Abstract 1822: Proteome signatures distinguish lung cancer subtypes, define metabolism states, and have prognostic impact
Bibliographic record
Abstract
Abstract Lung cancer is one of the most common cancers worldwide, and is the number one cause of cancer death in both men and women. Non-small cell lung cancer (NSCLC) accounts for 85% of lung cancers and is subdivided into two major histological subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SCC). There is an unmet need to better understand and stratify NSCLC according to distinctive molecular features that may help develop diagnostic and therapeutic strategies to improve patient outcomes. The proteome is expected to have a pronounced and direct effect on cancer phenotypes. Therefore, proteomic approaches hold promise as superior methods to characterize cancers, with the ultimate goal to translate research results into clinical utilities. Mass spectrometry (MS)-based quantitative comprehensive proteome analysis resolved the proteomes of human lung ADC and SCC primary tumor-derived xenografts. A multi-protein signature able to distinguish between ADC and SCC was identified and validated in an independent cohort of samples. The signature is comprised of various components of the epithelial barrier and metabolism enzymes. Signatures composed of metabolism proteins were found to be highly recapitulated between primary and matched xenograft tumors, and when extrapolated to DNA alterations in the encoding genes, to have prognostic impact for overall patient survival. The ability of NSCLC primary tumors to engraft in severely immune deficient mice is an independent predictor of shorter disease-free survival in early-stage NSCLC patients. Therefore, we sought to identify proteome signatures of engraftment, which would then be tested for prognostic impact. The proteomes of a series of >50 NSCLC primary tumors that engrafted or not were quantitatively compared by using the so-called super-SILAC method, in which a mixture of metabolically-labeled, stable-isotope-encoded NSCLC-derived cell lines were used as an internal standard. ADC and SCC tumors were analyzed, and a signature of proteins including enzymes involved in central carbon metabolism were identified as differentially expressed between engrafting and non-engrafting tumors. These results highlight the significance of metabolic remodeling as a feature that might be a determinant of more aggressive cancer phenotypes. These results support the further development of proteome signatures to diagnose, stratify, and precisely treat NSCLC. Citation Format: Wen Zhang, Paul Taylor, Lei Li, Yuhong Wei, Jiefei Tong, Vladimir Ignatchenko, Nhu-An Pham, Thomas Kislinger, Ming-sound Tsao, Michael Moran. Proteome signatures distinguish lung cancer subtypes, define metabolism states, and have prognostic impact. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1822. doi:10.1158/1538-7445.AM2015-1822
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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".