Integrated analysis and validation of ferroptosis-related genes and immune infiltration in acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) is indeed a significant cause of mortality and morbidity in individuals with coronary heart disease. Ferroptosis, an iron-dependent cell death, is characterized by the accumulation of intracellular lipid peroxides, which is implicated in cardiomyocyte injury. This study aims to identify biomarkers that are indicative of ferroptosis in the context of AMI, and to examine their potential roles in immune infiltration. METHODS: Firstly, the GSE59867 dataset was used to identify differentially expressed ferroptosis-related genes (DE-FRGs) in AMI. We then performed gene ontology (GO) and functional enrichment analysis on these DE-FRGs. Secondly, we analyzed the GSE76591 dataset and used bioinformatic methods to build ceRNA networks. Thirdly, we identified hub genes in protein-protein interaction (PPI) network. After obtaining the key DE-FRGs through the junction of hub genes with ceRNA and least absolute shrinkage and selection operator (LASSO). ImmucellAI was applied to estimate the immune cell infiltration in each sample and examine the relationship between key DE-FRGs and 24 immunocyte subsets. The diagnostic performance of these genes was further evaluated using the receiver operating characteristic (ROC) curve analysis. Ultimately, we identified an immune-related ceRNA regulatory axis linked to ferroptosis in AMI. RESULTS: Among 56 DE-FRGs identified in AMI, 41 of them were integrated into the construction of competitive endogenous RNA (ceRNA) networks. TLR4 and PIK3CA were identified as key DE-FRGs and PIK3CA was confirmed as a diagnostic biomarker for AMI. Moreover, CD4_native cells, nTreg cells, Th2 cells, Th17 cells, central-memory cells, effector-memory cells, and CD8_T cells had higher infiltrates in AMI samples compared to control samples. In contrast, exhausted cells, iTreg cells, and Tfh cells had lower infiltrates in AMI samples. Spearman analysis confirmed the correlation between 24 immune cells and PIK3CA/TLR4. Ultimately, we constructed an immune-related regulatory axis involving XIST and OIP5-AS1/miR-216a/PIK3CA. CONCLUSION: Our comprehensive analysis has identified PIK3CA as a robust and promising biomarker for this condition. Moreover, we have also identified an immune-related regulatory axis involving XIST and OIP5-AS1/miR-216a/PIK3CA, which may play a key role in regulating ferroptosis during AMI progression.

Antiplatelet effects of the CEACAM1-derived peptide QDTT.

Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) restricts platelet activation via platelet collagen receptor GPVI/FcRγ-chain. In this study, screening against collagen-induced platelet aggregation was performed to identify functional CEACAM1 extracellular domain fragments. CEACAM1 fragments, including Ala-substituted peptides, were synthesized. Platelet assays were conducted on healthy donor samples for aggregation, cytotoxicity, adhesion, spreading, and secretion. Mice were used for tail bleeding and FeCl3-induced thrombosis experiments. Clot retraction was assessed using platelet-rich plasma. Extracellular segments of CEACAM1 and A1 domain-derived peptide QDTT were identified, while N, A2, and B domains showed no involvement. QDTT inhibited platelet aggregation. Ala substitution for essential amino acids (Asp139, Thr141, Tyr142, Trp144, and Trp145) in the QDTT sequence abrogated collagen-induced aggregation inhibition. QDTT also suppressed platelet secretion and "inside-out" GP IIb/IIIa activation by convulxin, along with inhibiting PI3K/Akt pathways. QDTT curtailed FeCl3-induced mesenteric thrombosis without significantly prolonging bleeding time, implying the potential of CEACAM1 A1 domain against platelet activation without raising bleeding risk, thus paving the way for novel antiplatelet drugs.

What is the context? The study focuses on Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) and its role in platelet activation, particularly through the GPVI/FcRγ-chain pathway.The research aims to identify specific fragments of CEACAM1's extracellular domain that could restrict platelet activation, without increasing bleeding risk.What is new? The researchers identified a peptide called QDTT derived from the A1 domain of CEACAM1's extracellular segment. This peptide demonstrated the ability to inhibit platelet aggregation, secretion, and GP IIb/IIIa activation.The study also revealed that specific amino acids within the QDTT sequence were essential for its inhibitory effects on collagen-induced aggregation.What is the impact? The findings suggest that the A1 domain-derived peptide QDTT from CEACAM1 could serve as a potential basis for developing novel antiplatelet drugs. This peptide effectively limits platelet activation and aggregation without significantly prolonging bleeding time, indicating a promising approach to managing thrombosis and related disorders while minimizing bleeding risks.

Luteolin inhibits GPVI-mediated platelet activation, oxidative stress, and thrombosis.

Introduction: Luteolin inhibits platelet activation and thrombus formation, but the mechanisms are unclear. This study investigated the effects of luteolin on GPVI-mediated platelet activation in vitro and explored the effect of luteolin on thrombosis, coagulation, and platelet production in vivo. Methods: Washed human platelets were used for aggregation, membrane protein expression, ATP, Ca2+, and LDH release, platelet adhesion/spreading, and clot retraction experiments. Washed human platelets were used to detect collagen and convulxin-induced reactive oxygen species production and endogenous antioxidant effects. C57BL/6 male mice were used for ferric chloride-induced mesenteric thrombosis, collagen-epinephrine induced acute pulmonary embolism, tail bleeding, coagulation function, and luteolin toxicity experiments. The interaction between luteolin and GPVI was analyzed using solid phase binding assay and surface plasmon resonance (SPR). Results: Luteolin inhibited collagen- and convulxin-mediated platelet aggregation, adhesion, and release. Luteolin inhibited collagen- and convulxin-induced platelet ROS production and increased platelet endogenous antioxidant capacity. Luteolin reduced convulxin-induced activation of ITAM and MAPK signaling molecules. Molecular docking simulation showed that luteolin forms hydrogen bonds with GPVI. The solid phase binding assay showed that luteolin inhibited the interaction between collagen and GPVI. Surface plasmon resonance showed that luteolin bonded GPVI. Luteolin inhibited integrin αIIbß3-mediated platelet activation. Luteolin inhibited mesenteric artery thrombosis and collagen- adrenergic-induced pulmonary thrombosis in mice. Luteolin decreased oxidative stress in vivo. Luteolin did not affect coagulation, hemostasis, or platelet production in mice. Discussion: Luteolin may be an effective and safe antiplatelet agent target for GPVI. A new mechanism (decreased oxidative stress) for the anti-platelet activity of luteolin has been identified.

LENS: Landscape of Effective Neoantigens Software.

MOTIVATION: Elimination of cancer cells by T cells is a critical mechanism of anti-tumor immunity and cancer immunotherapy response. T cells recognize cancer cells by engagement of T cell receptors with peptide epitopes presented by major histocompatibility complex molecules on the cancer cell surface. Peptide epitopes can be derived from antigen proteins coded for by multiple genomic sources. Bioinformatics tools used to identify tumor-specific epitopes via analysis of DNA and RNA-sequencing data have largely focused on epitopes derived from somatic variants, though a smaller number have evaluated potential antigens from other genomic sources. RESULTS: We report here an open-source workflow utilizing the Nextflow DSL2 workflow manager, Landscape of Effective Neoantigens Software (LENS), which predicts tumor-specific and tumor-associated antigens from single nucleotide variants, insertions and deletions, fusion events, splice variants, cancer-testis antigens, overexpressed self-antigens, viruses, and endogenous retroviruses. The primary advantage of LENS is that it expands the breadth of genomic sources of discoverable tumor antigens using genomics data. Other advantages include modularity, extensibility, ease of use, and harmonization of relative expression level and immunogenicity prediction across multiple genomic sources. We present an analysis of 115 acute myeloid leukemia samples to demonstrate the utility of LENS. We expect LENS will be a valuable platform and resource for T cell epitope discovery bioinformatics, especially in cancers with few somatic variants where tumor-specific epitopes from alternative genomic sources are an elevated priority. AVAILABILITY AND IMPLEMENTATION: More information about LENS, including code, workflow documentation, and instructions, can be found at (https://gitlab.com/landscape-of-effective-neoantigens-software).

Comprehensive Analysis of the Immunogenomics of Triple-Negative Breast Cancer Brain Metastases From LCCC1419.

Background: Triple negative breast cancer (TNBC) is an aggressive variant of breast cancer that lacks the expression of estrogen and progesterone receptors (ER and PR) and HER2. Nearly 50% of patients with advanced TNBC will develop brain metastases (BrM), commonly with progressive extracranial disease. Immunotherapy has shown promise in the treatment of advanced TNBC; however, the immune contexture of BrM remains largely unknown. We conducted a comprehensive analysis of TNBC BrM and matched primary tumors to characterize the genomic and immune landscape of TNBC BrM to inform the development of immunotherapy strategies in this aggressive disease. Methods: Whole-exome sequencing (WES) and RNA sequencing were conducted on formalin-fixed, paraffin-embedded samples of BrM and primary tumors of patients with clinical TNBC (n = 25, n = 9 matched pairs) from the LCCC1419 biobank at UNC-Chapel Hill. Matched blood was analyzed by DNA sequencing as a comparison for tumor WES for the identification of somatic variants. A comprehensive genomics assessment, including mutational and copy number alteration analyses, neoantigen prediction, and transcriptomic analysis of the tumor immune microenvironment were performed. Results: Primary and BrM tissues were confirmed as TNBC (23/25 primaries, 16/17 BrM) by immunohistochemistry and of the basal intrinsic subtype (13/15 primaries and 16/19 BrM) by PAM50. Compared to primary tumors, BrM demonstrated a higher tumor mutational burden. TP53 was the most frequently mutated gene and was altered in 50% of the samples. Neoantigen prediction showed elevated cancer testis antigen- and endogenous retrovirus-derived MHC class I-binding peptides in both primary tumors and BrM and predicted that single-nucleotide variant (SNV)-derived peptides were significantly higher in BrM. BrM demonstrated a reduced immune gene signature expression, although a signature associated with fibroblast-associated wound healing was elevated in BrM. Metrics of T and B cell receptor diversity were also reduced in BrM. Conclusions: BrM harbored higher mutational burden and SNV-derived neoantigen expression along with reduced immune gene signature expression relative to primary TNBC. Immune signatures correlated with improved survival, including T cell signatures. Further research will expand these findings to other breast cancer subtypes in the same biobank. Exploration of immunomodulatory approaches including vaccine applications and immune checkpoint inhibition to enhance anti-tumor immunity in TNBC BrM is warranted.

NeoSplice: a bioinformatics method for prediction of splice variant neoantigens.

Motivation: Splice variant neoantigens are a potential source of tumor-specific antigen (TSA) that are shared between patients in a variety of cancers, including acute myeloid leukemia. Current tools for genomic prediction of splice variant neoantigens demonstrate promise. However, many tools have not been well validated with simulated and/or wet lab approaches, with no studies published that have presented a targeted immunopeptidome mass spectrometry approach designed specifically for identification of predicted splice variant neoantigens. Results: In this study, we describe NeoSplice, a novel computational method for splice variant neoantigen prediction based on (i) prediction of tumor-specific k-mers from RNA-seq data, (ii) alignment of differentially expressed k-mers to the splice graph and (iii) inference of the variant transcript with MHC binding prediction. NeoSplice demonstrates high sensitivity and precision (>80% on average across all splice variant classes) through in silico simulated RNA-seq data. Through mass spectrometry analysis of the immunopeptidome of the K562.A2 cell line compared against a synthetic peptide reference of predicted splice variant neoantigens, we validated 4 of 37 predicted antigens corresponding to 3 of 17 unique splice junctions. Lastly, we provide a comparison of NeoSplice against other splice variant prediction tools described in the literature. NeoSplice provides a well-validated platform for prediction of TSA vaccine targets for future cancer antigen vaccine studies to evaluate the clinical efficacy of splice variant neoantigens. Availability and implementation: https://github.com/Benjamin-Vincent-Lab/NeoSplice. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Stimulation of Oncogene-Specific Tumor-Infiltrating T Cells through Combined Vaccine and αPD-1 Enable Sustained Antitumor Responses against Established HER2 Breast Cancer.

PURPOSE: Despite promising advances in breast cancer immunotherapy, augmenting T-cell infiltration has remained a significant challenge. Although neither individual vaccines nor immune checkpoint blockade (ICB) have had broad success as monotherapies, we hypothesized that targeted vaccination against an oncogenic driver in combination with ICB could direct and enable antitumor immunity in advanced cancers. EXPERIMENTAL DESIGN: Our models of HER2+ breast cancer exhibit molecular signatures that are reflective of advanced human HER2+ breast cancer, with a small numbers of neoepitopes and elevated immunosuppressive markers. Using these, we vaccinated against the oncogenic HER2Δ16 isoform, a nondriver tumor-associated gene (GFP), and specific neoepitopes. We further tested the effect of vaccination or anti-PD-1, alone and in combination. RESULTS: We found that only vaccination targeting HER2Δ16, a driver of oncogenicity and HER2-therapeutic resistance, could elicit significant antitumor responses, while vaccines targeting a nondriver tumor-specific antigen or tumor neoepitopes did not. Vaccine-induced HER2-specific CD8+ T cells were essential for responses, which were more effective early in tumor development. Long-term tumor control of advanced cancers occurred only when HER2Δ16 vaccination was combined with αPD-1. Single-cell RNA sequencing of tumor-infiltrating T cells revealed that while vaccination expanded CD8 T cells, only the combination of vaccine with αPD-1 induced functional gene expression signatures in those CD8 T cells. Furthermore, we show that expanded clones are HER2-reactive, conclusively demonstrating the efficacy of this vaccination strategy in targeting HER2. CONCLUSIONS: Combining oncogenic driver targeted vaccines with selective ICB offers a rational paradigm for precision immunotherapy, which we are clinically evaluating in a phase II trial (NCT03632941).

Alkali Metal Cationization of Tumor-associated Antigen Peptides for Improved Dissociation and Measurement by Differential Ion Mobility-Mass Spectrometry.

Tandem mass spectrometry (MS/MS) is a highly sensitive and selective method for the detection of tumor-associated peptide antigens. These short, nontryptic sequences may lack basic residues, resulting in the formation of predominantly [peptide + H]+ ions in electrospray. These singly charged ions tend to undergo inefficient dissociation, leading to issues in sequence determination. Addition of alkali metal salts to the electrospray solvent can drive the formation of [peptide + H + metal]2+ ions that have enhanced dissociation characteristics relative to [peptide + H]+ ions. Both previously identified tumor-associated antigens and predicted neoantigen sequences were investigated. The previously reported rearrangement mechanism in MS/MS of sodium-cationized peptides is applied here to demonstrate complete C-terminal sequencing of tumor-associated peptide antigens. Differential ion mobility spectrometry (DIMS) is shown to selectively enrich [peptide + H + metal]2+ species by filtering out singly charged interferences at relatively low field strengths, offsetting the decrease in signal intensity associated with the use of alkali metal cations.

Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes.

Current tumor neoantigen calling algorithms primarily rely on epitope/major histocompatibility complex (MHC) binding affinity predictions to rank and select for potential epitope targets. These algorithms do not predict for epitope immunogenicity using approaches modeled from tumor-specific antigen data. Here, we describe peptide-intrinsic biochemical features associated with neoantigen and minor histocompatibility mismatch antigen immunogenicity and present a gradient boosting algorithm for predicting tumor antigen immunogenicity. This algorithm was validated in two murine tumor models and demonstrated the capacity to select for therapeutically active antigens. Immune correlates of neoantigen immunogenicity were studied in a pan-cancer data set from The Cancer Genome Atlas and demonstrated an association between expression of immunogenic neoantigens and immunity in colon and lung adenocarcinomas. Lastly, we present evidence for expression of an out-of-frame neoantigen that was capable of driving antitumor cytotoxic T-cell responses. With the growing clinical importance of tumor vaccine therapies, our approach may allow for better selection of therapeutically relevant tumor-specific antigens, including nonclassic out-of-frame antigens capable of driving antitumor immunity.

Alternative tumour-specific antigens.

The study of tumour-specific antigens (TSAs) as targets for antitumour therapies has accelerated within the past decade. The most commonly studied class of TSAs are those derived from non-synonymous single-nucleotide variants (SNVs), or SNV neoantigens. However, to increase the repertoire of available therapeutic TSA targets, 'alternative TSAs', defined here as high-specificity tumour antigens arising from non-SNV genomic sources, have recently been evaluated. Among these alternative TSAs are antigens derived from mutational frameshifts, splice variants, gene fusions, endogenous retroelements and other processes. Unlike the patient-specific nature of SNV neoantigens, some alternative TSAs may have the advantage of being widely shared by multiple tumours, allowing for universal, off-the-shelf therapies. In this Opinion article, we will outline the biology, available computational tools, preclinical and/or clinical studies and relevant cancers for each alternative TSA class, as well as discuss both current challenges preventing the therapeutic application of alternative TSAs and potential solutions to aid in their clinical translation.

Prognostic value of B cells in cutaneous melanoma.

BACKGROUND: Measures of the adaptive immune response have prognostic and predictive associations in melanoma and other cancer types. Specifically, intratumoral T cell density and function have considerable prognostic and predictive value in skin cutaneous melanoma (SKCM). Less is known about the significance of tumor-infiltrating B cells in SKCM. Our goal was to understand the prognostic and predictive value of B cell phenotypic subsets in SKCM using RNA sequencing. METHODS: We used our previously published algorithm, V'DJer, to assemble B cell receptor (BCR) repertoires and estimate diversity from short-read RNA sequencing (RNA-seq). We applied machine learning-based cellular phenotype classifiers to measure relative similarity of bulk tumor sample gene expression profiles and different B cell phenotypes. We assessed these aspects of B cell biology in 473 SKCM from the Cancer Genome Atlas Project (TCGA) as well as in RNA-seq data corresponding to tumor samples procured from patients who received CTLA-4 and PD-1 inhibitors for metastatic SKCM. RESULTS: We found that the BCR repertoire was associated with different clinical factors, such as tumor tissue site and sex. However, increased clonality of the BCR repertoire was favorably prognostic in SKCM and was prognostic even after first conditioning on various clinical factors. Mutation burden was not correlated with any BCR measurement, and no specific mutation had an altered BCR repertoire. Lack of an assembled BCR in pre-treatment tumor tissues was associated with a lack of anti-tumor response to a CTLA-4 inhibitor in metastatic SKCM. CONCLUSIONS: These findings suggest an important prognostic and predictive role for B cell characteristics in SKCM. This has implications for melanoma immunobiology and potential development of immunogenomics features to predict survival and response to immunotherapy.

Computational modeling and confirmation of leukemia-associated minor histocompatibility antigens.

T-cell responses to minor histocompatibility antigens (mHAs) mediate both antitumor immunity (graft-versus-leukemia [GVL]) and graft-versus-host disease (GVHD) in allogeneic stem cell transplant. Identifying mHAs with high allele frequency, tight binding affinity to common HLA molecules, and narrow tissue restriction could enhance immunotherapy against leukemia. Genotyping and HLA allele data from 101 HLA-matched donor-recipient pairs (DRPs) were computationally analyzed to predict both class I and class II mHAs likely to induce either GVL or GVHD. Roughly twice as many mHAs were predicted in HLA-matched unrelated donor (MUD) stem cell transplantation (SCT) compared with HLA-matched related transplants, an expected result given greater genetic disparity in MUD SCT. Computational analysis predicted 14 of 18 previously identified mHAs, with 2 minor antigen mismatches not being contained in the patient cohort, 1 missed mHA resulting from a noncanonical translation of the peptide antigen, and 1 case of poor binding prediction. A predicted peptide epitope derived from GRK4, a protein expressed in acute myeloid leukemia and testis, was confirmed by targeted differential ion mobility spectrometry-tandem mass spectrometry. T cells specific to UNC-GRK4-V were identified by tetramer analysis both in DRPs where a minor antigen mismatch was predicted and in DRPs where the donor contained the allele encoding UNC-GRK4-V, suggesting that this antigen could be both an mHA and a cancer-testis antigen. Computational analysis of genomic and transcriptomic data can reliably predict leukemia-associated mHA and can be used to guide targeted mHA discovery.

Molecular Subtype-Specific Immunocompetent Models of High-Grade Urothelial Carcinoma Reveal Differential Neoantigen Expression and Response to Immunotherapy.

High-grade urothelial cancer contains intrinsic molecular subtypes that exhibit differences in underlying tumor biology and can be divided into luminal-like and basal-like subtypes. We describe here the first subtype-specific murine models of bladder cancer and show that Upk3a-CreERT2; Trp53L/L; PtenL/L; Rosa26LSL-Luc (UPPL, luminal-like) and BBN (basal-like) tumors are more faithful to human bladder cancer than the widely used MB49 cells. Following engraftment into immunocompetent C57BL/6 mice, BBN tumors were more responsive to PD-1 inhibition than UPPL tumors. Responding tumors within the BBN model showed differences in immune microenvironment composition, including increased ratios of CD8+:CD4+ and memory:regulatory T cells. Finally, we predicted and confirmed immunogenicity of tumor neoantigens in each model. These UPPL and BBN models will be a valuable resource for future studies examining bladder cancer biology and immunotherapy.Significance: This work establishes human-relevant mouse models of bladder cancer. Cancer Res; 78(14); 3954-68. ©2018 AACR.

Systematic Analysis of Splice-Site-Creating Mutations in Cancer.

For the past decade, cancer genomic studies have focused on mutations leading to splice-site disruption, overlooking those having splice-creating potential. Here, we applied a bioinformatic tool, MiSplice, for the large-scale discovery of splice-site-creating mutations (SCMs) across 8,656 TCGA tumors. We report 1,964 originally mis-annotated mutations having clear evidence of creating alternative splice junctions. TP53 and GATA3 have 26 and 18 SCMs, respectively, and ATRX has 5 from lower-grade gliomas. Mutations in 11 genes, including PARP1, BRCA1, and BAP1, were experimentally validated for splice-site-creating function. Notably, we found that neoantigens induced by SCMs are likely several folds more immunogenic compared to missense mutations, exemplified by the recurrent GATA3 SCM. Further, high expression of PD-1 and PD-L1 was observed in tumors with SCMs, suggesting candidates for immune blockade therapy. Our work highlights the importance of integrating DNA and RNA data for understanding the functional and the clinical implications of mutations in human diseases.

HLAProfiler utilizes k-mer profiles to improve HLA calling accuracy for rare and common alleles in RNA-seq data.

BACKGROUND: The human leukocyte antigen (HLA) system is a genomic region involved in regulating the human immune system by encoding cell membrane major histocompatibility complex (MHC) proteins that are responsible for self-recognition. Understanding the variation in this region provides important insights into autoimmune disorders, disease susceptibility, oncological immunotherapy, regenerative medicine, transplant rejection, and toxicogenomics. Traditional approaches to HLA typing are low throughput, target only a few genes, are labor intensive and costly, or require specialized protocols. RNA sequencing promises a relatively inexpensive, high-throughput solution for HLA calling across all genes, with the bonus of complete transcriptome information and widespread availability of historical data. Existing tools have been limited in their ability to accurately and comprehensively call HLA genes from RNA-seq data. RESULTS: We created HLAProfiler ( https://github.com/ExpressionAnalysis/HLAProfiler ), a k-mer profile-based method for HLA calling in RNA-seq data which can identify rare and common HLA alleles with > 99% accuracy at two-field precision in both biological and simulated data. For 68% of novel alleles not present in the reference database, HLAProfiler can correctly identify the two-field precision or exact coding sequence, a significant advance over existing algorithms. CONCLUSIONS: HLAProfiler allows for accurate HLA calls in RNA-seq data, reliably expanding the utility of these data in HLA-related research and enabling advances across a broad range of disciplines. Additionally, by using the observed data to identify potential novel alleles and update partial alleles, HLAProfiler will facilitate further improvements to the existing database of reference HLA alleles. HLAProfiler is available at https://expressionanalysis.github.io/HLAProfiler/ .

Antigen-capturing nanoparticles improve the abscopal effect and cancer immunotherapy.

Immunotherapy holds tremendous promise for improving cancer treatment. To administer radiotherapy with immunotherapy has been shown to improve immune responses and can elicit the 'abscopal effect'. Unfortunately, response rates for this strategy remain low. Herein we report an improved cancer immunotherapy approach that utilizes antigen-capturing nanoparticles (AC-NPs). We engineered several AC-NP formulations and demonstrated that the set of protein antigens captured by each AC-NP formulation is dependent on the NP surface properties. We showed that AC-NPs deliver tumour-specific proteins to antigen-presenting cells (APCs) and significantly improve the efficacy of αPD-1 (anti-programmed cell death 1) treatment using the B16F10 melanoma model, generating up to a 20% cure rate compared with 0% without AC-NPs. Mechanistic studies revealed that AC-NPs induced an expansion of CD8+ cytotoxic T cells and increased both CD4+T/Treg and CD8+T/Treg ratios (Treg, regulatory T cells). Our work presents a novel strategy to improve cancer immunotherapy with nanotechnology.

Molecular and Clinical Characterization of a Claudin-Low Subtype of Gastric Cancer.

PURPOSE: Claudin-low molecular subtypes have been identified in breast and bladder cancers and are characterized by low expression of claudins, enrichment for epithelial-to-mesenchymal transition (EMT), and tumor-initiating cell (TIC) features. We evaluated whether the claudin-low subtype also exists in gastric cancer. MATERIALS AND METHODS: Four hundred fifteen tumors from The Cancer Genome Atlas (TCGA) gastric cancer mRNA data set were clustered on the claudin, EMT, and TIC gene sets to identify claudin-low tumors. We derived a 24-gene predictor that classifies gastric cancer into claudin-low and non-claudin-low subtypes. This predictor was validated with the Asian Cancer Research Group (ACRG) data set. We characterized molecular and clinical features of claudin-low tumors. RESULTS: We identified 46 tumors that had consensus enrichment for claudin-low features in TCGA data set. Claudin-low tumors were most commonly diffuse histologic type (82%) and originally classified as TCGA genomically stable (GS) subtype (78%). Compared with GS subtype, claudin-low subtype had significant activation in Rho family of GTPases signaling, which appears to play a key role in its EMT and TIC properties. In the ACRG data set, 28 of 300 samples were classified as claudin-low tumors by the 24-gene predictor and were phenotypically similar to the initially derived claudin-low tumors. Clinically, claudin-low subtype had the worst overall survival. Of note, the hazard ratios that compared claudin-low versus GS subtype were 2.10 (95% CI, 1.07 to 4.11) in TCGA and 2.32 (95% CI, 1.18 to 4.55) in the ACRG cohorts, with adjustment for age and pathologic stage. CONCLUSION: We identified a gastric claudin-low subtype that carries a poor prognosis likely related to therapeutic resistance as a result of its EMT and TIC phenotypes.

Claudin-low bladder tumors are immune infiltrated and actively immune suppressed.

We report the discovery of a claudin-low molecular subtype of high-grade bladder cancer that shares characteristics with the homonymous subtype of breast cancer. Claudin-low bladder tumors were enriched for multiple genetic features including increased rates of RB1, EP300, and NCOR1 mutations; increased frequency of EGFR amplification; decreased rates of FGFR3, ELF3, and KDM6A mutations; and decreased frequency of PPARG amplification. While claudin-low tumors showed the highest expression of immune gene signatures, they also demonstrated gene expression patterns consistent with those observed in active immunosuppression. This did not appear to be due to differences in predicted neoantigen burden, but rather was associated with broad upregulation of cytokine and chemokine levels from low PPARG activity, allowing unopposed NFKB activity. Taken together, these results define a molecular subtype of bladder cancer with distinct molecular features and an immunologic profile that would, in theory, be primed for immunotherapeutic response.

HiView: an integrative genome browser to leverage Hi-C results for the interpretation of GWAS variants.

BACKGROUND: Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits and diseases. However, most of them are located in the non-protein coding regions, and therefore it is challenging to hypothesize the functions of these non-coding GWAS variants. Recent large efforts such as the ENCODE and Roadmap Epigenomics projects have predicted a large number of regulatory elements. However, the target genes of these regulatory elements remain largely unknown. Chromatin conformation capture based technologies such as Hi-C can directly measure the chromatin interactions and have generated an increasingly comprehensive catalog of the interactome between the distal regulatory elements and their potential target genes. Leveraging such information revealed by Hi-C holds the promise of elucidating the functions of genetic variants in human diseases. RESULTS: In this work, we present HiView, the first integrative genome browser to leverage Hi-C results for the interpretation of GWAS variants. HiView is able to display Hi-C data and statistical evidence for chromatin interactions in genomic regions surrounding any given GWAS variant, enabling straightforward visualization and interpretation. CONCLUSIONS: We believe that as the first GWAS variants-centered Hi-C genome browser, HiView is a useful tool guiding post-GWAS functional genomics studies. HiView is freely accessible at: http://www.unc.edu/~yunmli/HiView .