Pre-Treatment Peripheral Blood Parameters as Prognostic Biomarkers in Cancer Patients Receiving Immune Checkpoint Inhibitors
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors have significantly improved outcomes in select cancers; however, not all patients respond to these therapies, and the duration of the response varies among responders. Markers predictive of the response to immunotherapy, such as PD-L1 expression determined by immunohistochemical staining of tumor sections and microsatellite status, have been identified. Some of these are used in companion diagnostics approved for clinical practice. Additional easy-to-use biomarkers may help clinicians to predict the efficacy of these drugs in individual patients. Materials and Methods: A retrospective review of the medical records of patients with metastatic cancer treated with immune checkpoint inhibitors in our cancer center was performed to identify the clinical and hematologic parameters associated with survival outcomes. Results: Among the 163 patients included in the study, most had lung cancer, followed by kidney cancer, melanoma, and bladder cancer. Most patients (61.3%) were male and had good performance status. Nivolumab and pembrolizumab were immune checkpoint inhibitors utilized in 85.9% of cases. Age, sex, and primary cancer type were not associated with survival outcomes. Among the peripheral blood parameters evaluated, lymphocytopenia was the strongest predictor of adverse survival outcomes in univariate analysis and the only clinical or hematologic biomarker that retained significance for overall survival (OS) prediction in multivariate analysis. Conclusion: Among the clinical and hematologic parameters routinely used in the clinic, a lymphocyte count below 1 x 109/ L was predictive of adverse OS in patients with metastatic cancers receiving immune checkpoint inhibitors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".