Abstract PO-015: The genetic landscape of head and neck cancer using brush biopsy
Bibliographic record
Abstract
Abstract Oral potentially malignant disorders (OPMDs) are characterized by the presence of dysplasia and DNA copy number aberrations (CNAs) and subsequently progress to oral squamous cell carcinoma (OSCC). OSCC has a 5-year survival rate of only about 50% and varies greatly depending on site and stage. The combination of next-generation sequencing and advanced computational data analysis approaches has revolutionized our understanding of the genomic underpinnings of cancer development and progression. This can reveal individual targetable alterations, mutational load, complex mutation signatures, and tumor-specific antigens, which might inform the utilization of targeted therapies and early diagnosis. Oral brush biopsy represents an attractive non-invasive advantage in the characterization of premalignant and malignant oral lesions. Still, a sufficient number of abnormal cells is mandatory to diagnose. The current study tested the possibility of recreating the genomic landscape of oral cancer using brush biopsy. Materials and methods: As a pilot study to address this question, we analyzed matched tissue and brush biopsy of a patient with synchronous OPMD and OSCC. Brush and tissue biopsy was taken from normal epithelium, OPMD, and OSCC. We scrutinized the evolution and heterogeneity of these three brush/tissue biopsy pairs. The techniques used include whole exome sequencing (WES), mutational signature analysis to evaluate smoking and other aberrant signatures, copy number analysis (using ASCAT), and subclonal architecture reconstruction (using DPclust). Results: To analyze intra-tumoral heterogeneity (ITH), ASCAT and DPclust were used to call copy number aberrations (CNA) and cluster somatic SNVs based on cancer cell fraction (CCF), respectively. We observed a large number of shared SNVs between the OPMD and OSCC lesions suggesting a common evolutionary origin. After adjusting the ploidy and purity based on DPClust results, ASCAT profiles revealed differences in aberrant tumor cell fraction, gains and loss of heterozygosity, and copy number neutral events between the samples. It showed almost similar profiles between the respective lesions' brush and tissue samples, suggesting that brush biopsy can recapitulate the genomic landscape. The OPMD and OSCC lesions showed loss of heterozygosity in chromosome 9 and chromosome 17, which play a potential role in tumorigenesis with loss of function of P53. We inferred the subclonal architecture of each sample, illuminating the clonal relationships between samples and distinct lineages of precursor clones developing independently. Conclusion: Collectively, these results emphasize the possibility of using brush biopsy to reconstruct the genomic characterization of OPMD and OSCC and show prognostic potential for diagnosis and intervention strategies and accelerate progress in precision cancer care. Citation Format: Evit John, Tom Lesluyes, Toby Baker, Xiao Zhao, Peter Van Loo. The genetic landscape of head and neck cancer using brush biopsy [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-015.
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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 | 0.001 | 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".