SITC perspective: leveraging patient enrichment biomarkers to accelerate early phase IO drug development
Bibliographic record
Abstract
Cancer immunotherapy (IO) enables patients to live well with cancer for many years, or even be cured. Several investigational IO agents recently failed in early-phase or late-phase trials, leading some to doubt the future of IO. Patient heterogeneity (eg, tumor characteristics, treatment history) increases the risk that a clinically active IO drug might be discarded. Enriching enrollment for patients with biomarkers hypothesized to reflect a higher probability of clinical benefit across clinical development should mitigate this risk. The Society for Immunotherapy of Cancer convened diverse IO stakeholders to discuss leveraging biomarkers at the earliest stages of drug development to accelerate the delivery of innovative IO agents to patients. This group developed a framework based on a biomarker-based enrichment strategy in early trials that evolves into the development of more precise predictive biomarkers in late phase trials. This framework integrates mechanistic insights related to the drug and its impact on the tumor microenvironment derived from preclinical data, digital pathology, exploratory multiomics, and artificial intelligence that are continuously refined through both adaptive and randomized clinical trials. Biomarker-based enrichment in early clinical development should de-risk late-stage trials, ultimately expanding the portfolio of innovative IO drugs available to patients.
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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 | 0.037 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".