Advertisement

Plasma Biomarker Enrichment of Clinical Prognostic Indices in Malignant Pleural Mesothelioma

Open ArchivePublished:February 20, 2016DOI:https://doi.org/10.1016/j.jtho.2016.02.006

      Abstract

      Objectives

      Prognostic models for malignant pleural mesothelioma (MPM) are needed to prevent potentially futile outcomes. We combined MPM plasma biomarkers with validated clinical prognostic indices to determine whether stratification of risk for death in 194 patients with MPM improved.

      Methods

      Individuals were recruited from three different centers: a discovery cohort (83 patients with MPM) created by combining patients from two U.S. centers and a separate, independent cohort from Canada (111 patients with MPM). Univariable and multivariable analyses were performed on the initial discovery and independent cohorts separately. In the multivariable analyses, prognostic factors were adjusted for the European Organisation for Research and Treatment of Cancer (EORTC) prognostic index (PI) of mesothelioma. The prognostic significance of adding plasma biomarker data to the PI was determined by using the likelihood ratio test, comparing models with and without the addition of biomarker to the clinical PI. The predictive ability of the biomarker was then assessed formally using Harrell’s C-index by applying the fitted model variables of the discovery cohort to the second, independent cohort, including and not including the biomarker with the PI.

      Results

      Higher levels of osteopontin and mesothelin were individually associated with worse prognosis after adjusting for the PI. In the independent cohort, incorporating either plasma osteopontin or mesothelin into the baseline predictive PI model substantively and statistically significantly improved Harrell’s C-statistic. In the final prognostic model, log-osteopontin, EORTC clinical prognostic index, and hemoglobin remained as independently significant predictors and the entire prognostic model improved the optimism-corrected Harrell’s C-index significantly, from 0.718 (0.67–0.77) to 0.801 (0.77–0.84).

      Conclusions

      These data suggest a possible role for preoperative plasma biomarkers to improve the prognostic capability of the EORTC PI of MPM.

      Keywords

      Despite growing reports describing improvement in median survival time for malignant pleural mesothelioma (MPM), current prognostic stratification methods remain suboptimal. Multiple single-institution series have attempted to correlate clinical factors, standard laboratory parameters, and pathologic features in an attempt to better define MPM prognosis. The European Organisation for Research and Treatment of Cancer (EORTC) Prognostic Index
      • Curran D.
      • Sahmoud T.
      • Therasse P.
      • et al.
      Prognostic factors in patients with pleural mesothelioma: the European Organization for Research and Treatment of Cancer experience.
      has been one standard for such prognostic quantification. Additionally, a surgery-based registry identified “best” clinical or pathologic stage, sex, age, histologic subtype, and curative intent surgery as associated with survival.
      • Rusch V.W.
      • Giroux D.
      • Kennedy C.
      • et al.
      Initial analysis of the International Association for the Study of Lung Cancer mesothelioma database.
      These factors were supplemented by white blood cell (WBC) count, hemoglobin (Hb) level, and platelet count.
      • Pass H.I.
      • Giroux D.
      • Kennedy C.
      • et al.
      Supplementary prognostic variables for pleural mesothelioma: a report from the IASLC staging committee.
      This registry serves as an excellent reference source for future studies; however, it does not have an embedded, prospective uniform biospecimen collection component.
      Our laboratory has long had an interest in osteopontin (OPN) as a potential biomarker in MPM. However, we recognized the importance of using plasma OPN (pOPN) instead of serum OPN and the existence of reproducibility issues depending on the enzyme-linked immunosorbent assay (ELISA) platform used.
      • Anborgh P.H.
      • Wilson S.M.
      • Tuck A.B.
      • et al.
      New dual monoclonal ELISA for measuring plasma osteopontin as a biomarker associated with survival in prostate cancer: clinical validation and comparison of multiple ELISAs.
      Reports of the prognostic value of OPN in other malignancies have been based on chemotherapy-treated patients,
      • Thoms J.W.
      • Dal P.A.
      • Anborgh P.H.
      • et al.
      Plasma osteopontin as a biomarker of prostate cancer aggression: relationship to risk category and treatment response.
      • Wu C.Y.
      • Wu M.S.
      • Chiang E.P.
      • et al.
      Elevated plasma osteopontin associated with gastric cancer development, invasion and survival.
      • Petrik D.
      • Lavori P.W.
      • Cao H.
      • et al.
      Plasma osteopontin is an independent prognostic marker for head and neck cancers.
      • Bramwell V.H.
      • Doig G.S.
      • Tuck A.B.
      • et al.
      Serial plasma osteopontin levels have prognostic value in metastatic breast cancer.
      in whom (in some cases) the ELISA results were associated with poor coefficients of variation. Moreover, studies in the literature analyzing serum, plasma mesothelin, or mesothelin-related peptide (MRP)
      • Hollevoet K.
      • Reitsma J.B.
      • Creaney J.
      • et al.
      Serum mesothelin for diagnosing malignant pleural mesothelioma: an individual patient data meta-analysis.
      • Dipalma N.
      • Luisi V.
      • Di S.F.
      • et al.
      Biomarkers in malignant mesothelioma: diagnostic and prognostic role of soluble mesothelin-related peptide.
      • Creaney J.
      • Francis R.J.
      • Dick I.M.
      • et al.
      Serum soluble mesothelin concentrations in malignant pleural mesothelioma: relationship to tumor volume, clinical stage and changes in tumor burden.
      have reported possible prognostic capabilities, but independent validations have been lacking. Fibulin-3 (FBLN3)
      • Creaney J.
      • Dick I.M.
      • Yeoman D.
      • et al.
      Auto-antibodies to beta-F1-ATPase and vimentin in malignant mesothelioma.
      • Pass H.I.
      • Goparaju C.
      Fibulin-3 as a biomarker for pleural mesothelioma.
      is a new plasma marker of MPM with no prognostic evaluations published to date.
      Using the highest-quality ELISAs available, we therefore designed a trial investigating OPN, MRP, and FBLN3 as prognostic factors among other variables, including EORTC Prognostic Index, stage, and other reported laboratory biomarkers, such as the absolute neutrophil-to-absolute lymphocyte ratios (NLRs)
      • Kao S.C.
      • Vardy J.
      • Chatfield M.
      • et al.
      Validation of prognostic factors in malignant pleural mesothelioma: a retrospective analysis of data from patients seeking compensation from the New South Wales Dust Diseases Board.
      in both cytoreduced and nonsurgical patients with MPM. We report that pretherapy pOPN levels were significantly associated with overall survival in mixed populations of patients with MPM in an initial discovery set, and this finding was confirmed in a second independent and blinded data set. Moreover, plasma OPN significantly improved the concordance index (C-index) when added to the EORTC Prognostic Index. These patient cohorts were used to describe for future validation a prognostic model for MPM combining plasma biomarker data with clinical variables.

      Methods

      Patient Populations

      We retrospectively analyzed patients with MPM who were prospectively recruited at the time of diagnosis from three different centers; they all provided signed informed consent to obtain plasma for biomarker studies. An initial cohort (n = 83) was created by combining patients from two centers: the New York University (NYU) Langone Medical Center (44 patients with MPM who were treated between 2007 and 2012) and the Barbara Ann Karmanos Cancer Institute (KCI) (39 patients with MPM who were treated between 1998 and 2006). A separate, independent cohort came from the Princess Margaret Cancer Centre (PMCC) (111 patients with MPM who were treated between 2004 and 2012); their levels of biomarkers were determined at NYU without advanced knowledge of their clinical and survival information. The sequencing of the component therapies for patients receiving multimodality therapy varied according to the individual institutions’ protocols (Supplementary Table 1). When performed, surgery included maximal cytoreduction by pleurectomy decortication, extended pleurectomy, or extrapleural pneumonectomy along with nodal sampling/dissection.
      • Rice D.
      • Rusch V.
      • Pass H.
      • et al.
      Recommendations for uniform definitions of surgical techniques for malignant pleural mesothelioma: a consensus report of the International Association for the Study of Lung Cancer International Staging Committee and the International Mesothelioma Interest Group.
      The EORTC clinical prognostic index (CPI) defined patients as having a good (<1.27) or poor prognosis (≥1.27) using a weighting score of Eastern Cooperative Oncology Group performance status, histologic diagnosis, sex, and pretreatment WBC counts.
      • Curran D.
      • Sahmoud T.
      • Therasse P.
      • et al.
      Prognostic factors in patients with pleural mesothelioma: the European Organization for Research and Treatment of Cancer experience.
      The Cancer and Leukemia Group B (CALGB) index used regression trees to examine prognostic variables in 337 patients treated in seven phase II clinical trials. Six prognostic groups were identified on the basis of age, performance status, Hb level, WBC count, and presence or absence of chest pain and weight loss.
      • Herndon J.E.
      • Green M.R.
      • Chahinian A.P.
      • et al.
      Factors predictive of survival among 337 patients with mesothelioma treated between 1984 and 1994 by the Cancer and Leukemia Group B.

      Specimen Characteristics and Plasma Biomarker Analyses

      Ethylenediaminetetraacetic acid (EDTA)-treated plasma samples were collected before therapy, within a few weeks of the initial histologic diagnosis of mesothelioma, at all three centers and stored locally at –80oC until use. ELISAs, in duplicate, were performed in the NYU Thoracic Surgical Laboratory for initial discovery, and second, independent cohorts were tested for OPN (R&D Systems, Minneapolis, MN), mesothelin (R&D Systems), and FBLN3 (USCN Life Sciences, Wuhan, Hubei, People's Republic China). All plasma biomarker analyses were performed blinded to patient information. The OPN ELISA from R&D Systems was chosen because it was shown by Anborgh
      • Anborgh P.H.
      • Wilson S.M.
      • Tuck A.B.
      • et al.
      New dual monoclonal ELISA for measuring plasma osteopontin as a biomarker associated with survival in prostate cancer: clinical validation and comparison of multiple ELISAs.
      to be the most consistent of the OPN ELISAs available. The MRP ELISA was used because the soluble MRP (SMRP) assay was commercialized and unavailable for research purposes in the United States and because data from our laboratory has demonstrated significant correlation between SMRP and MRP (r = 0.7314, p < 0.0001, 95% confidence interval [CI] for r = 0.5040–0.8640). Only one ELISA is commercially available for FBLN3.

      Statistical Analysis

      Clinicopathologic prognostic index, laboratory prognostic index, treatment prognostic index, CPI, and biomarker variables were compared across the initial discovery and independent cohorts using the chi-square and Kruskal-Wallis tests. Survival times were measured from start of first treatment (surgery, chemotherapy, or radiation) to death or last follow-up time; for patients receiving solely supportive care, survival times were measured from time of diagnosis to death or last follow-up. The primary analysis performed was to evaluate the role of the three plasma biomarkers as independent predictors of outcome using association analyses in the discovery data set and to confirm the significant factors in the second, independent data set. The discovery data were analyzed initially at NYU, whereas the PMCC data were analyzed at PMCC, independently and blinded to what was found at NYU. Final analyses for the manuscript were performed at PMCC. For this, the Kaplan–Meier method and log-rank tests were used to assess differences in overall survival curves. Cox proportional hazard models generated hazard ratios (HRs) and 95% CIs in univariable and multivariable analyses. The nonlinear effect of the clinical factors and biomarkers on overall survival was assessed using multiple fractional polynomials models.
      • Therneau T.M.N.
      • Granbsch P.M.
      Modeling Survival Data: Extending the Cox Model.
      • Harrell F.E.
      • Califf D.B.
      • Pryor K.L.
      • et al.
      Evaluating the yield of medical tests.
      • Royston D.
      • Altman D.
      Regression using fractional polynomials of continous covariatges parsimonious parametric modelling.
      • Sauerbrei W.
      • Royston D.
      Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials.
      The need for nonlinear transformations was observed for only mesothelin and OPN: the logarithmic transformation log(biomarker value/100) was used for all analyses, denoted by log-mesothelin and log-OPN, respectively.
      Univariable and multivariable analyses were performed on the discovery and independent cohorts separately. In separate multivariable analyses, prognostic factors were adjusted primarily for either the EORTC or CALGB CPIs. Because of the complexity of the CALGB CPI, its limited clinical use, and its conventional reclassification in subsequent use and because the original derivation using a partitioning approach had some categories based on very small numbers of patients, it was dichotomized into groups 4 to 6 versus 1 to 3. In contrast, the EORTC CPI was treated as a continuous variable. For Cox proportional hazard models, the proportionality of hazards assumption was assessed for each of the models in two ways: graphically by using Schoenfeld residuals and by formal testing.
      • Therneau T.M.N.
      • Granbsch P.M.
      Modeling Survival Data: Extending the Cox Model.
      The prognostic significance of adding plasma biomarker data to different CPIs was determined with the likelihood ratio test by comparing models with and without the addition of biomarker to the EORTC or CALGB CPIs. When a significant association (p < 0.05) was found between a plasma biomarker and survival in Cox models, the predictive ability of such a biomarker was then assessed formally using Harrell’s C-index.
      • Harrell F.E.
      • Califf D.B.
      • Pryor K.L.
      • et al.
      Evaluating the yield of medical tests.
      • Harrell F.E.
      • Lee K.
      • Mark D.B.
      Multivariable prognostic models: issues in developing models, evaluation assumptions and adequacy, and measuring and reducing errors.
      This was done by applying the fitted model variables of the initial cohort to the second, independent cohort. Furthermore, to measure the improvement of the discriminative power, Harrell's C-indices were calculated for each of the model cohorts, including the biomarker and the model with the clinical prognostic index alone. A bootstrap resampling algorithm was applied to estimate the CIs of the difference between the C-indices (CPI alone versus CPI plus biomarker) using 200 bootstrap replicates each time.
      Finally, a prognostic model was constructed using pooled data; it included significant plasma biomarkers and clinical prognostic factors selected on the basis of clinical rationale. Pooling was necessary to ensure that there were adequate events per predictor variable; however, using pooled data meant that no external validation was available. Thus, bias-corrected C-indices and CIs for the difference in C-indices between final prognostic models with or without plasma biomarkers were calculated through a bootstrap algorithm to account for optimism.
      • Harrell F.E.
      Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis.
      This difference in the C-indices reflects the improvement in the predictive ability attributed to the prognostic effect of log-OPN and log-mesothelin. Additionally, goodness of fit survival curves of the final models were assessed through visual inspection of observed versus predicted survival curves after grouping the risk scores derived from the prognostic model into tertiles.
      All analyses were performed in R, v.3.0.2 (R Development Core Team, R Foundation for Statistical Computing, Vienna, Austria). Parametric and Cox proportional hazards models were fitted using the survival package,
      • Becker R.A.
      • Chambers J.M.
      S: An Interactive Environment for Data Analysis and Graphics.
      C-indices were calculated using Hmisc package.

      Gibhub. Hmisc: Harrell Micscellaneous. https://github.com/harrelfe/Hmisc. Accessed March 7, 2016.

      Results

      Baseline information for the discovery (NYU/KCI) and second, independent (PMCC) cohorts is presented in Table 1. Compared with the NYU/KCI cohort, the PMCC cohort consisted of patients who had a more advanced stage of disease at diagnosis, worse performance status, more significant weight loss, and worse prognosis by the EORTC CPI. However, patients in the validation cohort also had higher baseline Hb levels and were more likely to have received aggressive cytoreductive surgery combined with radiation (p < 0.05, each comparison). The discovery and validation cohorts were also different in terms of the therapies used (Supplementary Table 1). In contrast, within the discovery cohort, patients of the subcohorts from KCI and NYU had similar baseline characteristics (Supplementary Table 2).
      Table 1Baseline Information for the Initial Discovery (NYU/KCI) and Independent (PMCC) Data Sets
      CovariateUnitsNYU/KCI (n = 83)PMCC (n = 111)p Value
      p Value comparing the NYU/KCI and PMCC data sets using nonparametric Kruskal-Wallis tests.
      Clinicopathologic Variables
      AgeMedian (range), y63 (34–86)65 (36–83)0.54
      Sex0.04
       Femalen (%)21 (25%)14 (13%)
       Malen (%)62 (75%)97 (87%)
      Performance status (ECOG)0.007
       0n (%)32 (39%)20 (18%)
       1n (%)37 (45%)66 (59%)
       2 or highern (%)14 (17%)25 (23%)
      Chest pain0.56
       Non (%)37 (47%)57 (51%)
       Yesn (%)42 (53%)54 (49%)
       Missingn40
      Weight loss<0.001
       Non (%)67 (85%)63 (57%)
       Yesn (%)12 (15%)48 (43%)
       Missingn40
      Histologic diagnosis1.00
       Epithelialn (%)58 (70%)77 (69%)
       Othern (%)25 (30%)34 (31%)
      Stage<0.001
       I/IIn (%)26 (31%)9 (8%)
       III/IVn (%)57 (69%)102 (92%)
      Laboratory Data
      Hemoglobin levelMedian (range), g/dL12.0 (8–15)13.3 (7–17)<0.001
       Missingn42
      White blood cell countMedian (range) × 109/L8.0 (4–33)8.0 (4–23)0.54
       Missingn41
      NLRMedian (range)3.2 (1.4–19)3.7 (1.3–15)0.31
       Missingn391
      Platelet countMedian (range) × 109/L302 (41–895)326 (136–880)0.15
       Missingn41
      Treatments
      Cytoreductive surgery0.02
       Non (%)32 (39%)62 (56%)
       Yesn (%)51 (61%)49 (44%)
      Chemotherapy0.26
       Non (%)19 (23%)34 (31%)
       Yesn (%)64 (77%)77 (69%)
      Radiation<0.001
       Non (%)79 (95%)47 (42%)
       Yesn (%)4 (5%)64 (58%)
      Clinical Prognostic Indices
      EORTC prognosis0.03
       Good prognosisn (%)26 (33%)20 (18%)
       Poor prognosisn (%)53 (67%)90 (82%)
       Missingn41
      CALGB
      The EORTC clinical prognostic index was divided into good and poor prognoses on the basis of a regression value cutpoint of 2.7. Weight loss was defined as a weight loss of 10% or more.
       Groups 1–3n (%)53 (67%)64 (58%)0.23
       Groups 4–6n (%)26 (33%)46 (42%)
       Missingn41
      Plasma Biomarkers
      Fibulin-3 levelMedian (range), ng/mL101 (40–316)54 (2–204)<0.001
       Missingn10
      Mesothelin levelMedian (range), ng/mL54 (4–272)43 (5–910)0.41
       Missingn10
      Osteopontin levelMedian (range), ng/mL120 (23–849)108 (19–588)0.24
      NYU, New York University; KCI, Karmanos Cancer Institute; PMCC, Princess Margaret Cancer Centre; ECOG, Eastern Cooperative Oncology Group; NLR, neutrophil-to-lymphocyte ratio; EORTC, European Organisation for Research and Treatment of Cancer; CALGB, Cancer and Leukemia Group B.
      a p Value comparing the NYU/KCI and PMCC data sets using nonparametric Kruskal-Wallis tests.
      b The EORTC clinical prognostic index was divided into good and poor prognoses on the basis of a regression value cutpoint of 2.7. Weight loss was defined as a weight loss of 10% or more.
      Although the distributions of plasma mesothelin and OPN levels were similar between the KCI/NYU and PMCC cohorts, the validation cohort had lower baseline levels of FBLN3 (Table 1 and Supplementary Fig. 1). More advanced stage of disease was associated with higher levels of plasma OPN and mesothelin (but not FBLN3) in the KCI/NYU cohort (Supplementary Table 3). In the PMCC cohort, more advanced stage of disease was associated with higher levels of plasma mesothelin only, but only 8% of this cohort had early Stage I/II disease.
      Despite their having more aggressive disease, with more aggressive management, patients in the validation cohort had better survival than did those in the discovery cohort (p = 0.04 by the log-rank test; Supplementary Fig. 2 and Table 2). Survival was similar in the NYU and KCI subcohorts that constituted the discovery cohort (p = 0.68). All deaths were attributable to MPM.
      Table 2Univariable Analysis of Baseline Characteristics on Overall Survival for the Initial Discovery (NYU/KCI) Data Set and for the Second, Independent (PMCC) Data Set
      Survival CharacteristicNYU/KCIPMCC
      Median follow-up time11 mo16 mo
      Median overall survival time11 (95% CI: 8–14) mo18 (95% CI: 14–22) mo
      Percentage alive at 6 mo70%86%
      Percentage alive at 12 mo41%67%
      Percentage alive at 24 mo23%26%
      CharacteristicHR (95% CI)p ValueGlobal p Value
      • Curran D.
      • Sahmoud T.
      • Therasse P.
      • et al.
      Prognostic factors in patients with pleural mesothelioma: the European Organization for Research and Treatment of Cancer experience.
      HR (95% CI)p ValueGlobal p Value
      Global p values compare across all levels of a single variable. Although discovery and validation analyses are provided in the same table, the analyses were performed sequentially.
      Clinicopathologic Variables
      Age per 10 year increase1.18 (0.9–1.5)0.171.34 (1.02–1.8)0.04
      Sex0.240.24
       Male vs. female1.40 (0.8–2.5)1.52 (0.8–3.1)
      Performance status (ECOG)<0.0010.011
       1 or higher vs. 02.99 (1.8–5.1)2.49 (1.2–5.0)
      Chest pain0.080.053
       Yes vs. no1.58 (1.0–2.6)1.54 (0.99–2.4)
      Weight loss<0.001<0.001
       Yes vs. no3.32 (1.7–6.4)2.8 (1.8–4.4)
      Histologic diagnosis0.14<0.001
       Other vs. epithelial1.48 (0.0–2.5)2.65 (1.6–4.3)
      Stage<0.0010.92
       III/IV vs. I/II3.93 (2.2–7.2)1.05 (0.5–2.4)
      Laboratory Data
      Hemoglobin level per 1g/dL increase0.83 (0.7,0.9)0.0030.73 (0.6–0.9)<0.001
      White blood cell count per 109/L increase2.08 (1.2–3.6)0.012.69 (1.5–4.8)0.001
      NLR per unit increase1.13 (1.0–1.2)0.0071.09 (1.0–1.2)<0.05
      Platelet count per 50 x 109/L increase1.15 (1.1–1.3)<0.0011.22 (1.1–1.3)<0.001
      Treatments
      Cytoreductive surgery<0.001<0.001
       Yes vs. no0.30 (0.2–0.5)0.37 (0.2–0.6)
      Chemotherapy0.0010.30
       Yes vs. no0.41 (0.2–0.7)0.78 (0.5–1.3)
      Radiation0.66<0.001
       Yes vs. no1.26 (0.5–3.5)0.42 (0.3–0.7)
      Clinical Prognostic Indices
      EORTC (per 1.0 unit increase in value)2.68 (1.8–4.1)<0.0013.07 (2.0–4.7)<0.001
      CALGB (Groups 4–6 vs. groups 1–3)4.07 (2.3–7.1)<0.0011.78 (1.1–2.8)0.01
      Plasma Biomarkers
      Each plasma biomarker was analyzed as a continuous variable. For the plasma biomarkers, the primary key findings are in the discovery cohort; univariable analysis of plasma biomarkers for the validation cohort is presented for completeness.
      Fibulin-3 per 50 ng/mL increase0.87 (0.7–1.1)0.231.32 (1.0–1.7)
      This value is being presented for completeness; as there was no significance in the discovery cohort, fibulin-3 was not formally validated.
      0.06
      Log-mesothelin per log(ng/100 mL) increase2.16 (1.5–3.1)<0.0011.28 (1.1–1.6)0.22
      Log-osteopontin per log(ng/100 mL) increase3.31 (2.3–4.8)<0.0013.93 (2.9–5.4)<0.001
      Note: Weight loss was defined as a weight loss of 10% or more.
      NYU, New York University; KCI, Karmanos Cancer Institute; PMCC, Princess Margaret Cancer Centre; HR, hazard ratio; CI, confidence interval; ECOG, Eastern Cooperative Oncology Group; NLR, neutrophil-to-lymphocyte ratio; EORTC, European Organisation for Research and Treatment of Cancer; CALGB – Cancer and Leukemia Group B.
      a Global p values compare across all levels of a single variable. Although discovery and validation analyses are provided in the same table, the analyses were performed sequentially.
      b Each plasma biomarker was analyzed as a continuous variable. For the plasma biomarkers, the primary key findings are in the discovery cohort; univariable analysis of plasma biomarkers for the validation cohort is presented for completeness.
      c This value is being presented for completeness; as there was no significance in the discovery cohort, fibulin-3 was not formally validated.
      In all survival analyses, no violations of the assumption of proportionality were observed. Nonlinear effects of mesothelin and OPN (but not FBLN3) on OS were observed (Supplementary Fig. 3); log-transformation was found to best address this issue, and log-mesothelin and log-OPN were used for all prognostic analyses. The relationships between individual clinicopathologic, laboratory, and treatment variables with survival in the discovery and validation data sets are presented in Table 2. Many of the non–treatment-related variables have been incorporated into the EORTC
      • Curran D.
      • Sahmoud T.
      • Therasse P.
      • et al.
      Prognostic factors in patients with pleural mesothelioma: the European Organization for Research and Treatment of Cancer experience.
      and CALGB
      • Herndon J.E.
      • Green M.R.
      • Chahinian A.P.
      • et al.
      Factors predictive of survival among 337 patients with mesothelioma treated between 1984 and 1994 by the Cancer and Leukemia Group B.
      CPIs, which were both significantly prognostic in both the initial discovery and second, independent cohorts (Table 2). Of the plasma biomarkers evaluated in the KCI/NYU cohort, higher levels of log-OPN and log-mesothelin (but not FBLN3) were each individually associated with worse prognosis after adjusting for CPIs (Table 3) and were therefore further evaluated for their predictive ability in the independent PMCC cohort. In the PMCC cohort, incorporating either plasma log-OPN or log-mesothelin into the baseline predictive CPI models substantively and significantly improved Harrell’s C-statistic as the CIs of the difference in C-statistics did not cross zero (Table 3).
      Table 3Predictive Models of Overall Survival for Log-Osteopontin and Log-Mesothelin
      CohortVariableAdjusted for EORTC CPIAdjusted for CALGB CPI
      HR (95% CI)p ValueHR (95% CI)p Value
      NYU/KCIlog-osteopontin2.70 (1.8–4.0)<0.0012.71 (1.8–4.1)<0.001
      log-mesothelin1.94 (1.4–2.8)<0.0011.63 (1.1–2.4)0.009
      PMCClog-osteopontin3.53 (2.6–4.9)<0.0014.05 (2.9–5.6)<0.001
      log-mesothelin1.27 (1.1–1.5)<0.0011.40 (1.2–1.7)<0.001
      Discovery (NYU/KCI) Cohort
      Prognostic VariablesEORTC CPICALGB CPI
      CPI alone (for log-osteopontin analysis),
      The baseline CPI-only C-indices reflect the patient samples available for the associated biomarker analyses; there was one missing log-mesothelin value (and no missing los-osteopontin values) in the discovery data set that results in minor differences in the CPI-only indices.
      C-index (95% CI)
      0.649 (0.59–0.70)0.641 (0.59–0.69)
      CPI alone (for log-mesothelin analysis),
      The baseline CPI-only C-indices reflect the patient samples available for the associated biomarker analyses; there was one missing log-mesothelin value (and no missing los-osteopontin values) in the discovery data set that results in minor differences in the CPI-only indices.
      C-index (95% CI)
      0.645 (0.59–0.70)0.640 (0.59–0.69)
      CPI + log-osteopontin, C-index (95% CI)0.767 (0.71–0.82)0.763 (0.71–0.81)
      CPI + log-mesothelin, C-index (95% CI)0.692 (0.63–0.76)0.724 (0.66–0.79)
      Improvement in Harrell’s C-indices when adding log-osteopontin
      Confidence intervals for the difference in Harrell’s C-indices were calculated using 200 bootstrap replicates. Intervals that did not cross zero are interpreted as having the Harrell’s C-index demonstrate significant improvement with the addition of the biomarker.
      0.118 (0.10–0.18)0.122 (0.11–0.18)
      Improvement in Harrell’s C-indices when adding log-mesothelin
      Confidence intervals for the difference in Harrell’s C-indices were calculated using 200 bootstrap replicates. Intervals that did not cross zero are interpreted as having the Harrell’s C-index demonstrate significant improvement with the addition of the biomarker.
      0.045 (0.03–0.11)0.084 (0.06–0.13)
      Validation (PMCC) Cohort
      Prognostic VariablesEORTC CPICALGB CPI
      CPI alone, C-index (95% CI)0.596 (0.55–0.64)0.602 (0.54–0.66)
      CPI + log-osteopontin, C-index (95% CI)0.811 (0.76–0.86)0.781 (0.73–0.83)
      CPI + log-mesothelin, C-index (95% CI)0.650 (0.58–0.72)0.649 (0.58–0.71)
      Improvement in Harrell’s C-indices when adding log-osteopontin
      Confidence intervals for the difference in Harrell’s C-indices were calculated using 200 bootstrap replicates. Intervals that did not cross zero are interpreted as having the Harrell’s C-index demonstrate significant improvement with the addition of the biomarker.
      0.216 (0.20–0.26)0.179 (0.16–0.23)
      Improvement in Harrell’s C-indices when adding log-mesothelin
      Confidence intervals for the difference in Harrell’s C-indices were calculated using 200 bootstrap replicates. Intervals that did not cross zero are interpreted as having the Harrell’s C-index demonstrate significant improvement with the addition of the biomarker.
      0.054 (0.03–0.12)0.047 (0.03–0.10)
      Note: (Top panel) Cox proportional hazard models of association; p values are derived from likelihood ratios comparing the models with and without the biomarker of interest. (Middle and bottom panels) Prognostic model evaluation comparing Harrell’s C-indices with and without the biomarker of interest in the model.
      Log-mesothelin and log-osteopontin are in log(ng/100mL) units.
      NYU, New York University; KCI, Karmanos Cancer Institute; PMCC, Princess Margaret Cancer Centre; CPI, clinical prognostic index; HR, hazard ratio; CI, confidence interval; EORTC, European Organisation for Research and Treatment of Cancer; CALGB, Cancer and Leukemia Group B.
      a The baseline CPI-only C-indices reflect the patient samples available for the associated biomarker analyses; there was one missing log-mesothelin value (and no missing los-osteopontin values) in the discovery data set that results in minor differences in the CPI-only indices.
      b Confidence intervals for the difference in Harrell’s C-indices were calculated using 200 bootstrap replicates. Intervals that did not cross zero are interpreted as having the Harrell’s C-index demonstrate significant improvement with the addition of the biomarker.
      In developing a prognostic model that includes log-OPN and log-mesothelin, we utilized the entire data set of individuals with complete data on all key variables (n = 154); missing individuals had clinicodemographic and outcomes data similar to those of the discovery cohort. Predictors included in this model were chosen on the basis of clinical factors: in addition to log-OPN and log-mesothelin, we included CPI, clinical stage, and three laboratory values associated with outcome in our data sets (Hb level, NLR, and platelet counts). The EORTC CPI was included in the model over the CALGB CPI because it had better performance in our population (Table 3), is the more commonly used CPI in clinical practice, and was generated from a regression model as a continuous variable. Within this prognostic model, log-OPN, EORTC CPI, and Hb level continued to remain as independently significant predictors, and the entire prognostic model improved the optimism-corrected Harrell’s C-index significantly from 0.718 (0.67–0.77) to 0.801 (0.77–0.84) (Table 4). Model calibration curves (Fig. 1) visually confirm good fit between predicted and observed survival curves when these prognostic scores are used.
      Table 4Prognostic Model Development: Pooled Analysis
      Hazard Ratio (95% Confidence Interval)p Value
      Log-osteopontin3.57 (2.5–5.1)<0.001
      Log-mesothelin0.97 (0.8–1.2)0.80
      EORTC CPI1.99 (1.4–2.9)<0.001
      Stage III/IV vs. I/II1.36 (0.8–2.4)0.30
      Hemoglobin level0.10 (0.0–0.3)<0.001
      Platelet count2.56 (0.6–10)0.19
      Neutrophil-to-lymphocyte ratio0.96 (0.5–2.0)0.91
      Prognostic modelC-Index, OriginalC-Index, Optimism-CorrectedDifference in C-Index
      Without log-osteopontin, log-mesothelin0.726 (0.68–0.78)0.718 (0.67–0.77)
      With log-osteopontin, log-mesothelin0.813 (0.78–0.85)0.801 (0.77–0.84)0.087 (0.08–0.12)
      Note: Because of missing data, only 154 pooled cases were analyzed. Selection of variables for the prognostic model was based on clinical rationale. The EORTC CPI was included in the model as it was the more commonly used CPI and because its general performance was better than with the CALGB CPI (see Table 3); as this CPI did not include stage, we included that variable separately. Finally, three laboratory variables not found in the EORTC CPI were also included. The regression equation generating the risk score is RS = 1.273 * log(osteopontin[ng/mL]/100) – 0.025 * log(Mesothelin(ng/mL)/100) + 0.686 * EORTC CPI – 2.349 * Hemoglobin(g/dL) – 0.0402 * NLR/10 + 0.941 * Platelets[×109 /L]/1000 + 0.307 (if stage III/IV; if I/II, omit this last term). Median (range) of risk score values is 0.046 (–2.88 to 3.28).
      CPI, clinical prognostic index; EORTC, European Organisation for Research and Treatment of Cancer.
      Figure thumbnail gr1
      Figure 1Visual inspection of model fit curves evaluating tertiles of the risk score generated from the pooled prognostic model. The observed (solid) and predicted (dashed) survival curves are presented. The darkest color represents the worse prognostic risk scores, with progressively lighter lines representing the higher tertiles of risk score. The tertile risk score ranges are as follows: worst prognosis category (risk scores –2.88 to –0.619), intermediate prognosis category (risk scores –0.619 to 0.710), and best prognosis (risk scores 0.71 to 3.28).

      Discussion

      Prognostic biomarkers, including clinicodemographic information, standard laboratory parameters, and novel plasma- or tissue-based tests have tremendous potential to improve care by assisting clinicians in appropriately tailoring therapies to individual patients' needs. The low disease incidence and diverse treatment strategies have hindered the discovery of prognostic biomarkers in MPM. Nonetheless, studies attempting to define novel prognostic markers in MPM have been reported, chiefly in patients receiving supportive care or chemotherapy only. Both the EORTC prognostic score and the CALGB index, which includes a combination of clinical and laboratory variables, have been validated prognostically in studies of noncytoreduced MPMs, and recently were again validated by Meniawy et al.
      • De R.A.
      • Richards W.G.
      • Yeap B.Y.
      • et al.
      Sequential binary gene ratio tests define a novel molecular diagnostic strategy for malignant pleural mesothelioma.
      in 274 patients with MPM; of the two prognostic indices, the EORTC prognostic index has been the more commonly used.
      Novel MPM prognostic biomarkers include tissue-based assays, including a four-gene expression test,
      • De R.A.
      • Richards W.G.
      • Yeap B.Y.
      • et al.
      Sequential binary gene ratio tests define a novel molecular diagnostic strategy for malignant pleural mesothelioma.
      expression arrays,
      • Pass H.I.
      • Liu Z.
      • Wali A.
      • et al.
      Gene expression profiles predict survival and progression of pleural mesothelioma.
      P16/CDKN2A homozygous deletion,
      • Lopez-Rios F.
      • Chuai S.
      • Flores R.
      • et al.
      Global gene expression profiling of pleural mesotheliomas: overexpression of aurora kinases and P16/CDKN2A deletion as prognostic factors and critical evaluation of microarray-based prognostic prediction.
      high nuclear grade,
      • Kadota K.
      • Suzuki K.
      • Colovos C.
      • et al.
      A nuclear grading system is a strong predictor of survival in epitheloid diffuse malignant pleural mesothelioma.
      and mir-29c*,
      • Pass H.I.
      • Goparaju C.
      • Ivanov S.
      • et al.
      hsa-miR-29c* is linked to the prognosis of malignant pleural mesothelioma.
      all of which have been reported to have prognostic significance; however, few have been blindly validated. The most extensively published candidate blood-based prognostic biomarkers include sMRP
      • Dipalma N.
      • Luisi V.
      • Di S.F.
      • et al.
      Biomarkers in malignant mesothelioma: diagnostic and prognostic role of soluble mesothelin-related peptide.
      • Hollevoet K.
      • Nackaerts K.
      • Thas O.
      • et al.
      The effect of clinical covariates on the diagnostic and prognostic value of soluble mesothelin and megakaryocyte potentiating factor.
      • Cristaudo A.
      • Bonotti A.
      • Simonini S.
      • et al.
      Combined serum mesothelin and plasma osteopontin measurements in malignant pleural mesothelioma.
      • Schneider J.
      • Hoffmann H.
      • Dienemann H.
      • et al.
      Diagnostic and prognostic value of soluble mesothelin-related proteins in patients with malignant pleural mesothelioma in comparison with benign asbestosis and lung cancer.
      • Grigoriu B.D.
      • Scherpereel A.
      • Devos P.
      • et al.
      Utility of osteopontin and serum mesothelin in malignant pleural mesothelioma diagnosis and prognosis assessment.
      and OPN, along with NLR and fibrinogen.
      • Ghanim B.
      • Klikovits T.
      • Hoda M.A.
      • et al.
      Ki67 index is an independent prognostic factor in epithelioid but not in non-epithelioid malignant pleural mesothelioma: a multicenter study.
      At least three single reports point to OPN as a possible prognostic factor in MPM in nonsurgical MPM cohorts.
      • Grigoriu B.D.
      • Scherpereel A.
      • Devos P.
      • et al.
      Utility of osteopontin and serum mesothelin in malignant pleural mesothelioma diagnosis and prognosis assessment.
      • Cappia S.
      • Righi L.
      • Mirabelli D.
      • et al.
      Prognostic role of osteopontin expression in malignant pleural mesothelioma.
      • Hollevoet K.
      • Nackaerts K.
      • Gosselin R.
      • et al.
      Soluble mesothelin, megakaryocyte potentiating factor, and osteopontin as markers of patient response and outcome in mesothelioma.
      The study by Hollevoet et al. involved 48 patients, all of whom received only chemotherapy, and the prognostic significance of OPN was not compared with the EORTC prognostic criteria. Grigoriu et al. found both SMRP and OPN to be prognostic in 91 patients with MPM who were treated with or without surgery; but again, they did not combine the markers with each other or with clinical prognostic criteria. It is interesting that in both these studies, as in ours, stage of disease did not persist as an independent predictor of death in multivariate analysis or in the final prognostic models. Nevertheless, biomarker studies of the prognostic implications of pOPN have been largely ignored owing to the inability to reproduce its value as a specific diagnostic marker of MPM and because of the aforementioned performance differences across available ELISA platforms. Another recently described biomarker, FBLN3,
      • Pass H.I.
      • Levin S.M.
      • Harbut M.R.
      • et al.
      Fibulin-3 as a blood and effusion biomarker for pleural mesothelioma.
      was reported to have diagnostic discrimination in plasma for diagnostic purposes but was not prognostic in plasma. Two studies have reported that FBLN3 may be prognostic when measured in the pleural effusions of patients with MPM.
      • Pass H.I.
      • Levin S.M.
      • Harbut M.R.
      • et al.
      Fibulin-3 as a blood and effusion biomarker for pleural mesothelioma.
      • Kirschner M.B.
      • Pulford E.
      • Hoda M.A.
      • et al.
      Fibulin-3 levels in malignant pleural mesothelioma are associated with prognosis but not diagnosis.
      The NLR could not be validated as a reliable prognostic biomarker in a recent report from Meniawy et al.
      • Meniawy T.M.
      • Creaney J.
      • Lake R.A.
      • et al.
      Existing models, but not neutrophil-to-lymphocyte ratio, are prognostic in malignant mesothelioma.
      The novelty of the present study, therefore, was to design a study that examined whether the most-studied MPM plasma and serum biomarkers have added value to known clinical prognostic indices. Using a carefully constructed and blinded discovery and validation analysis that combines all the plasma-based biomarkers as well as the most reliable clinical and laboratory parameters was also one of the novel aspects of the study. We examined most of the reported biomarkers in MPM in both cytoreduced (56%) and noncytoreduced patients (44%) and used an independent cohort for validation. The number of patients in the analysis (n = 188) is small compared with that in the recently published IASLC database (n = 1494), but it compares favorably with the number of patients analyzed by Curran et al.
      • Curran D.
      • Sahmoud T.
      • Therasse P.
      • et al.
      Prognostic factors in patients with pleural mesothelioma: the European Organization for Research and Treatment of Cancer experience.
      (n = 204) and most recently by Meniawy et al.
      • Meniawy T.M.
      • Creaney J.
      • Lake R.A.
      • et al.
      Existing models, but not neutrophil-to-lymphocyte ratio, are prognostic in malignant mesothelioma.
      To maintain maximum stringency, we required that all plasma biomarker levels be measured on samples obtained before the initiation of therapy and used a discovery set followed by a blinded validation cohort. Unlike MPM, other tumor types can allow for “matching” of cohorts owing to a large number of available patients. Our patients were recruited prospectively from institutions with considerable expertise in the treatment of MPM, but the components and timing of the multimodality treatment approaches were not uniform across the cohorts. In fact, the PMCC investigators are unique in their promising reports for preoperative radiation therapy and extrapleural pneumonectomy in MPM.
      • Cho B.C.
      • Feld R.
      • Leighl N.
      • et al.
      A feasibility study evaluating Surgery for Mesothelioma After Radiation Therapy: the “SMART” approach for resectable malignant pleural mesothelioma.
      The test set from KCI was collected before the discovery and validation sets, which were collected simultaneously from NYU and PMCC. Nevertheless, despite the study samples having been accrued over a 14-year period, the median survivals of the KCI and NYU samples were not significantly different when each site's patients with stage I/II disease (28 months versus 31 months, respectively, p = 0.48) and patients with stage III/IV disease (12 months versus 16 months, respectively, p = 0.19) were compared.
      Studying mesothelioma is a special case because of its rarity compared with lung cancer. Methodologies that can be readily applied to common cancers are difficult to apply to mesothelioma. In univariable analyses, some of the variables that were significantly associated with reduced survival across all cohorts have been previously reported to be similarly prognostic, including poor performance status, weight loss, low Hb level, high WBC count, increased NLR, high platelet count, and inability to cytoreduce. The EORTC prognostic index was significant in both the discovery and validation cohorts, and it was used as a surrogate for all non–laboratory-related clinical variables.
      As stated earlier, what has been lacking in previous reports on MPM biomarkers but addressed by us is whether any of the reported plasma biomarkers provide added value to the clinical CPI according to C-statistic comparison. Both log-OPN and log-mesothelin were predictive of survival when added to the EORTC CPI, and both increased the C-index for the discovery and validation sets. Only after we demonstrated that there was added value to both a discovery and validation set did we use a pooled analysis model, which should be validated in future trials. By using a bootstrap internal validation modeling approach, we attempted to compensate for overfit or “optimism,” and this approach has greater power in demonstrating improvement in prognostication. In our analysis, using separate development and validation data sets may actually be inferior to pooling all the data and using bootstrap internal validation. As endorsed by Harrell et al
      • Harrell F.E.
      Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis.
      and by Steyerberg and Lingsma,
      • Steyerberg E.W.
      • Lingsma H.F.
      Predicting citations: Validating prediction models 10.
      the pooled effect estimates in the model are more likely to be accurate than are the estimates in either group alone.
      As the MPM staging system undergoes revision, supplementary prognostic factors in addition to the usual clinicopathologic demographics could add value in the selection of patients for radical and potentially morbid procedures. These supplementary factors, including plasma biomarkers, could alert clinicians that certain patients with MPM are not candidates for cytoreductive surgery because their chance of survival is limited by a more aggressive phenotype. To potentially validate the CPI for the pooled data described in this study, an international effort to collect blood elements before treatment must be addressed. Such efforts should be limited not only to potential cytoreducible patients but also to those enrolled in ongoing novel therapeutic trials.

      Acknowledgments

      This study was supported by the following: the National Cancer Institute; funding from the Early Detection Research Network, National Institutes of Health, to Dr. Pass (U01 CA-111295); the Princess Margaret Hospital Foundation and the Princess Margaret Hospital Mesothelioma Research Program (funded by the Masters Insulators Association of Ontario, International Association of Heat and Frost Insulators and Asbestos Workers, Local 793, and other unions, and the Imperial Oil Charitable Foundation) for plasma banking; the M. Qasim Choksi Chair in Lung Cancer Translational Research (held by Dr. Tsao); the Alan B. Brown Chair in Molecular Genetics and CCO Chair in Experimental Therapeutics and Population Studies (both held by Dr. Liu); the Ontario Ministry of Health and Long-Term Care; and donations from Belluck and Fox, the Simmons Foundation, Levi, Phillips, and Konigsberg, the Stephen E. Banner Fund for Lung Cancer Research, the Rosenwald Family, and the Anderson family.

      Supplementary Data

      References

        • Curran D.
        • Sahmoud T.
        • Therasse P.
        • et al.
        Prognostic factors in patients with pleural mesothelioma: the European Organization for Research and Treatment of Cancer experience.
        J Clin Oncol. 1998; 16: 145-152
        • Rusch V.W.
        • Giroux D.
        • Kennedy C.
        • et al.
        Initial analysis of the International Association for the Study of Lung Cancer mesothelioma database.
        J Thorac Oncol. 2012; 7: 1631-1639
        • Pass H.I.
        • Giroux D.
        • Kennedy C.
        • et al.
        Supplementary prognostic variables for pleural mesothelioma: a report from the IASLC staging committee.
        J Thorac Oncol. 2014; 9: 856-864
        • Anborgh P.H.
        • Wilson S.M.
        • Tuck A.B.
        • et al.
        New dual monoclonal ELISA for measuring plasma osteopontin as a biomarker associated with survival in prostate cancer: clinical validation and comparison of multiple ELISAs.
        Clin Chem. 2009; 55: 895-903
        • Thoms J.W.
        • Dal P.A.
        • Anborgh P.H.
        • et al.
        Plasma osteopontin as a biomarker of prostate cancer aggression: relationship to risk category and treatment response.
        Br J Cancer. 2012; 107: 840-846
        • Wu C.Y.
        • Wu M.S.
        • Chiang E.P.
        • et al.
        Elevated plasma osteopontin associated with gastric cancer development, invasion and survival.
        Gut. 2007; 56: 782-789
        • Petrik D.
        • Lavori P.W.
        • Cao H.
        • et al.
        Plasma osteopontin is an independent prognostic marker for head and neck cancers.
        J Clin Oncol. 2006; 24: 5291-5297
        • Bramwell V.H.
        • Doig G.S.
        • Tuck A.B.
        • et al.
        Serial plasma osteopontin levels have prognostic value in metastatic breast cancer.
        Clin Cancer Res. 2006; 12: 3337-3343
        • Hollevoet K.
        • Reitsma J.B.
        • Creaney J.
        • et al.
        Serum mesothelin for diagnosing malignant pleural mesothelioma: an individual patient data meta-analysis.
        J Clin Oncol. 2012; 30: 1541-1549
        • Dipalma N.
        • Luisi V.
        • Di S.F.
        • et al.
        Biomarkers in malignant mesothelioma: diagnostic and prognostic role of soluble mesothelin-related peptide.
        Int J Biol Markers. 2011; 26: 160-165
        • Creaney J.
        • Francis R.J.
        • Dick I.M.
        • et al.
        Serum soluble mesothelin concentrations in malignant pleural mesothelioma: relationship to tumor volume, clinical stage and changes in tumor burden.
        Clin Cancer Res. 2011; 17: 1181-1189
        • Creaney J.
        • Dick I.M.
        • Yeoman D.
        • et al.
        Auto-antibodies to beta-F1-ATPase and vimentin in malignant mesothelioma.
        PLoS One. 2011; 6: e26515
        • Pass H.I.
        • Goparaju C.
        Fibulin-3 as a biomarker for pleural mesothelioma.
        N Engl J Med. 2013; 368: 190
        • Kao S.C.
        • Vardy J.
        • Chatfield M.
        • et al.
        Validation of prognostic factors in malignant pleural mesothelioma: a retrospective analysis of data from patients seeking compensation from the New South Wales Dust Diseases Board.
        Clin Lung Cancer. 2013; 14: 70-77
        • Rice D.
        • Rusch V.
        • Pass H.
        • et al.
        Recommendations for uniform definitions of surgical techniques for malignant pleural mesothelioma: a consensus report of the International Association for the Study of Lung Cancer International Staging Committee and the International Mesothelioma Interest Group.
        J Thorac Oncol. 2011; 6: 1304-1312
        • Herndon J.E.
        • Green M.R.
        • Chahinian A.P.
        • et al.
        Factors predictive of survival among 337 patients with mesothelioma treated between 1984 and 1994 by the Cancer and Leukemia Group B.
        Chest. 1998; 13: 723-731
        • Therneau T.M.N.
        • Granbsch P.M.
        Modeling Survival Data: Extending the Cox Model.
        Springer, New York, NY2000
        • Harrell F.E.
        • Califf D.B.
        • Pryor K.L.
        • et al.
        Evaluating the yield of medical tests.
        JAMA. 1982; 247 (2543–2546)
        • Royston D.
        • Altman D.
        Regression using fractional polynomials of continous covariatges parsimonious parametric modelling.
        J Royal Stat Soc. 2015; 43: 429-467
        • Sauerbrei W.
        • Royston D.
        Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials.
        J Royal Stat Soc. 1999; 162: 71-94
        • Harrell F.E.
        • Lee K.
        • Mark D.B.
        Multivariable prognostic models: issues in developing models, evaluation assumptions and adequacy, and measuring and reducing errors.
        Stat Med. 1996; 5: 361-387
        • Harrell F.E.
        Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis.
        Springer, New York, NY2001
        • Becker R.A.
        • Chambers J.M.
        S: An Interactive Environment for Data Analysis and Graphics.
        Wadsworth and Brooks/Cole, Pacific Grove, CA1984
      1. Gibhub. Hmisc: Harrell Micscellaneous. https://github.com/harrelfe/Hmisc. Accessed March 7, 2016.

        • Meniawy T.M.
        • Creaney J.
        • Lake R.A.
        • et al.
        Existing models, but not neutrophil-to-lymphocyte ratio, are prognostic in malignant mesothelioma.
        Br J Cancer. 2013; 109: 1813-1820
        • De R.A.
        • Richards W.G.
        • Yeap B.Y.
        • et al.
        Sequential binary gene ratio tests define a novel molecular diagnostic strategy for malignant pleural mesothelioma.
        Clin Cancer Res. 2013; 19: 2493-2502
        • Pass H.I.
        • Liu Z.
        • Wali A.
        • et al.
        Gene expression profiles predict survival and progression of pleural mesothelioma.
        Clin Cancer Res. 2004; 10: 849-859
        • Lopez-Rios F.
        • Chuai S.
        • Flores R.
        • et al.
        Global gene expression profiling of pleural mesotheliomas: overexpression of aurora kinases and P16/CDKN2A deletion as prognostic factors and critical evaluation of microarray-based prognostic prediction.
        Cancer Res. 2006; 66: 2970-2979
        • Kadota K.
        • Suzuki K.
        • Colovos C.
        • et al.
        A nuclear grading system is a strong predictor of survival in epitheloid diffuse malignant pleural mesothelioma.
        Mod Pathol. 2012; 25: 260-271
        • Pass H.I.
        • Goparaju C.
        • Ivanov S.
        • et al.
        hsa-miR-29c* is linked to the prognosis of malignant pleural mesothelioma.
        Cancer Res. 2010; 70: 1916-1924
        • Hollevoet K.
        • Nackaerts K.
        • Thas O.
        • et al.
        The effect of clinical covariates on the diagnostic and prognostic value of soluble mesothelin and megakaryocyte potentiating factor.
        Chest. 2012; 141: 477-484
        • Cristaudo A.
        • Bonotti A.
        • Simonini S.
        • et al.
        Combined serum mesothelin and plasma osteopontin measurements in malignant pleural mesothelioma.
        J Thorac Oncol. 2011; 6: 1587-1593
        • Schneider J.
        • Hoffmann H.
        • Dienemann H.
        • et al.
        Diagnostic and prognostic value of soluble mesothelin-related proteins in patients with malignant pleural mesothelioma in comparison with benign asbestosis and lung cancer.
        J Thorac Oncol. 2008; 3: 1317-1324
        • Grigoriu B.D.
        • Scherpereel A.
        • Devos P.
        • et al.
        Utility of osteopontin and serum mesothelin in malignant pleural mesothelioma diagnosis and prognosis assessment.
        Clin Cancer Res. 2007; 13: 2928-2935
        • Ghanim B.
        • Klikovits T.
        • Hoda M.A.
        • et al.
        Ki67 index is an independent prognostic factor in epithelioid but not in non-epithelioid malignant pleural mesothelioma: a multicenter study.
        Br J Cancer. 2015; 112: 783-792
        • Cappia S.
        • Righi L.
        • Mirabelli D.
        • et al.
        Prognostic role of osteopontin expression in malignant pleural mesothelioma.
        Am J Clin Pathol. 2008; 130: 58-64
        • Hollevoet K.
        • Nackaerts K.
        • Gosselin R.
        • et al.
        Soluble mesothelin, megakaryocyte potentiating factor, and osteopontin as markers of patient response and outcome in mesothelioma.
        J Thorac Oncol. 2011; 6: 1930-1937
        • Pass H.I.
        • Levin S.M.
        • Harbut M.R.
        • et al.
        Fibulin-3 as a blood and effusion biomarker for pleural mesothelioma.
        N Engl J Med. 2012; 367: 1417-1427
        • Cho B.C.
        • Feld R.
        • Leighl N.
        • et al.
        A feasibility study evaluating Surgery for Mesothelioma After Radiation Therapy: the “SMART” approach for resectable malignant pleural mesothelioma.
        J Thorac Oncol. 2014; 9: 397-402
        • Steyerberg E.W.
        • Lingsma H.F.
        Predicting citations: Validating prediction models 10.
        BMJ. 2008; 336: 789
        • Kirschner M.B.
        • Pulford E.
        • Hoda M.A.
        • et al.
        Fibulin-3 levels in malignant pleural mesothelioma are associated with prognosis but not diagnosis.
        Br J Cancer. 2015; 113: 963-969