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Clinical Studies

Integrated Analysis Of Immunotherapy Treated Clear Cell Renal Cell Carcinomas: An Exploratory Study

Sobottka, Bettina*; Nienhold, Ronny; Nowak, Marta*; Hench, Juergen; Haeuptle, Pirmin§; Frank, Angela; Sachs, Melanie; Kahraman, Abdullah*; Moch, Holger*; Koelzer, Viktor H.*; Mertz, Kirsten D.†,∥

Author Information
doi: 10.1097/CJI.0000000000000387

Abstract

Immune checkpoint inhibition (ICI) has become a new backbone in the treatment of advanced clear cell renal cell carcinoma (ccRCC)1–3 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 response12 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.

MATERIALS AND METHODS

Patient Cohort

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).

TABLE 1 - Cohort
Retrospective ccRCC Cohort Clinicopathologic Parameters, n=8 Responder (n=3), N Nonresponder (n=5), N
Age at initial diagnosis
 Age below 60 2 0
 Age 60 years or above 1 5
Sex
 Female 2 2
 Male 1 3
Tumor (T) stage
 T1 0 1
 T2 2 0
 T3 0 3
 T4 1 1
Fuhrman grade
 Grade 1 1 0
 Grade 2 1 2
 Grade 3 1 3
 Grade 4 0 0
First line treatment
 None 0 2
 TKI 3 3
ICI treatment
 Nivolumab 3 4
 Nivolumab+Ipilimumab 0 1
ICI cylces
 Average in months 84.7 2.8
Progression free survival
 Average in months 39.3 0
Detailed clinicopathologic information of the study cohort. Only primary tumors from clear cell renal cell carcinoma patients were included and are grouped according to their response to ICI treatment.
ccRCC indicates clear cell renal cell carcinomas; ICI, immune checkpoint inhibitor.

Nucleic Acid Extraction

DNA and RNA was extracted from FFPE tumor and adjacent healthy control tissues using the RecoverAll Total Nucleic Acid Isolation Kit (Catalog No. AM1975; Thermo Fisher Scientific, Waltham, MA). Nucleic acid concentrations were measured with Qubit dsDNA HS and RNA HS Assay Kits (Catalog No. Q32851 and Q32852; Thermo Fisher Scientific).

Next-generation Sequencing (NGS)

We performed targeted parallel sequencing of genomic DNA covering hotspot regions of 125 genes using a custom-adapted panel. This panel includes nonoverlapping target regions of the Oncomine Focus Assay, Oncomine Comprehensive Assay v3, Ion AmpliSeq Cancer Hotspot Panel v2, and AmpliSeq Colon and Lung Cancer Research Panel v2 (Supplemental Figs. 1A, B, Supplemental Digital Content 1, https://links.lww.com/JIT/A624). Tumor mutational burden was detected and calculated using the Oncomine Tumor Mutation Load Assay (Catalog No. A37909; Thermo Fisher Scientific) covering the complete coding regions of 409 genes (Supplementary Fig. 1C, Supplemental Digital Content 1, https://links.lww.com/JIT/A624).

Profiling of Immune Response by Targeted RNAseq

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.

Methylation and Copy Number Analysis

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 minfi18 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 Bioconductor21 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 down-sampled 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 GenVisR23 Bioconductor package (version 1.22.1).

Immunohistochemistry, Digitalization, and TLS Quantification

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 Group24 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. Marker-positive 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/mm2.26

T-Cell Receptor Sequencing

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 (Catalog No. A39359; Thermo Fisher Scientific). In brief, 250 ng of RNA extracted from FFPE tumor samples were used to generate first strand cDNA (SuperScript VILO cDNA Synthesis Kit, Catalog No. 11754250; Thermo Fisher Scientific). established gene segments for TCR recombination libraries were generated by amplification of the framework region 3 (FR3, V region), complementarity determining region 3 (CDR3, D region) and joining (J) region and barcoded with the Ion Torrent Dual Barcode Kit 1-96 (Catalog No. A39360; Thermo Fisher Scientific). The libraries were quantified (Ion Library TaqMan Quantitation Kit, Catalog No. 4468802; Thermo Fisher Scientific), equimolarly pooled and sequenced utilizing the Ion GeneStudio S5xl (Thermo Fisher Scientific). Demultiplexing and detection of individual T-cell clones were performed with the standard settings of the Torrent Suite (version 5.12.1) and the Oncomine TCR Beta-SR—w1.0—RNA—Single Sample workflow (version 1.0), provided as part of the Oncomine TCR Beta-SR Assay by Thermo Fisher Scientific.

RESULTS

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, https://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 single-nucleotide 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+ tumor-infiltrating 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, https://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).

F1
FIGURE 1:
Mutational and whole-genome wide copy number variation landscape in responders and nonresponders. Oncoprint visualizes per gene (left) the mutational frequencies (right) for our cohort including the mutation type (grey shading). The majority of clear cell renal cell carcinomas samples showed genetic alterations in VHL with or without co-altered genes. VHL, PBRM1, and BAP1 were the most frequently co-altered genes. Single mutated genes in nonresponders versus responders displayed no overlapping genetic alterations (A). Whole-genome wide SNP-based copy number variation analysis revealed the expected variations particularly in chromosome 3p with similar distributions between responders and nonresponders (B). SNP indicates single-nucleotide polymorphism.
F2
FIGURE 2:
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 compartment-specific (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.
F3
FIGURE 3:
Gene expression profiles of the tumor microenvironment. Heat-map of fold change expression of 398 genes between all tumors and matched normal tissue reveals a more inflammatory environment in tumor samples when compared with the healthy matched control tissue (A). Heat-map of top differentially (P<0.05) expressed gene expression profiles of responders and nonresponders, only B3GAT1 also reached a false discovery rate <0.05 (B). Similar distribution of genetic alterations and variant effects between nonresponders and responders (B, at the bottom) without any appreciable effect on gene expression. In detail, neither genetic alterations in PBRM1 (C) nor in BAP1 (D) showed differences in the expression of potential target genes. Heat-maps display log2 expression of each gene normalized to housekeeping genes.

DISCUSSION

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 ccRCC29,30 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 of neoantigenicity in ccRCC10 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 RCC32,33 that has recently been expanded by a comprehensive TCGA analysis revealing characteristic gains and losses particularly within chromosome 3p and 5q29 with only few studies available in the clinical context and with regard to metastatic disease.34

Unexpectedly we identified increased CD8+ tumor-infiltrating 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 findings6 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 publication6 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 tumor-infiltrating CD8+ T cells in ccRCC therefore continues to be controversial.15,16,37–39 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 microenvironment15,16 Moreover, the application of PD-L1 as a biomarker in ccRCC25 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 ccRCC41 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 pseudo-clonality 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 cells43 or TLS9 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 samples7 would provide a clear distinction remains to be determined, but certainly will not be feasible in the daily clinicopathologic routine.

ACKNOWLEDGMENTS

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.

Conflicts of Interest/Financial Disclosures

Supported by the Promedica Foundation F-87701-41-01 (V.H.K.).

V.H.K. has served as an invited speaker on behalf of Indica Labs. The remaining authors have declared that there are no financial conflicts of interest with regard to this work.

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Keywords:

immune phenotype; renal cell carcinoma; next-generation sequencing; digital pathology; personalized medicine

Supplemental Digital Content

Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.