Molecular characterization of cervical cancer histological subtypes: The VENUS study.
Bibliographic record
Abstract
5537 Background: While the landscape of cervical cancer treatment has undergone considerable advances, systemic treatment options following recurrence remain limited. There is paucity of data on predictive and prognostic molecular alterations within distinct histologic subtypes. Methods: In this prospective, translational, single-institution study (NCT03420118), patients diagnosed with cervical cancer were enrolled. Tumor tissue samples (biopsy or archival) were obtained at different time points from initial histological diagnosis to each progression, when feasible. Genomic DNA was extracted and analyzed, using the UHN Hi5 Panel and the Illumina TruSight Oncology 500 assays, to detect molecular alterations. Variant annotations were obtained from OncoKB database. In addition, clinical data for each patient, including histopathologic subtype, stage at diagnosis, and lines of treatment were collected. Results: 124 patients (median age 48 years; range 24-80) with a pathologic diagnosis of cervical cancer were enrolled. Clinical characteristics of patients with available tumor tissue samples (n=112) are detailed in the table. In 99 of the 112 (88.4%) available tumor tissue samples, 243 validated molecular alterations were identified across 88 different genes. Seven tumors had ≥ 5 alterations detected per sample; one squamous cell carcinoma (SCC) and one poorly differentiated adenocarcinoma (AC) had 14 alterations each. PIK3CA, the most frequently detected alteration (41/243; 16.8%), was identified in 22 of 58 (37.9%) SCC samples vs 8 of 38 (21.1%) AC samples (p-value 0.11). Other commonly detected alterations included TP53 (18/243; 7.4%) and KRAS (13/243; 5.3%), both identified more frequently in AC compared to SCC tumor samples (28.9% vs 3.4%; p-value < 0.001, and 23.7% vs 3.4%; p-value 0.006, respectively). KRAS alterations were predominantly G12D or G12V. No G12C variants were detected. All tumors were MSI-stable. Among rare histologic subtypes, unique alterations were detected. Adenosquamous subtypes had alterations of TET2, BARD1, BRAF, in addition to CCND3 and CCNE1amplifications, while neuroendocrine subtypes showed alterations of ATM, MYC, SDHB, TSC1, FANCE, and FGFR1. Overall survival did not show a significant correlation with molecular alterations among different subtypes. Conclusions: Genomic analysis of histologic subtypes of cervical cancer reveals distinctive molecular features which may allow the development of genomic biomarkers of resistance and sensitivity. [Table: see text]
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".