Effect of matched therapy in metastatic colorectal cancer on progression free survival in the phase I setting.
Bibliographic record
Abstract
619 Background: The benefits of matching targeted treatments to aberrations identified by molecular profiling (MP) is unclear. Outcomes in phase 1 settings have been traditionally reported across tumor histologies. We report outcomes based on a metastatic colorectal cancer (mCRC) population. Methods: Patients (pts) with mCRC receiving at least one dose of treatment on a phase 1 study were annotated for variants detected by MP. A precision oncology decision support (PODS) team determined variant function and actionability. A matched therapy (MT) was defined as allocation to a novel agent that targeted the aberration or predicted pathway deemed actionable by PODS. Progression-free survival (PFS) was estimated using the Kaplan-Meier method. A Cox proportional hazards model was used to estimate hazard ratios (HR). Results: A total of 370 patients enrolled onto 467 phase 1 trials were identified from January 2012 to April 2017. 106 enrolments were assigned to MT. Pts with microsatellite instability-high (MSI-H), BRAFV600E, PIK3CA mutation were more likely to be assigned a MT, while left-sided tumors and RAS mutant patients were less likely to be treated with a MT. Molecularly-targeted regimens (MTR) were utilized more frequently in MT while immune-targeting MTR was more common in non-MT. BRAFV600E mutations and HER2 amplification/overexpression made up 44.3% of MT. There was a significant difference in PFS between the MT vs non-MT group (HR 0.65, 95% CI 0.51-0.83, p = 0.016) in univariate analysis. The 6-month PFS probability was 31% (95% CI; 23-41%) versus 13% (95% CI; 10-17%) respectively. Other significant factors in univariate analysis associated with longer PFS were MSI-H, BRAFV600E and use of a regimen containing cytotoxic chemotherapy while RAS mutations were associated with shorter PFS. In multivariate analysis, after correcting for mutation status, allocation to a MT was associated with improved PFS (HR = 0.72, 95% CI 0.50-0.99, p = 0.043). Conclusions: Matching clinical trial enrollment to MP based on dedicated decision support is associated with improved outcomes in mCRC patients. The MT strategy is still hampered by a limited number of actionable variants, and is driven by a small number of active MTs.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".