Virtual microdissection in the molecular subtyping of head and neck squamous carcinoma—a ‘Virtual Reality’ of the tumor microenvironment?
Authors: B.B.Y. Ma
Journal: Annals of Oncology (2018)

The Cancer Genome Atlas (TCGA) embodies a vast treasury of unprocessed data that allow researchers to discover new insights into the molecular landscape of cancer (http://www.nature.com/tcga/). The use of advanced bioinformatics is essential for deciphering these raw data into datasets that are biologically meaningful to the clinicians and basic researchers. The systematic classification of head and neck squamous cancer (HNSC) into molecular subtypes was first achieved through the genomic sequencing of bulky tumors (i.e. stroma and tumor cells en bloc) by the TCGA, revealing four subtypes known as classical, atypical, basal and mesenchymal [1.Cancer Genome Atlas Network Comprehensive genomic characterization of head and neck squamous cell carcinomas.Nature. 2015; 517: 576-582Crossref PubMed Scopus (2477) Google Scholar]. Subsequently, single-cell sequencing (SCS) technology has enabled Puram et al. to construct an unadulterated view of the HNSC tumor microenvironment (TME) through the transcriptomic profiling of the separate stromal, immune and tumor compartments of 6000 cells that were micro-dissected from 18 tumors [2.Puram S.V. Tirosh I. Parikh A.S. et al.Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer.Cell. 2017; 171: 1611-1624.e24Abstract Full Text Full Text PDF PubMed Scopus (1014) Google Scholar]. Remarkably, their data could be mapped to three of the TCGA molecular subtypes but not the 'mesenchymal' subtype, which was later identified to be part of the stroma and not a distinctive tumor subtype [2.Puram S.V. Tirosh I. Parikh A.S. et al.Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer.Cell. 2017; 171: 1611-1624.e24Abstract Full Text Full Text PDF PubMed Scopus (1014) Google Scholar]. Due to the labor-intensive nature of SCS and the small sample size of tumors analyzed, the prognostic significance of this stromal/immune signature remained to be defined. In attempting to discern the immune classification of HNSC TME, other researchers digitally analyzed the transcriptomic data from the TCGA using complex bioinformatics. Mandal et al. interrogated the TCGA data from 280 HNSC tumors using a technique called single sample Gene Set Enrichment Analysis [3.Mandal R. Şenbabaoğlu Y. Desrichard A. et al.The head and neck cancer immune landscape and its immunotherapeutic implications.JCI Insight. 2016; 1: e89829Crossref PubMed Scopus (423) Google Scholar, 4.Barbie D.A. Tamayo P. Boehm J.S. et al.Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1.Nature. 2009; 462: 108-112Crossref PubMed Scopus (1855) Google Scholar]. They found that although HNSC has the most immune-infiltrated TME among a panel of 10 highly immune-infiltrated cancers, it also has the most immunosuppressive TME. Recently, two other groups adopted a tissue-agnostic approach to unravelling the immune molecular subtypes of cancers (including HNSC) by interrogating data from the TCGA Pan-Cancer Atlas. Thorsson et al. characterized the genomic data of over 10 000 tumors across 33 cancers (of which 500 were HNSC) and uncovered six TME immune subtypes termed C1–C6. Notably, they found that the TME of the 'classical' HNSC subtype is enriched with the C1 (Wound healing) and C2 (IFNγ dominant) subtypes [5.Thorsson V. Gibbs D.L. Brown S.D. et al.The immune landscape of cancer.Immunity. 2018; 48: 812-830.e14Abstract Full Text Full Text PDF PubMed Scopus (2223) Google Scholar]. Published in the same month as Thorsson et al., Tamborero et al. described six immune subtypes and three different phenotypes of cytotoxic immune-infiltrates in over 9000 tumors across 29 cancer types. Given the emerging clinical data on the efficacy of programmed death receptor 1 (PD-1) and CD152 (cluster of differentiation 152) inhibitors among subsets of patients with recurrent or metastatic HNSC [6.Seiwert T.Y. Burtness B. Mehra R. 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: 956-965Abstract Full Text Full Text PDF PubMed Scopus (1078) Google Scholar, 7.Ferris R.L. Blumenschein Jr, G. Fayette J. et al.Nivolumab for recurrent squamous-cell carcinoma of the head and neck.N Engl J Med. 2016; 375: 1856-1867Crossref PubMed Scopus (2887) Google Scholar], Tamborero et al. included datasets from melanoma patients who were treated with these agents and correlated these immune subtypes with patient's survival [8.Tamborero D. Rubio-Perez C. Muinos F. et al.A Pan-cancer landscape of interactions between solid tumors and infiltrating immune cell populations.Clin Cancer Res. 2018; 24: 3717-3728Crossref PubMed Scopus (164) Google Scholar]. However, the result was tested in a relatively small dataset and the authors focused more on the Pan-cancer landscape rather than on HNSC [8.Tamborero D. Rubio-Perez C. Muinos F. et al.A Pan-cancer landscape of interactions between solid tumors and infiltrating immune cell populations.Clin Cancer Res. 2018; 24: 3717-3728Crossref PubMed Scopus (164) Google Scholar]. In the article by Chen et al. [9.Chen Y.-P. Wang Y.-Q. Lv J.-W. et al.Identification and validation of novel microenvironment-based immune molecular subgroups of head and neck squamous cell carcinoma: implications for immunotherapy.Ann Oncol. 2019; 30: 68-75Abstract Full Text Full Text PDF PubMed Scopus (136) Google Scholar], instead of adopting a tissue-agnostic approach, they focused only on elucidating the immune TME subtypes in HSCC and any relevant clinical association with response to anti-PD-1 or CTLA-4 therapy [9.Chen Y.-P. Wang Y.-Q. Lv J.-W. et al.Identification and validation of novel microenvironment-based immune molecular subgroups of head and neck squamous cell carcinoma: implications for immunotherapy.Ann Oncol. 2019; 30: 68-75Abstract Full Text Full Text PDF PubMed Scopus (136) Google Scholar]. They performed a digital 'virtual microdissection' of the immune from the tumor-related transcriptomic data from 522 tumors analyzed by the TCGA ('the discovery set') and separated them into different clusters by unsupervised non-negative matrix factorization (NMF) [10.Lee D.D. Seung HS. Learning the parts of objects by non-negative matrix factorization.Nature. 1999; 401: 788-791Crossref PubMed Scopus (9439) Google Scholar]. Then, they validated their result in an independent set composed of six published datasets on HNSC (n=838). This approach of 'virtual microdissection' emulates the work by Moffitt et al. on the molecular subtyping of pancreatic ductal carcinoma [11.Moffitt R.A. Marayati R. Flate E.L. et al.Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma.Nat Genet. 2015; 47: 1168-1178Crossref PubMed Scopus (997) Google Scholar]. Chen et al. identified two immune TME subtypes in the discovery cohort, where 40% of HNSCs were found to belong to a novel immune class and the rest to a non-immune class. The immune class consists of two subclasses known as the active immune and the exhausted immune class. The active immune class consists mostly of Human Papilloma virus-positive (HPV+ve) tumors that are heavily infiltrated by tumor-infiltrating lymphocytes with increased cytolytic activity, with enrichment in a pro-inflammatory M1 macrophage signature. The exhausted immune class consists mainly of HPV-ve tumors with an immunosuppressive stroma that is enriched with anti-inflammatory M2 macrophages signatures and wingless/transforming growth factor-β activation [9.Chen Y.-P. Wang Y.-Q. Lv J.-W. et al.Identification and validation of novel microenvironment-based immune molecular subgroups of head and neck squamous cell carcinoma: implications for immunotherapy.Ann Oncol. 2019; 30: 68-75Abstract Full Text Full Text PDF PubMed Scopus (136) Google Scholar]. Comparing the immune subtypes described by Thorsson et al. and Tamborero et al., the analysis by Chen et al. is statistically robust because both the training and validation set of HNSC samples were of comparatively larger size. Integration of their model with the TCGA HNSC subtypes and the six Pan-cancer immune subtypes has shown consistency across these datasets. For example, there was a higher frequency of the active immune class among the 'atypical' TGCA subtype. When the data were integrated with the Pan-cancer immune subtypes, Chen et al. found that 99% of the HNSC samples belonged to the C1 and C2 subtypes where the respective survival curves actually overlapped in the report by Thorsson et al. [5.Thorsson V. Gibbs D.L. Brown S.D. et al.The immune landscape of cancer.Immunity. 2018; 48: 812-830.e14Abstract Full Text Full Text PDF PubMed Scopus (2223) Google Scholar]. In contrast, Chen et al.'s immune subtypes in HNSC were shown to be independent factors for overall survival (OS) (hazard ratio = 0.30, 95% confidence interval 0.09–0.97, P=0.04) [9.Chen Y.-P. Wang Y.-Q. Lv J.-W. et al.Identification and validation of novel microenvironment-based immune molecular subgroups of head and neck squamous cell carcinoma: implications for immunotherapy.Ann Oncol. 2019; 30: 68-75Abstract Full Text Full Text PDF PubMed Scopus (136) Google Scholar]. Moreover, the OS curve of the active immune class displayed a statistically significant separation from the other subtypes. This suggests that for HNSC, the tissue-agnostic Pan-cancer subtypes may not be as prognostically discriminating as the HNSC-specific subtypes described by Chen et al., possibly because the sample size of HNSC tumors included in Thorsson et al.'s analysis was inadequately powered to detect a survival difference. Chen et al.'s work has some limitations. Like Tamborero et al., they only used published datasets from 32 melanoma patients treated with PD-1/CTLA-4 inhibitors, which probably reflects a lack of publically available datasets for HNSCC patients who were treated with these agents [8.Tamborero D. Rubio-Perez C. Muinos F. et al.A Pan-cancer landscape of interactions between solid tumors and infiltrating immune cell populations.Clin Cancer Res. 2018; 24: 3717-3728Crossref PubMed Scopus (164) Google Scholar]. Another issue is that Chen et al.'s subtypes did not correlate with tumor mutational burden (TMB) and neoantigen load. Hanna et al. recently reported that TMB, somatic frameshift events in tumor suppressor genes and dense CD8+ T-cell infiltrates could predict response to PD-1 inhibitors in an observational study of 126 HNSC patients undergoing such treatment [12.Hanna G.J. Lizotte P. Cavanaugh M. et al.Frameshift events predict anti-PD-1/L1 response in head and neck cancer.JCI Insight. 2018; 3 (pii: doi 101172/jciinsight98811): 98811Crossref PubMed Scopus (143) Google Scholar]. Similarly, Thorsson et al. reported that higher neoantigen load was associated with better survival in the C1 and C2 subtypes which are enriched in the HSCC subsets [5.Thorsson V. Gibbs D.L. Brown S.D. et al.The immune landscape of cancer.Immunity. 2018; 48: 812-830.e14Abstract Full Text Full Text PDF PubMed Scopus (2223) Google Scholar]. Despite being a popular computational tool for analyzing gene expression data, a caveat to the NMF method is that it does not take into account of losses of gene expression arising from mechanisms such as homozygous deletion and epigenetic silencing [13.Ayars M. Goggins M. Pancreatic cancer: classifying pancreatic cancer using gene expression profiling.Nat Rev Gastroenterol Hepatol. 2015; 12: 613-614Crossref PubMed Scopus (8) Google Scholar]. 'Virtual microdissection' does not replace manual microdissection in the detection of these alterations [2.Puram S.V. Tirosh I. Parikh A.S. et al.Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer.Cell. 2017; 171: 1611-1624.e24Abstract Full Text Full Text PDF PubMed Scopus (1014) Google Scholar], nor does it replace the superiority of SCS technology in deciphering the intra-tumoral heterogeneity of cancers. In order to put these results from Chen and other researchers into clinical context for the practicing oncologists, one is obliged to reflect upon some recent advances in the development of biomarkers for HNSC. The preliminary result results of the Keynote 048 study (NCT02358031) have shown how a relatively simple immunohistochemical assay of an immune checkpoint protein (PD-L1) coupled with a standardized scoring method (Combined Positive Score) have allowed clinicians to select patients for treatment with a PD-1 inhibitor instead of the more toxic alternative of chemotherapy—despite all the imperfections of PD-L1 as a predictive biomarker [14.Burtness B. Harrington K. Greil R. et al.KEYNOTE-048: phase 3 study of first-line pembrolizumab (P) for recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC). In ESMO 2018 Congress. Munich, Germany.Ann Oncol. 2018; 29Google Scholar]. Another development is the clinical evaluation of an expanded immune/inflammatory gene signature and its integration with TMB and PD-L1 testing as predictive biomarkers of response to PD-1 inhibitor in HNSC [15.T Seiwert , R Haddad, J Bauml et al Abstract LB-339: biomarkers predictive of response to pembrolizumab in head and neck cancer (HNSCC). Am Assoc Cancer Res Ann Meeting; April 14-18, 2018; Chicago . Cancer Res78(Suppl 13).Google Scholar]. The data described by Chen et al. in this article are complementary to the literature and helps to construct a road-map for the future development of biomarkers and therapeutic targets for HNSC. Like any candidate biomarker, these immune subtypes will need to be prospective validated and the corresponding detection method rendered more technically reproducible before they can be applied in the clinics. To conclude, what do all these mega-data and virtual analyses mean to the HNSC researchers? According to the Merriam-Webster dictionary, Virtual Reality is 'an artificial environment which is experienced through sensory stimuli provided by a computer and in which one's actions partially determine what happens in the environment' (https://www.merriam-webster.com/). This implies that the act of searching may affect what the protagonist actually sees in the virtual environment. After all, asking the right question is the foundation of good science. None declared.