Primary Tumor Xenografts of Human Lung Adeno and Squamous Cell Carcinoma Express Distinct Proteomic Signatures
Bibliographic record
Abstract
Nonsmall cell lung carcinoma (NSCLC) accounts for 80% of lung cancers. The most prevalent subtypes of NSCLC are adenocarcinoma (ADC) and squamous cell carcinoma (SCC), which combined account for approximately 90%. Ten resected NSCLC patient tumors (5 ADC and 5 SCC) were directly introduced into severely immune deficient (NOD-SCID) mice, and the resulting xenograft tumors were analyzed by standard histology and immunohistochemistry (IHC) and by proteomics profiling. Mass spectrometry (MS) methods involving 1- and 2-dimensional LC-MS/MS, and multiplexed selective reaction monitoring (SRM, or MRM), were applied to identify and quantify the xenograft proteomes. Hierarchical clustering of protein profiles distinguished between the ADC and SCC subtypes. The differential expression of 178 proteins, including a comprehensive panel of intermediate filament keratin proteins, was found to constitute a distinctive proteomic signature associated with the NSCLC subtypes. Epidermal growth factor receptor (EGFR) was expressed in ADC and SCC xenografts, and EGFR network activation was assessed by phosphotyrosine profiling by Western blot analysis and SRM measurement of EGFR levels, and mutation analysis. A multiplexed SRM/MRM method provided relative quantification of several keratin proteins, EGFR and plakophilin-1 in single LC-MS/MS runs. The protein quantifications by SRM and MS/MS spectral counting were associated with superior dynamic range and reproducibility but were otherwise consistent with orthogonal methods including IHC and Western immuno blotting. These findings illustrate the potential to develop a comprehensive MS-based platform in oncologic pathology for better classification and potentially treatment of NSCLC patients.
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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 | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".