Potential Applications for Circulating Tumor Cells Expressing the Insulin-Like Growth Factor-I Receptor
Bibliographic record
Abstract
PURPOSE: To detect insulin-like growth factor-IR (IGF-IR) on circulating tumor cells (CTC) as a biomarker in the clinical development of a monoclonal human antibody, CP-751,871, targeting IGF-IR. EXPERIMENTAL DESIGN: An automated sample preparation and analysis system for enumerating CTCs (CellTracks) was adapted for detecting IGF-IR-positive CTCs with a diagnostic antibody targeting a different IGF-IR epitope to CP-751,871. This assay was used in three phase I trials of CP-751,871 as a single agent or with chemotherapy and was validated using cell lines and blood samples from healthy volunteers and patients with metastatic carcinoma. RESULTS: There was no interference between the analytic and therapeutic antibodies. Eighty patients were enrolled on phase I studies of CP-751,871, with 47 (59%) patients having CTCs detected during the study. Before treatment, 26 patients (33%) had CTCs, with 23 having detectable IGF-IR-positive CTCs. CP-751,871 alone, and CP-751,871 with cytotoxic chemotherapy, decreased CTCs and IGF-IR-positive CTCs; these increased toward the end of the 21-day cycle in some patients, falling again with retreatment. CTCs were commonest in advanced hormone refractory prostate cancer (11 of 20). Detectable IGF-IR expression on CTCs before treatment with CP-751,871 and docetaxel was associated with a higher frequency of prostate-specific antigen decline by >50% (6 of 10 versus 2 of 8 patients). A relationship was observed between sustained decreases in CTC counts and prostate-specific antigen declines by >50%. CONCLUSIONS: IGF-IR expression is detectable by immunofluorescence on CTCs. These data support the further evaluation of CTCs in pharmacodynamic studies and patient selection, particularly in advanced prostate cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".