The role of concurrent amplification of PD-L1, PD-L2 and JAK2 in metastatic lung adenocarcinoma as a biomarker of immune checkpoint inhibitor response: a case report
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors (ICIs) have transformed the landscape of care for many malignancies, including non-small cell lung cancer (NSCLC). That said, ICIs are associated with significant immune related adverse events (IrAE) and most patients do not benefit. As a result, significant efforts have been devoted to identifying biomarkers able to predict response to ICIs. Case Description: This report highlights a patient with diffusely metastatic NSCLC (adenocarcinoma) with RET translocation (RET-KIF5B fusion) who experienced an exceptional response to nivolumab. She was initially treated with standard of care platinum-based chemotherapy with a mixed response and significant toxicity after 3 cycles. She subsequently received 43 cycles of nivolumab on an expanded access program. In total, her disease remained stable 43 months after nivolumab had been stopped. On genetic testing, the patient’s tumor was found to harbour amplification of PD-L1, PD-L2 and JAK2. Discussion: This case highlights the potential value of PD-L1, PD-L2 and JAK2 gene cluster amplification as a biomarker of ICI sensitivity. Importantly, amplification of this gene cluster appears to confer sensitivity to nivolumab regardless of the presence of a RET rearrangement, a known driver mutation in NSCLC. This raises the question whether routine testing for rare but strong markers of ICI sensitivity can improve selection of upfront systemic therapy in patients with NSCLC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".