Targeted sequencing in a phase III trial of luminal breast cancer: Identification of novel targets.
Bibliographic record
Abstract
505 Background: The International Cancer Genome Consortium and The Cancer Genome Atlas have had a global transformative impact on our understanding of cancer. These programs have mapped the genomic landscape of common and rare tumors setting the scene for a comprehensive change in the approach to cancer diagnosis and treatment. However, the task remains incomplete until these mutational events are linked to clinical outcomes in the context of current therapeutic intervention to drive future stratified medicine approaches. Methods: We performed targeted sequencing in patients from the Tamoxifen Exemestane Adjuvant Multicentre trial. DNA was extracted and a 101 gene panel analysed using a novel mutation calling pipeline. Both a priori and machine learning analyses were performed using distant recurrence free survival as the primary endpoint. Results: In 1,491 successfully analyzed samples 1,070 (71.76%) samples exhibited at least one single nucleotide mutation (range 0-94, 1.828+/-0.133, mean+/-s.e.). 98/101 genes were mutated in at least one patient. Only variants in PIK3CA, TP53, MLL3, CDH1 were detected in 5% or more of samples. Twenty genes were associated with increased risk of recurrence in multivariate analyses corrected for clinic-pathological variables, 50% of these genes were involved in transcriptional regulation or RNA/protein processing. In a multivariate analysis, two combined signalling modules were independently prognostic for residual risk following hormone therapy (HRvalidation 3.10 95%CI 1.78-5.40 and HRvalidation 2.70 95%CI 1.57-4.64). Conclusions: We successfully performed a signalling pathway-based targeted sequencing analysis within predefined signalling modules. In supervised and unsupervised analyses we identified multiple signalling cassettes linked to poor outcome in patients with ER+ve breast cancers treated with modern endocrine therapy in the context of a phase III clinical trial. These results identify novel candidates as targets to treat endocrine refractory breast cancers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".