Reply: ‘Pre-treatment levels of circulating free IGF-1 identify NSCLC patients who derive clinical benefit from figitumumab’
Bibliographic record
Abstract
We read with great interest the Letter to the Editor from Shimokawa et al (2011) that reported significant associations between the expressions of the insulin-like growth factor type I receptor (IGF-IR) and those of E-cadherin and γ -catenin in non-small cell lung cancer (NSCLC) biopsies ( Shimokawa et al, 2011 ). These data reproduce previous observations from our group. We have also observed a correlation between the expressions of IGF-IR and E-cadherin, particularly in NSCLC tumours with a high degree of differentiation ( Gualberto et al, 2010 ). Furthermore, using unsupervised Bayesian clustering of epithelial-to-mesenchymal transition (EMT)- and IGF-IR-related markers, we identified three NSCLC subsets that resembled the steps of the EMT and we named epithelial-like, transitional-like and mesenchymal-like ( Gualberto et al, 2010 ). Several markers of the IGF-IR pathway such as nuclear insulin receptor substrate-1 were overexpressed in the transitional subset and a higher objective response rate to the combination of chemotherapy and the anti-IGF-IR antibody figitumumab was observed in patients with transitional tumours ( Gualberto et al, 2010 ). Thus, we agree with Shimokawa et al (2011) that analysis of tumour EMT status may contribute to a better understanding of the sensitivity to anti-IGF-IR 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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.022 | 0.029 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".