Vulvar Squamous Cell Carcinoma (VSCC) as Two Diseases: HPV Status Identifies Distinct Mutational Profiles Including Oncogenic Fibroblast Growth Factor Receptor 3
Bibliographic record
Abstract
Abstract Purpose: Patients with advanced or recurrent invasive vulvar squamous cell carcinoma (VSCC) have limited treatment options and a grave prognosis. Understanding the genomic landscape may facilitate the identification of new therapies and improve clinical outcomes. Experimental Design: A retrospective chart review and molecular analysis of patients with VSCC from 2000 to 2016 was performed at the Ottawa Hospital Research Institute. The presence of oncogenic human papillomavirus (HPV) was determined by nested PCR and amplified DNA was sequenced using the Ion AmpliSeq Cancer Hotspot v2 Panel. The patients were divided into two groups according to HPV status (HPV-positive versus HPV-negative) and clinical outcome correlated with mutation status using descriptive statistics. Results: In 43 VSCC patients, there was a high mutation rate in both HPV-positive (73%) and HPV-negative (90%) disease with the two subgroups expressing distinct genetic profiles. HPV-positive tumors were characterized by oncogenic mutations in PIK3CA (27%), FGFR3 (14%), and PTEN (9%), whereas HPV-negative tumors were found to have mutations in TP53 (57%), HRAS (24%), PI3KCA (19%), and CDKN2A (14%). Mutation S249C in FGFR3 occurred in 14% of HPV-positive tumors. While there were notable differences in the occurrence of TP53, HRAS, PTEN, and FGFR3 mutations according to HPV status, only the rate of TP53 mutations was statistically significant (P = 0.0004). No significant difference in prognosis was found between patients with HPV-positive and HPV-negative VSCC. Conclusions: HPV-positive VSCC is characterized by oncogenic FGFR3 mutations that helps classify this subtype as a separate disease. Inhibitors of FGFR3 merit consideration as a therapeutic strategy in this neglected cancer in women. Clin Cancer Res; 23(15); 4501–10. ©2017 AACR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.001 |
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; both teacher heads agree on what is shown here.
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".