Human papillomavirus (HPV) study of 691 pathological specimens from Quebec by PCR-direct sequencing approach
Bibliographic record
Abstract
Human papillomaviruses (HPV) are etiological agents of cervical cancer. In order to address clinical demand for HPV detection and sequence typing, mostly in pre-cancerous cervical lesions, we applied our two-tier PCR-direct sequencing (PCR-DS) approach based on the use of both MY09/MY11 and GP5 + /GP6 + sets of primers. We tested 691 pathological specimens, all of which were biopsies, 75% of which were diagnosed histologically as cervical intraepithelial neoplasia (CIN) grades I-III. In total, 484 samples (70%) tested HPV-positive, yielding 531 HPV sequences from 47 HPV types, including two novel types. Four most frequently found HPV types accounted for 52.9% of all isolates: HPV6, 16, 11, and 31 (21.5%, 20.0%, 7.0%, and 4.5%, respectively). Some interesting results are the following: all currently known high-risk HPV (14 types) and low-risk HPV (6 types) were detected; HPV18 was not the 1st or 2nd but rather the 4th-5th most frequent high-risk HPV type; the highest detection rate for HPV (86%) among samples suspected to be HPV-infected was found in the youngest age group (0-10 years old), including 70% (44/63) "genital" HPV types; HPV types of undetermined cervical cancer risk represented 19% and of the total HPV isolates but were strongly increased in co-infections (36.5% of all isolates). To our knowledge, this is the largest sequencing-based study of HPV. The HPV types of unknown cancer risk, representing the majority of the known HPV types, 27 of the 47 types detected in this study, are not likely to play a major role in cervical cancer because their prevalence in CIN-I, II, and III declines from 16% to 8% to 2.5%. The two-tier PCR-DS method provides greater sensitivity than cycle sequencing using only one pair of primers. It could be used in a simple laboratory setting for quick and reliable typing of known and novel HPV from clinical specimens with fine sequence precision. It could also be applied to anti-cancer vaccine development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".