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

doi:10.21203/rs.3.rs-1503364/v1

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

https://doi.org/10.21203/rs.3.rs-1503364/v1

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

Methods: 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 perfoming 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 predicator of patients prognosis after immunotherapy.

gene mutation

immunotherapy

immune checkpoint

LRP2

ICIs

Cancer immunotherapy with immune checkpoint inhibitors (ICIs) can enhance antitumor responses and significantly improve overall survival (OS) in cancer patients [1]. However, most patients do not benefit from immunotherapy. Identifying patients who can benefit from ICIs therapy 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 interply between tumor cells, tumor microenvironments, and host immunity, new predictive markers for individual immunotherapy still need to be explored.

Currently, prediction of response to ICIs response based on biomarker status has faced several obstacles, including non-standardized assays, various cut-off values, incomplete reporting of biomarker status, and dependence of biomarker utility on specific tissue types and clinical settings [5]. For example, internal and external factors, including ultraviolet light, tobacco smoking, aflatoxin B1 and benzene exposure, as well as viruses, may lead to mutational signatures that affect 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; in contrast, signatures associated with defects in DNA repair gene (MMR, POLE) are more obvious in endometrial, and colorectal cancers [7]. Thus, 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 performed a comprehensive analysis to examine the characteristics of Low-density lipoprotein receptor-related protein 2 (LRP2) mutation and its association with immunotherapy efficacy. We then developed a mutant LRP2 RNA expression signature (LMS), that was significantly associated with OS of patients receiving immunotherapy.

Data sources

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

LRP2 mutaion with immune microenvironment

To study the association between LRP2 mutations and immune characteristics, data was analyzed using TIMER2.0 (http://timer.cistrome.org), a database to explore the relationship between genomic changes or clinical results and immune infiltration in TCGA [9]. In addtion, CIBERSORT was applied to estimate the relative fractions of 22 infifiltrating immune cell types in each tumor sample using R package (http://cibersort.stanford.edu/). Besides, the differences in the expression levels of immune-related genes, which were quantified using the log2 (FPKM +1) values obtained, were also studied along with their functional classification [10].

Identify of differentially expressed genes, functional and pathway enrichment analysis

Differentially expressed genes (DEGs) between patitents wit LRP2 mutation and patients wihout no mutation in TCGA-UCES were identified using R package “Limma” . Then, the up-regulation genes in patients with LRP2 mutation was used for gene annotation enrichment analysis of metabolism-related genes (the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)) by using R package cluster profiler.

Gene set variation analysis

Gene set variation analysis (GSVA) can determine specific pathways based on transcriptomic data [11]. According to GSVA by “limma” package, each sample from the TCGA database got a score. Differential analyses were then conducted on the signature scores. The signatures with a log2 fold change (FC) > 0.4 (adjusted P < 0.05) were determined as significant differential expressed characteristics.

Prediction of LRP2 mutation to immunotherapy

In view of the three types of cancers with the highest mutaions in LRP2, including melanoma, endometrial cancer and lung cancer, we searched the studies involving in the application of ICIs in these tumor in “ClinicalTrials.gov” data. Then, we conducted a systematic search on PubMed database to find potential experiments on March 1. 2022. The selection criteria were prespecified. To be eligible, studies had to meet the following standards: (1) population: clinical trials including over 30 adult patients with solid tumor; (2) intervention: at least one arm in the trial was treated with ICIs irrespective the dosage and duration of the treatment; and (3) outcomes: reported information regarding LRP2 mutation status and OS. In addition, the reference lists of all trials fulfilling the eligibility criteria were also checked for possible relevant studies. When multiple publications of the same study appeared, only the most recent and/or most complete reporting study were included.

Data analysis of patients with ECs subtypes

To evluate the assocaition of LRP2 mutaion with EC molecular subtypes, we included two studies involving in the proteogenomic characterization [12-13]. One study from TCGA data, consist of 511 sample, and one study was from CPTAC data with 95 sample. The data about POLE, TP53, MSH6, MHS2, MLH1, and PMS2 were obtained to compare the difference between LRP2 mutation and LRP2 no mutation. The data for the association of LRP2 mutation with molecular subtyeps, TMB, MSI score, can be queried and downloaded from the cBioPortal for Cancer Genomics database.

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

Univariate cox regression analysis was performed to explore the effect of each gene on overall survival. Identified OS-associated genes were used to develop prognostic signatures. The least absolute shrinkage and selection operator (LASSO) Cox regression method was adopted to construct multivariable models using the “glmnet” package for R [14]. Only genes with non-zero coefficients in the LASSO regression model were selected for further calculate of LMS score. We calculated the LMS score for each patient using the following formula: LMS score=∑ⁿ_j=1Coefj*Xj , with Coefj indicating the coefficient and Xj representing the relative expression levels of each gene standardized by z-score. The median risk score was chosen as a cutoff value to dichotomize the TCGA-UCEC cohort separately. The prognostic model was verfied in the TCGA pan-cancer cortorts. LMS score in pan-cancer data were calculated using the same formula as TCGA-UCEC. Kaplan-Meier curve analysis was further performed to evaluate the relationship between LMS score and overall survival. The area under the curve (AUC) of the ROC curve was calculated and compared to examine the classifier performance. Calibration plot was used for predicting 1-year 3-year and 5-year OS.

Relationship of LMS score with immunotherapy

The patient’s response to immunotherapy was estimated by the tumor immune dysfunction and exclusion (TIDE) score [15] and immunophenoscore (IPS) [16]. The TIDE scores of pan-cancer patients were obtained from the TIDE web platform. We set the threshold of the TIDE score at 0, so patients with negative TIDE scores as responders. In general, patients with lower TIDE score and higher IPS are presumed to respond better to immunotherapy. To evaluate the relationship of LMS score to immunotherapy, gene expression profiles and clinical information of eight independent cohorts were downloaded from TIDE web platform to validate the predictive value of LMS in immunotherapy. Firstly, we calcuated the LMS score based on CD3D and ONECUT3 expression as well as LMS formula. Then, all samples were divided into high and low LMS group according to the median LMS score. According to Response Evaluation Criteria in Solid Tumors (RECIST) criteria, responders were defined as patients with a complete or partial responses (CR/PR) after immunotherapy; non-responders were those with stable disease (SD) or progressive disease (PD)[17]. We analyzed data from all included studies using Review Manager software (RevMan, version 5.2; Nordic Cochrane Centre, Copenhagen, Denmark). Weighted mean difference and RR with 95% CI were applied to compare dichotomous variables, respectively. Heterogeneity among the outcomes of combined trials was determined with the use of the chi² and I² tests. A probability value of 50% were suggestive of statistical heterogeneity. Funnel plots were used to assess the possibility of publication bias. Finally, to confirm the prognosis of LMS score for patients with immunotherapy, two cohort with large size of sample were analysed.

Statistics analysis

SPSS 24.0 (IBM, Armonk, NY, USA) was used for data processing and statistical analysis. Survival analysis was performed by Kaplan–Meier method and compared by log-rank test. The relations between various clinical characteristics and LRP2 mutation were evaluated with 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 whole exome sequences of 10,953 patients from 32 different cancer types. We identified 797 patients with LRP2 mutations.The mutant frequencies in different tumors was significantly different, with the higher LRP2 mutation in melanoma (28.18%), uterine carcinosarcoma (17.99%) and lung squamous cell carcinoma (16.32%) (Fig. 1A). In additon, of the 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 shown to be a useful biomarker for ICIs selection of some cancer types; high TMB in tumors favors the infiltration of immune effector cells, and antitumor immune response [18]. In TCGA cohort, there was a significant difference between TMB and various muation 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 as compared to that without LRP2 mutation (Fig.S1A). MSI has emerged as a major predictive marker for 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 that without LRP2 mutation in pan-cancer analysis ( P < 0.001) (Fig.1C). MSIsensor stratified by LRP2 mutation status showed that there was a significant difference between MSIsensor score and missense mutant and multiple mutations. To further vertify 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 MSIsensor and MSI MANTIS score were higher in patients with LRP2 mutation than those without LRP2 mutation in colorectal adenocarcinoma, endometheal 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 in 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 sigaling 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, showed in Fig.1F.

Then, we evaluated the effect of LRP2 on 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 mutantion and patients without LRP2 mutation (Fig.1F). The prognosis and survival 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.S2). A total of 64 patients were idendified 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). The outcomes indicated that LRP2 mutation was the main type in genomic alterations of tumors.

2.2 Relationship of LRP2 mutation with tumor microenviroment 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 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 recepotor (0.025), NK cell medicated cytotoxicity (0.0024) and toll-like recepotor signaling pathways (0.00062) (Fig.2C). In addtion, other highly enriched signaling pathways in LRP2 mutation were showed in Fig.S4A.

Then, we analyzed immune cell infiltration and immune checkpoint genes expression among different tumor types. The results showed that the coexpression 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 coexpression of LRP2 mutation was highly associated with most immune checkpoint genes in BRCA, COAD, UCEC and UCS (Fig.2E). Then, we analyzed the coexpression 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 immunetherapy (Table.S3). The results showed that the application of these ICIs in patients with LRP2 mutation exhibited higher complete response (CR) and partical response (PR) and low percetage 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 in EC. Two cohort (TCGA and CPTAC) were studied for the relationship of LRP2 mutation with EC molecular subtype. In TCGA-UCEC cohort, we identified 93 patients with LRP2 mutation in 573 patients (18%) (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 difference 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 confired the high enrichment level of immune related signaling pathways, such as T cell activation/differentiation, chemokine-medicated signaling pathway, response to chemokine, 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 with the outcomes from pan-cancer analysis, the 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 outcomes 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 the prognosis for these patients was excellent. MMRd tumor (MSI-H) had high mutation frequency, potential imunotherapeutic response and good prognosis. The CNA-low (also defined as p53 wt) subgroup was found to be quite heterogeneous and exhibited different prognosis since it covered multiple pathologic types of tumors. However, in CNA-high subgroup (also defined as p53 alterations) with p53 either overexpression or missense are associated with the worst prognosis of all molecular subtypes. The tumor subgroup includes most serous and mixed types, which occur at a higher stage, usually grade 3 [31]. In both cohorts, we evaluated the association between LRP2 mutation and four molecular subtypes in EC. In TCGA cohor, 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 CPTAC cohort, we identified 13 patients with LRP2 mutation in 95 patients (14%) (Fig.S7A), with missense mutaitons 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 significangly 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 upregulated in mutant LRP2 EC patients (Table.S4). Then, unitrivariate 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 muation 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.5D) and pan-cancer cohort (Fig.5E). 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 predicator of immunotherapy, we explored the relationship of LMS to TMB, MSI and the immune microenvironment. Our study unvealed 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 and positively associated with TMB in several cancer types, including UCEC, THYM, THCA, TGCT, LGG, and COAD (Fig.5C). Likewise, there was a significant association of LMS with MSI in UCEC, TGCT, OV, LUSC, and COAD (Fig.5D). To better understand the association of LMS with the immune-infiltratiing 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, high LMS was signaficantly associated with higher infiltration of CD8 T cell, activated CD4 T cell, M1 macrophage, and plasma, while exhibited low infiltration of M0 macrophage, resting memory CD4 T cell and activated dendritic cells (Fig.SA-B). At the same time, we also analyzed the relationships between immune checkpoint expression and LMS. Our results revealed that high LMS was significantly and positively associated with expression of important immune checkpoints expression, including CTLA4, PDCD1, TIGIT and GZMB in EC (Fig.S9C) and pan-cancer cohorts (Fig.S9D).

Recently, a new global transcriptomic immune classification of solid tumours has identified six immune subtypes (ISs) (C1-C6): wound healing (C1), IFN-g dominant (C2), inflammatory (C3), lymphocyte depleted (C4), immunologically quiet (C5) and TGF-b dominant (C6). Of the C1-C6 subtypes, C4 and C6 subtypes conferred the worst prognosis on their constituent tumors and displayed composite signatures reflecting a macrophage dominated, low lymphocytic infiltrate, with high M2 macrophage content. In contrast, C2 and C3, have the favorable prognosis [33]. In this study, we found that the high LMS group had a high percentage of C2 and C3 subtypes and a low percentage of C1 and C4 subytpes 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 the C4 subtype was lower than those in low LMS group (Fig.5F).

2.6 LRP2 mutation signature predicating the efficacy of immunotherpy

To infer the potential role of LMS in prdicating immunotherapy efficacy, we calculated IPS in pan-cancer patients. The results indicated that high LMS was assocaited with higher IPS compared to 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 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 immuotherapy.

Finally, to validate the predictive power of LMS in immunotherapy, we analyzed the all experimental studies on immunotherapy on the TIDE platform. According to the inclusion criteria, a total of 8 studies with 307 patietns was 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 resposne to immunotherapy and prognosis. In the cohort of Gide et al, a 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 confimred 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 changed the paradigm of cancer treatment; however, many patients do not benefit from them. The variability in ICIs response highlights the need to identify predictive biomarkers [34]. Several tumor-specific genes had been reported as markers for predicting the benefit of ICIs. These markers are still insufficient to determine when and how long to use ICIs [35]. In this study, we identified LRP2 mutation as a novel biomarker for predicating immnuetherapy response. In pan-cancer analysis, our results indicated that LRP2 mutations were associated with high immune cells infiltration, immune checkpoint gene expression and high enrichment of immune-related signaling pathways. Importantly, LRP2 mutation was associated with MHC molecular, receptors, immunostimulators and chemokines in specific tumor types. Then, we found that LRP2 mutaton was closely related to the immune microenvironment and prognosis of ECs and that LRP2-muataed patients correspondsed to the POLE and MSI-H subtypes in the molecular typing of ECs. Finally, we estimated a LRP2 mutation signature (LMS) score based on two improtant genes (CD3D, and ONECUT3), all of which were strongly related to better prognosis. We then found that high LMS was associated with high level of TMB, MSI and immune cell infiltration, therefore, LMS could be used to predicate the efficacy of immunotherapy.

LRP2 is a member of the low-density lipoprotein (LDL) receptor gene family and plays 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 context [37]. Its mutations leads to the loss of protein function, which is the basis of autosomal recessive disorder. LRP2 also mutated in many cancers-16% of colon and rectum adenocarcinoma, 19.7% of lung cancer and 9.3% of bladder cancer[38]. However, the frequancy of LRP2 mutations in gastric cancer and liver cancer is low [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 mutation was associated with high TMB and MSI level in various tumor types, especially for endometrial cancer, colorectal adenocarcinoma and esophagogastric adenocarcinoma. Then, we compared the immune microenvironment characteristics of LRP2-mutated and non-LRP2-mutated patients and the results showed that LRP2-mutated tumors exhibited an immune-inflammatory phenotype, characterized by high levels of immune cells infiltration and high expression of immune checkpint genes, immune receptors, immunostimulators, MHC molecules and chemokines genes. ICIs in inducing death of cancer cells is known to be influenced by the tumor microenvironment; an immunoactive "hot" microenvironment leads to a better response from the patient's antitumor immune system [41]. In this study, we found that LRP2 mutation was associated with higher CR and PR rate of ICIs and better prognosis in patients receiving immunotherapy. These evidences strongly supported that LRP2 mutation was strongly associated with immunotherapy and may be a biomarker for predicting clinical outcomes in patients receiving ICIs treatment.

In a pan-cancer analysis, we found that LRP2 mutations in EC were most strongly associated with immune cells, immune checkpoints and inflammation-related moleculars compared to other tumor types. Meanwhile, LRP2 mutation was associated with better OS, DSS and PFS compared with non-LRP2 mutation. Therefore, we explored the characteristics of LRP2 mutation in endometrial carcinomas. Similar to the pan-analysis, patients with LRP2 mutation in EC also had higher TMB, MSI and immune cell infiltration. Then, we found that LRP2 mutation was 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 chemokine and lymphocyte chemotaxis, suggesting that LRP2 mutation was involved in immune response. It is well known that 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 respone rate to immunotherapy. Therefore, we believed that LRP2 tumation 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 the single gene mutation signaling pathway [43]. To circumvent this, we searched for downstream transcriptional signatures based on RNA expression data that are highly and significantly upregulated in patients with LRP2 mutation relative to those without LRP2 mutation in EC. Lasson cox analysis indentified two importants 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 is involved in coding a protein complex, which 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. The 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. ONECUT3 has been shown to act through p53 to prevent epithelial-to-mesenchymal transition in lung cancer cells, setting a precedent for its role as a tumor-suppressor [46]. In this study, LMS reflect the value of LRP2 mutation in predicating the efficacy immunotherapy and prognosis. In variety cancer type, high LMS was associated with better prognosis, showing better prediction power than simple LRP2 mutation.

Then, we evaluated the effect of LMS on the immune microenvironment and the efficacy of immunotherapy. Notably, LMS were positively correlated with mutation counts, and MSI in the EC and pan-cancer cohort. In addtion, high LMS was associated with high infiltration level of CD8 T cells, plasma cells, activated CD4 T cells, and macrophage M1 as well as high expression level of immune checkpoint genes. Finally, we assessed the association of LMS with TIDE, IPS, immune dysfunciton, and immune exclusion and explored the efficacy of immunotherapy. These data demonstrated the potential value of LMS in predicating the efficacy of immunotherapy. However, available data remained of limited value in view of the few and small clinical studies and leads to a unreliable conclusion. Therefore, we performed a systematic review and metaanalysis to explore the association of LMS with immunotherapy efficacy. The results confirmed that LMS is an invaluable tool for selecting patietns who will benefit from immunotherapy and an improved prognositc marker for those receiving treatment.

In conclusion, the large scale, multi-data approach pioneered by TCGA has provided an unparalleled opportunity to better understand structural mechanisms of LRP2 mutation and its effect on immune microenvironemnt, immunotherapy and prognosis. We believed that this paper may facilitate the development of diagnostic and therapeutic tools based on a more robust knowledge of the LRP2 mutation in cancer.

Acknowledgements

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.

Availability of data and materials

Data are available in a public, open access repository.

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Competing interests

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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