Abstract C53: The genetic evolution of oral cancer fields
Bibliographic record
Abstract
Abstract Introduction: The evolution of oral cancer results from the accumulation of genetic alterations. Field cancerization, where histologically or molecularly abnormal cells surround a clinically visible tumor to a wider extent, imposes a challenge to delineate surgical boundaries. We applied a newly emerging optical technique using direct fluorescence visualization (FV) to redefine the field of alteration. Using genomic profiling we examined multiple biopsies within the field to assess clonal expansion of cells within this optically altered field. Experimental Approach: A hand-held FV device was used in the operating room to define the field that extended beyond the margins of clinically visible oral cancer. Multiple biopsies (N=15) were taken within the altered FV field (FV loss or FVL) and the surgical margins with no FVL, as controls, from three patients. Histological assessment and microdissection were performed for each biopsied sample. The genomic profile of each sample was generated using a tiling-path DNA microarray. A breakpoint detection algorithm was used to define genetic breakpoints and clonal ordering was performed to infer the sequence of genetic events of samples within each patient. Result: Early stage low-grade dysplasias were found within the FVL field in all patients, while no dysplasia was detected in the areas with no FVL. In general, each field is histologically and genetically heterogeneous. Specifically, patient A presented with a clinically identifiable SCC (#1), while another SCC (#3) was found in an area 10-mm anterior to SCC#1, which was not clinically apparent but showed FVL. A moderate dysplasia (#2) was found between SCC#1 and SCC#3. Interestingly, 5q, 8p, and 8q loss were common among all three samples (suggesting a common progenitor), while genetic alterations (e.g., high-level amplification on 9p22.3-pter) accumulated in both SCC#3 and dysplasia#2 but was absent in SCC#1. On the other hand, SCC#1 accumulated different genetic changes (e.g., gain of 11q13.2-q13.4 (CCND1)). This suggested that two clonal lineages were present within this cancerous field. Similarly, in patient B, the biopsies obtained revealed both common and different genetic signatures. For example, a moderate dysplasia showed genetic alterations specific to this lesion (e.g., high-level amplification of 8q11.21 (SNAI2)), while its corresponding carcinoma in situ harbored numerous different genetic alterations, including three regions of high-level amplification (e.g., 20q11.23 (SRC)). Genomic profiles of these samples suggest that two different genetic pathways diverged from a common progenitor, while subsequent genetic alterations accumulated for the formation of each unique subpopulation. In patient C, one genetic pathway was found governing the development of the clinically identifiable SCC, and increased genetic alterations were detected in the SCC compared to the mild dysplasia. All the controls did not show matching genetic changes. Conclusion: Our results indicate that the genetics of the oral cancer field is extremely dynamic, where different clones are evolving in the field. Genetic alterations occurring early in the genetic pathway may be important events that prime the area for further development of cancer. These findings provide evidence for the importance of implementing optical technologies in defining surgical margins as well as the importance of tailored targeted therapies to effectively treat different subclones of a field. Citation Information: Cancer Res 2009;69(23 Suppl):C53.
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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) | 0.002 | 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".