Plerixafor Improves Primary Tumor Response and Reduces Metastases in Cervical Cancer Treated with Radio-Chemotherapy
Bibliographic record
Abstract
Abstract Purpose: There is an important need to improve the effectiveness of radio-chemotherapy (RTCT) for cervical cancer. The CXCL12/CXCR4 pathway can influence RT response by recruiting normal myeloid cells to the tumor microenvironment that in turn can exert radioprotective effects, and may promote metastases. The objective of this study was to explore the efficacy and toxicity of combining RTCT with CXCL12/CXCR4 inhibition in cervical cancer. Experimental Design: CXCR4 expression was measured in 115 patients with cervical cancer. Two primary orthotopic cervical cancer xenografts (OCICx) with different levels of CXCR4 expression were treated with RT (30 Gy: 15 daily fractions) and weekly cisplatin (4 mg/kg), with or without the CXCR4 inhibitor Plerixafor (5 mg/kg/day). The endpoints were tumor growth delay and lymph node metastases. Acute intestinal toxicity was assessed using a crypt cell assay. Results: There was a fivefold variation in CXCR4 mRNA expression in the patient samples, and good correlation between the expression in patients and in the xenografts. The combination of RTCT and Plerixafor produced substantial tumor growth delay and reduced lymph node metastases compared with RTCT alone in both of the xenograft models. There was a trend toward reduced acute intestinal toxicity with the addition of Plerixafor to RTCT. There were no changes in normal organ morphology to suggest increased late toxicity. Conclusions: This study demonstrates that the addition of Plerixafor to standard RTCT improves primary tumor response and reduces metastases in cervical cancer with no increase in toxicity. This combination warrants further investigation in phase I/II clinical trials. Clin Cancer Res; 23(5); 1242–9. ©2016 AACR.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".