Table 4.

Response prediction to anti-PD-1 immunotherapy in HNSCC.

Gene alterations or signature	Testing platform	Response to anti-PD-1 checkpoint blockade	References
Combined Positive Score (CPS) for PD-L1 protein Expression	Immunohistochemistry on formalin-fixed paraffin-embedded tissue samples	CPS = number of PD-L1+ tumor cells, lymphocytes, and macrophages, divided by the total number of viable tumor cells, and multiplying by 100. In various trials, CPS ≥ 1 predicts response.	(7, 19, 97, 98)
MMR-deficient	Quantification of genomic MSI level (MSI intensity)	Higher insertion-deletion (Indel) load predicts response.	(99)
Apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like (APOBEC)-driven mutations	APOBEC enrichment scores.	Upregulated as an innate immune response particularly in HPV+ tumors. APOBEC3 mutation leads to driver mutation in PI3KCA.	(100–102)
Molecular exhausted immune class	Gene expression pattern analyzed by non-negative matrix factorization algorithm	Portends a worse prognosis than active immune class in overall survival.	(61)
Molecular active immune class		Better prognosis (overall survival) than exhausted class. May predict immune responses.	(61)
Interferon-γ signature (6-genes)	NanoString nCounter mRNA	Low signature score did not respond to pembrolizumab.	(103)
Expanded immune signature (18-genes)	NanoString nCounter mRNA	Low signature score did not respond to pembrolizumab.	(103)
Somatic frameshift events in tumor suppressor genes	Targeted massively parallel sequencing	More frequently seen in HPV- responders.	(104, 105)
Total mutational burden (TMB)	Targeted massively parallel sequencing	Predicts response in HPV- HNSCC.	(104, 105)
Microenvironment infiltrating arginase 1 (Arg1)+/CD68+ macrophage-mediated immune evasion	Enzyme-Linked Immunosorbent Assay (ELISA)	Plasma Arg1 level (ng/mL) to predict immune evasion (cutoff to be determined)	(74)