Impact of multi-gene mutational profiling on clinical trial outcomes in metastatic breast cancer
Bibliographic record
Abstract
PURPOSE: Next-generation sequencing (NGS) has identified recurrent genomic alterations in metastatic breast cancer (MBC); however, the clinical utility of incorporating routine sequencing to guide treatment decisions in this setting is unclear. We examine the frequency of genomic alterations in MBC patients from academic and community hospitals and correlate with clinical outcomes. METHODS: MBC patients with good performance status were prospectively recruited at the Princess Margaret Cancer Centre (PM) in Canada. Molecular profiling on DNA extracted from FFPE archival tissues was performed on the Sequenom MassArray platform or the TruSeq Amplicon Cancer Panel (TSACP) on the MiSeq platform. Clinical trial outcomes by RECIST 1.1 and time on treatment were reviewed retrospectively. RESULTS: From January 2012 to November 2015, 483 MBC patients were enrolled and 440 were genotyped. At least one somatic mutation was identified in 46% of patients, most commonly in PIK3CA (28%) or TP53 (13%). Of 203 patients with ≥ 1 mutation(s), 15% were treated on genotype-matched and 9% on non-matched trials. There was no significant difference for median time on treatment for patients treated on matched vs. non-matched therapies (3.6 vs. 3.8 months; p = 0.89). CONCLUSIONS: This study provides real-world outcomes on hotspot genotyping and small targeted panel sequencing of MBC patients from academic and community settings. Few patients were matched to clinical trials with targeted therapies. More comprehensive profiling and improved access to clinical trials may increase therapeutic options for patients with actionable mutations. Further studies are needed to evaluate if this approach leads to improved clinical outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".