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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)
  1. Anastasia Nikitina1,
  2. Alexander Zaytcev1,
  3. Jennifer Johnson2,
  4. Eric Mastrolonardo2,
  5. Madalina Tuluc2,
  6. Alban J Linnenbach2,
  7. Anastasia Sobol1,
  8. Tatiana Vasileva1,
  9. Arseniy Sokolov1,
  10. Daniiar Dyikanov1,
  11. Evgeniia Alekseeva1,
  12. Andrey Tyshevich1,
  13. Vladimir Kushnarev1,
  14. Christopher JH Davitt1,
  15. Michael F Goldberg1,
  16. Cagdas Tazearslan1,
  17. Adam J Luginbuhl2 and
  18. Alexander Bagaev1
  1. 1BostonGene, Corp., Waltham, MA, USA
  2. 2Thomas Jefferson University, Philadelphia, PA, USA
  • Journal for ImmunoTherapy of Cancer (JITC) preprint. The copyright holder for this preprint are the authors/funders, who have granted JITC permission to display the preprint. All rights reserved. No reuse allowed without permission.


Background While 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.

Methods Immunoprofiling 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.

Results Blood 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).

Conclusions Our results suggest that this integrated approach shows potential for the development of more accurate prediction of response to ICI therapies for HNSCC.


  1. Bagaev 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.2021.04.014

  2. Zaitsev 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.2022.07.006

  3. Luginbuhl 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-21-1816

Ethics Approval The cohort from Thomas Jefferson University was collected under identifier NCT03854032.

Abstract 222-K Figure 1

Classification of immune cells of the blood into five immunotypes

Abstract 222-K Figure 2

Immunotype 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 comparison

Abstract 222-K Figure 3

Tumor expression-based biomarkers of response (R) and non-response (NR) to nivolumab. *p < 0.05; **p < 0.01; ***p < 0.001

Abstract 222-K Figure 4

Transcriptome-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 5

Association 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

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