MétaCan
Menu
← Back to cohort

Abstract PR08: Pro-surfactant protein B as a biomarker for lung cancer prediction.

2014· article· en· W2146471206 on OpenAlexaffabout
Don D. Sin, Martin C. Tammemägi, Stephen Lam, Matt J. Barnett, Xiaobo Duan, Anthony Raymond Tam, Heidi Auman, Ziding Feng, Gary E. Goodman, Samir Hanash, Ayumu Taguchi

Bibliographic record

VenueClinical Cancer Research · 2014
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsCanadian Centre for Applied Research in Cancer Control
Fundersnot available
KeywordsLung cancerMedicineNational Lung Screening TrialLogistic regressionBiomarkerInternal medicineLung cancer screeningCancerOncologyLung

Abstract

fetched live from OpenAlex

Abstract Background: The National Lung Screening Trial reported that low-dose computed tomography (LDCT) screening reduced lung cancer mortality by 20% in adults at high risk for lung cancer. Despite these significant results, there are major concerns regarding lung cancer screening with LDCT. They include high false positivity, cost, and radiation exposure. Blood-based markers are a promising and attractive approach to complement LDCT because of the potential to identify those subjects that may be at increased risk of developing lung cancer, or that may be harboring early and potentially curable lung cancer, or that need to undergo further work-up for their indeterminate nodules. Our prior proteomic study suggested pro-surfactant protein B (SFTPB) as a promising circulating biomarker for non-small cell lung cancer (NSCLC). In this study we aimed to determine if plasma levels of pro-SFTPB are associated with lung cancer independently of known clinical risk factors, and improve lung cancer prediction beyond currently existing prediction models in individuals at high-risk for lung cancer at the screening setting. Methods: Pro-SFTPB levels were measured in 2,485 individuals, including 113 subjects later diagnosed as lung cancer, who enrolled in the Pan-Canadian Early Detection of Lung Cancer Study using plasma sample collected at the baseline visit. Multivariable logistic regression models were used to evaluate the predictive ability of pro-SFTPB in addition to known lung cancer risk factors. Calibration and discrimination were evaluated; the latter by an area under the receiver operator characteristics curve (AUC). Independent validation using a case-control study design was performed with serum samples collected in the Carotene and Retinol Efficacy Trial (CARET) participants consisting of 61 NSCLC subjects and matched 121 control subjects. Results: In the logistic model fully adjusted for lung cancer risk factors (age, sex, body mass index (BMI), personal history of cancer, family history of lung cancer, forced expiratory volume in 1 second percent predicted, average number of cigarettes smoked per day, and smoking duration), log-transformed pro-SFTPB (log-proSFTPB) was a significant independent predictor of lung cancer (odds ratio (OR) = 2.220, 95% confidence interval (CI) = 1.727-2.853, p < 0.001). The AUCs of the full model with and without pro-SFTPB were 0.741 (95% CI = 0.696-0.783) and 0.669 (95% CI = 0.620-0.717) (P value for difference in AUC = 0.0007). When the full model was estimated in 96 individuals with stage I or II lung cancer, log-proSFTPB remained a statistically significant predictor (OR = 2.195, 95%CI = 1.679-2.870; p < 0.001). In the CARET study, pro-SFTPB levels were significantly higher among NSCLC cases compared with controls (P < 0.0001) and ROC analysis yielded AUC of 0.683 (95% CI, 0.604-0.761). In multivariate logistic regression analysis, the risk of NSCLC increased along with the pro-SFTPB concentration gradient in the CARET set (Ptrend = 0.001, adjusted for matching variables, pack-years, years since quitting smoking, asbestos exposure, and BMI). Conclusions: Our study demonstrates that plasma pro-SFTPB is significantly and independently associated with lung cancer and adds to lung cancer prediction beyond those contributed by established risk factors in two independent cohorts. Furthermore, pro-SFTPB was associated with early stage lung cancer, suggesting its potential utility in predicting early staged NSCLC tumors, which may be amenable to surgical resection. This abstract is also presented as Poster A33. Citation Format: Don D. Sin, C Martin Tammemagi, Stephen Lam, Matt J. Barnett, Xiaobo Duan, Anthony Tam, Heidi Auman, Ziding Feng, Gary E. Goodman, Samir M. Hanash, Ayumu Taguchi. Pro-surfactant protein B as a biomarker for lung cancer prediction. [abstract]. In: Proceedings of the AACR-IASLC Joint Conference on Molecular Origins of Lung Cancer; 2014 Jan 6-9; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2014;20(2Suppl):Abstract nr PR08.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.164
GPT teacher head0.520
Teacher spread0.356 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueClinical Cancer Research→Same topicChronic Obstructive Pulmonary Disease (COPD) Research→French-language works237,207→