Response biomarkers: re-envisioning the approach to tailoring drug therapy for cancer
Bibliographic record
Abstract
BACKGROUND: The rapidly expanding arsenal of chemotherapeutic agents approved in the past 5 years represents significant progress in the field. However, this poses a challenge for oncologists to choose which drug or combination of drugs is best for any individual. Because only a fraction of patients respond to any drug, efforts have been made to devise strategies to personalize care. The majority of efforts have involved development of predictive biomarkers. While there are notable successes, there are no predictive biomarkers for most drugs. Moreover, predictive biomarkers enrich the cohort of individuals likely to benefit; they do not guarantee benefit. MAIN TEXT: There is a need to devise alternate strategies to tailor cancer care. One alternative approach is to enhance the current adaptive approach, which involves administration of a drug and cessation of treatment once progression is documented. This currently involves radiographic tests for the most part, which are expensive, inconvenient and imperfect in their ability to categorize patients who are and are not benefiting from treatment. A biomarker approach to categorizing response may have advantages. CONCLUSION: Herein, we discuss the state of the art on treatment response assessment. While the most mature technologies for response assessment involve radiographic tests such as CT and PET, reports are emerging on biomarkers used to monitor therapeutic efficacy. Potentially, response biomarkers represent a less expensive and more convenient means of monitoring therapy, although an ideal response biomarker has not yet been described. A framework for future response biomarker discovery is described.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".