Characterization of the tumor immune-microenvironment of adenocarcinoma of lung with a metastatic lesion in the pancreas treated successfully with first-line, single-agent pembrolizumab
Bibliographic record
Abstract
Single-agent immune checkpoint inhibitor therapy in advanced non-small cell lung cancer can significantly prolong progression-free and overall survival when compared with cytotoxic chemotherapy. Here, we report a case of newly diagnosed adenocarcinoma of the lung with a solitary brain metastasis and a biopsy confirmed adenocarcinoma in the tail of the pancreas. Cytomorphology and immunohistochemistry suggested the lung and pancreas tumors were distinct primaries. However, molecular analysis of the lung primary and tumor in the pancreas revealed the same mutations of functional significance in PIK3CA, NF1 and TP53, suggesting the tumors were clonal. A total of three cycles of single-agent pembrolizumab, and radiation to the lung and brain administered between cycles 1 and 2, resulted in marked responses in lung, brain and pancreatic tumors. Despite the discontinuation of the pembrolizumab after three cycles due to severe immune-mediated toxicities, the patient has had no progression 11 months after stopping all active treatment. Results of a novel 27-gene immuno-oncology (IO) expression assay revealed strong IO scores for the lung and pancreatic tumors, indicating a favorable tumor immune-microenvironment and possibly explaining the significant response.
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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) | 0.000 | 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) | 0.000 | 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 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".