Evaluation of CD20, CD22, and HLA-DR Targeting for Radioimmunotherapy of B-Cell Lymphomas
Bibliographic record
Abstract
Despite the promise of radioimmunotherapy using anti-CD20 antibodies (Ab) for the treatment of relapsed patients with indolent non-Hodgkin lymphoma (NHL), most patients treated with conventional doses of (131)I-tositumomab or (90)Y-ibritumomab eventually relapse. We did comparative assessments using conventional radioimmunotherapy targeting CD20, CD22, and HLA-DR on human Ramos, Raji, and FL-18 lymphoma xenografts in athymic mice to assess the potential for improving the efficacy of radioimmunotherapy by targeting other NHL cell surface antigens. Results of biodistribution studies showed significant differences in tumor localization consistent with variable antigenic expression on the different lymphoma cell lines. Interestingly, the radioimmunoconjugate that yielded the best tumor-to-normal organ ratios differed in each tumor model. We also explored administering all three (111)In-1,4,7,10-tetra-azacylododecane N,N',N'',N'''-tetraacetic acid antibodies in combination, but discovered, surprisingly, that this approach did not augment the localization of radioactivity to tumors compared with the administration of the best single radiolabeled Ab alone. These data suggest that conventional radioimmunotherapy using anti-CD20, anti-HLA-DR, or anti-CD22 Abs is effective when used singly and provides targeted uptake of radiolabel into the tumor that is dependent on the levels of antigen expression. Improvements in tumor-to-normal organ ratios of radioactivity cannot be achieved using directly labeled Abs in combination but may be afforded by novel pretargeting methods.
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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) | 0.001 | 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 | 0.000 |
| 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".