Nucleic acid-triggered tumoral immunity propagates pH-selective therapeutic antibodies through tumor-driven epitope spreading.

Important roles of humoral tumor immunity are often pointed out; however, precise profiles of dominant antigens and developmental mechanisms remain elusive. We systematically investigated the humoral antigens of dominant intratumor immunoglobulin clones found in human cancers. We found that approximately half of the corresponding antigens were restricted to strongly and densely negatively charged polymers, resulting in simultaneous reactivities of the antibodies to both densely sulfated glycosaminoglycans (dsGAGs) and nucleic acids (NAs). These anti-dsGAG/NA antibodies matured and expanded via intratumoral immunological driving force of innate immunity via NAs. These human cancer-derived antibodies exhibited acidic pH-selective affinity across both antigens and showed specific reactivity to diverse spectrums of human tumor cells. The antibody-drug conjugate exerted therapeutic effects against multiple cancers in vivo by targeting cell surface dsGAG antigens. This study reveals that intratumoral immunological reactions propagate tumor-oriented immunoglobulin clones and demonstrates a new therapeutic modality for the universal treatment of human malignancies.

Subclonal accumulation of immune escape mechanisms in microsatellite instability-high colorectal cancers.

BACKGROUND: Intratumor heterogeneity (ITH) in microsatellite instability-high (MSI-H) colorectal cancer (CRC) has been poorly studied. We aimed to clarify how the ITH of MSI-H CRCs is generated in cancer evolution and how immune selective pressure affects ITH. METHODS: We reanalyzed public whole-exome sequencing data on 246 MSI-H CRCs. In addition, we performed a multi-region analysis from 6 MSI-H CRCs. To verify the process of subclonal immune escape accumulation, a novel computational model of cancer evolution under immune pressure was developed. RESULTS: Our analysis presented the enrichment of functional genomic alterations in antigen-presentation machinery (APM). Associative analysis of neoantigens indicated the generation of immune escape mechanisms via HLA alterations. Multiregion analysis revealed the clonal acquisition of driver mutations and subclonal accumulation of APM defects in MSI-H CRCs. Examination of variant allele frequencies demonstrated that subclonal mutations tend to be subjected to selective sweep. Computational simulations of tumour progression with the interaction of immune cells successfully verified the subclonal accumulation of immune escape mutations and suggested the efficacy of early initiation of an immune checkpoint inhibitor (ICI) -based treatment. CONCLUSIONS: Our results demonstrate the heterogeneous acquisition of immune escape mechanisms in MSI-H CRCs by Darwinian selection, providing novel insights into ICI-based treatment strategies.

Quantifying immune-based counterselection of somatic mutations.

Somatic mutations in protein-coding regions can generate 'neoantigens' causing developing cancers to be eliminated by the immune system. Quantitative estimates of the strength of this counterselection phenomenon have been lacking. We quantified the extent to which somatic mutations are depleted in peptides that are predicted to be displayed by major histocompatibility complex (MHC) class I proteins. The extent of this depletion depended on expression level of the neoantigenic gene, and on whether the patient had one or two MHC-encoding alleles that can display the peptide, suggesting MHC-encoding alleles are incompletely dominant. This study provides an initial quantitative understanding of counter-selection of identifiable subclasses of neoantigenic somatic variation.

Neoantimon: a multifunctional R package for identification of tumor-specific neoantigens.

SUMMARY: It is known that some mutant peptides, such as those resulting from missense mutations and frameshift insertions, can bind to the major histocompatibility complex and be presented to antitumor T cells on the surface of a tumor cell. These peptides are termed neoantigen, and it is important to understand this process for cancer immunotherapy. Here, we introduce an R package termed Neoantimon that can predict a list of potential neoantigens from a variety of mutations, which include not only somatic point mutations but insertions, deletions and structural variants. Beyond the existing applications, Neoantimon is capable of attaching and reflecting several additional information, e.g. wild-type binding capability, allele specific RNA expression levels, single nucleotide polymorphism information and combinations of mutations to filter out infeasible peptides as neoantigen. AVAILABILITY AND IMPLEMENTATION: The R package is available at http://github/hase62/Neoantimon.

A Bayesian model integration for mutation calling through data partitioning.

MOTIVATION: Detection of somatic mutations from tumor and matched normal sequencing data has become among the most important analysis methods in cancer research. Some existing mutation callers have focused on additional information, e.g. heterozygous single-nucleotide polymorphisms (SNPs) nearby mutation candidates or overlapping paired-end read information. However, existing methods cannot take multiple information sources into account simultaneously. Existing Bayesian hierarchical model-based methods construct two generative models, the tumor model and error model, and limited information sources have been modeled. RESULTS: We proposed a Bayesian model integration framework named as partitioning-based model integration. In this framework, through introducing partitions for paired-end reads based on given information sources, we integrate existing generative models and utilize multiple information sources. Based on that, we constructed a novel Bayesian hierarchical model-based method named as OHVarfinDer. In both the tumor model and error model, we introduced partitions for a set of paired-end reads that cover a mutation candidate position, and applied a different generative model for each category of paired-end reads. We demonstrated that our method can utilize both heterozygous SNP information and overlapping paired-end read information effectively in simulation datasets and real datasets. AVAILABILITY AND IMPLEMENTATION: https://github.com/takumorizo/OHVarfinDer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ALPHLARD: a Bayesian method for analyzing HLA genes from whole genome sequence data.

BACKGROUND: Although human leukocyte antigen (HLA) genotyping based on amplicon, whole exome sequence (WES), and RNA sequence data has been achieved in recent years, accurate genotyping from whole genome sequence (WGS) data remains a challenge due to the low depth. Furthermore, there is no method to identify the sequences of unknown HLA types not registered in HLA databases. RESULTS: We developed a Bayesian model, called ALPHLARD, that collects reads potentially generated from HLA genes and accurately determines a pair of HLA types for each of HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, and -DRB1 genes at 3rd field resolution. Furthermore, ALPHLARD can detect rare germline variants not stored in HLA databases and call somatic mutations from paired normal and tumor sequence data. We illustrate the capability of ALPHLARD using 253 WES data and 25 WGS data from Illumina platforms. By comparing the results of HLA genotyping from SBT and amplicon sequencing methods, ALPHLARD achieved 98.8% for WES data and 98.5% for WGS data at 2nd field resolution. We also detected three somatic point mutations and one case of loss of heterozygosity in the HLA genes from the WGS data. CONCLUSIONS: ALPHLARD showed good performance for HLA genotyping even from low-coverage data. It also has a potential to detect rare germline variants and somatic mutations in HLA genes. It would help to fill in the current gaps in HLA reference databases and unveil the immunological significance of somatic mutations identified in HLA genes.

HLA Typing and Mutation Calling from Normal and Tumor Whole Genome Sequencing Data with ALPHLARD-NT.

HLA somatic mutations can alter the expression and function of HLA molecules, which in turn affect the ability of the immune system to recognize and respond to cancer cells. Therefore, it is crucial to accurately identify HLA somatic mutations to enhance our understanding of the interaction between cancer and the immune system and improve cancer treatment strategies. ALPHLARD-NT is a reliable tool that can accurately identify HLA somatic mutations as well as HLA genotypes from whole genome sequencing data of paired normal and tumor samples. Here, we provide a comprehensive guide on how to use ALPHLARD-NT and interpret the results.

DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates.

Messenger RNA splicing and degradation are critical for gene expression regulation, the abnormality of which leads to diseases. Previous methods for estimating kinetic rates have limitations, assuming uniform rates across cells. DeepKINET is a deep generative model that estimates splicing and degradation rates at single-cell resolution from scRNA-seq data. DeepKINET outperforms existing methods on simulated and metabolic labeling datasets. Applied to forebrain and breast cancer data, it identifies RNA-binding proteins responsible for kinetic rate diversity. DeepKINET also analyzes the effects of splicing factor mutations on target genes in erythroid lineage cells. DeepKINET effectively reveals cellular heterogeneity in post-transcriptional regulation.

Salivary Duct Carcinoma Arising in the Submandibular Gland in a Patient with Neurofibromatosis Type 1.

A 71-year-old man with neurofibromatosis type 1 (NF1) presented to our department with a 1-week history of a painful mass in the left submandibular area. Computed tomography (CT) and magnetic resonance imaging revealed an irregular-shaped tumor with a diameter of 2.0 cm in the left submandibular gland and a metastatic lymph node with a diameter of 1.0 cm adjacent to the tumor. Fluorodeoxyglucose-positron emission tomography/CT revealed increased uptake in the tumor. Fine-needle aspiration cytology revealed atypical cells, suggesting salivary duct carcinoma (SDC). Left neck dissection with resection of the tumor and submandibular gland was performed under general anesthesia. Histologic examination revealed ductal formation with a solid, cystic, cribriform, and papillary structure with intraductal comedonecrosis, diagnosing as SDC originating in the submandibular gland (pT3N1M0 pStage III). Mutational analysis of 160 cancer-related genes by next-generation sequencing (NGS) revealed a germline and frameshift mutation in the NF1 gene (p.R2408Kfs*14) and a somatic and frameshift mutation in the TP53 gene (p.C176Wfs*22). The patient received postoperative radiotherapy to the left neck area at 66 Gy. No evidence of recurrence or metastasis has been observed as of 10 months postoperatively. This is the first reported case of SDC in the submandibular gland in a patient with NF1. The mutational data by NGS may contribute to a better understanding of the oncogenesis of SDC in patients with NF1.

Single-cell colocalization analysis using a deep generative model.

Analyzing colocalization of single cells with heterogeneous molecular phenotypes is essential for understanding cell-cell interactions, and cellular responses to external stimuli and their biological functions in diseases and tissues. However, existing computational methodologies identified the colocalization patterns between predefined cell populations, which can obscure the molecular signatures arising from intercellular communication. Here, we introduce DeepCOLOR, a computational framework based on a deep generative model that recovers intercellular colocalization networks with single-cell resolution by the integration of single-cell and spatial transcriptomes. Along with colocalized population detection accuracy that is superior to existing methods in simulated dataset, DeepCOLOR identified plausible cell-cell interaction candidates between colocalized single cells and segregated cell populations defined by the colocalization relationships in mouse brain tissues, human squamous cell carcinoma samples, and human lung tissues infected with SARS-CoV-2. DeepCOLOR is applicable to studying cell-cell interactions behind various spatial niches. A record of this paper's transparent peer review process is included in the supplemental information.

BRAF V600E mutation co-existing with oncogenic mutations is associated with aggressive clinicopathologic features and poor prognosis in papillary thyroid carcinoma.

BACKGROUND: The aim of this study was to evaluate the correlation among mutations in cancer-related genes, clinicopathologic features, and clinical outcome in classical papillary thyroid carcinoma (PTC). PATIENTS AND METHODS: A total of 130 patients with classical PTC who underwent curative surgery between April 2012 and June 2023 at Hokuto Hospital were included. Mutations in targeted regions of 160 cancer-related genes were detected by next-generation sequencing (NGS)-based cancer panel testing. RESULTS: The BRAF V600E mutation was detected in 108 (83.1%) of 130 PTC patients. Among the 108 patients with the BRAF V600E mutation, other co-existing oncogenic mutations were found in 12 (9.2%) patients. When we divided into 3 groups of no mutations, BRAF V600E mutation alone, and BRAF V600E and other oncogenic mutations, significant differences were observed in terms of tracheal invasion (P = 0.0024), and bilateral neck lymph node metastasis (P = 0.0047). Kaplan-Meier analysis of overall survival (OS) revealed patients with BRAF V600E and other oncogenic mutations had significantly poorer survival than those with BRAF V600E mutation alone (P = 0.0026). Multivariate cox proportional hazard analysis revealed BRAF V600E and other oncogenic mutations was an independent prognostic factor for OS (HR: 10.559; 95%CI: 1.007-110.656, P = 0.0493). CONCLUSIONS: The BRAF V600E mutation co-existing with other oncogenic mutations but not the BRAF V600E mutation alone was associated with aggressive clinicopathologic features, resulting in poor prognosis in patients with classical PTC. Detection of oncogenic mutations using NGS-based cancer panel testing could enhance understanding of the clinical features of classical PTC.

A case of hyalinizing trabecular tumor of the thyroid: diagnostic significance of PAX8-GLIS3 fusion.

BACKGROUND: Hyalinizing trabecular tumor (HTT) is an uncommon follicular cell-derived thyroid tumor classified as a low-risk neoplasm by the World Health Organization Classification of Tumors of Endocrine Organs, 5th edition. The PAX8-GLIS3 gene fusion is reportedly a pathognomonic genetic alteration of HTT. CASE PRESENTATION: A 43-year-old Japanese female was incidentally discovered to have an 8-mm, well-defined, hypoechoic mass in the left lobe of the thyroid gland by ultrasound examination. Contrast-enhanced computed tomography scan revealed a solid mass exhibiting slight homogeneous enhancement in the lower pole of the thyroid gland. The mass was diagnosed as atypia of undetermined significance by fine-needle aspiration cytology. The patient underwent left hemithyroidectomy with routine central compartment dissection. Histologic findings revealed tumor cells with elongated nuclei and intranuclear pseudoinclusions arranged with trabeculae architecture or small nests in hyalinized stroma. Weak membranous and cytoplasmic staining was found by MIB1 (Ki-67) immunostaining. The final diagnosis was HTT of the thyroid gland. Next-generation sequencing genetic analysis of a surgical specimen revealed no pathologic mutations, including BRAF, H/K/NRAS, or RET-PTC fusions. The PAX8-GLIS3 fusion was detected by RT-PCR. CONCLUSIONS: A rare case of HTT was demonstrated through imaging, cytologic, histologic and molecular investigations. PAX8-GLIS3 fusion detected by RT-PCR and Sanger sequencing was confirmed to be a genetic hallmark of HTT.

Spatial and single-cell colocalisation analysis reveals MDK-mediated immunosuppressive environment with regulatory T cells in colorectal carcinogenesis.

BACKGROUND: Cell-cell interaction factors that facilitate the progression of adenoma to sporadic colorectal cancer (CRC) remain unclear, thereby hindering patient survival. METHODS: We performed spatial transcriptomics on five early CRC cases, which included adenoma and carcinoma, and one advanced CRC. To elucidate cell-cell interactions within the tumour microenvironment (TME), we investigated the colocalisation network at single-cell resolution using a deep generative model for colocalisation analysis, combined with a single-cell transcriptome, and assessed the clinical significance in CRC patients. FINDINGS: CRC cells colocalised with regulatory T cells (Tregs) at the adenoma-carcinoma interface. At early-stage carcinogenesis, cell-cell interaction inference between colocalised adenoma and cancer epithelial cells and Tregs based on the spatial distribution of single cells highlighted midkine (MDK) as a prominent signalling molecule sent from tumour epithelial cells to Tregs. Interaction between MDK-high CRC cells and SPP1+ macrophages and stromal cells proved to be the mechanism underlying immunosuppression in the TME. Additionally, we identified syndecan4 (SDC4) as a receptor for MDK associated with Treg colocalisation. Finally, clinical analysis using CRC datasets indicated that increased MDK/SDC4 levels correlated with poor overall survival in CRC patients. INTERPRETATION: MDK is involved in the immune tolerance shown by Tregs to tumour growth. MDK-mediated formation of the TME could be a potential target for early diagnosis and treatment of CRC. FUNDING: Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Science Research; OITA Cancer Research Foundation; AMED under Grant Number; Japan Science and Technology Agency (JST); Takeda Science Foundation; The Princess Takamatsu Cancer Research Fund.

Analyzing Antibody Repertoire Using Next-Generation Sequencing and Machine Learning.

Advances in high-throughput sequencing technologies have enabled comprehensive sequencing of the immune repertoire. Since repertoire analysis can help to explain the relationship between the immune system and diseases, several methods have been developed for repertoire analysis. Here, using simulated and real-world datasets, we describe how to use DeepRC, a method that applies cutting-edge machine learning techniques.

Internal carotid artery dissection caused by elongated styloid process.

A 69-year-old man with impaired consciousness, right hemiplegia, and aphasia was admitted to our emergency room for thorough examination. Magnetic resonance imaging (MRI) and 3-dimensional computed tomography (3D CT) scan of the head revealed a cerebral infarction due to dissection of the left internal carotid artery. Contrast-enhanced CT prior to internal carotid artery stenting showed that the left elongated styloid process ran in close proximity to the left internal carotid artery, with a minimum distance of 2 mm. The patient underwent stenting at the internal carotid artery 16 days after disease onset. The patient was referred to our department for left elongated styloid process resection to reduce the risk of further internal carotid artery injury. Resection of the left styloid process through a cervical incision was performed. Six months after surgery, there was no recurrence of the internal carotid artery dissection.

Topological data analysis of protein structure and inter/intra-molecular interaction changes attributable to amino acid mutations.

The presence of some amino acid mutations in the amino acid sequence that determines a protein's structure can significantly affect that 3D structure and its biological function. However, the effects upon structural and functional changes differ for each displaced amino acid, and it is very difficult to predict these changes in advance. Although computer simulations are very effective at predicting conformational changes, they struggle to determine whether the amino acid mutation of interest induces sufficient conformational changes, unless the researcher is a specialist in molecular structure calculations. Therefore, we created a framework that efficiently utilizes molecular dynamics and persistent homology methods to identify amino acid mutations that induce structural changes. We show that this framework can be used not only to predict conformational changes produced by amino acid mutations but also to extract groups of mutations that significantly alter similar molecular interactions, by capturing the resultant protein-protein interaction changes.

Spatial and single-cell transcriptomics decipher the cellular environment containing HLA-G+ cancer cells and SPP1+ macrophages in colorectal cancer.

The cellular interactions in the tumor microenvironment of colorectal cancer (CRC) are poorly understood, hindering patient treatment. In the current study, we investigate whether events occurring at the invasion front are of particular importance for CRC treatment strategies. To this end, we analyze CRC tissues by combining spatial transcriptomics from patients with a public single-cell transcriptomic atlas to determine cell-cell interactions at the invasion front. We show that CRC cells are localized specifically at the invasion front. These cells induce human leukocyte antigen G (HLA-G) to produce secreted phosphoprotein 1 (SPP1)+ macrophages while conferring CRC cells with anti-tumor immunity, as well as proliferative and invasive properties. Taken together, these findings highlight the signaling between CRC cell populations and stromal cell populations at the cellular level.

Bayesian statistical method for detecting structural and topological diversity in polymorphic proteins.

Polymorphisms in immune-related proteins and viral spike proteins are high and complicate host-virus interactions. Therefore, diversity analysis of such protein structures is essential to understand the mechanism of the immune system. However, experimental methods, including X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy, have several problems: (i) they are conducted under different conditions from the actual cellular environment, (ii) they are laborious, time-consuming, and expensive, and (iii) they do not provide information on the thermodynamic behaviors. In this paper, we propose a computational method to solve these problems by using MD simulations, persistent homology, and a Bayesian statistical model. We apply our method to eight types of HLA-DR complexes to evaluate the structural diversity. The results show that our method can correctly discriminate the intrinsic structural variations caused by amino acid mutations from the random fluctuations caused by thermal vibrations. In the end, we discuss the applicability of our method in combination with existing deep learning-based methods for protein structure analysis.

Immunogenomic pan-cancer landscape reveals immune escape mechanisms and immunoediting histories.

Immune reactions in the tumor microenvironment are an important hallmark of cancer, and emerging immune therapies have been proven effective against several types of cancers. To investigate cancer genome-immune interactions and the role of immunoediting or immune escape mechanisms in cancer development, we analyzed 2834 whole genome and RNA sequencing datasets across 31 distinct tumor types with respect to key immunogenomic aspects and provided comprehensive immunogenomic profiles of pan-cancers. We found that selective copy number changes in immune-related genes may contribute to immune escape. Furthermore, we developed an index of the immunoediting history of each tumor sample based on the information of mutations in exonic regions and pseudogenes and evaluated the immunoediting history of each tumor. Our immuno-genomic analyses of pan-cancers have the potential to identify a subset of tumors with immunogenicity and diverse backgrounds or intrinsic pathways associated with their immune status and immunoediting history.

Classification of primary liver cancer with immunosuppression mechanisms and correlation with genomic alterations.

BACKGROUND: The tumor microenvironment can be classified into immunologically active "inflamed" tumors and inactive "non-inflamed" tumors based on the infiltration of cytotoxic immune cells. Previous studies on liver cancer have reported a superior prognosis for inflamed tumors compared to non-inflamed tumors. However, liver cancer is highly heterogeneous immunologically and genetically, and a finer classification of the liver cancer microenvironment may improve our understanding of its immunological diversity and response to immune therapy. METHODS: We characterized the immune gene signatures of 234 primary liver cancers, mainly virus-related, from a Japanese population using RNA-Seq of tumors and matched non-tumorous hepatitis livers. We then compared them with the somatic alterations detected using the whole-genome sequencing. FINDINGS: Liver cancers expressed lower levels of immune marker genes than non-tumorous hepatitis livers, indicating immunosuppression in the tumor microenvironment. Several immunosuppression mechanisms functioned actively and mutually exclusively, resulting in four immune subclasses of liver cancer: tumor-associated macrophage (TAM), CTNNB1, cytolytic activity (CYT), and regulatory T cell (Treg). The CYT and Treg subclasses represented inflamed tumors, while the TAM and CTNNB1 subclasses represented non-inflamed tumors. The TAM subclass, which comprised 31% of liver cancers, showed a poor survival, expressed elevated levels of extracellular matrix genes, and was associated with somatic mutations of chromatin regulator ARID2. The results of cell line experiments suggested a functional link between ARID2 and chemokine production by liver cancer cells. INTERPRETATION: Primary liver cancer was classified into four subclasses based on mutually exclusive mechanisms for immunosuppression. This classification indicate the importance of immunosuppression mechanisms, such as TAM and Treg, as therapeutic targets for liver cancer. FUNDING: The Japan Agency for Medical Research and Development (AMED).