Data from Targeting Refractory Mantle Cell Lymphoma for Imaging and Therapy Using C-X-C Chemokine Receptor Type 4 Radioligands
Bibliographic record
Abstract
AbstractPurpose: Mantle cell lymphoma (MCL) is associated with poor survival. The purpose of this study was to assess whether the C-X-C chemokine receptor type 4 (CXCR4) is a useful target for imaging and radioligand therapy of MCL, using a novel pair of radioligands, [68Ga]Ga and [177Lu]Lu-BL02. Experimental Design: We performed a retrospective analysis of 146 patients with MCL to evaluate CXCR4 expression and its correlation with outcomes. Guided by in silico methods, we designed BL02, a new radioligand labelled with 68Ga or 177Lu for PET imaging and therapy, respectively. We performed imaging and biodistribution studies in xenograft models with varying CXCR4 expression. We evaluated [177Lu]Lu-BL02 in MCL models, and evaluated its potential for therapy in Z138 MCL xenografts. Results: Phosphorylated and nonphosphorylated CXCR4 expression were correlated with poor survival in patients with MCL and characterized by unique underlying molecular signatures. [68Ga]Ga-BL02 uptake correlated with CXCR4 expression, and localized lesions in a metastatic xenograft model. [177Lu]Lu-BL02 showed high uptake in MCL xenografts. Therapy studies with a single dose in the Z138 model showed tumor regression and improved survival compared with a control group. Upon regrowth, the treated mice experienced concurrent metastasis alongside localized xenograft regrowth, and recurrent lesions showed enhanced CXCR4 signaling. Conclusions: CXCR4 is an independent factor of poor prognosis for MCL and a promising target for imaging and radioligand therapy. [68Ga]Ga-BL02 showed high contrast to visualize CXCR4-expressing xenografts for PET imaging and [177Lu]Lu-BL02 induced rapid tumor regression in a preclinical model of MCL.
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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) | 0.003 | 0.001 |
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".