Abstract B1: Validation of a panel of candidate pancreatic cancer serum biomarkers identifies SYCN and REG1B to significantly improve the performance of CA19.9.
Bibliographic record
Abstract
The identification of new serum biomarkers with high sensitivity and specificity to aid in the detection and management of pancreatic cancer is an important priority in the field. Given that proteins which are secreted or shed from tumor cells and their microenvironment have the highest chance of reaching the circulation and serving as serum biomarkers, we previously characterized the proteomes of pancreatic cancer-related proximal biological fluids. Specifically, the proteomes of conditioned media (cell culture supernatants) from six pancreatic cancer cell lines (BxPc3, MIA-PaCa2, PANC1, CAPAN1, CFPAC1 and SU.86.86) and the normal human pancreatic ductal epithelial cell line HPDE, as well as pancreatic juice from patients with pancreatic ductal adenocarcinoma (PDAC) were analyzed using strong cation exchange liquid chromatography, followed by LC-MS/MS on an LTQ-Orbitrap mass spectrometer. Over 3,500 non-redundant proteins were identified with high confidence (≥ 2 peptides). Subsequent bioinformatics-based biomarker filtration criteria, incorporating both quantitative and qualitative evaluations, led to the generation of candidate pancreatic cancer biomarkers for validation. The present study details the validation of five selected candidates, regenerating islet-derived 1 beta (REG1B), syncollin (SYCN), anterior gradient homolog 2 protein (AGR2), polymeric immunoglobulin receptor (PIGR), and lysyl oxidase-like 2 (LOXL2), using enzyme-linked immunosorbent assays (ELISAs) in 388 serum samples. The samples included 182 PDAC, 92 healthy controls, 44 benign pancreatic diseases and 70 samples from individuals with other gastrointestinal cancers. Statistical analyses showed three of the tested candidates to be significantly elevated in PDAC versus healthy controls (p=1.95E-13, p=3.43E-11, 9.80E-07 for REG1B, SYCN and PIGR, respectively). CA19.9, the marker currently used clinically for pancreatic cancer management was also assessed (p=2.88E-24 in PDAC versus healthy controls). Individually, CA19.9 performed superiorly to the tested candidates in this sample set; however in combination, the addition of REG1B and SYCN was able to significantly improve the area under the receiver operating characteristic curve of CA19.9 to 0.92 (95% confidence interval of 0.8864-0.9486; p-value of panel in comparison to AUC of CA19.9 alone = 0.0012). The present study details a large validation of five candidates identified through comprehensive proteomics strategies, through which the addition of REG1B and SYCN in a panel with CA19.9 was found to improve the performance of CA19.9 alone. Further validation of these candidates in larger sample sets for purposes of pancreatic cancer detection and management is highly warranted, as is assessment of their performance when used in combination with other, yet untested, candidates. This work was supported by the Ontario Institute for Cancer Research, Grant # 10 NOV-498 (EPD Principal Investigator). Citation Format: Shalini Makawita, Apostolos Dimitromanolakis, Antoninus Soosaipillai, Ireena Soleas, Alison Chan, Steven Gallinger, Randy S. Haun, Ivan Blasutig, Eleftherios P. Diamandis. Validation of a panel of candidate pancreatic cancer serum biomarkers identifies SYCN and REG1B to significantly improve the performance of CA19.9. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Progress and Challenges; Jun 18-21, 2012; Lake Tahoe, NV. Philadelphia (PA): AACR; Cancer Res 2012;72(12 Suppl):Abstract nr B1.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".