Effectiveness of immune checkpoint inhibitor therapy on bone metastases in non-small-cell lung cancer
Bibliographic record
Abstract
Background: Bone metastases (BoMs) are prevalent in patients with metastatic non-small-cell lung cancer (NSCLC) however, there are limited data detailing how BoMs respond to immune checkpoint inhibitors (ICIs). The purpose of this study was to compare the imaging response to ICIs of BoMs against visceral metastases and to evaluate the effect of BoMs on survival. Materials and methods: A retrospective, multicentre cohort study was conducted in patients with NSCLC treated with nivolumab or pembrolizumab in Alberta, Canada from 2015 to 2020. The primary endpoint was the real-world organ specific progression free survival (osPFS) of bone versus visceral metastases. Visceral metastases were categorized as adrenal, brain, liver, lung, lymph node, or other intra-abdominal lesions. The secondary outcome was overall survival (OS) amongst patients with and without BoMs. Results: A total of 573 patients were included of which all patients had visceral metastases and 243 patients (42.4%) had BoMs. High PD-L1 expression was identified in 268 patients (46.8%). No significant difference in osPFS was observed between bone, liver, and intra-abdominal metastases (p=0.20 and p=0.76, respectively), with all showing shorter osPFS than other disease sites. There was no difference in the osPFS of extra-thoracic sites of disease in patients with high PD-L1 expression. There was significant discordance between visceral disease response and bone disease response to ICI (p=0.047). The presence of BoMs was an independent poor prognostic factor for OS (HR 1.26, 95%CI: 1.05-1.53, p=0.01). Conclusion: Metastatic bone, liver, and intra-abdominal lesions demonstrated inferior clinical responses to ICI relative to other sites of disease. Additionally, the presence of bone and liver metastases were independent poor prognostic factors for overall survival. This real-world data suggests that BoMs respond poorly to ICI and may require treatment adjuncts for disease control.
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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) | 0.001 | 0.001 |
| 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".