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BackgroundThe relationship between HPV status and immune checkpoint inhibitor (ICI) response in head and neck squamous cell carcinoma (HNSCC) is not fully understood. Previously, we showed that immune-inflamed TMEs are associated with ICI response in HPV- HNSCC.1 However, HPV+ tumors have distinct biological characteristics that influence the immune landscape. Therefore, our objective was to characterize the tumor microenvironment (TME) in HPV+ HNSCC relative to ICI response.MethodsComparative analysis of RNA-seq data from 74 HNSCC patients (40 HPV+ and 34 HPV-) receiving anti-PD-1 therapy and two public cohorts, TCGA-HNSC (n = 504)2 and GSE127165 (n = 112)3 was performed. Functional gene expression signature (FGES) analysis4 and Kassandra cellular deconvolution5 were performed. T and B cell receptor (TCR, BCR) repertoires were reconstructed using MiXCR.6 GATK Pathseq7 and Vi-Fi8 were used to identify viral reads and viral-host chimeric reads, respectively.ResultsCellular deconvolution of 594 HPV- and 164 HPV+ samples indicated HPV+ samples had higher immune (NK, CD8+ T, CD4+ T, and B) cell content, while HPV- samples were enriched with stromal components (fibroblasts and endothelial cells, p < 0.001). Congruent with these findings, HPV+ samples had stronger checkpoint inhibition, antitumor cytokine, and MHCII FGES and weaker CAF, extracellular matrix, and angiogenesis FGES (p < 0.001) compared to HPV- samples. Moreover, HPV+ tumors contained larger and more diverse TCR and BCR repertoires (p < 0.001).Pre-treatment HPV+ samples from two nivolumab-treated cohorts with 25 responders and 14 nonresponders were analyzed, but no associations were observed between ICI response and immune and stromal cell content in the TME, analyzed FGES, TCR and BCR diversity or clonality, viral transcript expression, or host genome integration. Interestingly, longitudinal analysis of 40 pre- and 37 post-treatment samples showed higher levels of EMT, CAF, macrophage, and checkpoint inhibition FGES in ICI responder and nonresponder post-treatment samples (p < 0.05), suggesting these TME dynamics were unrelated to outcomes.ConclusionsOur study highlighted the disparity in underlying mechanisms of ICI response between HPV- and HPV+ HNSCC patients. While factors like an immune-enriched TME and robust expression of checkpoint inhibition FGES are reportedly influential in HPV- cases, these and other factors were not associated with therapy response in HPV+ HNSCC patients. These results are likely due to the predominantly immune-enriched TME characteristic of HPV+ HNSCC. Our findings underscored the importance of considering HPV status when investigating the molecular determinants of ICI response in HNSCC.ReferencesN. Kotlov et al. ‘Abstract 1441: Predictors of treatment response in a preoperative window of opportunity trial of nivolumab in resectable squamous cell carcinoma of the head and neck,’ Cancer Res., Jul. 2021; 81(13_Supplement): 1441–1441, doi: 10.1158/1538-7445.AM2021-1441.The Cancer Genome Atlas Network, ‘Comprehensive genomic characterization of head and neck squamous cell carcinomas,’ Nature, Jan. 2015; 517(7536): 576–582, doi: 10.1038/nature14129.Y. Wu et al. ‘Circular RNA circCORO1C promotes laryngeal squamous cell carcinoma progression by modulating the let-7c-5p/PBX3 axis,’ Mol. Cancer, Jun. 2020;19(1): 99, doi: 10.1186/s12943-020-01215-4.A. Bagaev et al. ‘Conserved pan-cancer microenvironment subtypes predict response to immunotherapy,’ Cancer Cell, Jun. 2021; 39(6), 845–865.e7, doi: 10.1016/j.ccell.2021.04.014.A. Zaitsev et al. ‘Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes,’ Cancer Cell, Aug. 2022; 40(8): 879–894.e16, doi: 10.1016/j.ccell.2022.07.006.D. A. Bolotin et al. ‘MiXCR: software for comprehensive adaptive immunity profiling,’ Nat. Methods, May 2015;12(5): 380–381, doi: 10.1038/nmeth.3364.M. A. Walker et al. ‘GATK PathSeq: a customizable computational tool for the discovery and identification of microbial sequences in libraries from eukaryotic hosts,’ Bioinformatics, Dec. 2018; 34(24): 4287–4289, doi: 10.1093/bioinformatics/bty501.N. D. Nguyen, V. Deshpande, J. Luebeck, P. S. Mischel, and V. Bafna, ‘ViFi: accurate detection of viral integration and mRNA fusion reveals indiscriminate and unregulated transcription in proximal genomic regions in cervical cancer,’ Nucleic Acids Res., Apr. 2018; 46(7): 3309–3325, , doi: 10.1093/nar/gky180.Ethics ApprovalThe cohort from Thomas Jefferson University was collected under ClinicalTrials.gov identifiers NCT03238365 and NCT03854032.

Journal for immunotherapy of cancer, 2023-11, Vol.11 (Suppl 1), p.A634-A634

555 Challenges in predicting ICI response in HPV+ HNSCC: insights into the tumor microenvironment

BackgroundWhile PD-1 inhibitors are promising therapies for HNSCC, better methods are needed to predict response. We used integrated immunoprofiling of peripheral blood mononuclear cells (PBMCs) and RNA-seq of tumor tissue to identify novel predictors of anti-PD-1 response in HNSCC.MethodsImmunoprofiling using multiparameter flow cytometry was applied to PBMCs collected from a large internal cohort of cancer patients and healthy donors (n=850). Unsupervised clustering of normalized cell population frequencies from batched flow cytometry data utilizing a common backbone and variable functional staining panels was used to classify patients into five different immunotypes. We then analytically validated populations by cellular deconvolution of matched RNA-seq data with KassandraTM from the same specimens.2 PBMCs from previously untreated stage II-IV HNSCC patients (n=36) were analyzed at baseline and on-treatment with the anti-PD-1 inhibitor nivolumab ± an IDO inhibitor as a validation cohort. RNA-seq was retrospectively performed on tumors at baseline and on-treatment, along with transcriptome-based tumor microenvironment (TME) subtyping1 and cellular deconvolution with KassandraTM.2 All disease sites were assigned a pathologic Treatment Response (pTR)3 and analysis was completed based on primary site response alone and overall response (OR) based on all disease sites.ResultsBlood immunoprofiling of the internal cohort revealed five conserved immunotypes enriched in certain cell types (G1-naive T and B cells; G2-central memory CD4+ T cells; G3-transitional memory CD8+ T cells; G4-effector memory CD8+ T cells; G5-monocytes/granulocytes; figure 1), with immunotypes clustering to different disease states in these patients. We then stratified the 36 HNSCC patients treated with nivolumab into the same G1-G5 immunotypes as a validation cohort. At baseline, the G2 group had higher OR rates than other groups (p=0.02; figure 2). Baseline primary tumors showed OR correlated with PD-L1 and PD-L2 expression, interferon responsive genes, T-cell trafficking, and MHC class I pathway (higher values in Responders versus Non-responders, p<0.05; figure 3). Cell deconvolution showed greater CD8+ T cells in the TME correlated with primary site response (p<0.01). All 12 patients with immune-desert TMEs showed no primary site response (p=0.003); 4/5 patients with an immune-enriched TME showed a primary site response (p=0.002; figure 4). Primary tumors with fibrotic TMEs showed no response. However, in patients with a fibrotic TME and a positive OR, indicated by a significant pTR, the G2 immunotype was identified (figure 5).ConclusionsOur results suggest that this integrated approach shows potential for the development of more accurate prediction of response to ICI therapies for HNSCC.ReferencesBagaev A, Kotlov N, Nomie K, et al. Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell. 2021;39(6):845–865.e7. doi:10.1016/j.ccell.2021.04.014Zaitsev A, Chelushkin M, Dyikanov D, et al. Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes. Cancer Cell. 2022;40(8):879–894.e16. doi:10.1016/j.ccell.2022.07.006Luginbuhl AJ, Johnson JM, Harshyne LA, et al. Tadalafil enhances immune signatures in response to neoadjuvant nivolumab in resectable head and neck squamous cell carcinoma. Clin Cancer Res. 2022;28(5):915–927. doi:10.1158/1078-0432.CCR-21-1816Ethics ApprovalThe cohort from Thomas Jefferson University was collected under ClinicalTrials.gov identifier NCT03854032.Abstract 222-K Figure 1Classification of immune cells of the blood into five immunotypesAbstract 222-K Figure 2Immunotype analysis of the HNSCC cohort (n = 36) and treatment response (R) and non-response (NR) to nivolumab; Fisher’s exact test was used for statistical comparisonAbstract 222-K Figure 3Tumor expression-based biomarkers of response (R) and non-response (NR) to nivolumab. *p < 0.05; **p < 0.01; ***p < 0.001Abstract 222-K Figure 4Transcriptome-based classification of primary tumor samples of responders (R) and non-responders (NR) from the HNSCC cohort (n = 36) into four TME subtypes (Fibrotic; Immune Desert; Immune-Enriched, Fibrotic; Immune-Enriched, Non-fibrotic)Abstract 222-K Figure 5Association of TME subtypes (Fibrotic; Immune Desert; Immune-Enriched, Fibrotic; Immune-Enriched, Non-fibrotic) at baseline with primary and overall response to nivolumab. R - Response; NR - Non-response

Journal for immunotherapy of cancer, 2023-11, Vol.11 (Suppl 2), p.A1779-A1781

222-K Integrated immunoprofiling and RNA sequencing (RNA-seq) for anti-PD-1 response prediction in head and neck squamous cell carcinomas (HNSCC)

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555 Challenges in predicting ICI response in HPV+ HNSCC: insights into the tumor microenvironment

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222-K Integrated immunoprofiling and RNA sequencing (RNA-seq) for anti-PD-1 response prediction in head and neck squamous cell carcinomas (HNSCC)

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