Connective Tissue-Activating Peptide III: A Novel Blood Biomarker for Early Lung Cancer Detection
Bibliographic record
Abstract
PURPOSE: There are no reliable blood biomarkers to detect early lung cancer. We used a novel strategy that allows discovery of differentially present proteins against a complex and variable background. METHODS: Mass spectrometry analyses of paired pulmonary venous-radial arterial blood from 16 lung cancer patients were applied to identify plasma proteins potentially derived from the tumor microenvironment. Two differentially expressed proteins were confirmed in 64 paired venous-arterial blood samples using an immunoassay. Twenty-eight pre- and postsurgical resection peripheral blood samples and two independent, blinded sets of plasma from 149 participants in a lung cancer screening study (49 lung cancers and 100 controls) and 266 participants from the National Heart Lung and Blood Institute Lung Health Study (45 lung cancer and 221 matched controls) determined the accuracy of the two protein markers to detect subclinical lung cancer. RESULTS: Connective tissue-activating peptide III (CTAP III)/ neutrophil activating protein-2 (NAP-2) and haptoglobin were identified to be significantly higher in venous than in arterial blood. CTAP III/NAP-2 levels decreased after tumor resection (P = .01). In two independent population cohorts, CTAP III/NAP-2 was significantly associated with lung cancer and improved the accuracy of a lung cancer risk prediction model that included age, smoking, lung function (FEV(1)), and an interaction term between FEV(1) and CTAP III/NAP-2 (area under the curve, 0.84; 95% CI, 0.77 to 0.91) compared to CAPIII/NAP-2 alone. CONCLUSION: We identified CTAP III/NAP-2 as a novel biomarker to detect preclinical lung cancer. The study underscores the importance of applying blood biomarkers as part of a multimodal lung cancer risk prediction model instead of as stand-alone tests.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".