Prediction of Immune checkpoint inhibitors benefit from routinely measurable peripheral blood parameters
Bibliographic record
Abstract
Immunotherapy of cancer has been the most remarkable advance in cancer therapy in the last several years with the successful introduction of monoclonal antibody drugs that block inhibitory immune receptors to invigorate immune attack against tumors. With the introduction of these drugs the parallel need of predictive markers of response has arisen. Beyond markers from the tumor and the tumor micro-environment, the peripheral blood supplies several biomarkers for response that have been reported individually or in combinations to provide valuable predictive information. These include the number of circulating cell subsets, various ratios of them and combinations of factors that also encompass biochemical measurements such as LDH. Peripheral blood biomarkers are usually obtained during the routine patient evaluations, methodology is well-calibrated for the routine practice and their acquisition does not require extra infrastructure or financial resources. This paper will review available data on predictive markers for immune checkpoint inhibitors (ICIs) from peripheral blood, including biomarkers that have been less extensively studied. In addition, it will discuss ways forward for the use of peripheral blood biomarkers in immunotherapy prognostication.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".