The Value of Biomarkers in Optimizing the Use of Immuno-oncologic Therapy
Bibliographic record
Abstract
The development of therapies that restore or activate the host immune response - the socalled "immuno-oncologic" therapy - has improved the survival of some cancer patients harboring specific tumor types. These drugs, however, are very expensive which has greatly limited their use and consequently reduced the number of patients who could likely benefit. Not to mention, the proportion of patients who display a clinical benefit from therapy is limited. Thus, from a clinical and health economics perspective, there is a pressing need to identify and treat those patients for whom a given immuno- oncologic therapy is most likely to be beneficial. At this end, the identification, validation and use of biomarkers emerge as an important therapeutic tool. Here, we briefly review the state of immunologic biomarker development and utilization and make suggestions for interested clinicians, health policy makers and other stakeholders to prepare for the broader use of biomarkers associated with immuno-oncologic therapy in routine practice. The biomarker field is clearly in its earliest stages and there is no doubt that continued research will identify new biomarkers with valuable clinical indications. Of course, the clinical utility of a biomarker must consider patient preferences and perspectives. In addition, health economic analyses are crucial to better define the value of immunotherapy based on precision medicine strategies and promote value-based pricing.
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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 | 0.042 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".