LRP1B mutation is associated with tumor immune microenvironment and progression-free survival in lung adenocarcinoma treated with immune checkpoint inhibitors

Introduction

Lung cancer has been recognized as one of the world’s most lethal cancers (1). Lung adenocarcinoma (LUAD) accounts for more than 40% of lung cancer cases (2,3). Despite the development of therapeutic modalities such as targeted therapy, survival rates for patients with LUAD remain below 20% over 5 years (4,5). Surgery is currently the primary treatment, but even in stage I LUAD, 30% of patients still develop recurrence or metastasis within five years after undergoing radical surgery (6). Adjuvant chemotherapy has been shown to improve patient survival after surgery, but for some patients the toxicity associated with chemotherapy outweighs its potential benefits (7).

Immunotherapy has altered the prognosis of LUAD, of which immune checkpoint inhibitors (ICIs) have attracted considerable attention due to durable responsiveness and significant improvement in patients’ prognosis. ICIs mainly act by preventing the binding of inhibitory receptors (checkpoint block) to their ligands. Tumor mutational burden (TMB) and programmed death ligand-1 (PD-L1) are widely clinically used as predictive biomarkers. However, ICIs are effective in only a fraction of patients, possibly due to the spatial heterogeneity within the tumor (8), subjective analysis of PD-L1 (9), and lack of standardized thresholds for TMB determination (10). The positive response of LUAD to ICIs often relies on the interaction between tumor cells and immune cells and immune-related molecules in the tumor immune microenvironment (TIME), while major cellular components of the TIME also play an anti-tumor role during the pre-progression phase (11). It may be regulated by the involvement of T cell activation and CD4 memory T cell pathways (12). The TIME is a highly complex and interconnected spatial structure, in which the spatial structure of various cell types is determined by the vascular, lymphatic vessel and fibroblast matrix in the microenvironment. It contains a variety of functionally distinct immune cells and non-immune stromal cells and is important in immunotherapy (13-15). Growing evidence demonstrates that both immune and stromal cells influence tumor response to ICIs (16).

A low-density lipoprotein receptor-related protein, LRP1B, is implicated as strongly associated with cancer development and due to its frequent inactivation in non-small cell lung cancer (NSCLC), and was initially postulated to be a tumor suppressor gene (17). Studies have shown that specific genetic mutations affect the effects of ICIs, and EMT amplification has previously been found to be associated with prolonged progression-free survival (PFS) after receiving immunotherapy (18). Recently, LRP1B mutation was discovered to be associated with improved outcomes in multiple cancers receiving immunotherapy (19,20). However, the molecular mechanism of how LRP1B mutations are related to better prognosis in immunotherapy patients is unclear, and the most common explanation is that LRP1B mutation is associated with TMB (21). Here, we hypothesize that LRP1B mutation in LUAD may positively affect the TIME, ultimately affecting the outcome of patients treated with ICIs. We present the following article in accordance with the REMARK reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-23-39/rc).

Methods

The Cancer Genome Atlas (TCGA)-LUAD and ICIs cohorts

To assess the association of LPR1B mutation with the outcomes of patients receiving ICIs, we collected an LUAD cohort treated with programmed cell death protein 1 (PD-1) plus CTLA-4 blockade from a previous study (ICIs cohort) (22). A total of 59 LUAD patients with complete sequencing and clinical data were included. The clinical information and sequencing data of all patients were collected, including age, sex, treatment response, neoantigen loads (NALs), TMB, and gene mutation types and sites. PFS was defined as starting on the day of the first dose and ending on the date of disease progression, patient death, or study completion. The best overall response was assessed according to RECIST 1.1 criteria. We classified patients in this cohort into the LRP1B-mutated type (MT) group (n=15) and LRP1B-wild type (WT) group (n=44) according to the mutation status of LRP1B. Furthermore, TCGAbiolinks (23) was used to download data on the transcriptome, somatic mutations, and overall survival (OS) for the TCGA-LUAD cohort. We used cBioPortal (24) to obtain data on PFS for the TCGA-LUAD cohort. After excluding patients with missing data, a total of 507 LUAD patients were included. Differences in the survival of TCGA-LUAD patients according to LRP1B mutation status were analyzed.

Tumor mutation characteristics and tumor immunogenicity assessment

The somatic mutations of 59 LUAD patients from the ICIs cohort were obtained by whole exome sequencing (WES). In accordance with previous studies, TMB was defined as the overall number of non-synonymous mutations per megabase (Mb) in the coding region of the genes (25). NALs and immune scores were obtained from the previous study (26). LRP1B mutation frequencies, mutation types, and mutant structural domains were obtained from cBioPortal (24). Maftools was used to display the top 20 mutations and clinical features of the ICIs and TCGA-LUAD cohorts, as well as the LRP1B mutations in both cohorts (27). The correlation of the mutated genes was displayed by ComplexHeatmap in the R package (28).

Analysis of copy number variation

After quality control, data from the TCGA-LUAD cohort was retrieved from Affymetrix SNP 6.0 microarray (hg19) by Broad GDAC Firehose (http://gdac.broadinstitute.org/). GISTIC 2.0 (29) was conducted to analyze copy number variants (CNVs) and significantly amplified or deleted regions. The confidence interval (CI) was set to 0.99 (X chromosome excluded), and default parameters were set for others.

Immune characteristics analysis

The transcriptome of the TCGA-LUAD cohort was obtained via TCGAbiolinks. We performed Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) analysis (30) on the transcriptome and estimated the content of the 22 immune cells. We made a comparison of the immune genes between the LRP1B-MT and LRP1B-WT groups (26). The level of gene expression was assessed uniformly as log2 [transcripts per million (TPM) +1].

Analysis of pathway enrichment and DNA damage response and repair pathway

The limma package in R (31) was used to analyze differential gene expression between the LRP1B-MT group and LRP1B-WT groups in the TCGA-LUAD cohort. Any gene with an absolute value of log2foldchange (FC) greater than 0.5 and P value less than 0.05 was identified as significantly different. We used the gene set enrichment analysis (GSEA) to analyze the differential genes for gene annotation enrichment analysis in terms of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A P value <0.05 was considered statistically significant. We evaluated non-synonymous mutations in the DNA damage response and repair (DDR) pathways in the ICIs and TCGA-LUAD cohorts. Non-synonymous mutations in the LRP1B-MT and LRP1B-WT groups were analyzed. The GSEA and DDR pathway were referenced in the MSigDB database (Broad Institute, Cambridge, MA, USA). The DDR pathway gene set is available in Table S1 (32).

Analysis of TIME in clinical specimens

We retrospectively collected 20 LUAD patient samples which confirmed to be clinical stage IA according to the eighth edition of tumor-node-metastasis (TNM) staging from January 2020 to January 2021 who underwent partial pneumonectomy or radical lobectomy at the Department of Thoracic Surgery of the First Affiliated Hospital of Soochow University; all samples were sequenced using a gene panel. The inclusion criteria were as follows: (I) postoperative pathological diagnosis of lung adenocarcinoma; (II) no preoperative oncology-related treatment; (III) no distant metastases were detected during preoperative examination. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by institutional ethics board of the First Affiliated Hospital of Soochow University (No. 20202372). Individual consent for this retrospective analysis was waived. A total of 10 primary LUAD cases were identified as containing LRP1B mutation, and the other 10 cases as LRP1B WT. Sections were cut from formalin-fixed and paraffin-embedded LUAD tissue (4-µm thickness). Anti-CD4, anti-CD8, anti-PD-L1, and anti-CD68 staining were performed on tissue sections by multiplex immunohistochemistry (mIHC). The following antibodies were used: rabbit anti-CD4 (ZA0519, dilution 1:100), rabbit anti-CD8 (ZA0508, dilution 1:100), rabbit anti-PD-L1 (ZA0629, dilution 1:25), and mouse anti-CD68 (ZM0060, dilution 1:500). Antibodies were purchased from ZSGB-BIO (Beijing, China). Briefly, the sections were dewaxed with xylene, hydrated in 100%, 95%, 90%, 85%, 80%, and 75% ethanol for 5 minutes, and subsequently hydrated with distilled water. The antigen repair solution in the kit was used for microwave antigen repair, followed by incubation with primary antibody at 37 ℃ for 1 hour, washing, and incubation with secondary antibody at 37 ℃ for 10 minutes. Finally, 4',6-diamidino-2-phenylindole (DAPI) staining was performed at 25 ℃ for 5–10 minutes before sealing the sections. Table S2 provides information on the antibodies and their dilution concentrations. The mIHC staining was quantified digitally using HALO image analysis software (Indica Labs, Albuquerque, NM, USA).

Construction of a nomogram for predicting PFS

In the ICIs cohort, univariate and multivariate Cox regression analysis of PFS was performed using the survival package in R. A nomogram was constructed using rms in R to predict the probability of PFS at a specific time point; survROC and calibrate were used to determine the predictive power.

Statistical analysis

All analyses and statistical tests were conducted using the R software (version 4.1.1; The R Foundation for Statistical Computing, Vienna, Austria). Differences in the continuous variables between the 2 groups were evaluated by the Wilcoxon test, including TMB, NAL, immune gene expression, the scores of homologous recombination (HR) defects, and cytolytic activity. Differences in mutation frequency and clinical response to immunotherapy between the LRP1B-MT and LRP1B-WT groups were assessed by chi-squared test. The Kaplan-Meier (KM) and log-rank test were used to analyze survival-related data. All tests were set with P<0.05 indicating statistical significance.

Results

LRP1B is highly mutated in LUAD patients and is associated with multiple immune-related pathways

In this study, we found a high mutation frequency of LRP1B in the ICIs and TCGA-LUAD cohorts, 25% and 36%, respectively, and most of them were missense mutations. In the TCGA-LUAD cohort, the lollipop plot showed a higher number of splice mutations at the X470 splice/V470F mutation site (Figure 1A,1B). To further explore the functional impact caused by the LRP1B mutation, we analyzed the genes that were differentially expressed genes (DEGs) between the LRP1B-MT group and LRP1B-WT group in the TCGA-LUAD cohort, with 586 genes significantly up-regulated and 361 genes significantly down-regulated (Figure 1C). GO and KEGG enrichment analysis demonstrated that these DEGs were significantly enriched in 39 GO pathways and 1 KEGG pathway, including T cell differentiation and activation involved in immune response, T cell activation, immune response−activating cell surface receptor, which are closely related to the immune microenvironment (Figure 1D). We performed GSEA to further explore the pathways enriched by these DEGs. We found that DNA damage repair (DDR)-related pathways, such as cell cycle, DNA replication, HR, mismatch repair (MMR), and regulation of DNA repair, were significantly enriched in the LRP1B-MT group. In the TIME, the notch signaling pathway, PD-L1 expression, and the PD-1 checkpoint pathway in cancer were upregulated in the LRP1B-MT group. In contrast, some negative immune regulation pathways, including linoleic acid metabolism, arachidonic acid metabolism, fatty acid metabolism, and interleukin-6 production were enriched in the LRP1B-WT group. Additionally, we found that the activities of oncogenic signaling pathways such as sprouting angiogenesis were markedly increased in LRP1B-MT LUAD patients (Figure 1E,1F). These results indicate that LRP1B mutation may have a positive impact on antitumor immunity.

Figure 1 Mutation frequency of LRP1B and the associated pathways it affects. (A,B) Bar and lollipop plots showing the mutation frequency and mutation sites of LRP1B in the ICIs and TCGA-LUAD cohorts. (C) Volcano plot depicts differential genes between LRP1B-MT and WT patients, significantly up-regulated genes are indicated by red dots, significantly down-regulated genes are indicated by blue dots and non-significantly different genes are indicated by grey dots. (D) Significant enrichment pathways of differential genes under dissimilar LRP1B mutation status. (E) The upregulation and downregulation of KEGG-related pathways between the LRP1B-MT group and LRP1B-WT group in the TCGA-LUAD cohort. (F) The upregulation and downregulation of GO-related pathways between the LRP1B-MT group and LRP1B-WT group in the TCGA-LUAD cohort. ICIs, immune checkpoint inhibitors; TCGA, The Cancer Genome Atlas; LUAD, lung adenocarcinoma; VUS, variant of uncertain significance; SV, structural variation; BP, biological process; KEGG, Kyoto Encyclopedia of Genes and Genomes; MHC, major histocompatibility complex; ES, enrichment score; PD-L1, programmed death ligand-1; PD-1, programmed cell death protein 1; MT, mutated type; WT, wild type; GO, Gene Ontology.

Mutation landscape in LRP1B-mutated LUAD patients

We explored the differences in mutation driver genes under different LRP1B mutation statuses. The top 20 genes are demonstrated in Figure 2A. Clinical information for the LRP1B-MT and LRP1B-WT groups in the ICIs and TCGA-LUAD cohorts is displayed in Figure 2B. In the ICIs cohort, the top 5 mutated genes were TP53 (46%), RYR2 (42%), TTN (41%), KRAS (39%), and MUC16 (36%). There was a significant increase in mutation frequency of RYR2 (93% vs. 25%, P<0.001) and ANK2 (60% vs. 6%, P<0.001) in the LRP1B-MT group compared with the LRP1B-WT group. In the TCGA-LUAD cohort, the top 20 mutated genes were similar to those in the ICIs cohort, except for CSMD1, COL11A1, ZNF536, and NAV3. Figure 2C,2D show the co-occurrence and mutual exclusion analysis of the top 20 mutated genes in the ICIs and TCGA-LUAD cohorts. Figure 2E,2F show that 1q21.3 and 14q13.1 amplifications were enriched in the LRP1B-MT group. Additionally, the LRP1B-WT group had more amplifications on chromosomes 8 and 12, such as 8q24.21 and 12q15. The detailed mutation frequencies in the ICIs cohort are provided in Table S3.

Figure 2 The contrast of the frequently mutated gene and clinical data between LRP1B-MT and LRP1B-WT LUAD patients. (A,B) Oncoplot depicts the top 20 most frequently mutated genes for the LRP1B-MT and LRP1B-WT groups in the ICIs and TCGA-LUAD cohorts. (C,D) Heatmap shows co-occurring and mutually exclusive mutations of the top 20 mutated genes in the ICIs and TCGA-LUAD cohorts. (E,F) Status of LRP1B CNAs in the TCGA-LUAD cohort, with gains indicated in red and losses in blue. ICIs, immune checkpoint inhibitors; TMB, tumor mutational burden; WT, wild type; MT, mutated type; CR, complete response; NE, not evaluated; PD, progressive disease; PR, partial response; SD, stable disease; TCGA, The Cancer Genome Atlas; LUAD, lung adenocarcinoma; AJCC, American Joint Committee on Cancer; NA, not available; CNAs, copy number alterations.

TIME in LRP1B-mutated LUAD patients

The outcomes of patients undergoing ICIs were correlated with TIME, and the components in the TIME can be influenced by the somatic mutation of specific genes. We compared the TIME with different LRP1B mutation statuses in the TCGA-LUAD cohort. Figure 3A shows the expression of some immune activation-related genes, such as TAP1 (antigen processing gene) and MICB (antigen presentation gene), RALGPS2 (B cell-related gene), CD8A and GZMB (cytolytic activity gene), CXCL9 and CXCL10 (chemokines), and IFN-γ, ICAM1, and TNF (proinflammatory regulators). Their expression markedly increased in the LRP1B-MT group. In addition, Figure 3B shows that immune checkpoint biomarkers, including CD274, LAG3, EDNRB, and PDCD1, displayed increased expression in the LRP1B-MT group than those in the LRP1B-WT group. Additionally, we compared the score of immune signatures. Compared with the LRP1B-WT group, the scores of HR defects and cytolytic activity of the LRP1B-MT group were significantly higher (Figure 3C). Investigation of 22 immune cells in the TCGA-LUAD cohort demonstrated that activated immune cells, including M0 macrophages, M1 macrophages, CD8⁺ T cells, and activated CD4⁺ memory T cells were significantly enriched in the TIME of LRP1B-MT group (Figure 3D). In contrast, there were more immune cells with a resting function: CD4⁺ memory T, dendritic, and mast cells were all static in the TIME of the LRP1B-WT group. This suggests that LRP1B mutation may have an impact on the TIME, enhancing the immune-related pathway gene expression and facilitating the infiltration of activated immune cells.

Figure 3 TIME analysis of LRP1B mutated patients. (A) Up left: the comparison of the expression of potential antigen presentation-related genes between LRP1B-MT and LRP1B-WT tumors in the TCGA-LUAD cohort. Up middle: the expression differences of B cell-related genes in LRP1B-MT and LRP1B-WT tumors in the TCGA-LUAD cohort. Up right: the relative expression of cytolytic activity-related genes in LRP1B-MT and LRP1B-WT groups in the TCGA-LUAD cohort. Low: the relative expression of chemokine and proinflammatory mediators-related genes in LRP1B-MT and LRP1B-WT tumors in the TCGA-LUAD cohort. (B) The relative expression of the immune checkpoint-related genes in LRP1B-MT and LRP1B-WT in the TCGA-LUAD cohort. (C) Comparison of immune-related scores between LRP1B-MT and LRP1B-WT in the TCGA-LUAD cohort. (D) Infiltration frequencies of 22 immune cells between LRP1B-MT and LRP1B-WT in the TCGA-LUAD cohort using CIBERSORT analysis. *, P<0.05; **, P<0.01; ***, P<0.005; ****, P<0.001; “-”, not significant. TPM, transcripts per million; MT, mutated type; WT, wild type; CTA, cancer testicular antigens; NK, natural killer; TIME, tumor immune microenvironment; TCGA, The Cancer Genome Atlas; LUAD, lung adenocarcinoma; CIBERSORT, Cell-type Identification by Estimating Relative Subsets of RNA Transcripts.

Analysis of immune infiltration and PD-L1 expression in LRP1B-mutated LUAD patients in clinical specimens

To validate the bioinformatic analysis of the TIME in LRP1B-mutated LUAD patients, we performed mIHC analysis on clinically collected LUAD specimens. Typical images of immunostaining for CD8 and CD4 in the LRP1B MT and WT groups of LUAD are shown in Figure 4A,4B. The results showed an elevated level of CD8⁺ T cell infiltration in LRP1B-MT patients than that in LRP1B-WT patients (Figure 4C, P<0.01). However, no marked differences were observed in the CD4⁺ T cell infiltration levels (Figure 4D, P=0.14). Furthermore, Figure 5A,5B show higher PD-L1 expression in tumor tissues with the LRP1B mutation (P<0.01), which is similar to the results of our bioinformatics analysis. Although there was no significant difference in the total number of macrophages (P=0.25), PD-L1⁺ CD68⁺ macrophages had a higher level of infiltration in LRP1B-MT patients (P<0.05).

Figure 4 mIHC staining analysis of correlation between LRP1B mutation and tumor immune microenvironment in LUAD patients. (A) Representative images of CD8⁺ cells. (B) Representative images of CD4⁺ cells. (C) Differential analysis of the density of CD8⁺ cells in LUAD tumor between LRP1B-MT group and LRP1B-WT group. (D) The differential density of CD4⁺ cells in LUAD tumors between the LRP1B-MT and the LRP1B-WT. Nuclei (DAPI, blue), CD8 (cytoplasm, yellow), CD4 (cytoplasm, green). Magnification ×30. MT, mutated type; WT, wild type; DAPI, 4',6-diamidino-2-phenylindole; mIHC, multiplex immunohistochemistry; LUAD, lung adenocarcinoma.

Figure 5 Colocalization of PD-L1 with CD68⁺ macrophages was performed using mIHC. (A) Representative images of PD-L1⁺ and CD68⁺ cells. (B) Differential analysis of the density of PD-L1⁺ and CD68⁺ immune cells in LUAD tumor between LRP1B-MT group and LRP1B-WT group. Nuclei (DAPI, blue), PD-L1 (membrane, white), CD68 (cytoplasm, red). Magnification ×30. PD-L1, programmed death ligand-1; DAPI, 4',6-diamidino-2-phenylindole; MT, mutated type; WT, wild type; mIHC, multiplex immunohistochemistry; LUAD, lung adenocarcinoma.

Analysis of immunogenicity and DDR mutations in LRP1B-mutated LUAD patients

High immunogenicity benefits patients receiving immunotherapy, and TMB is regarded as an independent predictive biomarker. First, we compared the differences in TMB under different LRP1B mutation statuses. A markedly higher TMB level was observed in the LRP1B-MT group in both the ICIs and TCGA-LUAD cohorts (Figure 6A,6B). Additionally, we investigated the NAL in the ICIs and TCGA-LUAD cohorts. Markedly higher NAL in the LRP1B-MT group was observed than in the LRP1B-WT group (Figure 6C,6D). The DDR pathway is closely associated with genomic instability. In the ICIs cohort, mutation counts in the DDR-related pathways [base excision repair (BER), double-strand break (DSB) repair, Fanconi anemia (FA), HR, MMR, nucleotide excision repair (NER), non-homologous end-joining (NHEJ), and single-stranded DNA binding (SSB)] were statistically higher in the LRP1B-MT group (both P<0.05), except for HR (P=0.09) (Figure 6E). Figure 6F suggests that in the TCGA-LUAD cohort, LUAD patients with LRP1B-MT had higher mutations in all 8 DDR-related pathways compared to those with LRP1B-WT (both P<0.05).

Figure 6 Association between immunogenicity and LRP1B mutation. (A,B) TMB comparison between LRP1B-MT and LRP1B-WT in the ICIs and TCGA-LUAD cohorts. (C,D) NAL differences between LRP1B-MT and LRP1B-WT groups in the ICIs and TCGA-LUAD cohort. (E,F) Mutation counts in DNA damage repair pathways between LRP1B-MT and LRP1B-WT groups in the ICIs and TCGA-LUAD cohorts. *, P<0.05; **, P<0.01; ***, P<0.005; ****, P<0.001; and “-”, not significant. ICIs, immune checkpoint inhibitors; TMB, tumor mutational burden; TCGA, The Cancer Genome Atlas; LUAD, lung adenocarcinoma; MT, mutated type; WT, wild type; NAL, neoantigen loads; BER, base excision repair; DSB, double-strand break repair; FA, Fanconi anemia; HR, homologous recombination; MMR, mismatch repair; NER, nucleotide excision repair; NHEJ, non-homologous end-joining; SSB, single-stranded DNA binding; DDR, DNA damage response and repair.

LRP1B mutation improves the prognosis of ICIs-treated LUAD patients

The baseline data for patients in the ICIs cohort are demonstrated in Table 1. We conducted KM analysis to investigate the association between LRP1B mutation and the outcome of LUAD patients treated with ICIs. PFS was significantly prolonged (P=0.0016) for LRP1B-MT patients in comparison to LRP1B-WT patients (Figure 7A). Nonetheless, no significant difference was observed in PFS (P=0.43; Figure 7B) or OS (P=0.16; Figure 7C) between the LRP1B-MT and LRP1B-WT groups in the TCGA-LUAD cohort in which no patients received ICIs treatment. We observed that LRP1B-MT patients had a significantly higher proportion of durable clinical benefits (P=0.03; Figure 7D) and best overall response (P=0.02; Figure 7E) compared with LRP1B-WT patients in the ICIs cohort. According to these results, LRP1B mutation can be instrumental in improving the prognosis of patients treated with ICIs by affecting the TIME.

Figure 7 LUAD patients with LRP1B mutation who receive ICIs have better prognostic outcomes. (A) Kaplan-Meier analysis of PFS in LRP1B-MT group and LRP1B-WT group in the ICIs cohort. (B) Correlation between LRP1B mutation and PFS of patients in the TCGA-LUAD cohort. (C) Correlation between LRP1B mutation and OS of patients in the TCGA-LUAD cohort. (D,E) LUAD patients with LRP1B mutation have better immunotherapy outcomes. ICIs, immune checkpoint inhibitors; PFS, progression-free survival; MT, mutated type; WT, wild type; TCGA, The Cancer Genome Atlas; LUAD, lung adenocarcinoma; OS, overall survival; CR, complete response; NE, not evaluated; PD, progressive disease; PR, partial response; SD, stable disease.

Nomogram predicts short-term PFS in LUAD patients treated with ICIs

We firstly performed a univariate regression Cox analysis to explore the association between clinical factors and PFS in patients with LUAD. Figure 8A shows that in the univariate analysis, NAL [hazard ratio (HR) =0.491, 95% CI: 0.261–0.925, P=0.028] and LRP1B status (HR =0.265, 95% CI: 0.110–0.638, P=0.003) can predict the prognosis of LUAD patients. After adjusting for age, sex, NAL, TMB, and LRP1B status by multivariate Cox regression analysis, we found that only the age (HR =0.208, 95% CI: 1.035–4.179, P=0.04) and LRP1B status (HR =0.232, 95% CI: 0.084–0.645, P=0.005) can be considered independent predictors in the ICIs cohort (Figure 8B). We constructed a nomogram to predict the probability of PFS at 1 and 2 years for LUAD patients treated with ICIs (Figure 8C). The receiver operating characteristic (ROC) curve illustrates the reliable predictive power of the nomogram with area under the curve (AUC) values of 0.823 and 0.840 for 1 and 2 years, respectively (Figure 8D). The 1- and 2-year calibration curve for this nomogram also performs well (Figure 8E). Based on the above results, we believe that the constructed nomogram can help predict the short-term PFS probability for LUAD patients treated with ICIs.

Figure 8 LRP1B mutation is an independent prognostic factor for LUAD patients receiving ICIs and a nomogram of predicting PFS. (A,B) Forest plot showing the outcomes of univariate and multivariate cox regression analysis for the subgroups of age, sex, NAL, TMB, and LRP1B status in the ICIs cohort. (C) A nomogram for predicting PFS in patients receiving immunotherapy based on the results of multivariate Cox regression analysis. (D) ROC curves for nomogram. (E) Evaluation of 1-, 2-year calibration curves for nomogram. NAL, neoantigen load; TMB, tumor mutational burden; HR, hazard ratio; CI, confidence interval; MT, mutated type; WT, wild type; TPR, true positive rate; AUC, area under the curve; FPR, false positive rate; LUAD, lung adenocarcinoma; ICIs, immune checkpoint inhibitors; PFS, progression-free survival; ROC, receiver operating characteristic.

Discussion

The clinical application of immunotherapy in patients with LUAD has yielded remarkable results. It has been shown to improve overall survival in LUAD patients with few side effects and a long duration of action, even months or years after treatment has been stopped (33). In a retrospective cohort study of stage III melanoma, PD-1 immunotherapy significantly reduced regional recurrence and systemic metastasis rates compared to conventional therapy, suggesting that immunotherapy also plays an important role in reducing tumor micrometastasis. Nevertheless, many patients do not benefit from immunotherapy. It is noteworthy that neither PD-L1 expression nor TMB is an accurate predictor of whether a patient will benefit from immunotherapy (34). In our study, we discovered that LRP1B mutation was associated with known immunotherapy-related biomarkers, such as CD8⁺ T cell infiltration, pre-treatment expression of cytotoxic genes, and interferon-gamma (IFN-γ) (16). In addition, LRP1B mutations could enhance immunogenicity, including TMB, NAL, and mutations in DDR-related pathways, both in the ICIs and TCGA-LUAD cohorts.

Changes in the TIME have been demonstrated to influence the response to immunotherapy. Inflammatory TIME is associated with a high response rate to receive immunotherapy (35). The presence of a large number of tumor infiltrating lymphocytes, high density of CD8⁺ T cells and high expression of PD-L1 allows the patient to respond well to immunotherapy. Previous studies have suggested that immunotherapy works primarily by revitalizing the antitumor immune effect mediated by T cells (36). Among various cancer types, a higher density of CD8⁺ T cells usually indicates a better response to PD-1/PD-L1 blockade as well as longer survival, especially in the tumor core and the invasive margin (37,38). CD4⁺ T cells also play a key role in tumor immunotherapy. Several subpopulations of CD4⁺ Th1 cells have been shown to be more enriched in melanoma that responded to CTLA-4 blockade (39). In addition, activated M1 macrophages can promote CD8⁺ T cell activation via antigen presentation and secretion of cytokines (40). Our mIHC analysis revealed that higher levels of PD-L1⁺ macrophages (labeled by anti-CD68) infiltration could be observed in LUAD with LRP1B MT than that with LRP1B WT. The higher proportion of PD-L1⁺CD68⁺ macrophages was indicated for better immunotherapy outcomes in melanoma (41) and NSCLC (42). The pivotal position of T cells in immunotherapy depends on the interaction of IFN-γ in TIME components (16) secreted by cytotoxic T cells, Th1 cells, and natural killer (NK) cells; IFN-γ enhances the cytotoxicity of CD8⁺ T cells and NK cells, promotes antigen presentation and activates M1 macrophages (16). Also, it inhibits the growth of tumor cells and promotes their apoptosis (43). IFN-γ can upregulate PD-L1 expression on adjacent cells, which is related to a higher response rate in NSCLC patients who receive PD-1/PD-L1 blockade (44). Our study found that the LRP1B-MT group had more immune cell infiltration and higher INF-γ expression than the LRP1B-WT group, implying that the LRP1B mutation may be able to enhance LUAD patient response to immunotherapy by affecting the TIME in LUAD patients.

Moreover, CXCL9 and CXLC10 are secreted by T cells and tumor cells due to the positive feedback mechanism generated by INF-γ, thus increasing the infiltration of cytotoxic lymphocytes and NK cells in the TIME (45). Some studies have reported that the pre-treatment expression of cytotoxic genes (CD8A, GZMB) was associated with PD-1 blockade (46). Consistent with other studies, the expression of immune checkpoint genes (CD274 and PDCD1) was positively associated with the prognosis of immunotherapy (47). Although high expression of EDNRB and LAG3 is associated with poor prognosis in LUAD, but they are potential targets for the ICIs treatment (48-50). A study has also shown that the differential expression of immunotherapy-related genes in LUAD is related to the TMB and tumor immune cell infiltration (51). Therefore, LRP1B mutation can promote the secretion of inflammatory mediators and chemokines to recruit and enhance immune cytotoxicity while upregulating the expression of immune genes and ultimately enhancing the antitumor activity of the organism itself.

Genomic instability leads to the development of high immunogenicity, which is important for predicting clinical benefits for ICIs-treated patients (52). Studies have shown that high levels of TMB effectively predict the clinical benefit of ICIs, and LRP1B mutation status has also been reported to be closely associated with TMB levels (53). At the same time, certain somatic mutations in the genome can induce NAL to be expressed on the surface of tumor cells, thereby making NAL another important marker associated with the efficacy of immunotherapy (54). We found that LRP1B mutation was positively related to higher TMB and elevated NAL, which suggests that LRP1B mutation is a prognostic biomarker for ICIs-treated LUAD patients.

Mutations in LRP1B-MT patients were found to occur more frequently in genes related to DDR pathways. The normal activity of the DDR pathway can repair most DNA damage, but when genes in the pathway are altered, it will cause mis-repair or delayed repair, which will eventually lead to genomic instability (55). Genetic instability is associated with an activated immune microenvironment. In a study of colorectal cancer, it was found that MMR-deficient tumor tissues had a higher density of CD8⁺ lymph-like cells and a higher proportion of PD-L1 positive cells compared to MMR-normal tumor tissues (56). The accumulated defects in the DDR machinery can initiate the interferon gene (STING) pathway, which is associated with better response to ICIs (57,58). As expected, GSEA revealed that the major DDR pathway was significantly enriched in the LRP1B-MT group compared to in the LRP1B-WT group. Also, pathways related to NOTCH signaling, cell cycle, and immunoglobulin production were highly enriched in the LRP1B-MT group. Among them, the NOTCH signaling pathway not only regulates the functional state of immune cells but also determines their differentiation. The DLL1-NOTCH1/2 axis drives the differentiation of naïve CD8⁺ T cells to effector T cells by regulating the expression of granzyme B and perforin and can suppress tumor growth (59).

Recent reports have shown that lipid metabolism is associated with poorer immunotherapy outcomes. For example, cholesterol can inhibit T cell receptor signaling leading to T cell depletion (60). Our research examined the differences between the LRP1B-MT and LRP1B-WT group regarding pathways related to lipid metabolism. Among them, the fatty acid metabolic pathway was drastically downregulated in LRP1B-MT LUAD patients, predicting tumor progression and metastasis (61). In addition, interleukin-6, a pleiotropic pro-inflammatory factor, is considered a poor prognostic factor for T cell-mediated antitumor immunity ascribable to its role in inhibiting the conversion of CD4⁺ T cells into effector TH1 cells capable of secreting IFN-γ, and the interleukin-6-related pathway was significantly enriched in the LRP1B-WT group (62).

The common criteria used to evaluate immunotherapy include objective remission rates based on RECIST 1.1 criteria, progression-free survival, overall survival, and treatment-related adverse effects. By analyzing the ICIs and TCGA-LUAD cohorts, we noticed that the LUAD patients with LRP1B mutation had a better PFS and a higher proportion of durable clinical benefit after receiving ICIs treatment than LRP1B-WT patients in the ICIs cohort. However, we did not observe similar results in the TCGA-LUAD cohort. These results demonstrate that LRP1B mutation may enhance patients’ response to ICIs treatment by altering the TIME, providing evidence for LRP1B mutation as a prognostic biomarker in ICIs treatment. Our study provides a new clinical treatment option for LRP1B-mutated LUAD patients, but there are a few limitations. First, our mIHC cohort had a limited sample size and lacked RNA-seq data. To compensate for this limitation, we introduced the TCGA-LUAD cohort to compare the differences in TIME between the LRP1B-MT and LRP1B-WT groups. Second, although our preliminary results found that the LRP1B mutation may play a role in regulating the TIME, the molecular mechanism warrants further studies.

Conclusions

In conclusion, from a TIME perspective, our study explored the relationship between LRP1B mutation and clinical benefit of ICIs therapy in LUAD patients. Our results show that LRP1B mutation is a potential prognostic biomarker for ICIs treatment of LUAD. We believe that for patients with intermediate to advanced LUAD, the detection of LRP1B mutation should be included among the criteria for assessing the suitability of patients for ICIs treatment, in addition to testing for PD-L1 expression and TMB or NAL.

Acknowledgments

Funding: This work was supported by grants from the Major Project of Basic Science (Natural Science) Research in Jiangsu Higher Education Institutions in 2021 (No. 21KJA310003) and the Nurturing Project of Health Talents in Gusu district (No. GSWS2019027).

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-23-39/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-23-39/dss

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Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-23-39/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by institutional ethics board of the First Affiliated Hospital of Soochow University (No. 20202372). Individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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