Hong‐Shuai Li,Cheng‐Ming Liu,Yan Wang
RANKL acts an unfavorable prognostic biomarker and potential target in advanced KRAS‐mutated lung adenocarcinoma
denosumab,KRAS,lung adenocarcinoma,RANKL
Abstract Objective Advanced lung cancers carrying Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation remain a group that lacks effective treatments. Receptor activator of nuclear factorκB ligand (RANKL) has been demonstrated to drive malignant phenotypes in lung cancer; however, its role in KRAS‐mutant (mt) lung adenocarcinoma (LUAD) is not yet fully elucidated. Materials and methods The data used to explore expression and prognosis were obtained from The Cancer Genome Atlas, Genotype‐Tissue Expression databases, and from our hospital. The proliferation, invasion, and migration capacities of KRAS‐mt LUAD cells were evaluated. The prediction model was established via Lasso regression method. Results RANKL is strongly expressed in advanced KRAS‐mt LUAD, and significantly distinct association exists between high RANKL expression and poor survival. The enriched expression of RANKL in advanced KRAS‐mt LUAD was confirmed by specimens from our hospital. Further, although not statistically significant, our clinical cohort (n = 57) revealed a longer median progression‐free survival in advanced KRAS‐mt LUAD patients treated with RANKL inhibitor than those without (300 vs. 133 days, p = 0.210), but not in KRAS‐wt ones (208 vs. 250 days, p = 0.334). Decrease of KRAS‐mt LUAD cells’ capacity for proliferation, invasion, and migration was observed when RANKL was knocked down. Enrichment analysis suggested distinct roles of RANKL between KRAS‐mt and KRAS‐wt LUAD, with adhesion‐related pathways and molecules significantly downregulated in the KRAS‐mt RANKL‐high tumors. Finally, a model for predicting overall survival of KRAS‐wt LUAD was established according to four related key genes (BCAM, ICAM5, ITGA3, and LAMA3), which had good performance in prediction concordance. Conclusions RANKL acts as an unfavorable prognostic biomarker for patients with advanced KRAS‐mt LUAD. Inhibition of RANKL may be a feasible strategy for this subset of patients.
RANKL acts an unfavorable prognostic biomarker and potential target in advanced KRAS‐mutated lung adenocarcinoma

Advanced lung cancers carrying Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation remain a group that lacks effective treatments. Receptor activator of nuclear factorκB ligand (RANKL) has been demonstrated to drive malignant phenotypes in lung cancer; however, its role in KRAS‐mutant (mt) lung adenocarcinoma (LUAD) is not yet fully elucidated.

The data used to explore expression and prognosis were obtained from The Cancer Genome Atlas, Genotype‐Tissue Expression databases, and from our hospital. The proliferation, invasion, and migration capacities of KRAS‐mt LUAD cells were evaluated. The prediction model was established via Lasso regression method.

RANKL is strongly expressed in advanced KRAS‐mt LUAD, and significantly distinct association exists between high RANKL expression and poor survival. The enriched expression of RANKL in advanced KRAS‐mt LUAD was confirmed by specimens from our hospital. Further, although not statistically significant, our clinical cohort (n = 57) revealed a longer median progression‐free survival in advanced KRAS‐mt LUAD patients treated with RANKL inhibitor than those without (300 vs. 133 days, p = 0.210), but not in KRAS‐wt ones (208 vs. 250 days, p = 0.334). Decrease of KRAS‐mt LUAD cells’ capacity for proliferation, invasion, and migration was observed when RANKL was knocked down. Enrichment analysis suggested distinct roles of RANKL between KRAS‐mt and KRAS‐wt LUAD, with adhesion‐related pathways and molecules significantly downregulated in the KRAS‐mt RANKL‐high tumors. Finally, a model for predicting overall survival of KRAS‐wt LUAD was established according to four related key genes (BCAM, ICAM5, ITGA3, and LAMA3), which had good performance in prediction concordance.

RANKL acts as an unfavorable prognostic biomarker for patients with advanced KRAS‐mt LUAD. Inhibition of RANKL may be a feasible strategy for this subset of patients.

Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation accounts for ~ 10%~30% of driver mutations in non–small cell lung cancer (NSCLC). , Previous studies revealed that patients harboring KRAS mutation suffered from sub‐optimal chemotherapy efficacy and poor prognosis. , Although the development of KRAS inhibitors sotorasib and adagrasib has filled the void for patients with KRAS G12C mutations, 5.4%~16.1% of them showed primary drug resistance and almost all of them ended up with drug resistance. , Moreover, nearly 60% of patients with non‐KRAS G12C mutations are still without targeted drugs. Therefore, the exploration for non‐redundant alternative therapeutic targets and new combinations of treatment modalities is still of great importance for this subset of patients. Nuclear factor‐κB receptor activator ligand (RANKL) is a member of the tumor necrosis factor superfamily (TNFSF), encoded by the TNFSF11 gene, and is the shared ligand for RANK and osteoprotegerin (OPG) (fellow TNFSF members). Faget et al.8 and Rao et al.9 confirmed by public transcriptome data analysis that activation of the RANKL/RANK pathway was related with worse survival in patients with lung adenocarcinoma (LUAD). Interestingly, they also found a strong correlation between RANKL/RANK pathway and KRAS mutation, implying that KRAS‐mutant (KRAS‐mt) lung cancers have an over‐active RANKL/RANK pathway activity. , Chen et al. revealed the enhanced cell migration ability induced by the RANKL/RANK pathway in the KRAS‐mt cell line (A549) in vitro. Using a transgenic mouse model carrying KRAS G12D mutation, the blockade of the RANKL/RANK pathway was demonstrated to impair tumor growth, and the combination of RANKL inhibitor and chemotherapy further enhanced this tumor suppressive effect, , suggesting that RANKL/RANK pathway is a potential target for antitumor therapy, especially for KRAS‐mt lung cancer. However, because of the lack of KRAS‐wt LUAD as control group in the above‐mentioned in vivo or in vitro studies, the role of RANKL in KRAS‐mt LUAD is not yet clearly elucidated.

Raw data of RNA‐sequencing data and relevant clinical information of pan‐cancer including LUAD were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) manually, which contained a total of 516 tumor samples and 59 normal samples for LUAD samples. To increase the credibility of the comparison, we also retrieved additional data from the Genotype‐Tissue Expression (GTEx) dataset (https://commonfund.nih.gov/GTEx) for normal tissues as a supplement. Patients were divided into low‐ and high‐RANKL expression groups based on the median expression value of RANKL.

Differently expressed genes (DEGs) were identified using “limma” package with a fold‐change of 1.5 and an adjusted p‐value of <0.05. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted on the DEGs to explore their potential functions and identify related genes and pathways using the “clusterprofiler” package in R software.

To evaluate differences at the protein level, we evaluated RANKL expression levels using sections (4 μm) of frozen tissue of LUAD stored at our institution. Representative unstained sections of each patient's primary tumor were shipped to our hospital pathology laboratory for immunohistochemical staining of RANKL. The immunohistochemistry (IHC) score of RANKL was evaluated based on the evaluation criteria of the previously published method.

The “survival” R package and “survminer” R package were used to produce the Kaplan–Meier curves. Using the Cox univariate regression analysis, the forest plot was used to demonstrate the p‐value, hazard ratio (HR) and 95% confidence interval (CI) of each variable via the “forestplot” package. Progression‐free survival (PFS), overall survival (OS), and disease‐specific survival (DSS) were used as endpoints.

During the risk‐score model instruction, a Lasso regression was applied to minimize the risk of overfitting. We calculated the risk score as follows: risk score = ∑(Expi × coefi). The Coefi and Expi values represented, respectively, each gene's risk coefficient and expression. The median risk score was used to classify patients into low‐risk and high‐risk subgroups, followed by a Kaplan–Meier survival method and receiver operating characteristic analysis (ROC) plotting for model examination.

The human LUAD cell lines (H322 and A427) were obtained from the American Type Culture Collection (ATCC) and cultured in Dulbecco's Modified Eagle Medium high glucose (KeyGEN BioTECH)/ RPMI 1640 (KeyGEN BioTECH) with 10% fetal bovine serum, 100 units/mL penicillin and 100 μg/mL streptomycin at 37°C in a humidified chamber with 5% CO2.

To establish stable RANKL knockdown and overexpression cell lines, LUAD cells (H322 and A427) were infected with lentiviral particles purchased from OBiO Technology. After 24 h of transfection, the medium was changed and cultured at 37°C for another 72 h. Following puromycin screening (2 μg/mL), the RANKL gene knockdown and overexpression were validated by real‐time quantitative polymerase chain reaction analysis (qRT‐PCR).

H322 and A427 cells were plated in the 96‐well plates at 2.5 × 103 cells/well and grow in 10% fetal bovine serum (FBS) medium for 24 h. Each well was added with cell count kit‐8 (CCK‐8) solution (10 μL, Dojindo) after the transfection and cells went through 0 h, 24 h, 48 h, 72 h, and 96 h of incubation at 37°C in an incubator with 5% CO2, respectively. A spectrophotometer (SkanIt, Thermo Scientific) was used to read absorbance at 450 nm.

In the invasion assay, we coated Matrigel (BD) on the upper layer of transwell inserts (Corning), then seeded H322 and A427 cells suspended in serum‐free medium in the upper chamber and added medium containing 20% FBS into the lower chamber. After cell invasion was completed, cells on the upper layer were gently wiped off the membrane surface with a cotton swab, paraformaldehyde was used to fix the cells on the lower layer of the membrane, and then these cells were stained with 0.5% crystal violet (Solarbio). Final cell counting was performed from 5 microscopic fields under random selection.

H322 and A427 cells were cultured with serum‐free medium in 96‐well plates and reached to 90% confluence. A scratch across the center of the cell layer was produced using a sterile pipette tip. Real‐time live‐cell imaging was conducted every 4 h using IncuCyte S3 (Essen BioScience; Sartorius). Cell migration distance was estimated by Image J software.

Patients diagnosed as LUAD with bone metastases who have received chemotherapy as well as concomitant RANKL inhibitor (denosumab) or phosphates were identified from the Chinese National Cancer Center. To exclude the interference of the synergistic effect of RANKL inhibitor denosumab and immunotherapy reported in the previous literature in determining the direct tumor suppressive efficacy of denosumab, patients treated with denosumab combined with immunotherapy were excluded from this study. , , The study was carried out in accordance with the Declaration of Helsinki (as revised in 2013). Institutional review board approval of the study protocol was obtained (NCC‐008296) before study initiation.

Categorical variables were reported as numbers and percentages. The χ2 test was used for comparison between different clinical subgroups. The PFS was defined as the time from therapy initiation to disease progression or death from any cause. The data cutoff date of our clinical cohort was November 10, 2022. The Kaplan–Meier method was applied to evaluate the relationship between different variables and survival if not specified, and differences between survival curves were judged by the log‐rank test. All analyses and visualizations were done using the R statistical language (v3.3.1). Values of p < 0.05 were considered statistically significant.

We compared RANKL expression levels between cancer and matched normal samples from multiple cancers both in advanced (Figure 1(a), (b)) and non‐advanced (Figure S1A,B) settings. The expression of RANKL was significantly higher both in advanced and non‐advanced settings of multiple malignancies than in normal tissues. We, then, assessed the relationship between RANKL expression and prognosis in a variety of malignancies using Cox univariate analysis both in advanced (Figure 1(c)–(e)) and non‐advanced (Figure S1C–E) settings. Interestingly, we found that tumors including liver hepatocellular carcinoma, LUAD, and uterine corpus endometrial carcinoma that showed survival statistical significance in the advanced setting lost statistical significance in the non‐advanced stage. This suggests that for LUAD, RANKL may play a prognostic role mainly in the advanced setting.

Interestingly, it was suggested from the work by Faget et al. that KRAS‐mt LUAD expressed higher levels of RANKL than KRAS‐wt ones. We then validated this finding using the RNA‐sequencing data and clinical data from the TCGA database both in the advanced (Figure 2(a)) and non‐advanced settings (Figure S2A). Consistently, the expression of RANKL was significantly higher in KRAS‐mt LUAD than in KRAS‐wt ones both in advanced and non‐advanced settings. Specifically, we verified the level of RANKL expression between KRAS‐mt and KRAS‐wt LUAD in the advanced setting using surgical specimens collected from our hospital, and the results confirmed the TCGA findings (Figure 2(b)). We then assessed the relationship between RANKL expression and prognosis in KRAS‐mt and KRAS‐wt LUAD using Kaplan–Meier method both in advanced (Figure 2(c)–(e)) and non‐advanced (Figure S2B–D) settings. The findings revealed significant prognostic differences between advanced KRAS‐mt LUAD patients with low or high levels of RANKL expression, whereas this difference was not significant between advanced KRAS‐wt ones, suggesting that RANKL plays a poor prognostic role mainly in the KRAS‐mt setting (Figure 2(c)–(e)). Moreover, we did not observe the survival differences in non‐advanced LUAD cases (Figure S2B–D). This coincides with the findings in Section “RANKL is upregulated in LUAD, but only associated with poor survival in advanced LUAD,” which suggests that RANKL may play the tumor‐promoting role only in the advanced setting. Baseline characteristics of advanced KRAS‐mt/wt LUAD patients with low or high levels of RANKL expression from TCGA database were displayed in Table 1. We observed significantly higher proportion of T3 + T4 patients in KRAS‐mt RANKL‐high group (p = 0.016). To further confirm the prognostic role of RANKL in those advanced patients, the univariate and multivariate analyses on PFS, OS, and DSS were conducted, respectively, which revealed the independent prognostic role of RANKL both in PFS and OS (Figure 3(a)–(c)). However, we did not observe the independent role of RANKL in non‐advanced counterparts (Figure S3A–C).

To verify whether the application of RANKL inhibitor has clinical benefit, 57 patients diagnosed as LUAD with bone metastases who have received chemotherapy (±targeted agents but not immunotherapy as stated beforehand) as well as concomitant RANKL inhibitor (denosumab) or phosphates were identified. Baseline characteristics of different groups were displayed in Table 2. Although not statistically significant, this clinical cohort revealed a longer PFS in KRAS‐mt advanced LUAD patients treated with RANKL inhibitor than those without (300 vs. 133 days, p = 0.210), but not in KRAS‐wt ones (208 vs. 250 days, p = 0.334) (Figure 4(a)–(c)).

To further assess the influence of RANKL expression on malignant behavior between KRAS‐mt (A427 cell line) and KRAS‐wt (H322 cell line) lung cancer cells, RANKL knockdown short hairpin RNAs (shRNAs) (sh1 and sh2) were built and their regulatory effects on RANKL mRNA expression were verified by qRT‐PCR (Figure S4A,B). Subsequently, the CCK‐8 proliferation assay (Figure 5(a)) suggested a markedly attenuated proliferation ability of RANKL‐knock down cells, which was more pronounced in A427 cells. Wound healing assay also provided consistent results that RANKL knockdown by shRNAs transfection significantly attenuated the migration ability of A427 cells (Figure 5(b)). The transwell assay was used, and results revealed that sh‐RANKL significantly the invasive capabilities of A427 cells (Figure 5(c),(d)).

The KEGG enrichment analysis suggested that advanced KRAS‐mt LUAD are enriched for adhesion‐related pathways, especially for the cell adhesion molecules, whereas the KRAS‐wt ones are not (Figure 6(a)–(c)). Moreover, the cell adhesion molecules are markedly downregulated in RANKL‐high tumors (Figure 6(d)–(f)). Cell adhesion molecules have already been reported to be related to lung cancer cell invasion and migration. , Therefore, we hypothesized that KRAS‐mt RANKL‐high LUAD promotes biologically malignant behavior by changing the expression of cell adhesion molecules. We, then, selected the four most relevant DEGs (BCAM, ICAM5, ITGA3, and LAMA3) by the degree of statistical difference for further analysis. The expression analysis confirmed the differential expression of the four cell adhesion molecules between advanced KRAS‐mt, RANKL‐low, and RANKL‐high tumors, but not in advanced KRAS‐wt ones (Figure 7(a)–(d)). RANKL overexpression shRNA was built and the modulative effect on RANKL mRNA expression was validated via qRT‐PCR (Figure 7(e)–(h) and Figure S4C,D). No statistically significant positive correlations between RANKL expression and the four molecules was observed within advanced KRAS‐wt LUAD (Figure 7(i)–(l)), but within advanced KRAS‐mt ones (Figure 7(m)–(p)). In addition, we also performed stratified survival analysis based on the expression levels of each of the four molecules, which showed that all these molecules distinguished well between KRAS‐mt patients with different prognosis (Figure 7(q)–(t)).

Given that RANKL pathway‐related molecules play a regulatory role in the prognosis of KRAS‐mt LUAD, we constructed the risk‐score according to the RANKL gene and the four key DEGs (BCAM, ICAM5, ITGA3, and LAMA3). To determine the best prognostic signatures, Lasso analyses were performed. RANKL, BCAM, ITGA3, and LAMA3 remained after Lasso regression analyses according to the minimum partial likelihood deviance (Figure 8(a), (b)). The formula is following: risk‐score = (0.1223) × RANKL expression + (−0.627) × BCAM expression + (0.0167) × LAMA3 expression + (0.1492) × ITGA3 expression. The risk distribution plot showed that as risk‐score increased, survival time decreased while death or recurrence rates increased (Figure 8(c)). Kaplan–Meier analyses revealed significantly better OS of patients with low scores than did patients with high scores (log‐rank p = 0.00625) (Figure 8(d)). Area under curve (AUC) values of 0.689, 0.962, and 0.901 were calculated for the 1‐, 2‐, and 3‐year OS rates of risk‐score via the ROC curve, respectively (Figure 8(e)). The whole procedure of this work was presented in Figure 9.

Although previous studies have demonstrated the phenomenon of the high expression of RANKL in KRAS‐mt LUAD and the effectiveness of RANKL inhibitor in KRAS‐mt mice model, the different roles of RANKL/RANK pathway between KRAS‐wt and KRAS‐mt LUAD are not revealed, which concerns the precise selection of patients for receiving the RANKL inhibition therapy. Using the KRAS‐wt LUAD as control, our study revealed that RANKL further promoted the malignant phenotypes of KRAS‐mt LUAD relative to KRAS‐wt one and acted as a good prognostic biomarker for patients with advanced KRAS‐mt LUAD. Inhibition of RANKL may be a feasible strategy for treating advanced KRAS‐mt LUAD, which indicating the potential application of denosumab on this subset of patients. As known, in the bone microenvironment, RANKL is mainly produced by osteoblasts and is known to promote osteoclast differentiation under the action of macrophage colony‐stimulating factor and other factors, which in turn coordinates osteoclast‐mediated bone resorption and maintains the dynamic balance of bone activity. , , , As the first fully human anti‐RANKL monoclonal antibody, denosumab was shown to be non‐inferior to zoledronic acid (ZA) in delaying time to skeletal‐related event (SRE) in a randomized study enrolling multiple advanced cancer types (including lung cancer) and has been approved by the Food and Drug Administration for the prevention of SREs in solid tumors. Interestingly, the exploratory analysis also showed an OS benefit of denosumab over ZA on NSCLC patients with bone metastases. Considering the evidence suggesting the important role of RANKL in promoting tumor progression in a variety of malignancies and acting as a worse prognostic factor, , , , , it has been suggested that the direct inhibitory effect of denosumab on RANKL may provide a survival benefit for NSCLC patients. However, neither the ETOP/EORTC SPLENDOUR study nor the AMGEN‐249 study confirmed the added survival benefit of denosumab when combined with first‐line chemotherapy for NSCLC patients carrying bone metastases or not. , Interestingly, both the present study and previous studies revealed the higher levels of RANKL expression in KRAS‐mt LUAD patients, suggesting that this subset of patients may benefit more from treatment with RANKL inhibitors, which was unfortunately not investigated in the subgroup analyses of previous clinical studies. In our study, although not statistically significant, the small‐scale internal clinical cohort revealed a lower risk of disease progression in KRAS‐mt advanced LUAD patients treated with denosumab than those without (HR, 0.424; 95% CI, 0.086–2.088, p = 0.279), but not in KRAS‐wt ones. However, this conclusion needs to be confirmed by more clinical data. In fact, RANKL is expressed in both cancer cells and the tumor microenvironment (TIME). In the TIME, RANKL interacts with RANK to coordinate various immunosuppressive processes through a variety of mechanisms. Previous pre‐clinical and clinical studies have demonstrated the synergistic effect of RANKL inhibitor denosumab and immunotherapy during antitumor treatment. , , However, the potential survival benefit of denosumab for direct inhibition of RANKL was not taken into account in previous studies, especially for NSCLC patients carrying KRAS mutations. Therefore, in our study, to exclude the interference of this synergistic effect in determining the direct tumor suppressive efficacy of denosumab, patients treated with denosumab combined with immunotherapy were excluded from analyses. Evidence showed that RANKL is progressively upregulated throughout the progression and may only get involved in the later stage. , , , In the present study, we likewise found that RANKL expression increased with increasing staging, although no significant difference was found (Figure S5). Moreover, RANKL expression was not associated with the prognosis of non‐advanced KRAS‐mt patients, whereas it was significantly associated with that of advanced KRAS‐mt ones. The KEGG enrichment analysis suggested that advanced KRAS‐mt LUAD samples are enriched for adhesion‐related pathways, especially for the cell adhesion molecules. Cell adhesion molecules have already been reported to be related to lung cancer cell invasion and migration. , ITGA3, member of the integrin family, is an adhesion molecule expressed on the cell surface which can interact with extracellular matrix proteins. BCAM is a cell adhesion protein originally identified in the Lutheran blood group system and circulating sickle red cells, functioning as a receptor for the extracellular matrix protein. BCAM has been shown to play a functional role in the metastasis of multiple malignancies. Laminins (including LAMA3) are glycoproteins that are abundantly expressed in the tumor extracellular matrix, which have been demonstrated to be related with cancer progression and poor cancer prognosis. Therefore, we hypothesized that KRAS‐mt RANKL‐high LUAD promotes biologically malignant behavior by changing the expression of cell adhesion molecules. This study has some inherent limitations. First, the number of patients in the clinical validation cohort was too small, which may affect the statistical test validity. Second, the scale of patients for model construction was limited. Last, but not least, although the current first‐line treatment paradigm for KRAS‐mutated lung cancer is dominated by immunotherapy, our study reveals the antitumor effects of RANKL‐targeted therapy itself, which provides guidance for subsequent studies of combination therapy regimens for RANKL.

In conclusion, our study revealed that RANKL further promoted the malignant phenotypes of KRAS‐mt LUAD and acted as a good prognostic biomarker for patients with advanced KRAS‐mt LUAD. Inhibition of RANKL may be a feasible strategy for treating advanced KRAS‐mt LUAD, which indicating the potential application of denosumab on this subset of patients, but it needs more clinical data to confirm.

Hong‐Shuai Li: Methodology; software; validation; formal analysis; data curation; writing—original draft preparation; visualization; project administration. Cheng‐Ming Liu: Methodology; software; validation; formal analysis; data curation; writing—original draft preparation; visualization; project administration. Yan Wang: Conceptualization; investigation; resources; writing—review and editing; supervision; funding acquisition. All authors have read and agreed to the published version of the manuscript.

All authors completed the ICMJE uniform disclosure form. The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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