HIV Integration into the PTEN Gene and Its Tumor Microenvironment Implications for Lung Cancer
Bibliographic record
Abstract
Health outcomes for people with HIV (PWH) have improved significantly with combination antiretroviral therapy (ART), yet the risk of lung cancer remains elevated. While a single case cannot establish causality, we describe here an investigation of a 74-year-old male PWH with de novo high-grade neuroendocrine small cell lung carcinoma. To investigate the potential contribution of HIV to cancer development, we performed HIV integration site sequencing on blood, tumor, and non-tumor tissue samples from the patient. We analyzed integration site distribution, clonal expansion, and associated gene disruption. Phosphatase and Tensin Homolog (PTEN) expression was evaluated using immunofluorescence and microscopy. A total of 174 unique HIV integration sites were identified, with 29.9% (52/174) located in clonally expanded cells. The most frequent integration site in clonally expanded cells was within the PTEN gene, representing 4.2% to 16.7% of all HIV-infected cells across samples. PTEN expression was markedly reduced in tumor regions relative to non-tumor tissue. Areas positive for HIV p24 antigen showed minimal PTEN expression. These findings suggest that HIV integration into the PTEN gene, coupled with clonal expansion of HIV-infected cells, may impair anti-tumor immune responses and promote cancer progression in PWH.
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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 | 0.000 |
| 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) | 0.001 | 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".