Immunoglobulin G fragment C receptor polymorphisms and KRAS mutations: Are they useful biomarkers of clinical outcome in advanced colorectal cancer treated with anti‐EGFR‐based therapy?
Bibliographic record
Abstract
KRAS mutations have been identified as a strong predictor of resistance to anti-epidermal growth factor receptor (EGFR) therapies. Besides inhibiting the EGFR pathway, anti-EGFR monoclonal antibodies may exert antitumor effects through antibody-dependent cell-mediated cytotoxicity (ADCC). Through this mechanism, the antibody fragment C portion (Fcγ) interacts with Fc receptors (FcγRs) expressed by immune effectors cells. We investigated the association of FcγR polymorphisms and KRAS mutation with the clinical outcome of 104 refractory metastatic colorectal cancer (mCRC) patients treated with anti-EGFR antibodies. FcγRIIa-H131R and FcγRIIIa-V158F polymorphisms were analyzed in genomic DNA using a 48.48 dynamic array on the BioMark system (Fluidigm, South Sanfrancisco, CA, USA). Tumor tissues from 96 cases were screened for KRAS mutations. KRAS mutation was associated with a lower response rate (RR) (P = 0.035) and a shorter progression-free survival (PFS) (3 vs 7 months; P = 0.36). FcγRIIa-H131R and FcγRIIIa-V158F polymorphisms did not show statistically significant associations with response, PFS, or KRAS status. In the logistic regression analysis, KRAS status (P = 0.04) and skin toxicity (P = 0.03) were associated with RR. By multivariate analysis, the clinical risk classification (P = 0.006) and skin toxicity (P < 0.0001) were found to be independent risk factors for PFS. In conclusion, the FcγRIIa and FcγRIIIa polymorphisms are not useful as molecular markers for clinical outcome in mCRC patients. To date, the EORTC (European Organization for Research and Treatment of Cancer Classification), skin toxicity, and KRAS status are the only reliable biomarkers to identify patients that would benefit from anti-EGFR therapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".