Abstract 5073: Proteomic profiling of head and neck squamous cell carcinoma cell lines
Bibliographic record
Abstract
Abstract INTRODUCTION: Head and Neck Squamous Cell Carcinoma (HNSCC) is the sixth most common cancer worldwide with approximately 500,000 new cases diagnosed each year. Squamous cell carcinomas of the larynx (LSCC) and the hypopharynx (HSCC) are subtypes of HNSCC. The current diagnostic methods are not sensitive enough since pre-cancerous fields are often not visible to the naked eye during endoscopic examination and are difficult to detect even on histology. Discovery of novel biomarkers for HNSCC should lead to improved detection of HNSCC. Mass spectrometry-based proteomics methods have emerged as promising approaches for biomarker discovery. As one approach, mass-spectrometric identification of proteins shed or secreted from cancer cells can contribute to our understanding of tumour behaviour and to the identification of potential diagnostic biomarkers for HSCC and LSCC. EXPERIMENTAL DESIGN: In order to identify putative biomarkers for HNSCC detection, mass spectrometry-based proteomic profiling was performed on the conditioned media (i.e. secretome) of cancer cell lines of laryngeal and hypopharyngeal origin (UTSCC42a, UTSCC8, and FaDu). In addition, a human gene expression microarray was used to identify over-expressed genes in HNSCC cell lines in comparison to a control cell line. The protein expression data was integrated with gene expression microarray profiles and systematic bioinformatics data mining using publicly available resources (Human Protein Atlas and published proteomic/transcriptomic data) was used to prioritize the markers for validation. Subsequently, real-time quantitative PCR, Western Blotting, and immunohistochemistry (IHC), were performed to validate the over-expression of selected markers. RESULTS: Proteomic profiling of HNSCC cell lines resulted in 1850 protein identifications. By integrating the protein expression data with gene expression microarray profiles, we identified 90 putative protein biomarkers that were secreted or shed to the extracellular space and over-expressed in HNSCC cell lines, relative to controls. Subsequently, the over-expression of 5 markers was successfully validated at the transcriptional and translational levels using quantitative real-time PCR, Western Blotting, and IHC on the HNSCC cell lines, and xenograft tumour models. CONCLUSION: Secretome and transcriptome profiling of HNSCC cell lines enabled the identification of 90 putative HNSCC biomarkers for further validation, 5 of which were successfully validated in vitro. Several of these markers have been implicated in HNSCC, illustrating the robustness of our approach to biomarker discovery. Future validation steps will include examination of these proteins in primary HNSCC biopsies, and matching patient sera. Ultimately, identification of a panel of protein biomarkers in a biological fluid (e.g. serum) of HNSCC patients will allow the development of an effective diagnostic test for early diagnosis. 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 5073. doi:10.1158/1538-7445.AM2011-5073
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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) | 0.002 | 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".