Abstract 5068: Proteomics based prognostic signature of head and neck cancer
Bibliographic record
Abstract
Abstract Introduction. Molecular cancer diagnostics is rapidly moving beyond genomics to proteomics. Clinical cancer proteomics has the potential to discover, identify, and quantify novel biomarkers for early detection, diagnosis, prediction of the clinical outcome, and to develop effective therapeutic interventions by using these biomarkers as molecular targets. Despite therapeutic interventions, the five-year survival rate of head and neck cancer patients is less than 50%, and the prognosis of advanced HNSCC cases has not improved much over the past three decades. Methods. Using high-throughput proteomics, we identified a panel of biomarkers in head and neck cancer. This panel of markers was verified in a large independent cohort of paraffin embedded head and neck cancer tissues (n=100) using immunohistochemistry (IHC). Kaplan Meier analysis and multivariate logistic regression analysis was carried out to predict the prognosis of these head and neck cancer patients. Positive and negative predictive values (PPV and NPV respectively) were calculated as function of follow-up period. Results. Our immunohistochemical analysis demonstrated overexpression of a panel of proteins including 14-3-3zeta, 14-3-3sigma, heterogeneous nuclear ribonuclear protein K (HNRNPK), S100A7 and prothymosin alpha (PTMA) in head and neck cancer patients. Kaplan Meier analysis revealed shorter disease-free survival (DFS = 4 mths.) in head and neck cancer patients showing overexpression of the panel of proteins in comparison to patient cohort not showing overexpression of this panel (DFS = 41 mths., p < 0.001). Positive predictive value (PPV) and Negative predictive value (NPV) further verified the reduced disease free survival of these patients. Logistic regression analysis clearly demonstrated the relevance of this panel as the best prognostic signature for head and neck cancer patients (p < 0.001, Hazards ratio, H.R.=19.6, 95% C.I. = 2.3 – 8.5) in comparison to combinations of 2 – 4 markers and clincopathological parameters. Conclusions. In conclusion we identified, verified and developed a proteomics-based prognostic signature for head and neck cancer patients which may find its utility in follow-up clinics to accurately predict the recurrence of the disease after primary treatment. 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 5068. doi:10.1158/1538-7445.AM2011-5068
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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 | 0.001 |
| 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".