Abstract B181: TP53 mutation screening in patients with recurrent platinum-sensitive ovarian cancer.
Bibliographic record
Abstract
Abstract Background: TP53 is commonly mutated in ovarian cancer. As potential agents targeting TP53 mutant or wt are under development, the efficiency and accuracy for determination of TP53 mutations in a clinical trial was evaluated. Methods: Eligible patients, including relapsed, platinum-sensitive ovarian, fallopian tube, or primary peritoneal cancer, underwent pre-screening for a clinical trial with MK1775, eligibility for which was determined by the presence of functional TP53 mutations (Brana I et al, ASCO 2013). Single base substitution and single base deletion mutations in exons 2 - 11 and the flanking splice junction in the TP53 gene were analyzed using AmpliChip p53 Assay (Roche Molecular Diagnostics, California) on archival tumor specimens. Results: Seventy-five women were screened for eligibility based on TP53; 43 had TP53 mutations, 27 were TP53 wild-type (wt), and 5 undetermined (Table 1). No clustering of mutations to define hot spots was evident. The most commonly mutated region was exon 5 (11 mutations). Missense mutations were the most common mutation type (69.7%); although, nonsense (14 %), splice (14 %), and frame shift mutations (2.3%) were also observed (Table 1). Most of the missense mutations were in exons 5 to 8 (90%). The mean turnaround time from sample reception to final TP53 status report was 3 days (range 1:14). Conclusions: The TP53 mutational status has been successfully determined in 93.3% of patients with a short turnaround time, supporting the feasibility of TP53 determination in the clinical setting. In our patient population, 57.3% had a TP53 mutation. Potential geographic differences in the incidence TP53 mutations cannot be evaluated due to the limited sample size; this hypothesis should be further evaluated in a larger patient population Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B181. Citation Format: Irene Brana, Kathleen Moore, Stephen Welch, Ronnie Shapira-Frommer, Darlene Gibbon, Hal Hirte, Saul Rivkin, Robert Coleman, Gerry Prendergast, Katherine M. Moxley, Ying-Ming Jou, Michelle Marinucci, Tomoko Freshwater, Sharon McCarthy, Mark Lee Anthony, Shelonitda Rose, Amit M. Oza. TP53 mutation screening in patients with recurrent platinum-sensitive ovarian cancer. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B181.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".