Immunoprofiling as a predictor of patient’s response to cancer therapy—promises and challenges

Available online at www.sciencedirect.com

ScienceDirect Immunoprofiling as a predictor of patient’s response to cancer therapy—promises and challenges Daniel Bethmann1,2, Zipei Feng2,3 and Bernard A Fox2,4 Immune cell infiltration is common to many tumors and has been recognized by pathologists for more than 100 years. The application of digital imaging and objective assessment software allowed a concise determination of the type and quantity of immune cells and their location relative to the tumor and, in the case of colon cancer, characterized overall survival better than AJCC TNM staging. Subsequently, expression of PD-L1, by 50% or more tumor cells, identified NSCLC patients with double the response rate to anti-PD-1. Soon, automated staining methods will improve reproducibility of multiplex staining and allow for CLIA standards so that multiplex staining can be used to make clinical decisions. Ultimately, machinelearning algorithms will help interpret data from tissue images and lead to improved delivery of precision medicine. Addresses 1 Martin Luther University Halle-Wittenberg, Institute of Pathology, Halle, Germany 2 Robert W. Franz Cancer Research Center, Earle A. Chiles Research Institute, Providence Cancer Center, Portland, OR, United States 3 School of Medicine, Oregon Health & Science University, Portland, OR, United States 4 Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR, United States Corresponding author: Fox, Bernard A ([email protected], [email protected])

Current Opinion in Immunology 2017, 45:60–72

indicated a regressive process in melanoma [3], which was reinforced by Wade et al. when he described a regressing transplanted canine sarcoma as “the tumor being borne away on a lymphocyte tide” [4]. In 1912 De Fano concluded from a study on murine tumor grafts that a peritumoral infiltration of lymphocytes and plasma cells was an expression of a defensive mechanism akin to immunity [5]. Despite the rather weak associations of local immune response with improved prognosis in the original reports of MacCarty et al. in the 1920s [6], its conclusion earned strong affirmation in over 30 publications of non-lymphoid tumors until the mid-1970s [7]. From the late 1980s to early 1990s a series of reports in melanoma, head and neck cancer, breast cancer, and ovarian cancer demonstrated positive correlation between density of immune infiltrate and prognosis. Further characterization of these immune infiltrates were done starting in mid to late 1990s and established a favorable prognostic impact of CD8+ T cell in colorectal cancer (CRC), mycosis fungoides, and multiple other cancer types [8]. However, the vast majority of these studies were based on pathologists grading densities of lymphocytic infiltrate on a numeric scale, such as 1+, 2+ or 3+. Although this type of analysis is valuable and prognostic in small studies, it’s subject to inherent individual, or even day-to-day bias that decreases the consistency and reliability of the test as a prognostic biomarker [9].

This review comes from a themed issue on Tumour immunology Edited by Dmitry Gabrilovich and Robert L Ferris

http://dx.doi.org/10.1016/j.coi.2017.01.005 0952-7915/ã 2017 Published by Elsevier Ltd.

Introduction to immune infiltrates in cancer Immune cell infiltration is a common feature of many human solid tumors and has been the focus of studies for more than 100 years. Interest in the stromal response to neoplasia began in the latter half of the 19th century, when Virchow stated that the frequent presence of lymphatic cells in human tumors reflected the origin of cancer at sites of previous chronic inflammation [1]. Waldeyer et al. suggested that a local disturbance of connective tissue was an essential prelude to tumor growth [2]. In 1907 Handley described that a “round cell infiltrate” Current Opinion in Immunology 2017, 45:60–72

Evaluation of multiple immune markers and immune escape Over the past decade an increasing number of studies have characterized immune infiltrates for T cell subsets, B cells, NK cells, macrophages and FoxP3+ (possible regulatory) T cells with some studies including activation and functional markers as well [8]. A review of some of the most important studies across cancer types is summarized in Table 1. A key observation is that T cell infiltrates are not prognostic for a better outcome in all cancer entities, most remarkably shown in renal cell- [10,11,12] and prostate carcinoma [13,14] where strong T cell infiltrates are associated with a worse outcome. Explanations include the absence of tumor-specific T cells in the infiltrating cells. This may be secondary to absence or low expression of common tumor or tumor-associated antigens or the fostering of a tumor environment that is hostile to the development of an anti-cancer immune response [15], combined with a signal that recruits T cells into the tumor, for example, interferon-g (IFN-g) [16]. Alternatively, T cells may be specific for a tumor/tumorassociated antigen but this tumor may undergo functional www.sciencedirect.com

Immunoprofiling—promises and challenges Bethmann, Feng and Fox 61

Table 1 Association of immune cell infiltrates with prognosis in cancer Markers tested*

Type of assessment

Effect on prognosis* and significance ##, #

CD3, CD4, CD8, FoxP3, PD-1

Pathologist

CD8, CD20, CD45

Pathologist, Aperio Software Pathologist

High intratumoral number of CD3, CD4 and CD8 is favorable. High peritumoral number of PD-1+ lymphocytes is unfavorable [52]. ## High intratumoral density of CD8, CD45 and CD20 is favorable [53]. ## High intratumoral density of CD4 and CD8 as well as the presence of HLA-DR cells is favorable [54]. # High intratumoral number of CD4 and CD8 is favorable [55]. ## High intratumoral density of TILs* and prominent CLR* is favorable [56]. ## High number of CD8 and CD45RO at CT* and IM* is favorable [43]. ## High intratumoral density of CD8, CD45RO and FoxP3 is favorable [57]. ## High number of CD3, CD8 and CD45RO at CT* and IM* is favorable [58]. ## High number of CD3, CD8 and CD45RO at CT* and IM* is favorable [59]. ##

Histology Melanoma

CD4, CD8, CD68, HLA-DR

Colorectal cancer

Head and neck cancers

Breast cancer

CD3, CD4, CD8

Pathologist

TIL*, MSI*, CLR*

Pathologist

CD8, CD45RO

Pathologist

CD3, CD8, CD45RO, FoxP3 CD3, CD8, CD45RO, GZMB* CD3, CD8, CD45RO, variety of mRNA & T-cell markers CD1a, CD4, CD8, CD68, FoxP3 PD-1, PD-L1

Ariol Image Analysis System Spot Browser

CD3, CD4, CD8, FoxP3 CD4, CD8, CCR4

Pathologist

CD3, CD4, CD8, CD20, CD68, FoxP3, GZMB* CD4, CD25, CD69, FoxP3

Pathologist

CD4, CD8, IL4, CSF-1*

LSRII/FlowJo 8.8 Software

CD8, FoxP3

Pathologist

CD8, FoxP3, Vasohibin-1, CD31, EGFR, CK5/6, Ki67 CD8

Pathologist

CD4, CD8, CD68, IL34, CSF-1*

Aperio ScanScope + Spectrum Software, IHC-MARK algorithm Pathologist

CD8 Bladder cancer

CD3, CD8, CD15, CD45RO

www.sciencedirect.com

Spot Browser

Pathologist Randomized Phase 3 Trial

Pathologist

Pathologist

Pathologist

Pathologist

First author

Year

Kakavand

2015

Erdag

2012

Piras

2005

Al-Batran

2005

Rozek

2016

Mlecnik

2011

Nosho

2010

Galon

2006

Page`s

2005

High density of CD4 and CD8 is favorable [60]. ## Treatment with PL-1* mAb* Nivolumab significantly improves overall survival in recurrent, Platin-refractory HNSCC* [61]. ## High density of CD3 and CD8 at CT* and IM* is favorable [62]. ## Low number of CD8 at CT* and stroma as well as high stromal CCR4 is unfavorable. Low CD8/CCR4 ratio at CT* and stroma is unfavorable [63]. # Low number of CD8 at CT* is unfavorable [64]. #

Nguyen

2016

Ferris

2016

Balermpas

2014

Watanabe

2010

Distel

2009

High number of CD4 + CD69 + T-cells and CD4 + FoxP3 + Tregs at CT* is favorable [65]. # Macrophage-depletion by inhibition of CSF-1* delays tumor growth following radiotherapy. High intratumoral number of CD8, low number density of CD4 and low expression of IL4 delays tumor growth (murine model) [66]. ## High intratumoral and stromal number of CD8 and FoxP3 is favorable [67]. ## High number of CD8+ cells and high CD8+/FoxP3 ratio as well as high Ki67 is favorable [68]. ## High intratumoral and stromal number of CD8 is favorable [69]. # Blockade of Macrophage-recruitment by inhibition of CSF-1*in combination with Paclitaxel improves survival by slowing primary tumor development and reducing pulmonary metastasis [70]. #

Badoual

2006

Shiao

2015

Ali

2014

Miyashita

2014

Liu

2012

DeNardo

2011

High number of CD8 at CT* and IM* is favorable [71]. ## High intratumoral density of CD3 is favorable. High intratumoral density of CD15 is unfavorable [72]. #

Mahmoud

2011

Zhang

2016

Current Opinion in Immunology 2017, 45:60–72

62 Tumour immunology

Table 1 (Continued ) Histology

Ovarian cancer

Lung carcinoma

Urothelial cell carcinoma

Endometrial cancer

Oesophageal cancer

Type of assessment

Effect on prognosis* and significance ##, #

CD8, CD4, FoxP3, BTLA*, Cbl-b*

Markers tested*

Definiens Tissue Studio

CD4, CD8, FoxP3, IL-17 CD4, CD8, DC*, NK*

Pathologist, Image Pro Plus 5.1 Software Pathologist

Low intratumoral density of CD8 as well as high FoxP3/CD4, BTLA*/CD8 and Cbl-b*/CD8 ratios are unfavorable [73]. ## High intratumoral number of FoxP3 and IL-17 is unfavorable [74]. ##

CD3

Pathologist

CD3, CD8, CD45RO, FoxP3

Pathologist

CD3, CD8, FoxP3, Ki67 CD8, HLA-DMB

Pathologist

CD3, CD8

Pathologist

CD3, CD8, APM* (TAP1, TAP2, Tapasin, HLA-HC, b2m*) CD4, CD8, FoxP3

Pathologist

TIL* PD-1, PD-L1

Pathologist Pathologist

CD3, FoxP3, IL12Rb2, IL-7R

Pathologist

CD4, CD8, VEGF-A*, VEGFR-2*

Pathologist

TIL* CD8, CD68, c-kit mast cells CD4, CD8, Ki67

Pathologist Pathologist

CD8, CD103

Pathologist

CD8, MHC class I, NY-ESO-1 CD4, CD8, FoxP3

Pathologist

CD8, CD45RO, FoxP3

Pathologist

CD8, GZMB*

Pathologist, Image Pro Plus 4.5 Software Pathologist

CD8, CD68, CD163

Pathologist

Pathologist

Pathologist

Pathologist

CD4, CD57, CD8, FoxP3, IL-17 CD4, CD8, FoxP3

Pathologist

CD4, CD8, CD57

Pathologist

Current Opinion in Immunology 2017, 45:60–72

Pathologist

High intratumoral number of CD4, CD8 and DC* is favorable [75]. # High number of CD3 at CT* is favorable. High number in stroma is unfavorable [76]. # High intratumoral number of CD45RO, CD8 and FoxP3 as well as CD8/FoxP3 ratio is favorable [77]. # High intraepithelial number of CD8 is favorable. Low Ki67 is unfavorable [78]. # High intratumoral number of CD8 and presence of HLA-DMB is favorable [79]. ## High intratumoral number of CD3 and CD8 is favorable (only in serous subtype). [80] ## High intratumoral number of CD3 and CD8 and expression of APM is favorable [81]. ## High intratumoral CD8+ cells and high CD8/ CD4 and CD8/FoxP3 ratios are favorable [82]. ## High TIL* is favorable [83]. ## PD-L1 expression in  50% of tumor cells correlated with improved efficacy of anti-PD1 inhibitor pembrolizumab and overall survival [23]. # High density of FoxP3 and stromal FoxP3/ CD3 ratio as well as high expression of IL-7R is unfavorable. High expression of IL-12Rb2 is favorable [84]. ## Low intratumoral number of CD4 and CD8 is unfavorable. High intratumoral expression of VEGF-A* and VEGFR-2* is favorable [85]. ## Presence of TIL* is favorable [86]. ## High number of CD8 and CD68 in CT* > stroma is favorable [87]. ## High number of CD4 in stroma is favorable. High number of CD8 and high Ki67/CD8 ration at CT* is unfavorable [88]. # High intratumoral density of CD8 and CD103 is favorable [89]. ## High intratumoral number of CD8 is favorable [90]. ## High intratumoral number of FoxP3 and high FoxP3/CD8 ratio is unfavorable [91]. # High intratumoral number of CD8, CD8/ FoxP3 ratio and presence of CD45RO is favorable [92]. # High number of CD8 at IM* is favorable [93]. ##

High intratumoral infiltration of CD68 and CD163 is unfavorable [94]. ## High intratumoral density of CD8, CD57 and IL-17 is favorable [95]. # Low intratumoral density of FoxP3 is unfavorable [96]. ## High number of CD4 and CD8 in the stroma and high number of CD8 at CT* are favorable [97]. ##

First author

Year

Oguro

2015

Zhang

2013

Nakakubo

2003

Al-Attar

2010

Leffers

2009

Adams

2009

Callahan

2008

Clarke

2008

Han

2008

Sato

2005

Brambilla Garon

2016 2015

Suzuki

2013

Donnem

2010

Ruffini Kawai

2009 2008

Wakabayashi

2003

Wang

2015

Sharma

2007

Yamagami

2011

de Jong

2009

Kondratiev

2004

Sugimura

2015

Lv

2011

Yoshioka

2008

Cho

2003

www.sciencedirect.com

Immunoprofiling—promises and challenges Bethmann, Feng and Fox 63

Table 1 (Continued ) Type of assessment

Effect on prognosis* and significance ##, #

CD3, CD8, PD-1*, PDL1*

Markers tested*

Pathologist

CD8, FoxP3

Pathologist

CD1a, CD3, CD8, CD20, CD45RO, CD57, CD68, CD83, GZMB*, FoxP3, CD8, CD34, CD66b, TGF-b

Pathologist

High density of CD3 and CD8 at CT* and IM* as well as positive PD-L1 staining is favorable [98]. ## High intratumoral number of FoxP3 is unfavorable [99]. # High intratumoral density of CD45RO and low peritumoral density of CD57 are favorable. High CD45RO(intra)/CD57(peri) ratio is favorable [100]. # High intratumoral number of neutrophils and high neutrophils/CD8 ratio is unfavorable [101]. ## Low intratumoral number of FoxP3 and high intratumoral number of CD8 is favorable [102]. ##

Histology Hepatocellular carcinoma

CD3, CD4, CD8, FoxP3, GZMB*

Malignant pleural mesothelioma

Pancreatic cancer

Cervical cancer

Glioblastoma multiforme

CD8, CD68, CD163

Pathologist

Pathologist, computerized image analysis system from Hitachi/Leica Pathologist

CD4, CD8, NK*, panHLA class I CD3, CD4, CD45RO, CD8, CD25, FoxP3

Pathologist

CD8

Pathologist

CD3, CD8

Pathologist

CD3, CD4, CD8, CD68, FoxP3, GZMB*

Pathologist, COUNT Software

CD4, CD8, CD15, CD20, CD117, CD206

Pathologist

CD4, CD8, FoxP3, iNOS, CD163 CD4, CD8, M1, M2, CD68, FoxP3 CD66b

Pathologist

CD4, CD25, FoxP3

Pathologist

CD8, FoxP3, HLA-DR, PD-L1*

Pathologist, ImageJ, SlideBook 5.5 Reader

CD3, CD4, CD8, CD66b, CD163

Pathologist, newCAST Software

CD4, CD8, FoxP3, HLA-A

Pathologist

CD3, CD4, CD8, FoxP3

Aperio Scan Scope Software

FoxP3, p53, MGMT*, Ki67 CD3, FoxP3

Pathologist

www.sciencedirect.com

Pathologist, Aperio Image Scope

Pathologist

Pathologist

High intratumoral CD163/CD68 ratio is unfavorable [103]. # High intratumoral density of CD8 is favorable [104]. # High intratumoral level of CD8 and CD45RO is favorable. High intratumoral level of CD4 and CD25 is unfavorable [105]. # High intra- and peritumoral density of CD8 is favorable [106]. # High intratumoral density of CD3 and CD8 is favorable [44]. ## High intratumoral number of CD3 and CD4 is favorable. High intratumoral number of GZMB*-positive cells is unfavorable [107]. # Presence of tumor-associated CD15, CD20 and CD206 is unfavorable. Presence of tumor-associated CD4, CD8 and CD117 favorable [108]. ## High intra- and peritumoral number of FoxP3 and CD163 is unfavorable [109]. ## High number of M2, CD68, CD66b and FoxP3/CD4 ratio is unfavorable. High number of CD4, CD8 and M1/CD68 ratio is favorable. CD4high/CD8high/FoxP3 low and M1high/M2low is favorable (all at CT*) [110]. ## Low intratumoral number of CD4, CD25 and FoxP3 is favorable [111]. ## High intratumoral PD-L1* and HLA-DR cell rates and high number of FoxP3 are favorable. Low CD8/FoxP3 ratio is unfavorable in regard to lymph node metastasis [112]. ## High peritumoral number of CD66b, CD163 and low peritumoral number of CD8 is unfavorable. High stromal number of CD66b is unfavorable [113]. # High intratumoral number of CD8, low CD8/ CD4 and CD8/FoxP3 ratios and low HLA-A expression are unfavorable[114] # Low intratumoral CD4/FoxP3 ratio is unfavorable. High CD3/FoxP3 and CD8/FoxP3 ratios are favorable [115]. # High intratumoral density of FoxP3 is unfavorable [116]. ## No statistical effect on survival. [117]

First author

Year

Gabrielson

2016

Wang

2012

Gao

2012

Li

2011

Gao

2007

Cornelissen

2014

Yamada

2010

Anraku

2008

Hu

2015

Anitei

2014

Grabenbauer

2006

Wang

2016

Wartenberg

2015

Ino

2013

Hiraoka

2006

Heeren

2015

Carus

2013

Jordanova

2008

Sayour

2015

Yue

2015

Thomas

2015

Current Opinion in Immunology 2017, 45:60–72

64 Tumour immunology

Table 1 (Continued ) Histology Lymphomas: DLBCL*

Markers tested* CD4, CD8, CD56, CD68, CD137, CD163, PD-1*, PD-L1* CD3, FoxP3

FoxP3, CD10, bcl-6, MUM-1 TIA-1, FoxP3 Lymphomas: Follicular Lymphoma

Lymphomas: Mixed/Other

Renal cell carcinoma

Prostatic adenocarcinoma

Effect on prognosis* and significance ##, # The ratio of (CD4*CD8)/((CD163/CD68)[M2] *PD-L1) enables risk stratification with regard to 4-years overall survival [118]. ##

Keane

2015

Pathologist, Ariol System, Pannoramic Viewer + Densito-Quant module Pathologist

High density of tumor-infiltrating CD3 and FoxP3 are favorable [119]. ##

Coutinho

2015

High number of tumor-infiltrating FoxP3 is favorable [120]. ## Low number of tumor-infiltrating FoxP3 is favorable [121]. # Intrafollicular localization of CD4, CD25 and FoxP3 as well as perifollicular localization of CD8, CD25 and FoxP3 as well as high number of CD25 is favorable [122]. ## High number of tumor-infiltrating PD-1*+ cells is favorable [123]. ##

Lee

2008

Hasselblom

2007

Farinha

2010

Carreras

2009

Expression of CD69 on tumor cells is unfavorable. Dense interfollicular infiltrate of FoxP3 is favorable. Dense interfollicular infiltrate of CD68 is favorable in CVP-treated but unfavorable in Fludarabine-treated patients [124]. # CD4 + CD25 + FoxP3 + Tregs correlates with EBV* presence, yet not with survival [125]. #

de Jong

2009

Assis

2012

High number of tumor-infiltrating Th2-cells is favorable. High FoxP3/Th2 ratio is unfavorable [126]. # Low number of tumor-infiltrating CD8, CD56 and CD57 as well as high intratumoral number of GZMB* and TIA-1 are unfavorable [127]. # High number of tumor-infiltrating FoxP3 is favorable in follicular lymphoma, germinal center-like DLBCL and classical Hodgkin’s lymphoma but unfavorable in non germinale center-like DLBCL [128]. # High intratumoral number of CD8 is unfavorable [10]. ## High intratumoral number of FoxP3 is unfavorable [11]. # Presence of intratumoral CD4 + CD25 + FoxP3- T cells is unfavorable [12]. ## High intratumoral density of CD3, CD4 and CD8 is unfavorable [13]. ## High expression of CD3, M-CSF* and CSF-1R* in CT* and stroma are unfavorable [129]. ## High intratumoral number of CD4 and CD8 is unfavorable [14]. ##

Schreck

2009

A`lvaro-Naranjo

2005

Tzankov

2008

Mella

2015

Jensen

2009

Siddiqui

2007

Ness

2014

Richardsen

2008

Ka¨rja¨

2005

Pathologist

CD4, CD8, CD20, CD21, CD25, FoxP3

Pathologist

FoxP3, PD-1*

Pathologist, Ariol-SL50 Software Pathologist

CD3, CD4, CD8, CD20, CD21, CD23, CD68, CD69, FoxP3, Ki67

Lymphomas: Classical Hodgkin’s Lymphoma

Type of assessment Computer-based quantification

CD4, CD8, CD25, FoxP3

Pathologist

CD3, CD20, CD45RO, CD68, FoxP3, T-Bet, c-Maf CD4, CD8, CD21, CD56, CD57, GZMB*, TIA-1, S-100 FoxP3

Pathologist, HISTO Software Pathologist

Pathologist

CD8, TLR9*

ImageJ 1.44

CD3, CD4, CD8, FoxP3 CD4, CD25, FoxP3

Cast Software, Pathologist Pathologist

CD3, CD4, CD8, CD20

Pathologist

CD3, CD68, M-CSF*, CSF-1R*

Pathologist

CD4, CD8, CD20

Pathologist

First author

Year

*Abbreviations: APM, antigen presenting machinery; Cbl-b, Casitas–B-lineage lymphoma protein-b; b2m, beta 2 microglobulin; BTLA, B and T lymphocyte attenuator; CLR, Crohn’s like lymphoid reaction; CSF-1, colony-stimulating factor-1; CSF-1R, colony-stimulating factor-1 receptor; CT, core of the tumor; DC, dendritic cells; DLBCL, diffuse large B-cell lymphoma; EBV, Epstein-barr-virus; GZMB; Granzyme B; HLA-HC, HLA class I heavy chain; HNSCC, head and neck squamous cell carcinoma; IM, invasive margin of the tumor; mAb, monoclonal antibody; M-CSF, macrophage colony-stimulating factor; MGMT, methyl guanine methyl transferase; MSI, microsatellite instability; NK, natural killer cells; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; TIL, tumor infiltrating lymphocytes; TLR 9, toll-like receptor 9; VEGF-A, vascular endothelian growth factor A; VEGFR-2, VEGF receptor 2; #, significant effect on outcome (p < 0,05); ##, highly significant effect on outcome (p < 0,01); when different significance levels were reached for different parameters in the same study, an overall representative p-level was stated for the whole study. ## Denotes papers of high significance, # denotes papers of significance.

Current Opinion in Immunology 2017, 45:60–72

www.sciencedirect.com

Immunoprofiling—promises and challenges Bethmann, Feng and Fox 65

inactivation secondary to loss-of function mutations in Janus kinases 1 and 2 (JAK1, JAK2) genes [17]. Furthermore, the T cells may be inhibited through other suppressive elements, including regulatory T-cells (Tregs), subsets of T-helper populations (TH2, TH17), macrophages and myeloid-derived suppressor cells (MDSC) as well as immunosuppressive cytokines or other inhibitory molecules that downregulate the tumordestructive immune response [18–20]. In summary this suggests that it is not only the absolute number of T cells, but rather the function of the T cells and the stroma surrounding it that need to be evaluated as we consider prognostic biomarkers that will predict response to specific therapy.

PD-L1 expression: marker of response to checkpoint blockade One of the most studied predictive biomarkers for response to checkpoint blockade is PD-L1 expression. PD-L1 expression in pretreatment tumor specimens is thought to identify patients that are more likely to have a response to PD-1 pathway blockade [21]. In melanoma, patients with a high PD-L1 expression by immunohistochemistry (IHC) have a higher response rate to antiPD-1 compared to patients with low PD-L1 expression [22]. In non-small-cell lung cancer (NSCLC), first-line anti-PD-1 therapy with Pembrolizumab in patients with 50% of the tumor expressing PD-L1 was significantly more efficacious compared to standard 1st line chemotherapy [23] while first-line treatment with the anti-PD1 monoclonal antibody (mAb) Nivolumab set at a cut-off point of 5% PD-L1 tumor expression showed an unfavorable clinical outcome [24]. Other encouraging results were obtained in the context of Merkel- [25], renal cell[26] and bladder carcinoma [27,28] as well as hematologic diseases [29,30]. Yet, many patients with high PD-L1 tumors do not respond to anti-PD-1, while others with low or negative PD-L1 can demonstrate an objective clinical response [31]. In 2014, Ribas et al. added to this complexity when they discovered that tumor regression after PD-1-blockade in melanoma patients appeared to require an increased density of CD8+ T cells in close proximity to PD-1/PD-L1 expressing cells at the invasive tumor margin and inside the tumors [32]. However, most studies have not included T cell numbers in their evaluation and have focused solely on PD-L1 expression. While a lot of PD-L1 expression seems to be a good prognostic biomarker for response, uncertainty exists about the optimal cut-off point for determining what percentage of PD-L1 expression by the tumor should be used in the selection of patient’s checkpoint blockade. To evaluate PD-L1 tumor expression prior to treatment or placement in clinical trials, several different IHC assays have become available, though they are not standardized with respect to either quantity or distribution of expression. www.sciencedirect.com

This is a major problem as recently described by the “Blueprint PD-L1 IHC Assay Comparison Project”, where despite showing a similar analytical performance on tumor cell staining of PD-L1 expression by three of the four assays, there was a significant problem in the variability in immune cell staining. In as many as 37% of the cases, different PD-L1 classification would have been made depending on which assay/scoring system was used [33], meaning a potential undertreatment or overtreatment of cancer patients. Further controversy exists regarding the objective determination of PD-L1 protein levels and its reproducibility. In a study on NSCLC, McLaughlin et al. found a prominent heterogeneity within tumors and severe inter-assay variability or discordance which could be due to different antibody affinities, limited specificity, or distinct target epitopes [34]. Furthermore, in a study of Rehman et al. inter-observer reproducibility of PD-L1 assays were highly concordant for PD-L1 expression by the tumor, but not for stromal/ immune cell PD-L1 expression. However, scoring was similar among different blocks from each tumor, indicating that the spatial distribution of heterogeneity of expression of PD-L1 is within the area represented in a single block [35]. In contrast to that, Obeid et al. found that despite using automated assessment software, different tumor sampling strategies may yield discordant lymphocyte density results and different stratification for risk assessment [36]. The next step in the evolution of PD-1 immune checkpoint blockade is the administration of a combination immunotherapy. Recently clinical trials in patients with advanced melanoma, SCLC and NSCLC have reported increased objective response rates in patients treated with the combination of Nivolumab plus Ipilimumab (an antiCTLA-4 antibody) [31,37,38,39]. IHC analysis suggests that it is the patients with <5% tumor PD-L1 expression who benefit from the combination immunotherapy while patients with than 5% PD-L1 expressing tumors do not see added benefit to the combination over anti-PD-1 alone [31]. These are relevant findings, as the addition of Ipililumab to the treatment protocol adds significant side effects ranging from simple skin lesions to severe autoimmune inflammations [40,41].

Objective assessment of immune infiltrates Objective assessment using digitized slides and computer software is a critical step towards addressing the subjectivity and bias and improving reproducibility of immunoprofiling. Led by Jerome Galon and colleagues in 2006, quantitative assessment of CD3+, CD8+ and CD45RO+ T cells at the invasive margin and tumor center of colorectal cancer specimen, termed Immunoscore, was shown to be highly prognostic, and remarkably more so than conventional AJCC TNM staging [42,43], a notation which has since been shown again in rectal carcinoma [44]. These reports resulted in a Society for Current Opinion in Immunology 2017, 45:60–72

66 Tumour immunology

Immunotherapy of Cancer (SITC)-led global study to assess whether the Immunoscore could be validated as prognostic biomarker [45]. Ultimately, centers from 13 countries participated and documented the prognostic power of this assay (JITC 2016 published abstract). The Immunoscore test, while not yet approved by regulatory agencies, is available as a commercial test [46]. As this assessment was being performed, the digital imaging and objective assessment software provided staining intensities that could be used to assess the quality of staining and ultimately improve the interpretation of results. In our opinion, the validation of Immunoscore in a global study represents a critical first step for the development of a new generation of biomarker assessments that utilize automated staining, digital imaging and objective assessment software to assist pathologists in providing vital prognostic information for the stratification of patients enrolled on clinical trials.

Multiplex IHC: getting more from less Despite overwhelming evidence of the positive prognostic impact of T cell infiltrate, many patients with high immune infiltrate rapidly progress. Is this secondary to loss of HLA expression by the cancer, expression of immune inhibitory molecules or other suppressor cell populations? Evaluation of these possibilities can be aided by novel techniques such as multiplex IHC that help overcome some of the obstacles facing conventional IHC [47]. Rapid advancement in both brightfield and fluorescent imaging technologies have enabled the analysis of seven or more different markers simultaneously on a single slide [48,49]. Compared to single color IHC, the main advantage of multiplex IHC is twofold. First, because it allows for the analysis of multiple parameters simultaneously on a single slide, it significantly decreases the requirement for tissue, making the most out of small biopsies which is clinically relevant. Second, the simultaneous analysis of multiple immune cells allows for the study of their relationship to one another. An example is the relative ratio of CD8+ T cell to immune suppressors FoxP3 and PD-L1 increased the predictive power of tumor-infiltrating lymphocyte culture success and adds valuable information to their possible function in the tumor microenvironment [47,48].

Automation of multiplex IHC: staining and image analysis Moving forward, a standardized multiplex IHC platform that allows accurate identification and enumeration of immune subsets for clinical application is paramount. Current technology in multispectral imagine allows for manual staining and imaging of 7 or more markers. Currently, with multiplex platforms, significant manual manipulation is required from staining to antigen stripping to imaging. This not only creates variability, but also limits the number of slides one can do as each panel of stains can take 12 or more hours to accomplish. To Current Opinion in Immunology 2017, 45:60–72

advance the platform into full automation, our group investigated zinc-based stripping buffers as an alternative to microwaving [50]. Another group has successfully integrated the tyramide staining technology with existing single-color automated staining platforms, with successful staining of up to 5 different markers in a semi-automated way [51]. While this is not yet commercially available, several groups are working on this and we expect this to be available in the near future. Another major hurdle to the clinical application of this technology is the time required to process and analyze multispectral images. As we currently analyze multispectral data, substantial operator input is required to obtain an assessment and this interjects potential variability. What is needed is the development of improved software that can more readily automate analysis. Ultimately, for this methodology to see prime time in the clinical arena, where we believe it is desperately needed, it will require the development of solutions for processing and analysis of images in a quick, unbiased manner. Current existing platforms, although very useful and generating data that are igniting interest in the potential of this technology, are not yet ready to be the workhouse that we need them to be for stratifying patients on the next generation of clinical trials.

Application of machine learning to image analysis In addition to the challenges reviewed above, there are a myriad of possible cell density-, location- and relationship issues that are being conceived and evaluated by investigators. We imagine that these types of interrogations may be much better evaluated using machine learning protocols. In collaboration with the community, we envision an opportunity to create a global database of IHC and multispectral images, with associated treatment information and clinical outcomes. Machine-learning algorithms are being developed to address the large dataset generated from multiplex imaging. These algorithms, once trained can automatically process the images and identify features that correlate with outcome. This allows for automatic extraction of the most important features in an unbiased way with minimal user input. Additionally, a validation algorithm that allows pathologists to validate each image in a quick, intuitive fashion can be added.

Conclusions It has been said the “The tissue is the issue.” We believe that this statement is true and once tissue is acquired, the ability to assess relationships of immune cells to a patient’s cancer will represent a critical element in evaluating therapeutic options for that patient and ultimately to tailoring treatment. While we recognize that tumor heterogeneity and treatment-induced changes in the tumor environment represent potential hurdles to the application of this technology, we are encouraged by www.sciencedirect.com

Immunoprofiling—promises and challenges Bethmann, Feng and Fox 67

the success of the SITC-led global Immunoscore validation project, the role of PD-L1 expression in predicting response to anti-PD-1, and by data highlighted in Table 1 that document how assessment of immune cells has prognostic and predictive power. What is needed now is a push to move immunoprofiling panels that appear to provide important clinically relevant information to automated CLIA platforms that can be used to stratify patients for clinical trials, guide clinical decision making and ultimately tailor therapy for patients with cancer. The era of medicine when patients were treated without an assessment of the anti-cancer immune response should be over. The future of oncology will be in tailoring therapies to overcome suppressive mechanisms and developing strategies to induce destructive immunity against a wide spectrum of cancer antigens in the majority of patients that apparently lack effective anti-cancer immunity.

Acknowledgements This work was supported by The Murdoch Trust, the Oregon Clinical and Translational Research Institute (OCTRI, TL1TR000129) from the National Center for Advancing Translational Sciences (NCATS) at the National Institutes of Health (NIH), the Providence Medical Foundation, the Oral Maxofacial Surgery Foundation, Bob and Elsie Franz, The Chiles Foundation, Wes and Nancy Lematta, The Harder Family, Lynn and Jack Loacker.

References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as:  of special interest  of outstanding interest 1. Virchow R: Die Krankhaften Geschwu¨lste. 1863. Interest in the stromal response to neoplasia began in the latter half of the 19th century, when Virchow in this manuscript stated that the frequent presence of lymphatic cells in human tumors reflects the origin of cancer at sites of previous chronic inflammation. 2.

Waldeyer HGW: Die Entwicklung der Karzinome. Virchows Arch Path Anat 1872, 55:67.

3.

Handley WS: The Bunterian lectures on the pathology of melanotic growths in relation to their operative treatment. Lancet 1907, 169:927-933.

4.

Wade H: An experimental investigation of infective sarcoma in the dog, with a consideration of its relationship to cancer. J Path Bact 1908, 12:384.

5.

De Fano C: A cytological analysis of the reaction in animals resistant to implanted carcinomata. Fifth Sci Rep Imrp Cancer Res Fund 1912. [no volume].

6.

MacCarty WC, Mahle AE: Relation of differentiation and lymphocytic infiltration to postoperative longevity in gastric carcinoma. J Lab Clin Med 1921, 6:473.

7.

Underwood JC: Lymphoreticular infiltration in human tumours: prognostic and biological implications: a review. Br J Cancer 1974, 30:538-548.

8.

Fridman WH, Page`s F, Saute`s-Fridman C, Galon J: The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer 2012, 12:298-306.

9.

Holman CD, James IR, Heenan PJ, Matz LR, Blackwell JB, Kelsall GR, Singh A, ten Seldam RE: An improved method of analysis of observer variation between pathologists. Histopathology 1982, 6:581-589.

10. Mella M, Kauppila JH, Karihtala P, Lehenkari P,  Jukkola-Vuorinen A, Soini Y, Auvinen P, Vaarala MH, Ronkainen H, www.sciencedirect.com

Kauppila S et al.: Tumor infiltrating CD8+ T lymphocyte count is independent of tumor TLR9 status in treatment naı¨ve triple negative breast cancer and renal cell carcinoma. OncoImmunology 2015, 4:e1002726. 11. Jensen HK, Donskov F, Nordsmark M, Marcussen N, von der Maase H: Increased intratumoral FOXP3-positive regulatory immune cells during interleukin-2 treatment in metastatic renal cell carcinoma. Clin Cancer Res 2009, 15:1052-1058. 12. Siddiqui SA, Frigola X, Bonne-Annee S, Mercader M, Kuntz SM, Krambeck AE, Sengupta S, Dong H, Cheville JC, Lohse CM et al.: Tumor-infiltrating Foxp3-CD4+ CD25+ T cells predict poor survival in renal cell carcinoma. Clin Cancer Res 2007, 13:2075-2081. 13. Ness N, Andersen S, Valkov A, Nordby Y, Donnem T, Al-Saad S,  Busund L-T, Bremnes RM, Richardsen E: Infiltration of CD8+ lymphocytes is an independent prognostic factor of biochemical failure-free survival in prostate cancer. Prostate 2014, 74:1452-1461. 14. Ka¨rja¨ V, Aaltomaa S, Lipponen P, Isotalo T, Talja M, Mokka R: Tumour-infiltrating lymphocytes: a prognostic factor of PSAfree survival in patients with local prostate carcinoma treated by radical prostatectomy. Anticancer Res 2005, 25:4435-4438. 15. Stewart TJ, Abrams SI: How tumours escape mass destruction. Oncogene 2008, 27:5894-5903. 16. Kursunel MA, Esendagli G: The untold story of IFN-g in cancer biology. Cytokine Growth Factor Rev 2016, 31:73-81. 17. Zaretsky JM, Garcia-Diaz A, Shin DS, Escuin-Ordinas H, Hugo W,  Hu-Lieskovan S, Torrejon DY, Abril-Rodriguez G, Sandoval S, Barthly L et al.: Mutations associated with acquired resistance to PD-1 blockade in melanoma. N Engl J Med 2016, 375:819-829. Using whole exome sequencing, Zaretsky et al. demonstrated that acquired resistance to PD-1 blockade immunotherapy in patients with melanoma was associated with the functional inactivation of CD8+ T-cells by loss-of function mutations in Janus kinases 1 and 2 genes as well as the loss of MHC class I expression on the tumor surface as presented by a truncating mutation in the gene encoding b2-microglobulin. 18. Barth RJ, Camp BJ, Martuscello TA, Dain BJ, Memoli VA: The cytokine microenvironment of human colon carcinoma: lymphocyte expression of tumor necrosis factor-a and interleukin-4 predicts improved survival. Cancer 1996, 78:1168-1178. 19. Galon J, Mlecnik B, Bindea G, Angell HK, Berger A, Lagorce C, Lugli A, Zlobec I, Hartmann A, Bifulco C et al.: Towards the introduction of the Immunoscore in the classification of malignant tumours. J Pathol 2014, 232:199-209. 20. Ma W, Gilligan BM, Yuan J, Li T: Current status and perspectives in translational biomarker research for PD-1/PD-L1 immune checkpoint blockade therapy. J Hematol Oncol J Hematol Oncol 2016, 9:47. 21. Gandini S, Massi D, Mandala` M: PD-L1 expression in cancer patients receiving anti PD-1/PD-L1 antibodies: a systematic review and meta-analysis. Crit Rev Oncol Hematol 2016, 100:88-98. 22. Daud AI, Wolchok JD, Robert C, Hwu W-J, Weber JS, Ribas A,  Hodi FS, Joshua AM, Kefford R, Hersey P et al.: Programmed death-ligand 1 expression and response to the anti– programmed death 1 antibody pembrolizumab in melanoma. J Clin Oncol 2016, 34:4102-4109. In this clinical study, Daud et al. showed that in melanoma patients, the assessment of PD-L1 expression in pretreatment biopsy samples positively correlates with response rate, progression free- (PFS) and overall survival (OS) following the administration of anti-PD-1 antibody Pembrolizumab. However, some patients with PD-L1–negative tumors also achieved durable responses. 23. Garon EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP,  Patnaik A, Aggarwal C, Gubens M, Horn L et al.: Pembrolizumab for the treatment of non–small-cell lung cancer. N Engl J Med 2015, 372:2018-2028. In this clinical study (Keynote-001), the authors showed that in non-small-cell lung cancer (NSCLC), first-line anti-PD-1 therapy with Pembrolizumab in patients with 50% of the tumor expressing PD-L1 Current Opinion in Immunology 2017, 45:60–72

68 Tumour immunology

was significantly more efficacious compared to standard 1st line chemotherapy. 24. Socinski M, Creelan B, Horn L, Reck M, Paz-Ares L, Steins M, Felip E, Heuvel van den M, Ciuleanu TE, Badin F et al.: NSCLC, metastaticCheckMate 026: a phase 3 trial of nivolumab vs investigator’s choice (IC) of platinum-based doublet chemotherapy (PT-DC) as first-line therapy for stage iv/recurrent programmed death ligand 1 (PD-L1)-positive NSCLC. Ann Oncol 2016:27. LBA7_PR. 25. Nghiem PT, Bhatia S, Lipson EJ, Kudchadkar RR, Miller NJ, Annamalai L, Berry S, Chartash EK, Daud A, Fling SP et al.: PD-1 blockade with pembrolizumab in advanced Merkel-cell carcinoma. N Engl J Med 2016, 374:2542-2552. 26. Motzer RJ, Rini BI, McDermott DF, Redman BG, Kuzel TM, Harrison MR, Vaishampayan UN, Drabkin HA, George S, Logan TF et al.: Nivolumab for metastatic renal cell carcinoma: results of a randomized phase II trial. J Clin Oncol 2015, 33:1430-1437. 27. Sharma P, Callahan MK, Bono P, Kim J, Spiliopoulou P, Calvo E, Pillai RN, Ott PA, de Braud F, Morse M et al.: Nivolumab monotherapy in recurrent metastatic urothelial carcinoma (CheckMate 032): a multicentre, open-label, two-stage, multi-arm, phase 1/2 trial. Lancet Oncol 2016, 17:1590-1598. 28. Inman BA, Longo TA, Ramalingam S, Harrison MR: Atezolizumab: a PD-L1 blocking antibody for bladder cancer. Clin Cancer Res 2016 http://dx.doi.org/10.1158/1078-0432.ccr-16-1417. (epub ahead of print). 29. Armand P, Nagler A, Weller EA, Devine SM, Avigan DE, Chen Y-B, Kaminski MS, Holland HK, Winter JN, Mason JR et al.: Disabling immune tolerance by programmed death-1 blockade with pidilizumab after autologous hematopoietic stem-cell transplantation for diffuse large B-cell lymphoma: results of an international phase II trial. J Clin Oncol 2013, 31:4199-4206. 30. Westin JR, Chu F, Zhang M, Fayad LE, Kwak LW, Fowler N, Romaguera J, Hagemeister F, Fanale M, Samaniego F et al.: Safety and activity of PD1 blockade by pidilizumab in combination with rituximab in patients with relapsed follicular lymphoma: a single group, open-label, phase 2 trial. Lancet Oncol 2014, 15:69-77. 31. Larkin J, Chiarion-Sileni V, Gonzalez R, Grob JJ, Cowey CL,  Lao CD, Schadendorf D, Dummer R, Smylie M, Rutkowski P et al.: Combined nivolumab and ipilimumab or monotherapy in untreated melanoma. N Engl J Med 2015, 373:23-34. In this randomized, phase 3 clinical trial (Checkmate 067) of previously untreated patients with unresectable, metastatic melanoma, the authors showed that a therapy with nivolumab alone and the combination immunotherapy of nivolumab plus ipilimumab results in a significantly longer PFS in comparison with ipilimumab alone. Furthermore, IHC analysis suggests that it is the patients with <5% tumor PD-L1 expression who benefit from the combination immunotherapy while patients with than 5% PD-L1 expressing tumors do not see added benefit to the combination over anti-PD-1 alone. 32. Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJM,  Robert L, Chmielowski B, Spasic M, Henry G, Ciobanu V et al.: PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 2014, 515:568-571. In 2014, Ribas et al. discovered that tumor regression after PD-1blockade in melanoma patients appeared to require an increased density of CD8+ T cells in close proximity to PD-1 / PD-L1 expressing cells at the invasive tumor margin and inside the tumors. 33. Hirsch FR, McElhinny A, Stanforth D, Ranger-Moore J, Jansson M,  Kulangara K, Richardson W, Towne P, Hanks D, Vennapusa B et al.: PD-L1 immunohistochemistry assays for lung cancer: results from phase 1 of the blueprint PD-L1 IHC Assay Comparison Project [Internet]. J Thorac Oncol 2016 http://dx. doi.org/10.1016/j.jtho.2016.11.2228. (epub ahead of print). In this article, the “Blueprint PD-L1 IHC Assay Comparison Project”, an industrial-academic collaborative partnership, tested four PD-L1 IHC assays used in clinical trials and found that, despite showing a similar analytical performance on tumor cell staining of PD-L1 expression by three of the four assays, there was a significant problem in the variability in immune cell staining. Remarkably, in as many as 37% of the cases, different PD-L1 classification would have been made depending on which assay / scoring system was used.

Current Opinion in Immunology 2017, 45:60–72

34. McLaughlin J, Han G, Schalper KA, Carvajal-Hausdorf D, Pelekanou V, Rehman J, Velcheti V, Herbst R, LoRusso P,  Rimm DL: Quantitative Assessment of the Heterogeneity of PD-L1 Expression in Non–Small-Cell Lung Cancer. JAMA Oncol 2016, 2:46. Using both conventional IHC, quantitative IF and an automated image analysis on NSCLC specimen to compare PD-L1 expression in two different monoclonal antibodies, the authors showed a significant interassay heterogeneity with discordant expression rates at a frequency as high as 25%. 35. Rehman JA, Han G, Carvajal-Hausdorf DE, Wasserman BE,  Pelekanou V, Mani NL, McLaughlin J, Schalper KA, Rimm DL: Quantitative and pathologist-read comparison of the heterogeneity of programmed death-ligand 1 (PD-L1) expression in non-small cell lung cancer [Internet]. Mod Pathol 2016 http://dx.doi.org/10.1038/modpathol.2016.186. (epub ahead of print). Evaluating PD-L1 assay heterogeneity between different pathologists and different tissue blocks on NSCLC samples, Rehmann et al. found a high tumoral but only a low stromal/immune cell PD-L1 expression correlation factor. Furthermore, they demonstrated that scoring was similar among different blocks from each tumor, indicating that the spatial distribution of heterogeneity of expression of PD-L1 is within the area represented in a single tissue block. 36. Obeid JM, Wages NA, Hu Y, Deacon DH, Slingluff CL: Heterogeneity of CD8+ tumor-infiltrating lymphocytes in non-small-cell lung cancer: impact on patient prognostic assessments and comparison of quantification by different sampling strategies. Cancer Immunol Immunother 2016 http:// dx.doi.org/10.1007/s00262-016-1908-4. (epub ahead of print). 37. Hodi FS, Chesney J, Pavlick AC, Robert C, Grossmann KF,  McDermott DF, Linette GP, Meyer N, Giguere JK, Agarwala SS et al.: Combined nivolumab and ipilimumab versus ipilimumab alone in patients with advanced melanoma: 2-year overall survival outcomes in a multicentre, randomised, controlled, phase 2 trial. Lancet Oncol 2016, 17:1558-1568. In this multicentre, randomised clinical trial (CheckMate 069) of patients with previously untreated, unresectable stage III or IV melanoma, the authors showed that the first-line combination immunotherapy of nivolumab plus ipilimumab might lead to improved outcomes compared with first-line ipilimumab alone, though with the cost of significantly higher treatment-related side effects. 38. Hellmann MD, Rizvi NA, Goldman JW, Gettinger SN, Borghaei H,  Brahmer JR, Ready NE, Gerber DE, Chow LQ, Juergens RA et al.: Nivolumab plus ipilimumab as first-line treatment for advanced non-small-cell lung cancer (CheckMate 012): results of an open-label, phase 1, multicohort study [Internet]. Lancet Oncol 2016. In this phase 1, multicohort study (CheckMate 012) of patients with recurrent stage IIIb or stage IV, chemotherapy-naive NSCLC, the authors showed for the first time that the first-line combination immunotherapy of nivolumab plus ipilimumab had a tolerable safety profile, a high response rate and durable response rates. It is furthermore the first suggestion of an improved survival compared with anti-PD-1 monotherapy in this cancer entity. 39. Antonia SJ, Lo´pez-Martin JA, Bendell J, Ott PA, Taylor M, Eder JP, Ja¨ger D, Pietanza MC, Le DT, de Braud F et al.: Nivolumab alone and nivolumab plus ipilimumab in recurrent small-cell lung cancer (CheckMate 032): a multicentre, open-label, phase 1/2 trial. Lancet Oncol 2016, 17:883-895. 40. Friedman CF, Proverbs-Singh TA, Postow MA: Treatment of the immune-related adverse effects of immune checkpoint inhibitors: a review. JAMA Oncol 2016, 2:1346-1353. 41. Bertrand A, Kostine M, Barnetche T, Truchetet M-E, Schaeverbeke T: Immune related adverse events associated with anti-CTLA-4 antibodies: systematic review and metaanalysis [Internet]. BMC Med 2015:13. 42. Broussard EK, Disis ML: TNM staging in colorectal cancer: T Is for T cell and M is for memory. J Clin Oncol 2011, 29:601-603. 43. Mlecnik B, Tosolini M, Kirilovsky A, Berger A, Bindea G, Meatchi T,  Bruneval P, Trajanoski Z, Fridman W-H, Page`s F et al.: Histopathologic-based prognostic factors of colorectal cancers are associated with the state of the local immune reaction. J Clin Oncol 2011, 29:610-618.

www.sciencedirect.com

Immunoprofiling—promises and challenges Bethmann, Feng and Fox 69

In this report, Bernard Mlecnik and colleagues demonstrated that the quantitative assessment of CD3+, CD8+ and CD45RO+ T cells at the invasive margin and tumor center of colorectal cancer specimen, termed “Immunoscore”, was a highly prognostic biomarker, and remarkably superior to conventional AJCC TNM staging.

54. Piras F, Colombari R, Minerba L, Murtas D, Floris C, Maxia C, Corbu A, Perra MT, Sirigu P: The predictive value of CD8, CD4, CD68, and human leukocyte antigen-D-related cells in the prognosis of cutaneous malignant melanoma with vertical growth phase. Cancer 2005, 104:1246-1254.

44. Anitei M-G, Zeitoun G, Mlecnik B, Marliot F, Haicheur N, Todosi A M, Kirilovsky A, Lagorce C, Bindea G, Ferariu D et al.: Prognostic and predictive values of the immunoscore in patients with rectal cancer. Clin Cancer Res 2014, 20:1891-1899. Adding to the discussion about the “Immunoscore” being a vital prognostic tool, Anitei et al. demonstrated in this study on 111 rectal cancer patients that the densities of CD3+ and CD8+ lymphocytes and the associated Immunoscore were significantly correlated with differences in disease free- (DFS) and OS. In consensus to the results by Galon et al., Cox multivariate analysis here supports the advantage of the Immunoscore compared with current AJCC TNM-G staging in predicting recurrence and survival.

55. Al-Batran S-E, Rafiyan M-R, Atmaca A, Neumann A, Karbach J, Bender A, Weidmann E, Altmannsberger H-M, Knuth A, Ja¨ger E: Intratumoral T-cell infiltrates and MHC class I expression in patients with stage IV melanoma. Cancer Res 2005, 65:3937-3941.

45. Galon J, Page`s F, Marincola FM, Angell HK, Thurin M, Lugli A, Zlobec I, Berger A, Bifulco C, Botti G et al.: Cancer classification using the Immunoscore: a worldwide task force. J Transl Med 2012, 10:205. 46. Hermitte F: Biomarkers immune monitoring technology primer: ImmunoscoreJ colon. J Immunother Cancer 2016, 4:57. 47. Stack EC, Wang C, Roman KA, Hoyt CC: Multiplexed  immunohistochemistry, imaging, and quantitation: a review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis. Methods 2014, 70:46-58. In this article, Stack et al. summarizes different approaches towards multiplexed immunohistochemistry, imaging, and quantitation with an emphasis on Tyramide signal amplification, multispectral imaging and multiplex analysis. 48. Feng Z, Puri S, Moudgil T, Wood W, Hoyt CC, Wang C, Urba WJ,  Curti BD, Bifulco CB, Fox BA: Multispectral imaging of formalinfixed tissue predicts ability to generate tumor-infiltrating lymphocytes from melanoma. J Immunother Cancer 2015, 3:47. Using 7-color multispectral fluorescent immunohistochemistry to analyze the tumor microenvironment in melanoma patients, Feng et al. demonstrated that, while the presence of CD8+ T cells alone was insufficient, the assessment of the CD8+ to FoxP3+ in combination with the CD8+ to PD-L1+ ratio lead to high positive-predictive- and negative-predictive values towards successful tumor-reactive tumor-infiltrating lymphocytes (TIL) generation. 49. Remark R, Merghoub T, Grabe N, Litjens G, Damotte D,  Wolchok JD, Merad M, Gnjatic S: In-depth tissue profiling using multiplexed immunohistochemical consecutive staining on single slide. Sci Immunol 2016, 1 aaf6925–aaf6925. In this research article, Remark et al. propose a multiplexed chromogenbased IHC staining assay named “multiplexed immunohistochemical consecutive staining on single slide” (MICSSS), enabling the consecutive staining of up to 10 epitopes on the same FFPE tissue slide. 50. Feng Z, Jensen SM, Messenheimer DJ, Farhad M, Neuberger M, Bifulco CB, Fox BA: Multispectral imaging of T and B cells in murine spleen and tumor. J Immunol 2016, 196:3943-3950. 51. Zhang W, Hubbard A, Jones T, Bhaumik S, Racolta A, Lefever M,  Garsha K, Cummins N, Ventura F, Tang L: Automated 5-plex fluorescent immunohistochemistry with tyramide signal amplification using antibodies from the same species. J Immunother Cancer 2015, 3:P111. By using a fully automated heat deactivation process on the BenchMark ULTRA automated slide stainer, Zhang et al. demonstrated a fully automated 5-plex fluorescent IHC assay that achieved comparable staining results to the respective single-plex chromogenic IHC assays. 52. Kakavand H, Vilain RE, Wilmott JS, Burke H, Yearley JH, Thompson JF, Hersey P, Long GV, Scolyer RA: Tumor PD-L1 expression, immune cell correlates and PD-1+ lymphocytes in sentinel lymph node melanoma metastases. Mod Pathol 2015, 28:1535-1544. 53. Erdag G, Schaefer JT, Smolkin ME, Deacon DH, Shea SM, Dengel LT, Patterson JW, Slingluff CL: Immunotype and immunohistologic characteristics of tumor-infiltrating immune cells are associated with clinical outcome in metastatic melanoma. Cancer Res 2012, 72:1070-1080.

www.sciencedirect.com

56. Rozek LS, Schmit SL, Greenson JK, Tomsho LP, Rennert HS, Rennert G, Gruber SB: Tumor-infiltrating lymphocytes, Crohn’s-like lymphoid reaction, and survival from colorectal cancer. J Natl Cancer Inst 2016, 108 djw027. 57. Nosho K, Baba Y, Tanaka N, Shima K, Hayashi M, Meyerhardt JA, Giovannucci E, Dranoff G, Fuchs CS, Ogino S: Tumourinfiltrating T-cell subsets, molecular changes in colorectal cancer, and prognosis: cohort study and literature review. J Pathol 2010, 222:350-366. 58. Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Page`s C, Tosolini M, Camus M, Berger A, Wind P et al.: Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 2006, 313:1960-1964. 59. Page`s F, Berger A, Camus M, Sanchez-Cabo F, Costes A, Molidor R, Mlecnik B, Kirilovsky A, Nilsson M, Damotte D et al.: Effector memory T cells, early metastasis, and survival in colorectal cancer. N Engl J Med 2005, 353:2654-2666. 60. Nguyen N, Bellile E, Thomas D, McHugh J, Rozek L, Virani S, Peterson L, Carey TE, Walline H, Moyer J et al.: Tumor infiltrating lymphocytes and survival in patients with head and neck squamous cell carcinoma. Head Neck 2016, 38:1074-1084. 61. Ferris RL, Blumenschein GJ, Fayette J, Guigay J, Colevas AD,  Licitra L, Harrington K, Kasper S, Vokes EE, Vokes EE et al.: Nivolumab for recurrent squamous-cell carcinoma of the head and neck. N Engl J Med 2016, 375:1856-1867. In this clinical study, Ferris et al. depict that among patients with platinum-refractory, recurrent squamous-cell carcinoma of the head and neck, treatment with PD-1 monoclonal antibody nivolumab resulted in longer overall survival than treatment with standard, single-agent therapy. 62. Balermpas P, Michel Y, Wagenblast J, Seitz O, Weiss C, Ro¨del F, Ro¨del C, Fokas E: Tumour-infiltrating lymphocytes predict response to definitive chemoradiotherapy in head and neck cancer. Br J Cancer 2014, 110:501-509. 63. Watanabe Y, Katou F, Ohtani H, Nakayama T, Yoshie O, Hashimoto K: Tumor-infiltrating lymphocytes, particularly the balance between CD8+ T cells and CCR4+ regulatory T cells, affect the survival of patients with oral squamous cell carcinoma. Oral Surg Oral Med Oral Pathol Oral Radiol Endodontol 2010, 109:744-752. 64. Distel LV, Fickenscher R, Dietel K, Hung A, Iro H, Zenk J, Nkenke E, Bu¨ttner M, Niedobitek G, Grabenbauer GG: Tumour infiltrating lymphocytes in squamous cell carcinoma of the oro- and hypopharynx: prognostic impact may depend on type of treatment and stage of disease. Oral Oncol 2009, 45:e167-e174. 65. Badoual C, Hans S, Rodriguez J, Peyrard S, Klein C, Agueznay NEH, Mosseri V, Laccourreye O, Bruneval P, Fridman WH et al.: Prognostic value of tumor-infiltrating CD4+ T-cell subpopulations in head and neck cancers. Clin Cancer Res 2006, 12:465-472. 66. Shiao SL, Ruffell B, DeNardo DG, Faddegon BA, Park CC, Coussens LM: TH2-polarized CD4+ T cells and macrophages limit efficacy of radiotherapy. Cancer Immunol Res 2015, 3:518-525. 67. Ali HR, Provenzano E, Dawson S-J, Blows FM, Liu B, Shah M, Earl HM, Poole CJ, Hiller L, Dunn JA et al.: Association between CD8+ T-cell infiltration and breast cancer survival in 12 439 patients. Ann Oncol 2014, 25:1536-1543. 68. Miyashita M, Sasano H, Tamaki K, Chan M, Hirakawa H, Suzuki A, Tada H, Watanabe G, Nemoto N, Nakagawa S et al.: Tumor-infiltrating CD8+ and FOXP3+ lymphocytes in triplenegative breast cancer: its correlation with pathological Current Opinion in Immunology 2017, 45:60–72

70 Tumour immunology

complete response to neoadjuvant chemotherapy. Breast Cancer Res Treat 2014, 148:525-534. 69. Liu S, Lachapelle J, Leung S, Gao D, Foulkes WD, Nielsen TO: CD8 + lymphocyte infiltration is an independent favorable prognostic indicator in basal-like breast cancer. Breast Cancer Res 2012, 14:R48. 70. DeNardo DG, Brennan DJ, Rexhepaj E, Ruffell B, Shiao SL,  Madden SF, Gallagher WM, Wadhwani N, Keil SD, Junaid SA et al.: Leukocyte complexity predicts breast cancer survival and functionally regulates response to chemotherapy. Cancer Discov 2011, 1:54-67. In this article DeNardo et al. highlighted the importance of macrophages for tumor progression in demonstrating that blockade of macrophagerecruitment by inhibition of CSF-1 in combination with Paclitaxel improves survival of mammary tumor bearing mice via slowing primary tumor development and reducing pulmonary metastasis. 71. Mahmoud SMA, Paish EC, Powe DG, Macmillan RD, Grainge MJ, Lee AHS, Ellis IO, Green AR: Tumor-infiltrating CD8+ lymphocytes predict clinical outcome in breast cancer. J Clin Oncol 2011, 29:1949-1955. 72. Zhang Y, Ma C, Wang M, Hou H, Cui L, Jiang C, Sun J, Qu X: Prognostic significance of immune cells in the tumor microenvironment and peripheral blood of gallbladder carcinoma patients. Clin Transl Oncol 2016 http://dx.doi.org/ 10.1007/s12094-016-1553-6. (epub ahead of print). 73. Oguro S, Ino Y, Shimada K, Hatanaka Y, Matsuno Y, Esaki M, Nara S, Kishi Y, Kosuge T, Hiraoka N: Clinical significance of tumor-infiltrating immune cells focusing on BTLA and Cbl-b in patients with gallbladder cancer. Cancer Sci 2015, 106:1750-1760. 74. Zhang Y, Huang Y, Qin M: Tumour-infiltrating FoxP3+ and IL-17-producing T cells affect the progression and prognosis of gallbladder carcinoma after surgery. Scand J Immunol 2013, 78:516-522. 75. Nakakubo Y, Miyamoto M, Cho Y, Hida Y, Oshikiri T, Suzuoki M, Hiraoka K, Itoh T, Kondo S, Katoh H: Clinical significance of immune cell infiltration within gallbladder cancer. Br J Cancer 2003, 89:1736-1742.

prognosis in ovarian cancer. Proc Natl Acad Sci U S A 2005, 102:18538-18543. 83. Brambilla E, Le Teuff G, Marguet S, Lantuejoul S, Dunant A, Graziano S, Pirker R, Douillard J-Y, Le Chevalier T, Filipits M et al.: Prognostic effect of tumor lymphocytic infiltration in resectable non–small-cell lung cancer. J Clin Oncol 2016, 34:1223-1230. 84. Suzuki K, Kadota K, Sima CS, Nitadori J, Rusch VW, Travis WD, Sadelain M, Adusumilli PS: Clinical impact of immune microenvironment in stage I lung adenocarcinoma: tumor interleukin-12 receptor b2 (IL-12Rb2), IL-7R, and stromal FoxP3/CD3 ratio are independent predictors of recurrence. J Clin Oncol 2013, 31:490-498. 85. Donnem T, Al-Shibli K, Andersen S, Al-Saad S, Busund L-T, Bremnes RM: Combination of low vascular endothelial growth factor A (VEGF-A)/VEGF receptor 2 expression and high lymphocyte infiltration is a strong and independent favorable prognostic factor in patients with nonsmall cell lung cancer. Cancer 2010, 116:4318-4325. 86. Ruffini E, Asioli S, Filosso PL, Lyberis P, Bruna MC, Macrı` L, Daniele L, Oliaro A: Clinical significance of tumor-infiltrating lymphocytes in lung neoplasms. Ann Thorac Surg 2009, 87:365-372. 87. Kawai O, Ishii G, Kubota K, Murata Y, Naito Y, Mizuno T, Aokage K, Saijo N, Nishiwaki Y, Gemma A et al.: Predominant infiltration of macrophages and CD8+ T Cells in cancer nests is a significant predictor of survival in stage IV nonsmall cell lung cancer. Cancer 2008, 113:1387-1395. 88. Wakabayashi O, Yamazaki K, Oizumi S, Hommura F, Kinoshita I, Ogura S, Dosaka-Akita H, Nishimura M: CD4+ T cells in cancer stroma, not CD8+ T cells in cancer cell nests, are associated with favorable prognosis in human non-small cell lung cancers. Cancer Sci 2003, 94:1003-1009. 89. Wang B, Wu S, Zeng H, Liu Z, Dong W, He W, Chen X, Dong X, Zheng L, Lin T et al.: CD103+ tumor infiltrating lymphocytes predict a favorable prognosis in urothelial cell carcinoma of the bladder. J Urol 2015, 194:556-562.

76. Al-Attar A, Shehata M, Durrant L, Moseley P, Deen S, Chan S: T cell density and location can influence the prognosis of ovarian cancer. Pathol Oncol Res 2010, 16:361-370.

90. Sharma P, Shen Y, Wen S, Yamada S, Jungbluth AA, Gnjatic S, Bajorin DF, Reuter VE, Herr H, Old LJ et al.: CD8 tumor-infiltrating lymphocytes are predictive of survival in muscle-invasive urothelial carcinoma. Proc Natl Acad Sci 2007, 104:3967-3972.

77. Leffers N, Gooden MJM, de Jong RA, Hoogeboom B-N, ten Hoor KA, Hollema H, Boezen HM, van der Zee AGJ, Daemen T, Nijman HW: Prognostic significance of tumor-infiltrating Tlymphocytes in primary and metastatic lesions of advanced stage ovarian cancer. Cancer Immunol Immunother 2009, 58:449.

91. Yamagami W, Susumu N, Tanaka H, Hirasawa A, Banno K, Suzuki N, Tsuda H, Tsukazaki K, Aoki D: Immunofluorescencedetected infiltration of CD4+ FOXP3+ Regulatory T cells is relevant to the prognosis of patients with endometrial cancer. Int J Gynecol Cancer 2011, 21:1628-1634.

78. Adams SF, Levine DA, Cadungog MG, Hammond R, Facciabene A, Olvera N, Rubin SC, Boyd J, Gimotty PA, Coukos G: Intraepithelial T cells and tumor proliferation. Cancer 2009, 115:2891-2902. 79. Callahan MJ, Nagymanyoki Z, Bonome T, Johnson ME, Litkouhi B, Sullivan EH, Hirsch MS, Matulonis UA, Liu J, Birrer MJ et al.: Increased HLA-DMB expression in the tumor epithelium is associated with increased CTL infiltration and improved prognosis in advanced-stage serous ovarian cancer. Clin Cancer Res 2008, 14:7667-7673. 80. Clarke B, Tinker AV, Lee C-H, Subramanian S, van de Rijn M, Turbin D, Kalloger S, Han G, Ceballos K, Cadungog MG et al.: Intraepithelial T cells and prognosis in ovarian carcinoma: novel associations with stage, tumor type, and BRCA1 loss. Mod. Pathol 2008, 22:393-402. 81. Han LY, Fletcher MS, Urbauer DL, Mueller P, Landen CN, Kamat AA, Lin YG, Merritt WM, Spannuth WA, Deavers MT et al.: HLA class I antigen processing machinery component expression and intratumoral T-cell infiltrate as independent prognostic markers in ovarian carcinoma. Clin Cancer Res 2008, 14:3372-3379. 82. Sato E, Olson SH, Ahn J, Bundy B, Nishikawa H, Qian F, Jungbluth AA, Frosina D, Gnjatic S, Ambrosone C et al.: Intraepithelial CD8+ tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable Current Opinion in Immunology 2017, 45:60–72

92. de Jong RA, Leffers N, Boezen HM, ten Hoor KA, van der Zee AGJ, Hollema H, Nijman HW: Presence of tumor-infiltrating lymphocytes is an independent prognostic factor in type I and II endometrial cancer. Gynecol Oncol 2009, 114:105-110. 93. Kondratiev S, Sabo E, Yakirevich E, Lavie O, Resnick MB: Intratumoral CD8+ T lymphocytes as a prognostic factor of survival in endometrial carcinoma. Clin Cancer Res 2004, 10:4450-4456. 94. Sugimura K, Miyata H, Tanaka K, Takahashi T, Kurokawa Y, Yamasaki M, Nakajima K, Takiguchi S, Mori M, Doki Y: High infiltration of tumor-associated macrophages is associated with a poor response to chemotherapy and poor prognosis of patients undergoing neoadjuvant chemotherapy for esophageal cancer. J Surg Oncol 2015, 111:752-759. 95. Lv L, Pan K, Li X, She K, Zhao J, Wang W, Chen J, Chen Y, Yun J, Xia J: The accumulation and prognosis value of tumor infiltrating IL-17 producing cells in esophageal squamous cell carcinoma. PLoS One 2011, 6:e18219. 96. Yoshioka T, Miyamoto M, Cho Y, Ishikawa K, Tsuchikawa T, Kadoya M, Li L, Mishra R, Ichinokawa K, Shoji Y et al.: Infiltrating regulatory T cell numbers is not a factor to predict patient’s survival in oesophageal squamous cell carcinoma. Br J Cancer 2008, 98:1258-1263. 97. Cho Y, Miyamoto M, Kato K, Fukunaga A, Shichinohe T, Kawarada Y, Hida Y, Oshikiri T, Kurokawa T, Suzuoki M et al.: CD4 www.sciencedirect.com

Immunoprofiling—promises and challenges Bethmann, Feng and Fox 71

+ and CD8+ T cells cooperate to improve prognosis of patients with esophageal squamous cell carcinoma. Cancer Res 2003, 63:1555-1559. 98. Gabrielson A, Wu Y, Wang H, Jiang J, Kallakury B, Gatalica Z, Reddy S, Kleiner D, Fishbein T, Johnson L et al.: Intratumoral CD3 and CD8 T-cell densities associated with relapse-free survival in HCC. Cancer Immunol Res 2016, 4:419-430. 99. Wang F, Jing X, Li G, Wang T, Yang B, Zhu Z, Gao Y, Zhang Q, Yang Y, Wang Y et al.: Foxp3+ regulatory T cells are associated with the natural history of chronic hepatitis B and poor prognosis of hepatocellular carcinoma. Liver Int 2012, 32:644-655. 100. Gao Q, Zhou J, Wang X-Y, Qiu S-J, Song K, Huang X-W, Sun J, Shi Y-H, Li B-Z, Xiao Y-S et al.: Infiltrating memory/senescent T cell ratio predicts extrahepatic metastasis of hepatocellular carcinoma. Ann Surg Oncol 2012, 19:455-466. 101. Li Y-W, Qiu S-J, Fan J, Zhou J, Gao Q, Xiao Y-S, Xu Y-F: Intratumoral neutrophils: a poor prognostic factor for hepatocellular carcinoma following resection. J Hepatol 2011, 54:497-505. 102. Gao Q, Qiu S-J, Fan J, Zhou J, Wang X-Y, Xiao Y-S, Xu Y, Li Y-W, Tang Z-Y: Intratumoral balance of regulatory and cytotoxic T cells is associated with prognosis of hepatocellular carcinoma after resection. J Clin Oncol 2007, 25:2586-2593. 103. Cornelissen R, Lievense LA, Maat AP, Hendriks RW, Hoogsteden HC, Bogers AJ, Hegmans JP, Aerts JG: Ratio of intratumoral macrophage phenotypes is a prognostic factor in epithelioid malignant pleural mesothelioma. PLoS One 2014, 9:e106742. 104. Yamada N, Oizumi S, Kikuchi E, Shinagawa N, KonishiSakakibara J, Ishimine A, Aoe K, Gemba K, Kishimoto T, Torigoe T et al.: CD8+ tumor-infiltrating lymphocytes predict favorable prognosis in malignant pleural mesothelioma after resection. Cancer Immunol Immunother 2010, 59:1543-1549. 105. Anraku M, Cunningham KS, Yun Z, Tsao M-S, Zhang L, Keshavjee S, Johnston MR, de Perrot M: Impact of tumorinfiltrating T cells on survival in patients with malignant pleural mesothelioma. J Thorac Cardiovasc Surg 2008, 135:823-829. 106. Hu W-H, Miyai K, Cajas-Monson LC, Luo L, Liu L, Ramamoorthy SL: Tumor-infiltrating CD8+ T lymphocytes associated with clinical outcome in anal squamous cell carcinoma. J Surg Oncol 2015, 112:421-426. 107. Grabenbauer GG, Lahmer G, Distel L, Niedobitek G: Tumorinfiltrating cytotoxic T cells but not regulatory T cells predict outcome in anal squamous cell carcinoma. Clin Cancer Res 2006, 12:3355-3360. 108. Wang W-Q, Liu L, Xu H-X, Wu C-T, Xiang J-F, Xu J, Liu C, Long J, Ni Q-X, Yu X-J: Infiltrating immune cells and gene mutations in pancreatic ductal adenocarcinoma. Br J Surg 2016, 103:1189-1199. 109. Wartenberg M, Zlobec I, Perren A, Koelzer VH, Gloor B, Lugli A, Karamitopoulou E, Wartenberg M, Zlobec I, Perren A et al.: Accumulation of FOXP3+T-cells in the tumor microenvironment is associated with an epithelialmesenchymal-transition-type tumor budding phenotype and is an independent prognostic factor in surgically resected pancreatic ductal adenocarcinoma. Oncotarget 2015, 6:4190-4201. 110. Ino Y, Yamazaki-Itoh R, Shimada K, Iwasaki M, Kosuge T, Kanai Y, Hiraoka N: Immune cell infiltration as an indicator of the immune microenvironment of pancreatic cancer. Br J Cancer 2013, 108:914-923. 111. Hiraoka N, Onozato K, Kosuge T, Hirohashi S: Prevalence of FOXP3+ regulatory T cells increases during the progression of pancreatic ductal adenocarcinoma and its premalignant lesions. Clin Cancer Res 2006, 12:5423-5434. 112. Heeren AM, de Boer E, Bleeker MCG, Musters RJP, Buist MR, Kenter GG, de Gruijl TD, Jordanova ES, Heeren AM, de Boer E et al.: Nodal metastasis in cervical cancer occurs in clearly delineated fields of immune suppression in the pelvic lymph catchment area. Oncotarget 2015, 6:32484-32493. www.sciencedirect.com

113. Carus A, Ladekarl M, Hager H, Nedergaard BS, Donskov F: Tumour-associated CD66b+ neutrophil count is an independent prognostic factor for recurrence in localised cervical cancer. Br J Cancer 2013, 108:2116-2122. 114. Jordanova ES, Gorter A, Ayachi O, Prins F, Durrant LG, Kenter GG, van der Burg SH, Fleuren GJ: Human leukocyte antigen class I, MHC class I chain-related molecule A, and CD8+/regulatory T-cell ratio: which variable determines survival of cervical cancer patients? Clin Cancer Res 2008, 14:2028-2035. 115. Sayour EJ, McLendon P, McLendon R, Leon GD, Reynolds R, Kresak J, Sampson JH, Mitchell DA: Increased proportion of FoxP3+ regulatory T cells in tumor infiltrating lymphocytes is associated with tumor recurrence and reduced survival in patients with glioblastoma. Cancer Immunol Immunother 2015, 64:419-427. 116. Yue Q, Zhang X, Ye H, Wang Y, Du Z, Yao Y, Mao Y: The prognostic value of Foxp3+ tumor-infiltrating lymphocytes in patients with glioblastoma. J Neurooncol 2014, 116:251-259. 117. Thomas AA, Fisher JL, Rahme GJ, Hampton TH, Baron U, Olek S, Schwachula T, Rhodes CH, Gui J, Tafe LJ et al.: Regulatory T cells are not a strong predictor of survival for patients with glioblastoma. Neuro-Oncol 2015, 17:801-809. 118. Keane C, Vari F, Hertzberg M, Cao K-AL, Green MR, Han E, Seymour JF, Hicks RJ, Gill D, Crooks P et al.: Ratios of T-cell immune effectors and checkpoint molecules as prognostic biomarkers in diffuse large B-cell lymphoma: a populationbased study. Lancet Haematol 2015, 2:e445-e455. 119. Coutinho R, Clear AJ, Mazzola E, Owen A, Greaves P, Wilson A, Matthews J, Lee A, Alvarez R, da Silva MG et al.: Revisiting the immune microenvironment of diffuse large B-cell lymphoma using a tissue microarray and immunohistochemistry: robust semi-automated analysis reveals CD3 and FoxP3 as potential predictors of response to R-CHOP. Haematologica 2015, 100:363-369. 120. Lee N-R, Song E-K, Jang KY, Choi HN, Moon WS, Kwon K, Lee J-H, Yim C-Y, Kwak J-Y: Prognostic impact of tumor infiltrating FOXP3 positive regulatory T cells in diffuse large B-cell lymphoma at diagnosis. Leuk Lymphoma 2008, 49:247-256. 121. Hasselblom S, Sigurdadottir M, Hansson U, Nilsson-Ehle H, Ridell B, Andersson P-O: The number of tumour-infiltrating TIA1+ cytotoxic T cells but not FOXP3+ regulatory T cells predicts outcome in diffuse large B-cell lymphoma. Br J Haematol 2007, 137:364-373. 122. Farinha P, Al-Tourah A, Gill K, Klasa R, Connors JM, Gascoyne RD: The architectural pattern of FOXP3-positive T cells in follicular lymphoma is an independent predictor of survival and histologic transformation. Blood 2010, 115:289-295. 123. Carreras J, Lopez-Guillermo A, Roncador G, Villamor N, Colomo L, Martinez A, Hamoudi R, Howat WJ, Montserrat E, Campo E: High numbers of tumor-infiltrating programmed cell death 1–positive regulatory lymphocytes are associated with improved overall survival in follicular lymphoma. J Clin Oncol 2009, 27:1470-1476. 124. de Jong D, Koster A, Hagenbeek A, Raemaekers J, Veldhuizen D, Heisterkamp S, de Boer JP, van Glabbeke M: Impact of the tumor microenvironment on prognosis in follicular lymphoma is dependent on specific treatment protocols. Haematologica 2009, 94:70-77. 125. Assis MCG, Campos AHFM, Oliveira JSR, Soares FA, Silva JMK, Silva PB, Penna AD, Souza EM, Baiocchi OCG: Increased expression of CD4 + CD25 + FOXP3+ regulatory T cells correlates with Epstein–Barr virus and has no impact on survival in patients with classical Hodgkin lymphoma in Brazil. Med Oncol 2012, 29:3614-3619. 126. Schreck S, Friebel D, Buettner M, Distel L, Grabenbauer G, Young LS, Niedobitek G: Prognostic impact of tumourinfiltrating Th2 and regulatory T cells in classical Hodgkin lymphoma. Hematol Oncol 2009, 27:31-39. 127. A´lvaro-Naranjo T, Lejeune M, Salvado´-Usach MT, BoschPrı´ncep R, Reverter-Branchat G, Jae´n-Martı´nez J, Pons-Ferre´ LE: Tumor-infiltrating cells as a prognostic factor in Hodgkin’s Current Opinion in Immunology 2017, 45:60–72

72 Tumour immunology

lymphoma: a quantitative tissue microarray study in a large retrospective cohort of 267 patients. Leuk Lymphoma 2005, 46:1581-1591. 128. Tzankov A, Meier C, Hirschmann P, Went P, Pileri SA, Dirnhofer S: Correlation of high numbers of intratumoral FOXP3+ regulatory T cells with improved survival in germinal

Current Opinion in Immunology 2017, 45:60–72

center-like diffuse large B-cell lymphoma, follicular lymphoma and classical Hodgkin’s lymphoma. Haematologica 2008, 93:193-200. 129. Richardsen E, Uglehus RD, Due J, Busch C, Busund L-TR: The prognostic impact of M-CSF, CSF-1 receptor, CD68 and CD3 in prostatic carcinoma. Histopathology 2008, 53:30-38.

www.sciencedirect.com