Abstract 4573: Discovery of candidate biomarkers for colorectal cancer using mass spectrometry-based proteomic analysis of cell line conditioned media
Bibliographic record
Abstract
Abstract Colorectal cancer (CRC) represents the third leading cause of cancer-related deaths with approximately 655,000 deaths annually, worldwide. Specific biomarkers for early detection of CRC are needed, mainly because the currently applied serum biomarkers (CEA, CA 19.9) lack adequate sensitivity and specificity for screening purposes, while colonoscopy is invasive, with considerable cost, risks and inconvenience. In the present study, we utilized a mass spectrometry-based platform to analyze the proteome of the conditioned media from 12 CRC cell lines with diverse genetic profiles, to capture the disease heterogeneity. Proteins were trypsin-digested and fractionated by strong cation-exchange and reverse-phase liquid chromatography prior to mass spectrometric analysis with an LTQ Orbitrap instrument. The samples were run in triplicate and results were searched against the IPI human forward and reverse database, using MASCOT and X!Tandem search engines. We were able to identify approximately 2,000 unique proteins for each cell line, with reproducibility ranging from 71 to 82% among the triplicates. The efficiency of this approach was signified by the large number of known biomarkers, such as CEA, successfully identified in our list of candidates, as well as by the identification of internal control proteins, such as kallikreins 6, 7, 10, and 11, which were also quantified by ELISA in cancer cell supernatants. The acquired protein datasets were further subjected to bioinformatic analysis using Gene Ontology. The proteins were assigned a subcellular localization and 35-40% were classified as extracellular or plasma membrane proteins. Further mining of this vast database by using bioinformatics tools and experimental approaches (such as multiple reaction monitoring and ELISA) could lead to shortlists of proteins that may have utility as circulating CRC biomarkers. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4573.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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.
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".