| Literature DB >> 30008600 |
Guadalupe Aguilar-Madrid1, Beate Pesch2, Emma S Calderón-Aranda3, Katarzyna Burek2, Carmina Jiménez-Ramírez1,4, Cuauhtémoc Arturo Juárez-Pérez1, María Dolores Ochoa-Vázquez5, Luis Torre-Bouscoulet6, Leonor Concepción Acosta-Saavedra3, Isabel Sada-Ovalle7, Jorge García-Figueroa7, Isabel Alvarado-Cabrero8, Patricia Castillo-González9, Alejandra Renata Báez-Saldaña9, José Rogelio Pérez-Padilla10, Juvencio Osnaya-Juárez5, Rosa María Rivera-Rosales11, Eric Marco García-Bazán12, Yolanda Lizbeth Bautista-Aragón13, Elimelec Lazcano-Hernandez12, Daniel Alejandro Munguía-Canales14, Luis Marcelo Argote-Greene15, Dirk Taeger2, Daniel Gilbert Weber2, Swaantje Casjens2, Irina Raiko2, Thomas Brüning2, Georg Johnen2.
Abstract
Background: Diagnosis of malignant pleural mesothelioma (MPM) remains a challenge, especially when resources in pathology are limited. The study aimed to evaluate cost-effective tumor markers to predict the probability of MPM in plasma samples in order to accelerate the diagnostic workup of the tissue of potential cases.Entities:
Keywords: asbestos.; calretinin; diagnostic marker; mesothelin; mesothelioma
Mesh:
Substances:
Year: 2018 PMID: 30008600 PMCID: PMC6036095 DOI: 10.7150/ijms.23939
Source DB: PubMed Journal: Int J Med Sci ISSN: 1449-1907 Impact factor: 3.738
Description of the study population of cases with MPM and controls from Mexico
| Male | Female | ||||||
|---|---|---|---|---|---|---|---|
| Characteristics | Cases N (%) | Controls N (%) | Cases N (%) | Controls N (%) | |||
| Total | 63 | 172 | 12 | 68 | |||
| Age (years) | |||||||
| Median (IQR) | 64 (56-72) | 62 (55-71) | 0.59 | 62 (53-68) | 61 (53-70) | 0.82 | |
| Histologic diagnosis | |||||||
| Epithelioid | 52 (82.5) | 12 (100) | |||||
| Sarcomatoid | 3 (4.8) | - | |||||
| Other | 7 (11.1) | - | |||||
| Not specified | 1 (1.6) | - | |||||
| Smoking status | 0.0003 | - | |||||
| Never smoker | 21 (33.3) | 65 (37.8) | 8 (66.7) | 44 (64.7) | |||
| Former/current smoker | 42 (66.7) | 107 (62.2) | 4 (33.3) | 24 (35.3) | |||
| Occupational exposure to asbestos | <0.0001 | 0.24 | |||||
| Never | 4 (6.3) | 51 (29.7) | 8 (66.7) | 56 (82.4) | |||
| Ever | 59 (93.7) | 121 (70.3) | 4 (33.3) | 12 (17.7) | |||
aContinuous variables were compared using the Wilcoxon rank-sum test, categorical variables were compared using Fisher's exact test. IQR: interquartile range
Distribution of biomarker concentrations in plasma samples from cases with MPM and controls from Mexico
| Cases | Controls | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| N | Median | IQR | N | Median | IQR | ||||
| Males | 63 | 2.21 | 1.37-3.93 | 170 | 0.58 | 0.40-0.87 | <0.0001 | ||
| Age | <70 years | 41 | 2.04 | 1.28-3.77 | 120 | 0.52 | 0.38-0.76 | ||
| ≥70 years | 22 | 2.75 | 2.14-3.93 | 50 | 0.76 | 0.48-1.02 | |||
| Occupational exposure to asbestos | |||||||||
| Never | 4 | 1.10 | 0.66-2.76 | 51 | 0.61 | 0.43-0.85 | |||
| Ever | 59 | 2.29 | 1.56-3.93 | 119 | 0.55 | 0.40-0.89 | |||
| Females | 12 | 2.00 | 1.31-5.40 | 65 | 0.55 | 0.41-0.79 | <0.0001 | ||
| Age | <70 years | 9 | 1.46 | 1.28-3.24 | 48 | 0.55 | 0.41-0.78 | ||
| ≥70 years | 3 | 6.20 | 2.13-9.90 | 17 | 0.59 | 0.31-0.95 | |||
| Occupational exposure to asbestos | |||||||||
| Never | 8 | 1.80 | 1.29-5.40 | 53 | 0.55 | 0.38-0.79 | |||
| Ever | 4 | 2.56 | 1.57-5.01 | 12 | 0.55 | 0.45-0.87 | |||
| Males | 62 | 0.93 | <0.45-2.18 | 172 | <0.07 | <0.01-<0.22 | <0.0001 | ||
| Age | <70 years | 40 | 1.00 | <0.31-2.50 | 120 | <0.08 | <0.01-<0.23 | ||
| ≥70 years | 22 | 0.85 | 0.58-1.98 | 52 | <0.06 | <0.01-<0.22 | |||
| Occupational exposure to asbestos | |||||||||
| Never | 4 | 1.41 | <0.74-2.16 | 51 | <0.06 | <0.01-<0.20 | |||
| Ever | 58 | 0.86 | <0.45-2.18 | 121 | <0.08 | <0.01-<0.24 | |||
| Females | 12 | 0.86 | <0.25-1.33 | 68 | <0.22 | <0.10-0.48 | 0.0037 | ||
| Age | <70 years | 9 | <0.35 | <0.24-1.12 | 50 | <0.22 | <0.12-0.48 | ||
| ≥70 years | 3 | 3.09 | 0.97-4.98 | 18 | <0.18 | <0.03-<0.46 | |||
| Occupational exposure to asbestos | |||||||||
| Never | 8 | 1.04 | <0.55-2.16 | 56 | <0.22 | <0.10-0.48 | |||
| Ever | 4 | <0.25 | <0.13-<0.85 | 12 | <0.27 | <0.08-<0.47 | |||
| Males | 54 | 2.57 | 1.71-3.18 | 124 | 2.30 | 1.84-2.85 | 0.81 | ||
| Age | <70 years | 35 | 2.01 | 1.66-2.90 | 96 | 2.24 | 1.79-2.76 | ||
| ≥70 years | 19 | 3.04 | 1.97-3.41 | 28 | 2.79 | 2.19-3.38 | |||
| Occupational exposure to asbestos | |||||||||
| Never | 3 | 2.84 | 1.72-3.17 | 29 | 2.22 | 1.70-2.87 | |||
| Ever | 51 | 2.53 | 1.67-3.25 | 95 | 2.31 | 1.86-2.83 | |||
| Females | 10 | 2.25 | 1.77-2.43 | 63 | 2.05 | 1.62-2.72 | 0.84 | ||
| Age | <70 years | 8 | 2.07 | 1.59-2.36 | 47 | 2.01 | 1.58-2.51 | ||
| ≥70 years | 2 | 2.50 | 2.27-2.73 | 16 | 2.26 | 2.06-3.14 | |||
| Occupational exposure to asbestos | |||||||||
| Never | 6 | 2.10 | 1.77-2.30 | 51 | 2.05 | 1.66-2.72 | |||
| Ever | 4 | 2.33 | 1.82-3.19 | 12 | 2.09 | 1.26-2.66 | |||
aMesothelin and thrombomodulin concentrations were compared using the Wilcoxon rank-sum test, calretinin concentrations were compared using the Peto-Prentice test. IQR: interquartile range; '<' indicates percentiles below the limit of detection (LOD)
Odds ratios from logistic regression analyses as estimates of the relative risk for an MPM based on the plasma concentrations in cases and controls
| Study group | Characteristics | Intercept | Coefficient | OR (95% CI) |
|---|---|---|---|---|
| Males | Models | |||
| ln(Mesothelin)[nmol/L] | -1.15 | 2.68 | 14.51 (6.96-30.28) | |
| ln(Calretinin)[ng/mL] | 0.55 | 1.11 | 3.03 (2.20-4.18) | |
| ln(Thrombomodulin) [ng/mL] | -0.87 | 0.04 | 1.05 (0.44-2.48) | |
| ln(Mesothelin)[nmol/L] | -0.17 | 2.11 | 8.26 (3.77-18.10) | |
| ln(Calretinin)[ng/mL] | 0.59 | 1.80 (1.31-2.47) | ||
| Females | Models | |||
| ln(Mesothelin)[nmol/L] | -1.77 | 3.35 | 28.57 (4.09-199.4) | |
| ln(Calretinin)[ng/mL] | -0.72 | 0.99 | 2.69 (1.31-5.56) | |
| ln(Thrombomodulin)[ng/mL] | -1.86 | 0.02 | 1.02 (0.19-5.47) | |
| ln(Mesothelin)[nmol/L] | -1.36 | 3.08 | 21.86 (3.13-152.51) | |
| ln(Calretinin)[ng/mL] | 0.33 | 1.39 (0.67-2.88) |
OR: odds ratio; CI: confidence interval; ln: natural logarithm
Figure 1ROC curves of MPM biomarkers in incident cases and controls by gender (all CIs were 95%). A, Males: mesothelin (AUC=0.90, CI:0.85-0.95), calretinin (AUC=0.88, CI:0.82-0.94), and thrombomodulin (AUC=0.51, CI:0.41-0.61) B, Females: mesothelin (AUC=0.92, CI:0.79-1.00), calretinin (AUC=0.77, CI:0.61-0.93), and thrombomodulin (AUC=0.52, CI: 0.32-0.72).
Performance of mesothelin and calretinin in men and the probability of a diagnosis of MPM, conditional on the observed biomarker concentrations
| Probability % | Males | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | ||||||||||
| Cut off Mesothelin [nmol/L] | TPR | FPR | Cut off Calretinin [ng/mL] | TPR | FPR | Cut off Mesothelin [nmol/L] | Cut off Calretinin [ng/mL] | TPR | FPR | |||
| 90 | 3.48 | 0.33 | 0.01 | 4.47 | 0.10 | 0 | 3.66 | 0.85 | 0.37 | 0.01 | ||
| 80 | 2.62 | 0.43 | 0.01 | 2.18 | 0.26 | 0 | 2.15 | 1 | 0.53 | 0.01 | ||
| 70 | 2.09 | 0.57 | 0.01 | 1.36 | 0.40 | 0.01 | 1.08 | 4.25 | 0.63 | 0.01 | ||
| 60 | 1.81 | 0.63 | 0.03 | 0.87 | 0.52 | 0.02 | 1.46 | 0.79 | 0.73 | 0.03 | ||
| 50 | 1.56 | 0.73 | 0.03 | 0.62 | 0.68 | 0.05 | 0.74 | 3.82 | 0.76 | 0.05 | ||
| 40 | 1.31 | 0.78 | 0.06 | 0.41 | 0.76 | 0.10 | 0.97 | 0.74 | 0.84 | 0.07 | ||
| 30 | 1.12 | 0.79 | 0.11 | 0.29 | 0.81 | 0.18 | 1.01 | 0.33 | 0.84 | 0.11 | ||
| 20 | 0.92 | 0.84 | 0.22 | 0.18 | 0.87 | 0.34 | 0.65 | 0.58 | 0.85 | 0.14 | ||
| 10 | 0.68 | 0.90 | 0.40 | 0.09 | 0.92 | 0.49 | 1.31 | 0.01 | 0.92 | 0.33 | ||
Probability was used to estimate TPR and FPR: Probability = 1/(1+e^(-φ)). Logistic regression models: (1) with log-mesothelin as predictor, φ = exp [-1.15 + 2.68 * ln(mesothelin)]; (2) with log-calretinin as predictor, φ = exp [0.55 + 1.11 * ln(calretinin)]; (3) with log-mesothelin and log-calretinin, φ = exp [-0.17 + 2.11 * ln(mesothelin) + 0.59 * ln(calretinin)]. TPR: true positive rate; FPR: false positive rate
Performance of mesothelin and calretinin in women and the probability of a diagnosis of MPM, conditional on the observed biomarker concentrations
| Probability | Females | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | ||||||||||
| Cut off Mesothelin [nmol/L] | TPR | FPR | Cut off Calretinin [ng/mL] | TPR | FPR | Cut off Mesothelin [nmol/L] | Cut off Calretinin [ng/mL] | TPR | FPR | |||
| 90 | 3.24 | 0.42 | 0 | - | 0 | 0 | - | - | 0.33 | 0 | ||
| 80 | - | 0.42 | 0 | - | 0 | 0 | 2.13 | 3.09 | 0.50 | 0 | ||
| 70 | - | 0.42 | 0.02 | 4.98 | 0.08 | 0 | - | - | 0.50 | 0.02 | ||
| 60 | 1.87 | 0.58 | 0.02 | 3.09 | 0.17 | 0 | - | - | 0.58 | 0.02 | ||
| 50 | - | 0.58 | 0.02 | - | 0.17 | 0 | - | - | 0.58 | 0.02 | ||
| 40 | 1.48 | 0.58 | 0.03 | 1.44 | 0.25 | 0 | 1.35 | 1.23 | 0.75 | 0.02 | ||
| 30 | 1.35 | 0.75 | 0.03 | 0.97 | 0.50 | 0.06 | 1.21 | 1.12 | 0.75 | 0.05 | ||
| 20 | 1.14 | 0.92 | 0.12 | 0.49 | 0.58 | 0.24 | 1.05 | 0.72 | 0.83 | 0.09 | ||
| 10 | 0.90 | 0.92 | 0.20 | 0.23 | 0.92 | 0.49 | 0.90 | 0.23 | 0.92 | 0.20 | ||
Probability was used to estimate TPR and FPR: Probability = 1/(1+e^(-φ)). Because of the small number of female cases (12), not for all set probabilities corresponding marker concentrations were available. Logistic regression models: (1) with log-mesothelin as predictor, φ = exp [-1.77 + 3.35 * ln(mesothelin)]; (2) with log-calretinin as predictor, φ = exp [-0.72 + 0.99 * ln(calretinin)]; (3) with log-mesothelin and log-calretinin, φ = exp [-1.36 + 3.08 * ln(mesothelin) + 0.33 * ln(calretinin)]. TPR: true positive rate; FPR: false positive rate