Abstract SY16-01: Clinical trial design to match patients to treatment based on molecular profiling of tumors
Bibliographic record
Abstract
Abstract Recent advances in the systematic sequencing of cancer genomes have revealed that individual tumors frequently harbor “driver” somatic mutations that confer growth advantage and positive selection. Therefore, it seems likely that evaluating drug efficacy against tumors defined by a combination of histopathology and molecular genetic profiles will result in a greater therapeutic gain. The identification of specific somatic mutations and other genetic aberrations that drive cancers leave us on the threshold of a new era of “personalized cancer medicine,” in which specific biomarkers will be used to direct targeted agents only to those patients deemed most likely to respond, or avoided in those least likely to benefit. With the promise of personalized cancer medicine, there is an urgency to translate scientific discoveries in biomarker research rapidly and reliably to benefit patients, both in the setting of clinical trials and as standard of care. Recent examples of molecularly targeted agents which have obtained US FDA regulatory approval, such as vemurafenib for malignant melanoma with B-Raf V600E mutation and crizotinib for non-small cell lung carcinoma harboring EML4-ALK fusion, highlight that “driver” genomic alterations represent key predictors of clinical efficacy. The fundamental categories of cancer genomic aberrations including base mutation, copy number alteration and translocation/rearrangement, as well as epigenetic modifications of DNA or histones, are considered as being potential “druggable” characteristics of human malignancies. In the meantime, emerging technology renders parallel sequencing and profiling of such molecular characteristics increasingly feasible at point-of-care, using clinical tumor specimens. The optimal way to validate the personalized medicine approach remains a challenge for the clinical trialist. A tumor-based approach (e.g. BATTLE and I-SPY2 trials) requires access to different molecularly targeted agents, which can be difficult unless there is cooperation and collaboration of multiple industrial sponsors. A histology-independent, genomics-based approach is of interest but requires screening of large numbers of patients especially if mutations are uncommon in the selected tumor types for inclusion. Furthermore, it is possible that the functionality varies for the same mutation in different tumor types. Innovation in clinical trial designs is necessary to help address this impending paradigm shift in cancer treatment allocations. Additional considerations such as clonal evolution and tumor heterogeneity in malignancies, presence of crosstalks and emergence of resistance pathways, can further complicate the evaluation of personalized cancer medicine in the clinical trial setting. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr SY16-01. doi:1538-7445.AM2012-SY16-01
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".