Development of a diagnostic tool for the evaluation of progression risk of early oral premalignant lesions
Bibliographic record
Abstract
A51 Introduction: Forty percent of patients with oral squamous cell carcinoma (OSCC) do not survive past 5 years. Identification of oral premalignant lesions (OPLs) at high-risk for progression would allow for earlier intervention thus improving prognosis. Genetic alterations have been recognized as predictors of progression risk in OPLs. Here, we describe an “oral pre-cancer risk assessment array” (OPRA) dedicated to the evaluation of cancer risk of early premalignancies. >Methodologies: To select regions for OPRA, we analyzed 94 oral lesions by tiling-path array comparative genomic hybridization (CGH) to globally identify genetic signatures at a resolution of 50 kb. This whole genome array allowed the interrogation of 26,819 overlapping bacterial artificial chromosome (BAC) clones with complete coverage of the human genome. These lesions included 47 high-grade dysplasias (severe dysplasia and carcinoma in situ), 24 low-grade dysplasias (hyperplasia, mild and moderate dysplasia) with clinical outcome (follow-up ≥5 years), and 23 OSCCs. Genetic changes were detected by breakpoint algorithm aCGH-Smooth and were filtered for copy number polymorphisms previously identified in 95 non-cancer subjects. >Results: We first focused on 47 high-grade dysplasias, the premalignant stage that is associated with the strongest risk of progression. Genetic regions identified in high-grade dysplasias that are important for progression are likely to be maintained in OSCCs. Thus, regions recurrent in ≥20% high-grade dysplasias were compared with 23 OSCCs to identify the common minimal regions of alteration. Next, genetic alterations were assessed in early stage OPLs including 24 low-grade dysplasias (9 progressing and 15 non-progressing cases). The level of genomic instability was low in these lesions, however, alterations frequent in progressing low-grade dysplasias but infrequent in non-progressing cases were identified (p In total, 384 BACs representing 159 regions were selected from the RPCI-11 human BAC library. Additionally, 1152 random BACs were also incorporated in the array for normalization. All BACs were spotted in quadruplicates in this mini-chip. We demonstrate the detection of copy number alterations in clinical specimens in this mini-chip. These include both copy number gains and losses, with changes validated by the whole genome tiling-path array. >Conclusion: By whole genome profiling of OPLs and OSCCs, we obtained a priori knowledge of regions of interest that would minimize the cost and increase the robustness to target specific regions for the development of OPRA. This mini-chip will serve as a valuable diagnostic tool, and the use of DNA-based technology has practical advantages in clinical setting due to its inherent stability and ease of handling. This work represents the first attempt at the construction of a clinical tool that is well-targeted towards predicting outcome in early stage OPLs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".