A community‐based lung cancer rapid tissue donation protocol provides high‐quality drug‐resistant specimens for proteogenomic analyses
Bibliographic record
Abstract
BACKGROUND: For the advancement of cancer research, the collection of tissue specimens from drug-resistant tumors after targeted therapy is crucial. Although patients with lung cancer are often provided targeted therapy, post-therapy specimens are not routinely collected due to the risks of collection, limiting the study of targeted therapy resistance mechanisms. Posthumous rapid tissue donation (RTD) is an expedient collection process that provides an opportunity to understand treatment-resistant lung cancers. METHODS: Consent to participate in the thoracic RTD protocol was obtained during patient care. When death occurred, tumor and paired non-tumor, cytology, and blood specimens were collected within 48 hours and preserved as formalin-fixed and frozen specimens. Tissue sections were evaluated with hematoxylin and eosin staining and immunohistochemistry (IHC) against multiple biomarkers, including various programmed death ligand 1 (PD-L1) clones. Next-generation sequencing was performed on 13 specimens from 5 patients. RESULTS: Postmortem specimens (N = 180) were well preserved from 9 patients with lung cancer. PD-L1 IHC revealed heterogeneity within and between tumors. An AGK-BRAF fusion was newly identified in tumor from a donor with a known echinoderm microtubule-associated protein-like 4 to anaplastic lymphoma kinase (EML4-ALK) fusion and history of anaplastic lymphoma kinase (ALK) inhibitor therapy. RNA expression analysis revealed a clonal genetic origin of metastatic cancer cells. CONCLUSIONS: Post-therapy specimens demonstrated PD-L1 heterogeneity and an acyl glycerol kinase to B-rapidly accelerated fibrosarcoma (AGK-BRAF) fusion in a patient with an EML4-ALK-positive lung adenocarcinoma as a potential resistance mechanism to ALK inhibitor therapy. Rapid tissue donation collection of postmortem tissue from lung cancer patients is a novel approach to cancer research that enables studies of molecular evolution and drug resistance.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".