Integrated analysis of LRP2 mutation to immunotherapy efficacy in pan-cancer cohort

doi:10.21203/rs.3.rs-1503364/v2

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Research Article

Integrated analysis of LRP2 mutation to immunotherapy efficacy in pan-cancer cohort

https://doi.org/10.21203/rs.3.rs-1503364/v2

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Purpose Immunotherapy has emerged as a novel therapy, while many patients are refractory. Although, several biomarkers have been identified as predictive biomarkers for immunotherapy, such as tumor specific genes, PD-1/PD-L1, tumor mutation burn (TMB), and microsatellite instability (MSI), results remain unsatisfactory.

Methods We investigated the characteristics of low-density lipoprotein receptor-related protein 2 (LRP2) mutation in the cancer genome atlas (TCGA) and explored the potential association of LRP2 mutation with immunotherapy. Characteristic of LRP2 mutation in 33 cancer types was analyzed using large-scale public data. The association of LRP2 mutation with immune cell infiltration and immunotherapy efficacy was evaluated. Finally, a LPR2 mutation signature (LMS) was developed and validated by TCGA-UCEC and pan-cancer cohorts. Furthermore, we demonstrated the predictive power of LMS score in independent immunotherapy cohorts by performing a meta-analysis.

Results Our results revealed that patients with LRP2 mutant had higher TMB and MSI compared with patients without LRP2 mutation. Meanwhile, LRP2 mutation was associated with higher infiltration level of immune cells and higher expression of immune-related genes and signaling pathways. Patients with LRP2 mutation presented long overall survival (OS) after immunotherapy. In endothelial cancer (EC), patients with LRP2 mutation belonged to the POLE and MSI-H type of EC molecular types, with a better prognosis. Finally, we developed a LRP2 mutation signature (LMS), that was significantly associated with prognosis in patients receiving immunotherapy.

Conclusion These results indicated that LRP2 mutation can serve as a biomarker for the personalization of cancer immunotherapy. Importantly, LMS is a potential predictor of patients prognosis after immunotherapy.

gene mutation

immunotherapy

immune checkpoint

LRP2

ICIs

Cancer immunotherapy with immune checkpoint inhibitors (ICIs) can enhance the anti-tumor responses and significantly improve the overall survival (OS) of tumor patients [1]. However, most patients do not benefit from immunotherapy. Determining which patients may benefit from ICIs treatment is key to improving clinical outcomes [2]. To date, the FDA has approved PD-1, defective mismatch repair or microsatellite instability high (dMMR/MSI-H), and tumor mutation burden (TMB) to predict response to immunotherapy [3]. In addition, several genomic alterations, such as TP53, KRAS, KMT2C, CDKN2A/CDKN2B and MDM2/MDM4, have been reported to occur in tumors with ICIs therapy [4]. However, due to the complex interplay between tumor cells, tumor microenvironments, and host immunity, new predictive markers for individual immunotherapy still need to be explored.

Currently, the prediction of ICIs response based on biomarker status faces some obstacles, including non-standardized assays, various cut-off values, incomplete reporting of biomarker status, and the dependence of biomarker utility on specific tissue types and clinical settings [5]. For example, both internal and external factors, including ultraviolet light, tobacco smoking, aflatoxin B1 and benzene exposure, as well as viruses, may lead to mutational signatures that influence specific genomic alterations, TMB status and the emergence of neo-antigen immunogenicity. Therefore, TMB is not defined by a universal mutational signature across tumors [6]. Signatures associated with exogenous mutagens are more common in melanoma and lung cancer; Conversely, signatures associated with DNA repair gene defects (MMR, POLE) are more obvious in endometrial, and colorectal cancers [7]. Therefore, it is necessary to find specific markers for specific tumor types.

To date, clinical researchers have designed studies that provide robust tests to detect new biomarkers and comprehensive reporting on patient’s biomarker status. Here, we comprehensively analyzed the characteristics of Low-density lipoprotein receptor-related protein 2 (LRP2) mutation and its relationship to immunotherapy efficacy. We then developed a mutated LRP2 RNA expression signature (LMS), that was significantly associated with OS in patients receiving immunotherapy.

Data sources

The sequencing and clinical pathological data of more than 30 tumor patients were from the TCGA database. The prevalence analysis data on LRP2 mutations with CNA, 3D protein structure, TMB, MSI scores, and prognosis were obtained from cBioPortal for Cancer Genomics database (https://www.cbioportal.org) [8].

LRP2 mutation with immune microenvironment

In order to study the association between LRP2 mutations and immune signatures, TIMER2.0 (http://timer.cistrome.org) was used to display the association between genome alterations or clinical results and immune infiltration in TCGA [9]. In addition, CIBERSORT was used to estimate the relative fractions of 22 infiltrating immune cell types in each tumor sample using R package (http://cibersort.stanford.edu/). Differences in expression levels of immune-related genes quantified using the log2 (FPKM +1) values, were also studied for their functional characteristics [10].

Identification, function and pathway enrichment analysis of differentially expressed genes, Differentially expressed genes (DEGs) between patients with LRP2 mutation and those without mutation in TCGA were identified using R package “Limma”. Then, the gene annotation enrichment analysis of up-regulation genes in patients with LRP2 mutation was performed by R package cluster analyzer.

Gene set variation analysis

Gene set variation analysis (GSVA) can identify specific pathways according to transcriptome data [11]. According to the GSVA by “Limma” package, each sample in the TCGA database got a score. Then, the difference of signature score was analyzed. Signatures with a log2 fold change (FC) > 0.4 (adjusted P < 0.05) were identified as significant differential expressed characteristics.

Prediction of LRP2 mutation to immunotherapy

For the three types of cancers with the highest mutations in LRP2, including melanoma, endometrial cancer and lung cancer, we searched for studies involving ICIs in these tumor in “ClinicalTrials.gov” data. We then systematically searched PubMed database on March 1. 2022 for potential studies. Eligible studies must the following criteria: (1) population: clinical trial involving more than 30 adult solid tumors patients; (2) Intervention: at least one group received ICIs treatment, regardless of treatment dose and duration; (3) Results: LRP2 mutation status and OS report information. In addition, the reference list meeting the eligibility criteria was checked for possible relevant studies. When multiple publications of the same study were found, only recent and/or most complete reports were included.

Data analysis of patients with ECs subtypes

To evaluate the association between LRP2 mutation and EC molecular subtypes, we included two studies involving proteogenomic characterization [12-13]. One study was from TCGA data with a total of 511 samples; one study from CPTAC data with a total of 95 samples. POLE, TP53, MSH6, MHS2, MLH1, and PMS2 were obtained and the differences between LRP2 mutation and LRP2 non-mutation were compared. The Association of LRP2 mutation with molecular subtypes, TMB, MSI score, can be obtained from the cBioPortal for Cancer Genomics database.

Development and Testing of a LRP2 Mutant mRNA Expression Signature (LMS)

Univariate cox regression analysis was used to investigate the effect of each gene on OS. The identified OS-related genes were used to develop prognostic signatures. Multivariable models were constructed using the “Glmnet” package for R using the least absolute shrinkage and selection operator (LASSO) Cox regression method [14]. Only genes with non-zero coefficients in the LASSO regression model were used to further calculate the LMS scores. The following formula was used: LMS score=∑ⁿ_j=1Coefj*Xj, where Coefj represented the coefficient and Xj represented the relative expression of each gene, which was normalized by z-score. Then, the median of LMS score is selected as the critical value to classify the TCGA-UCEC cohort. This prognostic model was validated in the TCGA pan-cancer cohort. LMS scores in pan-cancer data were calculated using the same formula. Kaplan-Meier curve was further performed to assess the relationship between LMS score and OS. The area under the curve (AUC) of the ROC curve was calculated to test the classifier performance. Calibration plots were used to predict 1-year 3-year and 5-year OS.

Relationship of LMS score with immunotherapy

Tumor immune dysfunction and exclusion (TIDE) score [15] and immunophenoscore (IPS) were used to evaluate patient’s response to immunotherapy [16]. TIDE scores of pan-cancer patients were obtained from TIDE network platform. We set the threshold for TIDE score to 0, so patients with negative TIDE score were regarded as responders. In general, patients with lower TIDE scores and higher IPS were considered to have better respond to immunotherapy. To assess the relationship between LMS scores and immunotherapy, gene expression profiles and clinical information of eight independent cohorts were downloaded from TIDE network platform to validate the predictive value of LMS in immunotherapy. Firstly, we calculated the LMS score based on CD3D and ONECUT3 expression and the LMS formula. All samples were divided into high and low LMS groups according to the median LMS score. Then, according to Response Evaluation Criteria in Solid Tumors (RECIST) criteria, the responders were defined as patients with complete or partial response (CR/PR) after immunotherapy; non-responders were those with stable disease (SD) or progressive disease (PD) [17]. We used Review Manager software (RevMan, version 5.2; Nordic Cochrane Centre, Copenhagen, Denmark) to analyze data from all included studies. The weighted mean difference and the RR with 95% CI were used to compare dichotomous variables, respectively. Chi² and I² tests were used to test the heterogeneity between combined trial results. Probability value of 50% represented statistical heterogeneity. The possibility of publication bias was assessed using funnel plots. Finally, in order to confirm the prognosis of LMS score in immunotherapy patients, two large cohorts were analyzed.

Statistics analysis

SPSS 24.0 (IBM, Armonk, NY, USA) was used for data processing and statistical analysis. Kaplan–Meier method was used for survival analysis and log-rank test was used for comparison. The association between various clinical features and LRP2 mutations were assessed using χ2 test, Student’s t test, or Fisher’s exact test, depending on the context. P < 0.05 was considered statistically significant.

2.1 The prevalence of LPR2 somatic mutation in pan-cancer

We analyzed LRP2 mutations in the whole exome sequences of 10,953 patients from 32 different cancer types. We identified 797 patients with LRP2 mutations.The mutant frequency in different tumors was obviously different, with the higher LRP2 mutation in melanoma (28.18%), uterine carcinosarcoma (17.99%) and lung squamous cell carcinoma (16.32%) (Fig. 1A). In addition, among 797 LRP2 mutations, 651 (81.68%) were missense mutations, 100 (12.55%) were truncating mutations, 38 (4.77%) were splice site mutation and 8 (1.00%) were inframe mutation. These mutation appeared in a dispersed manner throughout the sequence and 3D protein structure (Fig.1B).

TMB has been proved to be a useful biomarker for ICIs selection of some cancer types; high TMB in tumors favors the infiltration of immune effector cells, and anti-tumor immune response [18]. In the TCGA cohort, there was significant differences between TMB and various mutation types (truncating mutant, missense mutant and multiple mutations) (Fig.1C). Then, we explored TMB level in different tumors. The results showed that among bladder urothelial cancinoma, rectal adenocarcinoma and endothelial carcinoma, patients with LRP2 mutation had significantly higher TMB level than those without LRP2 mutation (Fig.S1A). MSI has become the main predictor of the efficacy of ICIs over the last few years [19]. MSIsensor is an effective tool to obtain MSI status. The results showed that patients with LRP2 mutation had a higher MSIsensor scores than those without LRP2 mutation in pan-cancer analysis ( P < 0.001) (Fig.1C). According to the MSIsensor stratified by LRP2 mutation status, there was a significant difference between MSIsensor score and missense mutant and multiple mutations. To further verify the association between LRP2 mutation and MSI status, we also examined the difference of MSI MANTIS scores between patients with LRP2 mutant and without LRP2 mutation. The results showed that MSI MANTIS scores in patients with LRP2 mutant were significantly higher than that without LRP2 mutation (Fig.1C). Then, we analyzed the MSI level among different tumor types. The results exhibited that the MSIsensor and MSI MANTIS score were higher in patients with LRP2 mutation than those without LRP2 mutation in colorectal adenocarcinoma, endometrial carcinoma and esophagogastric adenocarcinoma (Fig.S1B-C).

MSH2, MSH6, MLH1, and PMS2 play a critical role in the process of mismatch repair (MMR). Any mutation of these four MMR genes may lead to MSI-H [20-21]. Here, we investigated the co-occurrence pattern of these four MMR mutant genes and LRP2 mutations (Fig. 1D). Compared with patients without LRP2 mutant, patients with LRP2 mutant harbored more MMR mutant (MSH6, 1.77% vs 12.2%; MSH2, 1.78% vs 10.78; MLH1, 1.60% vs 9.64%; PMS2, 2.25% vs 10.88%). In addition, we also found that LRP2 mutation was associated with common tumor-related signaling pathways, such PTK/RAS/PI3K, p53 and RB signaling pathways (Fig.1E) and homologous recombination repair genes (Table.S1), suggesting that LRP2 gene may regulate tumor through synergy with other mutant genes. Meanwhile, the top genes mutation with LRP2 mutation included WWOX, OSBPL11, and SNX4, as showed in Fig.1F.

Then, we evaluated the influence of LRP2 on the prognosis in pan-cancer analysis. The results showed that there was no significant difference in overall survival (OS) (P = 0.405), disease-specific survival (DSS) (P = 0.129) and progress-free survival (PFS) (P = 0.849) between patients with LRP2 mutation and those without LRP2 mutation (Fig.1G). The prognosis and survival rate for tumor patients in TCGA cohort were independent of LRP2 mutant status. Then, we compared the effect of LRP2 mutation on prognosis among different tumor types. The results showed that LRP2 mutation was associated with a better prognosis in endothelial cancer (OS, p<0.001), bladder urothelial cancinoma (OS, p=0.0061) and brain lower grade glioma (OS, p<0.0293) (Table.S2). In addition, we also examined the features of patients with CNA of LRP2 (Fig.S2A). A total of 64 patients were identified as 45 amplification and 19 deep deletion. The results showed that the CNA of LRP2 was not related to TMB level (P = 0.0709), MSI MANTIS scores (P = 0.436), OS (P = 0.488) and DSS (p=0.986)(Fig.S2B). The outcomes indicated that LRP2 mutation was the main type in genomic alterations of tumors.

2.2 Relationship of LRP2 mutation with tumor microenvironment and immunotherapy

In the TCGA cohort, our results showed that LRP2 mutation was associated with high infiltration level of CD8+ T cells, plasma cells, activated CD4+ memory T cells, and M1 macrophages, but low level of M2 macrophages and resting mast cells (Fig.2A and Fig.S3A). The expression level of PD-L1 in tumor tissues is one of the important biomarkers for patients to choose ICIs treatment [22-23]. In the TCGA cohort, we found that LRP2 mutation was associated with high expression level of multiple immune checkpoint genes, such as PDCD1, PDCD1LG2, LAG2, CD274 (PD1) and CTLA4 (Fig.2B and Fig.S3B). Notably, we found that patients with LRP2 mutation had higher enrichment of immune regulatory-related signaling pathways, such as T cell receptor (p=0.00073), B cell receptor (0.025), NK cell medicated cytotoxicity (0.0024) and toll-like receptor signaling pathways (0.00062) (Fig.2C). In addition, other highly enriched signaling pathways in LRP2 mutation were showed in Fig.S4A.

Then, we analyzed the immune cell infiltration and immune checkpoint genes expression among different tumor types. The results showed that the co-expression of LRP2 mutation with immune cells, such as CD8 T cells, M1 macrophages, plasma cell, and activated memory CD4 T cells, were highly correlation in several tumor types, especially for BRCA, COAD, LUAD, UCEC and UCS (Fig.2D). Similarly, the co-expression of LRP2 mutation was highly associated with most immune checkpoint genes in BRCA, COAD, UCEC and UCS (Fig.2E). Then, we analyzed the co-expression of LRP2 mutation with immune receptors, immunostimulators, MHC molecules, and chemokines in several tumor types, such as BLCA, COAD, LUAD, LUSC, SKCM, STAD, UCEC, and UCS, because they have high LRP2 mutation percentage (Fig.S4B). The results showed that the expression of most receptors, immunostimulators, MHC molecules and chemokines in COAD, UCEC, UCS and SKCM in patients with LRP2 mutation was higher than those without LRP2 mutation.

Then, we evaluated the relationship of LRP2 mutation with immunotherapy. Through rigorous screening, we identified three studies on melanoma [24-26] and two studies about lung cancer [27-29] that meet the included criteria. After reviewing the literature, we found that only two patients with lung cancer had LRP2 mutation, so these studies were further excluded. Finally, a total of 211 cases of melanoma with LRP2 mutation were applied to evaluate the effect of immunotherapy (Table.S3). The results showed that the application of these ICIs in patients with LRP2 mutation exhibited higher complete response (CR) and partial response (PR) and low percentage of progressive disease (PD) and stable disease (SD) (Fig.2F). Survival analysis showed that LRP2 mutation was associated with significantly improved OS (p=0.0235) (Fig.2G).

2.3 The characteristic of LRP2 mutation in EC

Based on the above results, we knew that LRP2 mutation may be a prognostic and a predictive biomarker for immunotherapy of EC. Two cohort (TCGA and CPTAC) studied the relationship of LRP2 mutation with EC molecular subtype. In the TCGA-UCEC cohort, we identified 93 patients with LRP2 mutation (18%) in 573 patients (Fig.3A). Survival analysis showed that high LRP2 mutation was associated with better OS (P=0.00644), DSS (p=0.0141) and PFS (p=0.00483) (Fig.3B), indicating that LPR2 mutation may be a prognostic factor of EC.Then, our results showed that LRP2 mutation was related to high TMB level, MSI MANTIS score and MSIsensor Score (Fig.3C). Then, we compared the mRNA differences between LRP2 mutation and LRP2 no mutation in EC. The results showed that LRP2 mutation was related to the high expression level of CXCL9, TIGIT, CXCR6, CXCL13. ICOS and LAG3, all of which were involved in immune regulation (Fig.3D). GO and KEGG analysis confirmed the high enrichment level of immune-related signaling pathways, such as T cell activation/differentiation, chemokine-medicated signaling pathway, response to chemokines, lymphocyte migration and chemokine signaling pathway (Fig.3E-F). Other highly enriched signaling pathways included DNA replication, cell cycle, homologous recombination, mismatch repair, NK cell-mediated cytotoxicity, N-glycan biosynthesis and TCA cycle (Fig.S5). Similar to the results of pan-cancer analysis, LRP2 mutation in TCGA-UCEC also presented higher infiltration CD8 T cell, plasma cells, and M1 macrophages than that without LRP2 mutation (Fig.S6A). In addition, our results showed that the expression of most immune receptors, immunostimulators, MHC molecules and chemokines in patients with LRP2 mutation was higher than those without LRP2 mutation in TCGA cohort (Fig.S6B-E).

The Cancer Genome Atlas (TCGA) performed a genome-wide analysis of 373 EC and identified four distinct groups: CNV-high, CNV-low, MSI-H, and POLE type [30]. POLE tumors were more common in earlier stages and have good prognosis for these patients. MMRd tumor (MSI-H) had high mutation frequency, potential immunotherapeutic response and good prognosis. It is found that CNA-low (also defined as p53 wt) is very heterogeneous and exhibited different prognosis, because it covers a variety of pathological types of tumors. However, the CNA-high subtype (also defined as p53 alterations) with p53 either over-expression or missense are associated with poor prognosis in all molecular subtypes [30]. The tumor subgroup includes most serous and mixed types, which occur at a higher stage, usually grade 3 [31]. In two cohorts, we evaluated the association between LRP2 mutation and four molecular subtypes in EC. In the TCGA cohort, we identified 95 patients with LRP2 mutation in 509 patients (19%) (Fig.S7A) and missense mutation was the main type of mutation (Fig.S7B). In the CPTAC cohort, we identified 13 patients with LRP2 mutation in 95 patients (14%) (Fig.S7A), with missense mutations being the main mutation type (Fig.S7B). Compared with patients without LRP2 mutation, patients with LRP2 mutation harbored more MMR mutant genes in TCGA and CPTAC cohort, indicating that patients with LRP2 mutation had high MSI levels (Fig.S7C). Then, we compared the association of LRP2 mutation with POLE and TP53 mutation in both cohorts. Our results showed that most patients with LRP2 mutation had higher percentage of POLE mutation and lower TP53 mutation than those without LRP2 mutation (p<0.001) (Fig.S7D-E).

Finally, we compared the association of patients with LRP2 mutation with EC molecular types. Our results showed that in TCGA cohort, 41 patients (43.16%) with LRP2 mutation belonged to POLE type and 39 patients (41.05%) with LRP2 mutation belonged to MSI-H type, but only 14 patients were CNV-low or CNV-high type (14.74%) (Fig.S7F). In CPTAC cohort, 6 patients with LRP2 mutation corresponded to POLE type, 6 patients were MSI-H type and only 1 patients belonged to CNV-low type (Fig.S7F). These outcomes indicated that LRP2 mutation can help to exclude highly malignant tumors and select tumors with high response of immunotherapy.

2.4 Development and validation of a prognostically useful LRP2 mutant signature

There are numerous studies examining whether genetic mutation affects the efficacy of immunotherapy and patients survival [32]. In this study, we found that LRP2 mutation were not statistically association with prognosis in most TCGA cancer types. Notably, LRP2 mutation in UCEC was significantly associated with OS, DSS and PFS (Table.S2). Using LRP2 mutation as a prognostic marker, while useful in some contexts, may not be useful in others. For example, LPR2 mRNA level and protein function can be inactivated by LRP2 mutation. In this study, we found no significant differences between LRP2-mutated and non-LRP2-mutated in the EC and pan-cancer cohort (Fig.4A). Thus, we sought to develop an RNA expression signature correlated with LRP2 mutation that might be more prognostic. We tested a mutant LRP2 expression signature based on the expression of genes consistently up-regulated in mutant LRP2 EC patients (Table.S4). Then, univariate regression analysis was undertaken to identify key prognostic markers in EC cohort (Fig.S8A). LASSO Cox analysis were performed (Fig.S8B) and two genes (CD3D and ONECUT3) were finally selected to establish a prognostic signature (Fig.S8C). The formula was shown as: LMS score =0.276627*expression level of CD3D+0.780825*expression level of ONECUT3. The two genes were risky prognostic genes (Fig.S8D). All patients were divided into high LMS and low LMS groups based on the optimal LMS score=0.585, that was founded by “Cutoff Finder”. Then, we compared the effect of LMS score in predicating prognosis with LRP2 mutation status and showed that LMS had higher accuracy with narrow confidence intervals than LRP2 mutation (Fig.4B). In EC and pan-cancer data, LRP2 mutation exhibited higher LMS score than those without LRP2 mutation (Fig.4C). Survival analysis showed that patients with high LMS score had longer overall survival compared with the low LMS group in the EC (Fig.4D) and pan-cancer cohort (Fig.4E). The area under the ROC curve and calibration plot was used for predicting 1-year 3-year and 5-year OS (Fig.S8E-F). Finally, we applied the Web-based software program Kaplan-Meier Plotter on 11 TCGA cancer types using these two gene signature and found a strong correlations between our prognostic calculations and the Kaplan-Meier Plotter-based calculations (Table.S5). Meanwhile, we demonstrated the association of LMS score with LRP2 mutational signature, survival status and gene expression in EC (Fig.4F) and pan-cancer cohort (Fig.4G).

2.5 Relationship of LMS score with TMB, MSI and immune microenvironment

To further evaluate the potential of LMS score as a predictor of immunotherapy, we explored the relationship of LMS to TMB, MSI and the immune microenvironment. Our study unveiled a positive correlation between LMS score and mutation count, MSI sensor score and MSI MANTIS score in EC (Fig.5A) and pan-cancer cohort (Fig.5B). Importantly, further analysis revealed that LMS score was significantly associated with TMB in several cancer types, including UCEC, THYM, THCA, TGCT, LGG, and COAD (Fig.5C). Similarly, there was a significant association of LMS with MSI in UCEC, TGCT, OV, LUSC, and COAD (Fig.5D). In order to better understand the relationship between LMS and immune-infiltrating microenvironment, we investigated the composition of 22 types of immune cells between high LMS and low LMS group in EC and pan-cancer cohort. Compared with the low LMS group,the infiltration of CD8 T cell, activated CD4 T cell, M1 macrophage, and plasma in the high LMS group increased significantly, while the infiltration of M0 macrophage, resting memory CD4 T cell and activated dendritic cells decreased significantly (Fig.S9A-B). At the same time, we also analyzed the relationships between immune checkpoint gene expression and LMS score. Our results revealed that high LMS was positively associated with the expression of CTLA4, PDCD1, TIGIT and GZMB in EC (Fig.S9C) and pan-cancer cohorts (Fig.S9D).

Recently, a new global transcriptomic immune classification of solid tumors has identified six immune subtypes (C1-C6): wound healing (C1), IFN-g dominant (C2), inflammatory (C3), lymphocyte depleted (C4), immunologically quiet (C5) and TGF-β dominant (C6). Among the C1-C6 subtypes, the prognosis of C4 and C6 subtypes was the worst and they displayed composite features reflecting a macrophage dominated, low lymphocytic infiltration, and high M2 macrophage content. In contrast, C2 and C3, have favorable prognosis [33]. This results found that high LMS group had a high percentage of C2 and C3 subtypes and a low percentage of C1 and C4 subtypes compared to those in low LMS group (Fig.5E). Similarly, in the pan-cancer analysis, nearly half of patients in high LMS group were C2 subtype, but the percentage of C4 subtype was lower than those in the low LMS group (Fig.5F).

2.6 LRP2 mutation signature predicating the efficacy of immunotherapy

In order to infer the potential role of LMS in predicating immunotherapy efficacy, we calculated the IPS in pan-cancer patients. The results indicated that high LMS was associated with higher IPS compared with low LMS score (Fig.6A). Then, we compared the difference of TIDE between high LMS group and low LMS group in UCEC, SKCM, and COAD. The results also showed that patients in high LMS group had higher immune dysfunction, and low TIDE and immune exclusion level than those in low LMS group (Fig.S10A-C), suggesting the potential better responses to immunotherapy.

Finally, to validate the predictive ability of LMS in immunotherapy, we analyzed all experimental studies on immunotherapy on the TIDE platform. According to the inclusion criteria, a total of 307 patients from 8 studies were included for further analysis (Table.S5). Then, we performed a meta-analysis, and the results showed that patients in high LMS group had higher response rate than those in low LMS group (OR 1.55. 95% CI 1.16-2.08, p=0.003) (Fig.6B). The funnel plot confirmed the reliability of the results (Fig.6C). Then, we included two studies to evaluate the association of LMS with clinical response to immunotherapy and prognosis. In the cohort of Gide et al, the high LMS score was associated with a better clinical response to immunotherapy (chi-square test, p<0.001) (Fig.6D). Survival analysis exhibited that patients with high LMS score had better prognosis than those with low LMS score (log-rank test, p=0.044) (Fig.6E). In the IMvigor210 cohort, the proportion of responders (CR/PR) was higher in the high LMS group than in low LMS score group (chi-square test, p < 0.05) (Fig.6F). Survival analysis also confirmed that patients with high LMS score had better prognosis than those with low LMS score (log-rank test, p=0.030) (Fig.6G). The ROC curve demonstrated that the survival AUC were 0.895, and 0.584 in Gide and IMvigor210 cohort, respectively, indicating a high accuracy of the model (Fig.6H).

Immune checkpoint inhibitors (ICIs) have dramatically changed the paradigm of cancer treatment; however, many patients do not benefit. The variability of ICIs response highlights the need to identify predictive biomarkers [34]. Several tumor-specific genes have been reported as markers for predicting the benefit of ICIs. These markers are still insufficient to determine when and how often ICIs should be used [35]. In this study, we identified LRP2 mutations as a novel biomarker for predicating immunotherapy response. In pan-cancer analysis, our results suggested that LRP2 mutations were associated with high immune cell infiltration, immune checkpoint gene expression and high enrichment of immune-related signaling pathways. Importantly, in specific tumor types, LRP2 mutations were associated with MHC molecules, immune receptors, immunostimulators and chemokines. We, then found that LRP2 mutations were closely associated with the immune microenvironment and prognosis of ECs and that patients with LRP2 mutations corresponded to the POLE and MSI-H subtypes in the molecular typing of ECs. Finally, we estimated a LRP2 mutation signature (LMS) based on two important genes (CD3D, and ONECUT3), all of which were strongly associated with better prognosis. We found that high LMS score was associated with high levels of TMB, MSI and immune cell infiltration, thus, LMS can be used to predicate the efficacy of immunotherapy. The potential biological functions of LRP2 mutation were summarized in Fig. 7.

LRP2 is a member of the low-density lipoprotein (LDL) receptor gene family that play a key role in embryonic development [36]. LRP2 has been shown to act as an auxiliary receptor for sonic hedgehog (SHH) and activate or inhibit this morphogenetic pathway depending on the cellular environment [37]. Its mutations lead to a loss of protein function, which is the basis of autosomal recessive genetic disorder. LRP2 is also mutated in many cancers–16% of colon and rectum adenocarcinoma, 19.7% of lung cancers and 9.3% of bladder cancers[38]. However, LRP2 mutations were less frequent in gastric cancer and liver cancer [39]. A recent study reported that LRP2 mutation was associated with high TMB in young patients with intrahepatic cholangiocarcinoma [40]. In this study, we found that LRP2 mutations were associated with high levels of TMB and MSI in various tumor types, particularly for endometrial cancer, colorectal adenocarcinoma and esophagogastric adenocarcinoma. Then, we compared the characteristics of the immune microenvironment of patients with LRP2-mutated and non-LRP2-mutated. The results showed that LRP2-mutated tumors exhibited an immunoinflammatory phenotype, characterized by high levels of immune cells infiltration and high expression of immune checkpoint genes, immune receptors, immunostimulators, MHC molecules and chemokine genes. ICIs is known to be influenced by TME in inducing cancer cell death; an immunoactive "hot" microenvironment leads to a better response from the patient's anti-tumor immune system [41]. In this study, we found that LRP2 mutations were associated with higher CR and PR rate in ICIs and better prognosis in immunotherapy patients. These findings strongly supported that LRP2 mutations was closely associated with immunotherapy and may be a biomarker for predicting clinical outcomes in patients treated with ICIs.

In a pan-cancer analysis, we found that LRP2 mutations in EC were most strongly associated with immune cells, immune checkpoints and inflammation-related molecules compared to other tumor types. Meanwhile, LRP2 mutations were associated with better OS, DSS and PFS than non-LRP2 mutations. Therefore, we explored the characteristics of LRP2 mutations in ECs. Similar to the pan-analysis, patients with LRP2 mutation in EC also had high TMB, MSI and immune cell infiltration. Then, we found that LRP2 mutation leaded to high expression of immune-related genes, such as CXCL9, CXCR6, CXCL13, ICOS and immune checkpoints (CTLA4 and LAG3). GO and KEGG also confirmed that LRP2 mutation are highly enriched for immune-related signaling pathways, such as T/B cell activation, chemokine-medicated signaling pathway, response to chemokines and lymphocyte chemotaxis, suggesting that LRP2 mutation was involved in immune response. EC can be divided into four subgroups (POLE, MSI-H, CNV-low and CNA-high types) [42]. The former two have a good prognosis, while the latter two tend to have a poor prognosis. According to the molecular subtypes of EC, most patients with LRP2 mutation belonged to POLE or MSI-H types, with a good prognosis and a high response rate to immunotherapy. Therefore, we believed that LRP2 mutation can help select patients who may benefit from immunotherapy.

Studies using genetic mutations as prognostic markers are prone to confounding, and many variables may contribute to this. One problem is the existence of mutation-independent mechanisms to inactivate single-gene mutation signaling pathway [43]. To circumvent the problem, we searched for downstream transcriptional signatures based on RNA expression data that are highly and significantly up-regulated in patients with LRP2 mutation relative to those without LRP2 mutation in EC. Lasson cox analysis identified two important genes (CD3D and ONECUT3). In order to validate the value of transcriptional signatures, pan-cancer data was used as a validate set. CD3D has been reported to be associated with a variety of cancers, including colon cancer, bladder cancer, and glioblastoma [44]. It involves coding a protein complex that is a vital portion of distinct chains than can bind to TCR and the ζ-chain to form TCR-CD3 complexes that promote T cell activation. ONECUT3 belongs to the onecut transcription factor family and plays an important role in regulating the development of diverse tissues [45]. However, the exact role of these factors has not been fully elucidated. In lung cancer cells, ONECUT3 has been shown to prevent epithelial-to-mesenchymal transition via p53, setting a precedent for its role as a tumor-suppressor [46]. In this study, LMS score can reflect the value of LRP2 mutation in predicating immunotherapy efficacy and prognosis. High LMS was associated with better prognosis, showing better prediction power than simple LRP2 mutation.

We then evaluated the effect of LMS on the immune microenvironment and the efficacy of immunotherapy. It is worth noting that LMS were positively correlated with mutation counts, and MSI in the EC and pan-cancer cohort. In addition, high LMS was associated with the high infiltration level of CD8 T cells, plasma cells, activated CD4 T cells, and macrophage M1 as well as high level of immune checkpoint genes expression. Finally, we evaluated the association of LMS with TIDE, IPS, immune dysfunction, and immune exclusion and explored the efficacy of immunotherapy. These data proved the potential value of LMS in predicating the efficacy of immunotherapy. However, due to the lack of clinical research, the conclusion was limited. Therefore, we performed a systematic review and meta-analysis to explore the association of LMS with immunotherapy efficacy. The results confirmed that LMS is an valuable tool for selecting patients who will benefit from immunotherapy and an improved prognostic marker for those receiving treatment.

In summary, the large scale, and multi-data research methods initiated by TCGA provided unparalleled opportunities for better understanding the structural mechanisms of LRP2 mutations and its impact on immune microenvironment, immunotherapy and prognosis. We believed that this paper may lead to the development of diagnostic and therapeutic tools based on a better understanding of LRP2 mutations in cancer.

Acknowledgement

None

Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 82173188) to KQ Hua, the Clinical Research Plan of SHDC (SHDC2020CR1045B, SHDC2020CR6009) to KQ Hua, the Shanghai Municipal Health Commission (20194Y0085) to CB Li, and the Shanghai “Rising Stars of Medical Talent” Youth Development Program (SHWSRS2020087) to CB Li.

Author Contribution CB Li and Y Ding contributed equally to this work. CB Li and Y Ding collected the data and did the statistical analysis, XY Zhang and KQ Hua organized and submitted the manuscript. KQ Hua guided the whole process.

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Data are available in a public, open access repository.

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The authors declare there was no commercial or financial conflict of interest.

Author details

¹Department of Obstetrics and Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.

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Integrated analysis of LRP2 mutation to immunotherapy efficacy in pan-cancer cohort

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