The treatment scenario of metastatic renal cell cancer (mRCC) has undergone a complete change in the last few years. Therapeutic options have progressed from non-specific immunotherapy with cytokines to targeted therapy with the development of tyrosine kinase inhibitors (TKI), and more recently to the novel immune checkpoint inhibitors (ICIs) such as anti-programmed death receptor 1 (PD-1), anti-programmed death receptor ligand 1 (PD-L1), and anti-cytotoxic T lymphocytes antigen 4 (CTLA-4) [1]. The CheckMate 025 was the first trial showing the efficacy of nivolumab, a human IgG4 anti-PD-1 antibody, in mRCC patients [2]. Later, the CheckMate 214 trial investigated the combination of nivolumab with another ICI, ipilimumab (anti-CTLA-4), and confirmed the benefit of the association, especially in patients with intermediate and poor-risk disease according to the IMDC (International Metastatic RCC Database Consortium) risk score [3]. More recently, the combined use of an ICI on top of a multitargeted receptor TKI evidenced a survival benefit and is therefore now another therapeutic option for patients with mRCC [4–6]. The opportunities to use ICIs in the future will, most likely, tremendously increase.

With the number of mRCC patients treated with immunotherapy rising year by year, clinicians have found themselves managing a new spectrum of adverse events (AEs) that are specific to this new class of therapeutic agents [7]. As expected, by stimulating the immune system to target malignancies, ICIs have also induced a wide range of immunologic AE (irAEs). The most common reported irAEs involve skin, gastrointestinal tract, endocrine glands, lung, and liver [8]. Little is known about the cellular and molecular mechanisms underlying most irAEs. However, emerging evidence from clinical trials and real-world studies indicate that irAE type and severity depend on the therapeutic target (i.e., CTLA-4 vs. PD-1/PD-L1), tumor type, and patient-intrinsic factors [9,10]. Most irAEs are mild and reversible if detected early and properly managed, but there is a noticeable proportion of patients who have experienced (grade ≥ 3) irAEs (31% of patients treated with CTLA-4 vs. 10% treated with PD-1) [11]. It has been reported that the incidence of irAEs is higher in ICI combination therapy than in monotherapy [12]. It must be noted that most clinical trials excluded cancer patients with underlying autoimmune disease or chronic infection. Therefore, irAEs in real-world clinical practice are expected to increase further.

However, despite the improved outcome of cancer treatment by ICIs, efficacy still remains limited. Many studies have searched for biomarkers predictive of response to immunotherapy. Some of these are already used in clinical practice including the PD-L1 tumor prediction score (TPS) and clinical prediction score (CPS) [13,14]. Other markers currently under evaluation include tumor mutational burden (TMB) [15], gene expression scores (GEP) [16], and tumor infiltrating lymphocytes (TILs) [17]. Other studies have suggested that peripheral blood markers could help predict the treatment response, but evidence is still scant [18–20]. In the context of this research irAEs have been proposed as potential clinical markers to predict response to ICI. This relationship, although documented in studies regarding non-small cell lung cancer (NSCLC) and melanoma [21–26] has been less studied in RCC [27–29]. Moreover, current data on the impact of specific types of irAEs on outcomes are not entirely consistent.

Our study represents a real-life observation concerning the onset and management of the most common side effects and the prognostic impact of irAEs in a cohort of mRCC real-world patients treated with nivolumab or nivolumab combined with ipilimumab at our Medical Oncology Unit, AOU Careggi (Firenze, Italy).

#### **2. Materials and Methods**

We retrospectively reviewed data from 43 patients treated at the Medical Oncology Unit, Careggi Hospital (Firenze, Italy) from March 2016 to March 2020.

Patient eligibility included age >18, histologically confirmed RCC with metastatic disease, and treatment with an immunotherapy agent. Patients were treated with nivolumab in monotherapy (3 mg/kg every two weeks or flat dose of 240 mg/every two weeks or 480 mg/every four weeks) or with the association of nivolumab 3 mg/kg and ipilimumab

1 mg/kg every three weeks for four cycles, then in monotherapy with nivolumab. PD-L1 status was not requested. Patients received therapy until either disease progression or unacceptable toxicity presented. All patients had measurable disease and the disease progression was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST v 1.1).

All patients evaluated had accurate clinical records of the irAEs with a description of their severity and their treatment. Toxicity was assessed according to Common Terminology Criteria for Adverse Events (CTCAE) version 4.0. IrAEs were defined as an adverse event with an immunological basis that required intensive monitoring or treatment with immunosuppressive agents or endocrine therapy. We divided irAEs into seven categories: pulmonary, hepatic, gastrointestinal, cutaneous, endocrine, rheumatological, and renal.

All data were analyzed anonymously; all patients signed an informed consent form for immunotherapy with particular specifications about the occurrence of possible adverse events. This study was conducted in accordance with the World Medical Association Declaration of Helsinki and independently reviewed and approved by the Regional Ethics Committee for Clinical Trials of the Tuscany Region (approval no.: 17332\_oss). All patient data were processed anonymously and de-identified prior to analysis.

Statistical analyses. Estimates of PFS in the irAE and non-irAE groups or different irAEs subgroups were calculated using the Kaplan–Meier method, and statistical significance was analyzed using the log-rank test. A significance level of *p* < 0.05 was employed for statistical analyses. First, at the univariate analysis, and then the Cox proportional hazard model was used to calculate the hazard ratios (HRs) and appropriate 95% CIs. Subsequently, the independent effect of each parameter on PFS was investigated by a multivariate Cox regression model. Data were analyzed using the statistical software Jamovi.

#### **3. Results**

#### *3.1. Patient Characteristics*

Between March 2016 and March 2020, 43 patients with mRCC were treated with ICIs (either nivolumab or nivolumab combined with ipilimumab) at our Medical Oncology Unit, AOU-Careggi (Florence). Table 1 summarizes the patients' clinical features.

Average age of enrolled patients at time of diagnosis was 64 years, ranging from 45 to 79 years; 81.4% (*n* = 35) male, and 18.6% (*n* = 8) female. A total of 53.4% of the patients were current or former smokers. The most frequent histology diagnosis was clear cell (83.7%), followed by papillary and chromophobe renal cancer. Most patients (*n* = 39/43, 91%) had a resected primary tumor. The most frequent site of metastases was lung (65% of patients), followed by lymph nodes (44%), bone (35%), liver (21%), and brain (7%).

Overall, 33 patients (76.7%) received nivolumab in monotherapy while 10 received (23.7%) nivolumab plus ipilimumab. Ten patients (23.2%) received ICI as the first line of therapy and 25 patients (58.1%) as second line. Overall, one patient achieved complete response (2.3%), six partial response (PR) (13.9%), 11 stable disease (SD) (25.6%), and the remaining 25 patients experienced progressive disease (PD) (58.1%).

Among patients treated with nivolumab (*n* = 33), 18 (54.5%) were non responders (defined as patients who experienced a progressive disease as best response), while among patients treated with nivolumab plus ipilimumab (*n* = 10), seven (70%) were non responders.

#### *3.2. Profile of Immune-Related Adverse Event (irAEs)*

Twenty-nine out of 43 patients (67.4%) experienced a total of seven different irAE categories (data are reported in Table 2). Baseline clinical features between patients with or without irAEs were not significantly different. In patients who developed irAEs, the median number of days before the onset was 59 days; 14 patients never developed irAEs until the end of the observation phase.

In total, we registered 49 irAEs: 20 (46.5%) developed endocrine-related events, nine (20.9%) developed skin reactions, seven (16.3%) developed hepatitis, five (11.6%) developed colitis (as diarrhea), four (9.3%) developed arthralgias/myalgias, three patients (9%) developed pneumonia, and one patient (2.3%) developed nephritis.

**Table 1.** Clinical features of the study population.

**Table 2.** Comparison between irAEs in mRCC patients treated with nivolumab or with nivolumab and ipilimumab.

According to the CTCAE grades, irAEs registered were mainly grades 1 and 2, and were included in a subgroup defined "non-serious AE". Within patients who developed any irAE, 19 experienced a non-serious irAE (65.5%), while 10 patients (34.5%) developed a serious irAE (grade 3 or grade 4). If we consider the total number of irAEs observed (*n* = 49), 12 irAEs were serious (24.5%), while 37 were classified as non-serious (75.5%).

Among the patients only treated in monotherapy with nivolumab (*n* = 33), three (9%) developed pneumonitis, five (15.1%) developed diarrhea, four (12.1%) developed hepatitis, eight (24.4%) developed skin reactions, four (12.1%) developed arthralgia/myalgia, and 14 (42.4%) developed endocrine-related events.

Regarding the cohort of patients treated with nivolumab in combination with ipilimumab (*n* = 10), three (30%) patients developed hepatitis, only one (10%) patient developed a skin reaction, six (60%) patients developed endocrine-related events, and one (10%) patient developed nephritis. No difference in the occurrence of AEs between mono and combination therapy could be found (*p* = 0.84).

Overall, the most frequent AEs were related to endocrine issues (46% of the patients with 19 events), with a percentage up to 40% of the total number of adverse events. The median time to first development of endocrine dysfunction was 80 days. Thyroid dysfunction was by far the most frequently encountered endocrine toxicity (*n* = 15/19), followed by hypophysitis and hyperglycemia. Some patients (*n* = 8) experienced early-stage thyrotoxicosis followed by a permanent stage of hypothyroidism.

#### *3.3. Relationship between irAEs and Patient Outcome*

PFS was significantly longer in the group of patients that developed a thyroid dysfunction (*p* = 0.028); the median PFS of the euthyroid group was 121 days (IQR 92–305.) while the median PFS of the thyroid dysfunction group was not reached (Figure 1).

**Figure 1.** Association between thyroid dysfunction during treatment and oncologic outcomes. Kaplan–Meier plots of progression-free survival. IrAEs = immune-related adverse events; PFS = progression-free survival; pts = patients.

In multivariate analysis, the development of thyroid toxicity was an independent prognostic factor for PFS (HR: 0.34 [95% CI 0.13–0.87] *p* = 0.025) (Table 3). On the other hand, although very rare, hypophysitis was related to significant shorter PFS (*p* = 0.048). Interestingly, endocrine toxicities were significantly higher in patients who had already performed two lines of therapy before immunotherapy (*p* = 0.022).

A significantly longer PFS was also found in patients who experienced skin irAEs (*p* = 0.41). The median PFS of the patients who did not experience skin toxicities was 120 days (IQR 61–336), while the median PFS of the skin irAE group was not reached (Figure 2).

Moreover, in the irAEs group, the PFS was significantly longer in patients who experienced two or more irAEs compared to only one or no irAEs (*p* = 0.016) (Figure 3). In multivariate analysis, the development of two or more irAEs was an independent prognostic factor for PFS (HR: 0.32 [95% CI 0.13–0.79] *p* = 0.014) (Table 3). Age, sex, current or former smoking status, and pathological subtypes were not associated with PFS.

**Table 3.** Univariate and multivariate analysis of progression-free survival.

HR = hazard ratio; CI = confidence interval; irAEs = immune-related adverse events.

**Figure 2.** Association between skin toxicity during treatment and oncologic outcomes. Kaplan–Meier plots of progression-free survival. irAEs = immune-related adverse events; PFS = progression-free survival; pts = patients.

**Figure 3.** Association between multiple irAEs during treatment and oncologic outcomes. Kaplan– Meier plot of progression-free survival. irAEs = immune-related adverse events; PFS = progressionfree survival; Pts = patients.

Additionally, we performed a complementary 16-week landmark analysis since patients with longer PFS could have a higher probability of developing AEs, which could lead

to analysis bias. The 16-week analysis confirmed that the occurrence of irAEs was significantly associated with prolonged median PFS for patients with skin toxicity and with two or more adverse events (PFS: NR vs. 120 days, *p* = 0.005 and PFS: NR vs. 120 days, *p* = 0.029, respectively) The same analysis for patients with thyroid dysfunction demonstrated similar tendencies, but was not statistically significant (*p* = 0.160).

#### **4. Discussion**

Immunotherapy potentiates a patient's immune system to fight cancer, and has become one of the standard treatments for RCC. Immune checkpoint blockade increases antitumor immunity by blocking intrinsic downregulators of immunity such as cytotoxic T-lymphocyte antigen 4 (CTLA-4) and programmed cell death 1 (PD-1) or its ligand, programmed cell death ligand 1 (PD-L1). By enhancing the activity of the immune system, immune checkpoint blockade can induce inflammatory side effects.

Within this study, we aimed to describe toxicities in a real-world cohort of mRCC patients and their potential association with treatment response.

The precise pathophysiology underlying irAEs is yet unknown, but various hypotheses have been made. Some potential mechanisms include increasing T cell activity against antigens that are present both in tumors and healthy tissue and increasing levels of preexisting autoantibodies [30]. Among other theories, the observation that gut microbiota is involved in the functions of intestinal CD4+ and CD8+ T, with anti-tumor immunological activity, is gaining strength and this could impact the efficacy of ICIs [31].

It must be noted that CTLA-4 and PD-1 inhibit the immune response in distinct ways, the first attenuating T-cell activation at a proximal step in the immune response [32] and the latter blocking T cells at later stages of the immune cascade in peripheral tissues [33]. These different functions are reflected in different toxicities. For instance, pneumonitis and thyroiditis appear to be more common with anti-PD-1 therapy, while colitis and hypophysitis seem to be more common with anti-CTLA-4 therapy [11].

Overall, our results confirmed a favorable toxicity profile in patients with mRCC as described in other real-life studies [28,34]. However, colitis was unexpectedly higher in the nivolumab group, however, the total number of cases was very low. We also found a higher frequency of AEs on the endocrine profile compared to previous studies, reaching 46% in our patients compared to 4% as reported by Verzoni [28] or 17.9% reported by Ishihara [29].

The spectrum of endocrinopathies reported in the literature in patients receiving ICIs is quite broad including hypophysitis, thyroiditis, and less frequently, primary adrenal insufficiency, hypogonadism, pancreatitis, hypercalcemia, and diabetes [35,36].The most frequent endocrine toxicities registered in our center in mRCC patients were thyroiditis (either as hypothyroidism or as thyrotoxicosis or both in the same patient), followed by hypophysitis and hyperglycemia. The reasons for such a high rate of endocrine toxicity are unclear. A potential explanation may be a specific search in our practice for signs and symptoms of endocrinopathies that often subtly present themselves with generic symptoms such as fatigue, nausea, and weight changes. Additionally, our patients have often undergone immunotherapy after having already experienced one or more lines of therapy with TKI, drugs that are known to often interfere with the endocrine system, especially the thyroid axis. In fact, endocrine toxicities were significantly higher in patients who had undergone multiple rounds of molecular-targeted therapies before developing endocrine toxicities while on immunotherapy. However, there are currently no studies on this topic. Further explanation could be a longer follow up period, which led to the discovery of a higher number of toxicities.

The correlation between irAEs and patient outcome has been recently described for different cancers [10,37,38]. The occurrence of irAEs has been found to be associated with favorable outcomes in melanoma [21–23], NSCLC [24–26] and, with only a few studies available, also in mRCC [27–29]. These studies corroborate the hypothesis of a direct link between antitumor response and auto-immune reactivity. There are two different hypothesized mechanisms that could lead to irAEs. The first one concerns preexisting selfreactive T-cells being deregulated while the second speculates a cross-reactivity between normal tissues and tumor associated antigens, which share similar targets [39,40].

However, to identify a relationship between AE and treatment response, it is important to consider AE classes individually. Although all patients experiencing irAEs of any class exhibited more favorable outcomes, only patients with thyroid dysfunction and cutaneous toxicity demonstrated significantly higher PFS.

The positive relationship between AEs of the endocrine spectrum and treatment response has been described in a few studies, both analyzed all together or individually as thyroid dysfunction. As above-mentioned, most studies have described a cohort of patients with melanoma [41] or NSCLC [42]. Less is known about endocrine irAEs as a prognostic factor in RCC [28]. Our study confirms this positive relationship with a percentage of responder patients who experienced an endocrine irAE of 64.7%. Interestingly, we noted a specific distribution of the subclasses of endocrine toxicity with respect to the response to treatment: thyroid dysfunction was confirmed to be a positive prognostic indicator while hypopituitarism, although rare, was a negative prognostic factor. However, this result should be carefully interpreted also by taking into account that endocrine toxicities showed two very different profiles between patients treated with combination therapy and monotherapy with nivolumab: thyroiditis were more represented in the group that performed only nivolumab, while all cases of hypophysitis were found in the combination therapy group. The connection that binds endocrine irAEs and efficacy is yet unknown. There are various possible hypotheses described in the literature. One of these assumes that the activation of pre-existing low-grade autoimmunity to thyroid glands could lead to thyroid irAEs and thus select patients with strong baseline immunity [43]. Another hypothesis suggests that thyroid antigens may have a common amino acid sequence with tumor epitopes. Thereby, the cross presentation of those epitopes could be associated with thyroid irAEs and this could facilitate the selection of patients who could benefit from immunotherapy [44].

The relationship between skin irAEs and treatment efficacy has also been previously described, mostly in melanoma and NSCLC [41,45]. A meta-analysis of ICI therapy in melanoma found that vitiligo was significantly associated with both longer PFS and OS [46]. In RCC, the only study that described the relation between skin irAEs and treatment response is the one by Verzoni [28]. Our data confirmed the positive association between skin irAEs and PFS with a significantly longer PFS in patients who experienced skin toxicity. Importantly, this association was confirmed in multivariable analysis, and to our knowledge, this is the first study indicating that this specific irAE represents an independent predictor of ICI efficacy in mRCC patients.

As an additional result, we described a positive correlation between the number of irAEs and patient outcome. Having ≥2 irAEs was associated with longer PFS compared with one or no irAE. This result has already been described in other retrospective real-world studies by Bouhlel [47] and Ricciuti [24], both in NSCLC. To the best of our knowledge, this study is the first to reveal this association in a mRCC cohort of patients. This finding further suggests a mechanistic association between irAEs and immunotherapy efficacy and indicates that the development of multiple immune-mediated toxicities might reflect sustained anti-tumor responses.

It could be argued that patients with longer response to treatment could develop a higher number of irAEs. In an attempt to address this potential confounding factor, we conducted a 16-week landmark analysis. The results confirmed (although the thyroid dysfunction did not reach statistical significance) that the occurrence of irAEs was significantly associated with prolonged median PFS. These findings further underline the association of early onset of irAEs and a durable clinical benefit in mRCC patients treated with immunotherapy.

The limitations in the interpretation of the study results are mainly impacted by the retrospective nature and the small sample size. Further limitations are the selection of patients, which is stricter in clinical trials than in real-life clinical practice. In addition, regarding the determination of outcome and follow up, patients enrolled in clinical trials followed regular and more frequent clinic visits as per pre-determined strict criteria, which are sometimes difficult to follow in a real-life scenario. Moreover, a precise definition and categorization of irAEs is still lacking, and the classification that we performed has not been validated or standardized.

However, despite these limitations, we were able to observe significant results that require further confirmation in prospective studies with larger cohorts.

#### **5. Conclusions**

Despite the small sample size, we observed that specific irAEs such as thyroid dysfunction and cutaneous reactions were associated with longer PFS and that patients that experienced more than one AE presented a better response to treatment. These results suggest that irAEs can be a surrogate marker of clinical benefit. Not every toxicity class was significantly associated with better clinical response. However, the two that showed a positive relationship (skin and thyroid), which were also the most frequent, could be very useful in a clinical context. Nevertheless, no definitive conclusions could be derived from these data considering the limited number of patients experiencing any specific class of irAEs.

Given the high percentage of endocrine, and in particular, thyroid AEs, it is essential to implement research and treatment of these particular toxicities during treatment. Since endocrine toxicities often present themselves with vague and unclear clinical pictures, close monitoring by an endocrinologist should be warranted, also given the positive correlation with the patient's outcome and therefore the importance of continuing immunotherapy for these patients. Further prospective studies are necessary to confirm our findings and to investigate the mechanistic association between AEs and clinical response in order to improve therapy with ICIs and the management of their toxicities.

**Author Contributions:** Conceptualization, S.P. and L.A.; Methodology, V.R. and L.D.; Interpretation of data: E.G., R.G., A.P., and A.M.; Data curation, A.P. and R.G.; Writing—original draft preparation, A.P. and S.P.; Writing—review & editing, L.A. and M.C.; Supervision, M.M.M. and L.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** This study was conducted in accordance with the World Medical Association Declaration of Helsinki and independently reviewed and approved by the Regional Ethics Committee for Clinical Trials of the Tuscany Region (approval no.: 17332\_oss).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Data available on request to the corresponding author.

**Acknowledgments:** We thank Susanna Bormioli Weber for the English language editing.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Article* **Nivolumab Reduces PD1 Expression and Alters Density and Proliferation of Tumor Infiltrating Immune Cells in a Tissue Slice Culture Model of Renal Cell Carcinoma**

**Philipp J. Stenzel 1,\*, Nina Hörner 1, Sebastian Foersch 1, Daniel-Christoph Wagner 1, Igor Tsaur 2, Anita Thomas 2, Axel Haferkamp 2, Stephan Macher-Goeppinger 1, Wilfried Roth 1, Stefan Porubsky 1,† and Katrin E. Tagscherer 1,†**

**Citation:** Stenzel, P.J.; Hörner, N.; Foersch, S.; Wagner, D.-C.; Tsaur, I.; Thomas, A.; Haferkamp, A.; Macher-Goeppinger, S.; Roth, W.; Porubsky, S.; et al. Nivolumab Reduces PD1 Expression and Alters Density and Proliferation of Tumor Infiltrating Immune Cells in a Tissue Slice Culture Model of Renal Cell Carcinoma. *Cancers* **2021**, *13*, 4511. https://doi.org/10.3390/ 10.3390/cancers13184511

Academic Editors: Claudia Manini and José I. López

Received: 23 August 2021 Accepted: 1 September 2021 Published: 8 September 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Simple Summary:** Immune checkpoint inhibitors (ICIs) have become a first-choice therapy option in the treatment of clear cell renal cell carcinoma (ccRCC). A predictive biomarker is urgently needed since not all patients respond and adverse events occur. Therefore, an ex vivo tissue slice culture (TSC) model was tested to investigate the effects of nivolumab on tumor infiltrating immune cells (TIIC). A decrease in programmed death receptor 1 expression, as well as effects on density and proliferation of TIIC, were observed. Thus, the TSC model could serve as a test platform for response prediction to ICIs.

**Abstract:** Background: In the treatment of clear cell renal cell carcinoma (ccRCC), nivolumab is an established component of the first-line therapy with a favorable impact on progression free survival and overall survival. However, treatment-related adverse effects occur and, to date, there is no approved predictive biomarker for patient stratification. Thus, the aim of this study was to establish an ex vivo tissue slice culture model of ccRCC and to elucidate the impact of nivolumab on tumor infiltrating immune cells. Methods: Fresh tumor tissue of ccRCC was treated with the immune checkpoint inhibitor nivolumab using ex vivo tissue slice culture (TSC). After cultivation, tissue slices were formalin-fixed, immunohistochemically stained and analyzed via digital image analysis. Results: The TSC model was shown to be suitable for ex vivo pharmacological experiments on intratumoral immune cells in ccRCC. PD1 expression on tumor infiltrating immune cells was dose-dependently reduced after nivolumab treatment (*p* < 0.01), whereas density and proliferation of tumor infiltrating T-cells and cytotoxic T-cells were inter-individually altered with a remarkable variability. Tumor cell proliferation was not affected by nivolumab. Conclusions: This study could demonstrate nivolumab-dependent effects on PD1 expression and tumor infiltrating T-cells in TSC of ccRCC. This is in line with results from other scientific studies about changes in immune cell proliferation in peripheral blood in response to nivolumab. Thus, TSC of ccRCC could be a further step to personalized medicine in terms of testing the response of individual patients to nivolumab.

**Keywords:** nivolumab; clear cell renal cell carcinoma; tissue slice culture; PD1; T-cells

#### **1. Introduction**

Renal cell carcinoma (RCC) is among the ten most frequent malignancies worldwide with increasing incidence and decreasing mortality [1]. The decrease in mortality is the

consequence of early diagnosis and a broad range of therapy options in advanced and metastasized stages, which applies to 20% of patients at the time of diagnosis and another 20% of patients during the clinical course after initial surgery [2]. For clear cell renal cell carcinoma (ccRCC), the most common histologic subtype of RCC, the combination of either tyrosine kinase inhibitor (TKI) and immune checkpoint inhibitor (ICI) or two ICIs are guideline-recommended first-line treatment options. Four comprehensive clinical trials (Checkmate 214 (ClinicalTrials.gov Identifier: NCT02231749), Keynote 426 (ClinicalTrials.gov Identifier: NCT02853331), Javelin 101 (ClinicalTrials.gov Identifier: NCT02684006), Checkmate9ER (ClinicalTrials.gov Identifier: NCT03141177)) have shown the superiority of either combined ICI therapy (nivolumab + ipilimumab) or combined TKI and ICI therapy (axitinib + pembrolizumab, axitinib + avelumab, cabozantinib + nivolumab) regarding overall survival (OS) or progression free survival (PFS) compared to standard-of-care sunitinib in patients with previously untreated advanced RCC [3–6]. The objective response rate (ORR) for the combination therapies including ICIs ranged from 42% [3] to 59.3% [4] compared to 25.7% to 35.7% for sunitinib alone [3–6]. Complete responses were rare in all studies. Treatment-related adverse events of grade 3 or higher occurred either in the sunitinib group [3] or in the combination therapy group [4,6] or showed no significant difference [5]. With the exception of the Javelin 101 trial, treatment was discontinued due to treatment-related adverse effects, more often in the group with combination therapy. In the Checkmate 214 trial, there were even eight treatment-related deaths in the group treated with the combination of ipilimumab and nivolumab compared to four treatment-related deaths in the sunitinib group [3].

Hence, stratifying patients eligible for therapy including ICIs remains a difficult task and predictive biomarkers are urgently needed. Programmed death receptor ligand 1 (PD-L1) expression of RCC has been examined in the above mentioned clinical trials, but has not been established as a reliable predictive biomarker for ICI [7]. A more dynamic approach is to measure blood parameters before or during ICI treatment. Serum levels of soluble programmed death receptor 1 (sPD1) and sPD-L1 correlated with OS and PFS of patients with RCC [8,9]. In patients with non-small cell lung cancer (NSCLC) an increased proliferation of CD8+ cytotoxic T-cells (CTL) in peripheral blood correlated with response to nivolumab therapy, whereas patients with progressive disease had no change in CTL proliferation or even a decrease [10,11]. In metastasized RCC, a high density of tumor infiltrating PD1+ CTLs correlated with higher ORR and prolonged PFS in a patient cohort treated with nivolumab and is, therefore, a promising candidate predictive biomarker for response to nivolumab [12,13].

In this study, we report our results regarding an ex vivo tissue slice culture (TSC) model with incubation of fresh vital ccRCC tumor tissue with nivolumab for 24 h or 72 h and consecutive quantification of immune cell density, proliferation, and distribution.

#### **2. Materials and Methods**

#### *2.1. Patients and Tissue Collection*

Twelve patients with ccRCC, surgically treated at the Department of Urology and Pediatric Urology of the University Medical Center Mainz from 2017 to 2020, were included in the study. Tissue collection was approved by ethics approval for the Tissue Biobank, University Medical Center Mainz (ethics approval: 837.031.15 (9799); date of approval: 2 October 2015). After arrival of the surgical specimen in the Institute of Pathology, tumors were macroscopically examined to confirm subtype (golden to yellow cut surface with hemorrhage) and tissue vitality. In cases of doubt, additional microscopic examination by frozen section of the intended area of sampling was performed. Exclusion criteria for tissue collection included a tumor size <1.0 cm to ensure reliable pathologic routine diagnostics, poor tissue quality with a high portion of necrotic tumor tissue, and non-clear cell morphology. Fresh vital tumor tissue (length: 10 mm; diameter: 6 mm) was collected from the tumor periphery by using a defined punching tool (KAI Medical Biopsy Punch, Solingen, Germany), stored in a 4 ◦C chilled Krebs-Henseleit-Buffer (Sigma-Aldrich/Merck, Darmstadt, Germany) and referred to the lab for TSC. To match tumor heterogeneity, at least two different tumor localizations were sampled.

#### *2.2. Ex Vivo Tissue Slice Culture*

The tissue culture protocol has been described in detail previously [14,15]. Briefly, tumor tissue was cut into slices of 300 μm thickness using a Vibratome VT1200 (Leica Microsystems, Mannheim, Germany). The first and the last slice of each tumor sample was immediately fixated in buffered 4% formalin. The other tissue slices were randomly assigned to control and intervention groups. Tissue slices were incubated at the air-mediuminterface in a 12-well plate with appropriate inserts. The used tissue culture medium was DMEM cell culture medium (ATCC, Manassas, CO, USA) with supplements (1% Penicillin/Streptomycin, 10% fetal calf serum (Sigma-Aldrich/Merck, Darmstadt, Germany)) and with or without nivolumab (Opdivo, Bristol-Myers Squibb, Munich, Germany). The medium including nivolumab in the therapy group was changed after 1 h and every additional 24 h. For the time of the experiment, tissue slices were kept in an incubator with a humidified atmosphere, a temperature of 37 ◦C, and 5% CO2. After 24 h or 72 h, respectively, tissue slices were harvested, fixated in buffered 4% formalin, and paraffin embedded. Figure 1 provides a detailed overview of the experimental setup.

#### *2.3. Treatment Regimen*

Tissue slices were incubated with increasing concentrations of nivolumab (0.1 μg/mL, 1 μg/mL, 10 μg/mL, and 100 μg/mL) or without nivolumab as control. Cultivation was usually performed in triplicates and in cases of limited amount of tumor tissue in duplicates.

**Figure 1.** Tissue culture workflow.

#### *2.4. Conventional and Immunohistochemical Staining*

Tissue slices were stained with hematoxilyn and eosin (HE) and tumor tissue vitality was confirmed. Slices with necrosis >50% were excluded from further immunohistochemical stainings. Slices were stained with antibodies against Ki67 (MIB-1, Dako, Glostrup, Denmark), PD-L1 (ab213524, Abcam, Cambridge, UK) or double stained using the Envision G/2 Doublestain System or Envision Flex Doublestain System (Dako). The antibody combinations were CD3 (IR503, Dako) + Ki67 (MIB-1, Dako), CD3 (IR503, Dako) + CK AE1/3 (IR053, Dako), CD8 (IR623, Dako) + Ki67 (MIB-1, Dako), PD1 (ab52587, Abcam) + Ki67

(MIB-1, Dako), and PD1 (ab52587, Abcam) + CK AE1/3 (IR053, Dako). All slides were stained with automatized immunostainers (Autostainer Plus, Dako).

#### *2.5. Digital Image Analysis*

Digitalization and digital image analysis were performed as previously described using a digital whole slide scanner (Nanozoomer, Hamamatsu Photonics, Hamamatsu, Japan) and the HALO® platform (Indica Labs, Corrales, NM, USA) [16]. Briefly, for the detection and quantification of stain-positive cells, the CytoNuclear module (v1.4–1.6) was used. To differentiate between tumor parenchyma and tumor-associated stroma, a tissue classifier was included. Localization of each detected cell in the tissue and its biomarker profile cells were saved and used for spatial analysis with the proximity tool included in HALO®. Vital and necrotic tumor areas were manually annotated and quantified via digital image analysis. PD-L1 status (tumor proportion score, TPS) was assessed by light microscopy (Olympus BX45, Olympus, Tokio, Japan).

#### *2.6. Spatial Distribution*

Tissue slices were immunohistochemically double stained for T-cells (brown) and for tumor cells (red). Digital image analysis was used for detection of T-cells (markup: red), tumor cells (markup: green), and other cells (markup: blue). Tissue was further classified into tumor area (classifier markup: red) and stroma (classifier markup: green) (Figure S1A). T-cells were dichotomized into "T-cells Tumor" and "T-cells Stroma" depending on the T-cells' localization (Figure S1B). The percental distribution of T-cells Tumor and T-cells Stroma within a diameter of 30 μm around tumor cells was quantified using the proximity tool implemented in HALO®.

#### *2.7. Statistical Analysis*

Data are given as mean ± standard deviation. In cases of high inter-individual variability of the examined parameters, data were normalized relative to baseline value or to control. For the comparison of two groups, the paired *t*-test was performed and, for the comparison of three or more groups, the one-way analysis of variance (one-way ANOVA) was performed. The necessary assumptions for the one-way ANOVA were tested with the Shapiro–Wilk test (normal distribution within the individual groups) and the Levene test (homogeneity of variances). In cases, where the assumption of normal distribution within the individual groups was violated, the Kruskal–Wallis test was alternatively performed. Post hoc tests for the one-way ANOVA were the Tukey test and, for the Kruskal–Wallis test, the Dunn test. All calculations were performed using Microsoft Excel (version 2012), R statistical software (version 4.0.3) and Rstudio (version 1.4.1103). Differences with *p*-values < 0.05 were considered significant.

#### **3. Results**

#### *3.1. Characteristics of Patient Collective*

Tumor tissues from 12 patients were treated with nivolumab for 24 h (tumors 1–7) or 72 h (tumors 8–12), respectively. Eleven specimens were from primary renal tumors and one from an adrenal gland metastasis. 63.6% (*n* = 7) of primary tumors were organ confined. The median age of patients at the moment of surgery was 65 years (mean 68.3 ± 10.0). Clinical follow-up data was available for nine patients. The median time of follow-up was 10.9 months (min. 1 month, max. 22.9 months, and the mean was 11.4 ± 8.3 months). By the end of follow-up, one patient had died of a disease unrelated to RCC, one was suffering from progressive disease, two showed a stable disease, and five showed no progress. Clinicopathological data including follow up are summarized in Table 1.

**Table 1.** Clinicopathological information of the patient collective.

*3.2. Tissue Slice Culture Is Possible for up to Three Days but Reduces Tumor Infiltrating Immune Cells*

Fresh vital tumor tissue of ccRCC was sampled close to the invasive margin (IM). Post hoc immunostaining of corresponding primary tumors showed that there were tumors with low (Figure S2A) and high (Figure S2B) amounts of tumor infiltrating PD1+ IC which were rather concentrated at the IM. The tumor tissue was cut into 300 μm thick slices and cultivated with increasing concentrations of nivolumab for 24 h or 72 h, respectively. With only one tumor sample which had to be excluded from further analysis due to extensive cultivation related necrosis, the success rate for the establishment of TSC corresponds to 92.3%. All tumors showed clear cell morphology and stayed negative for PD-L1 during TSC (tumor proportion score: 0%). Tumor infiltrating immune cells with PD1 expression (PD1+ IC) could be detected in every tumor (Figure 2). Baseline densities and proliferation rates of tumor infiltrating immune cells showed a high inter-individual variability (Table 2). After 24 h of TSC, the necrotic tumor area was non-significantly compared to baseline, whereas there was a significant increase in necrosis after 72 h (Figure 3A). Overall proliferation, including tumor cells, showed no significant change after 24 h and a non-significant increase after 72 h (Figure 3B). Tumor infiltrating PD1+ IC, proliferating PD1+ IC and proliferating T-cells were not altered significantly after 24 h of cultivation. Tumor infiltrating T-cells, CTL, and proliferating CTL were significantly decreased (Figure 3C). After 72 h of TSC, PD1+ IC, proliferating PD1+ IC, CTL, and proliferating CTL were all significantly decreased compared to baseline. Tumor infiltrating T-cells were not changed after 72 h of TSC and proliferating T-cells non-significantly increased (Figure 3D).

**Figure 2.** Histological morphology and PD-1/PD-L1 status of tumors. Clear cell morphology of tumors (HE-staining, left), PD1+ immune cells (brown) in the tumor tissue (red) (middle), and PD-L1 expression (brown) at baseline and after tissue slice culture for 24 h or 72 h. Interspersed PD1+ immune cells were present, however the tumor cells showed no PD-L1 expression. Bar indicates 50 μm.

**Table 2.** PD-L1 status and immune cell densities and proliferation rates at baseline.

 Distinct Reaction Patterns of Tumor Infiltrating Immune Cells in Response to Nivolumab*

A decreased density of PD1+ IC after nivolumab treatment was observed across all examined tumors, whereas T-cells and CTL and the corresponding proliferation fractions showed either a nivolumab dependent increase, decrease, or no alteration. Table 3 provides an overview of the reaction patterns of the individual tumors. Tumor 3 showed a significant decrease in PD1+ IC (*p* = 0.01) and a decrease by trend of proliferating PD1+ IC (*p* = 0.4), a consistent non-significant increase in tumor infiltrating T-cells (*p* = 0.3), proliferating T-cells (*p* = 0.4), CTL (*p* = 0.5), and proliferating CTL (*p* = 0.6) after nivolumab treatment (Figure 4A). In contrast, tumor 5 reacted with a significant decrease in PD1+ IC (*p* < 0.01), proliferating PD1+ IC (*p* < 0.01), T-cells (*p* = 0.01) and proliferating T-cells (*p* < 0.01), as well as a non-significant reduction of CTL (*p* = 0.4) and proliferating CTL (*p* = 0.3) (Figure 4B). Tumor 7 showed the third pattern, characterized by minor, non-significant changes in immune cell densities (T-cells: *p* = 0.8; CTL: *p* = 0.8) and proliferation fractions (Ki67+ PD1+ IC: *p* = 0.2; Ki67 + T-cells: *p* = 0.5; Ki67 + CTL: *p* = 0.9); however, the density of PD1+ IC was significantly decreased (*p* = 0.001) (Figure 4C). Overall proliferation (tumor 3: *p* = 0.99; tumor 5: *p* = 0.1; tumor 7: *p* = 0.3) and nivolumab-dependent necrosis (tumor 3: *p* = 0.1; tumor 5: *p* = 0.7; tumor 7: *p* = 0.5) were not significantly changed (Figure 3). The nivolumab-dependent decrease in PD1+ IC was significant after averaging the respective experiments with a duration of 24 h or 72 h (24 h: *p* < 0.01; 72 h: *p* < 0.01); the other parameters showed no significant changes (Figure 4).

**Table 3.** Nivolumab-dependent reaction patterns of tumor infiltrating immune cell densities and corresponding proliferation rates.

Abbreviations: CD3: T-cells; CD3-Ki67: proliferating T-cells; CD8: cytotoxic T-cells; CD8-Ki67: proliferating cytotoxic T-cells; PD1: programmed death receptor expressing immune cells; PD1-Ki67: proliferating programmed death receptor expressing immune cells; + nivolumab-dependent increase; - nivolumab-dependent decrease; ~ no nivolumab-dependent change; NA: data not available.

**Figure 3.** Influence of tissue slice culture (TSC) on tumor vitality, proliferation, and tumor infiltrating immune cells. Tumor tissue of clear cell renal cell carcinoma (ccRCC) was cultivated for 24 h to 72 h. Necrotic tumor area and the immunohistochemical stainings Ki67, Ki67-PD1, Ki67-CD3, and Ki67-CD8 were quantified by digital image analysis. (**A**) The percentage of necrotic tumor after 24 h (*n* = 7) or 72 h (*n* = 5) of TSC compared to baseline. Fold change of (**B**) overall proliferation, (**C**) tumor infiltrating PD1+ immune cells, T-cells, cytotoxic T-cells and their proliferating subsets after 24 h (*n* = 7) or (**D**) 72 h (*n* = 5) of TSC compared to baseline. Data were normalized relative to baseline values, if not stated otherwise, and given as mean ± standard deviation. The paired *t*-test was used for statistical analysis. \*: *p* < 0.05.

**Figure 4.** Nivolumab-dependent changes of tumor infiltrating immune cell densities and proliferation rates. Tissue slices of clear cell renal cell carcinoma (ccRCC) were immunohistochemically stained for Ki67-PD1, Ki67-CD3, and Ki67-CD8 after 24 h of cultivation with increasing concentrations of nivolumab. Three representative reaction patterns to nivolumab treatment are shown: (**A**) pattern A (tumor 3) with nivolumab-dependent increased tumor infiltrating T-cells, (**B**) pattern B (tumor 5) with nivolumab-dependent decreased tumor infiltrating T-cells, and (**C**) pattern C (tumor 7) without nivolumabdependent changes of tumor infiltrating T-cells. Data are given as mean ± standard deviation. For statistical analysis the one-way analysis of variance or the Kruskal–Wallis test with appropriate post hoc tests were used. *p*-values were corrected with the Bonferroni method. \*: *p* < 0.05.

#### *3.4. Spatial Distribution of Tumor Infiltrating Immune Cells under the Influence of Nivolumab*

In individual experiments, e.g., tumor 11, a minor shifting of tumor infiltrating Tcells toward tumor cells after treatment with 100 μg/mL nivolumab could be observed (Figure 5A), whereas there was a change in the distribution of stromal T-cells (Figure 5B). After averaging the experiments with a duration of 24 h, the distribution of tumor infiltrating PD1+ IC after nivolumab treatment was unaltered compared to control and the T-cells showed a non-significant shift toward tumor cells (Figure S5A). This effect was even more pronounced, yet still not significant, when looking at the corresponding stromal T-cell fraction (Figure S5A), whereas the stromal PD1+ IC were farther away compared to control. After 72 h, there was no major difference in tumor infiltrating T-cells after nivolumab treatment compared to control (Figure S5B).

**Figure 5.** Nivolumab-dependent spatial distribution of tumor infiltrating T-cells. Tumor tissue of clear cell renal cell carcinoma (ccRCC) cultivated 72 h with nivolumab. Tissue slices were immunohistochemically double stained for cytokeratin and CD3. The distance between (**A**) tumor infiltrating T-cells and (**B**) stromal T-cells to tumor cells was calculated by digital image analysis. Data are given as mean ± standard deviation.

#### **4. Discussion**

So far, there are no established predictive biomarkers to guide treatment in metastatic RCC. Therefore, a precise test system for response prediction is one option to better stratify patients who will benefit from nivolumab treatment. In this study, an ex vivo TSC model was tested to examine nivolumab-dependent effects on tumor infiltrating immune cells in human ccRCC tumor tissue.

The major finding of this study was the nivolumab-dependent significant reduction of PD1+ IC (Figure 4 and Figure S3). Immunohistochemical PD1 positivity of tumor infiltrating immune cells is widely considered as a biomarker for "exhausted immune cells", but with regard to T-cells and especially CTL, it is rather a biomarker for activated CTL [17,18]. In our previously published study about the prognostic value of tumor infitrating immune cells in ccRCC, PD1+ IC were associated with a favorable cancer-specific survival [16], indicative that PD1+ CTL comprise tumor-reactive CTL, as was shown in malignant melanoma [19]. Reduced PD1 expression after incubation with PD1-targeting agents, as described in the present study, has been demonstrated before: PD1 expression of peripheral CD8+ T-Cells of patients suffering from PDAC was reduced after incubation with nivolumab and also with pembrolizumab, another therapeutic anti-PD1-antibody [20]. In a xenograft mouse model of colon carcinoma and mammary carcinoma, a reduced frequency of PD1 + CD8+ T-cells and a decrease in PD1 levels below a certain threshold were associated with release from adaptive immune resistance [21]. In principle, this mechanism could also apply to ccRCC TSC. However, methods other than immunohistochemistry (IHC) are required for a more precise PD1 quantification, e.g., flow cytometry. So far, the decreased density in PD1+ IC after nivolumab treatment, assessed by IHC, could serve as a positive control for a successful nivolumab treatment in TSC. For further interpretation

of nivolumab-dependent effects, the focus of this study was on the differentially altered densities and proliferation rates of T-Cells and CTL after nivolumab treatment (Figure 4). This is in line with results examining proliferation of peripheral PD1+ CTL in response to nivolumab in NSCLC. Reduced CTL proliferation after nivolumab infusion correlated with progressive disease [10] and an early proliferative response of PD1+ CTL with (partial) response [11,22]. All in all, the number of tumors (*n* = 12) examined in this study was too small for further correlation of the observed reaction patterns and the clinical course of the included patients. Thus, for further validation of the reported results, these experiments need to be conducted in a larger patient cohort.

All of the examined tumors were immunohistochemically negative for PD-L1. However, PD-L1 expression in renal cell carcinoma has been shown to be a strong prognostic factor for poor outcome [23], but it only provides limited value on response prediction to nivolumab. The Checkmate025 [NCT01668784] trial showed the superiority of nivolumab over everolimus as a second-line therapy for patients with advanced RCC independent of PD-L1 expression [24]. Similarly, the consecutive Checkmate214 and Checkmate9ER trials showed the greater benefit of patients with RCC treated with combination therapies including nivolumab compared to the standard of care sunitinib, independent of the PD-L1 status of the primary tumors [3,6]. Thus, a lack of PD-L1 in the tumor tissue seems to have no major impact on response to nivolumab. Therefore, it is reasonable to investigate the effect of nivolumab on tumor infiltrating immune cells in PD-L1 negative ccRCC tumors, too.

The used ex vivo TSC bears several advantages to address this question compared to other established lab-based experimental designs. Firstly, primary and metastatic tumor tissue can be examined with TSC. Cultivation of several samples from different localizations within the tumor tissue allows for modelling tumor heterogeneity, especially with regard to PD-L1 expression [25]. Thus, at least two samples from different tumor localizations were taken. On the other hand, high tumor heterogeneity can result in the high variance of measured data; despite the lack of significant tendencies due to the high variances, these results should also be interpreted as representative for the whole tumor, because they comprise several tumor localizations. Secondly, the tumor microenvironment (TME) which is crucial for interactions between tumor cells and tumor-associated immune cells is transferred into TSC so that associations between therapy effects, e.g., necrosis or proliferation of tumor cells, and the TME can be discovered. One study using the TSC method for pharmacological experiments on RCC highlights the importance of PD-L1 expression and tumor infiltrating CTL [26]. Investigation in the TME can indeed be achieved with air-liquid interface patient-derived organoids or humanized mouse models, too, but establishing these is resource and time intensive [27,28]. Third, differently to common in vitro monolayer cell culture models, the three-dimensional architecture of the tumor is preserved in TSC. This leads to the conception that the tumor tissue reacts similarly in an ex vivo setting compared to the in vivo situation [29–31]. The TSC protocol used in this study has been established for ccRCC tumor tissue and was successfully used in a previous study in our lab (14). Nonetheless, there was increased necrosis of tumor tissue, unrelated to nivolumab treatment (Figure 3A). Tumor necrosis is not uncommon in ccRCC and is a poor prognostic marker for survival [32]. Therefore, a certain amount of tissue necrosis in TSC must be considered inevitable when screened for drug response and can, in principle, be kept low with experiment durations of 24 h (Figure 3A). In terms of the optimization of TSC, protocols studies have so far focused on improving TSC media compositions with regard to tumor cell vitality [33]. Overall proliferation was not significantly changed (Figure 3B) which implies that the used TSC medium composition is suitable to maintain tumor cell proliferation. In contrast, there was a marked drop of immune cell density and proliferation due to TSC alone (Figure 3C,D). CTL were extraordinarily affected which could explain the lack of nivolumab-dependent tumor necrosis in this study (Figure S3). While most TSC studies focus on effects on tumor cells, two have examined tumor infiltrating immune cells in ductal pancreatic adenocarcinoma and gastric carcinoma and found no significant reduction up to day 6 of TSC [34,35]. Thus, in further projects, TSC medium

composition should be reevaluated to support both tumor cell and immune cell vitality and proliferation.

In malignant melanoma, responders to ICI had significantly higher CTL densities within 20 μm around tumor cells compared to non-responders [36]. In this study, the spatial distribution of immune cells regarding the distance to tumor cells was investigated, too. The data show only minor nivolumab-dependent effects on the distribution of tumor infiltrating T-cells and PD1+ IC within 30 μm around tumor cells (Figure 5 and Figure S5). This is most likely due to the fact that the ccRCC tumor tissue punches that are used for TSC lack abundant tumor-associated stroma, which means that tumor infiltrating immune cells are close to tumor cells at any time during the experiment. Additionally, intact tissue slices are necessary for the measurement of spatial distribution since tearing of tissue slices is a major confounder. To circumvent these issues, live cell imaging of CTL, as was already established for lung tumor TSC [37], could provide a more detailed insight into the influence of nivolumab on CTL migration through the tumor and number of contacts to tumor cells.

The limitations of the study are: 1. The relatively low number of cases which are sufficient to document the potential and pitfalls of this method but is too low to prove that response to nivolumab can be predicted through TSC and the measurement of tumor infiltrating immune cells; 2. The implementation of clinical studies to correlate the results of this model with clinical therapies and outcomes is needed; 3. The tumor-inherent heterogeneity that—as discussed above—makes sampling at different locations necessary to achieve reliable results. This can turn out to be problematic especially in cases with poor tissue quality or large necrosis.

#### **5. Conclusions**

Taken together, the present study provides encouraging data that support the ex vivo TSC approach as a model to predict response to nivolumab in ccRCC. Yet, TSC conditions must be optimized in order to minimize effects on tumor infiltrating immune cells through TSC alone. This together with further research on the correlation of nivolumab-dependent changes in immune cell proliferation as a readout parameter for response of ccRCC patients to nivolumab treatment might be the way to establish TSC as a predictive test system.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/cancers13184511/s1. Figure S1: Spatial analysis of tumor infiltrating T-cells; Figure S2: Post hoc immunostaining of clear cell renal cell carcinoma (ccRCC); Figure S3: Nivolumab-dependent changes of overall proliferation and tumor necrosis; Figure S4: Nivolumab-dependent changes of overall proliferation, tumor necrosis and tumor infiltrating immune cell densities and proliferation rates; Figure S5: Nivolumab-dependent spatial distribution of tumor infiltrating PD1+ immune cells and T-cells.

**Author Contributions:** Conceptualization, P.J.S. and S.M.-G.; Data curation, P.J.S.; Formal analysis, P.J.S.; Investigation, P.J.S.; Methodology, P.J.S. and N.H.; Project administration, P.J.S.; Supervision, W.R., S.P. and K.E.T.; Validation, S.F., D.-C.W., I.T., A.T., A.H., S.M.-G., W.R., S.P. and K.E.T.; Visualization, P.J.S. and K.E.T.; Writing—original draft, P.J.S.; Writing—review & editing, P.J.S., W.R., S.P. and K.E.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** This study was covered by the ethics approval for the usage of excess tumor tissue within the scope of the terms and regulations of the Tissue Biobank, University Medical Center, Mainz, Germany (837.031.15 (9799); date of approval: 2 October 2015).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data presented in this study are available in this article and Supplementary Materials.

**Acknowledgments:** We thank Ron Unger for the proofreading as well as Silke Mitschke and Bonny Adami for excellent technical assistance. This project was supported by the Tissue Bank of the University Medical Center Mainz.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

#### *Article*

## **Molecular Subtypes Based on Genomic and Transcriptomic Features Correlate with the Responsiveness to Immune Checkpoint Inhibitors in Metastatic Clear Cell Renal Cell Carcinoma**

**ByulA Jee 1, Eunjeong Seo 1, Kyunghee Park 2, Yi Rang Kim 3, Sun-ju Byeon 4, Sang Min Lee 1, Jae Hoon Chung 1, Wan Song 1, Hyun Hwan Sung 1, Hwang Gyun Jeon 1, Byong Chang Jeong 1, Seong Il Seo 1, Seong Soo Jeon 1, Hyun Moo Lee 1, Se Hoon Park 5, Woong-Yang Park <sup>2</sup> and Minyong Kang 1,2,6,\***

**Simple Summary:** Immune checkpoint inhibitors (ICIs), such as programmed cell death protein 1 (PD-1) blockade, have proven to be the most effective agents for the management of many cancer types. Although ICIs are the current standard of care for treating metastatic clear cell renal cell carcinoma (ccRCC), 40–60% of patients still have intrinsic resistance to ICIs across multiple clinical trials. Therefore, identifying optimal biomarkers that can predict either responders or non-responders to ICIs has been of tremendous importance. Here, we generated targeted sequencing and whole transcriptomic sequencing of 60 patients with metastatic ccRCC treated with ICIs. Moreover, transcriptomic analysis was integrated to identify molecular subtypes using a total of 177 tumor samples by merging our data and published data derived from the CheckMate 025 trial. Our results show that these molecular subtypes are associated with specific genomic alterations, distinct molecular pathways, and differential clinical outcomes in patients with metastatic ccRCC treated with ICIs.

**Abstract:** Clear cell renal cell carcinoma (ccRCC) has been reported to be highly immune to and infiltrated by T cells and has angiogenesis features, but the effect of given features on clinical outcomes followed by immune checkpoint inhibitors (ICIs) in ccRCC has not been fully characterized. Currently, loss of function mutation in *PBRM1*, a PBAF-complex gene frequently mutated in ccRCC, is associated with clinical benefit from ICIs, and is considered as a predictive biomarker for response to anti-PD-1 therapy. However, functional mechanisms of *PBRM1* mutation regarding immunotherapy responsiveness are still poorly understood. Here, we performed targeted sequencing (*n* = 60) and whole transcriptomic sequencing (WTS) (*n* = 61) of patients with metastatic ccRCC treated by ICIs. By integrating WTS data from the CheckMate 025 trial, we obtained WTS data of 177 tumors and finally identified three molecular subtypes that are characterized by distinct molecular phenotypes and frequency of *PBRM1* mutations. Patient clustered subtypes 1 and 3 demonstrated worse responses and survival after ICIs treatment, with a low proportion of *PBRM1* mutation and angiogenesispoor, but were immune-rich and cell-cycle enriched. Notably, patients clustered in the subtype

**Citation:** Jee, B.; Seo, E.; Park, K.; Kim, Y.R.; Byeon, S.-j.; Lee, S.M.; Chung, J.H.; Song, W.; Sung, H.H.; Jeon, H.G.; et al. Molecular Subtypes Based on Genomic and Transcriptomic Features Correlate with the Responsiveness to Immune Checkpoint Inhibitors in Metastatic Clear Cell Renal Cell Carcinoma. *Cancers* **2022**, *14*, 2354. https://doi.org/10.3390/ cancers14102354

Academic Editors: Claudia Manini and José I. López

Received: 13 April 2022 Accepted: 4 May 2022 Published: 10 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

2 showed a better response and survival after ICIs treatment, with enrichment of *PBRM1* mutation and metabolic programs and a low exhausted immune phenotype. Further analysis of the subtype 2 population demonstrated that *GATM* (glycine amidinotransferase), as a novel gene associated with *PBRM1* mutation, plays a pivotal role in ccRCC by using a cell culture model, revealing tumor, suppressive-like features in reducing proliferation and migration. In summary, we identified that metastatic ccRCC treated by ICIs have distinct genomic and transcriptomic features that may account for their responsiveness to ICIs. We also revealed that the novel gene *GATM* can be a potential tumor suppressor and/or can be associated with therapeutic efficacy in metastatic ccRCC treated by ICIs.

**Keywords:** renal cell carcinoma; immune checkpoint inhibitor; responsiveness; molecular features

#### **1. Introduction**

Immune checkpoint inhibitors (ICIs), such as programmed cell death protein 1 (PD-1) blockade, have proven to be the most effective agents for the management of many cancer types [1]. Despite the low tumor mutational burden of renal cell carcinoma (RCC), it has unique immunologic features, including high immune infiltration score and increased infiltration by cytotoxic CD8+ T cells, which are known to be associated with the response to PD-1 blockade [2,3]. In this context, recent phase III clinical trials, such as CheckMate 025, CheckMate 214, and KEYNOTE 426, showed that ICIs-based regimens significantly improved the objective response and survival outcomes compared to that of tyrosine kinase inhibitors, particularly in advanced clear cell RCC (ccRCC) [4–7]. Although ICIs are the current standard of care for treating metastatic ccRCC, 40–60% of patients still have intrinsic resistance to ICIs across multiple clinical trials. Therefore, identifying optimal biomarkers that can predict either responders or non-responders to ICIs have been of tremendous importance [8].

While PD-ligand 1 expression is a conventional biomarker for predicting responsiveness to ICIs across various types of malignancies, data on RCC have been heterogeneous, and the predictive value of PD-ligand 1 expression is not clinically practical yet [8]. In contrast to other solid tumors, tumor mutational burden and neoantigen load, which have been the commonly explored predictors for ICIs therapy, were not associated with clinical responses to PD-1 blockade in advanced ccRCC [4]. Additionally, no difference in survival outcomes according to the patterns of CD8+ T cell infiltration in ccRCC patients treated with anti-PD-1 was found [4]. Recently, several studies reported that a loss of function (LOF) mutation in a PBAF-complex gene *PBRM1*, that is commonly mutated in ccRCC, was associated with better clinical benefit (CB) from ICIs [4,9,10]. In this context, a comprehensive understanding of the molecular mechanisms of *PBRM1* mutation in patients with ccRCC treated with ICIs could be critical for the development of a novel biomarker and to help predict which patients are most likely to benefit from ICIs treatment.

Here, targeted sequencing and whole transcriptomic sequencing of 60 patients with metastatic ccRCC treated with ICIs were performed. Moreover, transcriptomic analysis was integrated to identify molecular subtypes using a total of 177 tumor samples and by merging our data and published data derived from the CheckMate 025 trial [4]. Our results show that these molecular subtypes are associated with specific genomic alterations, distinct molecular pathways, and differential clinical outcomes in patients with metastatic ccRCC treated with ICIs.

#### **2. Materials and Methods**

#### *2.1. Patients*

The data of 60 patients with metastatic ccRCC treated with ICIs, particularly as either first-line (*n* = 8) (combination of anti-CTLA4 (ipilimumab) or anti-PD-1 (nivolumab)) or second-line (monotherapy of nivolumab) (*n* = 52) therapy from 2017 to 2020 at Samsung Medical Center, were retrospectively collected. The Institutional Review Board of our center approved the use of human archival tissues for this study (IRB no. SMC 2020-03-063).

Therapeutic responses of ICIs were determined every 3 to 4 months of treatment using abdomen-pelvis and chest-computed tomography scans. The responses were classified as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD), according to the RECIST 1.1 criteria [11]. The clinical benefit (CB), non-clinical benefit (NCB), or intermediate benefit (IB) classification was defined in a previous report [9]. Briefly, CB included patients with CR, PR, or SD with any reduction in tumors lasting at least 6 months. NCB was defined as patients who experienced PD and were discontinued from ICIs therapy within 3 months. All other patients were assigned to the IB group.

#### *2.2. Targeted Sequencing Preprocess*

Tumor tissues from 60 patients were used for targeted sequencing of 380 cancer-related genes (CancerSCAN Version 3.1, a targeted-sequencing platform designed at Samsung Medical Center) and extracted from formalin-fixed paraffin-embedded tissues. Most samples had a mean coverage of ~900× with coverage at hotspots well above the mean. The paired-end reads were aligned to the human reference genome (hg19) using BWA (Version 0.7.5). Then, SAMtools (Version 0.1.18), GATK (Version 3.1-1), and Picard (Version 1.93) were used for file handling, local realignment, and removal of duplicate reads, respectively. The base quality scores were recalibrated using the GATK BaseRecalibrator based on known single-nucleotide polymorphisms (SNPs) and indels from dbSNP138.

#### *2.3. RNA-Sequencing Preprocess*

To perform RNA sequencing using 60 metastatic ccRCC and 5 adjacent nontumor tissues, total RNA was extracted utilizing the RNA extraction kit (RNeasy Mini Kit, QIAGEN, Maryland, MD, USA), and RNA integrity was verified using a 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA). The libraries for sequencing were generated using the QuantSeq 3 Library Prep Kit (Lexogen Inc., Vienna, Austria) according to the manufacturer's instructions and sequenced on a HiSeq 2000 system (Illumina, San Diego, CA, USA). The reads were mapped to the hg19 human reference genome using STAR with default parameters. The number of reads mapped to each gene was calculated using RNA-Seq by Expectation-Maximization. Data processing and analysis were performed using the R/Bioconductor libraries. To preprocess the transcriptome data, genes with zero values across samples were filtered out. The data were normalized by subtracting the average expression values of the adjacent nontumor tissues per gene and centering the expression values of each sample and gene. In addition, three ccRCC samples that were outliers with respect to the overall mean and standard deviation and two ccRCC samples without information on the *PBRM1* mutation were filtered.

#### *2.4. Immunohistochemical Analysis*

To perform immunohistochemistry using 51 of the 60 available tissues of metastatic ccRCC patients, the Tissue microarray (TMA) method was applied. Briefly, representative tumor tissues (2 mm diameter) were taken from individual paraffin-embedded tumors (donor blocks) and arranged in new recipient paraffin blocks (tissue microarray blocks) using a trephine apparatus. One core tissue was taken from each case. Sections with a thickness of 5 mm were cut from each TMA block, deparaffinized, and dehydrated for immunohistochemical (IHC) staining. Ventana XT Benchmark (Ventana Medical Systems, Oro Valley, AZ, USA) for GATM (1:100 dilution, Catalog #ab32936, Abcam PLC, Cambridge, England, UK) was used IHC staining. Membranous and cytoplasmic staining of GATM was evaluated, and immunoreactivity for GATM was scored as follows: diffuse (more and equal 50% of tumor cells showed immunoreactivity) and moderate to strong expression of GATM was regarded as high expression (2+), diffuse or focal (less than 50% tumor cells showed immunoreactivity) and weak expression of GATM was regarded as low expression (1+), and no expression of GATM was regarded as no (0). Specimens with either weak

or high expression of GATM were classified as GATM-positive and specimens with no expression of GATM were considered as GATM-negative. IHC data were reviewed by a well-experienced pathologist (S.-j.B.) who was unaware of other clinical data.

#### *2.5. GSEA (Gene Set Enrichment Analysis) and ssGSEA (Single-Sample GSEA)*

To perform the GSEA, the Hallmark gene sets from the Molecular Signatures Database (MSigDB Version 7.0) were used [12]. ssGSEA was computed using the "GSVA" package [13].

#### *2.6. Immune Cell Type and Immune Type*

The proportion of immune cell types was calculated using CIBERSORTx [14]. The proportion of 10 immune cell types was calculated by aggregation (for example, the proportion of macrophages was aggregated by macrophages M0, M1, and M2). Immune subtypes, including active and exhausted, were conducted using the nearest template prediction algorithm based on the expression of active and normal stroma signatures [15].

#### *2.7. The Signatures of Differentially Expressed Genes (DEGs)*

To identify upregulated or downregulated genes using GSE102806, DEG sets from three conditions were calculated. DEG1 was calculated as shPBRM1 vs. shControl in replicate 1 of the 786-O cell line. DEG2 was calculated as shPBRM1 vs. shControl in replicate 2 of the 786-O cell line. DEG3 was calculated as a *PBRM1* mutation vs. *PBRM1* wild type in the A-704 cell line. DEG4 and DEG5 were calculated from merged data (*n* = 177). DEG4 was differentially expressed in subtype 2 (Figure S1 and Table S3). DEG5 was calculated as *PBRM1* mutation vs. *PBRM1* wild type. DEGs were subjected to *t*-test (GSE102806) and permuted *t*-test (merged data), and the cutoff options were *p* < 0.05, FDR < 0.05, and log2 fold differences > 0.5.

#### *2.8. Statistical Analysis*

DEGs were calculated using *t*-test and permutation *t*-test. The Kaplan–Meier method was used to estimate PFS and OS. Categorical variables between the two groups were compared using Fisher's exact test. One-way ANOVA was performed for the three groups. Student's *t*-test was performed for both groups. All statistical analyses were performed using the R software.

#### *2.9. Validation Sets*

To merge transcriptomic data for the classification of subtypes, CheckMate 025 data were obtained from Table S4 published in Nature Medicine by Braun et al. [4]. To validate our findings, datasets were obtained from the TCGA-KIRC and GEO websites (accessed date, 17 December 2020; accession numbers: GSE102806 and GSE105288).

#### *2.10. Cell Culture and Treatments*

786-O cells transfected with siScramble (siScr) or siPBRM1 (AM16708, Thermo Fisher, Waltham, MA, USA) were seeded before treatment at 60–80% confluent at the time of the experiment. Additionally, 0.5 μM actinomycin D (11805017, Thermo Fisher, Waltham, MA, USA) was used for the indicated time.

#### *2.11. Real-Time qPCR*

786-O or Caki cells were transfected with siScr or siPBRM1, and after 48 h, real-time RT-PCR assays were conducted following the manufacturer's instructions. Furthermore, 0.5 μg of total RNA was used as templates for reverse transcription through the ReverTra Ace qPCR RT Master Mix (Toyobo Co., Ltd., Kodakara island, Japan) according to the manufacturer's instructions. Real-time PCR analysis was performed using the QuantStudio system with SYBR Premix Ex Taq (Takara Co., Ltd., Otsu, Japan). GATM F-5 -CAC TAC ATC GGA TCT CGG CTT, GATM R-5 -CTA AGG GGT CCC ATT CGT TGT and USH1C

#### F-5 -TTC CGG CAT AAG GTG GAT TTT C, USH1C R-5 -GTA CAT TCG CAG CAC ATC ATA GA.

#### *2.12. Cell Migration Assays*

A total of 786-O cells were transfected with siRNAs against the control, *PBRM1*, or *GATM*. After 24 h, the 786-O cells seeded on glass-bottomed dishes pre-coated with fibronectin (100 g/mL) were scratched. After the cells reached 90% confluence, the monolayer was scratched with a pipette tip and incubated with a medium containing 300 μM H2O2 for 12 h in a humidified CO2 incubator at 37 ◦C.

#### *2.13. Colony Formation Assay*

Cells were transfected with the indicated siRNAs and seeded at 1000–2000 cells/well in 6-well plates. After 24 h, the medium was replaced with 300 μM H2O2 or low-glucose medium (11966025 Thermo Fisher) and incubated. After 7 days, the cells were fixed and stained with crystal violet. Triplicate wells were used for each experiment.

#### **3. Results**

#### *3.1. The Characteristics of Genomic Alterations in Patients with ccRCC Treated with ICIs*

The baseline demographics of patients with metastatic ccRCC in our cohort are described in Table S1. Additionally, treatment outcomes according to first- and second-line therapies are summarized in Table S2. Next, to evaluate the genomic landscape of patients with metastatic ccRCC (*n* = 60) treated with ICIs, we focused on targeted sequencing data to identify recurrently mutated genes in our cohort and found 17 recurrently altered genes. The most commonly altered genes in this cohort were *VHL* (*n* = 34, 56.7%), *PBRM1* (*n* = 18, 30.0%), *SETD2* (*n* = 16, 26.7%), and *BAP1* (*n* = 12, 20.0%), which were generally similar to those previously reported for ccRCC (Figure 1A) [4,9]. Next, when gene-specific alterations were compared between the clinical benefit (CB) group and the non-clinical benefit (NCB) group, only the *PBRM1* mutation among the 17 recurrently altered genes was significantly enriched in the CB group (Fisher's exact test, *p* = 0.03, odds ratio for CB = 3.67, 95% confidence interval (CI) = 0.98–14.69) (Figure 1B). As expected, patients with the *PBRM1* mutation had significantly prolonged overall survival (OS), not progression-free survival (PFS), compared to those with *PBRM1* wild type (log-rank test, *p* = 0.018) (Figure 1C). These results indicated that CB following ICIs therapy was more prominent in patients with metastatic ccRCC harboring the *PBRM1* mutation.

**Figure 1.** Overall mutational landscape in 60 patients with metastatic clear cell carcinoma treated with immune checkpoint inhibitors. (**A**) Heatmap shows 17 recurrently mutated genes in our cohort ordered by the number of mutation frequencies. (**B**) The plot shows the *p*-value and odds ratio for the 17 genes by performing Fisher's exact test between the clinical benefit versus non-clinical benefit groups. (Red dashed lines denote *p* < 0.05 and odds ratio > 1. Each black dot denotes the 17 recurrently mutated genes). (**C**) Kaplan–Meier plots show the overall survival (top) and progression-free survival (bottom) of patients who did or did not harbor mutations in *PBRM1*. PBRM1 MUT, PBRM1 mutation (blue); PBRM1 WT, PBRM1 wild type (yellow).

#### *3.2. The Characteristics of Molecular Subtypes in Patients with ccRCC Treated with ICIs*

To expand our understanding of the molecular phenotypes of metastatic ccRCC treated with ICIs, WTS data were generated using 177 tumor samples by merging our data and CheckMate 025 data. We aimed to identify molecular subtypes by utilizing four signatures associated with *PBRM1*-mutation and *PBRM1* LOF (Loss of Function). Two signatures of *PBRM1*-mutation were previously reported to be differentially expressed genes (DEGs) between patients with and without *PBRM1* mutations [16]. The signatures of *PBRM1* LOF also previously reported that high angiogenesis and less immunomodulation were related to phenotypes of *PBRM1* loss [17]. Unsupervised clustering analysis was performed based on the four signatures associated with *PBRM1* mutation and *PBRM1* LOF and identified three molecular subtypes in 177 patients (Figure 2, top). Patients with subtype 1 (*n* = 64, 36%) were characterized by the moderate expression of upregulated and downregulated

genes related to the *PBRM1* mutation with relatively lower expression of angiogenesis and a mixed pattern of immunomodulatory signature. Additionally, 12 patients with subtype 1 showed *PBRM1* mutations (20%). Interestingly, patients with subtype 2 (*n* = 75, 42%) were characterized by consistent expression patterns of upregulated and downregulated genes related to the *PBRM1* mutation, relatively higher expression of angiogenesis, and relatively lower expression of immunomodulatory signature with higher mutation rate of *PBRM1* (*n* = 44, 72%), compared to those with subtypes 1 and 3. The low expressions of immunomodulatory were seen in subtype 2, consistent with previous finding that *PBRM1* loss are associated with a nonimmunogenic tumor phenotype [17]. Patients with subtype 3 (*n* = 38, 22%) were characterized by the inverse expression of both upregulated and downregulated genes related to the *PBRM1* mutation, moderate expression of angiogenesis, and enrichment of higher immunomodulatory signature. Only five patients had the *PBRM1* mutations (8%) in subtype 3 (Figure 2, top).

**Figure 2.** Characteristics of subtypes based on the gene sets associated with *PBRM1* mutation and *PBRM1* loss of function. Heatmap of the single-sample gene set enrichment analysis shows the enrichment scores of the four gene sets in the merged data set (top). Mutation plot shows six commonly mutated genes by overlapping recurrently mutated genes from both our data and the CheckMate 025 data (middle). (Green color denotes harboring mutation). Radar plots show significantly enriched gene ontology terms using upregulated genes in each subtype (bottom).

We further characterized the prevalence of the six commonly mutated genes by overlapping recurrently mutated genes from our data and CheckMate 025 data and found a higher prevalence of alterations such as *VHL* (*n* = 36, 48%), *PBRM1* (*n* = 44, 72%) and *SETD2* (*n* = 28, 57%) in patients in subtype 2 (Figure 2, middle) than that of the other two subtypes. Next, to evaluate the key biological features related to these molecular subtypes, pairwise comparisons of each subtype were performed. Unique upregulated DEGs were identified in each molecular subtype (permuted *t*-test; *p* < 0.05, false discovery rate (FDR) < 0.05, and log2 fold difference > 0.5; Figure S1 and Table S3). First, 397 upregulated genes unique to subtype 1 were enriched in biological process terms for cell–cell signaling (ES = 2.46, *<sup>p</sup>* = 4.39 × <sup>10</sup><sup>−</sup>4) and cell development (ES = 2.34, *<sup>p</sup>* = 2.15 × <sup>10</sup>−5). Second, 1569 upregulated genes unique to subtype 2 with a high proportion of *PBRM1* mutations were associated with the metabolic process (ES = 25.93, *<sup>p</sup>* = 5.80 × <sup>10</sup>−42) and xenobiotic metabolism (ES = 12.56, *<sup>p</sup>* = 7.04 × <sup>10</sup><sup>−</sup>14). Finally, 459 upregulated genes unique to subtype 3 showed the activation of genes related to the cell cycle (ES = 12.55, *<sup>p</sup>* = 3.62 × <sup>10</sup>−23) and immune response (ES = 6.97, *<sup>p</sup>* = 1.19 × <sup>10</sup><sup>−</sup>10) (Figure 2, bottom). Taken together, the molecular stratification of 177 ccRCC tumors, treated with ICIs, was conducted into three subtypes with biologically distinct transcriptomes.

#### *3.3. Subtype 2 Is Associated with Higher Metabolic Processes and Lower Exhausted Immune Types than the Other Two Subtypes*

Next, when the treatment responses were examined according to each subtype, significant differences in the clinical responses and benefits were not found (Table S4). To evaluate the prognostic relevance of each subtype, the PFS and OS were compared according to each subtype. Notably, subtype 2, compared to that of subtypes 1 and 3, was significantly associated with OS (*p* = 0.0042) (Figure 3A) but not with PFS (*p* = 0.381) (Figure S2A). To further understand the molecular mechanisms of survival outcomes, transcriptomic pathway programs were explored by performing gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) using Hallmark gene sets (*n* = 50) in each subtype. Overall, 18 gene sets, including inflammatory response, oxidative phosphorylation, and E2F target pathways, were activated in each subtype (one-way analysis of variance (ANOVA) test, *p* < 0.0005, Figure 3B and Figure S2B). Both subtypes 1 and 3 were activated by immune-related pathways (INFLAMMATORY\_RESPONSE, COMPLEMENT, and IL6\_JAK\_STAT3). Subtype 3 differentiated from subtypes 1 and 2 through the enhanced activation of cell cycle progression pathways (G2M\_CHECKPOINT, E2F\_TARGETS, and MITOTIC\_SPINDLE). Particularly, in patients with subtype 2, metabolic-related pathways (OXIDATIVE\_PHOSPHORYLATION, FATTY\_ACID\_METABOLISM, and ADIPOGENE-SIS), the HYPOXIA and REACTIVE\_OXYGEN\_SPECIES pathways were activated. Previous reports demonstrated that ccRCC tumors with *PBRM1* mutations were activated with a hypoxic transcriptional signature, which is in agreement with our findings [9,17,18].

**Figure 3.** Patients in subtype 2 with enrichment of the *PBRM1* mutation are associated with metabolic pathways and transition of immune types. (**A**) Kaplan–Meier plot analysis of the overall survival for each subtype. (**B**) Heatmap of Hallmark gene sets. The mean Z score of the single-sample gene set enrichment analysis for each gene set was calculated (S1, Sub1; S2, Sub2; S3, Sub3). (**C**) CIBERSORTx findings show the proportion of distinct immune cell subpopulations (left) and the proportion of T cell subpopulations (right). (**D**) Barplots show the percentage of immune types, including active immune, exhausted immune and nonimmune types, in each subtype (left) and in the group with *PBRM1* mutation vs. the group without *PBRM1* mutation (right). (**E**) Kaplan–Meier plot analysis of progression-free survival for each immune type. AIT, active immune type (navy); EIT, exhausted immune type (green); NIT, non-immune type (gray).

#### *3.4. GATM Expression Associated with PBRM1 Mutation as a Novel Biomarker of Therapeutic Response in Patients with ccRCC Treated by ICIs*

Next, we aimed to identify key modulating genes associated with subtype 2 harboring a higher proportion of *PBRM1* mutations as well as showing the best survival outcomes. We analyzed cell line data (GSE102806), including 786-O and A-704 cell lines, which were associated with *PBRM1* LOF. First, we generated six gene sets, either upregulated or downregulated DEGs, by comparing treatment samples and control samples (see details in Section 2). To further evaluate the DEGs, including DEG1, DEG2, and DEG3, we applied the merged data of human samples (*n* = 177) using the DEGs and by performing ssGSEA

and identified distinct expression patterns in each subtype (Figure 4A). Notably, subtype 2 showed a significantly higher expression of the upregulated DEGs and a lower expression of the downregulated DEGs compared with subtypes 1 and 3. Likewise, tumors harboring the *PBRM1* mutation exhibited a higher expression of the upregulated DEGs, whereas the downregulated DEGs were not related compared with that of the *PBRM1* wild type (Figures 4B and S3A). These results suggested that upregulated DEGs rather than downregulated DEGs derived from cell line data were clearly validated in data derived from human samples showing the distinct characteristics of *PBRM1* LOF.

**Figure 4.** *GATM* expression is related to *PBRM1* mutation and *PBRM1* loss of function. (**A**) Heatmap of enrichment scores for the single-sample gene set enrichment analysis using upregulated and downregulated differentially expressed genes (DEGs) derived from cell line data GSE102806. (**B**) Boxplots summarizing the heatmap in (**A**) for each subtype (left) and in the group with *PBRM1* mutation vs. the group without *PBRM1* mutation (right) (S1, Sub1; S2, Sub2; S3, Sub3). (**C**) Venn diagram shows the overlapping of the five upregulated DEGs (left). Boxplots show the expression levels of *AMACR*, *SLC6A13*, *GATM*, and *USH1C* according to patients with and without *PBRM1* mutation in the merged data (right). (**D**) Kaplan–Meier plots show the overall survival of the patients stratified according the above or below the average *GATM* or *USH1C* in TCGA-KIRC (*n* = 530) and the merged data (*n* = 177). High expression (pink), low expression (sky blue).

Then, five DEGs were used (see details in Section 2), and 4 genes and 0 genes were identified as commonly upregulated or downregulated genes, respectively, by overlapping all five DEGs (Figure 4C, left and Figure S3B). The four identified genes, *AMACR*, *SLC6A3*, *GATM*, and *USH1C*, were upregulated and potentially associated with *PBRM1* mutation (Figure 4C, right). Among them, we focused on *GATM* (*<sup>p</sup>* = 1.01 × <sup>10</sup>−5) and *USH1C* (*<sup>p</sup>* = 1.94 × <sup>10</sup><sup>−</sup>4), which were significantly differentially expressed in tumors harboring the *PBRM1* mutation. In particular, the *GATM* gene showed a non-tumor-specific expression compared with that of the levels in tumor tissues in The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) data (*<sup>n</sup>* = 607, *<sup>p</sup>* = 3.07 × <sup>10</sup>−7; Figure S3C, left) and GSE105288 (*n* = 43, *p* = 0.045; Figure S3D, right). However, the *USH1C* gene showed tumor-specific expression compared with that of the levels of nontumor tissues in TCGA-KIRC data (*<sup>p</sup>* = 6.0 × <sup>10</sup>−9; Figure S3C, left) and GSE105288 (*<sup>p</sup>* = 0.503; Figure S3D, right). We then evaluated the clinical relevance of these two genes. When compared according to the expression levels of each gene, there were no differences in DFS (diseasefree survival) (Figure S3E) or PFS (Figure S3F). However, a higher expression of *GATM* was consistently correlated with clinical outcomes of OS in TCGA-KIRC data (*<sup>p</sup>* = 3.64 × <sup>10</sup><sup>−</sup>7; Figure 4D, left) and the merged data (*p* = 0.030; Figure 4D, right). A higher expression of *USH1C* was correlated with clinical outcomes of OS in TCGA-KIRC data (*p* = 0.00015; Figure 4D, left), whereas it did not reach statistical significance for OS in the merged data (*p* = 0.203; Figure 4D, right). Accordingly, GATM was prioritized as a potential driver in the following analysis.

#### *3.5. GATM Protein Levels Using Immunohistochemistry Are Related to Favorable Survival*

Next, to validate the prognostic role of GATM, we evaluated the GATM protein expression via IHC staining in 51 metastatic ccRCC patients and stratified it into two groups according to the status of GATM expression (GATM-positive and GATM-negative, respectively) (Figure 5A). Then, the association of GATM protein levels with survival outcomes was investigated. Notably, the GATM-positive group had the significantly better PFS (*p* = 0.0156; Figure 5B, top) and OS outcomes (*p* = 0.0013, Figure 5B, bottom) after ICIs therapy compared to GATM-negative group. We further analyzed GATM protein levels based on *PBRM1* mutation status and the molecular subtypes, and found that patients with *PBRM1* mutation exhibited a higher percentage of GATM-positive specimens than patients without the *PBRM1* mutation (Figure 5C, top). Additionally, we observed that subtypes 2 had a highest percentage of GATM-positive cases, whereas subtype 3, with aggressive phenotypes and poor survival, had the lowest percentage of GATM-positive specimens compared to that of subtype 1 and 2 (Figure 5C, bottom). These findings suggested that GATM protein levels are associated with *PBRM1* mutation status and less aggressive phenotypes and exhibit potential clinical utility as a prognostic marker for metastatic ccRCC with ICIs treatment.

**Figure 5.** Status of GATM protein expression in immunohistochemistry are related to favorable survival. (**A**) Immunohistochemistry of GATM expression using tissue microarray of 51 patients with metastatic clear cell renal cell carcinoma treated by immune checkpoint blockades. The GATMpositive group includes "high expression" of GATM (a ×4, and b; ×20) and "low expression" of GATM (c; ×4, and d; ×20). The GATM-negative group includes "no expression" of GATM (e; ×4, and f; ×20). (**B**) Kaplan–Meier plot analysis of progression-free survival and overall survival based on the status of GATM protein expression. GATM (+) = GATM-positive group; GATM (−) = GATM-negative group. (**C**) Barplots show the percentage of both GATM-positive (+, crimson), and GATM-negative groups (−, gray), respectively, in the patients with *PBRM1* mutation vs. the patients without *PBRM1* mutation (top), and each subtype (bottom).

#### *3.6. PBRM1 Deficiency and GATM Upregulation in Stress Conditions Reduce Cell Proliferation*

We examined whether *GATM* expression is regulated by the loss of *PBRM1* in ccRCC lines. *PBRM1* knockdown in Caki-1 and 786-O cells showed an increase in *GATM* or *USH1C* expression (Figures S4A and 6A). We also tested whether *GATM* expression was regulated by *PBRM1* at the transcriptional level using 0.5 μM actinomycin D (transcriptional inhibitor). As expected, increased levels of *GATM* transcripts were sustained upon *PBRM1* knockdown in 786-O cells treated with actinomycin D compared to that of control cells (Figure 6B). The results indicated that *GATM* expression was increased at the transcriptional level following *PBRM1* knockdown. *PBRM1* has been reported to protect cancer cells under high-stress conditions, and patients with ccRCC harboring *PBRM1* mutations show better responsiveness to PD-1 inhibitors [9,21]. Given the protective roles of *PBRM1* in stress response, we examined whether *GATM* induced by *PBRM1* deletion participated in the anticancer state under stress conditions. An in vitro wound healing assay was performed using 786-O cells. As shown in Figure 6C, 786-O cells transfected with siScr, siPBRM1, or siGATM were scratched and incubated with high concentrations of hydrogen peroxide (H2O2) for 12 h. *PBRM1* knockdown in the 786-O cells exposed to H2O2 led to little movement compared to that in control cells. Depletion of *GATM* in *PBRM1*-knockdown 786-O cells augmented the migration, and motility by *GATM* knockdown was similar to that in the control cells (Figure 6C). These data indicate that migration ability was lost by *PBRM1* loss-induced *GATM* upregulation.

**Figure 6.** Expression of *GATM* by *PBRM1* knockdown reduces proliferation of clear cell renal cell carcinoma cell lines under stress conditions. (**A**) *GATM* or *USH1C* expression levels in PBRM1 knockdown 786-O or Caki-1 cells. The 786-O or Caki-1 cells were transfected with siScr or siPBRM1 and qRT-PCR analysis was performed. (**B**) *GATM* expression levels in siScr or siPBRM1 were transfected into 786-O cells and treated with 0.5 μM ActD for the indicated time. (**C**) Wound healing assay was performed. The 786-O cells transfected with indicated siRNAs were incubated with medium containing 300 μM H2O2 for 12 h. (**D**) Colony formation was performed in *PBRM1* knockdown 786-O cells. The 786-O cells were transfected with the indicated siRNAs and incubated

with a medium containing 300 μM H2O2 or with a low-glucose medium (**E**) Kaplan–Meier plot analysis of overall survival based on *PBRM1* mutation and *GATM* expression using the merge data (*n* = 177, left). PBRM1\_MUT+HIGH\_GATM (*n* = 42, purple), PBRM1\_MUT+LOW\_GATM (*n* = 19, yellow), PBRM1\_WT+HIGH\_GATM (*n* = 55, blue), and PBRM1\_WT+LOW\_GATM (*n* = 61, gray). Kaplan– Meier plot analysis of overall survival based on *PBRM1* mutation and *GATM* expression using TCGA-KIRC (*n* = 430, right). PBRM1\_MUT+HIGH\_GATM (*n* = 92, purple), PBRM1\_MUT+LOW\_GATM (*n* = 46, yellow), PBRM1\_WT+HIGH\_GATM (*n* = 161, blue), and PBRM1\_WT+LOW\_GATM (*n* = 131, gray). The *p*-value was calculated using Student's *t*-test: \* *p* < 0.05 and \*\* *p* < 0.01. Data represent means ± SD from a representative experiment of at least two independent repeats (**A**,**B**,**D**).

Next, we examined the effect of *GATM* induced by *PBRM1* knockdown on the proliferation of 786-O cells under stress conditions such as that in H2O2 or low-glucose medium. *PBRM1*-depleted cells incubated with medium containing high concentrations of hydrogen peroxide or low glucose lost the ability to form colonies, which was recovered by silencing *GATM* (Figure 6D). The results suggested that the antiproliferative ability of the *PBRM1* mutation under stress conditions was accompanied by an increase in *GATM* expression.

More importantly, the association of *GATM* expression and *PBRM1* mutations with clinical prognosis was investigated; merged data (*n* = 177) derived from human samples were stratified into four groups according to the status of *PBRM1* mutation and *GATM* expression: (PBRM1\_MUT+HIGH\_GATM (*n* = 42), PBRM1\_MUT+LOW\_GATM (*n* = 19), PBRM1\_WT+HIGH\_GATM (*n* = 55) and PBRM1\_WT+LOW\_GATM (*n* = 61)). As expected, patients with the *PBRM1* mutation and high expression of *GATM* had the best PFS (*p* = 0.069; Figure S4B, left) and OS outcomes (*p* = 0.0012, Figure 6E, left). The molecular subtypes of the four groups were further analyzed, and it was found that patients in the PBRM1\_MUT+HIGH GATM group were enriched in subtype 2 (88%), and patients in the PBRM1\_WT+LOW\_GATM group were enriched in subtypes 1 (46%) and 3 (48%) (Figure S4C). More importantly, multivariate analysis of the merged data also revealed the prognostic significance of *GATM* expression and *PBRM1* mutation (hazard ratio [HR] = 2.067, 95% confidence interval [CI], 1.147–3.726; *p* = 0.016; Table 1). On further analysis, *PBRM1* mutation and *GATM* expression were associated with significant OS (*<sup>p</sup>* = 9.40 × <sup>10</sup>−7, Figure 6E, right) rather than PFS (*<sup>p</sup>* = 0.332, Figure S4B, right) in the TCGA-KIRC cohort. Although the TCGA-KIRC cohort was predominately composed of non-ICIs-treated patients, the survival result suggested that patients with the *PBRM1* mutation and high *GATM* expression are consistently associated with superior OS. These retrospective analysis results collectively suggest that the status of the *PBRM1* mutation and *GATM* expression were considered as prognostic key factors of RCC patients.

**Table 1.** Univariate and multivariate analyses for predicting the overall survival of patients with metastatic clear cell renal cell carcinoma treated with immune checkpoint inhibitors.

HR, Hazard Ratio; CI, Confidence Interval; IMDC, International Metastatic RCC Database Consortium; MUT, PBRM1 mutation; WT, PBRM1 wildtype; HIGH, GATM high expression; LOW, GATM low expression (\* *p* < 0.05, \*\* *p* < 0.005).

The *GATM* gene encodes glycine amidinotransferase, catalyzing the rate-limiting step in the synthesis of creatine, which plays a pivotal role in cancer progression and immunotherapy. Previous studies have demonstrated that creatine inhibits the growth of tumor cells both in vitro and in vivo and is associated with the regulation of T cell antitumor immunity [22,23]. In this context, the creatine metabolism level was examined for each subtype (*<sup>p</sup>* = 2.63 × <sup>10</sup><sup>−</sup>7; Figure S4D, left) as well as in patients with the *PBRM1* mutation vs. in patients without the *PBRM1* mutation (*p* = 0.048; Figure S4D, right). Interestingly, patients with subtype 2 and *PBRM1* mutation showed significantly elevated creatine metabolism. Next, the correlation between creatine metabolism and *GATM* expression levels was tested, revealing that the *GATM* expression had a significantly positive correlation with creatine metabolism (correlation coefficient, r = 0.49, *<sup>p</sup>* = 4.61 × <sup>10</sup>−12, Figure S4E). Collectively, activation of *GATM* may represent an efficient therapeutic approach for ccRCC harboring the *PBRM1* mutation under ICIs by regulating creatine metabolism to enhance antitumor T cell immunity in the tumor microenvironment.

#### **4. Discussion**

This study reports a comprehensive molecular analysis of patients with metastatic ccRCC receiving ICIs to investigate the role of tumor genomic and transcriptomic features in determining the response and survival outcomes following ICIs treatment. Our findings are summarized in Figure 7.

**Figure 7.** Graphical summary of the dynamics of genomic and transcriptomic aberrations in patients with metastatic clear cell renal cell carcinoma.

The dynamics of genomic and transcriptomic features, including the *PBRM1* mutation, metabolic process, active or exhausted immune types, and *GATM* expression, contributed to the responsiveness and prognosis to immunotherapy in metastatic ccRCC. More importantly, for the first time, we found that *GATM* plays a suppressive role by linking the the *PBRM1* mutation to patients with ccRCC treated with ICIs. Our unsupervised transcriptomic analysis based on signatures related to the the *PBRM1* mutation and *PBRM1* LOF identified three molecular subtypes. This subtyping approach in this study showed concordance with previous reports on gene expression-based subgrouping in large RCC datasets [10,24–26]. Indeed, we found an association between molecular subtypes and differential biological profiles and different prognoses to ICIs in patients with metastatic ccRCC. Patients in subtype 2 demonstrated favorable OS with a higher proportion of them with the *PBRM1* mutation and who are enriched with angiogenesis and metabolic pathways. In contrast, patients in subtypes 1 and 3 showed worse clinical outcomes with

a low proportion of them with the *PBRM1* mutation and who are downregulated with angiogenesis signature but upregulated with immune-related and cell-cycle pathways. Overall, the unique features of these subtypes provide their utility in understanding the differential prognosis and responsiveness to ICIs treatment.

One of our key findings is that subtype 2, with a higher proportion of the *PBRM1* mutation, showed enrichment of multiple metabolic pathways, including oxidation and phosphorylation, fatty acid metabolism, adipogenesis, and hypoxia pathways. In fact, hypoxia signaling as a master regulator is associated with the dysregulation of metabolic genes in RCC [27]. Indeed, previous studies have demonstrated that VHL loss and hypoxiainducible factor stabilization are associated with the reprogramming of metabolic pathways in RCC [28–30]. In summary, our results not only validated previous findings on RCC metabolism but also further explored the metabolic differences among the three subtypes. Interestingly, in-depth transcriptome analysis revealed an unexpected inconsistency between the response to ICIs and immune cell types. Additionally, previous studies demonstrated that *PBRM1* loss shows a nonimmunogenic tumor phenotype associated with ICIs [17], and CD8 T cell infiltration of immunofluorescence in CheckMate025 was not associated with response to PD-1 blockade [4]. Therefore, we investigated whether the association between immune cell types and response to ICIs would be better characterized by considering immune types, including active and exhausted immune subtypes. We found that tumors harboring the the *PBRM1* mutation or molecular subtype 2, which had the best survival outcome, showed a lower percentage of exhausted immune subtype compared to that of tumors with *PBRM1* wild type or subtypes 1 and 3. Previous studies have also shown that high proportion of exhausted immune types in tumor samples results in poor prognosis and an aggressive phenotype [15,31,32]. Taken together, these data suggest that ccRCC tumors can undergo a transition in immune subtypes from active to exhausted types, which may affect the tumor microenvironment to be different response to ICIs.

Moreover, our data indicated that *GATM* is a potential gene-linking *PBRM1* mutation and *PBRM1* LOF. *GATM* encodes glycine amidinotransferase, a mitochondrial enzyme that catalyzes the transfer of guanidinoacetic acid, which is a substrate for creatine synthesis. Interestingly, several studies have demonstrated that creatine inhibits the growth of tumor cells both in vitro and in vivo [22,33,34]. Furthermore, in mouse cancer models, treatment with creatine, either through intraperitoneal injection or through oral administration, effectively suppressed tumor growth, which was associated with a significant reduction in the number of exhausted T cell phenotypes among the tumor-infiltrating CD8+ T cells [22]. The exact mechanism by which creatine or *GATM*-attenuated cancer growth and related to immunotherapy is still unclear; however, a possible tumor-suppressive role of *GATM* was supported by the fact that low expression of *GATM* was associated with poor survival of patients with ccRCC treated with ICIs (as shown in Figure 4D). In addition, the status of GATM protein expression was significantly associated with PFS and OS after ICIs treatment (as shown in Figure 5B). Therefore, the activation of *GATM* has the potential to become an effective approach for enhancing the efficacy of ICIs therapies.

We acknowledged the limitations of the present study. First, the sample size was relatively small, which is the most critical pitfall of our study. To overcome this drawback, data derived from the CheckMate 025 study were merged. Second, a retrospective study was performed and samples were collected; therefore, there was an unavoidable risk of bias, such as selection and misclassification biases. Third, most patients were treated with anti-PD1 monotherapy as a second-line treatment after failure of the first-line targeted therapy. Thus, there was a discrepancy in the time between tumor sampling and anti-PD1 therapy, which means that there was time gap between genomic and/or transcriptomic status and ICIs treatment response status Actually, the population of this study included either firstline therapy (*n* = 8) or more than second-line treatments that failed withTyrosine Kinase inhibitors (TKIs, *n* = 52). The selective pressure of multiple lines of TKIs treatment could increase the genomic complexity of tumors and tumor microenvironments, thus influencing responsiveness to ICIs therapy. Fourth, our data were based on WTS analysis, not singlecell sequencing, resulting in a loss of immune cell compartment, which is particularly important for understanding the immune microenvironment and limiting the ability to perform such an immune cell type proportion analysis. Finally, further experiments should be performed to establish causality between the *PBRM1* mutation and *GATM* expression and to determine how *PBRM1-GATM* is involved in the responsiveness to ICIs treatment.

#### **5. Conclusions**

This study provides critical insight into genomic and transcriptomic mechanisms such as *PBRM1* mutations, metabolic processes, immune subtypes (active or exhausted), and *GATM* expression that contribute to the response to ICIs therapy in patients with metastatic ccRCC. Moreover, our data underscore the prognostic importance of the *PBRM1* mutation and *GATM* expression in patients with metastatic ccRCC treated with ICIs. Moving forward, it will be important to validate these findings in future clinical trials and to investigate the mechanisms on the interaction between the *PBRM1* mutation and *GATM* expression in the context of ICIs responsiveness.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/cancers14102354/s1, Figure S1: A heatmap showed up-regulated genes in each subtype; Figure S2: Kaplan-Meier plots for PFS and OS and ssGSEA analysis using HALLMARK gene sets; Figure S3: Kaplan-Meier plots and boxplots based on *GATM* and *USHIC* expressions; Figure S4: Kaplan-Meier plots based on *PBRM1* mutation and *GATM* expression and correlation plot between creatin metabolism and *GATM* expression; Table S1: Baseline clinical characteristics of patients with metastatic renal cell carcinoma (n = 60); Table S2: Baseline outcomes between patients receiving first-line and second-line therapies; Table S3: Upregulated genes in each subtype; Table S4: Clinical features of each subtype.

**Author Contributions:** Formal analysis, B.J., E.S. and S.-j.B.; resources, S.M.L., J.H.C., W.S., H.H.S., H.G.J., B.C.J., S.I.S., S.S.J., H.M.L. and S.H.P.; data curation, K.P.; writing—original draft preparation, B.J., E.S., S.-j.B. and M.K.; writing—review and editing, B.J. and M.K.; supervision, Y.R.K., W.-Y.P. and M.K.; project administration, M.K.; Funding acquisition, B.J., Y.R.K. and M.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1A6A3A11046 060 and NRF-2021R1I1A1A01040437). This work was also supported by grants from the NRF of Korea funded by the MSIT (NRF-2020R1A2C2007662 and NRF-2020R1C1C1005054). This work was also supported by Hallym University Research Fund (HURF) (no. 2018-30), by research grants from the National R&D Program for Cancer Control, Ministry of Health and Welfare, Republic of Korea (HA17C0039) and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HR20C0025).

**Institutional Review Board Statement:** The Institutional Review Board of Samsung Medical Center approved the use of human archival tissues for this study (IRB no. SMC 2020-03-063).

**Informed Consent Statement:** Written informed consent from enrolled patients who were alive during the study period was obtained. Any personal identifiers were removed using an anonymized processing protocol, and all methods used in this study were conducted in accordance with the Declaration of Helsinki guidelines.

**Data Availability Statement:** Whole transcriptomic sequencing data generated in this study are available from the corresponding author in this study<EMAIL_ADDRESS>upon request.

**Acknowledgments:** The authors would like to thank the patients and family members who gave their consent on presenting the data in this study, as well as the investigators and research staff involved in this study.

**Conflicts of Interest:** No potential conflict of interest was reported by the authors.

#### **Abbreviations**

ccRCC: clear cell renal cell carcinoma; TCGA: The Cancer Genome Atlas; GSEA: gene set enrichment analysis; ssGSEA: single-sample gene set enrichment analysis; DEG: differentially expressed gene; ES: enrichment score; OS: overall survival; PFS: progression-free survival; ICIs: immune checkpoint inhibitors; PD-1: programmed cell death protein 1; WTS: whole transcriptome sequencing; LOF: loss of function; CB: clinical benefit; NCB: non-clinical benefit.

#### **References**

## *Review* **Prognostic Factors for Localized Clear Cell Renal Cell Carcinoma and Their Application in Adjuvant Therapy**

**Kalle E. Mattila 1,\*, Paula Vainio <sup>2</sup> and Panu M. Jaakkola <sup>1</sup>**

**Simple Summary:** Approximately one fifth of patients with newly diagnosed renal cell carcinoma (RCC) present with metastatic disease and over one third of the remaining patients with localized RCC will eventually have metastases spread to distant sites after complete resection of the primary tumor in the kidney. Usually, disease recurrence is observed within the first five years of follow-up, but late recurrences after five years are seen in up to 10% of patients. Despite novel biomarkers, simple histopathological factors, such as tumor size, tumor grade, and tumor extension into the blood vessels or beyond the kidney, are still valid features in predicting the risk of disease recurrence after surgery. The optimal set of prognostic factors remains unclear. The results from ongoing placebo-controlled adjuvant therapy trials may elucidate prognostic features that help to define high-risk patients for disease recurrence.

**Abstract:** Approximately 20% of patients with renal cell carcinoma (RCC) present with primarily metastatic disease and over 30% of patients with localized RCC will develop distant metastases later, after complete resection of the primary tumor. Accurate postoperative prognostic models are essential for designing personalized surveillance programs, as well as for designing adjuvant therapy and trials. Several clinical and histopathological prognostic factors have been identified and adopted into prognostic algorithms to assess the individual risk for disease recurrence after radical or partial nephrectomy. However, the prediction accuracy of current prognostic models has been studied in retrospective patient cohorts and the optimal set of prognostic features remains unclear. In addition to traditional histopathological prognostic factors, novel biomarkers, such as gene expression profiles and circulating tumor DNA, are extensively studied to supplement existing prognostic algorithms to improve their prediction accuracy. Here, we aim to give an overview of existing prognostic features and prediction models for localized postoperative clear cell RCC and discuss their role in the adjuvant therapy trials. The results of ongoing placebo-controlled adjuvant therapy trials may elucidate prognostic factors and biomarkers that help to define patients at high risk for disease recurrence.

**Keywords:** adjuvant therapy; clear cell renal cell carcinoma; biomarker; prediction model; prognostic factor

#### **1. Introduction**

Renal cell carcinoma (RCC) is the third most common newly diagnosed urogenital cancer after prostate and bladder cancer. In 2020, the number of new kidney cancer diagnoses was over 400,000 and it caused nearly 180,000 deaths worldwide [1]. The most prevalent histological subtype, clear cell renal cell carcinoma (ccRCC), accounts for 75–80% of all RCCs and has been associated with inferior survival compared to papillary (10–15%) and chromophobe (5%) RCCs [2]. Localized RCC can be treated with curative intent by

**Citation:** Mattila, K.E.; Vainio, P.; Jaakkola, P.M. Prognostic Factors for Localized Clear Cell Renal Cell Carcinoma and Their Application in Adjuvant Therapy. *Cancers* **2022**, *14*, 239. https://doi.org/10.3390/ cancers14010239

Academic Editors: Claudia Manini and José I. López

Received: 27 November 2021 Accepted: 31 December 2021 Published: 4 January 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

radical (RN) or partial nephrectomy (PN). PN is preferred for smaller tumors (T1–2N0M0) if technically feasible without compromising the oncological outcome of surgery (negative surgical margins). Small renal tumors might be eligible for radiofrequency ablation. Lymph node dissection (LND) is not routinely performed unless there is a suspicion of metastatic lymph nodes preoperatively or during surgery. If macrovascular invasion is present, tumor thrombus is removed from the renal and caval vein during surgery [3,4].

Unfortunately, approximately 20% of RCC patients present with primarily metastatic disease and over one third of patients will eventually develop distant metastases [5]. Despite recent advances in the medical treatment of advanced RCC (antiangiogenic receptor tyrosine kinase inhibitors (TKI), immune checkpoint inhibitors (ICI) and TKI–ICI combinations), metastatic disease will, in most cases, lead to death. Individualized, risk-based, regular imaging follow-up after surgery for localized RCC for at least five years with thoracic and abdominal CT is recommended to detect disease progression (local recurrence or distant metastases) early. If detected early with few or solitary metastases, the patient may still be curable surgically or with high-dose radiation therapy and may be eligible for oncologic therapies [3,4]. The aim of this review is to provide an overview of the clinical prognostic models for localized ccRCC. Understanding the risk of disease progression after surgery of localized disease is essential for designing postoperative follow-up as well as for designing adjuvant drug trials in localized ccRCC.

#### **2. Histopathological and Clinical Prognostic Factors for Localized ccRCC**

The TNM classification of malignant tumors (American Joint Committee on Cancer (AJCC)) has been used since 1977 as a prognostic staging system for multiple solid tumors [6]. The staging of renal cell carcinoma based on pathologic examination and radiological imaging provides crucial prognostic information. Stage I (T1N0M0: tumor ≤ 7 cm) and stage II (T2N0M0: tumor > 7 cm) tumors are limited to the kidney, whereas stage III (T3N0, T1−3N1: tumor invades renal vein, perinephric tissues, or presents with regional lymph node metastases) and stage IV (T4NanyM0, TanyNanyM1: tumor extends beyond Gerota fascia or presents with distant metastases) tumors extend beyond the kidney [7]. In 1993–2004, 54.7%, 10.6%, 16.1%, and 18.6% of ccRCC tumors in the National Cancer Database were classified as stage I, II, III, and IV, respectively [8]. Stage I and II RCCs had significantly better 5-year survival rates (90.4% and 83.4%) compared to stage III and stage IV RCCs (66.0% and 9.1%) [8]. In a 2004–2015 Surveillance, Epidemiology, and End Results (SEER) database cohort (77% had ccRCC), the pathologic TNM stage was I (64.3%), II (10.9%), III (16.8%), and IV (8%) and the 5-year survival rates after nephrectomy were 97.4%, 89.9%, 77.9%, and 26.7% for stage I, II, III, and IV RCCs, respectively [9]. The proportion of stage I tumors has increased, probably due to the incidental detection of small renal tumors in abdominal imaging studies [8]. The increase in the survival rate of stage III and IV tumors is probably driven by VEGF-targeted TKI therapies introduced in the treatment of advanced RCC in the 2000s.

#### *2.1. Microscopical Histopathological Prognostic Factors*

In addition to the TNM stage, several histopathological factors affect the prognosis of localized ccRCC patients. Numerous tumor grading systems have been introduced to assess the histological differentiation of RCC cells. The Fuhrman and the WHO/ISUP grading systems are the most widely used. In 1982, Fuhrman developed a four-tiered grading system that is based on the assessment of nuclear size, nuclear shape, and nucleolar prominence. The estimated 5-year survival rate of RCC patients was 64% (grade I), 34% (grade II), 31% (grade III), and 10% (grade IV) [10]. In 2012, the International Society of Urological Pathology reformed the four-tiered grading system based on the prominence of nucleoli (grades 1–3) and grade 4 tumors showing extreme tumor nuclear pleomorphism, giant cells, or sarcomatoid/rhabdoid dedifferentiation [11].

Approximately 5% of RCCs undergo epithelial to mesenchymal transition and present with sarcomatoid differentiation, and sarcomatoid features have been observed in clear cell, papillary, and chromophobe RCCs [12]. Sarcomatoid morphology is associated with more aggressive cancer behavior: sarcomatoid RCCs (sRCCs) often present with a bulky primary tumor (higher size and stage) and higher tumor grade [12–14]. Metastases are seen in as much as 60–80% of newly diagnosed cases [12] and close to 80% of patients with localized sarcomatoid RCC have been observed to develop disease recurrence within two years after nephrecotomy [15,16]. Sarcomatoid RCCs have unfavorable prognosis compared to ccRCCs regardless of tumor stage: 5-year cancer-specific mortality estimates were 32%, 63%, and 82% for stage I–II, III, and IV sRCCs, compared to 6%, 20%, and 64% for stage I–II, III, and IV ccRCC patients [14].

Tumor necrosis is another established adverse histological feature in RCC. It is associated with a larger tumor size, higher grade, and higher proliferative activity, and it is considered to indicate biologically aggressive tumor behavior [17,18]. The presence of tumor necrosis has been reported in 21–32% of ccRCCs [19,20] and it has also been associated with inferior survival outcomes in multiple studies [20–25]. The combination of WHO/ISUP grading and tumor necrosis outperformed WHO/ISUP grading after adjusting for TNM stage. Researchers observed that the presence of tumor necrosis affected the prognosis, especially in WHO/ISUP grade 3 tumors. The 10-year cancer-specific survival was 62% in grade 3 tumors without necrosis but only 30% in grade 3 tumors with necrosis [26].

RCCs are highly vascularized, and microscopic vascular invasion is observed in 5.6–45% of tumors [19]. Tumor cells can spread via blood and lymph vessels to distant sites (lungs, bones, liver, etc.) and lymph nodes. Microvascular invasion is defined as tumor cells within small vessels in the tumor pseudocapsule, tumor, or renal parenchyma adjacent to the tumor [27]. Microvascular invasion (MVI) was more commonly present in ccRCC (29%) than in non-ccRCC (12%) and it was associated with metastatic spread and inferior survival in ccRCC patients [28]. MVI was found to be associated with a larger tumor size, higher Fuhrman grade, more advanced T stage, the presence of lymph node and distant metastases, as well as a shorter survival time in univariate but not in multivariate analysis [29]. In another cohort of RCC patients (93% had ccRCC), MVI was observed to correlate with metastases and shorter disease-free survival as well as cancer-specific survival [30].

Partial nephrectomy (PN) is the standard of treatment for small renal tumors if technically feasible. Positive surgical margins (PSM) have been observed in up to 18% of patients after surgery for localized RCC [31–34]. However, the effect of PSM on oncologic outcomes (recurrence-free and cancer-specific survival) is controversial. Local tumor recurrence in the surgical bed is uncommon. In a retrospective study, local tumor bed recurrence was found in only 1.9% of patients who underwent PN, and PSM were found in 15.9% of patients with local tumor bed recurrence, compared to 3% in the control group [35]. However, PSM is more common in patients with other adverse features (higher tumor stage, grade, multiple tumors, solitary kidney) and local recurrences are also observed in patients with negative surgical margins [32,33,35]. Therefore, imaging surveillance is preferred over radical nephrectomy in patients with PSM after PN.

#### *2.2. Macroscopical Histopathological Prognostic Factors*

Tumor extension into perirenal tissues, renal vein, and regional lymph nodes might be discovered in preoperative radiological imaging or during surgery but sometimes only after microscopical evaluation of resected tumor and regional lymph nodes. Tumor invasion into perirenal tissues (perirenal fat, renal sinus fat) or macrovascular invasion into the renal vein and inferior vena cava (IVC) or local lymph nodes (T3N0, T1–3N1, stage III) lead to inferior oncologic outcomes compared to stage I and II tumors [8,9]. Perinephric fat, renal sinus fat, and renal vein invasion were present in 26%, 9%, and 29% of T3a tumors and patients with multiple extrarenal extensions had inferior progression-free and overall survival [36]. The association of concomitant fat invasion and renal vein invasion with poorer cancer-specific survival has also been observed in other studies [37–39]. Upper pole RCCs may invade directly into the adrenal gland. These tumors are classified as T4 as

well as tumors extending beyond Gerota's fascia, leading to worse oncologic outcomes compared to T1–3N0/Nx tumors [8,9].

Tumor extension into the renal vein and inferior vena cava has been observed in 23% and 7–13% of patients, respectively [40,41]. Patients with venous invasion had significantly shorter survival compared to tumors limited to the kidney [41]. The Mayo Clinic's thrombus classification is commonly used to classify the level of tumor extension into the IVC [42]. The prognostic significance of the tumor thrombus level is controversial. In a retrospective multicenter evaluation of tumor thrombus level, 78%, 16%, and 5% of the patients had tumor extension into the renal vein, IVC below diaphragm, and above diaphragm, respectively [43]. The level of tumor thrombus in the IVC (below or above diaphragm) was not statistically significantly associated with the survival time, but patients with tumor thrombus in the IVC had shorter survival (18–26 months) compared to patients with tumor thrombus in the renal vein (52 months) [41,43]. In another multicenter study (89.9% had ccRCC), a higher tumor thrombus level was independently associated with shorter cancer-specific survival [44]. Notably, the patients in these studies were treated before modern TKI and ICI therapies. In a contemporary analysis of 6340 patients who underwent surgery for localized RCC (93.4% had ccRCC), only 3.6% of the patients had venous tumor thrombus and the level of thrombus was not associated with the risk of recurrence or death [45].

Lymph node dissection (LND) is not routinely performed during nephrectomy unless there is a suspicion of metastatic lymph nodes preoperatively or during surgery, and local lymph node status usually remains unknown (Nx). LND has not proven therapeutic but is a prognostic procedure to assess metastatic spread to regional (hilar, abdominal, para-aortic, and para caval) lymph nodes. In the SEER database analysis, 24.8% of patients (59.4% had ccRCC) underwent lymph node dissection (LND) and metastatic lymph nodes were observed in 17.1% (9.3% of T2 and 21.6% of T3) of the patients who underwent LND [46]. In another study, local lymph node metastases were present in 11% of nonmetastatic RCC patients (90.7% had ccRCC) who underwent nephrectomy [47]. The patients with regional lymph node metastases (T1–3N1) have as poor survival as stage IV patients [47,48]. The 5-year survival rates were 61.9%, 22.7%, and 15.6% for stage III lymph node negative, stage III lymph node positive, and stage IV patients, respectively (78.1% had ccRCC) [48].

#### **3. Prognostic Models for Localized RCC**

There are several postoperative prognostic models to assess the risk of RCC recurrence or death after surgery of localized RCC based on histopathological features, such as TNM stage, tumor size, tumor grade, coagulative necrosis, and microvascular invasion, and clinical manifestations, such as symptoms of the disease. Kattan et al. introduced the first nomogram in 2001 to assess the risk of disease recurrence for localized RCC [49], followed by the UISS, the SSIGN, the Cindolo, the Leibovich, the Sorbellini, and the Karakiewicz algorithms [50–55]. There are differences in the required prediction features and the prediction outcomes between these models. The majority (88–100%) of the patients included in these models have had clear cell RCC, although the Kattan, the UISS, the Cindolo, and the Karakiewicz models also included patients with papillary and chromophobe RCC. Because of marked differences in the histopathology and prognosis of clear cell, papillary, and chromophobe RCC subtypes, similar prediction models may not be optimal for different histological subtypes. Grading of chromophobe carcinoma is not recommended [19,27], which limits eligible prediction models for this subtype. Leibovich et al. introduced different algorithms for each histological subtype, aiming to improve the prediction accuracy. The study cohort included 75% clear cell, 17% papillary, and 6% chromophobe RCC patients [56]. Recently, Mattila et al. developed a prediction model for localized ccRCC that comprised only three features and included an easy-to-use nomogram for clinicians [57]. The properties of different prognostic models for localized RCC are described in Table 1.

#### **Table 1.** Postoperative prognostic models for localized RCC.

The prediction accuracy (concordance index, C-index) of these prognostic models had exceeded 0.8: SSIGN 0.82–0.84 [51], Leibovich 2003 0.82, Sorbellini 0.82, Leibovich 2018 0.83–0.86, Mattila 0.76–0.84. However, these prediction models are based on the analysis of retrospective patient cohorts. A prospective validation of prediction models in a cohort of 1647 nonmetastatic (≥T1b grade 3–4 or TanyN1M0) ccRCC patients enrolled in a sorafenib adjuvant therapy trial (ASSURE) resulted in considerably lower C-indices (0.57–0.69) for the UISS, SSIGN, Leibovich 2003, Kattan, MSKCC, Yayciogly, Karakiewicz, Cindolo, and 2002 TNM staging systems. All models demonstrated the best prediction accuracy during the first two years of follow-up after surgery [58]. Higher prediction accuracy for the first two years of follow-up was also found when comparing the Mattila

and the Leibovich 2003 models: C-indices were 0.81–0.88 (Mattila) and 0.76–0.88 (Leibovich 2003) during 0–24 months and 0.78–0.84 (Mattila) and 0.71–0.82 (Leibovich 2003) during 24–90 months [57]. Late disease recurrence after 5 years of follow-up has been observed in 5–11% of patients with localized RCC [59], and the prediction of these late events remains imprecise with present prognostic models [58].

#### **4. Current Applications of Biomarkers in Localized RCC**