A novel proteomic biomarker panel as a diagnostic tool for patients with ovarian cancer

Gynecologic Oncology 123 (2011) 308–313

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Gynecologic Oncology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y g y n o

A novel proteomic biomarker panel as a diagnostic tool for patients with ovarian cancer ☆ Claus Høgdall a,⁎, Eric T. Fung b, Ib J. Christensen c, Lotte Nedergaard d, Svend A. Engelholm e, Anette L. Petri a, Signe Risum e, Lene Lundvall a, Christine Yip b, Anette T. Pedersen a, Dorthe Hartwell a, Lee Lomas f, Estrid V.S. Høgdall g a

Gynecologic Clinic Rigshospitalet, University of Copenhagen, Copenhagen, Denmark Vermillion, Inc, Austin, Texas & Mountain View, California, USA c Finsen Laboratory, Rigshospitalet, Copenhagen Biocenter, Denmark d Department of Pathology, Rigshospitalet, University of Copenhagen, Denmark e Oncologic Clinic Rigshospitalet, University of Copenhagen, Copenhagen, Denmark f Bio-Rad Laboratories, Hercules, California, USA g Department of Pathology, Herlev University Hospital, University of Copenhagen, Denmark b

a r t i c l e

i n f o

Article history: Received 15 March 2011 Accepted 14 July 2011 Available online 19 August 2011 Keywords: Ovarian cancer Pelvic mass Proteomics Differential diagnosis Tumor markers

a b s t r a c t Background. Previous reports have shown that the proteomic markers apolipoprotein A1, hepcidin, transferrin, inter-alpha trypsin IV internal fragment, transthyretin, connective-tissue activating protein 3 and beta-2 microglobulin may discriminate between a benign pelvic mass and ovarian cancer (OC). The aim was to determine if these serum proteomic biomarkers alone as well as in combination with age and serum CA125, could be helpful in triage of women with a pelvic mass. Methods. We included prospectively 144 patients diagnosed with (OC), 40 with a borderline tumor and 469 with a benign tumor. Surface-enhanced laser desorption/ionization time of flight-mass spectrometry was used for analyses. The Danish Index (DK-Index) based on the proteomic data, age and CA125 was developed using logistic regression models. Results. Multivariate logistic regression analysis demonstrated that the selected proteomic markers, CA125 and age were independent predictors of OC and the combination of these is proposed as the DK-index. A sensitivity (SN) of 99% had a specificity (SP) of 57% for DK-index and 49% for CA125. At a SN of 95%, the SP increased to 81% for DK-index compared to 68% for CA125 alone. For stage I+II the SP was 58% for DK-index and 49% for CA125. For stage III+IV the corresponding values were 94% and 86% respectively. Conclusions. The DK-index warrants further evaluation in independent cohorts. © 2011 Elsevier Inc. All rights reserved.

Introduction Ovarian cancer (OC) is the most frequent cause of death among gynecologic malignancies [1]. The high frequency and poor prognosis emphasizes the need for better diagnostic and prognostic factors. One approach to improve survival is better triage of women with cancer pre-operatively. Studies have demonstrated that women with OC have better outcomes if they are operated by gynecologic oncologists [2–6]. Even with the most efficient diagnostic procedures available, it is not possible to preoperatively distinguish with certainty between a benign and a malignant pelvic mass. Consequently it is necessary to perform approximately 10 exploratory laparotomies in order to ☆ From the Danish “pelvic mass” ovarian cancer study. ⁎ Corresponding author at: The Gynecologic Clinic, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen, Denmark. E-mail address: [email protected] (C. Høgdall). 0090-8258/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.ygyno.2011.07.018

correctly diagnose 1 OC patient in a screening with 99.6% specificity (SP) [7].In the present consecutive cohort it was necessary to perform 4.5 laparotomies for each OC. Therefore efforts to develop methods to distinguish preoperatively between a benign and malignant pelvic mass are ongoing. A laboratory test that could further stratify this population would be a useful tool in clinical applications. CA125 is commonly used to evaluate a pelvic mass. However, it has limitations as approximately 20% have normal values and many benign cases have elevated levels. The search for useful serologic markers have intensively focused on multimarker combinations. Most combinations include CA125 and the newer marker HE4 [8–12]. In 2002, Petricoin et al. [13] discovered patterns of proteins in the blood of OC patients, and reported 100% sensitivity (SN) and 95% SP. Unfortunately, it has not been possible to reproduce the results [14]. In 2004, Zhang et al. [15] used a multivariate model to combine the biomarkers apolipoprotein A1 (APOA1), transthyretin (cysteinylated form) (TT) and inter-alpha trypsin inhibitor IV (internal fragment) (ITIH4) values from 503 patients. In combination with serum CA125,

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the markers had a SN of 74% and a SP of 94% for detecting OC, which was a modest improvement over CA125 alone. A large-scale multicenter study evaluated a set of seven biomarkers, for the detection of OC. Totally 607 sera from five studies were analyzed using SELDI-MS protocols optimized for the biomarkers. All biomarkers individually demonstrated statistically significant power for differentiation [16]. Based on these proteomic studies we decided to study the markers in a prospective collection of women from the Danish Pelvic Mass study. The aims were to determine if the seven serum biomarkers APOA1, TT, hepcidin (HEPC), ITIH4, transferrin (TrF), connective-tissue activating protein 3 (CTAP3) and beta-2 microglobulin (B2M), alone or in combination with serum CA125, could be helpful in further triaging women according to the likelihood that their mass is malignant.

serum CA125. PET/CT was performed when Risk of Malignancy Index (RMI) ≥200. Surgery was performed through a midline incision with the intention of radical surgery. All patients are on-line registered in the Danish Gynecological Cancer Database (DGCD) [18]. Blood, urine and tissue samples are handled according to biobanking guidelines. The Danish Ethical Committee approved the protocol No. KF01227/03 and KF01-143/04. Blood samples were collected within 14 days prior to surgery and were processed into serum by centrifugation for 10 min at 2000 ×g within maximally 6 h and stored in aliquots at − 80 °C until analyzed. All tissue specimens were examined by a pathologist specialized in gynecologic cancer. Histology types and FIGO stages are presented in Table 2.

Material and methods

Methods

Patients

Proteomic assays

Between September 2004 and August 2007, 653 patients were consecutively included in the “Pelvic Mass” study previously described in detail (Table 1) [17]. Briefly the main purpose of this prospective on-going study is to identify and characterize diagnostic and prognostic factors in patients with OC. Patients were examined with an abdominal and vaginal ultrasound, and measurement of

The assays have previously been described in detail [17]. Briefly Chromatographic SELDI-TOF-MS protocols have been developed to generate quantitative measurements for each of the markers. Standard curves were generated using known quantities of calibrates. Samples were processed in triplicate in 96 well bio-processors using Biomek2000. Samples were processed using a Tecan Aquarius™ robot,

Table 1 Basic data. Median (range; quartiles). Peak intensities (PI) are given for proteomic. No

Variable

Median

Minimum

Maximum

25% quartile

75% quartile

Benign

469

Borderline

40

Borderline+benign

509

Ovarian cancer

144

Age_years RMI APOA1_D B2M_B CTAP_D HEPC_ D ITIH4_D TT_D Trf_PR Pelvic_CA125 Probability, final Age_years RMI APOA1_D B2M_B CTAP_D HEPC_D e ITIH4_D TT_D Trf_PR Pelvic_CA125 Probability, final Age_years RMI APOA1_D B2M_B CTAP_D HEPC_D e ITIH4_D TT_D Trf_PR Pelvic_CA125 Probability, final Age_years RMI APOA1_D B2M_B CTAP_D HEP C_D ITIH4_D TT_D Trf_PR Pelvic_CA125 Probability, final

43.00 46.00 164.81 5.12 4.37 100.00 17.65 28.47 3.16 25.00 0.082 59.00 297.00 148.13 5.43 4.76 100.00 10.00 29.00 2.87 54.50 0.135 45 49 164.1 5.2 4.4 100 15.6 28.53.1 3.1 26 0.086 65.00 3555.00 125.42 7.43 5.39 166.95 10.00 18.99 2.21 549.50 0.486

19.00 2.00 59.63 1.99 1.88 100.00 10.00 5.69 1.33 2.00 0.007 22.00 13.00 56.11 2.94 1.70 100.00 10.00 13.52 1.57 11.00 0.002 19 2 56.1 2 1.7 100 10 5.7 1.3 2 0.002 31.00 6.00 25.17 2.89 1.95 100.00 10.00 3.16 1.25 6.00 0.036

90.00 17154.00 463.49 37.29 13.15 915.17 1027.06 64.62 7.13 1906.00 0.981 88.00 15975.00 271.72 22.25 11.71 339.87 459.79 47.96 6.71 1775.00 0.846 90 17154 463.5 37.3 13.2 915.2 1027 64.6 7.1 1906 0.981 89.00 155475.00 294.29 47.24 17.15 1246.94 319.44 51.54 4.64 17275.00 0.998

32.00 23.00 134.78 4.28 3.64 100.00 10.00 23.62 2.72 14.00 0.050 44.70 105.00 120.38 4.43 3.54 100.00 10.00 25.34 2.49 23.50 0.085 33 24 133 4.3 3.6 100 10 2.7 2.7 15 0.050 57.01 1165.50 100.56 5.43 4.44 100.00 10.00 13.59 1.90 189.00 0.230

58.00 135.00 204.82 6.35 5.32 110.16 25.00 35.01 3.56 46.00 0.150 68.00 759.00 202.12 7.24 6.04 100.77 22.59 34.14 3.07 150.00 0.225 59 156 203 6.4 5.4 109.5 25 3.6 3.6 50 0.155 73.88 9090.00 162.22 9.97 7.25 312.97 20.00 25.93 2.72 1556.00 0.784

proteomic model⁎

proteomic model*

proteomic model⁎

proteomic model⁎

*Estimated probability for OC for each individual based on the final proteomic model.

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Table 2 Histology types, grade and FIGO stage distribution. Histology type

Stage Benign

Benign

Borderline Cancer

Serous cystadenoma Mucinous cystadenoma Endometriosis Teratom Fibroma/leiomyoma Tubo-ovarian abcess/hydrosalpinx Functional/simple/hemorrhagic/paratubal cyst Other Normal finding at surgery Serous cystadenoma borderline Mucinous cystadenoma borderline Serous adeno carcinoma Mucinous adeno carcinoma Endometrioid adeno carcinoma Clear cell neoplasms Carcinosarcoma Non-differentiated carcinoma Grade 1 Grade 2 Grade 3 Not gradeable

Total

and protocols performed using different ProteinChip® Arrays. Arrays were analyzed on PCS4000 mass spectrometers (Bio-Rad). Peak intensity data were baseline subtracted and normalized using either total ion current factors or peak ratio factors. Data collection and analysis Arrays were processed in a ProteinChip SELDI System (Enterprise Edition, Bio-Rad Laboratories) using ProteinChip Data Management software v3.0. Data acquisition settings were optimized for the individual analytes and to provide the best performance. After all spectra were collected, data was archived and then imported into OvaCalc Software v3.1 (Vermillion Inc). This software package performed all calculations for the assay performance QC and the quantitative or semi-quantitative determinations of each of the analytes. Data acquisition and spectral processing ProteinChip arrays were placed in the ProteinChip reader Series 4000 mass spectrometer (Bio-Rad Laboratories) and mass spectra were acquired using settings optimized for the m/z range of 2–20 kDa. The spectra were externally calibrated using the “All-In-One” peptide and protein mass standards (Bio-Rad). The standards, ranging from 1–7 kDa for peptides and 7–66 kDa for proteins, were prepared on NP20 ProteinChip arrays. Spectra data were archived using ProteinChip Data Manager Software (Bio-Rad, version 3.0.7). Archived data files were imported into OvaCalc Software (version 3.2.7). Peak intensities of the relevant peaks associated with the analytes were processed and detected automatically. The baseline was subtracted (15× expected peak width based on mass) and the spectral intensities were normalized by total ion current (TIC) to an external factor of 1 between the mass range of 1.2 to 20 kDa. Spectral filtering was set to 0.2× expected peak width. CA125 Serum CA125 was analyzed using CA125II on the BRAHMS Kryptor. Intra-assay CV was 6.6% (n = 60), and the inter-assay CV was 6.2% (n = 10) at a control sample of 30 U/ml. RMI was calculated as previously described [17].

Total I

II

III

IV

70 45 150 53 42 11 81 13 4

469

20 16 7 1 9 3 0 0 12 6 2 0 56

1 0 11 1 2 1 0 0 4 8 3 0 16

2 0 66 2 3 3 0 1 4 37 33 1 77

1 0 30 2 0 0 2 0 2 10 18 4 35

70 45 150 53 42 11 81 13 4 24 16 114 6 14 7 2 1 22 61 56 5 653

Statistical analysis Testing for differences between groups was performed with the Wilcoxon rank sum test. The Spearman rank correlation was used as a measure of association between variables. The DK-index based on the proteomic data was developed using a logistic regression model. An analysis entering the actual levels on the log scale (log base 2) shows tests of type III hypothesis. CA125 was log transformed (log base 2). The Hosmer-Lemeshow test was used for tests of goodness of fit and internal validation was done using crossvalidation of random subsets. Discrimination of the variables was evaluated by the odds ratio (OR) and the estimated sensitivies and specificities. Estimates are presented with 95% confidence intervals (CI). Receiver operating characteristic curves (ROC) and the areas under the curve (AUC) are presented. Logistic regression analysis was used for multivariate analysis. Statistical significance was defined as P b 0.05. All statistical calculations were done using SAS (v9.1, SAS Institute, Cary, N.C., USA). Results Except for ITIH4 were the proteomic biomarkers significantly but not strongly correlated to each other. Testing for differences between the groups resulted in highly significant differences for all the variables (p b 0.0001) except for ITIH4 (p = 0.001). Significant correlations were also found between the proteomic markers and age, stage, CA125 and RMI (Table 3). Grade was weakly correlated to only B2M and Trf. No significant differences in the respective single proteomic markers were found by logistic regression analyses between benign and BOT. Benign and BOT cases therefore were combined into one non-cancer-group and evaluated together against OC. Neither was it possible to find any significant difference between any of the histological types (P = 0.57). The overall prevalence of OC in the study was 22% (144/653). Distribution of age, CA125, RMI, single proteomic markers and estimated probability for OC for each individual based on the final proteomic model stratified by OC and the non-cancer-groups is shown in Table 1. The distribution of tumor type in relation to histology, grade and stage is presented in Table 2. The odds ratios (OR) for the biomarkers were calculated for APOA1: OR: 0.98 (95% CI: 0.57–1.67), B2M: OR: 1.55 (95% CI: 1.04–

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Table 3 Rank correlations between age, stage, grade, CA125, RMI and the proteomic biomarkers. Spearman correlation coefficients, N = 653

Age (years) Stage Grade CA125 II (U/ml) RMI APOA1

APOA1

B2M

CTAP3

HEPC

ITIH4

TT

Trf

− 0.10 0.0079 − 0.31867 b.0001 − 0.05 0.58 − 0.30 b.0001 − 0.28 b.0001 1.00

0.48 b.0001 0.30552 b.0001 0.19 0.02 0.31 b.0001 0.44 b.0001 − 0.24 b.0001 1.00

0.21 b.0001 0.20968 0.0043 0.11 0.20 0.33 b.0001 0.35 b.0001 − 0.22 b.0001 0.13 b.0001 1.00

0.42 b.0001 0.39952 b.0001 0.05 0.56 0.31 b.0001 0.43 b.0001 − 0.23 b.0001 0.35 b.0001 0.19 b.0001 1.00

− 0.20 b.0001 − 0.05244 0.4796 0.015 0.85 − 0.17 b.0001 − 0.22 b.0001 0.13 0.0006 − 0.34 b.0001 − 0.18 b.0001 − 0.13 0.0009 1.00

0.19 b.0001 − 0.41843 b.0001 − 0.13 0.14 − 0.42 b.0001 − 0.40 b.0001 0.35 b.0001 − 0.33 b.0001 − 0.11 0.0059 − 0.24 b.0001 0.07 0.0587 1.00

− 0.53 b.0001 − 0.42260 b.0001 − 0.18 0.03 − 0.43 b.0001 − 0.55 b.0001 0.41 b.0001 − 0.41 b.0001 − 0.36 b.0001 − 0.49 b.0001 0.27 b.0001 0.30 b.0001 1.00

− 0.24 b.0001 − 0.22 b.0001 − 0.23 b.0001 0.13 0.0006 0.35 b.0001 0.41 b.0001

B2M CTAP3 HEPC ITIH4 TT Trf

0.13 b.0001 0.35 b.0001 − 0.34 b.0001 − 0.33 b.0001 − 0.41 b.0001

0.19 b.0001 − 0.19 b.0001 − 0.11 0.0059 − 0.36 b.0001

2.30), CTAP: OR: 1.49 (95% CI: 0.90–2.47), HEPC: OR: 1.45 (95% CI: 1.02–2.07), ITIH4: OR: 0.95 (95% CI: 0.77–1.16), TT: OR: 0.37 (95% CI: 0.24–0.59) and TrF: OR: 0.12 (95% CI: 0.05–0.29). An index based on the proteomic data was developed using logistic regression analysis. A reduced model selecting significant proteomic markers using backwards elimination was employed. The reduced model was composed of B2M: OR: 1.67 (95% CI: 1.15–2.43), TT: OR: 0.34 (95% CI: 0.22–0.54) and TrF: OR: 0.06 (95% CI: 0.03–0.13). The AUC for the reduced model was 0.866 (95% CI: 0.827–0.905) with a SN of 65% at 90% SP. Based on the regression coefficients of the logistic regression model, a final proteomic index was calculated as 6.194 + 0.512 × log2 B2M – 1.067 × log2 TT – 2.785 × log2 TrF. Using a 1000-fold cross validation technique with stepwise selection, it was demonstrated that the selected model estimates were robust. This final proteomic model is correlated to age (p b 0.0001), RMI (p b 0.0001) and CA125 (p b 0.0001). Menopausal status does not improve the fit to the model. RMI reflects age as well as CA125 and is not significant when included. Univariate and multivariate logistic regression analyses including the final proteomic model, CA125 and age are shown in Table 4. All 3 covariates were statistically significant suggesting that the covariates were independent predictors of OC. The resulting index is denoted the DK-index. The OR's shown for CA125 are for a twofold difference in level and for a 10 year difference in age. The DK-index is the linear combination of the 3 components of the multivariate model and the OR comparing an individual with an index value at the third quartile Table 4 Univariate and multivariate logistic regression analysis including the final proteomic model, CA125 and age. Univariate analysis p-value

Multivariate analysis

Covariate

OR (95% CI)

AUC

OR (95% CI)

p-value

AUC

Final proteomic model CA125 (log scale base 2) Age pr 10 years

2.72 b 0.0001 0.866 1.31 0.017 (2.29–3.22) (1.05–1.64) 2.89 b 0.0001 0.937 2.38 b0.0001 0.956 (2.44–3.42) (1.99–2.84) 2.04 b 0.0001 0.801 1.55 b0.0001 (1.77–2.36) (1.25–1.91)

− 0.13 0.0009 − 0.24 b.0001 − 0.49 b.0001

0.07 0.0587 0.27 b.0001

0.30 b.0001

to one at the first quartile was 31.19 (95% CI 17.97–54.12). The components of the DK-index remain statistically significant if ultrasound score (US) was included in the model (p = 0.018; OR = 2.30: 95% CI:1,16-4,59 for US 3 vs 1). Receiver operator characteristic curve analysis (ROC) was used to illustrate the diagnostic performance of respectively CA125 (AUC: 0.937;), the final proteomic model (AUC: 0.866) and the DK-index (AUC: 0.956). A stepwise improvement in performance is seen from the single markers to the DK-index (Fig. 1). The sensitivities and specificities for discriminating between OC and a benign/borderline or BOT tumor are shown in Table 5. Focusing at a very high SN of 99% there was a considerable gain in SP to 57% for DK-index from 49% for CA125 alone at a cutoff of 28 U/ml. At this SN the SP was only 1–2% for both respective markers in the stage I+II patient group. The strength of the markers at this very high SN is restricted to the stage III+IV group with a SP at 83% for DK-index and 59% for CA125. Looking at a SN at 95% for all stages the SP increased to 81% for DK-index from 68% for CA125, at a cutoff of 41 U/ml. For stage I+II increased the SP to 58% for DK-index from 49% for CA125. For stage III+IV were the corresponding values respectively 94% and 86%. Focusing at a SP at 90%, overall SN was 90%, 66% for FIGO stages I+II, and 97% for stages III+IV. In contrast, for CA125 alone at a cutoff of 120 U/ml, at 90% SP, overall SN was 84%, 54% for FIGO stages I+II, and 94% for stages III+IV. At 99% SP, overall SN was 51% for all OC stages, 23% for stages I+II and 64% for stages III+IV. For CA125 alone at a cutoff 467 U/ml, at 99% SP, overall SN was 53%,17% for FIGO stages I+II, and 66% for stages III+IV. The low SP and SN values for OC vs. BOT support the decision of combining benign and BOT in one group. At the traditional cutoff 35 U/ml for CA125 was SN = 88.6% for stages I+II and 98.2% for stages III+IV and SP = 61.5%. For the DK-index were the SP values respectively 77.2% and 81.7% at the same sensitivities. Discussion OC surgery has increasingly been centralized to tertiary gynecologic oncology centers. There is therefore a need for useful biomarkers to discriminate between patients with high or low risk of OC. In ovarian tumor triage, it is most interesting to have a high SN in order to reduce the risk of not detecting an OC. A very high marker SN often

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1.0

DK-index

0.8 Final proteomic model

Sensitivity

CA125

0.6

0.4

0.2

0.0 0.0

0.2

0.4

0.6

0.8

1.0

1 - specificity panel

CA125

DK-index

Final proteomic model

Fig. 1. Receiver operator curves for CA125, the final proteomic model and DK-index. The DK-index is based on the final proteomic model including CA125 and age.

results in an unacceptable SP. Compared to CA125 alone we find, that the DK-index results in higher SP values at very high SN levels both for OC stage I+II and advanced stage III+IV (Table 5). The DK-index may therefore be valuable in the primary triage of patients with suspicious symptoms or a pelvic mass [19]. Logistic regression analyses could not demonstrate a difference in DK-index scores between benign and BOT. The final results are therefore presented as a combined group of benign and BOT against OC patients. Clinically we find this acceptable as 36 BOT patients were stage I, one stage IIC (DK-index: 33% OC risk), one stage IIIA (DKindex: 74% OC risk) one stage IIIC (DK-index: 8% OC risk) and one stage IV (DK-index: 5.5% OC risk). At least two of the more advanced BOT would have been referred to a gynecological oncologist based on the DK-index. The overall 4 year survival rate at 95% for Danish BOT patients also justifies the combination [18]. The present proteomic markers have previously been reported as useful discriminatory tools [16]. We examined the discriminative strength of each marker by stepwise logistic regression and constructed a final proteomic model based on the three markers B2M, TT and TrF. Previously B2M has been found up-regulated in the fluid from ovarian tumors [20]. In blood B2M was found usable to Table 5 Sensitivities and specificities. Patient group

DK-index

CA125

Sensitivity

Specificity

Sensitivity

Specificity

All OC stages vs benign+borderline

99 95 90 74 51

57 81 90 95 99

Stage I+II OC vs benign+borderline

99 95 90 66 40 23 99 95 90 97 85 64 95 90 67 47

1 58 71 90 95 99 83 94 94 90 95 99 43 68 90 95

99 95 90 84 80 53 99 95 90 54 40 17 99 95 90 94 80 66 95 90 67 51

49 68 82 90 95 99 2 49 57 90 95 99 59 86 92 90 95 99 35 55 90 95

Stage III+IV OC vs benign+ borderline

All OC stages vs borderline

monitor the chemotherapy treatment [21,22]. Recently B2M was proposed as a potential target in OC treatment [23]. TT has previously been found to be down-regulated in OC. In combination with other proteins and CA125, TT was found to independently improve the detection of early OC [15,16,24–26]. Also TrF has previously been reported to be down-regulated and able to differentiate between benign tumors and OC [16,24,26,27]. These reports and our findings indicate, that the three biomarkers individually demonstrate a statistically significant discrimination and in combination independently predict OC. The markers in the DK-index are similar to those used in the test for ovarian tumor triage (OVA1). The OVA1 test uses B2M, APOA1, TrF, TT and CA125. Additionally, it incorporates menopausal status, in the sense that it uses different cutoffs based on menopausal status (OVA1, Fremont, CA: Vermillion, Inc; 2009). One difference is, that the OVA1 test is performed on standard immunoassays rather than using the SELDI-TOF-MS platform. The standard immune assay values are evaluated in an OvaCalc software program and OVA1 generated. Because the OvaCalc program used to generate OVA1 is not programmed for our type of SELDI-TOF-MS values, we are not able to evaluate OVA1 with our results. Only the cutoff 35 U/ml for CA125 is generally accepted [28]. The other presented CA125 cutoffs are only used for comparision with DKindex as illustration of the need for better diagnostic procedures . In this need is RMI increasingly used to select patients for referral to tertiary gynecologic oncology centers [29–31]. The optimal use of RMI requires vaginal and abdominal ultrasound equipment and an expert sonographer. In our construction of a diagnostic model we found that RMI reflected CA125 and that the use of the DK-index had sufficient power to give a risk estimate for OC without ultrasound. The information of menopause required in RMI could in the DK-index be replaced with the more simple information of age. The only requirement for using the DK-index is a person who can collect the blood sample, register the age and a sufficient transporting system to a qualified laboratory. The DK-index therefore may be valuable in the primary triage and correct referral to specialists for further evaluation including ultrasound or more advanced imaging. One of the major drawbacks for using proteomic based analyses is the requirement for correct handling of the blood samples [14,32]. The proteomic profiles have been shown to change with length of clotting time and temperature. The use of a proteomic based analysis, thus requires strict protocols for sampling, handling, storage and analysis. In the present study the influence of these variables was eliminated. A tight logistic protocol was used and all samples were time and temperature stamped for each procedure to secure the most reliable proteomic profiles. Only samples processed in less than 6 h and handled according to the protocol were analyzed. Other drawbacks

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for using proteomic based analyses include cost, processing, equipment variability and expertise. One solution could be development of immunobased analyses for measuring the specific proteins found in the proteomic analyses. One of the ultimate solutions to decrease the mortality of OC would be OC screening with a marker as DK-index. This issue is out of the scope of this study, as no healthy controls were examined and all included OC patients were clinical. Nevertheless may the results give an indication of this use. One of the requirements for such a marker is the ability to distinguish sufficiently between benign and early stage OC patients as indicated by a high SP close to the minimum of 99.6% [33]. If a marker has a high discrimination, corresponding to a high SP, between these groups, may the discrimination be expected to be higher when compared to healthy controls. The high SP values and corresponding increases in SN seen for the DK-index, compared to CA125 alone, merits the study of DK-index in OC screening trials. Before conclusion of the UKTOC screening trial and validation of the DK-index in studies with asymptomatic women we cannot recommend the routine use of DK-index for asymptomatic women [34]. Current strategies are directed at constructing marker panels with an increase in SN without any loss in SP. Such promising marker panels are increasingly being published [8,9,15,16,24,25,35–38]. When trying to compare the present DK-index with reported sensitivities, specificities and AUCs in these reports, we find that DK-index performs in the best category. Hutinen et al. reported 78% SN at 95% SP which is slightly higher than our results [9]. Other studies with HE4 report lower or almost equal results compared to DK-index [8,10,11]. We had no possibility to measure HE4 on the present samples. A comparison between the diagnostic powers of HE4 and DK-index will depend on future studies. In conclusion, we found the DK-index may be a new tool to differentiate between groups of pelvic mass patients. The performance is comparable to recent multimarker reports including CA125 and HE4. The DK-index deserves further investigation. Conflict of interest statement Christine Yip is an employee of Vermillion, Inc. and own stocks and stock options of the company. Eric Fung is an employee of Vermillion, Inc. and own stocks and stock options of the company. The remaining authors declare that there are no conflicts of interest.

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