Spatial resolution of the head and neck cancer tumor microenvironment to identify tumor and stromal features associated with therapy response.

Head and neck cancer immunotherapy spatial profiling spatial proteomics whole‐transcriptome analyses

Journal

Immunology and cell biology
ISSN: 1440-1711
Titre abrégé: Immunol Cell Biol
Pays: United States
ID NLM: 8706300

Informations de publication

Date de publication:
24 Jul 2024
Historique:
revised: 29 05 2024
revised: 30 06 2024
received: 20 02 2024
accepted: 05 07 2024
medline: 26 7 2024
pubmed: 26 7 2024
entrez: 24 7 2024
Statut: aheadofprint

Résumé

Head and neck cancer (HNC) is the seventh most common cancer globally, resulting in 440 000 deaths per year. While there have been advancements in chemoradiotherapy and surgery, relapse occurs in more than half of HNCs, and these patients have a median survival of 10 months and a 2-year survival of < 20%. Only a subset of patients displays durable benefits from immunotherapies in metastatic and recurrent HNC, making it critical to understand the tumor microenvironment (TME) underpinning therapy responses in HNC. To recognize biological differences within the TME that may be predictive of immunotherapy response, we applied cutting-edge geospatial whole-transcriptome profiling (NanoString GeoMx Digital Spatial Profiler) and spatial proteomics profiling (Akoya PhenoCycler-Fusion) on a tumor microarray consisting of 25 cores from 12 patients that included 4 immunotherapy-unresponsive (8 cores) and 2 immunotherapy-responsive patients (5 cores), as well as 6 immunotherapy naïve patients (12 cores). Through high-plex, regional-based transcriptomic mapping of the tumor and TME, pathways involved with the complement system and hypoxia were identified to be differentially expressed in patients who went on to experience a poor immunotherapy response. Single-cell, targeted proteomic analysis found that immune cell infiltration of the cancer cell mass and interactions of CD8 T cells with tumor and other immune cells were associated with positive immunotherapy response. The relative abundance of specific tumor phenotypes and their interactions with various immune cells was identified to be different between response groups. This study demonstrates how spatial transcriptomics and proteomics can resolve novel alterations in the TME of HNC that may contribute to therapy sensitivity and resistance.

Identifiants

pubmed: 39048134
doi: 10.1111/imcb.12811
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : PA Research Foundation (PARF)

Informations de copyright

© 2024 The Author(s). Immunology & Cell Biology published by John Wiley & Sons Australia, Ltd on behalf of the Australian and New Zealand Society for Immunology, Inc.

Références

Rad HS, Shiravand Y, Radfar P, et al. Understanding the tumor microenvironment in head and neck squamous cell carcinoma. Clin Transl Immunol 2022; 11: e1397.
Borel C, Jung AC, Burgy M. Immunotherapy breakthroughs in the treatment of recurrent or metastatic head and neck squamous cell carcinoma. Cancers (Basel) 2020; 12: 2691.
Dougan M, Luoma AM, Dougan SK, Wucherpfennig KW. Understanding and treating the inflammatory adverse events of cancer immunotherapy. Cell 2021; 184: 1575–1588.
Harrington KJ, Burtness B, Greil R, et al. Pembrolizumab with or without chemotherapy in recurrent or metastatic head and neck squamous cell carcinoma: updated results of the phase III KEYNOTE‐048 study. J Clin Oncol 2023; 41: 790–802.
Botticelli A, Cirillo A, Strigari L, et al. Anti–PD‐1 and anti–PD‐L1 in head and neck cancer: a network meta‐analysis. Front Immunol 2021; 12: e705096.
Gavrielatou N, Doumas S, Economopoulou P, Foukas PG, Psyrri A. Biomarkers for immunotherapy response in head and neck cancer. Cancer Treat Rev 2020; 84: 101977.
Wang L, Wang D, Yang L, et al. Cuproptosis related genes associated with Jab1 shapes tumor microenvironment and pharmacological profile in nasopharyngeal carcinoma. Front Immunol 2022; 13: 989286.
de Ruiter EJ, Ooft ML, Devriese LA, Willems SM. The prognostic role of tumor infiltrating T‐lymphocytes in squamous cell carcinoma of the head and neck: a systematic review and meta‐analysis. Onco Targets Ther 2017; 6: e1356148.
Lewis SM, Asselin‐Labat M‐L, Nguyen Q, et al. Spatial omics and multiplexed imaging to explore cancer biology. Nat Methods 2021; 18: 997–1012.
Berrell N, Sadeghirad H, Blick T, et al. Metabolomics at the tumor microenvironment interface: decoding cellular conversations. Med Res Rev 2024; 44: 1121–1146.
Hanahan D. Hallmarks of cancer: new dimensions. Cancer Discov 2022; 12: 31–46.
KRT13, FAIM2 and CYP2W1 mRNA expression in Oral squamous cell carcinoma patients with risk habits. Asian Pac J Cancer Prev 2015; 16: 953–958.
Li M, Tian P, Zhao Q, Ma X, Zhang Y. Potassium channels: novel targets for tumor diagnosis and chemoresistance. Front Oncol 2022; 12: 1074469.
O'Brien RM, Cannon A, Reynolds JV, Lysaght J, Lynam‐Lennon N. Complement in Tumourigenesis and the response to cancer therapy. Cancers (Basel) 2021; 13: 1209.
Liu CC, Greenwald NF, Kong A, et al. Robust phenotyping of highly multiplexed tissue imaging data using pixel‐level clustering. Nat Commun 2023; 14: 4618.
Monkman J, Taheri T, Ebrahimi Warkiani M, et al. High‐Plex and high‐throughput digital spatial profiling of non‐small‐cell lung cancer (NSCLC). Cancer 2020; 12: 3551.
Dumitru CS, Ceausu AR, Comsa S, Raica M. Loss of E‐cadherin expression correlates with Ki‐67 in head and neck squamous cell carcinoma. In Vivo 2022; 36: 1150–1154.
Liu N, Martin J, Bhuva DD, et al. hoodscanR: profiling single‐cell neighborhoods in spatial transcriptomics data. 2024. bioRxiv 2024.2003.2026.586902.
Feng Q, Liu Z, Yu X, et al. Lactate increases stemness of CD8 + T cells to augment anti‐tumor immunity. Nat Commun 2022; 13: 4981.
Jia Y, Guo B, Zhang W, et al. Pan‐cancer analysis of the prognostic and immunological role of GJB2: a potential target for survival and immunotherapy. Front Oncol 2023; 13: 1110207.
Cedzyński M, Świerzko AS. Components of the lectin pathway of complement in solid tumour cancers. Cancers (Basel) 2022; 14: 1543.
Revel M, Daugan MV, Sautés‐Fridman C, Fridman WH, Roumenina LT. Complement system: promoter or suppressor of cancer progression? Antibodies (Basel) 2020; 9: 57.
Wang H, Zheng Q, Lu Z, et al. Role of the nervous system in cancers: a review. Cell Death Dis 2021; 7: 76.
DePeaux K, Delgoffe GM. Metabolic barriers to cancer immunotherapy. Nat Rev Immunol 2021; 21: 785–797.
Leone RD, Powell JD. Fueling the revolution: targeting metabolism to enhance immunotherapy. Cancer Immunol Res 2021; 9: 255–260.
Colegio OR, Chu NQ, Szabo AL, et al. Functional polarization of tumour‐associated macrophages by tumour‐derived lactic acid. Nature 2014; 513: 559–563.
Tang Y, Gu S, Zhu L, Wu Y, Zhang W, Zhao C. LDHA: the obstacle to T cell responses against tumor. Front Oncol 2022; 12: 1036477.
Kao T‐W, Bai G‐H, Wang T‐L, et al. Novel cancer treatment paradigm targeting hypoxia‐induced factor in conjunction with current therapies to overcome resistance. J Exp Clin Cancer Res 2023; 42: 171.
Balar AV, Weber JS. PD‐1 and PD‐L1 antibodies in cancer: current status and future directions. Cancer Immunol Immunother 2017; 66: 551–564.
Kazanietz MG, Durando M, Cooke M. CXCL13 and its receptor CXCR5 in cancer: inflammation, immune response, and beyond. Front Endocrinol 2019; 10: 471.
Marques HS, de Brito BB, da Silva FAF, et al. Relationship between Th17 immune response and cancer. World J Clin Oncol 2021; 12: 845–867.
Mantovani A, Allavena P, Marchesi F, Garlanda C. Macrophages as tools and targets in cancer therapy. Nat Rev Drug Discov 2022; 21: 799–820.
Vermare A, Guérin MV, Peranzoni E, Bercovici N. Dynamic CD8+ T cell cooperation with macrophages and monocytes for successful cancer immunotherapy. Cancers (Basel) 2022; 14: 3546.
González‐Navajas JM, Fan DD, Yang S, et al. The impact of Tregs on the anticancer immunity and the efficacy of immune checkpoint inhibitor therapies. Front Immunol 2021; 12: 625783.
Jiang H, Zuo J, Li B, et al. Drug‐induced oxidative stress in cancer treatments: angel or devil? Redox Biol 2023; 63: 102754.
Hayes JD, Dinkova‐Kostova AT, Tew KD. Oxidative stress in cancer. Cancer Cell 2020; 38: 167–197.
Lainé A, Labiad O, Hernandez‐Vargas H, et al. Regulatory T cells promote cancer immune‐escape through integrin αvβ8‐mediated TGF‐β activation. Nat Commun 2021; 12: 6228.
Liu N, Bhuva DD, Mohamed A, et al. standR: spatial transcriptomic analysis for GeoMx DSP data. Nucleic Acids Res 2023; 52: e2.
Terry M, Therneau HS. Calculating samplesSize estimates for RNA Seq studies. R package version 1.44.0. ed2024.
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2009; 26: 139–140.
Ritchie ME, Phipson B, Wu D, et al. Limma powers differential expression analyses for RNA‐sequencing and microarray studies. Nucleic Acids Res 2015; 43: e47.
Bhuva DD, Tan CW, Liu N, et al. vissE: a versatile tool to identify and visualise higher‐order molecular phenotypes from functional enrichment analysis. BMC Bioinformatics 2024; 25: 64.
Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov Jill P, Tamayo P. The molecular signatures database Hallmark gene set collection. Cell Systems 2015; 1: 417–425.
Newman AM, Steen CB, Liu CL, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol 2019; 37: 773–782.
Bankhead, P, Loughrey MB, Fernández JA, et al. . QuPath: Open source software for digital pathology image analysis. Sci Rep. 2017; 7: 16878.
Stringer C, Wang T, Michaelos M, Pachitariu M. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 2020; 18: 100–106.
Hafemeister C, Satija R. Normalization and variance stabilization of single‐cell RNA‐seq data using regularized negative binomial regression. Genome Biol 2019; 20: 296.
Korsunsky I, Millard N, Fan J, et al. Fast, sensitive and accurate integration of single‐cell data with harmony. Nat Methods 2019; 16: 1289–1296.
Wolf FA, Angerer P, Theis FJ. SCANPY: large‐scale single‐cell gene expression data analysis. Genome Biol 2018; 19: 15.
Nirmal AJ, Chen Y, Sokolov A, Durieux T. labsyspharm/scimap. Release‐1.0.0 ed2023.

Auteurs

Naomi Berrell (N)

Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
Wesley Research Institute, Level 8 East Wing, The Wesley Hospital, Auchenflower, QLD, Australia.

James Monkman (J)

Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.

Meg Donovan (M)

Wesley Research Institute, Level 8 East Wing, The Wesley Hospital, Auchenflower, QLD, Australia.

Tony Blick (T)

Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.

Ken O'Byrne (K)

Princess Alexandra Hospital, Brisbane, QLD, Australia.

Rahul Ladwa (R)

Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
Princess Alexandra Hospital, Brisbane, QLD, Australia.

Chin Wee Tan (CW)

Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, VIC, Australia.
Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, Australia.

Arutha Kulasinghe (A)

Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
Wesley Research Institute, Level 8 East Wing, The Wesley Hospital, Auchenflower, QLD, Australia.

Classifications MeSH