Potential of Inflammatory Protein Signatures for Enhanced Selection of People for Lung Cancer Screening
Abstract
:Simple Summary
Abstract
1. Introduction
2. Methods
2.1. Study Design and Study Population
2.2. Laboratory Assay
2.3. Statistical Analysis
3. Results
3.1. Characteristics of Study Population and Assay Performance
3.2. Predictive Performance of Individual Markers
3.3. Predictive Performance of Multi-Marker Signatures
3.4. Predictive Performance of LC Risk Models and INS, Individually and in Combination
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AUC | area under the ROC curve |
AUC* | 0.632+ bootstrap adjusted area under the ROC curve |
CASP8 | caspase-8 |
CCL11 | eotaxin |
CCL25 | C-C motif chemokine 25 |
CDCP1 | CUB domain-containing protein 1 |
CD244 | natural killer cell receptor 2B4 |
CD8A | T-cell surface glycoprotein CD8 alpha chain |
CXCL10 | C-X-C motif chemokine 10 |
CXCL9 | C-X-C motif chemokine 9 |
CRP | C-reactive protein |
CV | coefficient of variance |
ESTHER | Epidemiologische Studie zu Chancen der Verhütung, Früherkennung und optimierten Therapie chronischer Erkrankungen in der älteren Bevölkerung |
FGF19 | fibroblast growth factor 19 |
IL12B | interleukin 12 subunit beta |
IL6 | interleukin 6 |
IL8 | interleukin 8 |
INS | inflammation protein biomarker score |
INS-pack-years | combined inflammation protein biomarker and pack-years score |
LC | lung cancer |
LCDRAT | Lung Cancer Death Risk Assessment Tool |
LCRAT | Lung Cancer Risk Assessment Tool |
LLP | Liverpool Lung Project Risk Model |
LLPi | Liverpool Lung Project Incidence Risk Model |
LDCT | low-dose computed tomography |
MMP1 | matrix metalloproteinase-1 |
MCP4 | monocyte chemotactic protein 4 |
N | number |
PEA | proximity extension assay |
PLCOM2012 | Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 |
Q | quartile |
QCC | quality control criteria |
ROC | receiver operating characteristics |
SCAD | smoothly clipped absolute deviation |
SCF | stem cell factor |
SD | standard deviation |
95% CI | 95% confidence intervals |
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Characteristics | Training Set | Validation Set | Overall | ||||||
---|---|---|---|---|---|---|---|---|---|
Incident LC Cases N (%) | Random Participants Free of LC N (%) | p Value | Incident LC Cases N (%) | Random Participants Free of LC N (%) | p Value | Incident LC Cases N (%) | Random Participants Free of LC N (%) | p Value | |
107 | 190 | 65 | 95 | 172 | 285 | ||||
Age (years) | |||||||||
50–59 | 35 (33) | 93 (49) | <0.01 | 18 (28) | 46 (48) | <0.01 | 53 (31) | 139 (49) | <0.01 |
60–69 | 56 (52) | 78 (41) | 37 (57) | 36 (38) | 93 (54) | 114 (40) | |||
70–75 | 16 (15) | 19 (10) | 10 (15) | 13 (14) | 26 (15) | 32 (11) | |||
Mean (SD) | 62.2 (6.3) | 60.2 (6.6) | 63.1 (6.7) | 60.7 (7.1) | 62.5 (6.5) | 60.4 (6.8) | |||
Median | 62.0 | 60.0 | 63.0 | 60.0 | 62.0 | 60.0 | |||
Gender | |||||||||
Female | 30 (28) | 76 (40) | <0.05 | 20 (31) | 31 (33) | 0.86 | 50 (29) | 107 (38) | 0.06 |
Male | 77 (72) | 114 (60) | 45 (69) | 64 (67) | 122 (71) | 178 (62) | |||
Smoking status | |||||||||
Former smoker | 44 (41) | 131 (69) | <0.01 | 21(32) | 52 (55) | <0.01 | 65 (38) | 183 (64) | <0.01 |
Current smoker | 63 (59) | 59 (31) | 44 (68) | 43 (45) | 107 (62) | 102 (36) |
Training Set N LC Cases—107 N LC-Free Participants—190 | Validation Set N LC Cases—65 N LC-Free Participants—95 | Proteins Included | ||
---|---|---|---|---|
AUC* | AUC (95% CI) | AUC (95% CI) | ||
INS | 0.770 | 0.771 (0.713–0.828) | 0.742 (0.667–0.818) | CASP8, CCL11, CDCP1, CD8A, CD244, CXCL10, FGF19, MCP4, SCF |
INS-pack-years | 0.796 | 0.811 (0.760–0.863) | 0.782 (0.711–0.854) | CASP8, CCL11, CCL25, CDCP1, CD8A, CD244, CXCL10, CXCL9, FGF19, MMP1 |
Model | Training Set N LC Cases—107 N LC-Free Participants—190 | Validation Set N LC Cases—65 N LC-Free Participants—95 | Proteins Included | ||||||
---|---|---|---|---|---|---|---|---|---|
AUCLCmodel (95% CI) | AUC* AUCLCmodel+INf (95% CI) | Improvement | p Val § | AUCLCmodel (95% CI) | AUCLCmodel+INf (95% CI) | Improvement | p Val § | ||
Bach [14] | 0.765 (0.711–0.820) | 0.807 * 0.811 (0.759–0.862) | 0.042 | 0.24 | 0.752 (0.676–0.828) | 0.770 (0.697–0.844) | 0.018 | 0.73 | CASP8, CDCP1, CD8A, CD244, CXCL10, FGF19, IL8 |
Spitz [15] | 0.678 (0.614–0.743) | 0.720 * 0.726 (0.666–0.786) | 0.042 | 0.29 | 0.673 (0.589–0.756) | 0.702 (0.622–0.782) | 0.029 | 0.67 | CDCP1, CXCL10, IL12B, IL8, SCF |
LLP [16] | 0.692 (0.629–0.756) | 0.789 * 0.795 (0.740–0.849) | 0.097 | <0.05 | 0.703 (0.618–0.787) | 0.756 (0.682–0.829) | 0.053 | <0.05 | CASP8, CCL11, CDCP1, CD8A, CD244, CXCL10, FGF19, IL8, SCF |
Hoggart [17] | 0.738 (0.679–0.798) | 0.791 * 0.800 (0.746–0.853) | 0.053 | 0.13 | 0.700 (0.617–0.783) | 0.745 (0.668–0.821) | 0.045 | 0.44 | CASP8, CCL11, CDCP1, CD8A, CD244, CXCL10, FGF19, IL8 |
PLCOM2012 [18] | 0.669 (0.609–0.730) | 0.790 * 0.791 (0.736–0.845) | 0.121 | <0.05 | 0.679 (0.594–0.763) | 0.749 (0.672–0.825) | 0.070 | <0.05 | CASP8, CCL11, CDCP1, CD8A, CD244, CXCL10, FGF19, IL8 |
LLPi [19] | 0.736 (0.679–0.793) | 0.746 * 0.747 (0.690–0.804) | 0.010 | 0.79 | 0.734 (0.655–0.813) | 0.743 (0.664–0.821) | 0.009 | 0.89 | CDCP1, CD244, IL12B, IL8 |
Pittsburgh Predictor [20] | 0.767 (0.713–0.821) | 0.800 * 0.801 (0.748–0.853) | 0.033 | 0.38 | 0.784 (0.712–0.857) | 0.794 (0.724–0.864) | 0.010 | 0.86 | CASP8, CDCP1, CD8A, CD244, CXCL10, IL8 |
LCRAT [21] | 0.775 (0.722–0.829) | 0.804 * 0.807 (0.756–0.859) | 0.029 | 0.40 | 0.763 (0.687–0.841) | 0.773 (0.700–0.845) | 0.010 | 0.87 | CASP8, CDCP1, CD8A, CD244, CXCL10, FGF19, IL8 |
LCDRAT [21] | 0.770 (0.716–0.825) | 0.781 * 0.785 (0.730–0.839) | 0.011 | 0.71 | 0.766 (0.690–0.842) | 0.775 (0.702–0.849) | 0.009 | 0.86 | CDCP1, CD244, CXCL10, IL12B, IL8 |
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Bhardwaj, M.; Schöttker, B.; Holleczek, B.; Benner, A.; Schrotz-King, P.; Brenner, H. Potential of Inflammatory Protein Signatures for Enhanced Selection of People for Lung Cancer Screening. Cancers 2022, 14, 2146. https://doi.org/10.3390/cancers14092146
Bhardwaj M, Schöttker B, Holleczek B, Benner A, Schrotz-King P, Brenner H. Potential of Inflammatory Protein Signatures for Enhanced Selection of People for Lung Cancer Screening. Cancers. 2022; 14(9):2146. https://doi.org/10.3390/cancers14092146
Chicago/Turabian StyleBhardwaj, Megha, Ben Schöttker, Bernd Holleczek, Axel Benner, Petra Schrotz-King, and Hermann Brenner. 2022. "Potential of Inflammatory Protein Signatures for Enhanced Selection of People for Lung Cancer Screening" Cancers 14, no. 9: 2146. https://doi.org/10.3390/cancers14092146