Abstract 3567: Loss of heterozygosity (LOH) profiles predict oral cancer recurrence in the Oral Cancer Recurrence Prediction Cohort
Bibliographic record
Abstract
Abstract Oral squamous cell carcinoma (SCC) has a poor prognosis with local recurrence occurring in up to 30% of cases. Oral mucosal changes are common at previously treated cancer sites; however, despite close follow-up, they often develop into recurrent tumor. LOH analysis has been shown to predict cancer recurrence in lesions at former tumour sites in retrospective studies. Objective: To determine the value of specific LOH markers in predicting oral cancer recurrence in samples from an ongoing longitudinal study. Design: Data were collected from 365 oral cancer patients enrolled in the Oral Cancer Recurrence Prediction Cohort. Methods: The study selection criteria included: 1) Patients originally diagnosed with carcinoma in situ (CIS) or SCC and treated with a curative intent (mostly Stage 0/I/II); 2) Appearance of a lesion at the tumor site during follow-up with biopsy histologically diagnosed as hyperplasia or low-grade (mild/moderate dysplasia) (higher diagnoses went to treatment); 3) Continued follow-up of patient; 4) Sufficient tissue in biopsy for LOH analysis. 71 patients met these criteria. Recurrence outcome was defined as development of severe dysplasia, CIS or SCC at the target site. Timeline was calculated from target date to outcome for recurring group or last follow-up date for non-recurring group. Target biopsies were analyzed using 19 microsatellite loci on 7 chromosome arms (3p, 4q, 8p, 9p, 11q, 13q, and 17p). Univariant Cox analysis was used to determine the prognostic value of clinical and microsatellite markers. Recursive partitioning trees and Kaplan Meier curves were constructed to create molecular models for both 9p and 3p. Results: Recurrence occurred in 26 or the 71 patients (36.6%) with 18 months median follow-up time (compared with 33 months in non-recurring patients). There were no differences between recurrent and non-recurrent groups for gender, ethnicity, tobacco habit, original diagnosis before treatment and histology of target biopsy (P>0.05). RR was significantly increased for cases with LOH at 9p, 3p, 8p, 13q and 17p (P<0.05) but not with 4q or 11q. Chromosomal loss of 3 or more arms had a 6.2-fold increase in recurrence risk. LOH at 9p and 3p increased the risk by 4.2- and 3.8-fold, respectively (Pα0.001) with LOH at both 9p and 3p having a RR of 7.1-fold (Pα0.001). 9p retention was the best independent negative predictor for disease recurrence. Conclusion: The study confirmed the value of microsatellite LOH markers in predicting oral cancer recurrence. The microsatellite markers could serve as an additional prognostic tool to better categorize future oral cancer patients into various risk groups. (Sponsored by NIH/NIDCR grants R01DE13124 and R01DE17013) Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3567. doi:1538-7445.AM2012-3567
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".