Sage Journals: Discover world-class research

Abstract

Background:

Renal cell carcinoma (RCC) is characterized by considerable heterogeneity and variable clinical outcomes. The identification of reliable biomarkers is crucial to improve prognostic assessment and therapeutic decision-making. CMTM6 (chemokine-like factor-like MARVEL transmembrane domain-containing protein 6) has emerged as a regulator of programmed death ligand-1 (PD-L1), a key immune checkpoint protein involved in tumor immune evasion.

Objectives:

The main objective of the study was to investigate the expression and prognostic significance of CMTM6 and to establish an automated immunohistochemical analysis.

Design:

This was a retrospective analysis conducted at a single center.

Methods:

Tumor samples from 111 patients who underwent partial nephrectomy for localized RCC between 2006 and 2019 were retrospectively analyzed. CMTM6 expression in tumor cells and tumor-infiltrating immune cells (ICs) was quantified using automated digital image analysis of immunohistochemically stained slides. Associations between CMTM6 expression, clinicopathological features, and patient outcomes were evaluated.

Results:

CMTM6 expression was heterogeneous across RCC samples, with significantly higher levels observed in clear cell RCC compared to non-clear cell subtypes. Elevated CMTM6 expression in ICs correlated with reduced overall survival (p = 0.049) and shorter time to metastasis (p = 0.009). Conversely, in patients with metastatic RCC receiving systemic therapy, lower CMTM6 expression was associated with shorter progression-free survival (p = 0.047). However, lower CMTM6 scores were also significantly associated with more unfavorable outcome according to prognostic group defined by the Stage, Size, Grade, and Necrosis risk model for localized RCC.

Conclusion:

CMTM6 expression represents a promising prognostic biomarker in RCC, with differential associations depending on disease stage and treatment setting. Its correlation with established clinical risk classifications underscores its potential utility in prognostic refinement. Given its regulatory role in PD-L1 expression, CMTM6 may also represent a therapeutic target, with implications for optimizing immunotherapeutic strategies in RCC. Further validation in larger cohorts and prospective studies are warranted.

Plain language summary

How CMTM6 expression relates to prognosis in renal cell carcinoma

Renal cell carcinoma (RCC) is the most common form of kidney cancer and remains a major clinical challenge due to its variable outcomes and limited predictive markers. To improve patient care, new biomarkers that may help predict prognosis and guide treatment decisions are under investigation. One such molecule is CMTM6 (CKLF-like MARVEL transmembrane domain-containing protein 6). CMTM6 is known to regulate PD-L1, a key immune checkpoint protein that allows cancer cells to evade the immune system. In this study, we analyzed tumour samples from 111 patients who underwent surgery for localized RCC. We applied automated digital image analysis to measure CMTM6 expression in both tumour cells and immune cells within the tumour microenvironment. Our results showed that CMTM6 expression was heterogeneous, with higher levels in clear cell RCC compared to non-clear cell RCC. Importantly, patients with elevated CMTM6 expression in immune cells had a shorter overall survival and developed metastases earlier. Interestingly, in patients with advanced disease receiving systemic therapy, lower levels of CMTM6 were linked to shorter progression-free survival as well as more unfavorable outcome according to prognostic group defined by the SSIGN risk model for localized RCC. These findings suggest that CMTM6 may represent a novel prognostic biomarker in RCC, offering additional insight beyond current clinical risk models. Since CMTM6 is directly linked to immune regulation through PD-L1, it may also represent a therapeutic target. Modulating CMTM6 could influence immune activity in the tumour microenvironment and potentially enhance the effectiveness of immunotherapy. In conclusion, this study highlights the clinical relevance of CMTM6 in RCC and the feasibility of CMTM6 immunohistochemistry in RCC tissue slides using digital image analysis software for automated slide evaluation.

Keywords

automated digital image analysis CMTM6 PD-L1 regulation prognostic biomarker renal cell carcinoma tumor microenvironment tumor-associated immune cells

Introduction

Renal cell carcinoma (RCC) is the third most common urological malignancy, comprising 2%–3% of global cancer cases, with peak incidence at 60–70 years^1,2 and a male predominance.^3,4 RCC cases are rising globally,⁵ with up to 30% presenting with recurrence or metastasis.^6,7 RCC is classified into three main subtypes: clear cell (ccRCC; 80%), papillary (pRCC; 10%–15%), and chromophobe (chRCC; 5%).⁸ Accurate tumor staging and grading are essential for prognosis and personalized treatment in RCC. Prognosis relies on tumor node metastasis (TNM) staging and histological grading,⁹ with the Fuhrman^10
–12 and WHO/International Society of Urological Pathology (ISUP) grading systems commonly used.^12,13 Although the WHO/ISUP system is widely applied in RCC, it is not suitable for chromophobe RCC, as nuclear grade does not reliably predict outcomes in this subtype. Instead, prognostic assessment for chRCC focuses on tumor size, necrosis, sarcomatoid differentiation, and TNM stage.^13,14

Beyond conventional TNM staging and histological grading, integrated prognostic models such as the Mayo Clinic Stage, Size, Grade, and Necrosis (SSIGN) score¹⁵ provide refined risk stratification for localized RCC. These tools combine clinicopathologic variables to predict recurrence and disease-specific survival, guiding individualized follow-up and management. The SSIGN score, which is based on tumor stage, size, grade, and histologic necrosis, stratifies patients into prognostic risk groups with high predictive accuracy, classifying scores of 0–2 as low risk, 3–5 as intermediate risk, and scores of 6 or higher as high risk. While primarily developed for ccRCC, the SSIGN framework can inform risk assessment in other subtypes with appropriate adjustments.¹⁶ For metastatic RCC (mRCC), risk stratification includes the Memorial Sloan Kettering Cancer Center (MSKCC)¹⁷ and International Metastatic RCC Database Consortium (IMDC) models.¹⁸

Until the advent of immune checkpoint inhibitors (ICIs), particularly programmed death-1 (PD-1) inhibitors such as nivolumab,^19,20 targeted therapies, including tyrosine kinase inhibitors (TKIs), mammalian target of rapamycin inhibitors, and vascular endothelial growth factor-neutralizing antibodies, had been the standard treatment for mRCC.^21
–28 Combination therapies that include PD-1 inhibitors with TKIs or cytotoxic T-lymphocyte-associated protein 4 inhibitors have improved survival, though monotherapy remains an option in selected cases.^29
–34

PD-L1 expression, assessed via immunohistochemistry (IHC), has been explored as a predictive biomarker for ICIs but remains unreliable due to intratumoral heterogeneity and assay variability.^35,36 Different scoring methods for PD-L1 staining have been developed to enhance its utility as a predictive biomarker for immunotherapy response. Among these, the combined positivity score (CPS), inflammatory cell score (ICS), and tumor proportion score (TPS) are the most commonly utilized approaches.³⁷ These methods have demonstrated predictive value in cancers such as bladder cancer and upper tract urothelial carcinoma (UTUC).^38
–40 However, in the context of RCC, comprehensive evaluations and direct comparisons of all three scoring methods are still lacking. This gap in research is compounded by conflicting results regarding PD-L1’s role as a predictive marker in RCC.⁴¹ In the Checkmate 214 trial, PD-L1 positivity correlated with better outcomes in ICI combination therapy,²⁹ but it was not predictive for nivolumab in second-line treatment.²⁰ Consequently, PD-L1 expression is not yet considered a reliable biomarker in clinical practice, and ICIs have been approved for mRCC regardless of PD-L1 status. Moreover, to date, there is no single predictive biomarker approved for selecting patients who are likely to benefit from ICIs targeting PD-1/PD-L1. This underscores the need for further investigation to identify biomarkers that can consistently predict responses to ICIs in RCC.

Post-translational modifications are crucial for regulating PD-L1 expression,⁴² with human chemokine-like factor-like MARVEL transmembrane domain containing family member 6 (CMTM6) emerging as a key modulator enhancing PD-L1 recycling and preventing degradation.⁴³ This stabilization also promotes tumor progression by inducing M2-like macrophages and activating Wnt/β-catenin signaling, which is vital for tumorigenesis and cancer stem cell maintenance.⁴⁴ In RCC, PD-L1’s prognostic impact depends on CMTM6 expression,⁴⁵ suggesting that CMTM6 may serve as an independent prognostic biomarker.⁴⁶

In a recent study, we demonstrated that the expression of CMTM6 on immune cells (ICs) was associated with progression-free survival (PFS) in patients with mRCC treated with nivolumab after at least one prior antiangiogenic therapy. Notably, both CMTM6 and PD-L1 exhibited substantial intratumoral heterogeneity, with significantly higher expression on IC compared to tumor cells (TCs). Furthermore, a positive correlation was observed between CMTM6 and PD-L1 expression, with CMTM6 levels being significantly higher on IC than PD-L1. These findings suggest that CMTM6 could enhance the prognostic value of PD-L1 and serve as a potential target for cancer immunotherapy.⁴⁷

However, the clinical significance of CMTM6 in RCC remains poorly understood. This study aimed to further clarify the role of CMTM6 expression in RCC patients. The first objective of our investigation was to analyze the stability and expression levels of CMTM6 in RCC, using IHC on tumor specimens from 111 RCC patients. The second objective was to explore the clinical relevance of CMTM6 in RCC, focusing on the association between CMTM6 expression and patient prognosis.

Patients and methods

The reporting of this study conforms to the STARD 2015 statement⁴⁸ (see Supplemental Table 1).

Study populations and data collection

After approval by the ethics committee of the Medical University of Innsbruck (study number 1335/2021) in accordance with the Declaration of Helsinki, we conducted a retrospective analysis of a database of patients with RCC treated in our clinic. Data were extracted from patients’ electronic medical records, including demographic information, smoking history, sequence of systemic therapy, detailed RCC histopathology, duration of treatment, best response to therapy, date of disease progression, and date of death or last follow-up at our outpatient department. In addition, we conducted a detailed assessment using the SSIGN risk model for patients with localized RCC, and the IMDC and MSKCC risk models for patients with mRCC.

Disease progression was assessed using computed tomography (CT). Overall survival (OS) was defined as the time from the date of diagnosis to the date of the last follow-up or death from any cause. Time to relapse (TTR) was defined as the interval from the date of diagnosis to the time of disease relapse, identified by the development of metastases detected on CT imaging during follow-up. In patients after disease progression, systemic therapy was initiated. PFS under this therapy was defined as the time from the initiation of treatment to disease progression, treatment discontinuation, loss to follow-up, or tumor-specific death. Continuation of therapy required an initial response of stable disease, partial remission, or complete remission.

Statistical analysis

Statistical analyses were performed using SPSS version 29 (IBM Corp., Chicago, IL, USA). The Kolmogorov–Smirnov test was employed to assess the normality of the data distribution. Descriptive statistics for continuous variables are presented as means with standard deviations for data that followed a normal distribution, and as medians with interquartile ranges (IQRs) for data that did not meet normality criteria. Categorical variables are reported as frequencies and percentages. Spearman’s rank correlation coefficient (r_s) was used to evaluate correlations between parameters. Comparisons of continuous variables between patient groups were conducted using the Student’s t test for normally distributed data and the Mann–Whitney U test for data that deviated from normal distribution.

For Kaplan–Meier survival analyses, patients were dichotomized into low- and high-expression groups using the median CMTM6 levels within each RCC tissue compartment and the median values of the applied scoring systems. Group differences were assessed using the log-rank test, with a two-sided α = 0.05. The median was selected as a cutoff because it provides an unbiased, distribution-independent threshold and ensures balanced group sizes, which supports statistical power and reproducibility in the absence of validated clinical cutoffs.⁴⁹

Tumor samples

Tumor specimens were obtained from the archives of the Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck and the archives of the INNPATH GmbH, Institute of Pathology, Tirol Kliniken, Innsbruck and were reviewed for diagnosis, by two pathologists with long-standing experience in uropathology (A.B. and B.Z.). One representative tumor block of every case was selected for further immunohistochemical analysis. Our study cohort included ccRCC, which comprised the majority of cases, chRCC and pRCC.

Immunohistochemistry

CMTM6 expression was assessed with a CMTM6 antibody (polyclonal, dilution 1:100, Invitrogen; Thermo-Fisher, Waltham, MA, USA). Staining was performed using an automated immunostainer (BenchMark ULTRA; Ventana Medical Systems, Tucson, AZ, USA). In brief, formalin fixed, paraffin-embedded tissue sections were cut at 1.5 µm. After deparaffinization, slides were treated with cell conditioning reagent 1 (CC1; Ventana Medical Systems) for antigen retrieval (heat, 95°C) and the primary antibody was incubated for 32 min at 37°C. For visualization, ultraView DAB Detection Kit (Ventana Medical Systems) was used according to the manufacture’s recommendation. Finally, slides were washed in distilled water, counterstained with hematoxylin (12 min) and bluing reagent (4 min), dehydrated in alcohols, cleared in xylene, and cover slipped with Tissue-tek mounting medium (Sakura Finetek, Tokyo, Japan). PD-L1 staining was performed in a selected cohort using the PD-L1 IHC 22C3 pharmDx Kit (Dako; Agilent) on the Autostainer LINK 48 (Dako; Agilent, Santa Clara, CA 95051, United States) according to the manufacturers protocol.

Evaluation of CMTM6 expression

CMTM6 expression was assessed using digital image analysis software for automated slide evaluation (QuPath-3.0.2, open source software). A composite classifier was developed using a machine learning approach with a training set of 18 specimens composed of clear cell, papillary, and chromophobe cancers and was then applied to the remaining cases (see Figure 1). To establish this classifier, the Random Trees classification method was used. In brief, object classification is done by annotating the training image with points or areas and assigning this classification for each marker separately to the annotations, which are then used to train the machine learning classifier. The following five classes were assigned: (1) TCs (blue), (2) tubuli/glomerula (dark green), (3) stroma/fibrous tissue (lime), (4) vessels (yellow), and (5) ICs (pink; see Figure 2). For each class, the total number of detections, positive and negative stained cells, and the percentage of positively stained cells were assessed by applying an intensity classifier.

Figure 1.

Exemplary cases of renal carcinoma stained with CMTM6 antibody and evaluated with a machine learning approach (tumor CMTM 6 negative: blue; tumor CMTM6 positive: red; ICs CMTM6 negative: pink; ICs CMTM6 positive: dark pink; stroma: green; vessels: yellow). Clear cell renal carcinoma with only slight CMTM6 staining in TCs (a) and results of automated analysis with only few positive (red) TCs (b). Another case of a clear cell renal carcinoma is depicted in (c) with a stronger CMTM6 staining and a broad peritumoral pseudocapsule with dense nodular IC infiltrates composed of mainly CMTM6 positive lymphocytes, which is highlighted in (d) by the applied machine learning algorithm. A papillary RCC with large numbers of macrophages in the papillae (e and f). Chromophobe RCC with peritumoral scattered lymphocytic infiltrates partially positive for CMTM6, TCs a slightly positive (g and h; all magnification 20×).

Figure 2.

Workflow of evaluation of CMTM6 expression applying a machine learning approach.

The same approach was used assessing PD-L1 expression in a subcohort of selected patients. Since PD-L1 expression is routinely quantified using the CPS, TPS, and ICS, while no standardized scoring system exists for CMTM6, we finally converted our results to fit the PD-L1 scoring system by calculating the number of positive cells compared to the total number of TC (see Figure 3).

Figure 3.

Scoring system routinely used for the assessment of the immunoscores CPS, TPS, and ICS for PD-L1 expression; expression in more than 1% of tumor cells is regarded as positive.

Results

Patients and tumor characteristics

The demographic and clinicopathological characteristics of the patient cohort (n = 111) are summarized in Table 1. The mean age of the patients was 61 years, with an age range of 33–84 years. The cohort consisted of 56.8% males and 43.2% females. All 111 included patients initially underwent surgical intervention, either in the form of radical nephrectomy or partial nephrectomy in the years 2006–2019. During follow-up, metastases were detected in 26 of the 111 patients, which were subsequently managed with systemic therapy (Table 2).

Table 1.

Patient and tumor characteristics.

Feature	Characteristics/values
Number of patients (all)	111
Age, n	Mean: 61 years, range 33–84 years
Sex, n (%)	Males/females	63 (56.8)/48 (43.2)
Risk factor, n (%)	Arterial hypertension (Y/N) Smoking (Y/N); Missing	67 (60.4)/44 (39.6) 30 (27)/80 (72.1); 1 (0.9)
Morphology, n (%)	Clear cell Papillary Chromophobe	80 (72.1) 22 (19.8) 9 (8.1)
pT, n (%)	1, 2, 3, 4, missing	78 (70.3), 12 (10.8), 19 (17.1), 1 (0.9), 1 (0.9)
Grade, n (%)	1, 2, 3, 4, missing	46 (41.4), 41 (36.9), 13 (11.7), 6 (5.4), 5 (4.5)
Tumor stage, n (%)	1, 2, 3, 4, missing	78 (70.3), 10 (9), 19 (17.1), 3 (2.7), 1 (0.9)
SSIGN	0–2, 3–5, >6	67 (60.4), 31 (27.9), 13 (11.7)
IMDC risk/MSKCC, n (%)^a	1, 2, 3	5 (19.2), 20 (76.9), 1 (3.8)
Sarcomatoid features, n (%)	(Y/N)	5 (4.5)/106 (95.5)

Based on 26 patients with metastatic RCC after diseases relapse.

IMDC, International Metastatic RCC Database Consortium; MSKCC, Memorial Sloan Kettering Cancer Center; RCC, renal cell carcinoma; SSIGN, Stage, Size, Grade, and Necrosis.

Table 2.

Systemic therapy for patients with metastatic RCC.

Feature	Characteristics/values
Number of patients (met)	26
1L therapy, n (%)	Sunitinib	15 (57.6)
	Pazopanib	8 (30.8)
	Bevacizumab	2 (7.7)
	Sorafenib	1 (3.8)
# Therapy lines, n (%)	1, 2, 3, 4	11 (42.3), 12 (46.2), 2 (7.7), 1 (3.8)

RCC, renal cell carcinoma.

Histological analysis revealed that the majority of the patients (72.1%) had ccRCC, while sarcomatoid features were identified only in five samples (4.5%).

Using the 2016 WHO grading system,⁸ 78 tumors (70.3%) were classified as T1, corresponding to tumor stage 1. In terms of tumor grade, 46 tumors (41.4%) were classified as G1, while 41 (36.9%), 13 (11.7%), and 6 (5.4%) were classified as G2, G3, and G4, respectively. Based on the SSIGN risk model, 67 of the 111 patients (60.4%) were categorized as low risk, 31 (27.9%) as intermediate risk, and 13 (11.7%) as high risk. According to the IMDC and MSKCC criteria, 5 patients (19.2%) were categorized as favorable risk, 20 patients (76.9%) as intermediate risk, and 1 patient (3.8%) as poor risk.

OS data from diagnosis to the last follow-up were available for all 111 patients, with a median OS of 47 months (range: 2–139 months; IQR: 58.0 months). During the observation period, 24 patients (21.6%) died. Twenty-six of the 111 patients (23.4%) relapsed with metastatic disease after initial diagnosis, with a median TTR of 6 months (range: 1–129 months; IQR: 17.3).

Patients with metastatic disease received between one and four lines of antiangiogenic therapies (demonstrated in Table 2). The median PFS during first-line therapy following diagnosis was 4.5 months (range: 1–82 months; IQR: 17 months).

CMTM6, PD-L1 expression, and correlation analysis

The highest expression levels of CMTM6 were observed in IC at 52.7% and TC at 51.8%. CMTM6-positive tumors were categorized as high for CPS, ICS, and TPS in 55%, 49.5%, and 47.7% of the cases, respectively (Table 3).

Table 3.

CMTM6 expression in RCC tissue, the calculated scoring systems (CPS, TPS, ICS), and the association with RCC morphology (ccRCC vs nccRCC).

RCC tissue, calculated scoring systems	Mean ± SD/median (IQR)	Morphology(p-value)
TC%	51.8 ± 24.8/52.7 (34.6)	0.040~
IC%	52.7 ± 27.8/56.7 (41.0)	0.034~/0.019 ^†
CPS	60.0 ± 31.1/58.4 (42.0)	0.016~
TPS%	51.1 ± 24.95/52.7 (34.7)	0.063~
ICS%	8.9 ± 13.5/3.9 (9.46)	0.006 ^†

In the dataset, p-values are labeled with “~” if derived from the T test and with “†” if derived from the Mann–Whitney U test. Statistically significant p-values are highlighted in bold.

ccRCC, clear cell RCC; CMTM6, CKLF-like MARVEL transmembrane domain containing protein 6; CPS, combined positivity score; IC, immune cells; ICS, inflammatory cell score; IQR, interquartile range; nccRCC, non-clear cell RCC; SD, standard deviation; TC, tumor cells; TPS, tumor proportion score.

We conducted an analysis of PD-L1 expression in a representative cohort of patients stratified by high and low CMTM6 expression levels, as well as by the presence or absence of disease progression at the time of analysis. Our findings revealed that only two of the examined RCC samples demonstrated positive PD-L1 expression according to the TPS scoring evaluation (12.5%), while the majority were negative. Conversely, PD-L1 expression on IC was positive in 50% of cases. However, overall PD-L1 staining was minimal across all samples, with a mean expression of 2.1% on TC and 9.5% on IC, rendering it difficult to detect in most cases. Consequently, our extended analysis focused on CMTM6 expression.

To assess potential relationships between CMTM6 expression across tumor and tumor-associated ICs, as well as the calculated scoring systems (CPS, TPS, and ICS), and clinicopathological parameters, the Student’s t test was applied for normally distributed data, while the Mann–Whitney U test was used for non-normally distributed data. The statistically significant results are shown in Table 3.

A significant association was observed between higher CMTM6 expression on IC, TC, as well as CPS and ICS and ccRCC (n = 80), compared to non-clear cell RCC (nccRCC; composed of pRCC and chRCC (n = 31; Figure 4).

Figure 4.

Association between the calculated scoring systems (CPS, TPS, ICS) and RCC morphology (ccRCC vs nccRCC). The bar graph displays the mean ± SD for each group, with statistical significance indicated as (*p < 0.05).

Survival analysis

Patients were dichotomized into low and high expression groups according to the median CMTM6 levels. Kaplan–Meier survival analyses revealed statistically significant differences between low and high CMTM6 expression in IC, using a median cutoff of 56.7%, with respect to OS from diagnosis to last follow-up (p = 0.049), TTR following diagnosis (p = 0.009), and PFS during first-line antiangiogenic therapy (p = 0.047). In contrast, CMTM6 expression in other RCC tissue compartments, as well as the median values of the evaluated scoring systems, showed no significant association with survival outcomes (Figure 5).

Figure 5.

Kaplan–Meier survival analysis was employed to explore the correlation between CMTM6 expression and survival outcomes. CMTM6 scores for immune cells were categorized into high and low groups using median value of 56.7% as cutoff point. The Log-rank test revealed a significant association between low CMTM6-IC score and OS from diagnosis to the last follow-up (a), as well as low CMTM6-IC score and time to relapse following the diagnosis (b). Conversely, a statistically significant association was found between high CMTM6 expression in IC and PFS during antiangiogenic first line therapy in patients with metastatic RCC (c).

Discussion

In recent years, combinations of ICI with TKI or ICI/ICI combinations are becoming increasingly common in the treatment of mRCC.^29
–31,33 However, not all patients benefit from these therapies, and some may experience adverse events. These challenges emphasize the need for reliable predictive biomarkers to identify patients who are likely to experience a more effective response, while sparing others from unnecessary or potentially harmful treatments.

CMTM6, a type-3 transmembrane protein, was found to be a regulator of the PD-L1 protein and displays remarkable specificity for PD-L1. CMTM6 co-localizes with PD-L1 at the plasmamembrane, preventing PD-L1 from being targeted for lysosome-mediated degradation.⁴³ The involvement of CMTM6 in multiple malignancies has already been shown.⁵⁰ We have also previously reported on CMTM6, its expression, and its association with oncological outcomes during therapy with nivolumab in mRCC.⁴⁷ Although based on a small patient cohort, we were able to demonstrate a significant association between CMTM6 expression on IC and outcomes in patients undergoing monotherapy with nivolumab. No significant associations were observed for CMTM6 expression on other components of RCC tissue.⁴⁷

In the current study, we were able to demonstrate CMTM6 detection on the protein level in a cohort of 111 RCC patients treated between 2006 and 2019. CMTM6 was detectable across various components of RCC tissue, with the highest expression levels observed in IC and TC. Remarkably, CMTM6 expression was significantly more prevalent in ccRCC compared to nccRCC. Furthermore, this relatively novel marker was proven to be stable and well-suited for the machine learning approach used for this analysis. In contrast, PD-L1 appeared to be less detectable. This is consistent with previous literature, which reports PD-L1 expression levels of up to 20% in ccRCC^51,52 and between 5% and 10% in nccRCC.⁵³

Moreover, a significant association of CMTM6 expression on IC with oncological outcomes has been shown. Patients with higher CMTM6 expression on IC had notably shorter TTR and OS.

Interestingly, in our subgroup analysis of 26 patients treated with antiangiogenic therapy, primarily using TKIs after disease progression, higher CMTM6 expression on IC was significantly associated with a positive therapeutic response to this treatment.

The use of kinase inhibitors often leads to the development of resistance, or even the overexpression of PD-L1, as alternate signaling pathways like STAT3 and ERK1/2 are activated. This phenomenon has already been observed in resistant non-small cell lung cancer.⁵⁴ For instance, epidermal growth factor receptor (EGFR) is associated with PD-L1 overexpression, which has been linked to a lack of response to anti-PD-1/PD-L1 immunotherapy.^55,56 Nonetheless, several studies have shown that receptor kinase inhibitors can enhance the response to PD-1/PD-L1 blocking therapies as well.^57
–59 These findings underscore the benefits of combining antiangiogenic agents with ICIs to enhance treatment response.

In this context, CMTM6, as a PD-L1 stabilizing protein, could play a central role. CMTM6 overexpression has been associated with OS in several cancer types, including glioblastoma and hepatocellular carcinoma.⁵⁰ In glioblastoma, high CMTM6 expression was identified as a potential predictor of poor prognosis and reduced survival.⁴⁶ In a cohort of 103 patients with invasive UTUCs, high levels of CMTM6 expression were observed. CMTM6 scores were significantly associated with various prognostic clinicopathological parameters, including WHO grade, tumor stage, inflammation, and necrosis.⁶⁰ In colorectal cancer, CMTM6 demonstrated a predictive value for anti-PD-L1/-PD-1 therapy response.⁶¹ In head and neck squamous cell carcinoma, tumors with high CMTM6 expression responded better to adjuvant therapy regimens compared to their low-CMTM6 counterparts,⁶² similar to our investigation in patients with mRCC undergoing ICI therapy with nivolumab.⁴⁷ However, the role of CMTM6 as a potential trigger of TKI resistance and the associated increase in PD-L1 expression requires further investigation. This is particularly important as our IHC analysis was performed on primary tumors and only served as a baseline and did not reflect treatment-related changes.

Our study has several limitations. First, the analysis employed calculated scoring systems such as CPS, TPS, and ICS, which are not standardized for use with CMTM6. Moreover, our study relied on a small patient cohort for the subgroup analysis of patients with metastatic disease. Due to the retrospective nature of our study, some data were unavailable, limiting the application of our analysis to the scoring models. This limitation applies not only to clinical parameters but also to follow-up data, which were unavailable for some patients. However, comprehensive follow-up, particularly for OS, is essential for ensuring reliable outcome assessments.⁶³ Furthermore, PD-L1 expression was low in our cohort. However, this finding is consistent with Möller et al.,⁶⁴ who reported only 6.3% positivity (cut-off 5%) in more than 1400 RCCs. Given that our cohort predominantly consisted of clear-cell RCC, this result is not unexpected.

Conclusion

However, in conclusion, in our cohort of RCC patients, we could demonstrate the association of CMTM6 as a co-marker for PD-L1, particularly in relation to oncological prognosis. Further studies are essential to clarify the role of CMTM6 in tumor progression and resistance mechanisms, particularly in its interaction with PD-L1 and its impact on responses to immunotherapies and other targeted treatments. Moreover, CMTM6 itself may emerge as a potential future treatment target.

Supplemental Material

sj-docx-1-tam-10.1177_17588359261430569 – Supplemental material for Association of CMTM6 expression with clinicopathological characteristics and prognostic implications in renal cell carcinoma

Supplemental material, sj-docx-1-tam-10.1177_17588359261430569 for Association of CMTM6 expression with clinicopathological characteristics and prognostic implications in renal cell carcinoma by Gennadi Tulchiner, Josef Fritz, Peter Rehder, Jasmin Bektic, Bettina Zelger, Lukas Jelisejevas, Andrea Brunner and Michael Ladurner in Therapeutic Advances in Medical Oncology

Footnotes

Acknowledgements

None.

Declarations

ORCID iDs

Gennadi Tulchiner

Lukas Jelisejevas

Michael Ladurner

Supplemental material

Supplemental material for this article is available online.

References

Ferlay

Soerjomataram

Dikshit

, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015; 136(5): E359–E386.

Siegel

Miller

Jemal

Cancer statistics, 2015. CA Cancer J Clin 2015; 65(1): 5–29.

Thorstenson

Bergman

Scherman-Plogell

, et al. Tumour characteristics and surgical treatment of renal cell carcinoma in Sweden 2005–2010: a population-based study from the national Swedish kidney cancer register. Scand J Urol 2014; 48(3): 231–238.

Concolino

Marocchi

Conti

, et al. Human renal cell carcinoma as a hormone-dependent tumor. Cancer Res 1978; 38(11 Pt 2): 4340–4344.

Flaifel

Xie

Braun

, et al. PD-L1 expression and clinical outcomes to cabozantinib, everolimus, and sunitinib in patients with metastatic renal cell carcinoma: analysis of the randomized clinical trials METEOR and CABOSUN. Clin Cancer Res 2019; 25(20): 6080–6088.

Cairns

Renal cell carcinoma. Cancer Biomark 2010; 9(1–6): 461–473.

Gupta

Miller

, et al. Epidemiologic and socioeconomic burden of metastatic renal cell carcinoma (mRCC): a literature review. Cancer Treat Rev 2008; 34(3): 193–205.

Moch

Cubilla

Humphrey

, et al. The 2016 WHO classification of tumours of the urinary system and male genital organs—Part A: renal, penile, and testicular tumours. Eur Urol 2016; 70(1): 93–105.

Sobin

Gospodarowicz

Wittekind

TNM classification of malignant tumors. 7th ed. Chichester, UK: Wiley-Blackwell, 2009.

10.

Kim

Alt

Weight

, et al. Independent validation of the 2010 American Joint Committee on Cancer TNM classification for renal cell carcinoma: results from a large, single institution cohort. J Urol 2011; 185(6): 2035–2039.

11.

Novara

Ficarra

Antonelli

, et al. Validation of the 2009 TNM version in a large multi-institutional cohort of patients treated for renal cell carcinoma: are further improvements needed? Eur Urol 2010; 58(4): 588–595.

12.

Paner

Stadler

Hansel

, et al. Updates in the eighth edition of the tumor-node-metastasis staging classification for urologic cancers. Eur Urol 2018; 73(4): 560–569.

13.

Delahunt

Cheville

Martignoni

, et al. The International Society of Urological Pathology (ISUP) grading system for renal cell carcinoma and other prognostic parameters. Am J Surg Pathol 2013; 37(10): 1490–1504.

14.

Przybycin

Cronin

Darvishian

, et al. Chromophobe renal cell carcinoma: a clinicopathologic study of 203 tumors in 200 patients with primary resection at a single institution. Am J Surg Pathol 2011; 35(7): 962–970.

15.

Frank

Blute

Cheville

, et al. An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the SSIGN score. J Urol 2002; 168(6): 2395–2400.

16.

Zigeuner

Hutterer

Chromecki

, et al. External validation of the Mayo Clinic stage, size, grade, and necrosis (SSIGN) score for clear-cell renal cell carcinoma in a single European centre applying routine pathology. Eur Urol 2010; 57(1): 102–109.

17.

Motzer

Bacik

Murphy

, et al. Interferon-alfa as a comparative treatment for clinical trials of new therapies against advanced renal cell carcinoma. J Clin Oncol 2002; 20(1): 289–296.

18.

Heng

Xie

Regan

, et al. External validation and comparison with other models of the International Metastatic Renal-Cell Carcinoma Database Consortium prognostic model: a population-based study. Lancet Oncol 2013; 14(2): 141–148.

19.

Long

Tykodi

Schneider

, et al. Assessment of nivolumab exposure and clinical safety of 480 mg every 4 weeks flat-dosing schedule in patients with cancer. Ann Oncol 2018; 29(11): 2208–2213.

20.

Motzer

Escudier

McDermott

, et al. Nivolumab versus everolimus in advanced renal-cell carcinoma. N Engl J Med 2015; 373(19): 1803–1813.

21.

Motzer

Hutson

Tomczak

, et al. Overall survival and updated results for sunitinib compared with interferon alfa in patients with metastatic renal cell carcinoma. J Clin Oncol 2009; 27(22): 3584–3590.

22.

Sternberg

Davis

Mardiak

, et al. Pazopanib in locally advanced or metastatic renal cell carcinoma: results of a randomized phase III trial. J Clin Oncol 2010; 28(6): 1061–1068.

23.

Motzer

Hutson

Cella

, et al. Pazopanib versus sunitinib in metastatic renal-cell carcinoma. N Engl J Med 2013; 369(8): 722–731.

24.

Rini

Escudier

Tomczak

, et al. Comparative effectiveness of axitinib versus sorafenib in advanced renal cell carcinoma (AXIS): a randomised phase 3 trial. Lancet 2011; 378(9807): 1931–1939.

25.

Motzer

Escudier

Oudard

, et al. Efficacy of everolimus in advanced renal cell carcinoma: a double-blind, randomised, placebo-controlled phase III trial. Lancet 2008; 372(9637): 449–456.

26.

Choueiri

Escudier

Powles

, et al. Cabozantinib versus everolimus in advanced renal cell carcinoma (METEOR): final results from a randomised, open-label, phase 3 trial. Lancet Oncol 2016; 17(7): 917–927.

27.

Choueiri

Halabi

Sanford

, et al. Cabozantinib versus sunitinib as initial targeted therapy for patients with metastatic renal cell carcinoma of poor or intermediate risk: the alliance A031203 CABOSUN trial. J Clin Oncol 2017; 35(6): 591–597.

28.

Motzer

Hutson

Glen

, et al. Lenvatinib, everolimus, and the combination in patients with metastatic renal cell carcinoma: a randomised, phase 2, open-label, multicentre trial. Lancet Oncol 2015; 16(15): 1473–1482.

29.

Motzer

Tannir

McDermott

, et al. Nivolumab plus ipilimumab versus sunitinib in advanced renal-cell carcinoma. N Engl J Med 2018; 378(14): 1277–1290.

30.

Rini

Plimack

Stus

, et al. Pembrolizumab plus axitinib versus sunitinib for advanced renal-cell carcinoma. N Engl J Med 2019; 380(12): 1116–1127.

31.

Choueiri

Powles

Burotto

, et al. Nivolumab plus cabozantinib versus sunitinib for advanced renal-cell carcinoma. N Engl J Med 2021; 384(9): 829–841.

32.

Motzer

Penkov

Haanen

, et al. Avelumab plus axitinib versus sunitinib for advanced renal-cell carcinoma. N Engl J Med 2019; 380(12): 1103–1115.

33.

Motzer

Alekseev

Rha

, et al. Lenvatinib plus pembrolizumab or everolimus for advanced renal cell carcinoma. N Engl J Med 2021; 384: 1289–1300.

34.

Ljungberg

Bedke

Capitanio

, et al. Reply to Yongbao Wei, Ruochen Zhang, and Le Lin’s letter to the editor re: Borje Ljungberg, Laurence Albiges, Yasmin Abu-Ghanem, et al. European Association of Urology Guidelines on renal cell carcinoma: the 2022 update. Eur Urol 2022; 82(4): e111–e112.

35.

Rijnders

de Wit

Boormans

, et al. Systematic review of immune checkpoint inhibition in urological cancers. Eur Urol 2017; 72(3): 411–423.

36.

Carretero-González

Lora

Martín Sobrino

, et al. The value of PD-L1 expression as predictive biomarker in metastatic renal cell carcinoma patients: a meta-analysis of randomized clinical trials. Cancers (Basel) 2020; 12(7): 1945.

37.

Schildhaus

HU.

[Predictive value of PD-L1 diagnostics]. Pathologe 2018; 39(6): 498–519.

38.

Krabbe

Heitplatz

Preuss

, et al. Prognostic value of PD-1 and PD-L1 expression in patients with high grade upper tract urothelial carcinoma. J Urol 2017; 198(6): 1253–1262.

39.

Galsky

Bajorin

Witjes

, et al. Disease-free survival analysis for patients with high-risk muscle-invasive urothelial carcinoma from the randomized CheckMate 274 trial by PD-L1 combined positive score and tumor cell score. Eur Urol 2023; 83(5): 432–440.

40.

Nakanishi

Wada

Matsumoto

, et al. Overexpression of B7-H1 (PD-L1) significantly associates with tumor grade and postoperative prognosis in human urothelial cancers. Cancer Immunol Immunother 2007; 56(8): 1173–1182.

41.

Zhang

Yin

Zhang

, et al. Differential expressions of PD-1, PD-L1 and PD-L2 between primary and metastatic sites in renal cell carcinoma. BMC Cancer 2019; 19(1): 360.

42.

Sun

Mezzadra

Schumacher

TN.

Regulation and function of the PD-L1 checkpoint. Immunity 2018; 48(3): 434–452.

43.

Burr

Sparbier

Chan

, et al. CMTM6 maintains the expression of PD-L1 and regulates anti-tumour immunity. Nature 2017; 549(7670): 101–105.

44.

Pang

Wang

Zhang

, et al. OSCC cell-secreted exosomal CMTM6 induced M2-like macrophages polarization via ERK1/2 signaling pathway. Cancer Immunol Immunother 2021; 70(4): 1015–1029.

45.

Zhao

Yang

, et al. An immunophenotyping of renal clear cell carcinoma with characteristics and a potential therapeutic target for patients insensitive to immune checkpoint blockade. J Cell Biochem 2019; 120(8): 13330–13341.

46.

Guan

Zhang

Zhao

, et al. CMTM6 overexpression is associated with molecular and clinical characteristics of malignancy and predicts poor prognosis in gliomas. EBioMedicine 2018; 35: 233–243.

47.

Tulchiner

Brunner

Schmidinger

, et al. CMTM6 expression as a potential biomarker for immunotherapy in metastatic renal cell carcinoma. BJU Int 2021; 128(1): 29–32.

48.

Bossuyt

Reitsma

Bruns

, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ (Clin Res Ed). 2015; 351: h5527.

49.

Tustumi

Choosing the most appropriate cut-point for continuous variables. Rev Col Bras Cir 2022; 49: e20223346.

50.

Zhao

Zhang

, et al. Prognostic implications of Pan-cancer CMTM6 expression and its relationship with the immune microenvironment. Front Oncol 2020; 10: 585961.

51.

Joseph

Millis

Carballido

, et al. PD-1 and PD-L1 expression in renal cell carcinoma with sarcomatoid differentiation. Cancer Immunol Res 2015; 3(12): 1303–1307.

52.

Campedel

Compérat

Cancel-Tassin

, et al. Prognostic value of programmed death ligand-1 and programmed death-1 expression in patients with upper tract urothelial carcinoma. BJU Int 2023; 132(5): 581–590.

53.

Choueiri

Fay

Gray

, et al. PD-L1 expression in nonclear-cell renal cell carcinoma. Ann Oncol 2014; 25(11): 2178–2184.

54.

Jiang

Guo

Liu

, et al. Continuous targeted kinase inhibitors treatment induces upregulation of PD-L1 in resistant NSCLC. Sci Rep 2019; 9(1): 3705.

55.

Liu

Shen

, et al. Immunotherapy strategy of EGFR mutant lung cancer. Am J Cancer Res 2018; 8(10): 2106–2115.

56.

Lee

Man

Lord

, et al. Clinical and molecular characteristics associated with survival among patients treated with checkpoint inhibitors for advanced non-small cell lung carcinoma: a systematic review and meta-analysis. JAMA Oncol 2018; 4(2): 210–216.

57.

Gettinger

Hellmann

Chow

LQM

, et al. Nivolumab plus erlotinib in patients with EGFR-mutant advanced NSCLC. J Thorac Oncol 2018; 13(9): 1363–1372.

58.

Amin

Plimack

Ernstoff

, et al. Safety and efficacy of nivolumab in combination with sunitinib or pazopanib in advanced or metastatic renal cell carcinoma: the CheckMate 016 study. J Immunother Cancer 2018; 6(1): 109.

59.

Motzer

Robbins

Powles

, et al. Avelumab plus axitinib versus sunitinib in advanced renal cell carcinoma: biomarker analysis of the phase 3 JAVELIN Renal 101 trial. Nat Med 2020; 26(11): 1733–1741.

60.

Kdimati

Christoph

Glass

, et al. Differential expression of CKLF-like MARVEL transmembrane domain-containing protein 6 and programmed cell death ligand 1 as prognostic biomarkers in upper tract urothelial carcinoma. Int J Mol Sci 2024; 25(6): 3492.

61.

Lan

, et al. CMTM6 expression in M2 macrophages is a potential predictor of PD-1/PD-L1 inhibitor response in colorectal cancer. Cancer Immunol Immunother 2021; 70(11): 3235–3248.

62.

Becker

Kluge

Schofeld

, et al. Identifying predictive biomarkers for head and neck squamous cell carcinoma response. Cancers (Basel) 2023; 15(23): 5597.

63.

Von Allmen

Weiss

Tevaearai

, et al. Completeness of follow-up determines validity of study findings: results of a prospective repeated measures cohort study. PLoS One 2015; 10(10): e0140817.

64.

Möller

Fraune

Blessin

, et al. Tumor cell PD-L1 expression is a strong predictor of unfavorable prognosis in immune checkpoint therapy-naive clear cell renal cell cancer. Int Urol Nephrol 2021; 53(12): 2493–2503.