Revisiting Risk and Benefit in Early Oncology Trials in the Era of Precision Medicine: A Systematic Review and Meta-Analysis of Phase I Trials of Targeted Single-Agent Anticancer Therapies
Bibliographic record
Abstract
PURPOSE Phase I trials are a crucial step in the evaluation of new cancer therapies. Historically, low rates of response (5%) and comparably high rates of death from toxicities (0.5%) have contributed to debates on the ethics and orientation of these trials. With the introduction of novel targeted therapies, a contemporary estimate is needed. METHODS We systematically searched PubMed, Embase, and ClinicalTrials.gov for reports of phase I oncology trials of single-agent targeted immunomodulators, molecularly targeted therapies, and antiangiogenic agents, published between January 2015 and July 2018. Adult and pediatric trials of solid and hematological malignancies were eligible. Treatment-related adverse events (grades 3, 4, and 5) and response rates (objective, complete, and partial) were extracted and analyzed. RESULTS One hundred and fifty-eight trial reports, covering 6,707 patients, were included. The rate of treatment-related deaths was 0.0% (95% CI, 0.0 to 0.1), while 13.2% of patients (9.5 to 17.3) experienced a grade 3 or 4 treatment-related toxicity. The combined objective response rate was 6.4% (4.6 to 8.5). Among trials using tumor biomarkers as eligibility criteria, the objective response rate was higher (12.0% [7.3 to 17.6] compared to 4.9% [2.5 to 5.7], P value < .01). The same was true of trials focusing on a single tumor type (13.4% [8.2 to 19.4]) compared to multiple tumor types (3.8% [2.5 to 5.3], P value < .01). CONCLUSION Reduced grade 5 risk and improved benefit appears to exist in modern phase I oncology trials, particularly in trials that target single tumor types and integrate biomarkers as eligibility criteria. These findings provide information to support informed consent discussions, highlight the need for improved reporting of phase I oncology trials, and provide direction for optimizing their design.
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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 | 0.105 | 0.212 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.030 | 0.037 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".