Abstract 4586: MRM-based targeted analysis of potential cancer markers in blood
Bibliographic record
Abstract
Abstract Introduction. Our previous discovery phase work using iTRAQ analysis on tissue homogenates from endometrial carcinoma, glioblastoma, head and neck squamous cell carcinoma have resulted in a number of potential markers for each of the types of cancer. A number of these potential markers were subsequently verified using conventional molecular biology approaches on the same as well as additional cohorts of samples. Recent studies using a new variant of the iTRAQ reagent, mTRAQ, in combination with multiple reaction monitoring (MRM) approaches on a hybrid triple quadrupole linear ion trap instrument also showed that the differential expression levels of a number of these potential markers could be observed in archived formalin fixed paraffin embedded tissue. We are therefore extending this approach to the detection and verification of differential expression of some of these potential markers in plasma. Methods. Serum or plasma samples from individuals diagnosed with EmCa or GBM or who had no known forms of cancer were studied. Each sample was individually depleted of the twenty most abundant proteins using an immunocapture column (Proteoprep 20, Sigma Aldrich). Samples were then enzymatically digested and labeled with one of the three versions of mTRAQ reagents. Data obtained during the discovery phase was used to direct the choice of MRM transitions. Transitions were generated for proteotypic peptides from each of the proteins of interest, each of which was targeted using a minimum of 2 peptides and 3 transitions per labeled peptide. Samples were mixed after labeling and analyzed using 2D LC-MRM analysis. An intermediate immunocapture step prior to digestion was incorporated to enrich for lower abundance proteins. Results: One of the potential markers of interest, clusterin, was successfully detected in sera from the three categories of patients, EmCa, GBM and non-malignant, however, no differential expression was observed. PIGR, which was another of the potential markers for EmCa, was observed exclusively in the serum from an EmCa patient. This confirmed its presence in sera and suggested that it might be possible to screen for its expression level therein. A third potential marker, Chaperonin 10, not initially observed using the direct approach described above, was detected after performing an immunocapture step prior to digestion and LC-MRM analysis. Current efforts are focused on titrating the antibody required to ensure any differential expression in plasma is accurately reflected in the results of the differential analyses. Conclusions: Our results have proved that the tissue-to-serum approach is feasible and that the differential expression of some potential markers detected in tissue are reflected in serum or plasma levels. 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 4586.
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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 | 0.001 | 0.002 |
| 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) | 0.011 | 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".