Mucosal Bioengineering: Gut in a Dish

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(but not a similar TAM population lacking. CD38 expression) often correlated with abundant tumor infiltration by TREG cells. Trends in Immunology, August 2017 ...

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Spotlight

Heavy Metal to Rock the Immune Infiltrate Lorenzo Galluzzi,1,2,3,* Takahiro Yamazaki,1 and Sandra Demaria1,2 Two resource articles recently published in Cell demonstrate that the elevated phenotypic complexity of the immune infiltrate in human lung adenocarcinomas and renal cell carcinomas can be reliably dissected with mass cytometry. These findings may pave the way to a new era of precision cancer immunotherapy. Accumulating clinical data demonstrate that the configuration of the immune infiltrate affects disease outcome in patients affected by a variety of solid malignancies [1]. However, dissecting an entity as heterogeneous and plastic as the immune infiltrate of solid tumors presents several challenges, including the need to interrogate multiple cell-surface and intracellular markers in the same test tube. Although flow cytometry has progressed tremendously and now allows for the simultaneous measurement of up to 25 markers, elaborate compensation matrices are required to properly discriminate between the partially overlapping emission spectra of fluorophores, which limits the applicability of this approach to precious clinical samples [2]. Two resource articles recently published in Cell demonstrate that mass cytometry provides a robust means to dissect the phenotypic and functional heterogeneity of the immune infiltrate in human specimens [3,4]. Mass cytometry (also known as CyTOF, for time-of-flight cytometry) is a variation of flow cytometry in which antibodies are tagged with isotopically pure heavy metal ions rather than fluorochromes. In this setting, detection relies on TOF mass spectrometry, which

enables complete discrimination between signals from different antibodies. Thus, mass cytometry circumvents the issue of spectral overlap associated with flow cytometry, allowing for the simultaneous quantification of up to 40 markers per cell [5]. These findings may pave the way to a novel branch of precision medicine in which personalized therapeutic decisions are guided by the configuration of the immunological tumor microenvironment, although further optimization is required for mass cytometry to become part of the clinical routine. Lavin et al. and Chevrier et al. harnessed mass cytometry to interrogate the immune infiltrate of lung adenocarcinomas and clear cell renal cell carcinomas (ccRCCs), respectively, resulting in a detailed profiling of both innate and adaptive components of the immunological tumor infiltrate [3,4]. Lavin et al. compared 18 untreated lung adenocarcinoma biopsies with normal lung and blood samples from the same patients. Among common features of lung tumorigenesis, they identified: (i) a depletion and/or functional impairment in CD141+ dendritic cells (DCs), CD16+ monocytes, CD16+ natural killer (NK) cells, and CD8+ cytotoxic T lymphocytes (CTLs); and (ii) an enrichment in CD4+CD25+FOXP3+ regulatory T (TREG) cells expressing high levels of exhaustion markers and immunosuppressive molecules like programmed cell death 1 (PDCD1, best known as PD-1), cytotoxic lymphocyte associated protein 4 (CTLA4), CD38, and ectonucleoside triphosphate diphosphohydrolase 1 (ENTPD1, best known as CD39), and tumor-associated macrophages (TAMs) expressing high levels of peroxisome proliferator-activated receptor g (PPARG, an immunosuppressive transcription factor) and interleukin-6 ([73_TD$IF]IL-6, a mitogenic and immunosuppressive cytokine). Interestingly, these changes were largely independent of tumor stage, as they could be detected in both early- and late-stage lesions. Along similar lines, the expression levels of PD-1 and its main ligand CD274

(best known as PD-L1) on macrophages and T cells, respectively, failed to vary across tumor stages. Moreover, some biomarkers previously thought to be associated with TAM-driven immunosuppression, such as CD206, were similarly expressed in macrophages from lung adenocarcinomas and paired normal lung samples, suggesting that they are rather linked to tissue of origin. However, this hypothesis was not formally addressed in biopsies from patients with nonmalignant lung disorders [3]. Chevrier et al. analyzed 73 biopsies from patients with ccRCC and five healthy matched kidney samples. No fewer than 11 CD8+[72_TD$IF] T cell phenotypes, eight CD4+ T cell phenotypes and 17 TAM phenotypes (defined by the expression of specific surface markers) emerged from this analysis. Tumor-infiltrating PD-1+ cells exhibited heterogeneous levels of other co-inhibitory receptors or exhaustion markers including CTLA4, CD38, hepatitis A virus cellular receptor 2 (HAVCR2, best known as TIM-3), but no lymphocyte-activating 3 [75_TD$IF](LAG3) expression. TREG cells and PD-1+[74_TD$IF] cells were present at different levels in ccRCC biopsies, correlating with tumor stage, but were almost absent in normal kidney samples. Although TAMs [76_TD$IF]were more heterogeneous than T lymphocytes among different patients, high levels of a peculiar TAM subset expressing CD38 (but not a similar TAM population lacking CD38 expression) often [7_TD$IF]correlated with abundant tumor infiltration by TREG [78_TD$IF]cells and PD-1+ cells, delineating a CD38dependent immunosuppressive pathway potentially amenable to therapeutic targeting. Finally, an applied multivariate statistical analysis identified a fraction of patients with reduced progression-free survival based on the co-enrichment of TAM subsets expressing high levels of HLA-DR, CD68, and CD64, but not CD11b or CD36 [4]. Altogether, these data demonstrate that mass cytometry can be harnessed for thorough characterization of tumor immune infiltration, not only for prognostic

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TNM staging Driver mutaons Mutaonal load Neoangen predicon Cancer cells

or nivolumab (the immunotherapeutic agents of choice in this setting), especially if PD-L1 is expressed at high levels within the malignant lesion. Over time, however, the same patient may become resistant to immunotherapy upon an increase in tumor-infiltrating TREG cells (or other detrimental changes in the tumor microenvironment). In this scenario, metronomic cyclophosphamide (or other immuno-, chemo- or radiotherapeutic interventions) might be advantageous to deplete TREG cells and perhaps restore the clinical efficacy of pembrolizumab or nivolumab [8]. This fictitious example underscores the importance of precisely characterizing the immunological tumor microenvironment for precision cancer (immuno)therapy (Figure 1).

nonimmune (CD45 ) compartments of the tumor microenvironment were not taken into account [3,4]. This precludes a quantitative assessment of the tumor infiltrate with respect to epithelial, endothelial, and stromal tumor components, which may also affect disease outcome and offer guidance to therapeutic choices [1]. Moreover, mass cytometry is unable to provide a spatial map of the tumor microenvironment, which [79_TD$IF]64has been shown to convey robust prognostic information in some settings [1]. Furthermore, the addition of mass cytometrybased platforms to the clinical routine remains unpractical, although considerable progress is being achieved in this sense [9]. Finally, while mass cytometry is advantageous in that it quantifies up to 40 biomarkers at the protein level Perhaps the main limitation of the studies (and hence properly addresses postfrom Lavin et al. and Chevrier et al. is that transcriptional expression control), it still offers a limited overview of the tumor infiltrate as compared to techniques that can measure the complete expression profile of a malignant lesion in an Tumor biopsy unbiased manner, such as RNA sequencing (RNAseq) [10]. We surmise that combining mass cytometry with Characterizaon of the TME approaches that provide an unbiased transcriptional profiling (RNAseq) and spatial information (mass cytometryAbundance Abundance based imaging, immunohistochemistry, Immunological status Composion Metabolism Acvaon status and immunofluorescence microscopy) Mitogenic signaling TCR clonality is key for fully dissecting the phenotypic Immune Stromal cells cells and functional complexity of the tumor (Immuno)therapeuc microenvironment.

or predictive purposes, but also as a guide towards therapeutic choices (Figure 1). Indeed, precisely identifying populations of innate or adaptive immune cells within the tumor microenvironment opens the possibility to use specific (immuno)therapeutics in a personalized manner. These agents include not only FDA-approved immune checkpoint blockers such as ipilimumab (targeting CTLA4), pembrolizumab and nivolumab (both targeting PD-1), atezolizumab and avelumab (both targeting PD-L1) [6], and experimental drugs directed against immunosuppressive enzymes like CD39 [7], but also chemo- and radiotherapeutic regimens that mediate on-target or offtarget immunostimulatory effects [8]. For instance, a metastatic non-small lung carcinoma patient progressing on platinumbased chemotherapy is expected to benefit from single-agent pembrolizumab

strategy

Conflict of interest Resistance

Clinical follow-up

Resistance

LG provides remunerated consulting to OmniSEQ (Buffalo, NY, USA). SD has served as consultant for Eisai Inc. (Tokyo, Japan), Lytix Biopharma (Tromsø, Norway), Nanobiotix (Paris, France), and EMD Serono

Clinical response

Inc. (Rockland, MA, USA).

Acknowledgments Figure 1. [6_TD$IF]Potential Precision Cancer (Immuno)Therapy Workflow. State-of-the-art technologies – LG and TY are supported by a departmental startup

including (but not limited to) flow and mass cytometry, immunohistochemistry, and immunofluorescence microscopy, as well as DNA sequencing and RNA sequencing – allow for a thorough characterization of the malignant, immune, and stromal components of the TME. Besides providing prognostic and/or predictive information, dissecting the phenotypic and functional complexity of the TME may guide the development of personalized (immuno)therapeutic regimens for both treatment-naïve and relapsing lesions. TCR, T cell receptor; TME, tumor microenvironment.

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grant from Weill Cornell Medical College and by Sotio a.c. (Prague, Czech Republic). SD is supported from NIH (R01 CA201246 and R01 CA198533), The Chemotherapy Foundation, and the Breast Cancer Research Foundation (BCRF-16-054).

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Department of Radiation Oncology, Weill Cornell Medical College, 10065 New York, NY, USA 2 [69_TD$IF]Sandra and Edward Meyer Cancer Center, 10065 New York, NY, USA Université Paris Descartes/Paris V[70_TD$IF], 75006 Paris, France

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*Correspondence: [email protected] (L. Galluzzi). http://dx.doi.org/10.1016/j.it.2017.05.007

2. Chattopadhyay, P.K. and Roederer, M. (2015) A mine is a terrible thing to waste: high content, single cell technologies for comprehensive immune analysis. Am. J. Transplant. 15, 1155–1161 3. Lavin, Y. et al. (2017) Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169, 750–765 4. Chevrier, S. et al. (2017) An immune atlas of clear cell renal cell carcinoma. Cell 169, 736–749 e718 5. Spitzer, M.H. and Nolan, G.P. (2016) Mass cytometry: single cells, many features. Cell 165, 780–791

References 1. Palucka, A.K. and Coussens, L.M. (2016) The basis of oncoimmunology. Cell 164, 1233–1247

6. Buque, A. et al. (2015) Trial watch: immunomodulatory monoclonal antibodies for oncological indications. Oncoimmunology 4, e1008814

7. Buque, A. et al. (2016) Trial watch-small molecules targeting the immunological tumor microenvironment for cancer therapy. Oncoimmunology 5, e1149674 8. Galluzzi, L. et al. (2015) Immunological effects of conventional chemotherapy and targeted anticancer agents. Cancer Cell 28, 690–714 9. Giesen, C. et al. (2014) Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 10. Paluch, B.E. et al. (2017) Robust detection of immune transcripts in FFPE samples using targeted RNA sequencing. Oncotarget 8, 3197–3205

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