Evaluation of NTRK3 mutation and a mutation-based gene set as biomarkers for immunotherapy outcomes in multiple cancers

Evaluation of NTRK3 mutation and a mutation-based gene set as biomarkers for immunotherapy outcomes in multiple cancers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of NTRK3 mutation and a mutation-based gene set as biomarkers for immunotherapy outcomes in multiple cancers Jing Huang, Jialu Liang, Kexi Wang, Kai Lei, Ruihao Liang, Jinsong Lei, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7437970/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Introduction: Immune checkpoint blockade (ICB) biomarkers like PD-L1 and TMB have limited utility. This study explores NTRK3 mutation as a novel ICB outcome biomarker and establishes a mutation-based gene set to optimize treatment decisions across cancers. Methods We analyzed immune landscapes of NTRK3 mutations using multi-omics data from TCGA pan-cancer cohorts (discovery cohort, n = 642; independent validation cohort, n = 1572) and our own cohort (n = 57). Key ICB outcomes (ORR, DCB, PFS, OS) were compared between NTRK3-mutated (NTRK3-Mut) and wild-type tumors. A mutation-based gene set containing NTRK3 was assessed via survival and clinical benefit analyses across multiple ICB-treated cohorts. Results NTRK3-Mut tumors in the discovery cohort showed significantly higher ORR (50.0% vs. 30.1%, P = 0.008) and DCB (60.9% vs. 42.6%, P = 0.020), improved PFS (HR = 0.65, P = 0.024), and a trend toward better OS (HR = 0.65, P = 0.086). These findings were validated in the independent and our own cohorts. Immune landscape analysis revealed NTRK3-Mut tumors had enhanced immunogenicity and a pro-inflammatory microenvironment versus NTRK3-Wild tumors. Additionally, the mutation-based gene set showed predictive performance comparable to TMB for identifying longer survival and higher DCB across cohorts. Conclusion NTRK3 mutation is a promising ICB biomarker. The gene set offers a practical tool to guide ICB decisions, refining immunotherapy biomarkers for better outcomes. NTRK3 biomarker immune checkpoint blockade (ICB) Tumor microenvironment multiple cancers Figures Figure 1 Figure 2 Figure 3 Figure 4 PLAIN LANGUAGE SUMMARY NTRK3 mutation could be a new and convenient tool to estimate whether patients could benefit from the immune checkpoint blockade (ICB) treatment in multiple solid cancers. Furthermore, a mutation-based gene set, including NTRK3, was established to guide ICB treatment decisions, with the efficacy comparable to TMB. Besides, it is an evolving list that could be improved continuously with the availability of more ICB cohorts. 1. Introduction A series of randomized controlled trials have revealed a promising survival advantage of immune checkpoint blockade (ICB) therapy in multiple tumors( 1 – 3 ). However, great differentiation existed in treatment outcomes, with some patients failing to benefit from ICB therapy( 4 , 5 ). Moreover, biomarkers already approved for clinical practice, such as programmed cell death 1 ligand 1 (PDL1) expression and microsatellite instability (MSI), still suffer from their shortcomings( 6 , 7 ). Thus, there is an urgent to identify complementary and reliable biomarkers for precision treatment. Growing evidence has shown that mutations in certain genes could influence the treatment outcomes of ICB therapy and have been proposed as convenient biomarkers. For example, on the one hand, mutations in the genes associated with interferon-receptor signaling and antigen presentation pathways, such as Janus kinase 1/2 (JAK1/2) and beta-2-microglobulin (B2M), might cause acquired resistance to PD-1 blockade immunotherapy in melanoma( 8 ). In non-small cell lung cancer (NSCLC), patients with epidermal growth factor receptor (EGFR) mutation generally failed to benefit from the ICB treatment, with the mechanism not fully understood( 9 ). On the other hand, tumors with Polymerase epsilon and delta (POLE and POLD1) mutations displayed enhanced antitumor immunity and were sensitive to ICB by potentially facilitating T cell recognition in multiple cancers( 10 , 11 ). Several other mutated genes were also found to be related to better ICB outcomes, though without mechanisms exploration so far( 12 – 14 ). The neurotrophic receptor tyrosine kinase 3 (NTRK3), a membrane-bound receptor, belongs to the NTRK family and is involved in a wide range of physiological activities. The NTRK3 expression was closely associated with tumor mutation burden and immune infiltrations in bladder cancer( 15 ). Besides, patients with NTRK3 mutation tended to have better overall survival (OS) after ICB treatment in lung Adenocarcinoma (LUAD)( 16 ). Nevertheless, whether the NTRK3 could influence the ICB response in multiple cancers remain unexplored. Moreover, the two studies lack validation using in-house data. Furthermore, patients with EGFR and HER2 mutations suffered from minimal benefit from ICB despite high PD-L1 expression in NSCLC( 17 ). JAK1/2 and STK11 mutations exhibited attenuated responsiveness to immunotherapy, even in high TMB context( 18 , 19 ). Both indicated there were factors beyond PD-L1 and TMB that may contribute to the response to ICB. A gene set consisting of those candidate genes with the predicting potential for ICB outcomes could be a reliable biomarker guiding immunotherapy. To date, several studies have defined new tumor mutation scores or constructed mutational signatures and revealed a promising efficiency in predicting the response of ICB of those tools, with the efficacy being even superior to TMB( 20 – 22 ). However, most efforts have so far focused on one specific tumor type, and the candidate genes were generally generated in one discovery cohort sequenced by the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) panel( 23 ). A study developing a mutation-based gene set for predicting ICB response in multiple cancers by collecting candidate genes through comprehensive literature research and further filtering out in whole-exome sequencing (WES) cohort is still lacking. In this work, we first evaluated the predictive value of NTRK3 mutation in multiple cancers based on discovery cohort (n = 642) and validation cohorts (n = 1572), and our internal cohort (n = 57). We further investigated the intrinsic and extrinsic immune context of NTRK3 mutation in TCGA pan-cancer data. Finally, we constructed and validated a mutation-based gene set, including the NTRK3, as a promising biomarker for immunotherapy in multiple cancers. 2. Methods 2.1. Clinical cohort and data sources To assess NTRK3 mutation’s predictive value in ICI therapy, we first consolidated eight cohorts into a discovery cohort, termed WES Cohort. Annotated clinical and mutational data were sourced from the cBioPortal database ( https://www.cbioportal.org ) and published studies( 24 – 31 ). Then, we utilized an integrated ICI cohort composed of multiple cancers from Samstein et al.( 32 ) (n = 1,661) to validate the predictive function of NTRK3 mutation. The validation cohort was sequenced by the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) panel( 23 ) and thus we named it the MSK cohort. The processed mutational and clinical data of the MSK cohort were downloaded from the cBioPortal database. The inclusion and exclusion of the WES and MSK cohorts were shown in Fig. S1 . To exclude that NTRK3-mutant (NTRK3-Mut) patients’ clinical benefits stemmed from general prognostic effects, we included two non-ICI cohorts. Annotated data of the non-ICI-treated cohort (n = 10,336) from Zehir et al. and The Cancer Genome Atlas (TCGA) solid tumors (n = 10,264) were obtained from the cBioPortal database and previous studies( 33 , 34 ). The detailed clinical information of the WES, MSK, Zehir, and TCGA cohorts was summarized in Table S1 -S4 . In addition, we acquired the mRNA expression profiles of 9,996 patients with 31 types of solid tumors from the TCGA websites by the “TCGAbiolinks” R package( 35 ). Those data were used to compare distinct immune landscapes between NTRK3-Mut and NTRK3-wildtype (NTRK3-Wild) tumors. 2.2. Patient enrollment We collected non-small cell lung cancer patients treated with anti-PD‐(L)1 in the Sun Yat-sen University Cancer Center from January 2020 to June 2023. The inclusion criteria included: 1) diagnosed as advanced or recurrent NSCLC patients; 2) ≥ 18 years; 3) can’t operate; 4) received anti‐PD‐(L)1 treatment; 5) sequenced by WES or next-generation. Those who were diagnosed with autoimmune disease, took prior immunotherapy for other reasons, or received targeted therapy were ruled out. Finally, we collected 57 patients. This study was approved by the Ethics and Research Committees of the Sun Yat-sen University Cancer Center and all patients provided written informed consent. 2.3. Bioinformatics analysis of extrinsic immune landscapes in NTRK3-Mut and NTRK3-Wild tumors The flow chart of this study was depicted in Fig. S2 . We defined nonsynonymous mutations as mutations that include translation start site, splice site, nonstop, nonsense, frameshift, and missense mutations, like previous studies( 10 , 21 ). Those patients with NTRK3 nonsynonymous mutations were classified as the NTRK3-Mut group, while the rest were in the NTRK3-Wild group. To investigate the different extrinsic immune landscapes of NTRK3-Mut and NTRK3-Wild tumors, we compared two groups in the following seven aspects: leukocyte fraction, lymphocyte fraction, and Tumor-infiltrating lymphocytes (TILs) regional fraction: we got those data for each TCGA solid sample from the study of Thorsson et al.( 36 ), who derived leukocyte fraction from TCGA DNA methylation data and calculated the lymphocyte fraction of each sample by the CIBERSORT approach( 37 ). The TILs regional fraction was evaluated by Saltz et al.( 38 ), who analyzed over 5000 H&E-stained images of TCGA with convolutional neural networks (CNNs). The immune infiltration scores: we acquired those data for each TCGA solid sample from the study of Danaher et al.( 39 ), who estimated 14 kinds of immune cell scores by 60 specific marker genes’ expression. Those immune cells included total TILs, B cells, DCs, macrophages, exhausted CD8 T cells, CD8 T cells, neutrophils, cytotoxic cells, Tregs, NK CD56dim cells, mast cells, NK cells, and Th1 cells. Immune signatures: we obtained twenty-nine classical immune signatures from He et al.( 40 ), and we further quantified the enrichment scores of those immune signatures for each TCGA solid sample using the “GSVA” R package( 41 ). Microenvironment immune cell abundance: we carried out both CIBERSORT( 37 ) and MCP-counter( 42 ) methods to calculate immune cell infiltrations in the NTRK3-Mut and NTRK3-Wild groups based on the RNA expression data of the TCGA cohort. Both methods were highly reproducible. Cytolytic activity(CYT) score: we calculated the cytolytic activity (CYT) score for the TCGA solid samples by performing the geometric mean of granzyme A (GZMA) and perforin 1 (PRF1) expression, as in a previous study( 43 ). TCR & BCR diversity: we obtained the T/B cell receptors (TCR/BCR) diversity scores of TCGA solid samples from Thorsson et al.( 36 ), who inferred the “TCR Shannon”, “TCR richness”, “BCR Shannon” and “BCR richness” based on the RNA-seq data. Chemokines and interleukin expression: we integrated the RNA expression of chemokines, interleukin, and its receptors of TCGA solid samples, and visualized the differential expression between NTRK3-Mut and NTRK3-Wild groups. 2.4. Bioinformatics analysis of intrinsic immune landscapes in NTRK3-Mut and NTRK3-Wild tumors We next explored the difference of intrinsic immune landscapes in NTRK3-Mut and NTRK3-Wild groups from four aspects below: Tumor Mutation Burden (TMB): the TMB was defined as the total number of nonsynonymous somatic, coding, base substitution, and indel mutations per megabase (Mb) of the genome examined( 44 ). After, we compared the TMB levels between NTRK3-Mut and NTRK3-Wild groups in WES, MSK, and TCGA pan-cancer cohorts. Tumor neoantigen: we got the neoantigen data from the TCGA pan-cancer immune project conducted by Thorsson et al.( 36 ), who quantified the “SNV neoantigen”, “Indel neoantigen”, “Non-silent mutation rate”, and “Silent mutation rate” for each TCGA tumor sample. Immune checkpoint blockade (ICB) targets expression: we extracted the RNA expression data of PD1, PDL1, CTLA4, TIGIT, and LAG3 from TCGA solid tumors, which was downloaded using the “TCGAbiolinks” R package( 35 ). Major histocompatibility complex (MHC) expression: Except for the ICB targets expression, we also collected the RNA expression of MHC I, MHC II, and other immune-related molecules of TCGA solid samples and further compared the expression of those genes between NTRK3-Mut and NTRK3-Wild groups. 2.5. Generation of the mutation-based gene set Given that multiple mutated genes (including NTRK3) reportedly influence ICB efficacy across cancers( 12 , 45 , 46 ), we hypothesized a gene set combining such genes could serve as a superior ICB efficacy biomarker. First, we conducted systematic PubMed searches using keywords: "Mutation(s)/Alteration(s)" & "Immune/Immune checkpoint/Immunotherapy/Anti-PD(L)1/Anti-CTLA4" & "Solid/Multiple/Pan-cancer(s)" and citation searches to identify candidate genes. Second, we validated these genes in MSK and WES cohorts, selecting only those with mutations associated with significantly better overall survival than wild type in both cohorts (P < 0.1) for the final gene set. Third, using the mutation-based gene set, we defined gene mutation burden (GMB) of a solid tumor sample as total nonsynonymous mutations in selected genes. Patients were divided into GMB-H (GMB > 1) and GMB-L (GMB ≤ 1) groups based on median GMB of all TCGA solid tumors. Finally, we compared GMB and TMB predictive efficacy in MSK and WES cohorts, with further validation in Rizvi et al.'s cohort( 47 ), which had mutation data sequenced by WES, TMB, and complete clinical data. 2.6. Statistical analysis Categorical variables like ICB therapy response were compared by the Fisher’s exact test in NTRK3-Mut and NTRK3-Wild groups, while continuous variables were compared by the Mann-Whitney U test in two groups. Overall survival (OS) and progression-free survival (PFS) analyses were carried out by Kaplan-Meier curves. The p-value, hazard ratio (HR), and 95% confidence interval (CI) were determined by the log-rank test. Univariate and multivariate analyses were performed to explore whether NTRK3 mutation was an independent survival factor in patients with ICB treatment. All statistical analyses were performed by SPSS v.26.0 (SPSS, Chicago, Illinois, USA) and R software (version 4.2.0). 3. Results 3.1. NTRK3-Mut predicted favorable immunotherapy response in the WES cohort We integrated eight ICB-treated cohorts, all sequenced by WES, into the discovery cohort (also the WES cohort). The baseline clinical characteristics of 642 patients in the WES cohort were shown in the Table 1 , which included non-small cell lung cancer (NSCLC) (n = 131), melanoma (n = 437), head and neck cancer (n = 12), renal cell carcinoma (n = 35), and urothelial cancer (n = 27) ( Fig. S1 ). The Objective Response Rate (ORR) in the NTRK3-Mut group was 19.9% higher than that in the NTRK3-Wild group (50% vs. 30.1%, Fisher’s test P = 0.008) (Fig. 1 A). Subgroup analysis further demonstrated the NTRK3-Mut patients tended to acquire better response after ICB treatment whatever the age, gender, tumor types, or ICB drugs with odds ratio (OR) value < 1 (Fig. 1 B). In addition, 60.9% patients in the NTRK3-Mut group achieved a durable clinical benefit (DCB), in contrast to only 42.6% in the NTRK3-Wild group (Fig. 1 C). Similarly, NTRK3-Mut patients also showed higher DCB than the other in different subgroups (Fig. 1 D). It is rational to speculate that the patients who were more likely to respond to ICB agents would have longer progression-free time. As expected, the median progression-free survival (PFS) of NTRK3-Mut patients was almost twice longer than that in NTRK3-Wild patients (8.37 vs. 4.07 months, P = 0.024, hazard ratio (HR) = 0.65) (Fig. 1 E). There was also a prominent overall survival (OS) benefit trends in the NTRK3-Mut patients than NTRK3-Wild patients (the median OS was 34.5 vs. 23.0 months, P = 0.086, HR = 0.65) (Fig. 1 F). Considering that the majority cancer type of the WES cohort was melanoma, we performed subgroup analyses of PFS and OS in different cancer types. The numerical PFS and OS benefit remained in melanoma and the other cancers ( Fig. S3A ). 3.2. Validation of the predictive value of NTRK3 status in the MSK cohort In addition, we validated the above results in an independent cohort with a larger sample size. The patients’ characteristics of the MSK cohort were exhibited in Table 1 , and 69 (4.4%) of 1572 patients were NTRK3-Mut. Detailed analysis of ORR, DCB, and OS between NTRK3-MUT and NTRK3-WT was presented in Fig. 1 G–I. The ORR of the NTRK3-Mut group was significantly higher than that of the NTRK3-Wild group (50% vs. 19.6%, Fisher’s test P = 0.022) (Fig. 1 G). similar results were also shown in the DCB analysis (66.7% vs. 27.9%, Fisher’s test P = 0.022) (Fig. 1 H). Besides, the NTRK3-Mut patients had longer OS than the other group (median OS not reached vs 18 months, hazard ratio [HR] = 0.42, P < 0.001) (Fig. 1 I). Subgroup analysis confirmed the OS benefits in the NTRK3-Mut patients in different cancer types, including bladder cancer, colorectal cancer, esophagogastric cancer, head and neck cancer, melanoma, and non-small cell lung cancer ( Fig. S3B) . We noticed that NTRK3-Mut patients of renal cell carcinoma seemed to not have a good prognosis trend, which may attribute to the limited samples. To exclude one possibility that the observed clinical benefits in NTRK3-Mut patients were just because of the general prognostic nature, we first compared the survival difference between NTRK3-Mut and NTRK3-Wild groups in the TCGA solid tumors. As shown in Fig. S4 , neither OS nor progression-free interval exhibited obvious benefits in the NTRK3-Mut patients. Given the relatively more early-stage cancers in the TCGA than the ICB-treated cohorts, we further evaluated the survival effects of NTRK3-Mut in the Zehir cohort (n = 10336) which had comparable clinical characteristics to the ICB-treated cohorts. The NTRK3-Mut patients also had no survival benefits no matter in all solid tumors or in the cancer types of the discovery cohort ( Fig. S4 ). 3.3. NTRK3-Mut is an independent predictor of immune response and prognosis in the WES and MSK cohort We were also interested in whether NTRK3-Mut was an independent predictor of immunotherapy outcomes. So, we next performed univariate and multivariate Logistic and Cox regression analyses in both WES and MSK cohorts. Clinical factors like age, gender, drug types, cancer types, and NTRK3 status were taken into consideration. The results of the univariate logistic analysis revealed the NTRK3-Mut status, along with age, and drug type were associated with better response after ICB treatment (odds ratio (OR) = 0.38, P < 0.05) in the WES cohort (Table 2 ). Afterward, the multivariate analysis including the age, drug type, and NTRK3 status uncovered that the NTRK3-Mut remained an independent predictor for ICB therapy response (OR = 0.34, P < 0.05). The univariate and multivariate logistic regression analyses in the MSK cohort confirmed the independent value of NTRK3 predicting the ICB response (Table 2 ). Similarly, the univariate Cox regression analyses in the WES cohort exhibited that both the cancer types and NTRK3 status could influence the PFS after ICB treatment (P < 0.05) ( Table S5 ). Subsequent multivariate analysis of the cancer types and NTRK3 status uncovered that the NTRK3-Mut remained an independent predictor for PFS in the WES cohort. As expected, after adjusting for drug type and cancer type, the NTRK3-Mut was also an independent predictive factor of OS based on multivariate Cox regression analysis in the MSK cohort ( Table S5 ). However, NTRK3-Mut didn’t significantly associate with OS in the WES cohort after multivariate analysis, though there was a trend of OS benefit with a hazard ratio (HR) = 0.78. Collectively, the NTRK3-Mut was an independent factor associated with better response and survival outcomes after ICB treatment while more validations of clinical cohorts were warranted. 3.4. The Association between NTRK3-Mut and outcomes after ICB treatment in our internal cohort We retrospectively enrolled 57 NSCLC patients with the inclusion criteria listed in the method part. The patients’ characteristics were exhibited in the Table S6 . A trend of better OS was observed in the NTRK3-Mut than in the NTRK3-Wild groups ( Fig. S5A ). The median OS of both groups wasn’t achieved due to the short follow-up time. The P value wasn’t significant because of the limited numbers of NTRK3-Mut patients (n = 3). The ORR also was obviously higher in the NTRK3-Mut group than the other. (66.7% VS. 16.7, Fisher’s test P = 0.09) ( Fig. S5B ). 3.5 Underlying extrinsic immune landscapes of the NTRK3-Mut and NTRK3-Wild patients To explore the mechanisms underlying NTRK3-Mut guided better clinical outcomes after immunotherapy, we first investigated the mutation landscape of the NTRK3 gene. The mutation was distributed evenly, with the most frequent mutation site in the NTRK3 genome being p.R153 and no hotspot mutations ( Fig. S6 ). From the view of pan-cancers, the mutation frequency of NTRK3 in different types of cancer differed greatly, with melanoma ranking first (14.3%), followed by uterine corpus endometrial carcinoma (12.9%), lung adenocarcinoma (9%) and lung squamous cell carcinoma (7.2%) ( Table S7 ). A key prerequisite for anti-tumor immunity was the infiltration of immune cells into the tumor microenvironment( 48 ). Thus, we compared the abundance of immune cell infiltrations between NTRK3-Mut and NTRK3-Wild groups by conducting multi-omics analysis of The Cancer Genome Atlas (TCGA) cohorts. We firstly observed the NTRK3-Mut patients owned a significantly larger leukocyte fraction estimated from DNA methylation data, and higher lymphocytes infiltration estimated from RNA sequencing data (P < 0.001) (Fig. 2 A-B). Second, using the data of Saltz et al.( 38 ), who calculated the TILs for each sample from hematoxylin and eosin-stained (H&E-stained) slides, we noticed consistent highly TILs in the NTRK3-Mut patients (P < 0.001) (Fig. 2 C). Specifically, the CD8 + T cells were significantly increased in the NTRK3-Mut patients than the NTRK3-Wild patients (P = 0.015) (Fig. 2 D). To cross-examine the above results, we further utilized different methods to evaluate and compared the immune cell infiltrations in two NTRK3 subgroups. With the MCP-counter method, we found that the NTRK3-Mut patients were characterized by enriched immune cells, especially CD8 + T cells, cytotoxic lymphocytes, and NK cells (Fig. 2 E). Considering the WES cohort (also the discovery cohort) only consisted of five kinds of tumors, we also investigated the immune cell abundance of those cancer types in details using CIBERSORT algorithm. The proportion of tumor-killing immune cells such as CD8 + T cells, and NK cells was generally higher in NTRK3-Mut groups of all kinds of cancer types (Fig. 2 F). In addition, most tumor-infiltrating leukocytes in the NTRK3-Mut patients showed significantly higher immune infiltration scores, which was measured with gene expression (Fig. 3 A). Collectively, the NTRK3-Mut patients tended to enrich more immune cells compared to the NTRK3-Wild patients. However, the higher immune cell infiltrations weren’t always related to favorable outcomes after ICB treatment, the gathered T cells performing their cytolytic function mattered( 43 , 49 , 50 ). As expected, the NTRK3-Mut patients showed significantly higher cytolytic activity (CYT) scores (P < 0.001) (Fig. 2 G). Moreover, we acquired Twenty-nine classical immune signatures from He et al.( 40 ) ( Table S8 ) and quantified the enrichment score in TCGA pan-cancer samples using the “GSVA” R package ( Table S9 ). The immune signatures were enriched in the NTRK3-Mut patients compared to NTRK3-WT patients (Fig. 3 B). When cancer evolved, the immune cells first recognized the neoantigens through the specific membrane receptors, then released chemokines to attract and activate more effector T cells, thus killing the tumor( 51 , 52 ). So, we next compared the abundance of T & B cell receptors (TCR/BCR) and chemokines. Compatible with previous results, the NTRK3-Mut patients exhibited higher TCR&BCR diversity (Fig. 3 C) and expression of immunostimulatory chemokines, such as CXCR3 and CXCR6( 53 , 54 )(Fig. 3 D). According to the above results, the NTRK3-Mut patients had more active tumor immune microenvironment than the NTRK3-Wild patients. 3.6 Underlying intrinsic immune landscapes of the NTRK3-Mut and NTRK3-Wild patients To unravel the intrinsic mechanism underlying the better response of NTRK3-Mut patients, we focused on the comparison of tumor immunogenicity, activation of immune checkpoints, and bulk RNA expression of antigen-processing machinery in two subgroups. Compared with the NTRK3-Wild patients, the NTRK3-Mut patients showed significantly higher TMB in the WES cohort, MSK cohort, and TCGA pan-cancer cohort (Fig. 4 A-C, P < 0.001 ). Besides, both the mutation and the neoantigen load were also highly generated in the NTRK3-Mut patients than the other group in the TCGA pan-cancer cohort (Fig. 4 D-G, P < 0.001 ). The immune checkpoint blockade (ICB) therapy mainly targeted the PD1/PDL1 and CTLA4 molecules( 5 , 55 ). Interestingly, the NTRK3-Mut patients exhibited upregulated bulk RNA expression of PD1, PDL1, CTLA4, TIGIT and LAG3 (Fig. 4 H-L, P < 0.05 ). Moreover, compared to the NTRK3-Wild patients, the NTRK3-Mut patients tended to express higher Major histocompatibility complex (MHC) I/II molecules and costimulatory molecules (Fig. 4 M), which were very important for immune-tumor cell interaction. From the above results, the NTRK3-Mut patients had enhanced tumor immunogenicity and activated immune checkpoint molecules’ expression, supporting better outcomes after ICB therapy. 3.7 Construction and validation of a mutation-based gene set for predicting ICB treatment efficacy Based on the literature researched with the keywords (see the “method” part) and citation searching, we collected twenty-eight single genes or gene groups, which were demonstrated to be associated with immunotherapy outcomes in multiple cancer types ( Table S10 ). We further filtered out those genes whose mutations weren’t related to better overall survival in the WES and MSK (P > 0.1) ( Table S10, Fig. S7, Fig. S8 ). At last, a mutation-based gene set consisting of twenty-three single genes was generated ( Table S11 ). To properly utilize those gene lists, we defined the gene mutation burden (GMB) of a solid tumor sample as the total nonsynonymous mutation numbers of the selected twenty-four genes, similar to TMB. We then depicted the landscape of the mutation-based gene set in the thirty-one solid cancers in the TCGA. The median GMB of solid tumors ranged from zero to eight, while the skin cutaneous melanoma had the most highly median GMB, and the median GMB of pan-cancer was one ( Fig. S9A) . Thus, we divided the solid patients into GMB-High (GMB > 1) and GMB-Low (GMB ≤ 1) groups based on the median pan-cancer GMB. We assumed the mutation-based gene set might serve as a promising biomarker for predicting ICB treatment outcomes. As supposed, the GMB-High patients reached significantly better OS than the other group in the WES cohort (P = 0.0084, HR = 1.39, 95%CI: 1.09–1.79) ( Fig. S9B ). In the MSK cohort, the GMB-High patients remained OS advantage (P < 0.001, HR = 1.56, 95%CI: 1.34–1.81) ( Fig. S10A ). Considering some genes weren’t included in the MSK-IMPACT panel, such as MUC4 and MUC16, we focused on the WES cohort for detailed exploration. The GMB-High patients still preserved OS benefits in different tumor subtype analyses ( Fig. S10B ). Moreover, the GMB-High patients also possessed longer PFS in the WES cohort ( Fig. S9C ). Compared with the GMB-Low patients, the GMB-High patients showed significantly higher durable clinical benefit (DCB) (48.1% vs. 32.7%, Fisher’s test P < 0.001) ( Fig. S9D ). For further validation, we performed PFS and response comparison in the Rizvi cohort( 47 ), which had thirty-four patients with non-small cell lung cancer (NSCLC) sequenced by the whole-exome sequencing. Consistent with the WES cohort, the GMB-High patients had better PFS (P = 0.026, HR = 2.76, 95%CI: 1.11–6.87) (Fig. S9E ) and higher makeup of DCB (56.5% vs. 12.5%, Fisher’s test P < 0.05) (Fig. S9F ) in the Rizvi cohort. We noticed that patients with some mutated genes suffered worse ICB outcomes, such as EGFR or ALK gene( 56 , 57 ). TMB was usually defined as the total number of nonsynonymous mutations per megabase (Mb) of the genome examined( 44 ), which meant the TMB contained all mutated genes, even the disadvantaged genes, in the whole-exome sequencing context. So, we assumed the GMB composed of the twenty-four genes might outperform the TMB for predicting ICB efficacy. The C-index was utilized to compare the performance of the GMB and TMB in MSK, WES, and Rizvi cohorts, as the C-index was one of the most commonly used measures for survival models( 58 ). Interestingly, in the MSK cohort, the C-index of GMB was larger than the TMB (0.577 vs. 0.544), while in the WES and Rizvi cohorts, the C-index of GMB was comparable to that of TMB (Fig. S9G-H ). 4. Discussion To date, dozens of researches have revealed single gene mutation was associated with survival benefits of ICB in multiple cancers( 10 , 59 , 60 ). However, the function of NTRK3 in ICB treatment remained elusive. Here, we demonstrated that NTRK3-Mut was related to superior efficacy and better survival outcomes in patients after ICB therapy and tested the results in our in-house data. Besides, we established a mutation-based gene set as a promising biomarker for clinical practice in predicting ICB efficacy. The public molecular profiles, even without ICB therapy, may still be valuable resources to derive surrogate biomarkers and uncovered the underlying immune mechanism( 36 , 61 ). Consistent with this idea, we utilized the multidimensional TCGA pan-cancer dataset to explore how the NTRK3-Mut may be involved in the response of ICB. A wonderful study characterized the factors resistance to ICB into two major categories: host (patient)-intrinsic and host (patient)-extrinsic factors( 62 ). Similarly, we investigated the mechanisms underlying the survival benefits of NTRK3-Mut in two aspects: tumor cell-extrinsic and tumor cell-intrinsic factors. On the one hand, the NTRK3-Mut tumors featured a pro-inflammatory immune activity. Specifically, the infiltrates of CD8 + T and NK cells were significantly more abundant in NTRK3-Mut tumors than the NTRK3-Wild tumors. The density of immune cells in the tumor environment was positively related to ICB response in multiple cancers( 3 , 63 ). Moreover, the activity of immune cells was also higher in the NTRK3-Mut tumors as they had higher cytolytic activity (CYT) scores( 43 ). The results above were confirmed by different methods, such as MCP-counter and GSVA. On the other hand, the NTRK3-Mut tumors also showed stronger immunogenicity, with higher TMB and neoantigens. Collectively, those results provided some hints into the biological underpinning of the NTRK3 mutation. Several studies could give us a hint on how to perform wet studies. Ma et al( 11 ) explored functional landscapes of POLE and POLD1 mutations with syngeneic murine models. Cai et al( 64 ) demonstrated that BCAT2 could shape a non-inflamed tumor microenvironment by negatively regulating proinflammatory chemokines in vivo, thus inducing resistance to anti-PD-1/PD-L1 immunotherapy. Interestingly, they also performed a multi-omics analysis to indicate that BCAT2 has an inhibitory effect on cytotoxic lymphocyte recruitment before wet studies. Basic research on the mechanism of NTRK3 was ongoing by our team and we expected to finish soon. Our results also constructed a mutation-based gene set as a reliable biomarker for predicting ICB outcomes. A closer examination of the final gene list would find some genes, such as ARID1A, belonging to DNA repair genes. The mutation of them indicated better immune response in various kinds of tumors( 65 ). Others were also well-known genes, like EPHA7/PTPRD/PTPRD, reported to influence the efficacy of ICB no matter in a single gene or a signature( 22 , 66 ), though with less mechanistic understanding. Several previous studies have established the mutation-based gene set to predict the ICB outcomes, even outperforming the TMB( 21 , 67 ). However, those studies were all based on cohorts sequenced by the MSK-IMPACT panel with limited gene candidates. To overcome this shortcoming, we filled out the gene panel based on WES cohorts. Indeed, our gene set included MUC16 and PAPPA2 genes, which were not included in the MSK-IMPACT panel. Besides, with more cohorts, we could improve the quality of the gene set, further promoting the predicting efficacy. Thus, the gene set could also be served as an evolving list that will be improved continuously with the availability of more ICB cohorts. For clinical implementation, we first provided NTRK3 mutation as a new and convenient tool to estimate whether patients could benefit from the ICB treatment. It was also a good candidate to be explored in wet lab research and may bring a new perspective into the mechanism underlying the response to ICB. Second, we established a mutation-based gene set for better assessment of patients who would respond to ICB than a single gene. Moreover, the efficacy of the gene set was comparable to TMB. PD-L1 expression and the TMB relied on tissue sample acquisition and may vary in different methods and platforms( 68 , 69 ). In contrast, gene mutation was much easier to achieve with tumor tissues or blood. From this perspective, the gene set was a surrogate biomarker for clinical implementation. We also noticed several limitations in this study. First, one critical bias may attribute to the characteristics of retrospective cohorts. Melanoma made up most of our discovery cohort. To eliminate such bias, we performed subtype and multivariable Cox proportional hazards regression analysis. In addition, we validated the results in multiple validation cohorts and in-house cohort. Second, although the gene set could further evolve and be improved, its utility in the clinic still needs to be evaluated in basic research and prospective clinical trials. 5. Conclusion In conclusion, we revealed that NTRK3-Mut was associated with better ICB outcomes in several retrospective cohorts. Moreover, we constructed a mutation-based gene set as an evolving list to guide ICB treatment decisions. Further prospective in vivo and in vitro experiments were warranted. Abbreviations ICB immune checkpoint blockade PDL1 programmed cell death 1 ligand 1 MSI microsatellite instability JAK1/2 Janus kinase 1/2 B2M beta-2-microglobulin EGFR epidermal growth factor receptor POLE polymerase epsilon POLD1 polymerase delta 1 NTRK3 neurotrophic receptor tyrosine kinase 3 TMB tumor mutation burden LUAD lung adenocarcinoma TCGA The Cancer Genome Atlas MSK-IMPACT Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets WES whole-exome sequencing TILs tumor-infiltrating lymphocytes CIBERSORT a computational method for immune cell fraction estimation MCP-counter a computational method for immune cell quantification CYT cytolytic activity TCR T-cell receptor BCR B-cell receptor MHC major histocompatibility complex Declarations Acknowledgements We acknowledge TCGA and cbioportal database for providing their platforms and contributors for uploading their meaningful datasets. Authors’ contributions S.H.C. and L.J.S.: Conceptualization; Funding acquisition; Supervision. J.H., J.L.L., and K.X.W.: Methodology; Writing – original draft. K.L. and R.H.L.: Formal analysis; Visualization. All the authors of the article agreed to be published in the journal. Funding This work was supported by Shenzhen High-level Hospital Construction Fund, Shenzhen Key Medical Discipline Construction Fund under Grant No. SZXK075; Sanming Project of Medicine in Shenzhen under Grant No. SZSM202211011; and Cancer Hospital, Chinese Academy of Medical Sciences, Shenzhen Center/Shenzhen Cancer Hospital Research Project under Grant No. SZ2020ZD010. Availability of data and R code Data used in this study from the TCGA, cbioportal database, and previous literatures can be accessed without restriction. In-house data can be accessible through email to the corresponding author Jinsong Lei. The R code is available in the following website: https://github.com/Jh14-code/NTRK3-mutation. Declarations Ethics approval and consent This study was approved by the Ethics and Research Committees of the Sun Yat-sen University Cancer Center, Guangzhou. Consent for publication No Consent for publication was needed. Competing interests The authors report there are no competing interests to declare. References Daud AI, Wolchok JD, Robert C, et al. Programmed Death-Ligand 1 Expression and Response to the Anti-Programmed Death 1 Antibody Pembrolizumab in Melanoma. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 2016;34(34):4102–9. Gandhi L, Rodríguez-Abreu D, Gadgeel S, et al. Pembrolizumab plus Chemotherapy in Metastatic Non-Small-Cell Lung Cancer. N Engl J Med. 2018;378(22):2078–92. Long J, Lin J, Wang A, et al. PD-1/PD-L blockade in gastrointestinal cancers: lessons learned and the road toward precision immunotherapy. Journal of hematology & oncology. 2017;10(1):146. Reck M, Rodríguez-Abreu D, Robinson AG, et al. Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. N Engl J Med. 2016;375(19):1823–33. Ribas A, Wolchok JD. Cancer immunotherapy using checkpoint blockade. Science (New York, NY). 2018;359(6382):1350–5. Havel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer. 2019;19(3):133–50. Hause RJ, Pritchard CC, Shendure J, et al. Classification and characterization of microsatellite instability across 18 cancer types. Nat Med. 2016;22(11):1342–50. Zaretsky JM, Garcia-Diaz A, Shin DS, et al. Mutations Associated with Acquired Resistance to PD-1 Blockade in Melanoma. N Engl J Med. 2016;375(9):819–29. Qiao M, Jiang T, Liu X, et al. Immune Checkpoint Inhibitors in EGFR-Mutated NSCLC: Dusk or Dawn? J Thorac Oncol. 2021;16(8):1267–88. Wang F, Zhao Q, Wang YN, et al. Evaluation of POLE and POLD1 Mutations as Biomarkers for Immunotherapy Outcomes Across Multiple Cancer Types. JAMA oncology. 2019;5(10):1504–6. Ma X, Riaz N, Samstein RM, et al. Functional landscapes of POLE and POLD1 mutations in checkpoint blockade-dependent antitumor immunity. Nature genetics. 2022;54(7):996–1012. Long J, Wang D, Yang X, et al. Identification of NOTCH4 mutation as a response biomarker for immune checkpoint inhibitor therapy. BMC medicine. 2021;19(1):154. Zhang Z, Wu HX, Lin WH, et al. EPHA7 mutation as a predictive biomarker for immune checkpoint inhibitors in multiple cancers. BMC medicine. 2021;19(1):26. Shang X, Zhang W, Zhang X, et al. PTPRD/PTPRT mutation as a predictive biomarker of immune checkpoint inhibitors across multiple cancer types. Front Immunol. 2022;13:991091. Zhang Z, Yu Y, Zhang P, et al. Identification of NTRK3 as a potential prognostic biomarker associated with tumor mutation burden and immune infiltration in bladder cancer. BMC cancer. 2021;21(1):458. Niu Y, Lin A, Luo P, et al. Prognosis of Lung Adenocarcinoma Patients With NTRK3 Mutations to Immune Checkpoint Inhibitors. Frontiers in pharmacology. 2020;11:1213. Negrao MV, Skoulidis F, Montesion M, et al. Oncogene-specific differences in tumor mutational burden, PD-L1 expression, and outcomes from immunotherapy in non-small cell lung cancer. Journal for immunotherapy of cancer. 2021;9(8). Shin DS, Zaretsky JM, Escuin-Ordinas H, et al. Primary Resistance to PD-1 Blockade Mediated by JAK1/2 Mutations. Cancer discovery. 2017;7(2):188–201. Skoulidis F, Goldberg ME, Greenawalt DM, et al. STK11/LKB1 Mutations and PD-1 Inhibitor Resistance in KRAS-Mutant Lung Adenocarcinoma. Cancer discovery. 2018;8(7):822–35. Pan D, Hu AY, Antonia SJ, et al. A Gene Mutation Signature Predicting Immunotherapy Benefits in Patients With NSCLC. J Thorac Oncol. 2021;16(3):419–27. Long J, Wang D, Wang A, et al. A mutation-based gene set predicts survival benefit after immunotherapy across multiple cancers and reveals the immune response landscape. Genome medicine. 2022;14(1):20. Li Y, Chen Z, Tao W, et al. Tumor mutation score is more powerful than tumor mutation burden in predicting response to immunotherapy in non-small cell lung cancer. Cancer immunology, immunotherapy: CII. 2021;70(8):2367–78. Cheng DT, Mitchell TN, Zehir A, et al. Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): A Hybridization Capture-Based Next-Generation Sequencing Clinical Assay for Solid Tumor Molecular Oncology. The Journal of molecular diagnostics: JMD. 2015;17(3):251–64. Liu D, Schilling B, Liu D, et al. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat Med. 2019;25(12):1916–27. Miao D, Margolis CA, Vokes NI, et al. Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors. Nature genetics. 2018;50(9):1271–81. Miao D, Margolis CA, Gao W, et al. Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma. Science (New York, NY). 2018;359(6377):801–6. Riaz N, Havel JJ, Makarov V, et al. Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab. Cell. 2017;171(4):934 – 49.e16. Hugo W, Zaretsky JM, Sun L, et al. Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma. Cell. 2016;165(1):35–44. Van Allen EM, Miao D, Schilling B, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science (New York, NY). 2015;350(6257):207–11. Snyder A, Makarov V, Merghoub T, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 2014;371(23):2189–99. Hellmann MD, Nathanson T, Rizvi H, et al. Genomic Features of Response to Combination Immunotherapy in Patients with Advanced Non-Small-Cell Lung Cancer. Cancer cell. 2018;33(5):843 – 52.e4. Samstein RM, Lee CH, Shoushtari AN, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nature genetics. 2019;51(2):202–6. Zehir A, Benayed R, Shah RH, et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017;23(6):703–13. Liu J, Lichtenberg T, Hoadley KA, et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. 2018;173(2):400 – 16.e11. Colaprico A, Silva TC, Olsen C, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2016;44(8):e71. Thorsson V, Gibbs DL, Brown SD, et al. The Immune Landscape of Cancer. Immunity. 2018;48(4):812 – 30.e14. Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nature methods. 2015;12(5):453–7. Saltz J, Gupta R, Hou L, et al. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell reports. 2018;23(1):181 – 93.e7. Danaher P, Warren S, Dennis L, et al. Gene expression markers of Tumor Infiltrating Leukocytes. Journal for immunotherapy of cancer. 2017;5:18. He Y, Jiang Z, Chen C, et al. Classification of triple-negative breast cancers based on Immunogenomic profiling. Journal of experimental & clinical cancer research: CR. 2018;37(1):327. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC bioinformatics. 2013;14:7. Becht E, Giraldo NA, Lacroix L, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17(1):218. Rooney MS, Shukla SA, Wu CJ, et al. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell. 2015;160(1–2):48–61. Yarchoan M, Hopkins A, Jaffee EM. Tumor Mutational Burden and Response Rate to PD-1 Inhibition. N Engl J Med. 2017;377(25):2500–1. Dong Y, Zhao L, Duan J, et al. PAPPA2 mutation as a novel indicator stratifying beneficiaries of immune checkpoint inhibitors in skin cutaneous melanoma and non-small cell lung cancer. Cell proliferation. 2022;55(9):e13283. Wu HX, Chen YX, Wang ZX, et al. Alteration in TET1 as potential biomarker for immune checkpoint blockade in multiple cancers. Journal for immunotherapy of cancer. 2019;7(1):264. Rizvi NA, Hellmann MD, Snyder A, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science (New York, NY). 2015;348(6230):124–8. Paijens ST, Vledder A, de Bruyn M, et al. Tumor-infiltrating lymphocytes in the immunotherapy era. Cellular & molecular immunology. 2021;18(4):842–59. Chen YP, Wang YQ, Lv JW, et al. Identification and validation of novel microenvironment-based immune molecular subgroups of head and neck squamous cell carcinoma: implications for immunotherapy. Annals of oncology: official journal of the European Society for Medical Oncology. 2019;30(1):68–75. Ayers M, Lunceford J, Nebozhyn M, et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. The Journal of clinical investigation. 2017;127(8):2930–40. Jia Q, Wu W, Wang Y, et al. Local mutational diversity drives intratumoral immune heterogeneity in non-small cell lung cancer. Nat Commun. 2018;9(1):5361. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74. Gunderson AJ, Yamazaki T, McCarty K, et al. TGFβ suppresses CD8(+) T cell expression of CXCR3 and tumor trafficking. Nat Commun. 2020;11(1):1749. Wang B, Wang Y, Sun X, et al. CXCR6 is required for antitumor efficacy of intratumoral CD8(+) T cell. Journal for immunotherapy of cancer. 2021;9(8). Brahmer J, Reckamp KL, Baas P, et al. Nivolumab versus Docetaxel in Advanced Squamous-Cell Non-Small-Cell Lung Cancer. N Engl J Med. 2015;373(2):123–35. Mazieres J, Drilon A, Lusque A, et al. Immune checkpoint inhibitors for patients with advanced lung cancer and oncogenic driver alterations: results from the IMMUNOTARGET registry. Annals of oncology: official journal of the European Society for Medical Oncology. 2019;30(8):1321–8. Hastings K, Yu HA, Wei W, et al. EGFR mutation subtypes and response to immune checkpoint blockade treatment in non-small-cell lung cancer. Annals of oncology: official journal of the European Society for Medical Oncology. 2019;30(8):1311–20. Pencina MJ, D'Agostino RB, Sr., D'Agostino RB, Jr., et al. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Statistics in medicine. 2008;27(2):157–72; discussion 207 – 12. Chen X, Su C, Ren S, et al. Pan-cancer analysis of KEAP1 mutations as biomarkers for immunotherapy outcomes. Ann Transl Med. 2020;8(4):141. Jiang T, Chen X, Su C, et al. Pan-cancer analysis of ARID1A Alterations as Biomarkers for Immunotherapy Outcomes. Journal of Cancer. 2020;11(4):776–80. Jiang P, Gu S, Pan D, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018;24(10):1550–8. Morad G, Helmink BA, Sharma P, et al. Hallmarks of response, resistance, and toxicity to immune checkpoint blockade. Cell. 2021;184(21):5309–37. Jedema I, Haanen JB. TIL classified to memory state are correlated with response to immune checkpoint blockade. Cell reports Medicine. 2022;3(6):100669. Cai Z, Chen J, Yu Z, et al. BCAT2 Shapes a Noninflamed Tumor Microenvironment and Induces Resistance to Anti-PD-1/PD-L1 Immunotherapy by Negatively Regulating Proinflammatory Chemokines and Anticancer Immunity. Advanced science (Weinheim, Baden-Wurttemberg, Germany). 2023;10(8):e2207155. Hsiehchen D, Hsieh A, Samstein RM, et al. DNA Repair Gene Mutations as Predictors of Immune Checkpoint Inhibitor Response beyond Tumor Mutation Burden. Cell reports Medicine. 2020;1(3). Bai H, Duan J, Li C, et al. EPHA mutation as a predictor of immunotherapeutic efficacy in lung adenocarcinoma. Journal for immunotherapy of cancer. 2020;8(2). Li Y, Chen Z, Wu L, et al. Novel tumor mutation score versus tumor mutation burden in predicting survival after immunotherapy in pan-cancer patients from the MSK-IMPACT cohort. Ann Transl Med. 2020;8(7):446. Addeo A, Banna GL, Weiss GJ. Tumor Mutation Burden-From Hopes to Doubts. JAMA oncology. 2019;5(7):934–5. Tsao MS, Kerr KM, Kockx M, et al. PD-L1 Immunohistochemistry Comparability Study in Real-Life Clinical Samples: Results of Blueprint Phase 2 Project. J Thorac Oncol. 2018;13(9):1302–11. Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table2.docx SupplementFigures.pdf SupplementTable.xlsx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 Apr, 2026 Reviews received at journal 16 Feb, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers agreed at journal 01 Dec, 2025 Reviews received at journal 22 Nov, 2025 Reviewers agreed at journal 21 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers invited by journal 12 Nov, 2025 Editor assigned by journal 05 Sep, 2025 Submission checks completed at journal 02 Sep, 2025 First submitted to journal 22 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7437970","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":549363716,"identity":"4d6c02ac-a4d6-4879-929f-4c9e0632c10c","order_by":0,"name":"Jing Huang","email":"","orcid":"","institution":"National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital \u0026 Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Huang","suffix":""},{"id":549363717,"identity":"6326e494-3d7f-42b3-bb99-fd022530e951","order_by":1,"name":"Jialu Liang","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Jialu","middleName":"","lastName":"Liang","suffix":""},{"id":549363718,"identity":"4e5167ae-0457-4dd0-8476-a97a117db390","order_by":2,"name":"Kexi Wang","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Kexi","middleName":"","lastName":"Wang","suffix":""},{"id":549363721,"identity":"1481adf7-0ba3-4072-b57b-197e65239347","order_by":3,"name":"Kai Lei","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Lei","suffix":""},{"id":549363722,"identity":"aabc810e-8756-4b95-8514-e8e01a9175e3","order_by":4,"name":"Ruihao Liang","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Ruihao","middleName":"","lastName":"Liang","suffix":""},{"id":549363723,"identity":"59e73242-1b2f-4e98-8fef-3b3c5bf154e7","order_by":5,"name":"Jinsong Lei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACPgbGBgOGClK0sIG1nCFNCxAwtpGkhb25ofDnvLrE+TOSH35gqLhn18B+9gB+LTwHG4x5tx1O3HAjzViC4UxxcgNPXgJ+LRKJDcaM2w4kbpDIAfqrLSGZQYLHAL8W+YcNhj/ngBxGtBYJYIjxNjAnNtyAaLEjrIUH6DCeY4eNN5x5ZiyRcCYhgY0nB78Wfvbjzwx/1NTJzm8HhtiHigR7fvYz+LWALAKpcGwAMRMYGBKJiSPmB0DCHsazx61wFIyCUTAKRioAAL0HP5UMcT+MAAAAAElFTkSuQmCC","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Jinsong","middleName":"","lastName":"Lei","suffix":""},{"id":549363726,"identity":"62276691-1718-4814-a3ae-ab1175a17acf","order_by":6,"name":"Songhua Cai","email":"","orcid":"","institution":"National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital \u0026 Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Songhua","middleName":"","lastName":"Cai","suffix":""}],"badges":[],"createdAt":"2025-08-23 02:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7437970/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7437970/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96631272,"identity":"5509ac78-bce5-49bd-929b-27c54cb737b0","added_by":"auto","created_at":"2025-11-24 12:45:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":251566,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscripv3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/4e46633aa1928db1d554fbf3.docx"},{"id":96631273,"identity":"c683947d-5bc4-4934-8fcc-8dc215d93c73","added_by":"auto","created_at":"2025-11-24 12:45:46","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17349,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/89a6c7622ca242dd17bf4730.docx"},{"id":96631276,"identity":"143d74bd-fd6d-43a2-8852-5cba2df60b67","added_by":"auto","created_at":"2025-11-24 12:45:46","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20674,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/97b903d568a30ab4ae9f25d4.docx"},{"id":96709065,"identity":"a0f09eef-8330-45b1-b480-7f77c1dd94c3","added_by":"auto","created_at":"2025-11-25 10:07:26","extension":"json","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8342,"visible":true,"origin":"","legend":"","description":"","filename":"746dd1f7c3f0489fbb771bc2b50ecca4.json","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/301e53e874297ce63a964286.json"},{"id":96631287,"identity":"2e256e88-2bfe-4d16-abc2-8651165b0619","added_by":"auto","created_at":"2025-11-24 12:45:47","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7451482,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/c80dd06f2af5caf10c0959c8.pdf"},{"id":96631282,"identity":"50d29c24-c85b-4cf7-9a8a-b9cab3c2b5cb","added_by":"auto","created_at":"2025-11-24 12:45:46","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5243255,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/5f0a564a6fb33058f53dbd76.xlsx"},{"id":96709289,"identity":"fd01099e-2c54-4bd2-a4d5-a37fdb34a6c3","added_by":"auto","created_at":"2025-11-25 10:08:37","extension":"xml","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":164987,"visible":true,"origin":"","legend":"","description":"","filename":"746dd1f7c3f0489fbb771bc2b50ecca41enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/c4fe4acf36fc991ebc1f4a4c.xml"},{"id":96631274,"identity":"15f34a5c-4686-457c-95ab-218f0cf7f7d5","added_by":"auto","created_at":"2025-11-24 12:45:46","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":570485,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/861b83c9b9e31d63ad607875.pdf"},{"id":96631278,"identity":"5a813c58-4617-4bf3-bf07-510eedf01ed0","added_by":"auto","created_at":"2025-11-24 12:45:46","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":689946,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/6562531c92f4d02228c6458e.pdf"},{"id":96709096,"identity":"1a5ef630-fd00-4dd7-b596-32a93ae5d1b6","added_by":"auto","created_at":"2025-11-25 10:07:38","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":727983,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/a40c6270c6bb15a5fef803f4.pdf"},{"id":96709312,"identity":"eb517744-72da-4908-8c0f-5b721800ece1","added_by":"auto","created_at":"2025-11-25 10:08:42","extension":"pdf","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":813737,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/870269f3afce2b17712c59f3.pdf"},{"id":96631285,"identity":"bd7dee4c-b34c-43f9-b190-6c70c571ff87","added_by":"auto","created_at":"2025-11-24 12:45:46","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163938,"visible":true,"origin":"","legend":"","description":"","filename":"746dd1f7c3f0489fbb771bc2b50ecca41structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/bc83bcc4d00a00146a6736aa.xml"},{"id":96631279,"identity":"19d0f419-0edf-4c73-ad3a-9a51a3e3f340","added_by":"auto","created_at":"2025-11-24 12:45:46","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179489,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/cdb01881f739cabb7bda0823.html"},{"id":96631266,"identity":"be6b8e19-e199-4ca1-beaa-8fa0b02e6120","added_by":"auto","created_at":"2025-11-24 12:45:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":393655,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between NTRK3 mutation and the clinical outcomes of ICB therapy in the discovery and validation cohort.\u003c/p\u003e\n\u003cp\u003e(A) The proportions of patients who achieved an objective response rate (ORR) in NTRK3-Mut and NTRK3-Wild tumors in the discovery cohort.\u003c/p\u003e\n\u003cp\u003e(B) Subgroup ORR analysis in the context of different ages, gender, tumor types, or ICB drugs in the discovery cohort.\u003c/p\u003e\n\u003cp\u003e(C) The proportions of patients who achieved a durable clinical benefit (DCB) in NTRK3-Mut and NTRK3-Wild tumors in the discovery cohort.\u003c/p\u003e\n\u003cp\u003e(D) Subgroup DCB analysis in the context of different ages, gender, tumor types, or ICB drugs in the discovery cohort.\u003c/p\u003e\n\u003cp\u003e(E) Kaplan-Meier survival curves of progression-free survival (PFS) comparing the predictive powers of NTRK3-Mut and NTRK3-Wild tumors in the discovery cohort.\u003c/p\u003e\n\u003cp\u003e(F) Kaplan-Meier survival curves of overall survival (OS) comparing the predictive powers of NTRK3-Mut and NTRK3-Wild tumors in the discovery cohort.\u003c/p\u003e\n\u003cp\u003e(G) The proportions of patients who achieved an objective response rate (ORR) in NTRK3-Mut and NTRK3-Wild tumors in the validation cohort.\u003c/p\u003e\n\u003cp\u003e(H) The proportions of patients who achieved a durable clinical benefit (DCB) in NTRK3-Mut and NTRK3-Wild tumors in the validation cohort.\u003c/p\u003e\n\u003cp\u003e(I) Kaplan-Meier survival curves of overall survival (OS) comparing the predictive powers of NTRK3-Mut and NTRK3-Wild tumors in the validation cohort.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/8f296095657b43073811757c.jpg"},{"id":96631268,"identity":"e4dbfc97-ea97-427d-a275-edc52bac99ea","added_by":"auto","created_at":"2025-11-24 12:45:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":313221,"visible":true,"origin":"","legend":"\u003cp\u003ePotential extrinsic immune landscapes of NTRK3-Wild and NTRK3-Mut tumors, comparing immune infiltrations and cytolytic activity scores.\u003c/p\u003e\n\u003cp\u003e(A) The difference of leukocyte fraction in the two subgroups. The fraction was estimated from TCGA DNA methylation data.\u003c/p\u003e\n\u003cp\u003e(B) The difference of lymphocytes in the two subgroups. The lymphocyte data was estimated from TCGA RNA-sequencing data.\u003c/p\u003e\n\u003cp\u003e(C) The difference of TIL regional fractions in the two subgroups. The fractions were estimated by processing H\u0026amp;E images.\u003c/p\u003e\n\u003cp\u003e(D) The difference of CD8+ T cell infiltrations in the two subgroups.\u003c/p\u003e\n\u003cp\u003e(E) Comparison of the ten cell populations estimated by the MCP-counter method in the two subgroups. The * represents P value \u0026lt; 0.05, ** P \u0026lt; 0.01, *** P \u0026lt; 0.001, ****P \u0026lt; 0.0001.\u003c/p\u003e\n\u003cp\u003e(F) Comparison of the different immune cells estimated by the CIBERSORT method in the two subgroups. We compared the mean immune cell infiltrations between the two groups and visualized the results by heatmap.\u003c/p\u003e\n\u003cp\u003e(G) Comparison of the cytolytic activity scores in the two subgroups.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/a44feb55820a593879de01d6.jpg"},{"id":96631270,"identity":"25f851e9-a6b1-4aeb-8293-067d2f61e3ce","added_by":"auto","created_at":"2025-11-24 12:45:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":464570,"visible":true,"origin":"","legend":"\u003cp\u003ePotential extrinsic immune landscapes of NTRK3-Wild and NTRK3-Mut tumors, comparing immune infiltration scores, immune signatures enrichment, the cell receptors diversity, and concentrations of chemokines.\u003c/p\u003e\n\u003cp\u003e(A) Comparison of the immune infiltration scores in the two subgroups. The * represents P value \u0026lt; 0.05, ** P \u0026lt; 0.01, *** P \u0026lt; 0.001, ****P \u0026lt; 0.0001.\u003c/p\u003e\n\u003cp\u003e(B) Comparison of the immune signatures enrichment in the two subgroups. Immune signatures enriched in NTRK3-Mut tumors are marked in grey; immune signatures enriched in NTRK3-Wild tumors are marked in green.\u003c/p\u003e\n\u003cp\u003e(C) Comparison of the T/B cell receptors (TCR/BCR) diversity in the two subgroups.\u003c/p\u003e\n\u003cp\u003e(D) Comparison of the expression of chemokines and interleukins in the two subgroups.\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/84c71a9ac5328f32884701ec.jpg"},{"id":96631271,"identity":"089bd73b-895a-464e-afdd-6d1b6787adab","added_by":"auto","created_at":"2025-11-24 12:45:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":451162,"visible":true,"origin":"","legend":"\u003cp\u003ePotential intrinsic immune landscapes of NTRK3-Wild and NTRK3-Mut tumors.\u003c/p\u003e\n\u003cp\u003e(A-C) Comparison of the TMB in the two subgroups of discovery, validation, and TCGA pan-cancer cohorts.\u003c/p\u003e\n\u003cp\u003e(D-G) Comparison of the non-silent mutation rate, silent mutation rate, SNV neoantigens, and indel neoantigens in the two subgroups of TCGA pan-cancer cohorts.\u003c/p\u003e\n\u003cp\u003e(H-L) Comparison of the PD1, PD-L1, CTLA4, TIGIT, and LAG3 mRNA expression in the two subgroups of TCGA pan-cancer cohorts.\u003c/p\u003e\n\u003cp\u003e(M) Comparison of the expression of MHC molecules, co-stimulators, and co-inhibitors in the two subgroups of TCGA pan-cancer cohorts.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/8fcbd518e27a597a3eb364bf.jpg"},{"id":96712661,"identity":"c0a5e7f9-0d8d-4eff-93ad-dcc021a9c93e","added_by":"auto","created_at":"2025-11-25 10:16:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2742176,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/9aaa75fb-b63d-4870-8e28-569022d98348.pdf"},{"id":96631267,"identity":"9ecbbcb0-2527-40ea-a3c7-8f3f03c0afdd","added_by":"auto","created_at":"2025-11-24 12:45:46","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17349,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/1cd5d8615455303ffe6cf818.docx"},{"id":96631269,"identity":"bd4fc089-979d-4c1e-bc16-39ee024b87dd","added_by":"auto","created_at":"2025-11-24 12:45:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20674,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/176c1eb66bb6c34b247e1afa.docx"},{"id":96631275,"identity":"258ec845-37f4-47b3-a41d-a7618277f898","added_by":"auto","created_at":"2025-11-24 12:45:46","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7451482,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/08cb839973ae2321a4762482.pdf"},{"id":96708848,"identity":"9a9ddf5d-5f16-4ba3-b13f-6098e6eb4335","added_by":"auto","created_at":"2025-11-25 10:05:37","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":5243255,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7437970/v1/8138a637dc4da33e6b91b5d5.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of NTRK3 mutation and a mutation-based gene set as biomarkers for immunotherapy outcomes in multiple cancers","fulltext":[{"header":"PLAIN LANGUAGE SUMMARY","content":"\u003cp\u003eNTRK3 mutation could be a new and convenient tool to estimate whether patients could benefit from the immune checkpoint blockade (ICB) treatment in multiple solid cancers. Furthermore, a mutation-based gene set, including NTRK3, was established to guide ICB treatment decisions, with the efficacy comparable to TMB. Besides, it is an evolving list that could be improved continuously with the availability of more ICB cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eA series of randomized controlled trials have revealed a promising survival advantage of immune checkpoint blockade (ICB) therapy in multiple tumors(\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, great differentiation existed in treatment outcomes, with some patients failing to benefit from ICB therapy(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Moreover, biomarkers already approved for clinical practice, such as programmed cell death 1 ligand 1 (PDL1) expression and microsatellite instability (MSI), still suffer from their shortcomings(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Thus, there is an urgent to identify complementary and reliable biomarkers for precision treatment.\u003c/p\u003e\u003cp\u003eGrowing evidence has shown that mutations in certain genes could influence the treatment outcomes of ICB therapy and have been proposed as convenient biomarkers. For example, on the one hand, mutations in the genes associated with interferon-receptor signaling and antigen presentation pathways, such as Janus kinase 1/2 (JAK1/2) and beta-2-microglobulin (B2M), might cause acquired resistance to PD-1 blockade immunotherapy in melanoma(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In non-small cell lung cancer (NSCLC), patients with epidermal growth factor receptor (EGFR) mutation generally failed to benefit from the ICB treatment, with the mechanism not fully understood(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). On the other hand, tumors with Polymerase epsilon and delta (POLE and POLD1) mutations displayed enhanced antitumor immunity and were sensitive to ICB by potentially facilitating T cell recognition in multiple cancers(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Several other mutated genes were also found to be related to better ICB outcomes, though without mechanisms exploration so far(\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The neurotrophic receptor tyrosine kinase 3 (NTRK3), a membrane-bound receptor, belongs to the NTRK family and is involved in a wide range of physiological activities. The NTRK3 expression was closely associated with tumor mutation burden and immune infiltrations in bladder cancer(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Besides, patients with NTRK3 mutation tended to have better overall survival (OS) after ICB treatment in lung Adenocarcinoma (LUAD)(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Nevertheless, whether the NTRK3 could influence the ICB response in multiple cancers remain unexplored. Moreover, the two studies lack validation using in-house data.\u003c/p\u003e\u003cp\u003eFurthermore, patients with EGFR and HER2 mutations suffered from minimal benefit from ICB despite high PD-L1 expression in NSCLC(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). JAK1/2 and STK11 mutations exhibited attenuated responsiveness to immunotherapy, even in high TMB context(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Both indicated there were factors beyond PD-L1 and TMB that may contribute to the response to ICB. A gene set consisting of those candidate genes with the predicting potential for ICB outcomes could be a reliable biomarker guiding immunotherapy. To date, several studies have defined new tumor mutation scores or constructed mutational signatures and revealed a promising efficiency in predicting the response of ICB of those tools, with the efficacy being even superior to TMB(\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). However, most efforts have so far focused on one specific tumor type, and the candidate genes were generally generated in one discovery cohort sequenced by the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) panel(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). A study developing a mutation-based gene set for predicting ICB response in multiple cancers by collecting candidate genes through comprehensive literature research and further filtering out in whole-exome sequencing (WES) cohort is still lacking.\u003c/p\u003e\u003cp\u003eIn this work, we first evaluated the predictive value of NTRK3 mutation in multiple cancers based on discovery cohort (n\u0026thinsp;=\u0026thinsp;642) and validation cohorts (n\u0026thinsp;=\u0026thinsp;1572), and our internal cohort (n\u0026thinsp;=\u0026thinsp;57). We further investigated the intrinsic and extrinsic immune context of NTRK3 mutation in TCGA pan-cancer data. Finally, we constructed and validated a mutation-based gene set, including the NTRK3, as a promising biomarker for immunotherapy in multiple cancers.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Clinical cohort and data sources\u003c/h2\u003e\u003cp\u003eTo assess NTRK3 mutation\u0026rsquo;s predictive value in ICI therapy, we first consolidated eight cohorts into a discovery cohort, termed WES Cohort. Annotated clinical and mutational data were sourced from the cBioPortal database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and published studies(\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28 CR29 CR30\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThen, we utilized an integrated ICI cohort composed of multiple cancers from Samstein et al.(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) (n\u0026thinsp;=\u0026thinsp;1,661) to validate the predictive function of NTRK3 mutation. The validation cohort was sequenced by the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) panel(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and thus we named it the MSK cohort. The processed mutational and clinical data of the MSK cohort were downloaded from the cBioPortal database. The inclusion and exclusion of the WES and MSK cohorts were shown in \u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eTo exclude that NTRK3-mutant (NTRK3-Mut) patients\u0026rsquo; clinical benefits stemmed from general prognostic effects, we included two non-ICI cohorts. Annotated data of the non-ICI-treated cohort (n\u0026thinsp;=\u0026thinsp;10,336) from Zehir et al. and The Cancer Genome Atlas (TCGA) solid tumors (n\u0026thinsp;=\u0026thinsp;10,264) were obtained from the cBioPortal database and previous studies(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The detailed clinical information of the WES, MSK, Zehir, and TCGA cohorts was summarized in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S4\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eIn addition, we acquired the mRNA expression profiles of 9,996 patients with 31 types of solid tumors from the TCGA websites by the \u0026ldquo;TCGAbiolinks\u0026rdquo; R package(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Those data were used to compare distinct immune landscapes between NTRK3-Mut and NTRK3-wildtype (NTRK3-Wild) tumors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Patient enrollment\u003c/h2\u003e\u003cp\u003eWe collected non-small cell lung cancer patients treated with anti-PD‐(L)1 in the Sun Yat-sen University Cancer Center from January 2020 to June 2023. The inclusion criteria included: 1) diagnosed as advanced or recurrent NSCLC patients; 2)\u0026thinsp;\u0026ge;\u0026thinsp;18 years; 3) can\u0026rsquo;t operate; 4) received anti‐PD‐(L)1 treatment; 5) sequenced by WES or next-generation. Those who were diagnosed with autoimmune disease, took prior immunotherapy for other reasons, or received targeted therapy were ruled out. Finally, we collected 57 patients. This study was approved by the Ethics and Research Committees of the Sun Yat-sen University Cancer Center and all patients provided written informed consent.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Bioinformatics analysis of extrinsic immune landscapes in NTRK3-Mut and NTRK3-Wild tumors\u003c/h2\u003e\u003cp\u003eThe flow chart of this study was depicted in \u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e. We defined nonsynonymous mutations as mutations that include translation start site, splice site, nonstop, nonsense, frameshift, and missense mutations, like previous studies(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Those patients with NTRK3 nonsynonymous mutations were classified as the NTRK3-Mut group, while the rest were in the NTRK3-Wild group.\u003c/p\u003e\u003cp\u003eTo investigate the different extrinsic immune landscapes of NTRK3-Mut and NTRK3-Wild tumors, we compared two groups in the following seven aspects:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eleukocyte fraction, lymphocyte fraction, and Tumor-infiltrating lymphocytes (TILs) regional fraction: we got those data for each TCGA solid sample from the study of Thorsson et al.(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), who derived leukocyte fraction from TCGA DNA methylation data and calculated the lymphocyte fraction of each sample by the CIBERSORT approach(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The TILs regional fraction was evaluated by Saltz et al.(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), who analyzed over 5000 H\u0026amp;E-stained images of TCGA with convolutional neural networks (CNNs).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe immune infiltration scores: we acquired those data for each TCGA solid sample from the study of Danaher et al.(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), who estimated 14 kinds of immune cell scores by 60 specific marker genes\u0026rsquo; expression. Those immune cells included total TILs, B cells, DCs, macrophages, exhausted CD8 T cells, CD8 T cells, neutrophils, cytotoxic cells, Tregs, NK CD56dim cells, mast cells, NK cells, and Th1 cells.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eImmune signatures: we obtained twenty-nine classical immune signatures from He et al.(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), and we further quantified the enrichment scores of those immune signatures for each TCGA solid sample using the \u0026ldquo;GSVA\u0026rdquo; R package(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMicroenvironment immune cell abundance: we carried out both CIBERSORT(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) and MCP-counter(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) methods to calculate immune cell infiltrations in the NTRK3-Mut and NTRK3-Wild groups based on the RNA expression data of the TCGA cohort. Both methods were highly reproducible.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCytolytic activity(CYT) score: we calculated the cytolytic activity (CYT) score for the TCGA solid samples by performing the geometric mean of granzyme A (GZMA) and perforin 1 (PRF1) expression, as in a previous study(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTCR \u0026amp; BCR diversity: we obtained the T/B cell receptors (TCR/BCR) diversity scores of TCGA solid samples from Thorsson et al.(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), who inferred the \u0026ldquo;TCR Shannon\u0026rdquo;, \u0026ldquo;TCR richness\u0026rdquo;, \u0026ldquo;BCR Shannon\u0026rdquo; and \u0026ldquo;BCR richness\u0026rdquo; based on the RNA-seq data.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eChemokines and interleukin expression: we integrated the RNA expression of chemokines, interleukin, and its receptors of TCGA solid samples, and visualized the differential expression between NTRK3-Mut and NTRK3-Wild groups.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Bioinformatics analysis of intrinsic immune landscapes in NTRK3-Mut and NTRK3-Wild tumors\u003c/h2\u003e\u003cp\u003eWe next explored the difference of intrinsic immune landscapes in NTRK3-Mut and NTRK3-Wild groups from four aspects below:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eTumor Mutation Burden (TMB): the TMB was defined as the total number of nonsynonymous somatic, coding, base substitution, and indel mutations per megabase (Mb) of the genome examined(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). After, we compared the TMB levels between NTRK3-Mut and NTRK3-Wild groups in WES, MSK, and TCGA pan-cancer cohorts.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTumor neoantigen: we got the neoantigen data from the TCGA pan-cancer immune project conducted by Thorsson et al.(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), who quantified the \u0026ldquo;SNV neoantigen\u0026rdquo;, \u0026ldquo;Indel neoantigen\u0026rdquo;, \u0026ldquo;Non-silent mutation rate\u0026rdquo;, and \u0026ldquo;Silent mutation rate\u0026rdquo; for each TCGA tumor sample.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eImmune checkpoint blockade (ICB) targets expression: we extracted the RNA expression data of PD1, PDL1, CTLA4, TIGIT, and LAG3 from TCGA solid tumors, which was downloaded using the \u0026ldquo;TCGAbiolinks\u0026rdquo; R package(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMajor histocompatibility complex (MHC) expression: Except for the ICB targets expression, we also collected the RNA expression of MHC I, MHC II, and other immune-related molecules of TCGA solid samples and further compared the expression of those genes between NTRK3-Mut and NTRK3-Wild groups.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Generation of the mutation-based gene set\u003c/h2\u003e\u003cp\u003eGiven that multiple mutated genes (including NTRK3) reportedly influence ICB efficacy across cancers(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), we hypothesized a gene set combining such genes could serve as a superior ICB efficacy biomarker.\u003c/p\u003e\u003cp\u003eFirst, we conducted systematic PubMed searches using keywords: \"Mutation(s)/Alteration(s)\" \u0026amp; \"Immune/Immune checkpoint/Immunotherapy/Anti-PD(L)1/Anti-CTLA4\" \u0026amp; \"Solid/Multiple/Pan-cancer(s)\" and citation searches to identify candidate genes.\u003c/p\u003e\u003cp\u003eSecond, we validated these genes in MSK and WES cohorts, selecting only those with mutations associated with significantly better overall survival than wild type in both cohorts (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1) for the final gene set.\u003c/p\u003e\u003cp\u003eThird, using the mutation-based gene set, we defined gene mutation burden (GMB) of a solid tumor sample as total nonsynonymous mutations in selected genes. Patients were divided into GMB-H (GMB\u0026thinsp;\u0026gt;\u0026thinsp;1) and GMB-L (GMB\u0026thinsp;\u0026le;\u0026thinsp;1) groups based on median GMB of all TCGA solid tumors.\u003c/p\u003e\u003cp\u003eFinally, we compared GMB and TMB predictive efficacy in MSK and WES cohorts, with further validation in Rizvi et al.'s cohort(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e), which had mutation data sequenced by WES, TMB, and complete clinical data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Statistical analysis\u003c/h2\u003e\u003cp\u003eCategorical variables like ICB therapy response were compared by the Fisher\u0026rsquo;s exact test in NTRK3-Mut and NTRK3-Wild groups, while continuous variables were compared by the Mann-Whitney U test in two groups. Overall survival (OS) and progression-free survival (PFS) analyses were carried out by Kaplan-Meier curves. The p-value, hazard ratio (HR), and 95% confidence interval (CI) were determined by the log-rank test. Univariate and multivariate analyses were performed to explore whether NTRK3 mutation was an independent survival factor in patients with ICB treatment. All statistical analyses were performed by SPSS v.26.0 (SPSS, Chicago, Illinois, USA) and R software (version 4.2.0).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. NTRK3-Mut predicted favorable immunotherapy response in the WES cohort\u003c/h2\u003e\n \u003cp\u003eWe integrated eight ICB-treated cohorts, all sequenced by WES, into the discovery cohort (also the WES cohort). The baseline clinical characteristics of 642 patients in the WES cohort were shown in the Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, which included non-small cell lung cancer (NSCLC) (n\u0026thinsp;=\u0026thinsp;131), melanoma (n\u0026thinsp;=\u0026thinsp;437), head and neck cancer (n\u0026thinsp;=\u0026thinsp;12), renal cell carcinoma (n\u0026thinsp;=\u0026thinsp;35), and urothelial cancer (n\u0026thinsp;=\u0026thinsp;27) (\u003cstrong\u003eFig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). The Objective Response Rate (ORR) in the NTRK3-Mut group was 19.9% higher than that in the NTRK3-Wild group (50% vs. 30.1%, Fisher\u0026rsquo;s test P\u0026thinsp;=\u0026thinsp;0.008) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Subgroup analysis further demonstrated the NTRK3-Mut patients tended to acquire better response after ICB treatment whatever the age, gender, tumor types, or ICB drugs with odds ratio (OR) value\u0026thinsp;\u0026lt;\u0026thinsp;1 (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). In addition, 60.9% patients in the NTRK3-Mut group achieved a durable clinical benefit (DCB), in contrast to only 42.6% in the NTRK3-Wild group (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). Similarly, NTRK3-Mut patients also showed higher DCB than the other in different subgroups (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). It is rational to speculate that the patients who were more likely to respond to ICB agents would have longer progression-free time. As expected, the median progression-free survival (PFS) of NTRK3-Mut patients was almost twice longer than that in NTRK3-Wild patients (8.37 vs. 4.07 months, P\u0026thinsp;=\u0026thinsp;0.024, hazard ratio (HR)\u0026thinsp;=\u0026thinsp;0.65) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE). There was also a prominent overall survival (OS) benefit trends in the NTRK3-Mut patients than NTRK3-Wild patients (the median OS was 34.5 vs. 23.0 months, P\u0026thinsp;=\u0026thinsp;0.086, HR\u0026thinsp;=\u0026thinsp;0.65) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF). Considering that the majority cancer type of the WES cohort was melanoma, we performed subgroup analyses of PFS and OS in different cancer types. The numerical PFS and OS benefit remained in melanoma and the other cancers (\u003cstrong\u003eFig. S3A\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Validation of the predictive value of NTRK3 status in the MSK cohort\u003c/h2\u003e\n \u003cp\u003eIn addition, we validated the above results in an independent cohort with a larger sample size. The patients\u0026rsquo; characteristics of the MSK cohort were exhibited in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, and 69 (4.4%) of 1572 patients were NTRK3-Mut. Detailed analysis of ORR, DCB, and OS between NTRK3-MUT and NTRK3-WT was presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eG\u0026ndash;I. The ORR of the NTRK3-Mut group was significantly higher than that of the NTRK3-Wild group (50% vs. 19.6%, Fisher\u0026rsquo;s test P\u0026thinsp;=\u0026thinsp;0.022) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eG). similar results were also shown in the DCB analysis (66.7% vs. 27.9%, Fisher\u0026rsquo;s test P\u0026thinsp;=\u0026thinsp;0.022) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eH). Besides, the NTRK3-Mut patients had longer OS than the other group (median OS not reached vs 18 months, hazard ratio [HR]\u0026thinsp;=\u0026thinsp;0.42, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eI). Subgroup analysis confirmed the OS benefits in the NTRK3-Mut patients in different cancer types, including bladder cancer, colorectal cancer, esophagogastric cancer, head and neck cancer, melanoma, and non-small cell lung cancer (\u003cstrong\u003eFig. S3B)\u003c/strong\u003e. We noticed that NTRK3-Mut patients of renal cell carcinoma seemed to not have a good prognosis trend, which may attribute to the limited samples.\u003c/p\u003e\n \u003cp\u003eTo exclude one possibility that the observed clinical benefits in NTRK3-Mut patients were just because of the general prognostic nature, we first compared the survival difference between NTRK3-Mut and NTRK3-Wild groups in the TCGA solid tumors. As shown in \u003cstrong\u003eFig. S4\u003c/strong\u003e, neither OS nor progression-free interval exhibited obvious benefits in the NTRK3-Mut patients. Given the relatively more early-stage cancers in the TCGA than the ICB-treated cohorts, we further evaluated the survival effects of NTRK3-Mut in the Zehir cohort (n\u0026thinsp;=\u0026thinsp;10336) which had comparable clinical characteristics to the ICB-treated cohorts. The NTRK3-Mut patients also had no survival benefits no matter in all solid tumors or in the cancer types of the discovery cohort (\u003cstrong\u003eFig. S4\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.3. NTRK3-Mut is an independent predictor of immune response and prognosis in the WES and MSK cohort\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe were also interested in whether NTRK3-Mut was an independent predictor of immunotherapy outcomes. So, we next performed univariate and multivariate Logistic and Cox regression analyses in both WES and MSK cohorts. Clinical factors like age, gender, drug types, cancer types, and NTRK3 status were taken into consideration. The results of the univariate logistic analysis revealed the NTRK3-Mut status, along with age, and drug type were associated with better response after ICB treatment (odds ratio (OR)\u0026thinsp;=\u0026thinsp;0.38, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the WES cohort (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Afterward, the multivariate analysis including the age, drug type, and NTRK3 status uncovered that the NTRK3-Mut remained an independent predictor for ICB therapy response (OR\u0026thinsp;=\u0026thinsp;0.34, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The univariate and multivariate logistic regression analyses in the MSK cohort confirmed the independent value of NTRK3 predicting the ICB response (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, the univariate Cox regression analyses in the WES cohort exhibited that both the cancer types and NTRK3 status could influence the PFS after ICB treatment (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (\u003cstrong\u003eTable S5\u003c/strong\u003e). Subsequent multivariate analysis of the cancer types and NTRK3 status uncovered that the NTRK3-Mut remained an independent predictor for PFS in the WES cohort. As expected, after adjusting for drug type and cancer type, the NTRK3-Mut was also an independent predictive factor of OS based on multivariate Cox regression analysis in the MSK cohort (\u003cstrong\u003eTable S5\u003c/strong\u003e). However, NTRK3-Mut didn\u0026rsquo;t significantly associate with OS in the WES cohort after multivariate analysis, though there was a trend of OS benefit with a hazard ratio (HR)\u0026thinsp;=\u0026thinsp;0.78. Collectively, the NTRK3-Mut was an independent factor associated with better response and survival outcomes after ICB treatment while more validations of clinical cohorts were warranted.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. The Association between NTRK3-Mut and outcomes after ICB treatment in our internal cohort\u003c/h2\u003e\n \u003cp\u003eWe retrospectively enrolled 57 NSCLC patients with the inclusion criteria listed in the method part. The patients\u0026rsquo; characteristics were exhibited in the \u003cstrong\u003eTable S6\u003c/strong\u003e. A trend of better OS was observed in the NTRK3-Mut than in the NTRK3-Wild groups (\u003cstrong\u003eFig. S5A\u003c/strong\u003e). The median OS of both groups wasn\u0026rsquo;t achieved due to the short follow-up time. The P value wasn\u0026rsquo;t significant because of the limited numbers of NTRK3-Mut patients (n\u0026thinsp;=\u0026thinsp;3). The ORR also was obviously higher in the NTRK3-Mut group than the other. (66.7% VS. 16.7, Fisher\u0026rsquo;s test P\u0026thinsp;=\u0026thinsp;0.09) (\u003cstrong\u003eFig. S5B\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Underlying extrinsic immune landscapes of the NTRK3-Mut and NTRK3-Wild patients\u003c/h2\u003e\n \u003cp\u003eTo explore the mechanisms underlying NTRK3-Mut guided better clinical outcomes after immunotherapy, we first investigated the mutation landscape of the NTRK3 gene. The mutation was distributed evenly, with the most frequent mutation site in the NTRK3 genome being p.R153 and no hotspot mutations (\u003cstrong\u003eFig. S6\u003c/strong\u003e). From the view of pan-cancers, the mutation frequency of NTRK3 in different types of cancer differed greatly, with melanoma ranking first (14.3%), followed by uterine corpus endometrial carcinoma (12.9%), lung adenocarcinoma (9%) and lung squamous cell carcinoma (7.2%) (\u003cstrong\u003eTable S7\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eA key prerequisite for anti-tumor immunity was the infiltration of immune cells into the tumor microenvironment(\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e). Thus, we compared the abundance of immune cell infiltrations between NTRK3-Mut and NTRK3-Wild groups by conducting multi-omics analysis of The Cancer Genome Atlas (TCGA) cohorts. We firstly observed the NTRK3-Mut patients owned a significantly larger leukocyte fraction estimated from DNA methylation data, and higher lymphocytes infiltration estimated from RNA sequencing data (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). Second, using the data of Saltz et al.(\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e), who calculated the TILs for each sample from hematoxylin and eosin-stained (H\u0026amp;E-stained) slides, we noticed consistent highly TILs in the NTRK3-Mut patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Specifically, the CD8\u0026thinsp;+\u0026thinsp;T cells were significantly increased in the NTRK3-Mut patients than the NTRK3-Wild patients (P\u0026thinsp;=\u0026thinsp;0.015) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eTo cross-examine the above results, we further utilized different methods to evaluate and compared the immune cell infiltrations in two NTRK3 subgroups. With the MCP-counter method, we found that the NTRK3-Mut patients were characterized by enriched immune cells, especially CD8\u0026thinsp;+\u0026thinsp;T cells, cytotoxic lymphocytes, and NK cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE). Considering the WES cohort (also the discovery cohort) only consisted of five kinds of tumors, we also investigated the immune cell abundance of those cancer types in details using CIBERSORT algorithm. The proportion of tumor-killing immune cells such as CD8\u0026thinsp;+\u0026thinsp;T cells, and NK cells was generally higher in NTRK3-Mut groups of all kinds of cancer types (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF). In addition, most tumor-infiltrating leukocytes in the NTRK3-Mut patients showed significantly higher immune infiltration scores, which was measured with gene expression (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). Collectively, the NTRK3-Mut patients tended to enrich more immune cells compared to the NTRK3-Wild patients.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eHowever, the higher immune cell infiltrations weren\u0026rsquo;t always related to favorable outcomes after ICB treatment, the gathered T cells performing their cytolytic function mattered(\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e). As expected, the NTRK3-Mut patients showed significantly higher cytolytic activity (CYT) scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG). Moreover, we acquired Twenty-nine classical immune signatures from He et al.(\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e) (\u003cstrong\u003eTable S8\u003c/strong\u003e) and quantified the enrichment score in TCGA pan-cancer samples using the \u0026ldquo;GSVA\u0026rdquo; R package (\u003cstrong\u003eTable S9\u003c/strong\u003e). The immune signatures were enriched in the NTRK3-Mut patients compared to NTRK3-WT patients (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eWhen cancer evolved, the immune cells first recognized the neoantigens through the specific membrane receptors, then released chemokines to attract and activate more effector T cells, thus killing the tumor(\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e). So, we next compared the abundance of T \u0026amp; B cell receptors (TCR/BCR) and chemokines. Compatible with previous results, the NTRK3-Mut patients exhibited higher TCR\u0026amp;BCR diversity (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC) and expression of immunostimulatory chemokines, such as CXCR3 and CXCR6(\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e)(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). According to the above results, the NTRK3-Mut patients had more active tumor immune microenvironment than the NTRK3-Wild patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Underlying intrinsic immune landscapes of the NTRK3-Mut and NTRK3-Wild patients\u003c/h2\u003e\n \u003cp\u003eTo unravel the intrinsic mechanism underlying the better response of NTRK3-Mut patients, we focused on the comparison of tumor immunogenicity, activation of immune checkpoints, and bulk RNA expression of antigen-processing machinery in two subgroups. Compared with the NTRK3-Wild patients, the NTRK3-Mut patients showed significantly higher TMB in the WES cohort, MSK cohort, and TCGA pan-cancer cohort (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA-C, P\u0026thinsp;\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e). Besides, both the mutation and the neoantigen load were also highly generated in the NTRK3-Mut patients than the other group in the TCGA pan-cancer cohort (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD-G, P\u0026thinsp;\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe immune checkpoint blockade (ICB) therapy mainly targeted the PD1/PDL1 and CTLA4 molecules(\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e). Interestingly, the NTRK3-Mut patients exhibited upregulated bulk RNA expression of PD1, PDL1, CTLA4, TIGIT and LAG3 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eH-L, P\u0026thinsp;\u003cstrong\u003e\u0026lt;\u0026thinsp;0.05\u003c/strong\u003e). Moreover, compared to the NTRK3-Wild patients, the NTRK3-Mut patients tended to express higher Major histocompatibility complex (MHC) I/II molecules and costimulatory molecules (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eM), which were very important for immune-tumor cell interaction. From the above results, the NTRK3-Mut patients had enhanced tumor immunogenicity and activated immune checkpoint molecules\u0026rsquo; expression, supporting better outcomes after ICB therapy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Construction and validation of a mutation-based gene set for predicting ICB treatment efficacy\u003c/h2\u003e\n \u003cp\u003eBased on the literature researched with the keywords (see the \u0026ldquo;method\u0026rdquo; part) and citation searching, we collected twenty-eight single genes or gene groups, which were demonstrated to be associated with immunotherapy outcomes in multiple cancer types (\u003cstrong\u003eTable S10\u003c/strong\u003e). We further filtered out those genes whose mutations weren\u0026rsquo;t related to better overall survival in the WES and MSK (P\u0026thinsp;\u0026gt;\u0026thinsp;0.1) (\u003cstrong\u003eTable S10, Fig. S7, Fig. S8\u003c/strong\u003e). At last, a mutation-based gene set consisting of twenty-three single genes was generated (\u003cstrong\u003eTable S11\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eTo properly utilize those gene lists, we defined the gene mutation burden (GMB) of a solid tumor sample as the total nonsynonymous mutation numbers of the selected twenty-four genes, similar to TMB. We then depicted the landscape of the mutation-based gene set in the thirty-one solid cancers in the TCGA. The median GMB of solid tumors ranged from zero to eight, while the skin cutaneous melanoma had the most highly median GMB, and the median GMB of pan-cancer was one (\u003cstrong\u003eFig. S9A)\u003c/strong\u003e. Thus, we divided the solid patients into GMB-High (GMB\u0026thinsp;\u0026gt;\u0026thinsp;1) and GMB-Low (GMB\u0026thinsp;\u0026le;\u0026thinsp;1) groups based on the median pan-cancer GMB.\u003c/p\u003e\n \u003cp\u003eWe assumed the mutation-based gene set might serve as a promising biomarker for predicting ICB treatment outcomes. As supposed, the GMB-High patients reached significantly better OS than the other group in the WES cohort (P\u0026thinsp;=\u0026thinsp;0.0084, HR\u0026thinsp;=\u0026thinsp;1.39, 95%CI: 1.09\u0026ndash;1.79) (\u003cstrong\u003eFig. S9B\u003c/strong\u003e). In the MSK cohort, the GMB-High patients remained OS advantage (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;1.56, 95%CI: 1.34\u0026ndash;1.81) (\u003cstrong\u003eFig. S10A\u003c/strong\u003e). Considering some genes weren\u0026rsquo;t included in the MSK-IMPACT panel, such as MUC4 and MUC16, we focused on the WES cohort for detailed exploration. The GMB-High patients still preserved OS benefits in different tumor subtype analyses (\u003cstrong\u003eFig. S10B\u003c/strong\u003e). Moreover, the GMB-High patients also possessed longer PFS in the WES cohort (\u003cstrong\u003eFig. S9C\u003c/strong\u003e). Compared with the GMB-Low patients, the GMB-High patients showed significantly higher durable clinical benefit (DCB) (48.1% vs. 32.7%, Fisher\u0026rsquo;s test P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cstrong\u003eFig. S9D\u003c/strong\u003e). For further validation, we performed PFS and response comparison in the Rizvi cohort(\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e), which had thirty-four patients with non-small cell lung cancer (NSCLC) sequenced by the whole-exome sequencing. Consistent with the WES cohort, the GMB-High patients had better PFS (P\u0026thinsp;=\u0026thinsp;0.026, HR\u0026thinsp;=\u0026thinsp;2.76, 95%CI: 1.11\u0026ndash;6.87) \u003cstrong\u003e(Fig. S9E\u003c/strong\u003e) and higher makeup of DCB (56.5% vs. 12.5%, Fisher\u0026rsquo;s test P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cstrong\u003e(Fig. S9F\u003c/strong\u003e) in the Rizvi cohort.\u003c/p\u003e\n \u003cp\u003eWe noticed that patients with some mutated genes suffered worse ICB outcomes, such as EGFR or ALK gene(\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e). TMB was usually defined as the total number of nonsynonymous mutations per megabase (Mb) of the genome examined(\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e), which meant the TMB contained all mutated genes, even the disadvantaged genes, in the whole-exome sequencing context. So, we assumed the GMB composed of the twenty-four genes might outperform the TMB for predicting ICB efficacy. The C-index was utilized to compare the performance of the GMB and TMB in MSK, WES, and Rizvi cohorts, as the C-index was one of the most commonly used measures for survival models(\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e). Interestingly, in the MSK cohort, the C-index of GMB was larger than the TMB (0.577 vs. 0.544), while in the WES and Rizvi cohorts, the C-index of GMB was comparable to that of TMB \u003cstrong\u003e(Fig. S9G-H\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo date, dozens of researches have revealed single gene mutation was associated with survival benefits of ICB in multiple cancers(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). However, the function of NTRK3 in ICB treatment remained elusive. Here, we demonstrated that NTRK3-Mut was related to superior efficacy and better survival outcomes in patients after ICB therapy and tested the results in our in-house data. Besides, we established a mutation-based gene set as a promising biomarker for clinical practice in predicting ICB efficacy.\u003c/p\u003e\u003cp\u003eThe public molecular profiles, even without ICB therapy, may still be valuable resources to derive surrogate biomarkers and uncovered the underlying immune mechanism(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Consistent with this idea, we utilized the multidimensional TCGA pan-cancer dataset to explore how the NTRK3-Mut may be involved in the response of ICB. A wonderful study characterized the factors resistance to ICB into two major categories: host (patient)-intrinsic and host (patient)-extrinsic factors(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Similarly, we investigated the mechanisms underlying the survival benefits of NTRK3-Mut in two aspects: tumor cell-extrinsic and tumor cell-intrinsic factors. On the one hand, the NTRK3-Mut tumors featured a pro-inflammatory immune activity. Specifically, the infiltrates of CD8\u0026thinsp;+\u0026thinsp;T and NK cells were significantly more abundant in NTRK3-Mut tumors than the NTRK3-Wild tumors. The density of immune cells in the tumor environment was positively related to ICB response in multiple cancers(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). Moreover, the activity of immune cells was also higher in the NTRK3-Mut tumors as they had higher cytolytic activity (CYT) scores(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The results above were confirmed by different methods, such as MCP-counter and GSVA. On the other hand, the NTRK3-Mut tumors also showed stronger immunogenicity, with higher TMB and neoantigens. Collectively, those results provided some hints into the biological underpinning of the NTRK3 mutation.\u003c/p\u003e\u003cp\u003eSeveral studies could give us a hint on how to perform wet studies. Ma et al(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) explored functional landscapes of POLE and POLD1 mutations with syngeneic murine models. Cai et al(\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e) demonstrated that BCAT2 could shape a non-inflamed tumor microenvironment by negatively regulating proinflammatory chemokines in vivo, thus inducing resistance to anti-PD-1/PD-L1 immunotherapy. Interestingly, they also performed a multi-omics analysis to indicate that BCAT2 has an inhibitory effect on cytotoxic lymphocyte recruitment before wet studies. Basic research on the mechanism of NTRK3 was ongoing by our team and we expected to finish soon.\u003c/p\u003e\u003cp\u003eOur results also constructed a mutation-based gene set as a reliable biomarker for predicting ICB outcomes. A closer examination of the final gene list would find some genes, such as ARID1A, belonging to DNA repair genes. The mutation of them indicated better immune response in various kinds of tumors(\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Others were also well-known genes, like EPHA7/PTPRD/PTPRD, reported to influence the efficacy of ICB no matter in a single gene or a signature(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e), though with less mechanistic understanding. Several previous studies have established the mutation-based gene set to predict the ICB outcomes, even outperforming the TMB(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). However, those studies were all based on cohorts sequenced by the MSK-IMPACT panel with limited gene candidates. To overcome this shortcoming, we filled out the gene panel based on WES cohorts. Indeed, our gene set included MUC16 and PAPPA2 genes, which were not included in the MSK-IMPACT panel. Besides, with more cohorts, we could improve the quality of the gene set, further promoting the predicting efficacy. Thus, the gene set could also be served as an evolving list that will be improved continuously with the availability of more ICB cohorts.\u003c/p\u003e\u003cp\u003e For clinical implementation, we first provided NTRK3 mutation as a new and convenient tool to estimate whether patients could benefit from the ICB treatment. It was also a good candidate to be explored in wet lab research and may bring a new perspective into the mechanism underlying the response to ICB. Second, we established a mutation-based gene set for better assessment of patients who would respond to ICB than a single gene. Moreover, the efficacy of the gene set was comparable to TMB. PD-L1 expression and the TMB relied on tissue sample acquisition and may vary in different methods and platforms(\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). In contrast, gene mutation was much easier to achieve with tumor tissues or blood. From this perspective, the gene set was a surrogate biomarker for clinical implementation.\u003c/p\u003e\u003cp\u003eWe also noticed several limitations in this study. First, one critical bias may attribute to the characteristics of retrospective cohorts. Melanoma made up most of our discovery cohort. To eliminate such bias, we performed subtype and multivariable Cox proportional hazards regression analysis. In addition, we validated the results in multiple validation cohorts and in-house cohort. Second, although the gene set could further evolve and be improved, its utility in the clinic still needs to be evaluated in basic research and prospective clinical trials.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, we revealed that NTRK3-Mut was associated with better ICB outcomes in several retrospective cohorts. Moreover, we constructed a mutation-based gene set as an evolving list to guide ICB treatment decisions. Further prospective in vivo and in vitro experiments were warranted.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eICB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eimmune checkpoint blockade\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePDL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eprogrammed cell death 1 ligand 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emicrosatellite instability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJAK1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJanus kinase 1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ebeta-2-microglobulin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eepidermal growth factor receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePOLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003epolymerase epsilon\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePOLD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003epolymerase delta 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNTRK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eneurotrophic receptor tyrosine kinase 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003etumor mutation burden\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLUAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elung adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTCGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMSK-IMPACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMemorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ewhole-exome sequencing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTILs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003etumor-infiltrating lymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCIBERSORT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ea computational method for immune cell fraction estimation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMCP-counter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ea computational method for immune cell quantification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCYT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ecytolytic activity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT-cell receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB-cell receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emajor histocompatibility complex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge TCGA and cbioportal database for providing their platforms and contributors for uploading their meaningful datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.H.C. and L.J.S.: Conceptualization; Funding acquisition; Supervision.\u003c/p\u003e\n\u003cp\u003eJ.H., J.L.L., and K.X.W.: Methodology; Writing – original draft.\u003c/p\u003e\n\u003cp\u003eK.L. and R.H.L.: Formal analysis; Visualization.\u003c/p\u003e\n\u003cp\u003eAll the authors of the article agreed to be published in the journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Shenzhen High-level Hospital Construction Fund, Shenzhen Key Medical Discipline Construction Fund under Grant No. SZXK075; Sanming Project of Medicine in Shenzhen under Grant No. SZSM202211011; and Cancer Hospital, Chinese Academy of Medical Sciences, Shenzhen Center/Shenzhen Cancer Hospital Research Project under Grant No. SZ2020ZD010.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and R code\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used in this study from the TCGA, cbioportal database, and previous literatures can be accessed without restriction. In-house data can be accessible through email to the corresponding author Jinsong Lei. The R code is available in the following website: https://github.com/Jh14-code/NTRK3-mutation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics and Research Committees of the Sun Yat-sen University Cancer Center, Guangzhou.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo Consent for publication\u0026nbsp;was needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report there are no competing interests to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDaud AI, Wolchok JD, Robert C, et al. Programmed Death-Ligand 1 Expression and Response to the Anti-Programmed Death 1 Antibody Pembrolizumab in Melanoma. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 2016;34(34):4102\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGandhi L, Rodr\u0026iacute;guez-Abreu D, Gadgeel S, et al. Pembrolizumab plus Chemotherapy in Metastatic Non-Small-Cell Lung Cancer. N Engl J Med. 2018;378(22):2078\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLong J, Lin J, Wang A, et al. PD-1/PD-L blockade in gastrointestinal cancers: lessons learned and the road toward precision immunotherapy. Journal of hematology \u0026amp; oncology. 2017;10(1):146.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReck M, Rodr\u0026iacute;guez-Abreu D, Robinson AG, et al. Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. N Engl J Med. 2016;375(19):1823\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRibas A, Wolchok JD. Cancer immunotherapy using checkpoint blockade. Science (New York, NY). 2018;359(6382):1350\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHavel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer. 2019;19(3):133\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHause RJ, Pritchard CC, Shendure J, et al. Classification and characterization of microsatellite instability across 18 cancer types. Nat Med. 2016;22(11):1342\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZaretsky JM, Garcia-Diaz A, Shin DS, et al. Mutations Associated with Acquired Resistance to PD-1 Blockade in Melanoma. N Engl J Med. 2016;375(9):819\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQiao M, Jiang T, Liu X, et al. Immune Checkpoint Inhibitors in EGFR-Mutated NSCLC: Dusk or Dawn? J Thorac Oncol. 2021;16(8):1267\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang F, Zhao Q, Wang YN, et al. Evaluation of POLE and POLD1 Mutations as Biomarkers for Immunotherapy Outcomes Across Multiple Cancer Types. JAMA oncology. 2019;5(10):1504\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa X, Riaz N, Samstein RM, et al. Functional landscapes of POLE and POLD1 mutations in checkpoint blockade-dependent antitumor immunity. Nature genetics. 2022;54(7):996\u0026ndash;1012.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLong J, Wang D, Yang X, et al. Identification of NOTCH4 mutation as a response biomarker for immune checkpoint inhibitor therapy. BMC medicine. 2021;19(1):154.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Z, Wu HX, Lin WH, et al. EPHA7 mutation as a predictive biomarker for immune checkpoint inhibitors in multiple cancers. BMC medicine. 2021;19(1):26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShang X, Zhang W, Zhang X, et al. PTPRD/PTPRT mutation as a predictive biomarker of immune checkpoint inhibitors across multiple cancer types. Front Immunol. 2022;13:991091.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Z, Yu Y, Zhang P, et al. Identification of NTRK3 as a potential prognostic biomarker associated with tumor mutation burden and immune infiltration in bladder cancer. BMC cancer. 2021;21(1):458.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNiu Y, Lin A, Luo P, et al. Prognosis of Lung Adenocarcinoma Patients With NTRK3 Mutations to Immune Checkpoint Inhibitors. Frontiers in pharmacology. 2020;11:1213.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNegrao MV, Skoulidis F, Montesion M, et al. Oncogene-specific differences in tumor mutational burden, PD-L1 expression, and outcomes from immunotherapy in non-small cell lung cancer. Journal for immunotherapy of cancer. 2021;9(8).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShin DS, Zaretsky JM, Escuin-Ordinas H, et al. Primary Resistance to PD-1 Blockade Mediated by JAK1/2 Mutations. Cancer discovery. 2017;7(2):188\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSkoulidis F, Goldberg ME, Greenawalt DM, et al. STK11/LKB1 Mutations and PD-1 Inhibitor Resistance in KRAS-Mutant Lung Adenocarcinoma. Cancer discovery. 2018;8(7):822\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePan D, Hu AY, Antonia SJ, et al. A Gene Mutation Signature Predicting Immunotherapy Benefits in Patients With NSCLC. J Thorac Oncol. 2021;16(3):419\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLong J, Wang D, Wang A, et al. A mutation-based gene set predicts survival benefit after immunotherapy across multiple cancers and reveals the immune response landscape. Genome medicine. 2022;14(1):20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Chen Z, Tao W, et al. Tumor mutation score is more powerful than tumor mutation burden in predicting response to immunotherapy in non-small cell lung cancer. Cancer immunology, immunotherapy: CII. 2021;70(8):2367\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng DT, Mitchell TN, Zehir A, et al. Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): A Hybridization Capture-Based Next-Generation Sequencing Clinical Assay for Solid Tumor Molecular Oncology. The Journal of molecular diagnostics: JMD. 2015;17(3):251\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu D, Schilling B, Liu D, et al. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat Med. 2019;25(12):1916\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiao D, Margolis CA, Vokes NI, et al. Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors. Nature genetics. 2018;50(9):1271\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiao D, Margolis CA, Gao W, et al. Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma. Science (New York, NY). 2018;359(6377):801\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRiaz N, Havel JJ, Makarov V, et al. Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab. Cell. 2017;171(4):934\u0026thinsp;\u0026ndash;\u0026thinsp;49.e16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHugo W, Zaretsky JM, Sun L, et al. Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma. Cell. 2016;165(1):35\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Allen EM, Miao D, Schilling B, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science (New York, NY). 2015;350(6257):207\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSnyder A, Makarov V, Merghoub T, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 2014;371(23):2189\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHellmann MD, Nathanson T, Rizvi H, et al. Genomic Features of Response to Combination Immunotherapy in Patients with Advanced Non-Small-Cell Lung Cancer. Cancer cell. 2018;33(5):843\u0026thinsp;\u0026ndash;\u0026thinsp;52.e4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSamstein RM, Lee CH, Shoushtari AN, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nature genetics. 2019;51(2):202\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZehir A, Benayed R, Shah RH, et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017;23(6):703\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Lichtenberg T, Hoadley KA, et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. 2018;173(2):400\u0026thinsp;\u0026ndash;\u0026thinsp;16.e11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eColaprico A, Silva TC, Olsen C, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2016;44(8):e71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThorsson V, Gibbs DL, Brown SD, et al. The Immune Landscape of Cancer. Immunity. 2018;48(4):812\u0026thinsp;\u0026ndash;\u0026thinsp;30.e14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNewman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nature methods. 2015;12(5):453\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaltz J, Gupta R, Hou L, et al. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell reports. 2018;23(1):181\u0026thinsp;\u0026ndash;\u0026thinsp;93.e7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDanaher P, Warren S, Dennis L, et al. Gene expression markers of Tumor Infiltrating Leukocytes. Journal for immunotherapy of cancer. 2017;5:18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe Y, Jiang Z, Chen C, et al. Classification of triple-negative breast cancers based on Immunogenomic profiling. Journal of experimental \u0026amp; clinical cancer research: CR. 2018;37(1):327.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eH\u0026auml;nzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC bioinformatics. 2013;14:7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBecht E, Giraldo NA, Lacroix L, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17(1):218.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRooney MS, Shukla SA, Wu CJ, et al. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell. 2015;160(1\u0026ndash;2):48\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYarchoan M, Hopkins A, Jaffee EM. Tumor Mutational Burden and Response Rate to PD-1 Inhibition. N Engl J Med. 2017;377(25):2500\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong Y, Zhao L, Duan J, et al. PAPPA2 mutation as a novel indicator stratifying beneficiaries of immune checkpoint inhibitors in skin cutaneous melanoma and non-small cell lung cancer. Cell proliferation. 2022;55(9):e13283.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu HX, Chen YX, Wang ZX, et al. Alteration in TET1 as potential biomarker for immune checkpoint blockade in multiple cancers. Journal for immunotherapy of cancer. 2019;7(1):264.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRizvi NA, Hellmann MD, Snyder A, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science (New York, NY). 2015;348(6230):124\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaijens ST, Vledder A, de Bruyn M, et al. Tumor-infiltrating lymphocytes in the immunotherapy era. Cellular \u0026amp; molecular immunology. 2021;18(4):842\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen YP, Wang YQ, Lv JW, et al. Identification and validation of novel microenvironment-based immune molecular subgroups of head and neck squamous cell carcinoma: implications for immunotherapy. Annals of oncology: official journal of the European Society for Medical Oncology. 2019;30(1):68\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAyers M, Lunceford J, Nebozhyn M, et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. The Journal of clinical investigation. 2017;127(8):2930\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJia Q, Wu W, Wang Y, et al. Local mutational diversity drives intratumoral immune heterogeneity in non-small cell lung cancer. Nat Commun. 2018;9(1):5361.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGunderson AJ, Yamazaki T, McCarty K, et al. TGFβ suppresses CD8(+) T cell expression of CXCR3 and tumor trafficking. Nat Commun. 2020;11(1):1749.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang B, Wang Y, Sun X, et al. CXCR6 is required for antitumor efficacy of intratumoral CD8(+) T cell. Journal for immunotherapy of cancer. 2021;9(8).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrahmer J, Reckamp KL, Baas P, et al. Nivolumab versus Docetaxel in Advanced Squamous-Cell Non-Small-Cell Lung Cancer. N Engl J Med. 2015;373(2):123\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMazieres J, Drilon A, Lusque A, et al. Immune checkpoint inhibitors for patients with advanced lung cancer and oncogenic driver alterations: results from the IMMUNOTARGET registry. Annals of oncology: official journal of the European Society for Medical Oncology. 2019;30(8):1321\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHastings K, Yu HA, Wei W, et al. EGFR mutation subtypes and response to immune checkpoint blockade treatment in non-small-cell lung cancer. Annals of oncology: official journal of the European Society for Medical Oncology. 2019;30(8):1311\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePencina MJ, D'Agostino RB, Sr., D'Agostino RB, Jr., et al. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Statistics in medicine. 2008;27(2):157\u0026ndash;72; discussion 207\u0026thinsp;\u0026ndash;\u0026thinsp;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen X, Su C, Ren S, et al. Pan-cancer analysis of KEAP1 mutations as biomarkers for immunotherapy outcomes. Ann Transl Med. 2020;8(4):141.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang T, Chen X, Su C, et al. Pan-cancer analysis of ARID1A Alterations as Biomarkers for Immunotherapy Outcomes. Journal of Cancer. 2020;11(4):776\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang P, Gu S, Pan D, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018;24(10):1550\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorad G, Helmink BA, Sharma P, et al. Hallmarks of response, resistance, and toxicity to immune checkpoint blockade. Cell. 2021;184(21):5309\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJedema I, Haanen JB. TIL classified to memory state are correlated with response to immune checkpoint blockade. Cell reports Medicine. 2022;3(6):100669.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCai Z, Chen J, Yu Z, et al. BCAT2 Shapes a Noninflamed Tumor Microenvironment and Induces Resistance to Anti-PD-1/PD-L1 Immunotherapy by Negatively Regulating Proinflammatory Chemokines and Anticancer Immunity. Advanced science (Weinheim, Baden-Wurttemberg, Germany). 2023;10(8):e2207155.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHsiehchen D, Hsieh A, Samstein RM, et al. DNA Repair Gene Mutations as Predictors of Immune Checkpoint Inhibitor Response beyond Tumor Mutation Burden. Cell reports Medicine. 2020;1(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBai H, Duan J, Li C, et al. EPHA mutation as a predictor of immunotherapeutic efficacy in lung adenocarcinoma. Journal for immunotherapy of cancer. 2020;8(2).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Chen Z, Wu L, et al. Novel tumor mutation score versus tumor mutation burden in predicting survival after immunotherapy in pan-cancer patients from the MSK-IMPACT cohort. Ann Transl Med. 2020;8(7):446.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAddeo A, Banna GL, Weiss GJ. Tumor Mutation Burden-From Hopes to Doubts. JAMA oncology. 2019;5(7):934\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsao MS, Kerr KM, Kockx M, et al. PD-L1 Immunohistochemistry Comparability Study in Real-Life Clinical Samples: Results of Blueprint Phase 2 Project. J Thorac Oncol. 2018;13(9):1302\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"NTRK3, biomarker, immune checkpoint blockade (ICB), Tumor microenvironment, multiple cancers","lastPublishedDoi":"10.21203/rs.3.rs-7437970/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7437970/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e\u003cp\u003eImmune checkpoint blockade (ICB) biomarkers like PD-L1 and TMB have limited utility. This study explores NTRK3 mutation as a novel ICB outcome biomarker and establishes a mutation-based gene set to optimize treatment decisions across cancers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analyzed immune landscapes of NTRK3 mutations using multi-omics data from TCGA pan-cancer cohorts (discovery cohort, n\u0026thinsp;=\u0026thinsp;642; independent validation cohort, n\u0026thinsp;=\u0026thinsp;1572) and our own cohort (n\u0026thinsp;=\u0026thinsp;57). Key ICB outcomes (ORR, DCB, PFS, OS) were compared between NTRK3-mutated (NTRK3-Mut) and wild-type tumors. A mutation-based gene set containing NTRK3 was assessed via survival and clinical benefit analyses across multiple ICB-treated cohorts.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eNTRK3-Mut tumors in the discovery cohort showed significantly higher ORR (50.0% vs. 30.1%, P\u0026thinsp;=\u0026thinsp;0.008) and DCB (60.9% vs. 42.6%, P\u0026thinsp;=\u0026thinsp;0.020), improved PFS (HR\u0026thinsp;=\u0026thinsp;0.65, P\u0026thinsp;=\u0026thinsp;0.024), and a trend toward better OS (HR\u0026thinsp;=\u0026thinsp;0.65, P\u0026thinsp;=\u0026thinsp;0.086). These findings were validated in the independent and our own cohorts. Immune landscape analysis revealed NTRK3-Mut tumors had enhanced immunogenicity and a pro-inflammatory microenvironment versus NTRK3-Wild tumors. Additionally, the mutation-based gene set showed predictive performance comparable to TMB for identifying longer survival and higher DCB across cohorts.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eNTRK3 mutation is a promising ICB biomarker. The gene set offers a practical tool to guide ICB decisions, refining immunotherapy biomarkers for better outcomes.\u003c/p\u003e","manuscriptTitle":"Evaluation of NTRK3 mutation and a mutation-based gene set as biomarkers for immunotherapy outcomes in multiple cancers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-24 12:45:41","doi":"10.21203/rs.3.rs-7437970/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-06T12:03:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T04:11:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2026-01-28T07:33:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47730206365338638485413134715290402602","date":"2025-12-01T16:40:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-22T18:01:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268496367636910582369498076958037037414","date":"2025-11-21T05:26:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313602061074755549945787554883074498439","date":"2025-11-12T13:59:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-12T09:55:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-05T11:25:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-02T13:56:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-08-23T02:04:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f1ba110-5934-4753-8ca7-291434c5189f","owner":[],"postedDate":"November 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T12:11:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-24 12:45:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7437970","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7437970","identity":"rs-7437970","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}