key: cord-0024409-zn4l3taq
authors: Sobottka, Bettina; Nienhold, Ronny; Nowak, Marta; Hench, Juergen; Haeuptle, Pirmin; Frank, Angela; Sachs, Melanie; Kahraman, Abdullah; Moch, Holger; Koelzer, Viktor H.; Mertz, Kirsten D.
title: Integrated Analysis Of Immunotherapy Treated Clear Cell Renal Cell Carcinomas: An Exploratory Study
date: 2021-08-18
journal: J Immunother
DOI: 10.1097/cji.0000000000000387
sha: f9f2bb416c72b4df89e0cd0088a96010fd8bdf26
doc_id: 24409
cord_uid: zn4l3taq

Molecular or immunological differences between responders and nonresponders to immune checkpoint inhibitors (ICIs) of clear cell renal cell carcinomas (ccRCCs) remain incompletely understood. To address this question, we performed next-generation sequencing, methylation analysis, genome wide copy number analysis, targeted RNA sequencing and T-cell receptor sequencing, and we studied frequencies of tumor-infiltrating CD8(+) T cells, presence of tertiary lymphoid structures (TLS) and PD-L1 expression in 8 treatment-naive ccRCC patients subsequently treated with ICI (3 responders, 5 nonresponders). Unexpectedly, we identified decreased frequencies of CD8(+) tumor-infiltrating T cells and TLS, and a decreased expression of PD-L1 in ICI responders when compared with nonresponders. However, neither tumor-specific genetic alterations nor gene expression profiles correlated with response to ICI or the observed immune features. Our results underline the challenge to stratify ccRCC patients for immunotherapy based on routinely available pathologic primary tumor material, even with advanced technologies. Our findings emphasize the analysis of pretreated metastatic tissue in line with recent observations describing treatment effects on the tumor microenvironment. In addition, our data call for further investigation of additional parameters in a larger ccRCC cohort to understand the mechanistic implications of the observed differences in tumor-infiltrating CD8(+) T cells, TLS, and PD-L1 expression.

I mmune checkpoint inhibition (ICI) has become a new backbone in the treatment of advanced clear cell renal cell carcinoma (ccRCC) [1] [2] [3] as reflected in the recent European Association of Urology, European Society for Medical Oncology, and National Comprehensive Cancer Network guidelines. 4 Despite its success, the response to immunotherapy remains clinically unsatisfactory in many RCC patients. The underlying mechanisms are incompletely understood but there is emerging evidence that they may relate to differences within the tumor microenvironment. 5 Recently, single-cell transcriptome analysis revealed the functional effect of treatment on the tumor immune microenvironment of ccRCC. 6 Integrative analysis of tumor intrinsic genetic alterations and RNA expression identified molecular subsets of RCC, and provided important insights into the molecular basis of treatment response to ICI. 7 A broad summary of these molecular subsets was suggested, dichotomizing ccRCC into angiogenic-poorly immunogenic, frequently PBRM1 mutated, and proliferative-inflamed tumors, the latter commonly carrying loss of function mutations in BAP1 and/or CDKN2A. 8 However, the composition and spatial distribution of the tumor immune infiltrate, the effect of tertiary lymphoid structures (TLS) 9 and their contribution to immune checkpoint inhibitor response remain poorly investigated.

From a clinicopathologic point of view, an improved pretreatment stratification of ccRCC patients for immunotherapy by a combined assessment of tumor-related and microenvironment-associated factors would be desirable. Common predictive biomarkers of immune checkpoint blockade response do not appear to be useful in ccRCC for three reasons: (i) The overall tumor mutational burden in ccRCC is usually low; rare neoantigens predominantly arise from frameshift mutations caused by insertions-deletions (INDELs), splice variants, and post-translational modifications. 10, 11 (ii) PD-L1 expression that is usually regarded as a surrogate marker of a pre-existing immune response 12 can be increased in some tumors merely as a consequence of HIF binding to the PD-L1 promoter. 13, 14 ( iii) The role of tumor-infiltrating CD8 + T cells, usually associated with a good prognosis in most malignancies, 12 remains controversial mainly because of methodological issues of previous studies such as the failure to control for the different histologic RCC subtypes. 15, 16 To explore potential differences in tumor-related and microenvironment-associated factors of ICI treated ccRCC patients, we performed a multimodal investigation by combining molecular and histologic methods including digital image quantification techniques in a well-defined group of ICI responding (n = 3) and nonresponding (n = 5) ccRCC. For integrated tumor profiling, we combined a suite of analyses: comprehensive targeted parallel sequencing, whole-genome copy number variation (CNV) analysis, targeted transcriptomics and T-cell receptor (TCR) sequencing, spatially resolved digital immunoprofiling, PD-L1 expression, and histologic assessment of TLS. With the selection of the 2 extremes of the clinical spectrum to ICI response, we hypothesized to uncover biological differences by this integrative in-depth analysis.

Ethical approval was obtained from the Ethics Committee of Northwestern and Central Switzerland (Project-ID 2016-01499). We collected treatment-naive ccRCC cases from the archives of the Institute of Pathology, Cantonal Hospital Baselland, Liestal, Switzerland between 2001 and 2018. Patients showed no synchronous metastases but developed metachronous metastases as indicated. After surgical resection of the primary tumor, the majority of the patients had received first line treatment with tyrosine kinase inhibitors before ICI therapy (anti-PD1, Nivolumab) was initiated upon disease progression, in 1 case in conjunction with anti-CTLA-4 treatment (Ipilimumab). Treatment with ICI varied between 1 and 96 cycles, and ICI response was assessed by imaging (Table 1) . 

We analyzed the differential expression of 398 genes using the Oncomine Immune Response Research Assay (OIRRA, Catalog No. A32881; Thermo Fisher Scientific) as recently described. 17 NGS libraries were prepared according to the manufacturer's description. Demultiplexing and quantification of transcript levels were performed with the standard setting of the ImmuneResponseRNA plugin (version 5.12.0.1) within the Torrent Suite (version 5.12.1), provided as part of the OIRRA. Differential gene expression analysis was performed using the Transcriptome Analysis Console Software (version 4.0.1) from Thermo Fisher Scientific.

A volume of 500 ng genomic DNA from each sample was subjected to bisulfite conversion using the EZ DNA Methylation Kit (Catalog No. D5001; Zymo, Irvine, CA) according to the manufacturer's guidelines. The Infinium Human Methylation EPIC 850 k array was used to obtain genome wide DNA methylation profiles from FFPE tumor samples, according to the manufacturer's instructions (Illumina, USA). Data (IDAT files) were analyzed through the minfi 18 R package. Methylation patterns were subjected to unsupervised machine learning on the form of uniform manifold approximation projection (UMAP, 19 ), all as previously described. 20 Copy number calculations on the IDAT files were performed using R (version 4.0.4) and the conumee Bioconductor 21 package (version 1.24.0). Before the calculations, IDAT files were imported to R using the minfi Bioconductor package (v.1.36.0) and converted to conumee compatabile MethylSet objects. Probe sets were downsampled to an Illumina 450K array and intensities were normalized using a reference probe set comprising 33 individuals. 22 Normalized probe intensities were subsequently clustered into segments using the circular binary segmentation algorithm with an acceptance significance level of alpha = 0.05, 10,000 permutations, minimum of 5 probes per segment and without undoing change-points. Copy number spectrum plots were generated using the cnSpec function in the GenVisR 23 Bioconductor package (version 1.22.1). 

Immunohistochemical analysis was performed utilizing the monoclonal mouse-antihuman CD8 (clone 4B11; Leica Biosystems, Muttenz, Switzerland) and the monoclonal rabbit-anti-PD-L1 (clone SP263; Roche Diagnostics, Rotkreuz, Switzerland) antibodies with pretreatments according to the manufacturers' instructions. Antibody binding was visualized using either OptiView (Roche Diagnostics) or Bond Polymer Refine Red Detection (Leica Biosystems) kits. All immunohistochemical stains were part of the diagnostic routine. Stained slides were digitalized on a Hamamatsu NanoZoomer S360 scanner at 40× magnification (Hamamatsu Photonics, Hamamatsu City, Japan). Digital slide review, annotation of tumor regions as recommended by the International Immuno-Oncology Biomarker Working Group 24 and quality controls were performed by an expert image analyst (M.N.) and reviewed by a board-certified pathologist (V.H.K.). A deep neural network (DNN) algorithm (DeepNet, HALO AI on HALO 3.1.1076; Indica Labs, Corrales, NM) was trained using pathologist annotations to automatically localize and measure tumor areas, desmoplastic and inflamed stromal regions in RCC resection specimens and to exclude glass background, areas of tumor necrosis, debris, and tissue folds. Mark-up images for tissue classification were generated, and the accuracy of tissue classification was confirmed by pathologist review. For cell-level analysis, nuclear segmentation was performed using a seeded watershed and optimized using cell-morphometric parameters. Markerpositive cells in stromal and epithelial regions were quantified according to pathologist-set thresholds. CD8 + counts were normalized by area to determine infiltration density in each tumor region and localize it to the stromal or intraepithelial compartment. PD-L1 expression was scored as the combined positive score (percentage of PD-L1 expressing tumor and infiltrating immune cells relative to the total number of tumor cells) by visual assessment of the digital slides. 25 TLS were defined as aggregates of lymphocytes with formation of a lymphoid based on hematoxylin and eosin stain and were counted with frequency/mm 2 . 26

Identification and quantification of unique T-cell clones to determine clonality and diversity of productive T lymphocytes was performed using the Oncomine TCR Beta-SR Assay 

To identify molecular and morphological differences between ccRCCs of ICI nonresponders and responders we used a multidimensional approach including routine diagnostic, emerging, and exploratory technologies for in-depth investigation of 8 ccRCC patients with differential treatment response (Table 1) . Less than half of the patients (n = 3) responded well to ICI therapy as documented by imaging, while the remaining patients showed nonresponse or progressive disease. Comprehensive NGS confirmed the presence of loss of function mutations in the VHL gene in 5 of 8 cases, either in isolation or in conjunction with PBRM1 or BAP1 alterations (Fig. 1A) . One tumor in each group, responders and nonresponders, was VHL wild type, carrying genetic variants in PBRM1 alone or together with BAP1, and 1 nonresponder tumor was wild type for VHL, BAP1, and PBRM1 (Fig. 1A) . All tumors displayed single mutated genes interpreted as passenger mutations (Fig. 1A) . The tumor mutational burden was low (< 10 mutations/megabase) for all cases and no increased INDEL frameshifts could be detected (data not shown). For methylation analysis the investigated ccRCC cases were subjected to UMAP against a public reference dataset (n = 18,228), mainly comprising samples from the TCGA, the top 25,000 differentially methylated probes, determined by SD-based ranking. As expected, all analyzed cases (n = 7) grouped within the cluster formed by ccRCC (n = 332) samples (Supplemental Fig. 2 , Supplemental Digital Content 2, http://links.lww.com/JIT/A625). However, UMAP analysis did not show separation of these specimens that correlated with their documented immune response state. Most chromosomal aberrations as analyzed by a singlenucleotide polymorphism-based array occurred particularly as chromosome 3p losses and chromosome 5q gains without major differences between responders and nonresponders (Fig. 1B) . To analyze markers of ICI response, we assessed tumor-infiltrating CD8 + T cells and PD-L1 expression by immunohistochemistry. As there are yet no established guidelines for CD8 + T-cell assessment in ccRCC, we based our approach on the recently published recommendations of the International Immuno-Oncology Biomarker Working Group. 24 CD8 stained whole slides were scanned at high resolution and quantitatively evaluated by digital image analysis to derive the overall and compartment-specific CD8 + T-cell infiltration densities (ie, specific assessment of the tumor center and the invasive margin; Fig. 2A ). We observed poorly (Fig. 2B) and highly (Fig. 2C) CD8 + T-cell-infiltrated tumors. Quantitative analysis revealed an overall (Fig. 2D) and spatial (Fig. 2E ) trend toward higher CD8 + tumorinfiltrating T cells in nonresponders across all tumor compartments. Likewise, increased amounts of TLS (Fig. 2F) and PD-L1 expression (Fig. 2G) were detected in nonresponders. Both CD8-infiltration density and PD-L1 expression were independent of the underlying profile of genetic alterations (data not shown). TCR sequencing revealed increased evenness, diversity and TCRβ clones in responders yet with reduced convergence frequencies (Supplemental Fig. 3 , Supplemental Digital Content 3, http://links.lww.com/JIT/A626). Of particular interest for the analysis of ICI response biomarkers in ccRCC are transcriptional differences in the tumor microenvironment of CD8 + T-cell-rich but nonresponding ccRCCs in comparison to CD8 + T-cell-poor but responding ccRCC samples. Despite clear differences in the inflammatory milieu between tumor and matched healthy normal samples (Fig. 3A) we could not observe any significant differences in gene expression profiles between responding and nonresponding tumors (Fig. 3B) . Also, no substantial differences of genetic variant effects and gene expression could be detected with respect to ICI treatment response (Fig. 3B) or alone (Figs. 3C, D) .

Here we present a multidimensional analysis of treatment-naive ccRCC resection specimens subsequently treated with immune checkpoint inhibitors to uncover cellular, molecular, and histologic differences of ICI responders and nonresponders in ccRCC. We focused on samples that represent the typically available tissue within pathology departments, and combined established methods like NGS and immunohistochemistry with novel technologies with potential for integration in future pathology workflows such as digital quantification of immunohistochemistry, targeted RNA and TCR sequencing.

In contrast to previous reports, 27,28 we did not observe a higher frequency of loss of function variants in PBRM1 or BAP1 or a particular immunogenic phenotype in association with treatment response. While whole-exome sequencing or whole-genome sequencing might have elucidated additional genetic alterations between responders and nonresponders, our comprehensive NGS panel covered the known relevant genes in ccRCC 29, 30 necessary for the classification of ccRCCs into the 2 large groups associated with an either angiogenic or proinflammatory microenvironment. 8 Methylation analysis reliably grouped the investigated ccRCC samples into the expected ccRCC clusters, yet without distinct features between responders and nonresponders. In our cohort, the tumor mutational burden was low as expected, and consistently, INDEL frameshifts which were proposed as a potential source . CD8 + tumor-infiltrating T cells, TLS, and PD-L1 expression in responders versus nonresponders. CD8 stained whole sections were digitalized and annotated according to the state-of-the-art recommendations including the tumor center (red) and the invasive margin (blue) with lymphocyte poor areas (green) (A). The CD8 + T-cell infiltrate was sparse (far left and middle B) or dense (far left and middle C) and was analyzed at single-cell resolution including spatial distribution (far right B and C). Absolute (D) and compartmentspecific (E) CD8 + T-cell densities, as well as the presence of TLS (F) and PD-L1 expression depicted as the combined positivity score (CPS) (G) were increased in nonresponders compared with responders. iCD8 + T cells = intratumoral CD8 + T cells, sCD8 + T cells = stromal CD8 + T cells, imCD8 + T cells = invasive margin CD8 + T cells. BCL2  IFI44L  TNFRSF14  MYC  CCL2  NFKBIA  HIF1A  PGF  VEGFA  VCAM1  HLA-A  CMKLR1  IDO2  HLA-DQA2  FCRLA  TRIM29  CEACAM1  CDKN3  CCR6  KRT7  B3GAT1  EOMES   VHL  PBRM1  BAP1   wildtype  missense  frameshift  nonsense   Non-responder  Responder   C  B   Stat1   CCR5   D   VEGFA  EGFR  TGFB1  CD68  TAP1  HIF1A  CCL2  KLF2  MTOR  EGFR3  CD83  MRC1  PTGS2  HGF  CEACAM1  CA4  GATA4  KRT7  GZMA  ITGAL  CD276  IL2RB  NKG7  CCL5  OAS1  IFI44L  FAS  IFI27  PSMB9  PGF  CCL20  TRIM29  VTCN1  IL6  B3GAT1  MELK  CD27  CD70  CDKN2A  CX3CR1  HLA- of neoantigenicity in ccRCC 10 were rare without significant differences in responders versus nonresponders (data not shown). As abnormalities in chromosomal structure increase genetic variability and could be another source of mutational burden, 31 we performed whole-genome CNV analysis. Our data reflect the known CNV landscape of RCC 32,33 that has recently been expanded by a comprehensive TCGA analysis revealing characteristic gains and losses particularly within chromosome 3p and 5q 29 with only few studies available in the clinical context and with regard to metastatic disease. 34 Unexpectedly we identified increased CD8 + tumorinfiltrating T cells, TLS, and PD-L1 expression in ICI nonresponders when compared with responders. Unlike most studies, we present a spatially resolved, highly standardized, and reproducible assessment of tumor-infiltrating CD8 + T cells by employing digital image analysis according to the state-of-the-art guidelines for tumor infiltrating lymphocytes assessment. 24, 35 As standardized PD-L1 assessment algorithms in ccRCC are not available, we reported our findings as the combined positivity score. 25 All 3 parameters are generally regarded as indicators of a successful pretreatment immune response. 9, 12 Our observations associating elevated CD8 + T-cell frequencies with poor treatment response are particularly counterintuitive, because CD8 + T cells represent the major target population of anti-PD1 treatment. 36 Very recent findings 6 suggest that both the tumor and the tumor immune microenvironment are shaped by the respective treatment performed after excision of the primary tumor. These observations may explain the observed discrepancy between tissue level features and outcome considering the time interval between resection and immune oncology treatment in the present study. Likewise, in light of this recent publication 6 our data strongly support the evaluation of progressing tumor lesions as the original primary tumor may not reflect the immune cell composition that is indicative of immunotherapy response. In line with prior reports, the predictive and prognostic role of tumorinfiltrating CD8 + T cells in ccRCC therefore continues to be controversial. 15, 16, [37] [38] [39] Some studies have found high CD8 + T cells in ccRCCs of poor ICI responders. 15, 39 Conflicting findings about the predictive value of CD8 + T-cell frequencies may result from (i) the lack of standardized and quantitative tumor-infiltrating immune cell assessment algorithms, (ii) investigation of different tumor compartments, (iii) heterogeneous assessment of additional markers relating to immune phenotypes, (iv) lack of spatial information and (v)-maybe most importantly-no clear separation of the different histologic RCC entities within investigated cohorts, leading to a mixture of different histologic subtypes in these previous investigations with associated differences in the tumor microenvironment 15, 16 Moreover, the application of PD-L1 as a biomarker in ccRCC 25 remains controversial as it can be increased upon persistent HIF expression or hypoxia. 13, 14, 40 To elucidate potential underlying mechanisms for the observed differential CD8 + T-cell infiltrate, TLS densities, and PD-L1 expression between responders and nonresponders, we performed targeted gene expression analysis by employing a commercially available targeted NGS assay (OIRRA) designed for the quantification of immune cell and inflammatory transcripts in FFPE samples. No significant differences nor strong tendencies in gene expression could be detected between responders and nonresponders in our cohort. Likewise, no correlations between gene expression profiles and the observed genetic alterations were found in opposite to recent reports, 7, 8 even when focusing on known pathways like activation of the CCL5-CCR5 pathway in BAP1 mutated ccRCC 41 or decreased STAT1 phosphorylation/decreased expression of interferon gamma target genes in PBRM1 mutated ccRCC. 27 Still, potential differences within the tumor microenvironment cannot be excluded, because we used FFPE and not fresh frozen tissue for improved RNA quality and used a targeted RNAseq assay comprising 398 immune response related genes. Additional TCR sequencing suggested increased clonal T cells in responders. However, these results may correspond to pseudoclonality in view the few tumor-infiltrating T cells in responders, and must therefore be interpreted with caution. 42 This focused study of ccRCC responder versus nonresponder samples has inherent shortcomings related to the number of samples, treatment heterogeneity, and limited documentation of the established RECIST criteria. However, we chose to focus on samples that represent the typically available tissue within pathology departments to probe which biomarkers may become feasible in clinical routine. Unique features demanding additional tissue requirements like slow-/or snap-frozen tumor and/or healthy control tissue will remain difficult to employ and may not prevail in the diagnostic routine. We achieved an in-depth analysis of RCC cases stratified by immunotherapy response through the combination of comprehensive molecular studies with spatially resolved tissue microenvironment analysis. Our results on conceptually novel biomarkers like tumor-infiltrating CD8 + T cells 43 or TLS 9 and established biomarkers in immune oncology like PD-L1 expression are unexpected, remain unexplained and emphasize the need for further investigation in a larger cohort overcoming the limitations of our small sample size. Whether the combination of additional information such as blood parameters or a transcriptome-based molecular stratification of the samples 7 would provide a clear distinction remains to be determined, but certainly will not be feasible in the daily clinicopathologic routine.

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The authors thank Susanne Dettwiler and Fabiola Prutek from the Department of Pathology and Molecular Pathology, University Hospital Zurich for outstanding tissue management and technical assistance.