The Challenges of Validating in Precision Medicine: The Case of Excision Repair Cross-Complement Group 1 Diagnostic Testing
Bibliographic record
Abstract
Personalized medicine relies upon the successful identification and translation of predictive biomarkers. Unfortunately, biomarker development has often fallen short of expectations. To better understand the obstacles to successful biomarker development, we systematically mapped research activities for a biomarker that has been in development for at least 12 years: excision repair cross-complement group 1 protein (ERCC1) as a biomarker for predicting clinical benefit with platinum-based chemotherapy in non-small cell lung cancer. We found that although research activities explored a wide range of approaches to ERCC1 testing, there was little replication or validation of techniques, and design and reporting of results were generally poor. Our analysis points to problems with coordinating and standardizing research in biomarker development. Clinically meaningful progress in personalized medicine will require concerted efforts to address these problems. In the interim, health care providers should be aware of the complexity involved in biomarker development, cautious about their near-term clinical value, and conscious of applying only validated diagnostics in the clinic. THE ONCOLOGIST: 2017;22:89-96 IMPLICATIONS FOR PRACTICE: : Many hospitals, policy makers, and scientists have made ambitious claims about the promise of personalizing cancer care. When one uses a case example of excision repair cross-complement group 1 protein-a biomarker that has a strong biological rationale and that has been researched for 12 years-the current research environment seems poorly suited for efficient development of biomarker tests. The findings provide grounds for tempering expectations about personalized cancer care-at least in the near term-and shed light on the current gap between the promise and practice of personalized medicine.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".