Characterizing the tumor microenvironment in rare renal cancer histological types

Introduction

Tumor formation and progression are influenced by two main factors: genomic changes and the rearrangement of the components of the tumor microenvironment (TME) [1, 2]. The TME is tumor dependent [3] and although various immune cells may be recruited to the tumor site, their tumor killing functions are often inhibited, resulting in tumor progression. Thus, understanding the mechanisms, and cells, governing immune evasion in the TME is essential to identify novel strategies to disrupt tumor interactions with its surrounding environment and effectively treat cancer [4].

Several of the immunosuppressive components within the TME have been characterized and used in the development of novel immunotherapies, including in kidney cancer, the eighth most common malignancy in the United States [5]. However, most investigations of the TME in renal cancers have focused on clear cell renal cell carcinoma (ccRCC), which is the most frequent renal histotype in adults with a 5-year survival rate of 68–75% [6]. ccRCCs show abundant immune cell infiltration [7-9] and there is a role for anti-angiogenic therapies as this TME has been linked to increased angiogenic activity following Von Hippel Lindau tumor suppressor loss [10, 11]. Clinical analysis of immune checkpoint inhibitors alone [12, 13], or in combination with anti-angiogenic therapies [14], has shown promise in relation to improved clinical outcomes for patients with ccRCC in a few studies, highlighting the relevance of the TME in facilitating therapeutic approaches and precision medicine. However, to date, little analysis of the TME has been carried out in rarer forms of renal cancers, which often present with more aggressive or metastatic disease at diagnosis [15].

Papillary renal cell carcinoma (pRCC) accounts for 10–20% of all RCC and is histologically characterized by a proliferation of papillae composed of fibrovascular cores lined by tumor cells [16]. pRCC can be further subtyped as type 1 (pRCC1) and type 2 (pRCC2), the latter being more aggressive than the former [17] with 5-year disease-specific survival rates of pRCC1 and pRCC2 of 94.5 and 66.4%, respectively [18]. Collecting duct carcinoma (CDC) arises from the collecting duct in the renal medulla [19]. It comprises less than 1% of all primary renal tumors and is highly aggressive, with most subjects presenting with metastatic disease [20] at the time of diagnosis and a 5-year survival rate of just 8.8% [21]. Urothelial carcinoma arises from the urothelium of the renal pelvis and is a highly variable and aggressive disease [22]. High-grade papillary urothelial carcinomas (HGUC) grow more quickly than low-grade disease, and are more likely to metastasize, with a 5-year survival rate of 6% for metastatic disease [23].

With the aim to better understand the TME in renal cell carcinomas, we characterized the TME in 103 rare kidney cancer tissue samples by applying machine learning algorithms to digitized whole slide sections stained immunohistochemically for immune, endothelial, and epithelial-to-mesenchymal markers.

Materials and methods Sample collection and subject information

This study was based on archived samples collected at the Regina Elena Cancer Institute, Rome, Italy, including 103 tumor samples from 83 subjects. Specifically, we analyzed 88 primary tumor samples (20 pRCC1, 49 pRCC2, 14 CDC, 5 HGUC), 13 metastatic tissue samples, including both distant metastasis and lymph node metastasis (8 pRCC2, 4 CDC, 1 HGUC), and 2 recurrences (2 pRCC2) (supplementary material, Table S1). All tissue specimens were derived from surgical resection. Written informed consent to allow banking of biospecimens for future scientific research was obtained from each subject. This work was excluded from the NCI IRB Review per 45 CFR 46 and NIH policy for the use of specimens/data by the Office of Human Subjects Research Protections (OHSRP) of the National Institutes of Health. The data were anonymized. The pathology of all tissue samples underwent centralized review by a specialist in uropathology (SS) to confirm the diagnosis. Tumor tissue samples were formalin fixed, paraffin embedded, sectioned at 3 μm thickness, mounted on SuperFrost Plus slides (Menzel-Gläser, Braunschweig, Germany), and stained with hematoxylin and eosin (H&E). H&E slides were inspected for the presence of neoplastic cells and the percentages of epithelial and mesenchymal regions on each slide were estimated by an expert pathologist (SS). Samples with compromised fixation/processing were excluded from the study.

Immunohistochemistry

Slides were baked at 60 °C for 1 h prior to immunostaining. Immunohistochemistry (IHC) for detection of 10 markers per tissue sample was performed on automated staining platforms using the following antibodies (supplementary material, Table S2): pan-macrophage marker (CD68), M2-like macrophage/tumor-associated macrophage (TAM) marker (CD163), pan-T-cell markers (CD3), cytotoxic T-cell maker (CD8), B-cell marker (CD20), marker of immune suppression (PD-L1), angiogenic marker (CD31), proliferation marker (Ki67), marker of tumor tissue (PanCK), and marker of epithelial-to-mesenchymal transition (EMT) (vimentin). In addition, staining of LAG3 (a marker of NK cells and T cells) was performed but expression levels were below detection in all samples. Staining was carried out as per the manufacturer's protocols and recommendations. After deparaffinization, rehydration, and antigen retrieval in citrate buffer (10 mm, pH 6.1), the tissue sections were stained for the marker of interest (supplementary material, Table S2). Positive control human tissues were used for all markers (supplementary material, Table S2).

Immunoreactions were revealed by Bond Polymer Refine Detection on an automated autostainer (Bond™Max, Leica Biosystems, Milan, Italy). Standard processing steps were performed according to the manufacturer's instructions. Diaminobenzidine was used as chromogenic substrate. Stained slides were rinsed in distilled water, dehydrated, cleared in xylene, and cover slipped prior to scanning.

IHC for PDL-1 and vimentin was conducted at the Molecular Digital Pathology Laboratory of the National Cancer Institute, NIH. All other IHC slides were prepared at the Regina Elena Cancer Institute, Rome, Italy.

Image analysis

Slides were scanned using an Aperio AT2 DX scanner (Leica Biosystems, Richmond, IL, USA) at ×40 magnification. Scanned whole slide images were analyzed using the HALO image analysis platform (Indica labs, Albuquerque, NM, USA). The HALO Random Forest classifier was trained to differentiate between tissue, glass, and folds/debris and to provide quantitative data on the total tissue area on each slide. Within the tissue area, regions of interest, i.e. the tumor tissue islands, were identified by a pathologist (PL) for analysis. Image analysis settings were based on differences in protein expression and tissue localization for the different markers. For CD68, CD163, CD3, CD8, CD20, PanCK, and vimentin, which are typically localized to membranous and/or cytoplasmic compartments, we used the HALO ‘Area quantification v1.0’ algorithm, a powerful approach for analyzing tissues where clustering and aggregation inhibit reliable cell segmentation, to estimate the positive staining area (Figure 1). The percent positive marker area was calculated by dividing the positive staining area by the total tissue area and multiplying by 100. For CD31, Ki67, and PDL1, the ‘Object colocalization v1.2’ algorithm, which detects positive staining cells or objects (e.g. CD31+ endothelial cells) based on their size and shape, was used for determining the total number of positively staining objects per mm2 of tissue area (Figure 1). Algorithms to measure Ki67 and vimentin were trained on the entire tissue, considering both tumor and non-tumor regions equally for training.

image

Representative images of the comparative quantification of tumor island staining by light microscopy and by digital image analysis using the HALO imagine analysis software for each marker. CD68, papillary renal cell carcinoma (pRCC) type 1; CD3, collecting duct carcinoma; Ki67, high-grade urothelial carcinoma; all other markers, pRCC type 2.

To investigate immune infiltration patterns by histological subtype, each image was spatially characterized into tumor core, proximal tumor periphery, and distal tumor periphery, and marker expression was assessed within these compartments. In addition to careful pathology annotations, delineation of the tumor on the slide was enhanced using PanCK staining. In general, machine learning algorithms were trained to identify and annotate regions on representative sections with positive PanCK staining. For each subject, annotated PanCK regions from the representative section were digitally overlayed on serial sections from the other markers through a process of digital image registration. Accordingly, separate PanCK with CD3, CD20, or CD68 images were successfully merged by using the HALO image registration tool. For each marker, concentric rings of 140 μm in width from the tumor core to 300 μm outside the tumor region (i.e. distal tumor periphery) were analyzed using the HALO ‘Infiltration analysis’ module (supplementary material, Figure S1). Percent positive staining area and object count per mm2 were determined within each concentric region.

Statistical analyses

To compare the TME by histological types, we calculated the rate of immune markers expression (i.e. the area of tissue with positive stain divided by the total tissue area) and tested differences between individual histological types using Mann–Whitney, and across all histological types using Kruskal–Wallis tests. A single sample, chosen at random, was included from tumors with multiple samples. We examined correlations across markers using Spearman's rank correlation coefficient r.

To examine the associations between TME marker expression (outcome) and histological types (exposure), we fitted Poisson regression models on the log of the area of tissue with positive stain. To take into account the variability in tissue size on each slide, the log of the total tissue area per slide was included as an offset. Correlations across multiple samples from the same tumor were accommodated by using generalized estimating equations [24] with the independent working correlation to obtain variance estimates (Proc Genmod). pRCC2 was used as the reference group to ensure stable estimates, as it had the largest sample size (n = 49). To identify confounders of the association between TME markers and histological types, we verified whether marker expression (in quartiles) was associated with clinical features (listed in Table 1) using chi-square or Fisher's exact tests (data not shown), and whether clinical features were associated with histological types using Fisher's exact tests (supplementary material, Table S3). Poisson models were first only adjusted for histological type, and then additionally adjusted for age in categories fitted with a trend (<51, 51–61, 62–70, >70), sex, tumor size (<4, 4–7, >7 cm) [25], and clinical stage (I, II, III, IV). The incidence rate ratio (IRR) estimated from the Poisson model represents the ratio of the percent positive area in a specific histological type compared to pRCC2. A Wald-based P heterogeneity was calculated to test differences of marker expression across all four histological types. In sensitivity analyses, we additionally adjusted the Poisson model of the immune markers for the rate of immune marker expression from the same cell types (i.e. T cells or macrophages) to control for any possible residual confounding.

Table 1. Summary of clinical characteristics of study participants. n (%) Age at surgery (n = 83) <51 16 (19.28) 51–61 23 (27.71) 62–70 22 (26.51) >70 22 (26.51) Gender (n = 83) Male 63 (75.90) Female 20 (24.10) Histology (n = 83) pRCC type 1 20 (24.10) pRCC type 2 44 (53.00) CDC 14 (16.90) HGUC 5 (6.00) Clinical stage (n = 83) I 32 (38.60) II 7 (8.40) III 21 (25.30) IV 23 (27.70) Tumor size, cm (n = 83) <4 16 (19.50) 4–7 31 (37.80) >7 35 (42.70) Metastasis at diagnosis (n = 83) Yes 20 (24.10) No 63 (75.90)

Next, we compared the median expression of markers inside the tumor with that on the proximal (tumor border) and distal periphery using Wilcoxon matched-pairs signed rank test. To assess marker expression by distance from the center of the tumor island region to the periphery, we fitted linear models to the log-transformed marker values, adjusted for the log-transformed bandwidth area. P values of <0.05 were considered significant. All statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA).

Results

As expected [26], the majority of the subjects were male (76%) (Table 1). The mean age of participants was 61 years (range 56–76 years). Over half (53%) of the samples analyzed were pRCC2. Clinical stage and tumor size showed even distribution across categories. Finally, 24% of subjects presented with metastasized tumors (Table 1).

We analyzed 103 tumor samples from 83 subjects. Variability in expression across all markers was observed when we compared multiple samples from the same primary tumor (supplementary material, Figure S2), highlighting the heterogeneity of the TME in these cancers. In contrast, we observed no significant variation between marker expression in primary tumors and matched metastatic (n ≤ 13) or recurrent (n = 2) tissue samples (supplementary material, Figure S3). However, although not statistically different, PD-L1 showed a suggestive increase in the metastatic tissue samples (median positive cells/mm2 = 17.6 (lymph node and distant metastatic samples) versus 5.30 (primary matched samples), n = 13, p = 0.1099; nodes only, n = 4, p = 0.1250; distant metastatic samples only, n = 9, p = 0.820) (supplementary material, Figure S3 and Table S4). Moderate correlation was observed between expression of markers from the same cell types, i.e. macrophages (CD68 and CD163; r = 0.634) and T cells (CD3 and CD8; r = 0.48) (supplementary material, Table S5). Furthermore, B-cell marker showed moderate and significant correlation with the pan-T-cell marker CD3 (r = 0.693) (supplementary material, Table S5), suggesting that activation of adaptive immune cells may occur concurrently.

Several immune markers showed different rates of expression across histological types (Figure 2). Kruskal–Wallis analysis showed that CD68 (p = 0.0005), CD20 (p = 0.0003), and Ki67 (p = 0.0053) expression varied the greatest between all histological types (not shown on graphs). Overall, expression of macrophage markers (i.e. CD68 and CD163) was highest in the pRCC histological types (pRCC2 versus CDC, CD68: p = 0.006; pRCC2 versus HGUC, CD68: p = 0.01, CD163; p = 0.046, Mann–Whitney test). However, lymphocyte markers (i.e. CD3, CD8, and CD20) showed a trend of higher expression in the CDC or HGUC subtypes, with CD20 expression significantly higher in CDC versus pRCC2 (p = 0.04). Moreover, pRCC1 had significantly lower expression of CD8 (p = 0.0083), CD20 (p < 0.001), PDL-1 (p = 0.0161), CD31 (p = 0.039), and Ki67 (p = 0.0245) in comparison to the more clinically aggressive pRCC2 (Figure 2).

image

Scatter plot representation of median ± range of the percentage positive tissue area for markers measured by IHC and quantified by digital pathology using the HALO image analysis platform. Data were analyzed by unpaired Mann–Whitney test for differences between individual histological types compared. pRCC2 acted as comparison group for all analysis, as it had the largest subject size. pRCC2, papillary renal cell carcinoma type 2 (n = 44) (red); pRCC1, papillary renal cell carcinoma type 1 (n = 20) (green); CDC, collecting duct carcinoma (n = 14) (blue); HGUC, high-grade urothelial carcinoma (n = 5) (magenta). ****p < 0.0001; ***p < 0.0005; **p < 0.005; *p < 0.05.

In multivariable Poisson models, CDC showed a higher lymphocytic infiltration in comparison to pRCC2, i.e. high T cells (CD3; IRR = 2.82, 95% confidence interval [CI] = 1.69–4.69, p < 0.001) and B cells (CD20; IRR = 4.94, 95% CI = 2.63–9.28, p < 0.001) (Table 2), but a lower infiltration of macrophages (CD68; IRR = 0.06, 95% CI = 0.00–0.95, p = 0.046). However, no variation across histological types was found for CD8, a marker of cytotoxic T cells. Additionally, PDL1 expression was higher in pRCC2 versus CDC (IRR = 0.11, 95% CI = 0.02–0.73, p = 0.02).

Table 2. Univariable and multivariable analyses of the association between TME marker expression and kidney tumor histological types. Univariable model Multivariable model* Marker Histology n IRR (95% CI) P value IRR (95% CI) P value CD68 pRCC type 2 45 Ref. Ref. pRCC type 1 16 2.16 (1.09–4.29) 0.0276 1.48 (0.61–3.63) 0.3884 CDC and other 14 0.06 (0.00–1.04) 0.0532 0.06 (0.00–0.95) 0.0458 HGUC 5 0.02 (0.00–33.94) 0.3160 0.03 (0.00–35.21) 0.3277 P heterogeneity 0.0662 0.2522 CD163 pRCC type 2 49 Ref. Ref. pRCC type 1 20 0.84 (0.43–1.64) 0.6090 0.57 (0.26–1.27) 0.1676 CDC and other 14 0.97 (0.39–2.42) 0.9473 0.96 (0.38–2.38) 0.9239 HGUC 5 0.22 (0.01–8.21) 0.4142 0.31 (0.01–10.72) 0.5170 P heterogeneity 0.3327 0.571 CD3 pRCC type 2 48 Ref. Ref. pRCC type 1 20 0.95 (0.54–1.68) 0.8589 0.79 (0.41–1.49) 0.4632 CDC and other 14 2.50 (1.56–4.02) <0.0001 2.82 (1.69–4.69) <0.0001 HGUC 5 0.71 (0.24–2.12) 0.5395 1.03 (0.31–3.41) 0.9590 P heterogeneity 0.4508 0.4002 CD8 pRCC type 2 47 Ref. Ref. pRCC type 1 19 0.65 (0.33–1.28) 0.2145 0.5 (0.23–1.07) 0.0726 CDC and other 14 1.45 (0.82–2.55) 0.1976 1.56 (0.86–2.83) 0.1405 HGUC 5 1.42 (0.60–3.37) 0.4202 1.88 (0.72–4.91) 0.1974 P heterogeneity 0.364 0.1848 CD20 pRCC type 2 48 Ref. Ref. pRCC type 1 19 0.38 (0.12–1.24) 0.1087 0.33 (0.09–1.24) 0.0914 CDC and other 13 4.35 (2.49–7.58) <0.0001 4.94 (2.63–9.28) <0.0001 HGUC 5 1.07 (0.30–3.76) 0.9175 1.29 (0.31–5.34) 0.7210 P heterogeneity 0.1631 0.3931 PDL1 pRCC type 2 44 Ref. Ref. pRCC type 1 15 0.11 (0.01–1.40) 0.0886 0.16 (0.02–1.71) 0.1306 CDC and other 14 0.23 (0.03–2.15) 0.1986 0.11 (0.02–0.73) 0.0224 HGUC 5 0.17 (0.00–16.47) 0.4521 0.05 (0.00–2.39) 0.1310 P heterogeneity 0.4983 0.4304 CD31 pRCC type 2 49 Ref. Ref. pRCC type 1 18 0.67 (0.48–0.92) 0.0143 0.76 (0.54–1.06) 0.1043 CDC and other 14 1.05 (0.78–1.41) 0.7553 1.14 (0.86–1.52) 0.3496 HGUC 5 0.97 (0.52–1.83) 0.9322 1.06 (0.58–1.94) 0.8387 P heterogeneity 0.0902 0.216 Ki67 pRCC type 2 48 Ref. Ref. pRCC type 1 18 0.51 (0.29–0.88) 0.0162 0.47 (0.25–0.87) 0.0166 CDC and other 14 0.43 (0.20–0.90) 0.0263 0.47 (0.22–1.00) 0.0487 HGUC 5 0.31 (0.08–1.24) 0.0973 0.32 (0.08–1.31) 0.1142 P heterogeneity 0.0474 0.0282 Data were analyzed by Poisson regression. Marker positive expression tissue area and total tissue area were quantified using HALO algorithms: ‘Area quantification v1.0’ for CD163, CD68, CD3, CD8, and CD20, and ‘Object colocalization v1.2’ for PDL-1, CD31, and Ki67. Data were modeled as area of tissue with positive stain/positive object count as the dependent variable, and the log of the total tissue area per slide as an offset. Ref., reference category. * Adjusted for sex, age at surgery, clinical stage, and tumor size. Bold values are statistically significant (P < 0.05).

Moreover, pRCC1 showed lower expression of Ki67, a marker of cell proliferation, in comparison to pRCC2 (IRR = 0.47, 95% CI = 0.21–0.92, p = 0.017). Adjusting for markers expressed on the same cell types did not substantially alter the findings (supplementary material, Table S6). We could not stratify analysis of marker expression by tumor size or stage because of small numbers in some categories.

By quantifying the percentage of epithelial and mesenchymal regions on each H&E slide, we investigated whether the proportions of these tissue composition metrics differed by histological type. We found the proportion of epithelium on the slide to be higher among pRCC (mean [SD] = 80% [11%] and 78% [12%] for pRCC1 and pRCC2, respectively) and CDC (mean [SD] = 72% [17%]) than HGUC (mean [SD] = 50% [29%]) histological subtypes (supplementary material, Figure S4A). In contrast, HGUC (mean [SD] = 50% [26%]) showed a higher proportion of mesenchymal regions than the other histological types (mean [SD] = 22% [11%], 20% [11%], and 28% [17%] for pRCC2, pRCC1, and CDC, respectively; supplementary material, Figure S4B). We assessed EMT by using IHC staining of vimentin as a surrogate marker, which is in keeping with previous reports [27]. No significant variation in vimentin expression was observed by histological type (supplementary material, Figure S4C).

Finally, in an effort to ascertain if immune cells remain at the tumor periphery or can penetrate the tumor tissue core, we carried out immune marker spatial analysis using the HALO 3.1 imaging platform (supplementary material, Figure S1). Notably, CD68 expression was consistently expressed both inside the

留言 (0)

沒有登入
gif