Abstract 5105: Towards a multiparametric biomarker panel for pancreatic cancer detection
Bibliographic record
Abstract
Abstract Pancreatic cancer is one of the most highly lethal of all solid malignancies for which serological biomarkers to aid in the detection and clinical management of patients are urgently needed. To date, the lack of a single highly specific and sensitive marker for pancreatic cancer detection has led to a growing consensus in the field towards the development of panels of biomarkers, where-by the combinatorial assessment of multiple biomarkers will likely result in increased sensitivity and specificity. In this respect, we previously characterized the conditioned media of six pancreatic cancer cell lines (MIA-PaCa2, BxPc3, PANC1, Su.86.86, CAPAN1, CFPAC1) and the normal human pancreatic ductal epithelial cell line HPDE, as well as six pancreatic juice samples from pancreatic ductal adenocarcinoma patients using strong cation exchange followed by reverse-phase coupled online to an LTQ-Orbitrap mass spectrometer. This resulted in the identification of 3810 non-redundant proteins in the cell lines and 648 proteins in the pancreatic juice. All proteins were identified with 2 or more peptides. Through subsequent bioinformatic analyses which included hierarchical clustering, label-free protein quantification between the cancer and normal cell lines, cellular localization, tissue specificity and integration of the proteomes from the multiple biological fluids, a list of candidate pancreatic cancer biomarkers was generated. Preliminary verification of selected candidates using enzyme-linked immunosorbent assays (ELISAs) in a screening set of plasma samples from pancreatic cancer patients and healthy age-sex matched controls (n=40) show five proteins (designated PANC1, PANC2, PANC3, PANC4 and PANC5) to be significantly increased in pancreatic cancer plasma (p=0.0011, p<0.0001, p<0.0001, p<0.0001 and p=0.0098, respectively). Individually, these proteins did not exhibit improved area under the curve (AUC) in comparison to CA19.9 levels measured in the same samples; however the combination of the five proteins with CA19.9 showed improved AUC to CA19.9 alone (CA19.9 alone AUC = 0.97; all proteins with CA19.9 AUC = 1.0). These data suggest that this novel panel warrants further evaluation as a pancreatic cancer diagnostic tool. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5105. doi:10.1158/1538-7445.AM2011-5105
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".