Abstract PO-028: Clinical and genomic correlates of intratumoral HPV for oropharyngeal and cervical cancers
Bibliographic record
Abstract
Abstract Introduction: p16 immunohistochemistry (IHC) is the most commonly used test for diagnosing Human Papillomavirus (HPV) related oropharyngeal cancers. HPV DNA or RNA tests have been used increasingly more recently. However, the significance of the intratumoral HPV expression levels is not established yet. Here, we analyzed the intratumoral HPV expression levels using The Cancer Genome Atlas (TCGA) database. Methods: HPV levels were analyzed for oropharyngeal and cervical cancers using the TCGA database at cBioPortal.1,2 Alphapapilloma virus genus was included in the analysis to capture all high-risk HPV types. The association of HPV levels with genomic and clinical features was assessed using either a Spearman correlation coefficient (continuous variables) or a rank-sum test (categorical variables). Results: In oropharyngeal cancer patients, the genomic analysis revealed that CYLD-mutated HPV-positive tumors had significantly higher HPV levels (p=0.0162). There was no difference in virus levels depending on PIK3CA mutation (p=0.370). Gene expression analysis showed a significant correlation of virus levels with MAP3K7 (TAK1) expression (r=0.37, p<0.01). There was no difference in clinical features or survival depending on HPV levels. There was no significant difference among the common oncogenic mutations depending on HPV levels in cervical cancer patients. However, patients with low virus levels had shorter overall survival (PS) and disease-specific survival (DSS) (p=0.01 and 0.027, respectively). Patients with adenocarcinoma had higher levels of the virus when compared to squamous cell carcinoma. Discussion: Our study showed that intratumoral HPV level was correlated with CYLD mutations and TAK1 expression in oropharyngeal cancer patients. CYLD mutations are common in HPV-related head&neck cancers, occurring in up to 20% of tumors. CYLD is known to negatively regulate the NF-κB pathway by inactivating TAK1 and IKK complexes.3 Clinical outcomes were not different depending on the HPV levels in patients with oropharyngeal cancers in our study; however, there were a small number of patients in this study (n=48). Another HPV-related cancer, cervical cancer, was more commonly represented in the TCGA database (n=297). The patients with low HPV levels had poor prognosis in this larger cohort. HPV-directed therapies are emerging for HPV-related cancers. Understanding the role of the intratumoral HPV levels can help to improve viral targeted therapies. References: Cerami et al. The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data. Cancer Discovery. May 2012 2; 401. Gao et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013). Cui et al. CYLD Alterations in the Tumorigenesis and Progression of Human Papillomavirus-Associated Head and Neck Cancers. Mol Cancer Res. 2021 Jan;19(1):14-24. Citation Format: Emrullah Yilmaz, Jacob A. Miller, Shlomo A. Koyfman, Jessica L. Geiger, Natalie Silver, Daniel McGrail. Clinical and genomic correlates of intratumoral HPV for oropharyngeal and cervical cancers [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-028.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".