Does HPV Subtype Predict Outcomes in Head and Neck Cancers?
Bibliographic record
Abstract
Background. Recently, reanalysis of The Cancer Genome Atlas study demonstrated that human papillomavirus (HPV) genotypes in head and neck cancers other than HPV-16 have inferior survival to HPV-16-positive tumors. We aimed to examine the association of HPV subtypes and survival in a large cohort of patient samples from our institution. Methods. Fresh frozen primary site biopsy samples were collected either in clinic or at the time of surgery. Patient demographic, staging, and survival data were also collected. Tumors were tested for HPV subtypes by quantitative polymerase chain reaction (qPCR). Univariable and multivariable analyses were performed using Cox proportional hazards regression. Results. 280 patient biopsy samples were collected between 2011 and 2017. Mean ± standard deviation (SD) age was 61.9 ± 11.1 years and most patients (78%) were male. The majority of cancers were of the oral cavity (60%) or oropharynx (25%) and 30% had HPV-positive disease. Median follow-up was 3.76 years and 96/280 patients (34%) developed recurrences. Patients with p16-positive versus negative disease had significantly improved 5-year overall survival (OS, 77.6% vs. 53.3%; <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>p</a:mi> <a:mo>=</a:mo> <a:mn>0.009</a:mn> </a:math> ) and progression-free survival (PFS, 67.3% vs. 41.0%, <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>p</c:mi> <c:mo>=</c:mo> <c:mn>0.006</c:mn> </c:math> ). Similarly improved 5-year OS and PFS were observed for patients with HPV-positive versus negative disease (65.0% vs. 55.0%, <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>p</e:mi> <e:mo>=</e:mo> <e:mn>0.084</e:mn> </e:math> ; 53.3% vs. 43.2%, <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" id="M4"> <g:mi>p</g:mi> <g:mo>=</g:mo> <g:mn>0.072</g:mn> </g:math> , resp.). Patients with HPV-16 compared to other HPV diseases had worse 5-year OS and PFS (62.1% vs. 88.9%, <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" id="M5"> <i:mi>p</i:mi> <i:mo>=</i:mo> <i:mn>0.273</i:mn> </i:math> ; 49.0% vs. 88.9%, <k:math xmlns:k="http://www.w3.org/1998/Math/MathML" id="M6"> <k:mi>p</k:mi> <k:mo>=</k:mo> <k:mn>0.081</k:mn> </k:math> , resp.). Conclusions. In contrast to the data derived from The Cancer Genome Atlas, patients with HPV-16 tumors trended towards decreased PFS and OS compared with tumors driven by other HPV genotypes. Further larger multi-institutional studies are necessary to understand the relationship between other HPV genotypes and survival in head and neck squamous cell carcinomas.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".