1472 Assessing the correlation between CD8 cell PET Imaging with 89-Zr-Crefmirlimab Berdoxam and CD8 cell immunohistochemistry in patients with advanced cancer receiving immunotherapy
Bibliographic record
Abstract
Background CD8 T-cells (CD8s) mediate the effects of most cancer immunotherapies. CD8s are typically assessed by biopsy which is inherently limited by sample availability, intratumoral and intrapatient heterogeneity, and difficulty with repeated, longitudinal assessment. Non-invasive CD8 PET imaging with 89-Zr-Crefmirlimab Berdoxam (crefmirlimab) could circumvent these barriers and has previously demonstrated feasibility and safety. Methods We conducted a Phase II, prospective multicenter study to test the correlation between crefmirlimab PET signal and CD8 cell quantity by immunohistochemistry (IHC) in patients with solid tumors receiving standard of care immunotherapy. Patients underwent a baseline CD8 PET scan within 1 week prior to starting immunotherapy. A second crefmirlimab PET scan was performed 4-6 weeks after starting immunotherapy. Pre-treatment tissue and a biopsy 4-6 weeks on-treatment were used for CD8 IHC assessment by SP-57 antibody stain. Bone biopsies and those with <5% tumor were excluded. The primary endpoint was the correlation between PET uptake in the biopsied tumors [SUVmax, SUVmean, SUVpeak; normalized to reference tissue] and CD8 IHC results [CD8 cells/mm2] using the Spearman’s correlation coefficient. Results Among 52 enrolled patients with ≥1 crefmirlimab scan and corresponding biopsy, 48 patients had 35 baseline biopsies and 34 on-treatment biopsies evaluable for the primary endpoint. Eight solid tumor types were represented with renal cell carcinoma (RCC, n=21 samples), melanoma (n=23), and non-small cell lung cancer (NSCLC, n=17) being the most common. Among the examined imaging parameters, SUVmean of the biopsied tumor, normalized to Aorta (SUVmean/SUVaorta) provided the best correlation. For all 69 biopsied lesions, the SUVmean/SUVmean aorta correlated with CD8 cell density [cells per mm2] by IHC with a Spearman’s correlation coefficient of 0.58 (95% CI: 0.385 - 0.697). For the 35 baseline biopsies the correlation was 0.66 (95%CI: 0.387 - 0.825), and for the 34 on-treatment biopsies the correlation was 0.48 (95% CI: 0.148 - 0.713). The correlation for RCC, melanoma, and NSCLC was 0.77 (95% CI: 0.552 - 0.913), 0.55 (95% CI: 0.084 - 0.727), and 0.54 (95% CI: -0.121 - 0.774), respectively. The mean SUVmean lesion/SUVmean aorta and mean CD8 cell density were 1.71 (IQR: 0.93-1.55) and 509 (IQR:114-461) at baseline and 2.43 (IQR: 0.80-3.60) and 759 (IQR:158-963) post-treatment respectively. Conclusions Non-invasive CD8 PET scanning with crefmirlimab correlates with CD8 assessment by IHC and permits whole patient, longitudinal CD8s assessment. Crefmirlimab imaging is under investigation as a biomarker for immunotherapy responsiveness in ongoing trials (NCT05013099) and could ultimately provide a useful tool for immunotherapy drug development and clinical management. Trial Registration NCT03802123 Ethics Approval The study was conducted in accordance with the Declaration of Helsinki and the International Conference on Harmonization Guidelines for Good Clinical Practice (ICH-GCP) All patients provided written informed consent.
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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 | 0.000 | 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".