Nutritional status associates with immunotherapy clinical outcomes in recurrent or metastatic head and neck squamous cell carcinoma patients
Bibliographic record
Abstract
BACKGROUND: Beyond programmed death-ligand 1 (PD-L1) assessed by the combined positive score (CPS) and tumor mutational burden (TMB), no other biomarkers are approved for immunotherapy interventions. Here, we investigated whether additional clinical and pathological variables may impact on immunotherapy outcomes in recurrent or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) patients. METHODS: R/M HNSCC patients treated with immunotherapy were reviewed. Analyzed variables at baseline included: clinicopathological, laboratory, and variables reflecting the host nutritional status such as the prognostic nutritional index (PNI) and albumin. The primary endpoint was progression free survival (PFS). The secondary endpoints were overall survival (OS) and objective response rate (ORR). Univariable and multivariable Cox models were fitted and random forest algorithm was used to estimate the importance of each prognostic variable. RESULTS: A total of 100 patients were treated with immunotherapy; 50% with single agent and 50% with experimental immunotherapy combinations. In the multivariable analysis, both ECOG performance status (HR: 1.73; 95%CI 1.07-2.82; p = 0.03) and PNI levels (10-point increments, HR: 0.66; 0.46-0.95; p = 0.03) were significantly associated with PFS. However, the derived neutrophil to lymphocyte ratio (dNLR) and lactate dehydrogenase (LDH) were not significantly associated with PFS (p-values > 0.15). In the OS analysis, albumin and PNI were the only statistically significant factors in the multivariable model (p < 0.001). CONCLUSIONS: In our cohort, PNI and ECOG performance status were most strongly associated with PFS in R/M HNSCC patients treated with immunotherapy. These results suggest that parameters informative of nutritional status should be considered before immunotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".