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Soluble TIM-3, likely produced by myeloid cells, predicts resistance to immune checkpoint inhibitors in metastatic clear cell renal cell carcinoma

Journal of Experimental & Clinical Cancer Research volume 44, Article number: 54 (2025) Cite this article

Abstract

Background

Immunotherapies targeting PD-1 and CTLA-4 are key components of the treatment of metastatic clear cell renal cell carcinoma (mccRCC). However, they have distinct safety profiles and resistance to treatment can occur. We assess soluble TIM-3 (sTIM-3) in the plasma of mccRCC patients as a potential theranostic biomarker, as well as its source and biological significance.

Methods

We analyzed the association between sTIM-3 and overall survival (OS), tumor response, and common clinical and biological factors in two mccRCC cohorts treated with anti-PD-1 (nivolumab, n = 27), anti-PD-1 or anti-PD-1 + anti-CTLA-4 (nivolumab + ipilimumab – N + I, n = 124). The origin and role of sTIM-3 are studied on tumor and blood samples, using multiplex immunohistochemistry and flow cytometry, as well as analyses of publicly available single-cell transcriptomic (scRNAseq) and mass cytometry data.

Results

sTIM-3 is significantly elevated in the plasma of treatment-naive mccRCC. It shows distinct associations with survival on anti-PD-1 vs anti-PD-1 + anti-CTLA-4: under nivolumab monotherapy, sTIM-3-high patients have a significantly reduced survival compared to sTIM-3-low patients, while they have similar survival probabilities under N + I. sTIM-3 is independent from other clinical and biological factors. Myeloid immune cells appear as the prominent source of sTIM-3, which may indicate their dysfunctional role in the antitumor immune response.

Conclusions

sTIM-3 appears to be a promising biomarker for optimizing treatment strategies in ccRCC as well as a potential therapeutic target, linked with to the immune myeloid compartment. Future investigations are warranted in patients treated with anti-PD-1 + antiangiogenic therapies.

Background

Immune checkpoint inhibitors (ICI) were introduced in clear cell renal cell carcinoma (ccRCC) as a second-line treatment in the metastatic setting (mccRCC) with the approval of nivolumab monotherapy, an antibody blocking the-programmed cell death protein-1 (PD-1) [1]. Anti-PD-1 were later combined in the first line with ipilimumab, an anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4) or antiangiogenic tyrosine kinase inhibitors (TKI) [2]. The International Metastatic RCC Database Consortium (IDMC) score is the only validated theranostic biomarker for mccRCC and previously helped selecting nivolumab + ipilimumab (N + I) over sunitinib [3, 4]. No predictive biomarkers exist to discern patients who may achieve maximal efficacy with N + I rather than with other anti-PD-1-based regimens.

T cell immunoglobulin and mucin domain-containing molecule 3 (TIM-3) is an immunomodulatory transmembrane protein classically ascribed as an exhaustion marker on T cells, conferring dysfunctionality [5]. In ccRCC, TIM-3-expressing T cells have been associated with poor prognosis [6, 7] and resistance to anti-PD-1 monotherapy [8, 9] or combined with antiangiogenics [10]. Clinical trials targeting TIM-3 in mccRCC are underway [11, 12]. Soluble isoforms of TIM-3 (sTIM-3) are generated through proteolytic cleavage and can be quantified in human plasma [13, 14]. Blood-based biomarkers have the advantages of ready accessibility and proximity to baseline measurements. We assessed whether plasma sTIM-3 is associated with resistance of mccRCC to ICI, given the detrimental value of TIM-3 expression in the TME [15] and sTIM-3 association with prognosis regardless of treatment [16].

Here we show that an acute sTIM-3 increase is induced via antitumor immunization in mice. We assess chronic plasma sTIM-3 elevation in mccRCC patients, its association with outcomes under ICI. We investigate the source of sTIM-3 by analyzing tumor specimens and peripheral blood mononuclear cells (PBMC) from mccRCC patients.

Material and methods

Patient cohorts and sample collection

ICI-treated mccRCC patients were participants from the Colcheckpoint (nivolumab monotherapy after prior antiangiogenic therapy) and BIONIKK (first line nivolumab monotherapy or N + I [17]) independent cohorts approved by the French Health authorities and ethics committee [CPP Ile-de-France 8 (ref.16.10.69) and CPP Ouest I SI CNRIPH n18.11.21.67518 respectively]. Healthy adult human samples were drawn from blood donations. Plasma and PBMC were collected immediately before ICI initiation for mccRCC (baseline). Multiplex immunohistochemistry (IHC) was performed on formalin-fixed, paraffin-embedded (FFPE) archival tissue of primary tumors from participants of the BIONIKK cohort. All the participants provided written informed consent.

Mouse models

TC-1 cells were obtained from TC Wu's laboratory (John Hopkins Hospital) and injected into anesthetized animals. Mice were vaccinated with 200 µg G15F peptide from the HPV-16 E7 protein (Genosphere Biotechnologies) and 2 µg of α-galactosyl-ceramide (α-Gal-Cer) adjuvant (Funakoshi Co.).

CD8 + T cells depletion was performed in vivo by intraperitoneal injections of a depleting antibody (100 µg/100 µl anti-mouse CD8α rat IgG2bκ, clone 2.43 InVivoMab™, BioXCell).

Experiments were conducted on 8-week-old female C57BL/6 J mice (Janvier Labs) and were approved by the Ethics Committee of the University Paris Cité (CEEA34).

sTIM-3 quantification

Human plasma samples were isolated by centrifugation and frozen at −80 °C for storage. After thawing the samples, sTIM-3 was quantified using the Luminex® Multiplex ProcartaPlex™ Human Immuno-Oncology Checkpoint Marker Panel 1 (14-Plex) kit (Thermo Fisher®), following the manufacturer’s instructions. Measurements were acquired using a BioPlex-200 (BioRad®).

sTIM-3 was quantified in mouse serum by ELISA (Mouse Tim-3 SimpleStep ELISA® kit—ab255721 Abcam®), absorbance was measured on a Spectrostar microplate reader and converted to concentrations using BMG Labtech software.

Multiplex IHC

FFPE slides were incubated with specific antibodies (details in Supp. Table 2.A) and stained in a BOND-RX automate (Leica®) using Opal™ secondary antibodies and fluorescent reagents. The specificity of TIM-3 staining was confirmed in additional ccRCC samples using the corresponding isotype control. Images were acquired in a Vectra Polaris scanner (Akoya/PerkinElmer) at 20X magnification and analyzed using HALO™ (v.3.6.4, IndicaLab®).

Flow cytometry

PBMC were isolated by Ficoll™ density gradient centrifugation and frozen for storage. PBMC were thawed, marked for viability (Zombie NIR, BioLegend®) and stained with antibodies against human markers (details in Supp. Table 2.B). Acquisition was performed on a Navios 10 colors cytometer (Beckman Coulter®), analyses were performed using Kaluza® (v 2.1). Thresholds for TIM-3 and CD14 positivity were set with the corresponding control isotypes.

Single-cell RNA sequencing (scRNAseq) data analyses

ScRNAseq data from ccRCC and non-cancerous human kidneys were obtained from publicly available datasets [18,19,20,21,22] and analyzed in R (v.4.1.1) using Seurat (v.5.0.3). Cells were annotated through SingleR (v.1.8.1) and manually. Differential gene expression (DEG) and gene set enrichment analyses (GSEA) were performed using MAST (v.1.20.0) and enrichR (v.3.2). Pseudobulk analysis of scRNAseq data was performed using DESeq2 (v.1.34.0) on aggregated transcripts counts.

Statistical analysis

Comparisons were performed for quantitative continuous variables using Mann–Whitney U (a.k.a. Wilcoxon ranks sum), Kruskal–Wallis, paired Wilcoxon, Friedman and Student’s t tests. Appropriate tests were chosen depending on the distribution and the dependent/independent nature of the observations. Given the sufficient maturity of the data, Overall survival (OS) was chosen as the primary outcome for measuring ICI efficacy [23, 24]. OS probabilities were estimated using the Kaplan–Meier method, differences were quantified by Cox hazard ratios (HR) and tested using the log-rank test. Two-tailed p-values < 0.05 were considered statistically significant. P-value adjustments for multiple testing were performed using the Benjamini–Hochberg method for large-scale differential gene expression analyzes. In this case, adjusted p-values < 0.05 were considered to be statistically significant. sTIM3 values were batch-corrected through the ComBat() function from the sva R package (v. 3.50.0) prior to classification of individuals for comparisons between the nivolumab and N + I arms of the BIONIKK cohort (Supp. Figure 1). Analyses were performed using the R software (v.4.3.2; R Foundation for Statistical Computing, Vienna, Austria).

Results

sTIM-3 is elevated in mccRCC patients and associated with OS under ICI

Plasma sTIM-3 levels were significantly increased in treatment-naive mccRCC patients (median = 110.33 pg/L, IQR 65.36 to 151.10) from the BIONIKK cohort, compared to healthy adults (median = 46.10 pg/L, IQR 40.81 to 50.42, p < 10–3) (Fig. 1).

Fig. 1
figure 1

sTIM-3 plasma levels in ccRCC patients. sTIM-3 plasma levels were measured in treatment-naive ccRCC patients from the BIONIKK cohort before ICI treatment, and in healthy adults

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High baseline sTIM-3 (above the median within each cohort) was associated with poor OS of ccRCC patients on nivolumab monotherapy, in both the Colcheckpoint (n = 27, HR for death sTIM-3-high vs sTIM-3-low = 2.67, p = 0.03) and BIONIKK (n = 45, HR sTIM-3-high vs sTIM-3-low = 2.36, p = 0.04) cohorts (Fig. 2A & B).

Fig. 2
figure 2

Association of sTIM-3 plasma levels with OS in ccRCC patients under anti-PD1. Patients were categorized in sTIM-3 high or low, depending on whether their individual values of plasma sTIM-3 fell below or above the median value within each cohort. OS was estimated using the Kaplan–Meier method and differences between sTIM-3-low and -high groups were tested with the log rank method. Patients’ characteristics are found in Supplementary Table 1. A Colcheckpoint cohort (n = 27). B BIONIKK cohort (n = 45)

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Interestingly, there was no OS difference according to sTIM-3 stratification in patients from the BIONIKK trial treated with N + I (n = 79, Supp. Figure 2.A). Furthermore, we found that sTIM-3-low participants in the nivolumab monotherapy and N + I arms had comparable OS, whereas for sTIM-3-high participants, N + I was significantly superior to nivolumab (Supp. Figure 2.B). To verify that the results in the N + I group were not confounded by IMDC scores (Supp. Table 1.C), a 2-variables Cox model for OS including IMDC and sTIM-3 categories was constructed. Still, the adjusted analysis showed no relationship between sTIM-3 category and OS under N + I (HR high vs low = 1.0, 95% IC 0.40—2.72) (Supp. Figure 3).

This suggests that sTIM-3 can distinguish mccRCC patients likely to achieve prolonged survival under anti-PD-1 monotherapy, whereas the anti-PD-1 + anti-CTLA4 combination retains its efficacy in sTIM-3-high patients.

sTIM-3 is independent from clinical and biological prognostic markers

To assess the specificity of plasma sTIM-3 as a biomarker in the context of metastatic ccRCC, we tested its association with known baseline clinical and biological prognostic markers. IMDC categorization was not associated with sTIM-3 (Fig. 3A) and sTIM-3 was neither correlated with systemic inflammation markers included in the score (neutrophil and platelet counts, decreased hemoglobin) nor with other biomarkers (Supp. Figure 4 and Supp. Figure 6).

Fig. 3
figure 3

Comparison of sTIM-3 plasma levels in participants of BIONIKK (n = 133) categorized according to IMDC score and tumor burden. A sTIM-3 versus IMDC score calculated at baseline before ICI initiation. B sTIM-3 vs. the number of metastatic sites at baseline (1 site or ≥ 2 sites). C sTIM-3 vs. history of primary tumor removal (previous nephrectomy)

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Unlike CRP (Supp. Figure 5), sTIM-3 was not associated with tumor burden proxies (number of metastatic sites or presence of primary tumor, Fig. 3B & C). Hence, sTim-3 does not simply reflect a higher tumor burden or systemic inflammation, it conveys additional information about the status of the disease. In addition, these results suggest a source other than tumor cells for sTIM-3.

Plasma sTIM-3 is likely produced by myeloid cells in ccRCC

In situ multiplex IHC of ccRCC tumors

We assessed the expression of TIM-3 by tumor cells – defined as pancytokeratin and/or PAX8 positive cells—and tested its association with plasma sTIM-3 in a subset of BIONIKK participants (n = 22) (Fig. 4A).

Fig. 4
figure 4

TIM-3 IHC detection on ccRCC tumors. 10 random tumor regions evenly spread on the slides were selected for analyzes. A custom phenotyping algorithm was used for quantification of total PAX8 + Cytokeratin + tumor cells and manual counting was performed for quantification of TIM3 + tumor cells. A Absorption view (× 20) of a ccRCC primary tumor FFPE sample analyzed with multiplex IHC. Blue: DAPI; Yellow: PanCytokeratine-PAX; Red: TIM-3. Arrows: TIM-3 positive tumor cells; Arrowhead: TIM-3 positive non-tumor cell. B % of TIM-3-positive tumor cells among tumor cells in each sample (n = 22). C Association of plasma sTIM-3 with TIM-3 tumor status

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There was high intra-tumor and inter-sample variability in TIM-3 staining of tumor cells (Fig. 4B), with a median of 0.34% (IQR 0.05% to 5.61%) positive tumor cells. Tumors with > 1% TIM-3 positive tumor cells were classified as TIM-3 positive. TIM-3 positivity was neither associated with higher levels of plasma sTIM-3 (Fig. 4C), nor with survival under nivolumab monotherapy (Supp. Figure 7).

The abundance of TIM-3-positive non-tumor cells did not appear to be correlated with plasma sTIM-3 (Spearman correlation = −0.26, p = 0.25, Supp. Figure 8). We further characterized non-tumor TIM-3 positive cells with additional broad immune markers (CD3, CD4, CD8 and CD68). Interestingly, a substantial proportion of intratumor myeloid cells was TIM-3 positive. They seemed to be overall at least as abundant as TIM-3 positive lymphoid cells (Supp. Figure 9). Nonetheless, no direct correlation between the abundance of the different TIM-3 positive immune populations and sTIM-3 plasma levels was evidenced (not shown). This might be partly due to the sensitivity of IHC to the shedding of TIM-3 (see Discussion).

scRNAseq of ccRCC tumors

The main hypothesis for the production of sTIM-3 in human is its shedding from double-positive HAVCR2 + ADAM + cells, co-expressing HAVCR2 (encoding TIM-3) and one or both of the metalloproteinases (ADAM10 and ADAM17) for which the cleaving activity of membrane TIM-3 is reported [13, 14]. We quantified the expression of these genes in several scRNAseq datasets of ccRCC and healthy kidney samples, the results are shown in Obradovic et al. dataset (Fig. 5).

Fig. 5
figure 5

Obradovic et al. ccRCC scRNAseq dataset. A Tumor and adjacent tissue samples. Upper section: UMAP of cell lineages; middle section: HAVCR2 expression; lower section: HAVCR2 + ADAM + double-positive cells repartition. B % of cells in the pooled dataset expressing HAVCR2 and ADAM10 and/or ADAM17 in broad lineages. HAVCR2 + ADAM10 +—detection of ADAM10 transcripts but not ADAM17; HAVCR2 + ADAM17 +—detection of ADAM17 transcripts but not ADAM10, HAVCR2 + ADAM10 + 17 +—detection of both metalloproteinases. C Comparison of the proportions of HAVCR2 + ADAM + cells within the lymphoid and myeloid lineages, for each patient in tumor samples

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Although some CD8 + and NK lymphocyte clusters had high transcription levels of HAVCR2, myeloid clusters (Supp. Fig. 10,11,12,13) were identified as the most enriched lineage in HAVCR2 + ADAM + cells, with even distributions of the three different double-positive types (Fig. 5B; Supp. Fig. 10 & 12). HAVCR2 expression was detected in renal tissue clusters (tumor and healthy samples) but they contained marginal proportions of HAVCR2 + ADAM + cells (Fig. 5A & B, Supp. Fig. 11 & 12). Consistent findings were reproduced in the three other scRNAseq datasets.

HAVCR2 + ADAM + enriched monocytes clusters were common to paired tumors and PBMC in a dataset of 3 ccRCC patients [20]. Consistent with our flow cytometry data, HAVCR2 expression in PBMC was mainly found in monocytes and NK clusters but was quasi-undetectable in T cells (Supp. Fig. 13).

Principal component analysis (PCA) of the top 200 most variable genes showed a clear separation of double-positive and non-double-positive pseudobulk samples generated from Obradovic et al. dataset, despite technical batch variations, suggesting a distinct transcriptomic state for HAVCR2 + ADAM + myeloid cells (Supp. Fig. 14).

A differential gene expression profile comparing HAVCR2 + ADAM + myeloid cells with other myeloid cells was established using Seurat implementation of the MAST algorithm. Among the top DEG, the putative pro-tumor markers TREM2 and APOE were found increased in HAVCR2 + ADAM + myeloid cells (Khantakova, Brioschi, et Molgora 2022; Bancaro et al. 2023), while NLRP3 was decreased, which may be linked to decreased APC functionality through inflammasomes (Dixon et al. 2021). GSEA suggested global downregulation of proteins synthesis in HAVCR2 + ADAM + myeloid cells (Supp. Fig. 15), as wells as cellular respiration (oxidative phosphorylation).

These results favor the hypothesis of myeloid cells being the major source of sTIM3 in mccRCC. sTIM-3 could reflect the presence of dysfunctional HAVCR2 + ADAM + myeloid cells with globally decreased anabolic activity [25].

Mass cytometry of ccRCC tumors

In order to quantify the expression of TIM-3 by the different cell lineages of the ccRCC TME at the protein level, we reanalyzed publicly available mass cytometry data of 72 ccRCC tumors and 5 healthy kidney samples published by Chevrier et al. [26]. TIM-3 appeared to be fairly expressed by lymphoid and myeloid cells but much less expressed by non-immune cells (Supp. Fig. 16 & 17). There was a significant difference in median signal intensity depending on the cell lineage (p < 0.0001), TIM-3 detection was generally higher for lymphoid and myeloid cells than for non-immune cells in all ccRCC samples (p = 5.33 × 10–13 and 1.15 × 10–12 respectively), in line with our findings on the scRNAseq datasets (Supp. Figure 16.B). Notably, the median TIM-3 signal intensity was null in non-immune cells of kidney samples from healthy donors (Supp. Figure 16.C).

PBMC cytometry of ccRCC patients

We quantified TIM-3-positive (TIM3 +) cells in PBMC from mccRCC patients (Fig. 6A), their median proportion was 22.7% (IQR 14.9% to 27.3%) (Fig. 6B).

Fig. 6
figure 6

Flow cytometry quantification of TIM-3-positive (TIM3 +) cells in PBMC of ccRCC patients (Colcheckpoint cohort, n = 27). A Gating strategy; we considered CD3-SSC-Ahigh cells as myeloid cells, CD3 + SSC-Alow as T cells and CD3-SSC-Alow as non-T lymphoid cells. B Percentage of TIM3 + cells within PBMC of Colcheckpoint participants. C Proportion of CD3- myeloid, CD3- lymphoid and CD3 + lymphoid cells within TIM3 + cells in PBMC of Colcheckpoint participants

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CD3-TIM3 + myeloid cells were the most common type of TIM3 + PBMC (median 61.9% of TIM3 + PBMC), followed by CD3-TIM3 + lymphoid cells. Surprisingly, T cells (CD3 + TIM3 +) accounted for a minority of TIM3 + PBMC (Fig. 6C). Given their intermediate CD4 positivity (Supp. Figure 20) and that their proportion among TIM3 + PBMC was the same as that of CD14 + TIM3 + PBMC on a separate panel (Supp. Figure 21), we hypothesized that most CD3-TIM3 + myeloid cells had a monocytic origin. CD3-TIM3 + lymphoid PBMC were most likely NK cells, given their CD3-CD8low phenotype and that B cells (quantified with CD19 on another panel, not shown) were not found in TIM3 + PBMC. These results are consistent with data from the literature on peripheral blood cells from healthy donors, showing TIM-3 detection on a high proportion of monocytes, a moderate proportion of NK cells, and low or absent detection on T cells and granulocytes (Kikushige et al. 2010; [13]).

Discussion

Plasma sTIM-3 is a promising blood-based biomarker associated with ICI efficacy in mccRCC, with differential effects on nivolumab versus N + I, and is independent of common clinical or inflammatory markers. An association with the efficacy of current anti-PD-1 + TKI regimens is also plausible and will be investigated in future research. If a differential effect on N + I compared to anti-PD-1 + TKI is confirmed, this could make sTIM-3 a theranostic biomarker. Defining an sTIM-3-high population for which the addition of anti-CTLA-4 is needed to overcome resistance to anti-PD-1 would facilitate the indication of N + I, for which the benefit-risk balance is delicate. sTIM-3 is also promising for other cancers where anti-PD-1 monotherapies are challenged by combinations, without strong decisional criteria (e.g. melanoma and lung cancer). Of note, slightly higher plasma sTIM-3 levels were found in men (median = 117.21 pg/L in men vs. 87.59 pg/L in women, unadjusted p = 0.048, Supp. Figure 6.B). If a true association between sex and sTIM-3 levels exists, to our knowledge, there is no obvious mechanism to explain this result and it is unlikely to affect the predictive value of sTIM-3.

TIM-3 is known for its inhibitory role at the membrane of T cells, its shedding into sTIM-3 by activated CD8 + T cells has been reported [14]. Unexpectedly, our study suggests that myeloid cells are the major source of sTIM-3 in mccRCC. TIM-3 was found on tumor-associated macrophages (TAM) and associated with PFS, in another cohort of ccRCC, and was induced on monocytes co-cultured with RCC cell-lines. However, the patients were not treated with ICI and monocytes were obtained from healthy volunteers [27]. LPS-activated monocytes from healthy donors and myeloid leukemia cells shed sTIM-3 upon activation of ADAMs [13, 28]. In mccRCC, we show that myeloid cells represent the majority of TIM-3-positive PBMC and the most enriched lineage in HAVCR2-expressing cells within the TME. It must be noted that the 2E2 mAb used by Chevrier et al. and by us to detect TIM-3 in cytometry experiments most likely binds to a part of TIM-3’s ectodomain that is shed [13, 14]. Nevertheless, our analyses of scRNAseq data showed more co-expression of ADAM10/17 in myeloid cells from the TME and PBMC samples compared to lymphoid cells. A bias leading to underestimation of TIM-3 expression from its membrane detection, because of the shedding of the marker, is hence more likely to occur for myeloid cells, especially in the TME where the activity of ADAM is known to increase [29]. Consistent with this hypothesis, the median signal intensity for TIM-3 was higher in lymphoid cells than in myeloid cells in most ccRCC samples from Chevrier et al. dataset (Supp. Figure 16.B, Supp. Fig. 17). Besides, TIM-3 was detected at the apical border of some healthy and cancerous proximal tubule cells in IHC experiments (Fig. 4A). One hypothesis is that sTIM-3 undergoes glomerular filtration with re-uptake of the antigen by these cells [30]. Still, TIM-3 appeared to be fairly expressed by immune cells but with null median signal intensities for non-immune cells in most samples of Chevrier et al. dataset (Supp. Fig. 16.B & C). Finally, granulocytes are quasi-absent from PBMC and from the scRNAseq datasets we reanalyzed but their role as source of sTIM-3 cannot be excluded. Nevertheless, it can be noted that existing studies from the literature report no TIM-3 expression on peripheral granulocytes from healthy adult donors, unlike monocytes (Kikushige et al. 2010b; [13],Hakim et al. 2020; H. Wang et al. 2022).

A secreted sTIM-3 isoform is derived from the alternative splicing of murine TIM-3 mRNA [31]. Nonetheless, mouse embryonic fibroblasts shed transfected human TIM-3 and murine TIM-2 (TIM family) via ADAM10/17, supporting the relevance of studying sTIM-3 in this species [13, 32]. To assess sTIM-3 at the early stage of the tumor response, we used a syngeneic TC-1 mouse tumor model, which is spontaneously poorly immunogenic but for which a specific antitumor immune response can be triggered via vaccination. Control mice solely inoculated with TC-1 cells showed no significant variation in plasma sTIM-3 18 days after the graft, whereas vaccinated mice exhibited a significant increase (Supp. Figure 22). This suggests a prominent role of the immune response in the production of sTIM-3 in this model, rather than the growth of the tumor burden per se. Moreover, in a second experiment, sTIM-3 increase upon antitumor immunization was found at earlier time-points, as soon as 4 days after the vaccine administration (e.g. 11 days after tumor graft) and was not modified by CD8 + T cells depletion (Supp. Figure 23). The timing of sTIM-3 increase and its sustainability despite CD8 + T cells elimination favor the hypothesis of the early release of sTIM-3 by innate immune cells, possibly myeloid cells, in this model. Confirming experiments of myeloid cells depletion are being conducted in this model.

Regarding the biological meaning of sTIM-3 in mccRCC patients, there are two (non-exclusive) possibilities: On one hand, sTIM-3 could have per se a pro-tumor and/or immunosuppressive effect. For instance, it has been shown that sTIM-3 inhibits interleukin-2 expression, which is known for its major role in T cell immunity and as one of the first immunotherapies with antitumor activity in mccRCC [28, 33]. On the other hand, plasma sTIM-3 could be a marker of pro-tumor processes or cell types, such as ADAM10/17 activity in the TME [34, 35]. McDermott et al. reported an association between high expression of a gene set related to myeloid inflammation and reduced PFS of metastatic ccRCC patients treated with atezolizumab (anti-PD-L1), alone or combined with bevacizumab, in the IMmotion150 trial [36]. Recently, Vanmeerbeek et al. identified TAM co-expressing TIM-3 and VSIR, associated with resistance to ICI. Interestingly, HAVCR2 was one of the most transcriptionally enriched genes in myeloid cells from ICI-unresponsive patients in their cross-cancer dataset [37]. We show that HAVCR2 + ADAM + myeloid cells have increased transcription of TREM2 and APOE, which have been proposed as pro-tumor macrophage markers [38]. The release of sTIM-3 may also reflect a deleterious state of over-activation of myeloid APC, which are required to prime T cells and sustain the response to ICI [39,40,41]. In an experimental mouse model of systemic inflammatory response syndrome, intravenous injection of LPS, which is also known to induce sTIM-3 release by monocytes, could recreate a state of immune paralysis [42]. GSEA suggest that HAVCR2 + ADAM + myeloid APC have globally impaired transcriptional and translational capacities (Supp. Figure 15) [25, 43].

Understanding the biological meaning of plasma sTIM-3 will be crucial for TIM-3-targeted therapies: According to the relevant hypotheses, strategies to treat sTIM-3 high patients would be to either block the putative deleterious action of sTIM-3, inhibit ADAMs’ activities, deplete TIM-3 + pro-tumor myeloid cells or restore the function of paralyzed APC.

Conclusion

  • Plasma sTIM-3 is elevated in mccRCC and independent of clinical and inflammatory prognostic markers. It is a promising blood-based biomarker associated with ICI efficacy in this setting.

  • The immune myeloid compartment is a predominant candidate as the source of plasma sTIM-3 in mccRCC.

  • Further functional studies will precise the biological role of sTIM-3 in mccRCC and the ensuing therapeutic targets.

Data availability

The human datasets generated during the current study are not publicly available due to the local regulation on patients’ data protection. Requests for anonymized patient data will be examined on an individual basis by relevant administration committees of the Colcheckpoint and BIONIKK cohorts’ data. The processed data generated by the authors from animal experiments are available upon request from the corresponding authors. The R code generated by the authors for bioinformatics and statistical analyses is available upon request from the corresponding authors. The publicly archived scRNAseq datasets reanalyzed during the current study are available at the following addresses: https://github.com/Aleksobrad/single-cell-rcc-pipeline; https://singlecell.broadinstitute.org/single_cell/study/SCP1288/tumor-and-immune-reprogramming-during-immunotherapy-in-advanced-renal-cell-carcinoma#study-summary; https://github.com/ncborcherding/ccRCC; and http://www.kidneycellatlas.org/. The publicly archived mass cytometry dataset reanalyzed during the current study is available at the following address: https://premium.cytobank.org/cytobank/projects/875.

Abbreviations

ADAM10:

A disintegrin and metalloproteinase domain 10

ADAM17:

A disintegrin and metalloproteinase domain 17

APC:

Antigen presenting cells

APOE :

Apolipoprotein E

ccRCC:

Clear cell renal cell carcinoma

CD:

Cluster of differentiation

CRP:

C-reactive protein

CTLA-4:

Cytotoxic t-lymphocyte-associated protein 4

FFPE:

Formalin-fixed, paraffin-embedded

GAL-9:

Galectin-9

GSEA:

Gene set enrichment analysis

HAVCR2:

Hepatitis a virus cellular receptor 2

HPV-16:

Human papillomavirus 16

HR:

Hazard ratio

IC:

Immune checkpoints

ICI:

Immune checkpoint inhibitors

IFN:

Interferon

IHC:

Immunohistochemistry

IL:

Interleukin

IMDC:

International metastatic rcc database consortium

IQR:

Interquartile ranges

LPS:

Lipopolysaccharide

mAb:

Monoclonal antibodies

MFI:

Median fluorescence intensity

MHC:

Major histocompatibility complex

NK:

Natural killer

NLR:

Neutrophil-to-lymphocyte ratio

NLRP3:

Nlr family pyrin domain containing 3

NSCLC:

Non-small cell lung cancer

ORR:

Overall response rate

OS:

Overall survival

PAX8:

Paired box 8

PBMC:

Peripheral blood mononuclear cells

PD-1:

Programmed cell death protein 1

PFS:

Progression-free survival

PS:

Performance status

sADAM10:

Soluble ADAM10

sADAM17:

Soluble ADAM17

scRNAseq:

Single-cell RNA sequencing

TAM:

Tumor associated macrophages

TCGA:

The cancer genome genome atlas

TCR:

T cell receptor

TILs:

Tumor-infiltrating lymphocytes

TIM-3:

 T cell immunoglobulin and mucin domain-containing molecule 3

TKI:

Tyrosine kinase inhibitors

TME:

Tumor microenvironment

Tregs:

Regulatory T cells

TREM2:

Triggering receptor expressed on myeloid cells 2

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Acknowledgements

The authors thank Marine Sroussi, Aurélien De Reynies, Pierre Gestraud, Lorette Noiret and Aleksandar Obradovic for their discussions and advice on scRNAseq data analyzes; Stéphane Chevrier for his advice on mass cytometry data exploitation; Florian Da Silva and Chloé Broudin for their help on tumor samples logistics; Jean-Philippe Empana for his advice on survival models; Clémence Granier for inspiring scientific discussions.

Funding

With financial support from ITMO Cancer of Aviesan within the framework of the 2021–2030 Cancer Control Strategy, on funds administered by Inserm. ITMO Cancer was not involved in the design and conduct of the study, management and analysis of the data, or approval of the manuscript.

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Author notes

  1. Nadine Benhamouda, Thi Tran and Hugo Roux contributed equally to this work.

  2. Eric Tartour and Stéphane Oudard contributed equally as co-last authors of this article.

Authors and Affiliations

  1. Université Paris Cité, INSERM U970, PARCC, Paris, France

    Ivan Pourmir, Nadine Benhamouda, Thi Tran, Joséphine Pineau, Alain Gey, Eric Tartour & Stéphane Oudard

  2. Medical Oncology Department, Georges Pompidou European Hospital, CARPEM Cancer Institute, AP-HP Centre, Université Paris Cité, Paris, France

    Ivan Pourmir & Stéphane Oudard

  3. Thoracic Oncology Department, Georges Pompidou European Hospital, CARPEM Cancer Institute, AP-HP Centre, Université Paris Cité, Paris, France

    Ivan Pourmir

  4. Immunology Department, Georges Pompidou European Hospital, AP-HP Centre, Université Paris Cité, Paris, France

    Nadine Benhamouda, Joséphine Pineau, Alain Gey, Andyara Munoz, Nesrine Mabrouk & Eric Tartour

  5. Institut Curie, PSL University, Inserm U932, Immunity and Cancer, Paris, France

    Hugo Roux

  6. Medical Oncology Department, Clinique Kuindo Magnin, Nouméa, Nouvelle Calédonie, France

    Nicolas Epaillard

  7. Pathology Department, Georges Pompidou European Hospital, AP-HP Centre, Université Paris Cité, INSERM UMR970, Equipe Labellisée Ligue Contre Le Cancer, Paris, France

    Virginie Verkarre

  8. Medical Oncology Department, Hôpital Foch, Suresnes, France

    Yann-Alexandre Vano

  9. Centre de Recherche Des Cordeliers, INSERM1138, Inflammation Complément Et Cancer, Université Paris Cité, Paris, France

    Yann-Alexandre Vano

  10. Paris – Cardiovascular Research Center, European Georges Pompidou Hospital, 56 Rue Leblanc, Paris, 75015, France

    Ivan Pourmir, Eric Tartour & Stéphane Oudard

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  1. Ivan Pourmir
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Contributions

IP, NB, HR, AG, ET, SO contributed in the conception and design of the study. IP, NB, HR, AG, TT, JP, AM, NM, NE contributed to data acquisition. IP, ET, SO wrote the first draft. NB, HR, TT, JP performed critical revisions of the manuscript. IP performed statistical analyses, figures and tables generation. IP, ET, SO obtained funding. YV provided material. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Ivan Pourmir, Eric Tartour or Stéphane Oudard.

Ethics declarations

Ethics approval and consent to participate

The Colcheckpoint and BIONIKK cohorts have been approved by the French Health authorities and ethics committee [CPP Ile-de-France 8 (ref.16.10.69) and CPP Ouest I SI CNRIPH n18.11.21.67518 respectively]. All the participants provided written informed consent. Animal experiments were approved by the Ethics Committee of the University Paris Cité (CEEA34).

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Not applicable.

Competing interests

V.V. has received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events and support for attending meetings and/or travel from MSD. Y.V. has received consulting fees from BMS, Ipsen, Eisai, MSD, Pfizer; research grants from BMS, Ipsen. S.O. has received consulting fees, payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events and support for attending meetings and/or travel from Pfizer, Novartis, Ipsen, Eisai, BMS, Merck; has participated in data safety monitoring board or advisory board from Roche, Ipsen, Eisai. The remaining authors declare that they have no competing interests.

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Pourmir, I., Benhamouda, N., Tran, T. et al. Soluble TIM-3, likely produced by myeloid cells, predicts resistance to immune checkpoint inhibitors in metastatic clear cell renal cell carcinoma. J Exp Clin Cancer Res 44, 54 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13046-025-03293-y

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Journal of Experimental & Clinical Cancer Research

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