Abstract 3753: Quantitation of EGFR and phosphoEGFR in FFPE tissue
Bibliographic record
Abstract
Abstract The epidermal growth factor receptor (EGFR) is a drug target for both small molecule and antibody therapeutic approaches for several cancers; however, current methods of selecting patients that will most likely respond to anti-EGR therapy are not effective. Better methods for patient stratification are needed. To this end we have developed an approach which can determine both absolute EGFR levels and the phosphorylation status of EGFR directly in formalin-fixed paraffin-embedded (FFPE) patient tissue. This approach is based on the Liquid Tissue®-SRM technology platform, a combination of tissue microdissection, Liquid Tissue® processing which turns dissected tissue to a complete solubilized tryptic digest, and mass spectrometry-based selected reaction monitoring (SRM). This approach was used to measure the EGFR protein and its phosphorylation status in formalin fixed tissue culture cells, xenograft tumors, and patient tumor tissue. For assay development, 3 distinct tryptic peptides were assessed for absolute protein quantitation and multiple peptides where specific residues are known to become phosphorylated (pT693 and pY1197) were assessed for assaying the phosphorylation status of the EGFR protein. We demonstrate the ability to detect and quantify the EGFR protein and to monitor its phosphorylation status directly in patient tumor tissue. This approach offers a dynamic range and quantification which is superior to traditional IHC methods, and that could be used to identify and stratify patients most likely to benefit from anti-EGFR therapies. 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 3753.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".