Abstract A33: Loss of tumor suppressors KAI1 and p27 identifies a unique subgroup of primary melanoma patients with poor survival
Bibliographic record
Abstract
Abstract Objective: Melanoma is a highly heterogeneous disease. A certain subgroup of patients with primary melanomas exhibits a greater potential to develop metastatic disease and thus has worse survival. The present study attempts to identify the molecular features shared by metastatic melanomas and the subgroup of primary melanomas, and use the information to assist clinicians to design personalized therapy for primary melanoma patients. Patients and methods: Seven previously reported biomarkers, including BRAF, Dicer, Fbw7, KAI1, MMP2, p27 and Tip60, were investigated. A training cohort with 250 melanoma patients (145 primary melanomas and 105 metastatic melanomas) and an independent cohort with 92 primary melanoma patients were used for the study. Logistic regression analysis was used to identify discriminate biomarkers of metastatic melanoma from primary melanoma. Kaplan-Meier survival and multivariate Cox proportional hazard regression analysis were performed to assess the significance of the molecular signature in the prognosis of melanoma. Results: In the training cohort, we found the loss expression of KAI1 and p27 to be most significant between metastatic and primary melanoma. The primary melanoma patients with loss expression of both KAI1 and p27 had poor 5-year survival in both training cohort (P = 0.002) and independent cohort (P = 0.03). Multivariate Cox regression analysis showed that the KAI1-/p27- signature was an independent factor for disease- specific survival (P = 0.004). More important, compared to KAI1 and p27 as an individual prognostic marker, the KAI1-/p27- signature is more closely associated with melanoma patient survival: the P value is 0.004 for KAI1-/p27-, and 0.044, 0.181 for KAI1 and p27, respectively. Conclusion: Loss of both KAI1 and p27 defines a subgroup of primary melanoma patients with poor prognosis, and the combination signature is better than individual biomarker. The clinical significance of KAI1 and p27 warrants further assessment in prospective clinical trials. Citation Format: Yabin Cheng, Guohong Zhang, Yun Tang, Guangdi Chen, Gholamreza Safaee, Annand Rotte, Magdalena Martinka, Kevin McElwee, Youwen Zhou. Loss of tumor suppressors KAI1 and p27 identifies a unique subgroup of primary melanoma patients with poor survival. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Melanoma: From Biology to Therapy; Sep 20-23, 2014; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(14 Suppl):Abstract nr A33.
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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 | 0.000 |
| 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) | 0.003 | 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 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".