | 7.40E−05 | 4.64E−11 | 4.81E−08 | 2.15E−09 | 0.342001882 | 1 | 1 | 0.026901128 |
p-values for the individual proteins in the discovery and replication steps in the comparison of endometrial cancer to benign tumors, population controls and ovarian cancer
Uniprot no | Uniprot protein name | Proseek protein ID | Cancer | Endometrial cancer versus benign cysts | Endometrial cancer versus ovarian cancer 1–4 | Endometrial cancer versus population controls | |||
---|---|---|---|---|---|---|---|---|---|
Discovery | Replication | Discovery | Replication | Discovery | Replication | ||||
Q16651 | Prostasin | CVD2_173_PRSS8 | OE | 4.36E−09 | 6.88E−08 | 1 | 3.22E−07 | 0.01381015 | 0.928202706 |
P21741 | Midkine | ONC2_161_MK | OE | 3.65E−08 | 0.119795419 | 1 | 8.20E−09 | 0.026477007 | 1 |
Q14508 | Major epididymis-specific protein E4 (WAP four-disulfide core domain protein 2) | ONC2_182_WFDC2 | OE | 6.28E−09 | 1.71E−10 | 1 | 2.83E−12 | 0.290677608 | 0.00019993 |
P22301 | Interleukin 10 | INF_162_IL-10 | OE | 4.23E−12 | 1 | 1 | 1 | 0.065101325 | 1 |
P35318 | Adenomedulin | CVD2_103_ADM | E | 2.48E−07 | 0.004662956 | 0.005277639 | 1 | 5.23E−05 | 0.608471686 |
P35475 | Al pha-L-iduronidase | CVD2_116_IDUA | E | 1.05E−05 | 0.954600936 | 6.83E−10 | 1 | 6.52E−08 | 0.443914788 |
P09237 | Matrilysin | CVD2_167_MMP-7 | E | 7.21E−06 | 0.000194133 | 1 | 0.112528602 | 0.001380847 | 0.000174466 |
P15090 | Fatty acid-binding protein, adipocyte | CVD3_129_FABP4 | E | 1.10E−06 | 1 | 0.01262521 | 1 | 0.000231174 | 0.023151067 |
Q8NBP7 | Proprotein convertase subtilisin/kexin type 9 | CVD3_161_PCSK9 | E | 1.43E−05 | 1 | 2.42E−05 | 1 | 0.002207816 | 1 |
Q01638 | Interleukin-1 receptor-like 1 | CVD3_176_ST2 | E | 1.35E−06 | 0.010291606 | 0.023539896 | 1 | 0.000249695 | 0.242686447 |
Q9UBR2 | Cathepsin Z | CVD3_185_CTSZ | E | 3.20E−07 | 1 | 3.59E−06 | 1 | 2.28E−06 | 1 |
015467 | C–C motif chemokine 16 | CVD3_196_CCL16 | E | 2.70E−06 | 1 | 0.48003849 | 0.006484386 | 0.005950521 | 1 |
Q9UBT3 | Dickkopf-related protein 4 | NEU_187_Dkk-4 | E | 2.60E−09 | 1 | 4.27E−05 | 0.252055749 | 0.000134061 | 0.022042978 |
P15692 | Vascular endothelial growth factor A | INF_102_VEGF-A | E | 0.00150827 | 1.88E−06 | 1 | 4.56E−05 | 0.156267791 | 1 |
P05231 | Interleukin-6 | INF_113_IL-6 | E | 0.000735048 | 1.41E−05 | 1 | 5.49E−06 | 1 | 1 |
P08581 | Hepatocyte growth factor receptor | INF_156_HGF | E | 6.46E−05 | 6.46E−05 | 6.46E−05 | 6.46E−05 | 6.46E−05 | 0.010198277 |
Estimates of sensitivity and specificity for the protein panel based on the discovery and replication data
Comparison | Proteins | Best point | AUC | Minimum specificity of 0.95 (screening) | Minimum sensitivity of 0.95 (diagnostic) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Sensitivity | Specificity | Sensitivity | Specificity | Sensitivity | Specificity | |||||||||
Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | |||
Analysis discovery | ||||||||||||||
Benign tumors versus ovarian cancer stage I–II | All | 0.68 | 0.07 | 0.73 | 0.08 | 0.72 | 0.33 | 0.11 | 0.96 | 0.01 | 0.96 | 0.01 | 0.07 | 0.08 |
Benign tumors versus ovarian cancer stage III–IV | All | 0.93 | 0.04 | 0.93 | 0.03 | 0.95 | 0.86 | 0.07 | 0.96 | 0.01 | 0.96 | 0.00 | 0.54 | 0.25 |
Benign tumors versus ovarian cancer stage I–IV | All | 0.79 | 0.04 | 0.85 | 0.04 | 0.86 | 0.61 | 0.07 | 0.96 | 0.01 | 0.96 | 0.01 | 0.18 | 0.10 |
Population ontrols versus ovarian cancer stage I–II | All | 0.70 | 0.07 | 0.75 | 0.07 | 0.74 | 0.43 | 0.07 | 0.96 | 0.01 | 0.97 | 0.01 | 0.02 | 0.05 |
Population controls versus ovarian cancer stage III–IV | All | 0.94 | 0.03 | 0.96 | 0.03 | 0.97 | 0.92 | 0.05 | 0.96 | 0.01 | 0.97 | 0.01 | 0.47 | 0.29 |
Population controls versus ovarian stage I–IV | All | 0.75 | 0.05 | 0.87 | 0.05 | 0.86 | 0.66 | 0.06 | 0.95 | 0.00 | 0.96 | 0.01 | 0.13 | 0.10 |
Benign tumors versus endometrial cancer | All | 0.77 | 0.05 | 0.79 | 0.05 | 0.83 | 0.45 | 0.11 | 0.96 | 0.01 | 0.97 | 0.01 | 0.09 | 0.11 |
Population controls versus endometrial cancer | All | 0.69 | 0.06 | 0.78 | 0.06 | 0.76 | 0.43 | 0.07 | 0.95 | 0.00 | 0.97 | 0.01 | 0.07 | 0.07 |
Ovarian cancer versus endometrial cancer | All | 0.84 | 0.04 | 0.88 | 0.04 | 0.89 | 0.64 | 0.17 | 0.97 | 0.01 | 0.96 | 0.01 | 0.42 | 0.16 |
Replication | ||||||||||||||
Benign tumors versus ovarian cancer stage I–II | All | 0.73 | 0.08 | 0.74 | 0.07 | 0.76 | 0.29 | 0.11 | 0.97 | 0.01 | 0.97 | 0.01 | 0.19 | 0.14 |
Benign tumors versus ovarian cancer stage III–IV | All | 0.88 | 0.03 | 0.92 | 0.03 | 0.92 | 0.81 | 0.07 | 0.96 | 0.01 | 0.96 | 0.01 | 0.58 | 0.15 |
Benign tumors versus ovarian cancer stage I–IV | All | 0.83 | 0.04 | 0.87 | 0.04 | 0.89 | 0.68 | 0.10 | 0.97 | 0.01 | 0.96 | 0.01 | 0.48 | 0.12 |
Population ontrols versus ovarian cancer stage I–II | All | 0.62 | 0.09 | 0.68 | 0.11 | 0.63 | 0.24 | 0.12 | 0.97 | 0.01 | 0.97 | 0.01 | 0.06 | 0.10 |
Population controls versus ovarian cancer stage III–IV | All | 0.85 | 0.03 | 0.92 | 0.04 | 0.89 | 0.78 | 0.09 | 0.97 | 0.01 | 0.96 | 0.01 | 0.35 | 0.15 |
Population controls versus ovarian stage I–IV | All | 0.77 | 0.05 | 0.85 | 0.05 | 0.86 | 0.62 | 0.10 | 0.96 | 0.01 | 0.96 | 0.01 | 0.21 | 0.11 |
Benign tumors versus endometrial cancer | All | 0.70 | 0.06 | 0.67 | 0.07 | 0.72 | 0.22 | 0.09 | 0.97 | 0.01 | 0.96 | 0.01 | 0.25 | 0.08 |
Population controls versus endometrial cancer | All | 0.64 | 0.07 | 0.72 | 0.08 | 0.71 | 0.24 | 0.14 | 0.97 | 0.01 | 0.96 | 0.01 | 0.10 | 0.06 |
Ovarian cancer versus endometrial cancer | All | 0.73 | 0.03 | 0.77 | 0.04 | 0.78 | 0.36 | 0.06 | 0.96 | 0.00 | 0.96 | 0.00 | 0.11 | 0.06 |
Benign tumors versus ovarian cancer stage I–II | CA125/HE4 | 0.73 | 0.08 | 0.73 | 0.07 | 0.78 | 0.38 | 0.11 | 0.97 | 0.01 | 0.97 | 0.01 | 0.30 | 0.16 |
Benign tumors versus ovarian cancer stage III–IV | CA125/HE4 | 0.87 | 0.03 | 0.87 | 0.04 | 0.90 | 0.74 | 0.06 | 0.97 | 0.02 | 0.96 | 0.01 | 0.37 | 0.12 |
Benign tumors versus ovarian cancer stage I–IV | CA125/HE4 | 0.79 | 0.03 | 0.87 | 0.04 | 0.87 | 0.67 | 0.06 | 0.97 | 0.01 | 0.96 | 0.01 | 0.39 | 0.10 |
Population ontrols versus ovarian cancer stage I–II | CA125/HE4 | 0.59 | 0.08 | 0.77 | 0.10 | 0.66 | 0.35 | 0.11 | 0.97 | 0.01 | 0.97 | 0.01 | 0.14 | 0.07 |
Population controls versus ovarian cancer stage III–IV | CA125/HE4 | 0.84 | 0.03 | 0.89 | 0.04 | 0.86 | 0.74 | 0.07 | 0.97 | 0.01 | 0.96 | 0.00 | 0.17 | 0.09 |
Population controls versus ovarian stage I–IV | CA125/HE4 | 0.77 | 0.03 | 0.88 | 0.05 | 0.81 | 0.65 | 0.08 | 0.96 | 0.00 | 0.96 | 0.01 | 0.14 | 0.07 |
Reporter operator characteristic (ROC) for combinations of proteins in the discovery step. a Benign tumors versus Ovarian cancer stage I–II. b Benign tumors versus Ovarian cancer stage III–IV. c Benign tumors versus Ovarian cancer stage I–IV. d Population controls versus Ovarian cancer stage I–II. e Population controls versus Ovarian cancer stage III–IV. f Population controls versus Ovarian cancer stage I–IV. g Benign tumors versus Endometrial cancer. h Controls versus Endometrial cancer. i Ovarian cancer stage I-IV versus endometrial cancer versus
The 15-protein panel was also able to distinguish population controls from OC stage I–IV with a sensitivity = 0.75 and specificity = 0.87 (AUC = 0.86), from OC stage I–II with a sensitivity = 0.70 and specificity = 0.75 (AUC = 0.74) and from OC stage III–IV with a sensitivity = 0.94 and specificity = 0.96 (AUC = 0.97) (Table 4). At a specificity of 0.96, as a cutoff value for the biomarker panel to be useful in population screening, the sensitivity to distinguish controls from OC stage I–IV was 0.66, from OC stage I–II it was 0.43 and from OC stage III–IV it was 0.92 (Table 4).
Correlation between CA125 values from the multiplex PEA used in the discovery step and from clinical ELISA, for benign tumors (in black) and ovarian cancer patients (in red)
For EC the 16-protein panel distinguished benign tumors from EC with a sensitivity = 0.77 and specificity = 0.79 (AUC = 0.83). OC stage I–IV could be distinguished from EC with a sensitivity of 0.84 and specificity of 0.88 (AUC = 0.89). Finally, the EC patients could be distinguished from population controls with a sensitivity = 0.69 and specificity = 0.78 (AUC = 0.76) (Table 4).
Replication step
Correlation in protein abundance between the 92-plex panels and the focused protein panels for the six proteins a CA125, b IL10, c ENRAGE, d HE4, e MK, f Dkk4
Reporter operator characteristic (ROC) for combinations of proteins in replication stage. The blue line is using CA125 and HE4 only. a Benign tumors versus Ovarian cancer stage I–II. b Benign tumors versus Ovarian cancer stage III–IV. c Benign tumors versus Ovarian cancer stage I–IV. d Population controls versus Ovarian cancer stage I–II. e Population controls versus Ovarian cancer stage III–IV. f Population controls versus Ovarian cancer stage I–IV. g Benign tumors versus Endometrial cancer. h Controls versus Endometrial cancer. i Ovarian cancer stage I–IV versus endometrial cancer versus
The selected protein panel distinguished population controls from OC stage I–IV with a sensitivity = 0.77 and specificity = 0.85 (AUC = 0.86), from OC stage I–II with a sensitivity = 0.62 and specificity = 0.68 (AUC = 0.63), and from OC stage III–IV with a sensitivity = 0.85 and specificity = 0.92 (AUC = 0.89) (Table 4, Fig. 6). At a minimum specificity of 0.95, the sensitivity to distinguish population controls from OC stage I–IV was 0.62, from OC stage I–II it was 0.24, and from OC stage III–IC it was 0.78 (Table 4).
Correlation between AUC values for some of the proteins overlapping between our study and that by Boylan et al. [5] for the comparison between benign tumors and ovarian cancer stage III–IV (left panel) and I–II (right panel)
Of the 16 proteins selected for EC, an association was replicated for nine (PRSS8, MK, WFDC2 (HE4), ADM, MMP-7, ST2, VEGF-A, IL-6, HGF), while seven proteins showed no association (IL-10, IDUA, FABP-4, PCSK9, CTSZ, CCL16, Dkk-4). The protein panel distinguished between benign tumors and EC with a sensitivity = 0.70 and a specificity = 0.67 (AUC = 0.72). Population controls could be distinguished from EC with a sensitivity = 0.64 and specificity = 0.72 (AUC = 0.71).
Of the 13 proteins that differed significantly between OC stage I–IV and EC in the discovery data, six (hK11, MUCIN-16, FR-alpha, NTRK3, EN-RAGE, HGF) showed a significant difference in the replication step (Tables 2, 3). OC stage I–IV could be distinguished from EC with a sensitivity = 0.73 and a specificity = 0.77 (AUC = 0.78).
Discussion
We have searched for plasma proteins that could distinguish between patients with the gynecologic cancers OC and EC and benign tumors, using a two-step study design and a scalable technology for measuring protein abundance. The high degree of multiplexing of PEA panels enabled us to screen 441 unique proteins in search of suitable biomarker candidates. The ability to scale the PEA technology was then used to design two focused multiplex panels for the proteins selected in the discovery step. The abundance estimates for the proteins measured both in discovery step and the replication step showed high correlation and similar precision, testifying to that the PEA technology is scalable without compromising the performance for detection of individual proteins. In fact, by using a smaller Integrated Fluidic Circuit for the analysis, we were able to combine PEA assays for proteins with a greater difference in abundance than is possible using the 92-protein Integrated Fluidic Circuits.
Proteins identified
Among the proteins we identified as biomarker candidates for OC some have been discussed previously [5], such as Mucin-16 (CA125) and Major epididymis-specific protein E4 (HE4), Midkine (MK) [16, 17], Kallikrein-11 (hK11) [18], Folate receptor alpha (FR) [19, 20] and Prostasin (PRSS8) [21, 22]. Boylan et al. [5] also using PEA technology further reported an association of OC with Interleukin-6 (IL-6) [23–25], Kallikrein-6 (KLK6) [26], Furin (FUR) [27], Chemokine (C-X-C motif) ligand 13 (CXCL13) and Tumor necrosis factor ligand superfamily member 14 (TNFSF14), but none of these proteins were among our top candidates. A number of the proteins we selected for the replication step were not studied by Boylan et al. [5] such as Interleukin-8 (IL-8), Nectin-4 (PVRL4), Interleukin-17C (IL17C), Poly (ADP-ribose) polymerase-1 (PARP-1), Superoxide dismutase (Mn), mitochondrial (SOD2) and Protein S100-A12 (EN-RAGE). Several of these have previously been noted in connection to OC. The level of Interleukin-8 has been proposed as a diagnostic and prognostic biomarker for OC [28–30]. Nectin-4 is overexpressed in epithelial cancers including OC and has been proposed as a therapeutic target [31]. Interleukin-17C has been shown to be tumor-promoting in OC cell models [32]. Poly (ADP-ribose) polymerase 1 is overexpressed in OC and may enhance angiogenesis by upregulating Vascular endothelial growth factor A (VEGF-A) [33]. Genetic variation in Neurotrophic tyrosine receptor kinase 3 (NTRK3) has been associated with prognosis of OC and suggested to predict platinum resistance in OC patients [34]. Superoxide dismutase (Mn), mitochondrial, is highly expressed in OC and has been shown to increase tumor development and metastatic spread [35].
Among the nine proteins (Prostasin (PRSS8), Midkine (Mk), Major epididymis-specific protein E4 (HE4), Adenomedulin (ADM), Matrilysin (MMP-7), Interleukin-1 receptor-like 1 (ST2), Vascular endothelial growth factor A (VEGF-A), Interleukin-6 (IL-6), Hepatocyte growth factor receptor (HGF)) associated with EC in the replication step, several have been discussed in relation to EC. Midkine has been proposed as a serum biomarker for high-risk EC patients, and preoperative serum levels have been shown to correlate with lymph node metastasis [36]. Adrenomedullin expression is upregulated in post-menopausal endometria, and is increased during progression from benign endometrium to type-1 adenocarcinoma [37, 38]. Fatty acid-binding protein (FABP4) has been proposed as a diagnostic biomarker for EC [39, 40] and showed a significant association in our discovery step analysis, but not in the replication step.
Preoperative diagnostic
Our protein panel has a sensitivity = 0.83 and specificity = 0.87 to distinguish between benign tumors and OC stage I–IV. The specificity of our protein panel is somewhat lower than the 0.91 of Overa®, while the sensitivity is higher than the 0.69 reported for Overa® [4]. Focusing on OC stage III–IV we have a sensitivity = 0.88 and specificity = 0.92, which is significantly higher than using only CA125 and HE4.
A test for triaging women with adnexal ovarian mass should have better performance than TVU. A recent study showed that TVU in the hands of specially trained gynecologic sonographers can achieve an AUC = 0.92 [41]. However, the performance of ordinary gynecologists is generally lower. For instance, among women with a TVU indication of adnexal ovarian mass that are diagnosed by surgical sampling, 58% have been reported to have benign tumors, 30% have OC stage I–IV and the remaining 15% borderline tumors [42, 43]. Among the 30% of women with OC, 15% have OC stage III–IV. Based on these estimates, clinical diagnosis by surgical sampling has a specificity for OC stage I–IV of 0.30 and for OC III–IV of 0.15. At a minimum sensitivity of 0.96, used as a threshold for a preoperative diagnostic test, our protein panel can distinguish between benign tumors and OC stage III–IV with a specificity of 0.58. This indicates that the number of women with OC stage III–IV among those stratified for surgical sampling based on TVU could be increased from 15% when using TVU to 58% by using the protein panel. For OC stage I–IV, the protein panel show a specificity of 0.48, while the specificity of TVU is 0.30. This correspond to a 50% increase of the specificity. The combined use of both TVU and the biomarker test is likely to give even higher specificity.
Boylan et al. [5] used the ONC Iv2 panel to search for candidate biomarkers for OC. Several of the proteins found to be associated with OC in their study were also on our list of candidates, such as Major epididymis-specific protein E4, Midkine, Kallikrein-11, Folate receptor alpha. Interleukin-6, and Prostasin. Their list of top proteins had consistently higher AUC values than our estimates (Fig. 7). This may reflect differences in design between the studies. Boylan et al. [5] used a single set of cases and controls for identification of proteins, while we used a two-step analysis with two independent set of clinical materials, and in the replication step we also included OC patients from two different hospitals. Our study thus includes several factors that could introduce variation, such as multiple patient cohorts from both the same and different hospitals, different PEA analysis rounds and using both 92-plex and focused PEA panels, and finally the use of a replication step to verify initial findings. Together these factors are likely to reduce the overall performance characteristics of the assay, while at the same time result in more realistic performance indicators.
We also identified a set of protein biomarkers that can be used to distinguish between benign tumors and EC. Biomarkers have previously been described for EC, and good diagnostic accuracy has been reported for e.g. Major epididymis-specific protein E4 (HE4), Growth/differentiation factor 15 (GDF-15), C-Jun-amino-terminal kinase-interacting protein 4 (JIP-4), JNK-interacting protein 4 (SPAG9), Chitinase-3-like protein 1 (YKL-40), Interleukin-31 (IL-31) and Interleukin-33 (IL-33) [11]. Further studies are needed to compare the performance of these candidates with the ones identified in the present study.
Population screening
Our results indicate that the focused protein panel at a specificity of 0.96 has a high sensitivity to distinguish population controls from women with OC stage I–IV and stage III–IV, while lower for stage I–II. To determine the potential of using the protein panel in population screening, further studies are needed of based on samples collected at distinct time-points prior to diagnosis. A recent evaluation of four markers (CA125, HE4, CA72.4, and CA15.3) for OC showed that the performance declined with increasing time between sample collection and time of diagnosis [44]. Serial sampling could enable the use of individual baseline values, and testing women at 3 months rather than 6-12-month intervals using ROCA has been shown to result in better sensitivity and high specificity for detection of early-stage disease [45].
In summary, we have used a two-step strategy to identify plasma proteins that can be used to distinguish benign tumors from OC or EC and for differential diagnostic procedures of women with suspicious pelvic mass. The biomarker panels could be useful in population screening and to identify women in need of TVU examination at a specialized gynecologic ultrasound unit.
Notes
Declarations
Authors' contributions
The authors contributed to the different aspects of the study as follows: PI of the study: UG. Study design: UG, KSU. Collection and characterization of clinical materials: KSU, KS, ML. Generation of protein data: ML, EA. Analysis of protein data: MB, SE. Statistical analysis: MB, SE, ML, EA. Writing of the paper: All authors read and approved the final manuscript.
Acknowledgements
See Funding.
Availability of data and materials
The data will be made available upon publication.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
The study was approved by the Regional Ethics Committee in Uppsala (Dnr: 2016/145) and Gothenburg (Dnr: 201-15).
Funding
The study was funded by the Swedish Cancer Foundation, The Swedish Foundation for Strategic Research (SSF), the Swedish Research Council (VR), VINNOVA (SWELIFE) and Olink Proteomics.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
References
- Ferley, GLOBOSCAN 2012: Estimated Cancer incidence, mortality and prevlance worldwide 2012. 2012.Google Scholar
- Moore RG, et al. Evaluation of the diagnostic accuracy of the risk of ovarian malignancy algorithm in women with a pelvic mass. Obstet Gynecol. 2011;118(2 Pt 1):280–8.View ArticleGoogle Scholar
- Coleman RL, et al. Validation of a second-generation multivariate index assay for malignancy risk of adnexal masses. Am J Obstet Gynecol. 2016;215(1):82e1–11.View ArticleGoogle Scholar
- Simmons AR, et al. Validation of a biomarker panel and longitudinal biomarker performance for early detection of ovarian cancer. Int J Gynecol Cancer. 2016;26(6):1070–7.View ArticleGoogle Scholar
- Boylan KLM, et al. A multiplex platform for the identification of ovarian cancer biomarkers. Clin Proteomics. 2017;14:34.View ArticleGoogle Scholar
- Lu KH, et al. A 2-stage ovarian cancer screening strategy using the Risk of Ovarian Cancer Algorithm (ROCA) identifies early-stage incident cancers and demonstrates high positive predictive value. Cancer. 2013;119(19):3454–61.View ArticleGoogle Scholar
- Jacobs IJ, et al. Ovarian cancer screening and mortality in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial. Lancet. 2016;387(10022):945–56.View ArticleGoogle Scholar
- Andreano A, et al. MR diffusion imaging for preoperative staging of myometrial invasion in patients with endometrial cancer: a systematic review and meta-analysis. Eur Radiol. 2014;24(6):1327–38.View ArticleGoogle Scholar
- Salvesen HB, et al. Integrated genomic profiling of endometrial carcinoma associates aggressive tumors with indicators of PI3 kinase activation. Proc Natl Acad Sci USA. 2009;106(12):4834–9.View ArticleGoogle Scholar
- Supernat A, et al. A multimarker qPCR platform for the characterisation of endometrial cancer. Oncol Rep. 2014;31(2):1003–13.View ArticleGoogle Scholar
- Rizner TL. Discovery of biomarkers for endometrial cancer: current status and prospects. Expert Rev Mol Diagn. 2016;16:1315–36.View ArticleGoogle Scholar
- Assarsson E, et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS ONE. 2014;9(4):e95192.View ArticleGoogle Scholar
- R Core Team, R: A Language and Environment for Statistical Computing. 2014, R Foundation for Statistical Computing.Google Scholar
- Hong MG, et al. Multidimensional normalization to minimize plate effects of suspension bead array data. J Proteome Res. 2016;15(10):3473–80.View ArticleGoogle Scholar
- Assarsson E, Lundberg M. Development and validation of customized PEA biomarker panels with clinical utility, in advancing precision medicine: current and future proteogenomic strategies for biomarker discovery and development. Washington, DC: Science/AAAS; 2017. p. 33–6.Google Scholar
- Wu X, et al. Midkine as a potential diagnostic marker in epithelial ovarian cancer for cisplatin/paclitaxel combination clinical therapy. Am J Cancer Res. 2015;5(2):629–38.PubMedPubMed CentralGoogle Scholar
- Rice GE, Edgell TA, Autelitano DJ. Evaluation of midkine and anterior gradient 2 in a multimarker panel for the detection of ovarian cancer. J Exp Clin Cancer Res. 2010;29:62.View ArticleGoogle Scholar
- McIntosh MW, et al. Validation and characterization of human kallikrein 11 as a serum marker for diagnosis of ovarian carcinoma. Clin Cancer Res. 2007;13(15 Pt 1):4422–8.View ArticleGoogle Scholar
- O’Shannessy DJ, et al. Serum folate receptor alpha, mesothelin and megakaryocyte potentiating factor in ovarian cancer: association to disease stage and grade and comparison to CA125 and HE4. J Ovarian Res. 2013;6(1):29.View ArticleGoogle Scholar
- Kurosaki A, et al. Serum folate receptor alpha as a biomarker for ovarian cancer: implications for diagnosis, prognosis and predicting its local tumor expression. Int J Cancer. 2016;138(8):1994–2002.View ArticleGoogle Scholar
- Mok SC, et al. Prostasin, a potential serum marker for ovarian cancer: identification through microarray technology. J Natl Cancer Inst. 2001;93(19):1458–64.View ArticleGoogle Scholar
- Tamir A, et al. The serine protease prostasin (PRSS8) is a potential biomarker for early detection of ovarian cancer. J Ovarian Res. 2016;9:20.View ArticleGoogle Scholar
- Berek JS, et al. Serum interleukin-6 levels correlate with disease status in patients with epithelial ovarian cancer. Am J Obstet Gynecol. 1991;164(4):1038–42 (discussion 1042–3).View ArticleGoogle Scholar
- Tempfer C, et al. Serum evaluation of interleukin 6 in ovarian cancer patients. Gynecol Oncol. 1997;66(1):27–30.View ArticleGoogle Scholar
- Block MS, et al. Plasma immune analytes in patients with epithelial ovarian cancer. Cytokine. 2015;73(1):108–13.View ArticleGoogle Scholar
- Tamir A, et al. Kallikrein family proteases KLK6 and KLK7 are potential early detection and diagnostic biomarkers for serous and papillary serous ovarian cancer subtypes. J Ovarian Res. 2014;7:109.View ArticleGoogle Scholar
- Page RE, et al. Increased expression of the pro-protein convertase furin predicts decreased survival in ovarian cancer. Cell Oncol. 2007;29(4):289–99.PubMedPubMed CentralGoogle Scholar
- Nolen BM, Lokshin AE. Protein biomarkers of ovarian cancer: the forest and the trees. Future Oncol. 2012;8(1):55–71.View ArticleGoogle Scholar
- Sanguinete MMM, et al. Serum IL-6 and IL-8 correlate with prognostic factors in ovarian cancer. Immunol Invest. 2017;46(7):677–88.View ArticleGoogle Scholar
- Hibbs K, et al. Differential gene expression in ovarian carcinoma: identification of potential biomarkers. Am J Pathol. 2004;165(2):397–414.View ArticleGoogle Scholar
- Boylan KL, et al. The expression of Nectin-4 on the surface of ovarian cancer cells alters their ability to adhere, migrate, aggregate, and proliferate. Oncotarget. 2017;8(6):9717–38.View ArticleGoogle Scholar
- Charles KA, et al. The tumor-promoting actions of TNF-alpha involve TNFR1 and IL-17 in ovarian cancer in mice and humans. J Clin Invest. 2009;119(10):3011–23.View ArticleGoogle Scholar
- Wei W, et al. PARP-1 may be involved in angiogenesis in epithelial ovarian cancer. Oncol Lett. 2016;12(6):4561–7.View ArticleGoogle Scholar
- Ge L, et al. Copy number variations of neurotrophic tyrosine receptor kinase 3 (NTRK3) may predict prognosis of ovarian cancer. Medicine (Baltimore). 2017;96(30):e7621.View ArticleGoogle Scholar
- Hemachandra LP, et al. Mitochondrial superoxide dismutase has a protumorigenic role in ovarian clear cell carcinoma. Cancer Res. 2015;75(22):4973–84.View ArticleGoogle Scholar
- Tanabe K, et al. Midkine and its clinical significance in endometrial carcinoma. Cancer Sci. 2008;99(6):1125–30.View ArticleGoogle Scholar
- Evans JJ, et al. Adrenomedullin interacts with VEGF in endometrial cancer and has varied modulation in tumours of different grades. Gynecol Oncol. 2012;125(1):214–9.View ArticleGoogle Scholar
- Bozkurt KK, et al. The role of immunohistochemical adrenomedullin and Bcl-2 expression in development of type-1 endometrial adenocarcinoma: adrenomedullin expression in endometrium. Pathol Res Pract. 2016;212(5):450–5.View ArticleGoogle Scholar
- Li Z, et al. Prognostic evaluation of epidermal fatty acid-binding protein and calcyphosine, two proteins implicated in endometrial cancer using a proteomic approach. Int J Cancer. 2008;123(10):2377–83.View ArticleGoogle Scholar
- Li Z, et al. Proteomics-based approach identified differentially expressed proteins with potential roles in endometrial carcinoma. Int J Gynecol Cancer. 2010;20(1):9–15.View ArticleGoogle Scholar
- Timmerman D, et al. Predicting the risk of malignancy in adnexal masses based on the simple rules from the international ovarian tumor analysis group. Am J Obstet Gynecol. 2016;214(4):424–37.View ArticleGoogle Scholar
- Partheen K, Kristjansdottir B, Sundfeldt K. Evaluation of ovarian cancer biomarkers HE4 and CA-125 in women presenting with a suspicious cystic ovarian mass. J Gynecol Oncol. 2011;22(4):244–52.View ArticleGoogle Scholar
- Kristjansdottir B, Levan K, Partheen K, Sundfeldt K. Diagnostic performance of the biomarkers HE4 and CA125 in type I and type II epithelial ovarian cancer. Gynecol Oncol. 2013;131(1):52–8.View ArticleGoogle Scholar
- Terry KL, et al. A prospective evaluation of early detection biomarkers for ovarian cancer in the European EPIC cohort. Clin Cancer Res. 2016;22(18):4664–75.View ArticleGoogle Scholar
- Skates SJ, et al. Early detection of ovarian cancer using the risk of ovarian cancer algorithm with frequent CA125 testing in women at increased familial risk—combined results from two screening trials. Clin Cancer Res. 2017;23(14):3628–37.View ArticleGoogle Scholar