Abstract IA11: Using Molecular Profiling Strategies in Clinical Trials to Understand Resistance Mechanisms in the Era of Personalized Cancer Medicine
Bibliographic record
Abstract
Abstract Resistance to molecularly targeted agents remains a key challenge in cancer therapeutics. Even in the setting of oncogenically addicted tumor types matched with potent and specific kinase inhibitors, the duration of drug response is limited. For example, in the phase III trial of vemurafenib versus dacarbazine in patients with advanced malignant melanoma harboring BRAF V600E mutations, 48% of patients in the vermurafenib arm achieved an objective response with a progression-free survival of 5.3 months (Chapman et al. N Engl J Med 2011). A caveat to this clearly positive clinical trial is that there are patients whose tumors have the same mutations who did not respond to vemurafenib and amongst those who did, the duration of response remains limited. In tumors that harbor driver mutations or other genetic aberrations, mechanisms of resistance are of relevance, including both primary resistance that renders them insensitive to specific inhibitors, and evolved/acquired resistance that results in tumor growth despite initial sensitivity. Comprehensive molecular profiling of pre-treatment tumor samples and at progression would be informative to offer insight into such mechanisms of primary and evolved/acquired resistance and guide clinical trial designs to counteract them. Combinatorial strategies may be considered upfront for patients who have tumor characteristics suggestive of primary resistance. For patients with sensitive disease which evolve/acquire resistance during treatment, a repeat tumor biopsy may help detect newly emerged aberrations that are potentially druggable. Additional areas of interest in defining tumor resistance include the observations that same mutation may have different degrees of functionality in different tumor types (e.g. BRAF mutations in melanoma versus colorectal cancer) and different mutations of the same gene may confer different drug sensitivities (e.g. activating and inactivating BRAF mutations), These context-dependent phenomena further complicate the planning of histology-agnostic, mutation-specific clinical trials using targeted agents. Efforts to provide detailed tumor molecular profiling information in clinical trials are relevant to help distinguish signatures of sensitivity versus resistance.
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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 | 0.030 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".