scholarly journals Essential gene expression pattern of head and neck squamous cell carcinoma revealed by tumor-specific expression rule based on single-cell RNA sequencing

Author(s):  
Xiangtian Yu ◽  
Zhenjia Wang ◽  
Tao Zeng
2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A947-A947
Author(s):  
Diana Graves ◽  
Aleksandar Obradovic ◽  
Michael Korrer ◽  
Yu Wang ◽  
Sohini Roy ◽  
...  

BackgroundUse of anti-PD-1 immune checkpoint inhibitors (ICI) is currently the first line therapy for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), but critical work remains in identifying factors guiding resistance mechanisms.1 2 While recent studies have specifically implicated cancer-associated fibroblasts (CAFs) as potential mediators of immunotherapy response, the immunoregulatory role of CAFs in head and neck cancer has not been thoroughly explored.3–5MethodsTo determine if there are changes in cell populations associated with anti-PD-1 therapy in head and neck cancer patients, we performed high dimensional single-cell RNA sequencing (scRNA-SEQ) from a neoadjuvant trial of 50 advanced-stage head and neck squamous cell carcinoma (HNSCC) patients that were treated with the anti-PD-1 therapy, nivolumab, for the duration of one month. Tumor specimens were analyzed pre- and post-treatment with single-cell RNA sequencing performed on 4 patients as well as bulk RNA sequencing on 40 patients. Matched scRNA-SEQ data was analyzed using the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) and Virtual Inference of Protein-activity by Enriched Regulon (VIPER) bioinformatic analysis platform to determine TME cells that correlated with response and resistance to nivolumab.6 For CAF functional studies, surgical tumor specimens were processed and enriched for CAF subtypes, and these were co-cultured with T cells from peripheral blood and tumor infiltrating lymphocytes.ResultsWe identified 14 distinct cell types present in HNSCC patients. Of these 14 cell types, the fibroblast subtype showed significant changes in abundance following nivolumab treatment. We identified 5 distinct clusters of cancer-associated fibroblast subsets (HNCAF-0, 1, 2, 3, and 4) of which, two clusters, HNCAF-0 and HNCAF-3 were predictive of patient response to anti-PD-1 therapy. To determine the significance of these CAF subsets’ function, we isolated HNCAF-0/3 cells from primary HNSCC tumor specimens and co-cultured with primary human T cells. Analysis by flow cytometry showed that HNCAF-0/3 reduced TGFβ-dependent PD-1+TIM-3+ exhaustion of T cells and increased CD103+NKG2A+ resident memory phenotype and cytotoxicity to enhance overall function.ConclusionsTo our knowledge, we are the first to characterize CAF heterogeneity within the head and neck TME and show direct immunostimulatory activity of CAFs. Our findings demonstrate the functional importance of CAF subsets in modulating the immunoregulatory milieu of the human HNSCC, and we have identified clinically actionable CAF subtypes that can be used as a biomarker of response and resistance in future clinical trials.Trial RegistrationNCT03238365ReferencesFerris RL, Blumenschein Jr G, Fayette J, Guigay J, Colevas AD, Licitra L, Harrington K, Kasper S, Vokes EE, Even C, et al. Nivolumab for recurrent squamous-cell carcinoma of the head and neck. N Engl J Med 2016;375:1856–1867.Seiwert TY, Burtness B, Mehra R, Weiss J, Berger R, Eder JP, Heath K, McClanahan T, Lunceford J, Gause C, et al. Safety and clinical activity of pembrolizumab for treatment of recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-012): an open-label, multicentre, phase 1b trial. Lancet Oncol 2016;17:956–965.Dominguez CX, Muller S, Keerthivasan S, Koeppen H, Hung J, Gierke S, Breart B, Foreman O, Bainbridge TW, Castiglioni A, et al. Single-cell RNA sequencing reveals stromal evolution into LRRC15(+) myofibroblasts as a determinant of patient response to cancer immunotherapy. Cancer Discov 2020;10:232–253.Feig C, Jones JO, Kraman M, Wells RJ, Deonarine A, Chan DS, Connell CM, Roberts EW, Zhao Q, Caballero OL, et al. Targeting CXCL12 from FAP-expressing carcinoma-associated fibroblasts synergizes with anti-PD-L1 immunotherapy in pancreatic cancer. Proc Natl Acad Sci U S A 2013;110:20212–20217.Kieffer Y, Hocine HR, Gentric G, Pelon F, Bernard C, Bourachot B, Lameiras S, Albergante L, Bonneau C, Guyard A, et al. Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer. Cancer Discov 2020;10:1330–1351.Obradovic A, Chowdhury N, Haake SM, Ager C, Wang V, Vlahos L, Guo XV, Aggen DH, Rathmell WK, Jonasch E, et al. Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages. Cell 2021;184:2988–3005.Ethics ApprovalPatients provided informed consent for this work. All experimental procedures were approved by the Institutional Review Board of Vanderbilt University Medical Center (IRB: 171883).


Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1230
Author(s):  
Zongtai Qi ◽  
Yating Liu ◽  
Michael Mints ◽  
Riley Mullins ◽  
Reilly Sample ◽  
...  

Complexities in cell-type composition have rightfully led to skepticism and caution in the interpretation of bulk transcriptomic analyses. Recent studies have shown that deconvolution algorithms can be utilized to computationally estimate cell-type proportions from the gene expression data of bulk blood samples, but their performance when applied to tumor tissues, including those from head and neck, remains poorly characterized. Here, we use single-cell data (~6000 single cells) collected from 21 head and neck squamous cell carcinoma (HNSCC) samples to generate cell-type-specific gene expression signatures. We leverage bulk RNA-seq data from >500 HNSCC samples profiled by The Cancer Genome Atlas (TCGA), and using single-cell data as a reference, apply two newly developed deconvolution algorithms (CIBERSORTx and MuSiC) to the bulk transcriptome data to quantitatively estimate cell-type proportions for each tumor in TCGA. We show that these two algorithms produce similar estimates of constituent/major cell-type proportions and that a high T-cell fraction correlates with improved survival. By further characterizing T-cell subpopulations, we identify that regulatory T-cells (Tregs) were the major contributor to this improved survival. Lastly, we assessed gene expression, specifically in the Treg population, and found that TNFRSF4 (Tumor Necrosis Factor Receptor Superfamily Member 4) was differentially expressed in the core Treg subpopulation. Moreover, higher TNFRSF4 expression was associated with greater survival, suggesting that TNFRSF4 could play a key role in mechanisms underlying the contribution of Treg in HNSCC outcomes.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jili Cui ◽  
Lian Zheng ◽  
Yuanyuan Zhang ◽  
Miaomiao Xue

AbstractHead and neck squamous cell carcinoma (HNSCC) is the sixth most common type of malignancy in the world. DNA cytosine-5-methyltransferase 1 (DNMT1) play key roles in carcinogenesis and regulation of the immune micro-environment, but the gene expression and the role of DNMT1 in HNSCC is unknown. In this study, we utilized online tools and databases for pan-cancer and HNSCC analysis of DNMT1 expression and its association with clinical cancer characteristics. We also identified genes that positively and negatively correlated with DNMT1 expression and identified eight hub genes based on protein–protein interaction (PPI) network analysis. Enrichment analyses were performed to explore the biological functions related with of DNMT1. The Tumor Immune Estimation Resource (TIMER) database was performed to explore the relationship between DNMT1 expression and immune-cell infiltration. We demonstrated that DNMT1 gene expression was upregulated in HNSCC and associated with poor prognosis. Based on analysis of the eight hub genes, we determined that DNMT1 may be involved in cell cycle, proliferation and metabolic related pathways. We also found that significant difference of B cells infiltration based on TP 53 mutation. These findings suggest that DNMT1 related epigenetic alterations have close relationship with HNSCC progression, and DNMT1 could be a novel diagnostic biomarker and a promising therapeutic target for HNSCC.


2018 ◽  
Author(s):  
Neeraja M Krishnan ◽  
Hiroto Katoh ◽  
Vinayak Palve ◽  
Manisha Pareek ◽  
Reiko Sato ◽  
...  

AbstractTumor suppression by the extracts of Azadirachta indica (neem) works via anti-proliferation, cell cycle arrest, and apoptosis, demonstrated previously using cancer cell lines and live animal models. However, very little is known about the molecular targets and pathways that the neem extracts and the associated compounds act through. Here, we address this using a genome-wide functional pooled shRNA screen on head and neck squamous cell carcinoma cell line treated with crude neem leaf extracts, known for their anti-tumorigenic activity. By analyzing differences in global clonal sizes of the shRNA-infected cells cultured under no treatment and treatment with neem leaf extract conditions, assayed using next-generation sequencing, we found 225 genes affected the cancer cell growth in the shRNA-infected cells treated with neem extract. Pathway enrichment analyses of whole-genome gene expression data from cells temporally treated with neem extract revealed important roles played by the TGF-β pathway and HSF-1-related gene network. Our results indicate that neem extract simultaneously affects various important molecular signaling pathways in head and neck cancer cells, some of which may be therapeutic targets for this devastating tumor.


2021 ◽  
Vol 11 ◽  
Author(s):  
Guanying Feng ◽  
Feifei Xue ◽  
Yingzheng He ◽  
Tianxiao Wang ◽  
Hua Yuan

ObjectivesThis study aimed to identify genes regulating cancer stemness of head and neck squamous cell carcinoma (HNSCC) and evaluate the ability of these genes to predict clinical outcomes.Materials and MethodsThe stemness index (mRNAsi) was obtained using a one-class logistic regression machine learning algorithm based on sequencing data of HNSCC patients. Stemness-related genes were identified by weighted gene co-expression network analysis and least absolute shrinkage and selection operator analysis (LASSO). The coefficient of LASSO was applied to construct a diagnostic risk score model. The Cancer Genome Atlas database, the Gene Expression Omnibus database, Oncomine database and the Human Protein Atlas database were used to validate the expression of key genes. Interaction network analysis was performed using String database and DisNor database. The Connectivity Map database was used to screen potential compounds. The expressions of stemness-related genes were validated using quantitative real‐time polymerase chain reaction (qRT‐PCR).ResultsTTK, KIF14, KIF18A and DLGAP5 were identified. Stemness-related genes were upregulated in HNSCC samples. The risk score model had a significant predictive ability. CDK inhibitor was the top hit of potential compounds.ConclusionStemness-related gene expression profiles may be a potential biomarker for HNSCC.


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