Abstract 5097: Proteomic characterization of non-small cell lung cancer (NSCLC): Discovery and validation of NSCLC protein signatures and signaling networks
Bibliographic record
Abstract
Abstract Lung cancer is the leading cause of cancer death worldwide. Non-small 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 (XT) 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 (mSRM, 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 >175 proteins was found to constitute a distinctive proteomic signature associated with NSCLC subtype, and an mSRM method was developed that provided relative quantification of a subset of highly differentially expressed proteins (i.e. >10-fold; p <0.05) that distinguished ADC and SCC subtypes. The mSRM assay was further developed for the absolute quantification of tumor signature proteins, and also the epidermal growth factor receptor (EGFR) and phosphorylated EGFR, and was successfully applied with formalin-fixed paraffin-embedded tissue sections following laser micro dissection and Liquid Tissue® processing (Expression Pathology, Rockville, MD). To further validate the XT models and to define tumor-specific protein signatures, proteomic profiles encompassing ∼1500 proteins were compared between 5 (each) resected NSCLC patient tumors (PT), corresponding XT, and normal lung tissue (N). Subsets of proteins were identified that, based on their quantified differential expression patterns, distinguished between N and PT, and validated the fidelity of tumor signatures in the XT models. Protein quantifications by mSRM 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. Expression Pathology Inc. 9620 Medical Center Dr. Rockville, MD 20850 Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5097. doi:10.1158/1538-7445.AM2011-5097
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
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".