In this study we developed a prognostic biomarker signature for OS based on 3 pretreatment peripheral blood biomarkers previously shown to correlate with oncologic outcomes for immunotherapy in R/M HNSCC: LDH, % lymphocytes, and abx neutrophils [3]. Using this biomarker signature, we stratified patients into high, medium, and low risk groups (trichotomized signature) as well as high versus low-risk groups (dichotomized signature). Currently the standard biomarker to select patients more likely to benefit from PD-1 inhibitors relies on PD-L1 expression of tumor cells, lymphocytes, and macrophages to calculate CPS. However, PD-L1-negative tumors occasionally respond to PD-1 inhibitors and even among tumors with high PD-L1 expression or favorable CPS, only a minority will respond to PD-1 inhibitors, indicating the importance of other mechanisms through with ICIs work [1, 7].

Robust literature exists demonstrating the importance of immune cells both in the tumor microenvironment and in the periphery in regards to immunotherapy response [7]. Lymphocytes, particularly NK cells and CD8 + cytotoxic T cells, play a fundamental role in antitumor immunity, with multiple studies in melanoma, non-small cell lung cancer, and more recently HNSCC showing elevated peripheral lymphocytes to be associated with improved response and survival in the ICI treatment setting [3, 7,8,9]. In fact, directly inhibiting egress of lymphocytes from lymph nodes, such as through administration of fingolimod, an immunomodulator used to treat multiple sclerosis, has been shown to reduce the efficacy of immunotherapy [7]. In contrast, neutrophils promote carcinogenesis through multiple mechanisms including production of various cytokines, growth factors, proteases, and reactive oxygen species [10]. Several studies in solid tumors, including head and neck, have demonstrated that elevated peripheral neutrophils are associated with poor survival and response to immunotherapy [3, 11,12,13]. LDH is likewise negatively correlated with oncologic outcomes in immunotherapy, most notably in melanoma, and recently shown in head and neck [3, 14,15,16]. It is a key enzyme in anaerobic glycolysis, which allows for proliferation of aggressive tumors under hypoxic conditions.

The utility of PBBMs in outcome prognostication in R/M HNSCC receiving ICI is a subject of ongoing scientific inquiry. To our knowledge, our data represents the only PBBM prognostic score relying on three routinely obtained laboratory results. Our observations complement early reported efforts at generating prognostic survival models in the immunotherapy treatment setting for HNSCC. Issa et al. recently developed and internally validated a nomogram to prognosticate survival using age and other variables they found to be associated with OS, including p16 status, neutrophils, lymphocytes, albumin, hemoglobin, and LDH [17]. While the PBBMs incorporated in our prognostic signature are consistent with those selected by Issa et al., our model relies on percent rather than absolute lymphocytes, as we previously found percent lymphocytes to be more strongly correlated with OS than absolute lymphocytes on our elastic net variable selection analysis [3]. Additionally, our signature allows for application of LDH as a continuous rather than dichotomous (high/low) variable used in the nomogram, allowing for increased information from this variable in the model.

The applicability and innovation of our prognostic signature lies in its low cost, ease of use and interpretation, as well as reliance on routinely-obtained bloodwork. Used as an adjunct or alternative to CPS, which requires an invasive procedure to obtain tissue for analysis, a prognostic signature based on peripheral blood may improve patient selection for expensive and potentially toxic immunotherapy without increasing morbidity. Although our trichotomized prognostic signature did not show good separation between the low and medium-risk groups in our relatively limited testing dataset, when we combined low and medium-risk groups in the dichotomized prognostic signature, we were crucially able to identify a high-risk group least likely to have a survival benefit from ICIs. This high-risk group may require more frequent monitoring and/or alternate or intensified therapies other than current standard ICI treatment regimens. With the significantly higher cost of ICIs compared to cytotoxic therapy, as well as the potential for severe immune-related adverse events, appropriate patient selection for these drugs is paramount [18]. The dichotomized prognostic signature has the potential to significantly improve patient selection for ICIs and warrants validation in an external, independent cohort.

Our study has several limitations, including the retrospective design and single institutional cohort, which likely reflects practice patterns at other tertiary academic centers but may not fully capture diversity in clinical practice throughout the field. Additionally, non-FDA-approved PD-1 inhibitor treatment regimens were included, although the vast majority (78.1%) of patients received an FDA-approved PD-1 inhibitor monotherapy. The majority of our patients underwent ICI treatment as second-line therapy, so our results may not extrapolate to the first-line ICI treatment setting. Our study did not include correlation with CPS biomarker, as most patients in our cohort began ICI treatment before routine use of CPS. Whether the high-risk score indicates an overall worse-performing group independent of ICI treatment is also not possible to conclude from our results. Studies evaluating the predictive potential of PBBMs for oncologic outcomes after treatment with ICIs as well as evaluating on-treatment or post-treatment PBBMs would add valuable insight into our understanding of PBBMs in the immunotherapy treatment setting.

Our work demonstrates a promising OS prognostic signature developed from previously identified PBBMs that can identify a subset of patients with R/M HNSCC at high risk for poor survival benefit from treatment with ICIs. The prognostic signature is also significant for PFS in both the training and testing datasets (Supplementary analysis). Ongoing work from our group aims to validate our prognostic signature in an external, independent cohort and compare with CPS biomarker.