Integrated genomic and molecular characterization of cervical cancer
Bibliographic record
Abstract
Cervical cancer remains one of the leading causes of cancer-related deaths worldwide. Here we report the extensive molecular characterization of 228 primary cervical cancers, one of the largest comprehensive genomic studies of cervical cancer to date. We observed notable APOBEC mutagenesis patterns and identified SHKBP1, ERBB3, CASP8, HLA-A and TGFBR2 as novel significantly mutated genes in cervical cancer. We also discovered amplifications in immune targets CD274 (also known as PD-L1) and PDCD1LG2 (also known as PD-L2), and the BCAR4 long non-coding RNA, which has been associated with response to lapatinib. Integration of human papilloma virus (HPV) was observed in all HPV18-related samples and 76% of HPV16-related samples, and was associated with structural aberrations and increased target-gene expression. We identified a unique set of endometrial-like cervical cancers, comprised predominantly of HPV-negative tumours with relatively high frequencies of KRAS, ARID1A and PTEN mutations. Integrative clustering of 178 samples identified keratin-low squamous, keratin-high squamous and adenocarcinoma-rich subgroups. These molecular analyses reveal new potential therapeutic targets for cervical cancers. This paper describes molecular subtypes of cervical cancers, including squamous cell carcinoma and adenocarcinoma clusters defined by HPV status and molecular features, and distinct molecular pathways that are activated in cervical carcinomas caused by different somatic alterations and HPV types. Cervical cancer is one of the main causes of cancer-related deaths worldwide, and 95% of cases result from human papilloma virus (HPV) infection. The Cancer Genome Atlas Research Network now reports the genomic and molecular characterization of 228 primary cervical cancers. The authors identify significantly mutated genes and pathways that differ by cervical cancer subtype, and find that keratin-low squamous, keratin-high squamous and adenocarcinoma-rich clusters are marked by different HPV types and molecular features.
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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 | 0.002 | 0.001 |
| 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".