Circulating tumor cells enumeration and characterization in patients with lung cancer treated with immunotherapy.
Bibliographic record
Abstract
e15030 Background: Programmed death-ligand 1 (PD-L1) is a predictive biomarker for immunotherapy in the treatment of non-small cell lung cancer (NSCLC). Assessing PD-L1 expression from the tumor specimen can be challenging because of tissue accessibility, heterogeneity, and dynamic changes in PD-L1 expression that may impact the status of PD-L1 during disease evolution and treatment. Hence, assessing PD-L1 status from archival tumor might not reflect its actual state on the tumor and having a real-time assessment of its expression with the use of non-invasive techniques such circulating tumor cells (CTCs) is useful. Methods: We conducted a single centre prospective study to detect CTCs in the blood of patients with stage III-IV NSCLC treated with immunotherapy using the standard CellSearch technology for CTC enumeration and the Epic Sciences technology for CTCs enumeration and assessment of PD-L1 protein expression on CTCs. CTCs were detected at baseline before treatment initiation and after 2 cycles of treatment. Study endpoints included CTCs detection and concordance between the 2 technologies, PD-L1 expression assessment on CTCs with the Epic Sciences technology, and concordance with tissue-based expression. Comparisons were made using Pearson correlation coefficient (PCC) and intraclass correlation coefficient (ICC) for count data and percentage of perfect agreement. Results: Between 2019 and 2022, 48 patients treated with immunotherapy were enrolled in the study. The mean ± standard deviation (SD) age was 66.8 ± 10.3 years, 63% were females, 42% received combination chemotherapy and immunotherapy, and 44% had adenocarcinoma histology. The tissue PD-L1 expression was high (≥50%), intermediate (1-49%), and low (<1%) in 36%, 31% and 33% of patients respectively. The mean ± SD baseline CTCs count per 7.5ml was 1.97 ± 4 with CellSearch and 1.38 ± 2.72 per ml with Epic Sciences. After 2 treatment cycles, 22% and 41% of patients had an increase in their CTCs count with CellSearch and Epic Sciences, respectively and 30% and 29% had a decrease in their CTCs. The PCC and ICC for baseline CTCs detected by CellSearch and Epic Sciences were 0.72 and 0.61, respectively and for CTCs detected after 2 cycles of treatment by the 2 technologies were -0.16 and 0.45 respectively. One patient had PD-L1 expression on their CTCs with the Epic Sciences technology and there was no association between the PD-L1 expression on CTCs and on matched tissue samples. Conclusions: This study showed a moderate to strong correlation between the 2 technologies for baseline CTCs detection and moderate to poor correlation for CTCs detection after 2 cycles of therapy. No association was found between CTCs and tissue PD-L1 expression. Future work needs to further investigate the role of PD-L1 expression on CTCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".