Beta-Testing of Next-Generation DNA Sequencing for Patients With Advanced Cancers Treated at Community Hospitals
Bibliographic record
Abstract
Multiple nonrandomized studies have demonstrated that cancer drug treatments selected on the basis of tumor genomic alterations are superior to nontargeted therapies. A pooled analysis of phase II single-agent studies reported that biomarkermatched therapy was an independent predictor of improved outcome. However, the prospective SHIVA randomized trial failed to show an improvement in progressionfree survival with the use of molecularly targeted agents matched to genomic alterations outside of their indications when compared with physician’s choice of nonmatched standard therapy. Many large academic institutions and cooperative research groups have launched nextgeneration sequencing (NGS) testing initiatives to facilitate enrollment in precision medicine clinical trials. There are also ongoing umbrella (histology-specific) and basket (histology-agnostic, aberrationspecific) clinical trials that incorporate the results of NGS testing for treatment assignment to genotype-matched therapies (Lung Master Protocol [Lung-MAP], NCT02154490; A Biomarker-Integrated Targeted Therapy Study-2 [BATTLE-2], NCT01248247; National Cancer Institute [NCI]-Molecular Analysis for Therapy Choice [MATCH], NCT02465060; My Pathway, NCT02091141). NGS profiling is also commercially available, and despite limited evidence of clinical usefulness, it is used increasingly to guide use of off-label therapy for patients with advanced refractory cancers. There are limited data about the clinical impact of targeted NGS profiling for patients treated at community hospitals. Although the majority of patients with cancer in the United States are treated in community settings, most clinical trial participants receive their primary cancer treatment at academic centers. It is estimated that only 3% of adult patients with cancer participate in clinical trials in the United States, and patients in rural areas have lower rates of participation compared with those in metropolitan regions. Even among patients who travel to an academic center for a clinical trial assessment, many ultimately do not participate because of the logistical challenges of receiving treatment far away from their home. For example, Meric-Bernstam et al recently reported their experience with 2,000 consecutive patients with advanced solid tumors who underwent genomic profiling at MD Anderson Cancer Center. They found that 17% of patients did not return to MD Anderson after testing, and 13% chose to be treated closer to home, underscoring the importance of integrating genomic profiling within the context of community-based cancer care. Mantripragada et al describe the results of a genomic profiling program that enrolled 200 patients with advanced solid tumors and hematologic malignancies
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".