Landscape and functional impact of missense mutations of immune response genes in human cancer

Landscape and functional impact of missense mutations of immune response genes in human cancer | 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 Brief Communication Landscape and functional impact of missense mutations of immune response genes in human cancer Montse Sanchez-Cespedes, Cristina Diaz, Juan Morillas, Pablo Navajas-Chocarro, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7187015/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Apr, 2026 Read the published version in Cancer Gene Therapy → Version 1 posted 21 You are reading this latest preprint version Abstract Tumor-intrinsic defects in immune recognition and interferon-gamma (IFNγ) signaling pathways facilitate immune evasion and limit the efficacy of immune checkpoint blockade (ICB). Here, we delineate the mutational landscape and functional consequences of amino acid substitutions in key immune-related genes, B2M , CALR , IFNGR1 , IFNGR2 , JAK1 , and JAK2 , across more than 12 000 primary tumors and cancer cell lines. Genomic alterations affecting the coding regions of at least one of these genes were identified in approximately 11% of cancers, with missense variants accounting for 55% of these events. B2M exhibited the highest mutation frequency per base pair, the mutations predominantly involving truncating variants. A curated set of 215 missense mutations in B2M , IFNGR1 , IFNGR2 , and JAK2 was interrogated using SIFT, PolyPhen-2, and AlphaMissense, yielding predicted pathogenicity rates of 52%, 35%, and 27%, respectively. Functional assays revealed JAK2 and IFNGR1 variants that impaired IFNγ-mediated transcriptional activation and growth suppression, and B2M variants that disrupted HLA class I complex formation. Notably, AlphaMissense predictions showed the highest concordance with experimental data. These findings provide a detailed mutational map of antigen presentation and IFNγ-response components in cancer, offering a resource of specific mutations in immune pathways that compromise tumor immunogenicity and may influence the response to ICB. Health sciences/Biomarkers Biological sciences/Cancer Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Immune evasion is an important hallmark of tumor development that enables cancer cells to avoid or bypass recognition and elimination by the host immune system 12 . The specificity of T-cell activation against tumor cells relies on the recognition of tumor cognate neoantigens, which are processed and properly presented by the HLA-I complex at the surface membrane of the cancer cells 3 . The HLA-I complex consists of a heavy chain-I (HLA-I) molecule (either HLA-A, B, or C) and an invariant light chain (B2M). Genetic inactivation of the HLA-I genes or of B2M has been reported in human cancer 2 4 . B2M inactivation has been shown to impair anchoring of the HLA-I complex to the cell surface 4 , and B2M loss-of-function mutations or HLA-I low-level expression are involved in the resistance to immune checkpoint blockade (ICB) treatment 4 5 6 7 8 9 . The maturation and correct localization of the HLA-I complex requires the participation of other proteins, including CANX, CALR, PDIA3, TAP1, TAP2, and TAPBP 3 , some of which are known to be genetically inactivated in cancer, although at a very low frequency 2 . Furthermore, alterations affecting the response to interferon gamma (IFNγ), which can directly inhibit tumor cell growth and promote apoptosis, also contribute to immune evasion 10 11 . IFNγ is a cytokine produced predominantly by the T-lymphocytes and natural killer cells in response to a variety of inflammatory or immune stimuli 11 . IFNγ binds to its cognate receptor, composed of the IFNGR1 and IFNGR2 subunits, thereby promoting the recruitment and activation of Janus kinases (JAK1 and JAK2), which, in turn, phosphorylate and activate the signal transducer and activator of transcription 1 (STAT1). STAT1 then homodimerizes and translocates to the nucleus, where it binds to specific promoter elements and modulates the transcription of genes related to the immune response, including those involved in immunorecognition and antigen presentation 11 . Previous studies have shown the presence of genetic defects in IFNGR1/2 or JAK1/2 that render tumor cells refractory to IFNγ 12 13 14 . Gene expression of IFNγ-responsive genes were found to be necessary, but not always sufficient, to produce a clinical benefit to treatment with ICB 15 16 . In addition, in melanoma, gene-inactivating mutations in these molecules have been associated with resistance to ICB 17 . More recently, we reported a deficient response to IFNγ in some lung cancer cells together with constitutive low levels of IFNγ-stimulated genes related to the oncogenic activation of MYC 14 . Given the potential impact of tumor-intrinsic defects in HLA-I assembly or IFNγ signaling on the clinical efficacy of current and emerging immunotherapies, in this paper we have analyzed the mutational landscape of genes involved in HLA-I complex formation and IFNγ response across more than 12 000 tumors, and evaluated the functional consequences of selected amino acid substitutions. METHODS Genes, variant selection and computational predictions Mutation frequency and type for B2M , CALR , IFNGR1 , IFNGR2 , JAK1 , and JAK2 genes were retrieved from the cBioPortal database ( https://www.cbioportal.org/ ) using 12 706 samples, including both primary tumors (TCGA Pan-Cancer Atlas) and cancer cell lines (CCLE 2019 datasets). To identify the potential germline polymorphisms among the selected changes, we used the ClinVar database ( https://www.ncbi.nlm.nih.gov/clinvar/ ), as well as UNIPROT ( https://www.uniprot.org/ ) and COSMIC ( https://cancer.sanger.ac.uk/cosmic/login ). The pathogenicity of the selected variants was determined using three in silico tools. Polymorphism Phenotyping (PolyPhen-2) (available at http://genetics.bwh.harvard.edu/pph2/ ) predicts the effect of substitutions on protein structure and function using physical and comparative methods. Scores range from 0 to 1, which are interpreted as follows: 0.0–0.15, benign; 0.15–0.85, possibly damaging; >0.85, probably damaging 18 . Sorting Intolerant from Tolerant (SIFT) (available at http://blocks.fhcrc.org/sift/SIFT.html ) evaluates the potential impact of amino acid substitutions based on sequence conservation and physicochemical properties. Scores range from 0 to 1, with variants predicted to be deleterious if the score is ≤ 0.05, and to be tolerated if it is > 0.05. DeepMind AlphaMissense (AlphaMissense) estimates the likelihood of pathogenicity of missense variants, providing a score and the classification. AlphaMissense outputs included the score and pathogenicity classification (benign, ambiguous, or pathogenic). Scores were interpreted as follows: 0–0.33, benign; 0.34–0.564, ambiguous; 0.565–1, pathogenic 19 . Cancer cell lines Cancer cell lines were obtained from recognized biorepositories: ATCC (Rockville, MD), ECACC (Salisbury, UK), RIKEN Cell Bank (Tokyo, Japan), and DSMZ (Braunschweig, Germany). One cancer cell line (PDC11) was derived from a previous study 20 . Cells were cultured under supplier-recommended conditions with medium supplemented with 10% heat-inactivated fetal bovine serum and 1% penicillin–streptomycin. Cultures were maintained at 37°C in a humidified atmosphere with 5% CO₂. Adherent cells were cultured using trypsin-EDTA. Cell line identity was verified by genotyping hallmark mutations and compared with public databases. Mycoplasma contamination was routinely tested for and, when found, excluded. Construction of expression vectors and lentiviral infections Full-length cDNAs of IFNGR1 (NM_000416.3), IFNGR2 (NM_005534.4), JAK2 (NM_004972.4), and B2M (NM_004048.4) were generated by reverse transcription of RNA from EBC-1 cells, using SuperScript™ II reverse transcriptase (Invitrogen) and random primers (Promega). cDNAs were PCR-amplified with Phusion High-Fidelity DNA Polymerase (Thermo Scientific) and cloned into pLVX-Puro (Clontech) at appropriate restriction sites. Inserts were validated by Sanger sequencing (Supplementary Table S1 ). Lentiviral particles were produced by co-transfecting HEK-293T cells with the pLVX vectors and packaging plasmids psPAX and pMD2.G (Sigma-Aldrich). Target cells were infected with lentiviral supernatants containing either wild-type and mutant constructs, or empty vectors. Stable cell lines were selected with puromycin (1 µg/mL, ANT-PR-1, Invivogen) for 72 hours. Site-directed mutagenesis Point mutations were introduced into wild-type expression plasmids using the Q5 Site-Directed Mutagenesis Kit (New England Biolabs) following the manufacturer’s protocols. Mutation-specific primers were designed using the NEBaseChanger® tool ( https://nebasechanger.neb.com/ ). All mutated constructs were validated by Sanger sequencing (primer sequences illustrated in Supplementary Table S1 ). Antibodies, western blots and immunofluorescence Primary antibodies used for western blots included anti-TUBULIN (T6199, mouse, 1:10 000, Sigma-Aldrich), anti-β-ACTIN (13854, mouse, 1:10 000, Sigma-Aldrich), anti-IFNGR1 (10808-1-AP, rabbit, 1:750, Proteintech), anti-IFNGR2 (10266-1-AP, rabbit, 1:500, Proteintech), anti-JAK2 (3230, rabbit, 1:1 000, Cell Signaling), anti-STAT1 (9175, rabbit, 1:1 000, Cell Signaling), anti-pSTAT1 (9167, rabbit, 1:1 000, Cell Signaling), anti-IRF1 (8478, rabbit, 1:1 000, Cell Signaling), Anti-B2M (BBM.1, Santa Cruz Biotechnology, Inc., and D8P1H, 1:1 000, Cell Signaling Technology). The anti-HLA-I (EMR8-5, mouse, 1:500, Abcam) was used for immunofluorescence. For western blotting, whole-cell lysates were prepared in lysis buffer (2% SDS, 50 mM Tris-HCl pH 6.8, 10% glycerol, and protease/phosphatase inhibitors; Roche). Protein concentrations were quantified using the Bio-Rad DC Protein Assay. Equal amounts of protein (15–40 µg) were resolved by SDS-PAGE, transferred to nitrocellulose membranes, and blocked in 5% non-fat dry milk. Membranes were incubated overnight at 4°C with primary antibodies for 1.5 hours at room temperature with IRDye 680CW or 800CW secondary antibodies (LI-COR, 1:10 000). Fluorescent signals were acquired using the Odyssey CLx system and Image Studio Lite software. For IFNGR1 and IFNGR2, chemiluminescence detection was used (1:1 000 Immobilon, Millipore). For immunofluorescence, cells were fixed with 4% formaldehyde (PanReac AppliChem) for 20 minutes at room temperature and blocked with 20% goat serum in PBS for 1 hour. Samples were incubated overnight at 4°C with a primary anti-HLA-I antibody diluted in blocking buffer, in a humidified chamber to prevent evaporation. After washing with PBS containing 0.1% Tween-20, cells were incubated with Alexa Fluor 568 goat anti-mouse secondary antibody (1:500 dilution; Invitrogen) for 1.5 hours at room temperature in the dark. Nuclei were counterstained with DAPI (0.1 µg/mL) and mounted using Immu-Mount (Epredia). Quantitative RT-PCRs To assess mRNA expression levels, total RNA was extracted and reverse-transcribed using SuperScript™ II reverse transcriptase (Invitrogen) and random primers (Promega). Quantitative PCR was performed using SYBR Green PCR Master Mix (Applied Biosystems) on a QuantStudio 7 Real-Time PCR System (Thermo Fisher), and data were analyzed with QuantStudio Design & Analysis v1.5.1 software. Primer sequences are provided in Supplementary Table S1 . IFNγ toxicity and cell growth assays To evaluate IFNγ-induced toxicity, lung cancer cell lines stably expressing the different variants were seeded at 10 000 cells per well in 48-well plates and treated with increasing concentrations of recombinant human IFNγ (PeproTech) for 10 days. Following treatment, cells were washed with PBS and fixed with cold methanol for 30 minutes. Fixed cells were stained with 0.5% Crystal Violet solution for 30 minutes, then rinsed thoroughly with PBS and water. After drying, the dye was solubilized with 10% acetic acid under shaking for 15 minutes, and absorbance was measured at 560 nm using a Synergy H1 Microplate Reader (BioTek). Statistical and bioinformatic analysis . IC 50 calculations were performed using Prism software (GraphPad). RNA-sequencing and single nucleotide polymorphism (SNP) data were downloaded from the indicated databases of lung primary tumors (lung adenocarcinomas and lung squamous cell carcinomas projects) as indicated, using TCGAbiolinks (version 2.36.0) and processed as Summarized Experiment objects (version 1.38.1). Gene expression counts were filtered (CPM > 1) and normalized using the variance stabilizing transformation (VST) in DESeq2 (version 1.49.2). SNPs were integrated using GenomicRanges (version 1.60.0). The gene signature was standardized (z-score per gene) and visualized as a heatmap using pheatmap (version 1.0.13), alongside clinical and genomic annotations. All analyses were performed in R (version 4.5) with Bioconductor (version 3.21) to ensure reproducibility. RESULTS Mutational profile of components of the immune response in human cancer Whole exome sequencing data from primary tumors and cancer cell lines (TCGA Pan-Cancer Atlas and CCLE 2019 datasets) were obtained from the cBioportal database, drawn from 33 studies and including 12 706 tumors from 12 692 cancer patients. We tested for the presence and type of alterations in the coding regions and intron-exon boundaries of some genes directly involved in the response to IFNγ (i.e., IFNGR1, IFNGR2, JAK1 , and JAK2 ) or in immune recognition/antigen presentation (i.e., B2M and CALR ) that were already known to be mutated in human cancer 2 3 4 6 13 . After discarding changes that were likely to constitute rare polymorphisms, 1 257 (~ 10%) mutations were considered tumor-specific alterations (designated as Group I) (Fig. 1A). About half of them were missense, resulting in amino acid substitutions of unknown effect on the encoded protein (Table 1 ). B2M was the gene with the highest frequency of mutations per base pair in coding DNA (0.6), followed by JAK1 and IFNGR2 (0.1 each), IFNGR1 , JAK2 , and CALR (0.09 each) ( Table 1 ). Except for B2M , the frequency of missense changes in these genes was > 50% ( Table 1 ; Fig. 1A). When analyzed by tumor type, colorectal and endometrial tumors had the highest mutation rates of all the genes (Fig. 1B). These are the common types of cancer associated with deficient DNA mismatch repair (MMR) activity, due to germline or somatic alterations in MMR genes 21 22 . Other tumor types, such as melanoma and lung cancer, exhibited high mutation rates, whereas breast cancer, gliomas/glioblastomas, and pancreatic cancer showed a lower frequency of mutations across all genes. Computational determination of the effects of amino acids substitutions in immune response-related genes While most truncating mutations inactivate the function of the encoded protein, the impact of amino acid substitutions on its activity remains uncertain. Here, we conducted a detailed analysis of the profile and predicted the functional effects of selected missense changes in Group I. We focused on genes for which we had previously reported alterations that affect the IFNγ response ( IFNGR1 , IFNGR2 , and JAK2 ) or HLA-I localization ( B2M ) 4 13 and discarded those changes that were likely to be passenger mutations (affecting mostly colorectal and endometrial cancers with inactivation of MMR genes such as MLH1 , MSH2 , MSH6 , and PMS2 ). Next, we selected substitutions that were recurrent or adjacent and affected highly conserved amino acids (Fig. 1C). The curated selection yielded 226 missense alterations (Group II): 60 in IFNGR1 , 37 in IFNGR2 , 107 in JAK2 , and 22 in B2M . Some of the alterations were recurrent, which meant there were 215 unique changes in total (Supplementary Table S2 ). To assess the functional impact and pathogenicity of these amino acid substitutions, we compared the predictions of three silico tools: PolyPhen-2, SIFT, and DeepMind AlphaMissense. The latter is of a newer generation of tools that are based on deep machine learning. We compared the concordance of the three algorithms and found an overall agreement among them of ~ 50% (113/215). Eighteen per cent (39 of 215) had consensus on predicted damaging/pathogenic effects (Fig. 1C-D, Supplementary Table S2 ). AlphaMissense was the most conservative tool for predicting variants as pathogenic, identifying only 27% (n = 59) as such, compared with 52% (n = 111) classified as deleterious by SIFT, and 33% (n = 72) labeled as probably damaging by PolyPhen-2 (Supplementary Fig. S2 ). We selected 17 of the 215 unique missense variants, based on their recurrence, amino acid conservation and location within the protein, to investigate their impact on the protein’s function (Group III) (Fig. 1A). Functional assessment of amino acid substitutions at IFNGR1, IFNGR2 , and JAK2 For IFNGR1, we cloned the following changes: p.Y40C and p.N41S, contiguous within the extracellular domain, and p.R480I, located at the C-terminus (Supplementary Table S2 ; Fig. 2B). These substitutions, along with the wild-type IFNGR1 and an empty vector (EV), were overexpressed in a previously established IFNGR1-deficient, patient-derived cancer cell line, established in our laboratory from a lung cancer patient (PDC11) 20 (Fig. 2A). The ectopic expression of wild-type IFNGR1 restored IFNγ signaling, as evidenced by the increase in pSTAT1 and IRF1 levels, as well as increased expression of CD274 , IDO1 , and ICAM1 . Similarly, the p.R480I-IFNGR1 protein rescued IFNγ signaling, indicating that this variant does not impair protein function. In contrast, the p.Y40C-IFNGR1 and p.N41S-IFNGR1 variants attenuated pathway activation to different extents, whereby there was a moderate increase in pSTAT1 and IRF1 levels and no or minimal transcriptional activation of IFNγ-targets in response to IFNγ (Fig. 2C, D). These variant proteins also failed to induce IFNγ-mediated cell growth suppression (Fig. 2E). Three IFNGR2 variants were selected: two that affect the same residue in the extracellular domain (p.R114C and p.R114H), and one at the C-terminus (p.Q290P) (Fig. 1C; Fig. 2B). These variants, along with the wild-type control, were overexpressed in the H157 cancer cell line, in which we found biallelic inactivation of IFNGR2 (Fig. 2A). All these IFNGR2 variants activated IFNγ signaling and transcriptional IFNγ-targets to the same extent as occurred in the wild-type (Supplementary Fig. S2 ). The p.Q290P change was highly recurrent (n = 5), and a different substitution at the same residue (p.Q290H) was identified in two samples (Supplementary Table S2 ). All these variants were reported in the CCLE, but not in the TCGA, suggesting that, even though they are not listed in the ClinVar database, they may be germline polymorphisms. Six distinct JAK2 variants were selected: one affecting a highly conserved residue in the N-terminal FERM domain (p.R133W), four located in the JH1 domain (p.I559L, p.F560S, p.D569Y, and p.A676D), and one located within the JH2 (pseudokinase) domain (p.D894G) (Fig. 1C; Fig. 2B). These proteins, along with the wild-type control, were overexpressed in three JAK2 -mutant cancer cell lines (Fig. 2A). The p.R133W-JAK2 and p.A676D-JAK2 proteins did not restore IFNγ signaling to the same extent as did the wild-type protein, but a consistent attenuation of transactivation of IFNγ-targets was observed across all three models. This impairment was accompanied by a failure to induce IFNγ-mediated growth inhibition, similar to what was observed in the EV (Fig. 2E-F). In contrast, the other four protein variants exhibited functional properties comparable to those of the wild-type protein in all assays (Fig. 2C-F; Supplementary Fig. S2 B-C). These findings indicate that p.R133W and p.A676D are functionally inactive forms of the JAK2 protein. All selected variants were predicted to be deleterious by SIFT, and were classified as probably or possibly damaging by PolyPhen-2. AlphaMissense benign predictions were fully consistent with the functional data, while its pathogenicity predictions were concordant in about 50% of cases (Fig. 2G). Overall, about one third of the variants tested were dysfunctional. AlphaMissense provided the most accurate functional predictions of the algorithms evaluated. Assessment of amino acid substitution functionality at B2M The B2M protein acts as a chaperone, maintaining the structural stability of the HLA-I complex and its position on the cell surface 3 . As mentioned above, the anchoring of HLA-I complex to the cell surface is impaired in cancer cells carrying B2M -inactivating mutations, which prevents T-cells from recognizing the tumor cells. To study the effects of different amino acid substitutions on the function of B2M, we took advantage of one of the various B2M-deficient cancer cell lines, the H2009 lung cancer cell line, which is known to feature genetic inactivation of B2M 4 (Fig. 3A). We had previously tested three amino acid substitutions at B2M found in human tumors (p.R32H, p.E67Q and p.W80G), and found that only the p.W80G change affects the maturation and transportation of the HLA-I complex to the cell surface 4 . For the current work, we selected five B2M variants (p.L12P, p.Y30C, p.H51P, p.D54N, and p.H104R), three of which are located in the C1-set (Ig-like) domain (Fig. 1B, Fig. 2B). All variants, along with the wild-type control, were overexpressed in H2009 cells. The p.H51P variant was not detected by western blot, despite showing mRNA expression, suggesting that this protein may be subject to degradation (Fig. 3C). In contrast, the p.L12P variant protein exhibited an apparent increase in molecular weight compared to the wild type, possibly due to alterations in its secondary structure affecting electrophoretic mobility (Fig. 3C). The EV cells showed HLA-I protein accumulation in punctate clusters throughout the cytoplasm. In contrast, the expression of wild-type B2M redirected HLA-I to the cell surface (Fig. 3D). None of the tested variants, except for p.D54N-B2M, were capable of restoring proper localization of the HLA-I complex, indicating that these variants are dysfunctional (Fig. 3D). We cannot currently rule out the possibility that p.D54N-B2M interferes with other aspects of the function of the HLA-I complex. In conclusion, approximately 60% of all B2M variants tested were shown to be inactive. Of the computational tools evaluated, AlphaMissense and PolyPhen-2 accurately predicted the functional impact of the variants in nearly 90% of the cases (Fig. 3E). Mutation status of immune response-related genes and IFNγ-gene expression signature Next, we analyzed an IFNγ signature (IFNγsign) consisting of the most differentially expressed genes identified in a previous study 13 across a large cohort of lung primary tumors from the TCGA dataset (n = 1 160), encompassing lung adenocarcinomas and squamous cell carcinomas. Somatic alterations in B2M , IFNGR1 , IFNGR2 , or JAK2 were present in 69 tumors (17%; Supplementary Table S3). As we previously reported 14 , lung squamous cell carcinomas exhibited lower levels of overall expression of IFNγsign transcripts than did lung adenocarcinomas (Fig. 3F). Unsupervised clustering revealed three main clusters. Clusters I and III, which showed low to moderate IFNγsign expression levels, each contained distinct sub-clusters corresponding to different histopathological subtypes. In contrast, cluster II exhibited the highest levels of IFNγsign transcripts, did not differentiate between histopathological subtypes, and encompassed all normal lung tissue samples. Mutations in JAK2 , IFNGR1 , and IFNGR2 were distributed across all clusters, but those that AlphaMissense predicted to be pathogenic or to result in truncated proteins tended to group together. There was no clear association between inactivation of components of the IFNγ signaling pathway and low levels of global IFNγsign expression in primary lung tumors. However, normal tissue exhibited high IFNγsign transcript levels, raising the possibility that normal cell contamination in tumor samples may mask reduced IFNγsign expression in some of these tumors. DISCUSSION Our analysis found that approximately one tenth of all human cancers harbor genetic alterations in at least one of the following genes: IFNGR1 , IFNGR2 , JAK1 , JAK2 , B2M , and CALR. These alterations were most frequent in tumor types associated with MMR deficiency (e.g., colorectal and endometrial cancers) and with exposure to well-established carcinogens (e.g., lung cancer and melanoma) 21 22 25 , suggesting that some are passenger mutations unrelated to tumorigenesis. The enrichment of truncating mutations in specific genes, particularly B2M , points to a prominent role in cancer development. Here, we have studied the impact on protein function of missense mutations found in IFNGR1 , IFNGR2 , JAK2 , and B2M. JAK2 domains include, from N- to C-terminal, a FERM, an SH2-like, a kinase-like or pseudokinase (JH2), and a tyrosine kinase (JH1) 26 . JH2 autoinhibits JH1 activity, and many activating mutations found in myeloproliferative neoplasms (e.g., p.V617F) cluster in this region 27 . Interestingly, among the four variants we studied within the JH2 domain, only one (p.A676D) impaired the function of the protein. This substitution, which replaces a small, non-polar amino acid with the negatively charged aspartate, could affect the proper activation of JH1. On the other hand, the p.R133W change, at the FERM domain, also gave rise to a non-functional protein. In this case, the replacement of arginine with hydrophobic tryptophan could disrupt receptor binding. Regarding IFNGR1, two of the IFNGR1 variants tested (p.Y40C and p.N41S) significantly impaired signaling by IFNγ. Both variants are within the extracellular domain and thus may disrupt the ability of the receptor to bind IFNγ. Germ-line mutations at IFNGR1 lead to immunodeficiency 28 . Different mutations have been found resulting in different degrees of severity of the disease. Neither the p.Y40C nor the p.N41S variants have been associated with this disease (www.LOVD.nl/IFNGR1). It is intriguing that some of the JAK2 and IFNGR1 dysfunctional variants retained some ability to activate STAT, even though their capacity to trigger transactivation and cell death by IFNγ was greatly reduced. It is possible that the activation of STAT1 and the resulting increase in IRF1 must exceed a certain threshold to drive transactivation of IFNγ-targets and achieve effective suppression of cell growth. We recently observed a similar defective response to IFNγ in a subset of cancer cell lines that was associated with MYC-oncogenic activation 14 . B2M consists of a single immunoglobulin-fold domain and lacks transmembrane, intracellular, and separate extracellular domains. Combining our previous findings 4 with the current results, we report the testing of eight B2M mutations that are distributed throughout the protein. Most of these mutations impaired B2M function, preventing proper formation of the HLA class I complex at the cell surface and, consequently, compromising antigen presentation and T-cell recognition. Only three variants did not seem to affect HLA-I complex formation, although we cannot rule out other functional consequences. The high frequency of mutations that impair the formation of the HLA-I complex, whether through truncations or amino acid substitutions, highlights that B2M inactivation is an important strategy for tumor immune evasion. In our study, AlphaMissense has demonstrated strong predictive performance in identifying deleterious missense variants and may serve as a valuable surrogate when functional validation is not feasible. Finally, our analysis did not reveal a clear association between inactivation of components of the IFNγ signaling pathway and low global IFNγsign expression in primary lung tumors, although we cannot discard a masking effect by normal cell contamination. Additionally, some tumors lacking alterations in IFNγ signaling components also showed very low levels of IFNγsign expression, suggesting the involvement of other intrinsic and extrinsic mechanisms, as previously reported 14 29 30 31 . In conclusion, our findings provide a detailed mutational map of antigen presentation and IFNγ-response components in cancer, offering a resource made up of specific mutations in immune pathways that could compromise tumor immunogenicity and that may influence response to immunotherapies. Table 1 Frequency and percentage of mutations of genes from the cBioportal database (TCGA Pan-Cancer Atlas and CCLE 2019 datasets) (Group I). IFNGR1 IFNGR2 JAK1 JAK2 B2M CALR Total Missense Frequency 89 64 223 198 44 76 694 % 67 63 56 66 20 71 55 Truncated Frequency 41 25 164 90 129 13 462 % 31 24.5 41 30 59 12 37 Others Frequency 3 13 11 12 44 18 101 % 2 13 3 4 20 17 8 Total Frequency 133 102 398 300 217 107 1 257 Mutations/base pair 0.09 0.1 0.1 0.09 0.6 0.09 0.09 Declarations COMPETING INTERESTS The authors declare no competing interest. FUNDING This work was supported by the Spanish Government’s Ministerio de Ciencia e Innovación, Proyectos de Generación de Conocimiento. European Regional Development Fund, ‘A way to make Europe’ ERDF (grant PID2023-150559OB-100 to MS-C and grant PID2021-125282OB-I00 to ME), and by the Departament de Recerca i Universitats de la Generalitat de Catalunya, Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) (project 2021SGR01377 to MS-C and 2021SGR01494 to ME). C Diaz and J Morillas are supported by pre-doctoral contracts from the AGAUR (2023 FI-100667) and the Spanish Ministry of Education (FPU21/00047), respectively. We thank CERCA Programme/Generalitat de Catalunya for institutional support. AUTHORS CONTRIBUTIONS Conception and design: MS-C, ME, and CAD; development of methodology: CAD, VP and FS; acquisition of data: CAD, PN-C, JM, VP; analysis and interpretation of data: CAD, ME and MS-C; writing of the manuscript and study supervision: MS-C. ACKNOWLEDGMENTS The authors thank Isabel Bartolessis (Cancer Genetics Group) at IJC for technical assistance. References Chen DS, Mellman I. 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Hubbard SR. Mechanistic insights into regulation of JAK2 tyrosine kinase. Front Endocrinol (Lausanne) 2017; 8: 361. Vainchenker W, Kralovics R. Genetic basis and molecular pathophysiology of classical myeloproliferative neoplasms. Blood 2017; 129: 667–679. van de Vosse E, van Dissel JT. IFN-γR1 defects: Mutation update and description of the IFNGR1 variation database: van de VOSSE and van DISSEL. Human Mutation 2017; 38: 1286–1296. Mugarza E, van Maldegem F, Boumelha J, Moore C, Rana S, Llorian Sopena M et al. Therapeutic KRASG12C inhibition drives effective interferon-mediated antitumor immunity in immunogenic lung cancers. Sci Adv 2022; 8: eabm8780. Wawrzyniak P, Hartman ML. Dual role of interferon-gamma in the response of melanoma patients to immunotherapy with immune checkpoint inhibitors. Mol Cancer 2025; 24: 89. Tani T, Mathsyaraja H, Campisi M, Li Z-H, Haratani K, Fahey CG et al. TREX1 inactivation unleashes cancer cell STING-interferon signaling and promotes antitumor immunity. Cancer Discov 2024; 14: 752–765. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files SupplementaryTablesCDiazrevPM.pdf Supplementary Tables SupplementaryFiguresCADiaz.pdf Supplementary Figures Cite Share Download PDF Status: Published Journal Publication published 04 Apr, 2026 Read the published version in Cancer Gene Therapy → Version 1 posted Editorial decision: revise 05 Nov, 2025 Review # 8 received at journal 18 Sep, 2025 Review # 6 received at journal 15 Sep, 2025 Review # 4 received at journal 13 Sep, 2025 Review # 5 received at journal 09 Sep, 2025 Review # 3 received at journal 06 Sep, 2025 Review # 2 received at journal 30 Aug, 2025 Reviewer # 8 agreed at journal 18 Aug, 2025 Reviewer # 7 agreed at journal 18 Aug, 2025 Reviewer # 6 agreed at journal 18 Aug, 2025 Reviewer # 5 agreed at journal 13 Aug, 2025 Reviewer # 4 agreed at journal 06 Aug, 2025 Reviewer # 3 agreed at journal 05 Aug, 2025 Reviewer # 2 agreed at journal 04 Aug, 2025 Review # 1 received at journal 04 Aug, 2025 Reviewer # 1 agreed at journal 04 Aug, 2025 Reviewers invited by journal 03 Aug, 2025 Submission checks completed at journal 01 Aug, 2025 First submitted to journal 31 Jul, 2025 Unknown event 29 Jul, 2025 Editor assigned by journal 28 Jul, 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. 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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-7187015","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Brief Communication","associatedPublications":[],"authors":[{"id":494848615,"identity":"40a19fd0-821e-4c7a-afa9-ae738d99a07d","order_by":0,"name":"Montse Sanchez-Cespedes","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-6045-5627","institution":"Josep Carreras Leukemia Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Montse","middleName":"","lastName":"Sanchez-Cespedes","suffix":""},{"id":494848616,"identity":"b1756326-6811-4a0a-a897-22e127f131aa","order_by":1,"name":"Cristina Diaz","email":"","orcid":"","institution":"Josep Carreras Leukemia Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Diaz","suffix":""},{"id":494848617,"identity":"d3cf7b23-63a2-4e23-9647-bb600a9e0461","order_by":2,"name":"Juan Morillas","email":"","orcid":"","institution":"Josep Carreras Leukemia Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Morillas","suffix":""},{"id":494848618,"identity":"9347683a-6625-4f15-bb27-dab7c6cbaa50","order_by":3,"name":"Pablo Navajas-Chocarro","email":"","orcid":"","institution":"Josep Carreras Leukemia Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Navajas-Chocarro","suffix":""},{"id":494848619,"identity":"39134801-3fb9-41db-9757-78927128c39c","order_by":4,"name":"Fernando Setien","email":"","orcid":"","institution":"Josep Carreras Leukaemia Research Institute (IJC)","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"","lastName":"Setien","suffix":""},{"id":494848620,"identity":"bf1c59dc-3feb-4a39-8d9a-922e92694de9","order_by":5,"name":"Valentina Provenzano","email":"","orcid":"","institution":"Josep Carreras Leukemia Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Valentina","middleName":"","lastName":"Provenzano","suffix":""},{"id":494848621,"identity":"4c61e970-1f2d-447a-83d1-bceaf2a4acc3","order_by":6,"name":"Manel Esteller","email":"","orcid":"https://orcid.org/0000-0003-4490-6093","institution":"Josep Carreras Leukaemia Research Institute (IJC)","correspondingAuthor":false,"prefix":"","firstName":"Manel","middleName":"","lastName":"Esteller","suffix":""}],"badges":[],"createdAt":"2025-07-22 12:25:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7187015/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7187015/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41417-026-01024-9","type":"published","date":"2026-04-04T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88518266,"identity":"33e76080-7178-47bf-a8b4-11c439b4ba11","added_by":"auto","created_at":"2025-08-07 09:22:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55099,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMutational profile of components of the immune response in human cancer. A\u003c/strong\u003eWorkflow for selecting variants for the analyses. Of the 12 706 tumors (from 12 692 patients), there were 1 257 somatic variants in the indicated immune-related genes (Group I). MMR, mismatch repair. \u003cstrong\u003eB\u003c/strong\u003e Bar plot depicting the number of truncating (nonsense, frameshift, and splice site) and missense changes for the indicated genes and the most frequent tumor types (Group I, from the TCGA database). The number of tumors of each type is also indicated. \u003cstrong\u003eC\u003c/strong\u003e Schematic representation of JAK2, IFNGR1, IFNGR2, and B2M proteins, including their main domains and the locations of their respective variants (Group II). The predicted effect of each variant, according to SIFT, PolyPhen-2, and AlphaMissense, is indicated by color codes. Some amino acids have been highlighted, including those with recurrent changes. \u003cstrong\u003eD\u003c/strong\u003e Venn diagram depicting the overlap between the indicated prediction tools for the functional effects of the mutations (from Group II). The diagram on the left includes all the mutations; that on the right shows only those predicted to be deleterious (SIFT), probably damaging (PolyPhen-2), or pathogenic (AlphaMissense).\u003c/p\u003e","description":"","filename":"Figure1CDiaz1copy.png","url":"https://assets-eu.researchsquare.com/files/rs-7187015/v1/1a03dfc1333ce286f3b71012.png"},{"id":88519200,"identity":"501a045d-9607-4b34-ac33-4991e343bd3a","added_by":"auto","created_at":"2025-08-07 09:30:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4101756,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional analysis of missense alterations in the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eIFNGR1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eIFNGR2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eJAK2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e genes.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e Description of the mutational status of the indicated genes and cell lines. The histopathology of the cell lines is also indicated. LuSCC, lung squamous cell carcinoma; LuAD, lung adenocarcinoma. HD, homozygous deletion. \u003cstrong\u003eB\u003c/strong\u003e Multiple sequence alignment of the regions containing the amino acid changes selected for functional testing in each of the indicated proteins (Group III). \u003cstrong\u003eC\u003c/strong\u003e, Western blot showing protein levels in the indicated cancer cells ectopically expressing different mutant forms of IFNGR1, or JAK2, compared with wild-type and empty vector (EV) controls, following treatment with IFNγ (30 nM). Experiments were independently repeated at least once, yielding similar results on each occasion. \u003cstrong\u003eD \u003c/strong\u003eHeatmap showing changes in mRNA levels, based on quantitative RT-PCR (relative to \u003cem\u003eActin\u003c/em\u003e), of selected IFNγ target genes in the indicated cancer cell lines ectopically expressing the specified mutant proteins. \u003cstrong\u003eE\u003c/strong\u003e Representative clonogenic assays for the indicated cells infected with EV and with wild-type (WT) or the indicated mutants and treated with IFNγ at the indicated concentrations or untreated (for 10 days). \u003cstrong\u003eF\u003c/strong\u003e Viability of indicated cell lines and conditions, as in \u003cstrong\u003eE.\u003c/strong\u003e Lines, number of viable cells relative to the untreated cells. NR, not reached. IC\u003csub\u003e50\u003c/sub\u003e, half maximal inhibitory concentration.\u003cstrong\u003e G\u003c/strong\u003e Dot plot showing the scores assigned by the indicated algorithms (SF, SIFT; PH, PolyPhen-2; and AM, AlphaMissense) to each of the specified amino acid substitutions and genes. Squares on the right represent the functional data obtained in the current study.\u003c/p\u003e","description":"","filename":"Figure2CDiaz1copy.png","url":"https://assets-eu.researchsquare.com/files/rs-7187015/v1/af0f0cf3cd2337bfb4c178c2.png"},{"id":88519944,"identity":"4d6095a1-d860-4cb0-be94-7c927f955d8b","added_by":"auto","created_at":"2025-08-07 09:38:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6167096,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of the wild-type and mutant forms of B2M on HLA-I protein localization\u003c/strong\u003e. \u003cstrong\u003eA\u003c/strong\u003e Description of the mutational status of \u003cem\u003eB2M \u003c/em\u003ein the H2009 cancer cell line. LuAD, lung adenocarcinomas.\u003cstrong\u003e B\u003c/strong\u003e Multiple sequence alignment of the regions containing the amino acid changes selected for functional testing in each of the indicated proteins (Group III). \u003cstrong\u003eC \u003c/strong\u003eLeft panel: mRNA levels of \u003cem\u003eB2M \u003c/em\u003e(relative to \u003cem\u003eActin\u003c/em\u003e) for each indicated variant, assessed by real-time quantitative PCR. Lines represent mean ± SD. Right panel: Western blot analysis of ectopically expressed the indicated B2M proteins in H2009 B2M-mutant cells. EV, empty vector; TUBULIN, protein loading control. \u003cstrong\u003eD\u003c/strong\u003eImmunofluorescence of the HLA-I proteins in the H2009 cells infected with EV and with wild-type (WT) or the indicated mutants. Nuclei were stained with DAPI. Representative fluorescent images are shown. Scale bar, 25 µm. \u003cstrong\u003eE \u003c/strong\u003eDot plot showing the scores assigned by the indicated algorithms (SF, SIFT; PH, PolyPhen-2; and AM, AlphaMissense) to each of the specified B2M amino acid substitutions. Squares represent the functional data obtained in the current study. \u003cstrong\u003eF \u003c/strong\u003eUnsupervised hierarchical clustering and dendrograms were generated using a selection of the most upregulated genes from the IFNG signature (derived from GEO: GSE109720), reflecting gene expression profiles of primary lung tumors from TCGA. The status of the \u003cem\u003eIFNGR1\u003c/em\u003e, \u003cem\u003eIFNGR2, JAK2\u003c/em\u003e, and \u003cem\u003eB2M \u003c/em\u003egenes, including mutation types and tumor histopathology, is indicated. Tumor samples outlined by rectangles highlight groups with truncated or missense mutations predicted to be pathogenic by AlphaMissense. All amino acid substitutions in B2M were predicted to be pathogenic (Supplementary Table S3).\u003c/p\u003e","description":"","filename":"Figure3CDiazcopy.png","url":"https://assets-eu.researchsquare.com/files/rs-7187015/v1/256151f05135c7d32c786633.png"},{"id":106172086,"identity":"2ac670c4-1c16-43ba-8ab0-e16dd2716c92","added_by":"auto","created_at":"2026-04-05 07:05:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15107857,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7187015/v1/c9beabc6-1d00-4f70-b37c-9b789f2f1723.pdf"},{"id":88518268,"identity":"544fbecd-4953-49c8-b8f8-90f21ba72371","added_by":"auto","created_at":"2025-08-07 09:22:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":164062,"visible":true,"origin":"","legend":"Supplementary Tables","description":"","filename":"SupplementaryTablesCDiazrevPM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7187015/v1/00b0974abe7c53eedb7b8471.pdf"},{"id":88518272,"identity":"1473cbaf-f1b1-4a32-9f45-c4a1b6e7b605","added_by":"auto","created_at":"2025-08-07 09:22:06","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":729608,"visible":true,"origin":"","legend":"Supplementary Figures","description":"","filename":"SupplementaryFiguresCADiaz.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7187015/v1/26db0605ac256d795f00eee4.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Landscape and functional impact of missense mutations of immune response genes in human cancer","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eImmune evasion is an important hallmark of tumor development that enables cancer cells to avoid or bypass recognition and elimination by the host immune system\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The specificity of T-cell activation against tumor cells relies on the recognition of tumor cognate neoantigens, which are processed and properly presented by the HLA-I complex at the surface membrane of the cancer cells\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The HLA-I complex consists of a heavy chain-I (HLA-I) molecule (either HLA-A, B, or C) and an invariant light chain (B2M). Genetic inactivation of the HLA-I genes or of B2M has been reported in human cancer\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e 4\u003c/sup\u003e. \u003cem\u003eB2M\u003c/em\u003e inactivation has been shown to impair anchoring of the HLA-I complex to the cell surface\u003csup\u003e4\u003c/sup\u003e, and B2M loss-of-function mutations or HLA-I low-level expression are involved in the resistance to immune checkpoint blockade (ICB) treatment\u003csup\u003e4 5 6 7 8 9\u003c/sup\u003e. The maturation and correct localization of the HLA-I complex requires the participation of other proteins, including CANX, CALR, PDIA3, TAP1, TAP2, and TAPBP\u003csup\u003e3\u003c/sup\u003e, some of which are known to be genetically inactivated in cancer, although at a very low frequency\u003csup\u003e2\u003c/sup\u003e. Furthermore, alterations affecting the response to interferon gamma (IFNγ), which can directly inhibit tumor cell growth and promote apoptosis, also contribute to immune evasion\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e 11\u003c/sup\u003e. IFNγ is a cytokine produced predominantly by the T-lymphocytes and natural killer cells in response to a variety of inflammatory or immune stimuli\u003csup\u003e11\u003c/sup\u003e. IFNγ binds to its cognate receptor, composed of the IFNGR1 and IFNGR2 subunits, thereby promoting the recruitment and activation of Janus kinases (JAK1 and JAK2), which, in turn, phosphorylate and activate the signal transducer and activator of transcription 1 (STAT1). STAT1 then homodimerizes and translocates to the nucleus, where it binds to specific promoter elements and modulates the transcription of genes related to the immune response, including those involved in immunorecognition and antigen presentation\u003csup\u003e11\u003c/sup\u003e. Previous studies have shown the presence of genetic defects in IFNGR1/2 or JAK1/2 that render tumor cells refractory to IFNγ\u003csup\u003e12 13 14\u003c/sup\u003e. Gene expression of IFNγ-responsive genes were found to be necessary, but not always sufficient, to produce a clinical benefit to treatment with ICB\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e 16\u003c/sup\u003e. In addition, in melanoma, gene-inactivating mutations in these molecules have been associated with resistance to ICB\u003csup\u003e17\u003c/sup\u003e. More recently, we reported a deficient response to IFNγ in some lung cancer cells together with constitutive low levels of IFNγ-stimulated genes related to the oncogenic activation of MYC\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGiven the potential impact of tumor-intrinsic defects in HLA-I assembly or IFNγ signaling on the clinical efficacy of current and emerging immunotherapies, in this paper we have analyzed the mutational landscape of genes involved in HLA-I complex formation and IFNγ response across more than 12 000 tumors, and evaluated the functional consequences of selected amino acid substitutions.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cb\u003eGenes, variant selection and computational predictions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMutation frequency and type for \u003cem\u003eB2M\u003c/em\u003e, \u003cem\u003eCALR\u003c/em\u003e, \u003cem\u003eIFNGR1\u003c/em\u003e, \u003cem\u003eIFNGR2\u003c/em\u003e, \u003cem\u003eJAK1\u003c/em\u003e, and \u003cem\u003eJAK2\u003c/em\u003e genes were retrieved 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) using 12 706 samples, including both primary tumors (TCGA Pan-Cancer Atlas) and cancer cell lines (CCLE 2019 datasets). To identify the potential germline polymorphisms among the selected changes, we used the ClinVar database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/clinvar/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/clinvar/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), as well as UNIPROT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and COSMIC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancer.sanger.ac.uk/cosmic/login\u003c/span\u003e\u003cspan address=\"https://cancer.sanger.ac.uk/cosmic/login\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe pathogenicity of the selected variants was determined using three \u003cem\u003ein silico\u003c/em\u003e tools. Polymorphism Phenotyping (PolyPhen-2) (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genetics.bwh.harvard.edu/pph2/\u003c/span\u003e\u003cspan address=\"http://genetics.bwh.harvard.edu/pph2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) predicts the effect of substitutions on protein structure and function using physical and comparative methods. Scores range from 0 to 1, which are interpreted as follows: 0.0\u0026ndash;0.15, benign; 0.15\u0026ndash;0.85, possibly damaging; \u0026gt;0.85, probably damaging\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Sorting Intolerant from Tolerant (SIFT) (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://blocks.fhcrc.org/sift/SIFT.html\u003c/span\u003e\u003cspan address=\"http://blocks.fhcrc.org/sift/SIFT.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) evaluates the potential impact of amino acid substitutions based on sequence conservation and physicochemical properties. Scores range from 0 to 1, with variants predicted to be deleterious if the score is \u0026le;\u0026thinsp;0.05, and to be tolerated if it is \u0026gt;\u0026thinsp;0.05. DeepMind AlphaMissense (AlphaMissense) estimates the likelihood of pathogenicity of missense variants, providing a score and the classification. AlphaMissense outputs included the score and pathogenicity classification (benign, ambiguous, or pathogenic). Scores were interpreted as follows: 0\u0026ndash;0.33, benign; 0.34\u0026ndash;0.564, ambiguous; 0.565\u0026ndash;1, pathogenic\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCancer cell lines\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCancer cell lines were obtained from recognized biorepositories: ATCC (Rockville, MD), ECACC (Salisbury, UK), RIKEN Cell Bank (Tokyo, Japan), and DSMZ (Braunschweig, Germany). One cancer cell line (PDC11) was derived from a previous study\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Cells were cultured under supplier-recommended conditions with medium supplemented with 10% heat-inactivated fetal bovine serum and 1% penicillin\u0026ndash;streptomycin. Cultures were maintained at 37\u0026deg;C in a humidified atmosphere with 5% CO₂. Adherent cells were cultured using trypsin-EDTA. Cell line identity was verified by genotyping hallmark mutations and compared with public databases. Mycoplasma contamination was routinely tested for and, when found, excluded.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction of expression vectors and lentiviral infections\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFull-length cDNAs of \u003cem\u003eIFNGR1\u003c/em\u003e (NM_000416.3), \u003cem\u003eIFNGR2\u003c/em\u003e (NM_005534.4), \u003cem\u003eJAK2\u003c/em\u003e (NM_004972.4), and \u003cem\u003eB2M\u003c/em\u003e (NM_004048.4) were generated by reverse transcription of RNA from EBC-1 cells, using SuperScript\u0026trade; II reverse transcriptase (Invitrogen) and random primers (Promega). cDNAs were PCR-amplified with Phusion High-Fidelity DNA Polymerase (Thermo Scientific) and cloned into pLVX-Puro (Clontech) at appropriate restriction sites. Inserts were validated by Sanger sequencing (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Lentiviral particles were produced by co-transfecting HEK-293T cells with the pLVX vectors and packaging plasmids psPAX and pMD2.G (Sigma-Aldrich). Target cells were infected with lentiviral supernatants containing either wild-type and mutant constructs, or empty vectors. Stable cell lines were selected with puromycin (1 \u0026micro;g/mL, ANT-PR-1, Invivogen) for 72 hours.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSite-directed mutagenesis\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePoint mutations were introduced into wild-type expression plasmids using the Q5 Site-Directed Mutagenesis Kit (New England Biolabs) following the manufacturer\u0026rsquo;s protocols. Mutation-specific primers were designed using the NEBaseChanger\u0026reg; tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nebasechanger.neb.com/\u003c/span\u003e\u003cspan address=\"https://nebasechanger.neb.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All mutated constructs were validated by Sanger sequencing (primer sequences illustrated in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAntibodies, western blots and immunofluorescence\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrimary antibodies used for western blots included anti-TUBULIN (T6199, mouse, 1:10 000, Sigma-Aldrich), anti-β-ACTIN (13854, mouse, 1:10 000, Sigma-Aldrich), anti-IFNGR1 (10808-1-AP, rabbit, 1:750, Proteintech), anti-IFNGR2 (10266-1-AP, rabbit, 1:500, Proteintech), anti-JAK2 (3230, rabbit, 1:1 000, Cell Signaling), anti-STAT1 (9175, rabbit, 1:1 000, Cell Signaling), anti-pSTAT1 (9167, rabbit, 1:1 000, Cell Signaling), anti-IRF1 (8478, rabbit, 1:1 000, Cell Signaling), Anti-B2M (BBM.1, Santa Cruz Biotechnology, Inc., and D8P1H, 1:1 000, Cell Signaling Technology). The anti-HLA-I (EMR8-5, mouse, 1:500, Abcam) was used for immunofluorescence.\u003c/p\u003e\u003cp\u003eFor western blotting, whole-cell lysates were prepared in lysis buffer (2% SDS, 50 mM Tris-HCl pH 6.8, 10% glycerol, and protease/phosphatase inhibitors; Roche). Protein concentrations were quantified using the Bio-Rad DC Protein Assay. Equal amounts of protein (15\u0026ndash;40 \u0026micro;g) were resolved by SDS-PAGE, transferred to nitrocellulose membranes, and blocked in 5% non-fat dry milk. Membranes were incubated overnight at 4\u0026deg;C with primary antibodies for 1.5 hours at room temperature with IRDye 680CW or 800CW secondary antibodies (LI-COR, 1:10 000). Fluorescent signals were acquired using the Odyssey CLx system and Image Studio Lite software. For IFNGR1 and IFNGR2, chemiluminescence detection was used (1:1 000 Immobilon, Millipore).\u003c/p\u003e\u003cp\u003eFor immunofluorescence, cells were fixed with 4% formaldehyde (PanReac AppliChem) for 20 minutes at room temperature and blocked with 20% goat serum in PBS for 1 hour. Samples were incubated overnight at 4\u0026deg;C with a primary anti-HLA-I antibody diluted in blocking buffer, in a humidified chamber to prevent evaporation. After washing with PBS containing 0.1% Tween-20, cells were incubated with Alexa Fluor 568 goat anti-mouse secondary antibody (1:500 dilution; Invitrogen) for 1.5 hours at room temperature in the dark. Nuclei were counterstained with DAPI (0.1 \u0026micro;g/mL) and mounted using Immu-Mount (Epredia).\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuantitative RT-PCRs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess mRNA expression levels, total RNA was extracted and reverse-transcribed using SuperScript\u0026trade; II reverse transcriptase (Invitrogen) and random primers (Promega). Quantitative PCR was performed using SYBR Green PCR Master Mix (Applied Biosystems) on a QuantStudio 7 Real-Time PCR System (Thermo Fisher), and data were analyzed with QuantStudio Design \u0026amp; Analysis v1.5.1 software. Primer sequences are provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIFNγ toxicity and cell growth assays\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate IFNγ-induced toxicity, lung cancer cell lines stably expressing the different variants were seeded at 10 000 cells per well in 48-well plates and treated with increasing concentrations of recombinant human IFNγ (PeproTech) for 10 days. Following treatment, cells were washed with PBS and fixed with cold methanol for 30 minutes. Fixed cells were stained with 0.5% Crystal Violet solution for 30 minutes, then rinsed thoroughly with PBS and water. After drying, the dye was solubilized with 10% acetic acid under shaking for 15 minutes, and absorbance was measured at 560 nm using a Synergy H1 Microplate Reader (BioTek).\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical and bioinformatic analysis\u003c/b\u003e. IC\u003csub\u003e50\u003c/sub\u003e calculations were performed using Prism software (GraphPad).\u003c/p\u003e\u003cp\u003eRNA-sequencing and single nucleotide polymorphism (SNP) data were downloaded from the indicated databases of lung primary tumors (lung adenocarcinomas and lung squamous cell carcinomas projects) as indicated, using TCGAbiolinks (version 2.36.0) and processed as \u003cem\u003eSummarized Experiment\u003c/em\u003e objects (version 1.38.1). Gene expression counts were filtered (CPM\u0026thinsp;\u0026gt;\u0026thinsp;1) and normalized using the variance stabilizing transformation (VST) in DESeq2 (version 1.49.2). SNPs were integrated using GenomicRanges (version 1.60.0). The gene signature was standardized (z-score \u003cem\u003eper\u003c/em\u003e gene) and visualized as a heatmap using pheatmap (version 1.0.13), alongside clinical and genomic annotations. All analyses were performed in R (version 4.5) with Bioconductor (version 3.21) to ensure reproducibility.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eMutational profile of components of the immune response in human cancer\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhole exome sequencing data from primary tumors and cancer cell lines (TCGA Pan-Cancer Atlas and CCLE 2019 datasets) were obtained from the cBioportal database, drawn from 33 studies and including 12 706 tumors from 12 692 cancer patients. We tested for the presence and type of alterations in the coding regions and intron-exon boundaries of some genes directly involved in the response to IFNγ (i.e., \u003cem\u003eIFNGR1, IFNGR2, JAK1\u003c/em\u003e, and \u003cem\u003eJAK2\u003c/em\u003e) or in immune recognition/antigen presentation (i.e., \u003cem\u003eB2M\u003c/em\u003e and \u003cem\u003eCALR\u003c/em\u003e) that were already known to be mutated in human cancer\u003csup\u003e2 3 4 6 13\u003c/sup\u003e. After discarding changes that were likely to constitute rare polymorphisms, 1 257 (~\u0026thinsp;10%) mutations were considered tumor-specific alterations (designated as Group I) (Fig.\u0026nbsp;1A). About half of them were missense, resulting in amino acid substitutions of unknown effect on the encoded protein (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cem\u003eB2M\u003c/em\u003e was the gene with the highest frequency of mutations per base pair in coding DNA (0.6), followed by \u003cem\u003eJAK1\u003c/em\u003e and \u003cem\u003eIFNGR2\u003c/em\u003e (0.1 each), \u003cem\u003eIFNGR1\u003c/em\u003e, \u003cem\u003eJAK2\u003c/em\u003e, and \u003cem\u003eCALR\u003c/em\u003e (0.09 each) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Except for \u003cem\u003eB2M\u003c/em\u003e, the frequency of missense changes in these genes was \u0026gt;\u0026thinsp;50% \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;1A). When analyzed by tumor type, colorectal and endometrial tumors had the highest mutation rates of all the genes (Fig.\u0026nbsp;1B). These are the common types of cancer associated with deficient DNA mismatch repair (MMR) activity, due to germline or somatic alterations in MMR genes\u003csup\u003e21 22\u003c/sup\u003e. Other tumor types, such as melanoma and lung cancer, exhibited high mutation rates, whereas breast cancer, gliomas/glioblastomas, and pancreatic cancer showed a lower frequency of mutations across all genes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComputational determination of the effects of amino acids substitutions in immune response-related genes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhile most truncating mutations inactivate the function of the encoded protein, the impact of amino acid substitutions on its activity remains uncertain. Here, we conducted a detailed analysis of the profile and predicted the functional effects of selected missense changes in Group I. We focused on genes for which we had previously reported alterations that affect the IFNγ response (\u003cem\u003eIFNGR1\u003c/em\u003e, \u003cem\u003eIFNGR2\u003c/em\u003e, and \u003cem\u003eJAK2\u003c/em\u003e) or HLA-I localization (\u003cem\u003eB2M\u003c/em\u003e)\u003csup\u003e4 13\u003c/sup\u003e and discarded those changes that were likely to be passenger mutations (affecting mostly colorectal and endometrial cancers with inactivation of MMR genes such as \u003cem\u003eMLH1\u003c/em\u003e, \u003cem\u003eMSH2\u003c/em\u003e, \u003cem\u003eMSH6\u003c/em\u003e, and \u003cem\u003ePMS2\u003c/em\u003e). Next, we selected substitutions that were recurrent or adjacent and affected highly conserved amino acids (Fig.\u0026nbsp;1C). The curated selection yielded 226 missense alterations (Group II): 60 in \u003cem\u003eIFNGR1\u003c/em\u003e, 37 in \u003cem\u003eIFNGR2\u003c/em\u003e, 107 in \u003cem\u003eJAK2\u003c/em\u003e, and 22 in \u003cem\u003eB2M\u003c/em\u003e. Some of the alterations were recurrent, which meant there were 215 unique changes in total (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo assess the functional impact and pathogenicity of these amino acid substitutions, we compared the predictions of three \u003cem\u003esilico\u003c/em\u003e tools: PolyPhen-2, SIFT, and DeepMind AlphaMissense. The latter is of a newer generation of tools that are based on deep machine learning. We compared the concordance of the three algorithms and found an overall agreement among them of ~\u0026thinsp;50% (113/215). Eighteen per cent (39 of 215) had consensus on predicted damaging/pathogenic effects (Fig.\u0026nbsp;1C-D, Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). AlphaMissense was the most conservative tool for predicting variants as pathogenic, identifying only 27% (n\u0026thinsp;=\u0026thinsp;59) as such, compared with 52% (n\u0026thinsp;=\u0026thinsp;111) classified as deleterious by SIFT, and 33% (n\u0026thinsp;=\u0026thinsp;72) labeled as probably damaging by PolyPhen-2 (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe selected 17 of the 215 unique missense variants, based on their recurrence, amino acid conservation and location within the protein, to investigate their impact on the protein\u0026rsquo;s function (Group III) (Fig.\u0026nbsp;1A).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional assessment of amino acid substitutions at IFNGR1, IFNGR2\u003c/b\u003e, \u003cb\u003eand JAK2\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor IFNGR1, we cloned the following changes: p.Y40C and p.N41S, contiguous within the extracellular domain, and p.R480I, located at the C-terminus (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e; Fig.\u0026nbsp;2B). These substitutions, along with the wild-type IFNGR1 and an empty vector (EV), were overexpressed in a previously established IFNGR1-deficient, patient-derived cancer cell line, established in our laboratory from a lung cancer patient (PDC11)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;2A). The ectopic expression of wild-type IFNGR1 restored IFNγ signaling, as evidenced by the increase in pSTAT1 and IRF1 levels, as well as increased expression of \u003cem\u003eCD274\u003c/em\u003e, \u003cem\u003eIDO1\u003c/em\u003e, and \u003cem\u003eICAM1\u003c/em\u003e. Similarly, the p.R480I-IFNGR1 protein rescued IFNγ signaling, indicating that this variant does not impair protein function. In contrast, the p.Y40C-IFNGR1 and p.N41S-IFNGR1 variants attenuated pathway activation to different extents, whereby there was a moderate increase in pSTAT1 and IRF1 levels and no or minimal transcriptional activation of IFNγ-targets in response to IFNγ (Fig.\u0026nbsp;2C, D). These variant proteins also failed to induce IFNγ-mediated cell growth suppression (Fig.\u0026nbsp;2E).\u003c/p\u003e\u003cp\u003eThree IFNGR2 variants were selected: two that affect the same residue in the extracellular domain (p.R114C and p.R114H), and one at the C-terminus (p.Q290P) (Fig.\u0026nbsp;1C; Fig.\u0026nbsp;2B). These variants, along with the wild-type control, were overexpressed in the H157 cancer cell line, in which we found biallelic inactivation of \u003cem\u003eIFNGR2\u003c/em\u003e (Fig.\u0026nbsp;2A). All these IFNGR2 variants activated IFNγ signaling and transcriptional IFNγ-targets to the same extent as occurred in the wild-type (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The p.Q290P change was highly recurrent (n\u0026thinsp;=\u0026thinsp;5), and a different substitution at the same residue (p.Q290H) was identified in two samples (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). All these variants were reported in the CCLE, but not in the TCGA, suggesting that, even though they are not listed in the ClinVar database, they may be germline polymorphisms.\u003c/p\u003e\u003cp\u003eSix distinct JAK2 variants were selected: one affecting a highly conserved residue in the N-terminal FERM domain (p.R133W), four located in the JH1 domain (p.I559L, p.F560S, p.D569Y, and p.A676D), and one located within the JH2 (pseudokinase) domain (p.D894G) (Fig.\u0026nbsp;1C; Fig.\u0026nbsp;2B). These proteins, along with the wild-type control, were overexpressed in three \u003cem\u003eJAK2\u003c/em\u003e-mutant cancer cell lines (Fig.\u0026nbsp;2A). The p.R133W-JAK2 and p.A676D-JAK2 proteins did not restore IFNγ signaling to the same extent as did the wild-type protein, but a consistent attenuation of transactivation of IFNγ-targets was observed across all three models. This impairment was accompanied by a failure to induce IFNγ-mediated growth inhibition, similar to what was observed in the EV (Fig.\u0026nbsp;2E-F). In contrast, the other four protein variants exhibited functional properties comparable to those of the wild-type protein in all assays (Fig.\u0026nbsp;2C-F; Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB-C). These findings indicate that p.R133W and p.A676D are functionally inactive forms of the JAK2 protein.\u003c/p\u003e\u003cp\u003eAll selected variants were predicted to be deleterious by SIFT, and were classified as probably or possibly damaging by PolyPhen-2. AlphaMissense benign predictions were fully consistent with the functional data, while its pathogenicity predictions were concordant in about 50% of cases (Fig.\u0026nbsp;2G). Overall, about one third of the variants tested were dysfunctional. AlphaMissense provided the most accurate functional predictions of the algorithms evaluated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of amino acid substitution functionality at B2M\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe B2M protein acts as a chaperone, maintaining the structural stability of the HLA-I complex and its position on the cell surface\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. As mentioned above, the anchoring of HLA-I complex to the cell surface is impaired in cancer cells carrying \u003cem\u003eB2M\u003c/em\u003e-inactivating mutations, which prevents T-cells from recognizing the tumor cells. To study the effects of different amino acid substitutions on the function of B2M, we took advantage of one of the various B2M-deficient cancer cell lines, the H2009 lung cancer cell line, which is known to feature genetic inactivation of B2M\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;3A). We had previously tested three amino acid substitutions at B2M found in human tumors (p.R32H, p.E67Q and p.W80G), and found that only the p.W80G change affects the maturation and transportation of the HLA-I complex to the cell surface\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. For the current work, we selected five B2M variants (p.L12P, p.Y30C, p.H51P, p.D54N, and p.H104R), three of which are located in the C1-set (Ig-like) domain (Fig.\u0026nbsp;1B, Fig.\u0026nbsp;2B). All variants, along with the wild-type control, were overexpressed in H2009 cells. The p.H51P variant was not detected by western blot, despite showing mRNA expression, suggesting that this protein may be subject to degradation (Fig.\u0026nbsp;3C). In contrast, the p.L12P variant protein exhibited an apparent increase in molecular weight compared to the wild type, possibly due to alterations in its secondary structure affecting electrophoretic mobility (Fig.\u0026nbsp;3C). The EV cells showed HLA-I protein accumulation in punctate clusters throughout the cytoplasm. In contrast, the expression of wild-type B2M redirected HLA-I to the cell surface (Fig.\u0026nbsp;3D). None of the tested variants, except for p.D54N-B2M, were capable of restoring proper localization of the HLA-I complex, indicating that these variants are dysfunctional (Fig.\u0026nbsp;3D). We cannot currently rule out the possibility that p.D54N-B2M interferes with other aspects of the function of the HLA-I complex. In conclusion, approximately 60% of all B2M variants tested were shown to be inactive. Of the computational tools evaluated, AlphaMissense and PolyPhen-2 accurately predicted the functional impact of the variants in nearly 90% of the cases (Fig.\u0026nbsp;3E).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMutation status of immune response-related genes and IFNγ-gene expression signature\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNext, we analyzed an IFNγ signature (IFNγsign) consisting of the most differentially expressed genes identified in a previous study\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e across a large cohort of lung primary tumors from the TCGA dataset (n\u0026thinsp;=\u0026thinsp;1 160), encompassing lung adenocarcinomas and squamous cell carcinomas. Somatic alterations in \u003cem\u003eB2M\u003c/em\u003e, \u003cem\u003eIFNGR1\u003c/em\u003e, \u003cem\u003eIFNGR2\u003c/em\u003e, or \u003cem\u003eJAK2\u003c/em\u003e were present in 69 tumors (17%; Supplementary Table S3). As we previously reported\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, lung squamous cell carcinomas exhibited lower levels of overall expression of IFNγsign transcripts than did lung adenocarcinomas (Fig.\u0026nbsp;3F). Unsupervised clustering revealed three main clusters. Clusters I and III, which showed low to moderate IFNγsign expression levels, each contained distinct sub-clusters corresponding to different histopathological subtypes. In contrast, cluster II exhibited the highest levels of IFNγsign transcripts, did not differentiate between histopathological subtypes, and encompassed all normal lung tissue samples. Mutations in \u003cem\u003eJAK2\u003c/em\u003e, \u003cem\u003eIFNGR1\u003c/em\u003e, and \u003cem\u003eIFNGR2\u003c/em\u003e were distributed across all clusters, but those that AlphaMissense predicted to be pathogenic or to result in truncated proteins tended to group together. There was no clear association between inactivation of components of the IFNγ signaling pathway and low levels of global IFNγsign expression in primary lung tumors. However, normal tissue exhibited high IFNγsign transcript levels, raising the possibility that normal cell contamination in tumor samples may mask reduced IFNγsign expression in some of these tumors.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur analysis found that approximately one tenth of all human cancers harbor genetic alterations in at least one of the following genes: \u003cem\u003eIFNGR1\u003c/em\u003e, \u003cem\u003eIFNGR2\u003c/em\u003e, \u003cem\u003eJAK1\u003c/em\u003e, \u003cem\u003eJAK2\u003c/em\u003e, \u003cem\u003eB2M\u003c/em\u003e, and \u003cem\u003eCALR.\u003c/em\u003e These alterations were most frequent in tumor types associated with MMR deficiency (e.g., colorectal and endometrial cancers) and with exposure to well-established carcinogens (e.g., lung cancer and melanoma)\u003csup\u003e21 22 25\u003c/sup\u003e, suggesting that some are passenger mutations unrelated to tumorigenesis. The enrichment of truncating mutations in specific genes, particularly \u003cem\u003eB2M\u003c/em\u003e, points to a prominent role in cancer development. Here, we have studied the impact on protein function of missense mutations found in \u003cem\u003eIFNGR1\u003c/em\u003e, \u003cem\u003eIFNGR2\u003c/em\u003e, \u003cem\u003eJAK2\u003c/em\u003e, and \u003cem\u003eB2M.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eJAK2 domains include, from N- to C-terminal, a FERM, an SH2-like, a kinase-like or pseudokinase (JH2), and a tyrosine kinase (JH1)\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. JH2 autoinhibits JH1 activity, and many activating mutations found in myeloproliferative neoplasms (e.g., p.V617F) cluster in this region\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Interestingly, among the four variants we studied within the JH2 domain, only one (p.A676D) impaired the function of the protein. This substitution, which replaces a small, non-polar amino acid with the negatively charged aspartate, could affect the proper activation of JH1. On the other hand, the p.R133W change, at the FERM domain, also gave rise to a non-functional protein. In this case, the replacement of arginine with hydrophobic tryptophan could disrupt receptor binding. Regarding IFNGR1, two of the IFNGR1 variants tested (p.Y40C and p.N41S) significantly impaired signaling by IFNγ. Both variants are within the extracellular domain and thus may disrupt the ability of the receptor to bind IFNγ. Germ-line mutations at IFNGR1 lead to immunodeficiency\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Different mutations have been found resulting in different degrees of severity of the disease. Neither the p.Y40C nor the p.N41S variants have been associated with this disease (www.LOVD.nl/IFNGR1). It is intriguing that some of the JAK2 and IFNGR1 dysfunctional variants retained some ability to activate STAT, even though their capacity to trigger transactivation and cell death by IFNγ was greatly reduced. It is possible that the activation of STAT1 and the resulting increase in IRF1 must exceed a certain threshold to drive transactivation of IFNγ-targets and achieve effective suppression of cell growth. We recently observed a similar defective response to IFNγ in a subset of cancer cell lines that was associated with MYC-oncogenic activation\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eB2M consists of a single immunoglobulin-fold domain and lacks transmembrane, intracellular, and separate extracellular domains. Combining our previous findings\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e with the current results, we report the testing of eight \u003cem\u003eB2M\u003c/em\u003e mutations that are distributed throughout the protein. Most of these mutations impaired B2M function, preventing proper formation of the HLA class I complex at the cell surface and, consequently, compromising antigen presentation and T-cell recognition. Only three variants did not seem to affect HLA-I complex formation, although we cannot rule out other functional consequences. The high frequency of mutations that impair the formation of the HLA-I complex, whether through truncations or amino acid substitutions, highlights that B2M inactivation is an important strategy for tumor immune evasion. In our study, AlphaMissense has demonstrated strong predictive performance in identifying deleterious missense variants and may serve as a valuable surrogate when functional validation is not feasible.\u003c/p\u003e\u003cp\u003eFinally, our analysis did not reveal a clear association between inactivation of components of the IFNγ signaling pathway and low global IFNγsign expression in primary lung tumors, although we cannot discard a masking effect by normal cell contamination. Additionally, some tumors lacking alterations in IFNγ signaling components also showed very low levels of IFNγsign expression, suggesting the involvement of other intrinsic and extrinsic mechanisms, as previously reported\u003csup\u003e14 29 30 31\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn conclusion, our findings provide a detailed mutational map of antigen presentation and IFNγ-response components in cancer, offering a resource made up of specific mutations in immune pathways that could compromise tumor immunogenicity and that may influence response to immunotherapies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFrequency and percentage of mutations of genes from the cBioportal database (TCGA Pan-Cancer Atlas and CCLE 2019 datasets) (Group I).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eIFNGR1\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eIFNGR2\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eJAK1\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eJAK2\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eB2M\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eCALR\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e694\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTruncated\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e462\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOthers\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1 257\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMutations/base pair\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCOMPETING INTERESTS\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e\u003cp\u003eThis work was supported by the Spanish Government\u0026rsquo;s Ministerio de Ciencia e Innovaci\u0026oacute;n, Proyectos de Generaci\u0026oacute;n de Conocimiento. European Regional Development Fund, \u0026lsquo;A way to make Europe\u0026rsquo; ERDF (grant PID2023-150559OB-100 to MS-C and grant PID2021-125282OB-I00 to ME), and by the Departament de Recerca i Universitats de la Generalitat de Catalunya, Ag\u0026egrave;ncia de Gesti\u0026oacute; d\u0026rsquo;Ajuts Universitaris i de Recerca (AGAUR) (project 2021SGR01377 to MS-C and 2021SGR01494 to ME). C Diaz and J Morillas are supported by pre-doctoral contracts from the AGAUR (2023 FI-100667) and the Spanish Ministry of Education (FPU21/00047), respectively. We thank CERCA Programme/Generalitat de Catalunya for institutional support.\u003c/p\u003e\u003ch2\u003eAUTHORS CONTRIBUTIONS\u003c/h2\u003e\u003cp\u003eConception and design: MS-C, ME, and CAD; development of methodology: CAD, VP and FS; acquisition of data: CAD, PN-C, JM, VP; analysis and interpretation of data: CAD, ME and MS-C; writing of the manuscript and study supervision: MS-C.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGMENTS\u003c/h2\u003e\u003cp\u003eThe authors thank Isabel Bartolessis (Cancer Genetics Group) at IJC for technical assistance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen DS, Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature 2017; 541: 321\u0026ndash;330.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaigi M, Alburquerque-Bejar JJ, Sanchez-Cespedes M. 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Cancer Discov 2024; 14: 752\u0026ndash;765.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-gene-therapy","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cgt","sideBox":"Learn more about [Cancer Gene Therapy](http://www.nature.com/cgt/)","snPcode":"41417","submissionUrl":"https://mts-cgt.nature.com/cgi-bin/main.plex","title":"Cancer Gene Therapy","twitterHandle":"@cgtnature","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7187015/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7187015/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTumor-intrinsic defects in immune recognition and interferon-gamma (IFNγ) signaling pathways facilitate immune evasion and limit the efficacy of immune checkpoint blockade (ICB). Here, we delineate the mutational landscape and functional consequences of amino acid substitutions in key immune-related genes, \u003cem\u003eB2M\u003c/em\u003e, \u003cem\u003eCALR\u003c/em\u003e, \u003cem\u003eIFNGR1\u003c/em\u003e, \u003cem\u003eIFNGR2\u003c/em\u003e, \u003cem\u003eJAK1\u003c/em\u003e, and \u003cem\u003eJAK2\u003c/em\u003e, across more than 12 000 primary tumors and cancer cell lines. Genomic alterations affecting the coding regions of at least one of these genes were identified in approximately 11% of cancers, with missense variants accounting for 55% of these events. \u003cem\u003eB2M\u003c/em\u003e exhibited the highest mutation frequency per base pair, the mutations predominantly involving truncating variants. A curated set of 215 missense mutations in \u003cem\u003eB2M\u003c/em\u003e, \u003cem\u003eIFNGR1\u003c/em\u003e, \u003cem\u003eIFNGR2\u003c/em\u003e, and \u003cem\u003eJAK2\u003c/em\u003e was interrogated using SIFT, PolyPhen-2, and AlphaMissense, yielding predicted pathogenicity rates of 52%, 35%, and 27%, respectively. Functional assays revealed JAK2 and IFNGR1 variants that impaired IFNγ-mediated transcriptional activation and growth suppression, and B2M variants that disrupted HLA class I complex formation. Notably, AlphaMissense predictions showed the highest concordance with experimental data. These findings provide a detailed mutational map of antigen presentation and IFNγ-response components in cancer, offering a resource of specific mutations in immune pathways that compromise tumor immunogenicity and may influence the response to ICB.\u003c/p\u003e","manuscriptTitle":"Landscape and functional impact of missense mutations of immune response genes in human cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 09:22:01","doi":"10.21203/rs.3.rs-7187015/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-11-05T10:22:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-18T23:20:08+00:00","index":8,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-15T16:46:08+00:00","index":6,"fulltext":"This content is not 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