Unveiling Immunological Signatures and Predictors of Response to Immunosuppressive Therapy in Acquired Aplastic Anaemia".

Acquired Aplastic Anaemia (AA) often results from immune destruction of hematopoietic stem and progenitor cells. However, only 60-70% of patients with AA respond to immunosuppressive therapy (IST). There is lack of strong predictive marker for response to IST which can help therapy. Our study sought to pinpoint unique immune markers in AA patients and validate established predictors for response to IST. We enrolled 51 severe AA patients and analyzed 57 immunological parameters via flow cytometry. Additionally, we measured paroxysmal nocturnal hemoglobinuria (PNH) clone, telomere length, and thrombopoietin (TPO) levels prior to IST. After a 6-months follow-up, response was observed. Patients with AA had a distinct immunological signature characterized by absolute lymphopenia, skewed CD4/CD8 ratio with expansion of CD8 T cells with activated and senescent phenotype. Treg counts were reduced, while proportion of Treg A and B was comparable to controls. Treatment response was correlated with elevated Absolute Neutrophil Count (ANC), Absolute Reticulocyte Count (ARC), and reduced CD57+ CD8+ naive cells and B cell % before therapy. However, predictors like TPO, telomere length, and PNH did not emerge as indicators of treatment response. Identifying predictors for treatment response in AA is challenging due to abnormal haematopoiesis, genetic mutations, and treatment variables.

Circulating immune signatures in chronic pancreatitis with and without preceding acute pancreatitis: A pilot study.

OBJECTIVE: To investigate profiles of circulating immune signatures in healthy controls and chronic pancreatitis patients (CP) with and without a preceding history of acute pancreatitis (AP). METHODS: We performed a phase 1, cross-sectional analysis of prospectively collected serum samples from the PROspective Evaluation of Chronic Pancreatitis for EpidEmiologic and Translation StuDies (PROCEED) study. All samples were collected during a clinically quiescent phase. CP subjects were categorized into two subgroups based on preceding episode(s) of AP. Healthy controls were included for comparison. Blinded samples were analyzed using an 80-plex Luminex assay of cytokines, chemokines, and adhesion molecules. Group and pairwise comparisons of analytes were performed between the subgroups. RESULTS: In total, 133 patients with CP (111 with AP and 22 without AP) and 50 healthy controls were included. Among the 80 analytes studied, CP patients with a history of AP had significantly higher serum levels of pro-inflammatory cytokines (interleukin (IL)-6, IL-8, IL-1 receptor antagonist, IL-15) and chemokines (Cutaneous T-Cell Attracting Chemokine (CTACK), Monokine induced Gamma Interferon (MIG), Macrophage-derived Chemokine (MDC), Monocyte Chemoattractant Protein-1 (MCP-1)) compared to CP without preceding AP and controls. In contrast, CP patients without AP had immune profiles characterized by low systemic inflammation and downregulation of anti-inflammatory mediators, including IL-10. CONCLUSION: CP patients with a preceding history of AP have signs of systemic inflammatory activity even during a clinically quiescent phase. In contrast, CP patients without a history of AP have low systemic inflammatory activity. These findings suggest the presence of two immunologically diverse subtypes of CP.

Identification of Proteome-Based Immune Subtypes of Early Hepatocellular Carcinoma and Analysis of Potential Metabolic Drivers.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, ranking fourth in frequency. The relationship between metabolic reprogramming and immune infiltration has been identified as having a crucial impact on HCC progression. However, a deeper understanding of the interplay between the immune system and metabolism in the HCC microenvironment is required. In this study, we used a proteomic dataset to identify three immune subtypes (IM1-IM3) in HCC, each of which has distinctive clinical, immune, and metabolic characteristics. Among these subtypes, IM3 was found to have the poorest prognosis, with the highest levels of immune infiltration and T-cell exhaustion. Furthermore, IM3 showed elevated glycolysis and reduced bile acid metabolism, which was strongly correlated with CD8 T cell exhaustion and regulatory T cell accumulation. Our study presents the proteomic immune stratification of HCC, revealing the possible link between immune cells and reprogramming of HCC glycolysis and bile acid metabolism, which may be a viable therapeutic strategy to improve HCC immunotherapy.

Metabolic and immune-related gene signatures: Predictive stratification and prognostic implications in gastric cancer.

BACKGROUND: Gastric cancer, marked by its heterogeneous nature, showcases various molecular subtypes and clinical trajectories. This research delves into the significance of metabolic and immune-driven pathways in gastric cancer, constructing a prognostic signature derived from differentially expressed metabolic and immune-correlated genes (DE-MIGs). METHODS: Metabolic and immune-associated gene were sourced from the GeneCards database. Differential expression analysis on the TCGA-STAD dataset was executed using the limma package, unveiling 51 DE-MIGs that underwent functional enrichment scrutiny. The LASSO Cox regression methodology guided the creation of the prognostic signature, and individual patient risk scores were determined. Assessment tools like CIBERSORT, ESTIMATE and ssGSEA were deployed to study the immune microenvironment, while mutation profiles, genomic stability, resistance to chemotherapy and immunotherapy responsiveness were scrutinized across distinct signature categorizations. RESULTS: Among the identified DE-MIGs, 26 were significantly tied to the overall survival of gastric cancer patients. The developed prognostic signature proficiently differentiated patients into high-risk and low-risk cohorts, with the latter showing markedly better outcomes. The study underscored the centrality of the immune microenvironment in influencing gastric cancer outcomes. Key pathways such as TGF-Beta, TP53 and NRF2 dominated the high-risk group, whereas the LRTK-RAS and WNT pathways characterized the low-risk group. Interestingly, the low-risk segment also manifested a heightened tumor mutation burden and enhanced susceptibility to immunotherapy. CONCLUSIONS: Our findings introduce a pivotal prognostic signature, rooted in DE-MIGs, that effectively segregates gastric cancer patients into distinct risk-based segments. Insights into the influential role of the immune microenvironment in gastric cancer progression pave the way for more refined therapeutic interventions.

Immune signatures of the POLE mutation in endometrial carcinomas: a systematic study based on TCGA data and clinical cohort validation.

Background: POLE is a critical biomarker for endometrial cancer (ECs) prognosis and therapeutic decision. However, the immune infiltration and immunotherapy-related gene expression in the tumor microenvironment (TME) of POLE-mutated ECs remain unresolved. Methods: The TCGA database was used to characterize the TME of POLE mutants, which primarily included immune cells and co-expression genes. We used immunohistochemistry (IHC) to determine immune cell abundance and PD-L1 expression in 104 EC tissues, including 11 POLE mutants and 93 wild-type. Results: The bioinformatic study found significant differences in gene expression of the chemokine family, immune-cell markers, and lysozyme in POLE mutants, along with immune response activation. In POLE-mutated ECs, the abundance of CD4+T, CD8+T, M1 macrophages, and dendritic cells increased considerably. Furthermore, POLE mutations may enhance immune cell recruitment or activation and lymphocyte homing in ECs. POLE mutants also had increased expression of immune-checkpoint suppressor genes such as PD-L1, CTLA-4, TIM-3, and others. The tumor mutation burden (TMB) was higher in ECs with POLE mutation. In the validation cohort, we discovered that POLE mutations were related to the immune infiltration abundance of CD8+, CD4+, and Foxp3+ cells and PD-L1 expression by IHC. The prognosis of TCGA-ECs showed that the survival time of the CD8, CD4, PD-L1, or Foxp3 over-expression subgroup of the POLE mutants was significantly prolonged compared to the down-regulation subgroup or the POLE wild-type. Conclusion: The infiltration abundance of CD8+ T, CD4+ T, Foxp3+ T cells, and the expression of PD-L1 harbor crucial value for the prognosis or individualized therapy of POLE-mutated ECs.

Lung immune signatures define two groups of end-stage IPF patients.

BACKGROUND: The role of the immune system in the pathobiology of Idiopathic Pulmonary Fibrosis (IPF) is controversial. METHODS: To investigate it, we calculated immune signatures with Gene Set Variation Analysis (GSVA) and applied them to the lung transcriptome followed by unbiased cluster analysis of GSVA immune-enrichment scores, in 109 IPF patients from the Lung Tissue Research Consortium (LTRC). Results were validated experimentally using cell-based methods (flow cytometry) in lung tissue of IPF patients from the University of Pittsburgh (n = 26). Finally, differential gene expression and hypergeometric test were used to explore non-immune differences between clusters. RESULTS: We identified two clusters (C#1 and C#2) of IPF patients of similar size in the LTRC dataset. C#1 included 58 patients (53%) with enrichment in GSVA immune signatures, particularly cytotoxic and memory T cells signatures, whereas C#2 included 51 patients (47%) with an overall lower expression of GSVA immune signatures (results were validated by flow cytometry with similar unbiased clustering generation). Differential gene expression between clusters identified differences in cilium, epithelial and secretory cell genes, all of them showing an inverse correlation with the immune response signatures. Notably, both clusters showed distinct features despite clinical similarities. CONCLUSIONS: In end-stage IPF lung tissue, we identified two clusters of patients with very different levels of immune signatures and gene expression but with similar clinical characteristics. Weather these immune clusters differentiate diverse disease trajectories remains unexplored.

High-dimensional single-cell analysis unveils distinct immune signatures of peripheral blood in patients with pancreatic ductal adenocarcinoma.

Introduction: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with poor response to immune checkpoint inhibitors. The mechanism of such poor response is not completely understood. Methods: We assessed T-cell receptor (TCR) repertoire and RNA expression at the single-cell level using high-dimensional sequencing of peripheral blood immune cells isolated from PDAC patients and from healthy human controls. We validated RNA-sequencing data by performing mass cytometry (CyTOF) and by measuring serum levels of multiple immune checkpoint proteins. Results: We found that proportions of T cells (CD45+CD3+) were decreased in PDAC patients compared to healthy controls, while proportion of myeloid cells was increased. The proportion of cytotoxic CD8+ T cells and the level of cytotoxicity per cell were increased in PDAC patients, with reduced TCR clonal diversity. We also found a significantly enriched S100A9+ monocyte population and an increased level of TIM-3 expression in immune cells of peripheral blood in PDAC patients. In addition, the serum level of soluble TIM-3 (sTIM-3) was significantly higher in PDAC patients compared to the non-PDAC participants and correlated with worse survival in two independent PDAC cohorts. Moreover, sTIM-3 exhibited a valuable role in diagnosis of PDAC, with sensitivity and specificity of about 80% in the training and validation groups, respectively. We further established an integrated model by combining sTIM-3 and carbohydrate antigen 19- 9 (CA19-9), which had an area under the curve of 0.974 and 0.992 in training and validation cohorts, respectively. Conclusion: Our RNA-seq and proteomic results provide valuable insight for understanding the immune cell composition of peripheral blood of patients with PDAC.

Immune desert in MMR-deficient tumors predicts poor responsiveness of immune checkpoint inhibition.

Background: Although many efforts have been devoted to identify biomarkers to predict the responsiveness of immune checkpoint inhibitors, including expression of programmed death-ligand 1 (PD-L1) and major histocompatibility complex (MHC) I, microsatellite instability (MSI), mismatch repair (MMR) defect, tumor mutation burden (TMB), tertiary lymphoid structures (TLSs), and several transcriptional signatures, the sensitivity of these indicators remains to be further improved. Materials and methods: Here, we integrated T-cell spatial distribution and intratumor transcriptional signals in predicting the response to immune checkpoint therapy in MMR-deficient tumors including tumors of Lynch syndrome (LS). Results: In both cohorts, MMR-deficient tumors displayed personalized tumor immune signatures, including inflamed, immune excluded, and immune desert, which were not only individual-specific but also organ-specific. Furthermore, the immune desert tumor exhibited a more malignant phenotype characterized by low differentiation adenocarcinoma, larger tumor sizes, and higher metastasis rate. Moreover, the tumor immune signatures associated with distinct populations of infiltrating immune cells were comparable to TLSs and more sensitive than transcriptional signature gene expression profiles (GEPs) in immunotherapy prediction. Surprisingly, the tumor immune signatures might arise from the somatic mutations. Notably, patients with MMR deficiency had benefited from the typing of immune signatures and later immune checkpoint inhibition. Conclusion: Our findings suggest that compared to PD-L1 expression, MMR, TMB, and GEPs, characterization of the tumor immune signatures in MMR-deficient tumors improves the efficiency of predicting the responsiveness of immune checkpoint inhibition.

Checkpoint Inhibitors in Urothelial Carcinoma-Future Directions and Biomarker Selection.

CONTEXT: Several recent phase 2 and 3 trials have evaluated the efficacy and toxicity of checkpoint inhibitor (CPI) therapy for urothelial carcinoma (UC) in the metastatic, localized muscle-invasive UC (MIUC), upper tract UC, and non-muscle-invasive bladder cancer (NMIBC) disease state. OBJECTIVE: To assess the outcomes and toxicity of CPIs across the treatment landscape of UC and contextualize their application to current real-world treatment. EVIDENCE ACQUISITION: We queried PubMed, Web of Science, and EMBASE databases and conference abstracts to identify prospective trials examining CPIs in UC. The primary endpoints included overall survival, recurrence-free survival, and toxicity (when available). A secondary analysis included biomarker evaluation of response. EVIDENCE SYNTHESIS: We identified 21 trials, 12 phase 2 and nine phase 3 trials, in which a CPI was used for metastatic UC (seven), MIUC (nine), and NMIBC (five). For first-line (1L) metastatic UC, concurrent chemotherapy with CPIs failed to show superiority. Improved overall and progression-free survival for switch maintenance avelumab (after achieving stable disease or response with induction systemic chemotherapy) has established the current standard of care for 1L metastatic UC. A single-agent CPI is a consideration for patients unable to tolerate chemotherapy. CPIs in the perioperative setting are limited to only the adjuvant treatment with nivolumab after radical surgery for MIUC in patients at a higher risk of recurrence based on pathologic stage. Only pembrolizumab is approved by the Food and Drug Administration for carcinoma in situ unresponsive to bacillus Calmette-Guérin (BCG) in patients who are not fit for or who refuse radical cystectomy. Trials investigating CPIs in combination with multiple immune regulators, antibody drug conjugates, targeted therapies, antiangiogenic agents, chemotherapy, and radiotherapy are enrolling patients and may shape the future treatment of patients with UC. CONCLUSIONS: CPIs have an established role across multiple states of UC, with broadened applications likely to occur in the future. Several combinations are being evaluated, while the development of predictive biomarkers and their validation may help identify patients who are most likely to respond. PATIENT SUMMARY: Our findings highlight the broad activity of checkpoint inhibitors in urothelial carcinoma, noting the need for further investigation for the best application of combinations and patient selection to patient care.

Classification of triple-negative breast cancer based on pathway enrichment levels.

Triple-negative breast cancer (TNBC) is highly heterogeneous in the stromal and immune microenvironment, genome integrity, oncogenic signatures, and metabolic processes. However, a classification of TNBC by combining these features remains lacking. Here we classified TNBC based on the enrichment levels of 14 pathways related to immune, stromal, oncogenic and metabolic signatures by unsupervised clustering. In three bulk tumor transcriptome datasets and a single cell RNA-Seq (scRNA-seq) dataset, we consistently identified four TNBC subtypes: Imm-E, Str-E, DR-E, and Met-E. Imm-E displayed the strongest immune signatures, the lowest intratumor heterogeneity (ITH), the highest global methylation levels, the lowest tumor purity, and the best prognosis. Str-E showed the strongest stromal signatures and TGF-ß signaling. DR-E had the highest DNA repair activity, cell cycle activity, and genomic instability. Met-E demonstrated the strongest hormone metabolism and signaling, the highest ITH, the lowest genomic instability, and the worst prognosis. The subtyping of TNBC provides novel insights into tumor biology and clinical implications for this disease.

The prognostic signature based on glycolysis-immune related genes for acute myeloid leukemia patients.

Acute myeloid leukemia (AML) is widely considered an immunoresponsive malignancy. However, potential association between glycolysis-immune related genes and AML patients' prognosis has been seldom studied. AML-related data was downloaded from TCGA and GEO databases. We grouped patients according to Glycolysis status, Immune Score and combination analysis, basing on which overlapped differentially expressed genes (DEGs) were identified. The Risk Score model was then established. The results showed that totally 142 overlapped genes were probably correlated with glycolysis-immunity in AML patients, among which 6 optimal genes were screened to construct Risk Score. High Risk Score was an independent poor prognostic factor for AML. In conclusion, we established a relatively reliable prognostic signature of AML based on glycolysis-immunity related genes, including METTL7B, HTR7, ITGAX, TNNI2, SIX3 and PURG.

Identifying key transcription factors and miRNAs coregulatory networks associated with immune infiltrations and drug interactions in idiopathic pulmonary arterial hypertension.

BACKGROUND: The deregulated genetic factors are critically associated with idiopathic pulmonary arterial hypertension (IPAH) development and progression. However, the identification of hub-transcription factors (TFs) and miRNA-hub-TFs co-regulatory network-mediated pathogenesis in IPAH remains lacking. METHODS: We used GSE48149, GSE113439, GSE117261, GSE33463, and GSE67597 for identifying key genes and miRNAs in IPAH. We used a series of bioinformatics approaches, including R packages, protein-protein interaction (PPI) network, and gene set enrichment analysis (GSEA) to identify the hub-TFs and miRNA-hub-TFs co-regulatory networks in IPAH. Also, we employed a molecular docking approach to evaluate the potential protein-drug interactions. RESULTS: We found that 14 TFs encoding genes, including ZNF83, STAT1, NFE2L3, and SMARCA2 are upregulated, and 47 TFs encoding genes, including NCOR2, FOXA2, NFE2, and IRF5 are downregulated in IPAH relative to the control. Then, we identified the differentially expressed 22 hub-TFs encoding genes, including four upregulated (STAT1, OPTN, STAT4, and SMARCA2) and 18 downregulated (such as NCOR2, IRF5, IRF2, MAFB, MAFG, and MAF) TFs encoding genes in IPAH. The deregulated hub-TFs regulate the immune system, cellular transcriptional signaling, and cell cycle regulatory pathways. Moreover, the identified differentially expressed miRNAs (DEmiRs) are involved in the co-regulatory network with hub-TFs. The six hub-TFs encoding genes, including STAT1, MAF, CEBPB, MAFB, NCOR2, and MAFG are consistently differentially expressed in the peripheral blood mononuclear cells of IPAH patients, and these hub-TFs showed significant diagnostic efficacy in distinguishing IPAH cases from the healthy individuals. Moreover, we revealed that the co-regulatory hub-TFs encoding genes are correlated with the infiltrations of various immune signatures, including CD4 regulatory T cells, immature B cells, macrophages, MDSCs, monocytes, Tfh cells, and Th1 cells. Finally, we discovered that the protein product of STAT1 and NCOR2 interacts with several drugs with appropriate binding affinity. CONCLUSIONS: The identification of hub-TFs and miRNA-hub-TFs co-regulatory networks may provide a new avenue into the mechanism of IPAH development and pathogenesis.

Intravenous immunoglobulins, cyclosporine, and best supportive care in epidermal necrolysis: Diverse effects on systemic inflammation.

BACKGROUND: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are rare but potentially life-threatening cutaneous adverse reactions. There is still no consensus on adjuvant treatments, and little is known about their effects on systemic inflammation in SJS/TEN. Our aim was to characterize the systemic and cutaneous immune profiles of SJS/TEN patients and to investigate whether/how intravenous immunoglobulins (IVIG), cyclosporine A (CSA), and best supportive care only (BSCO) affected the systemic immune signature and clinical outcome (6 week-mortality, complications, hospitalization stay). METHODS: We included 16 patients with SJS/TEN, treated with high-dose IVIG (n = 8), CSA (n = 4) or BSCO (n = 4). Serial serum samples were obtained prior-, 5-7 days, and 21 days after treatment onset. Serum levels of inflammation-/immune response-associated proteins were measured by high-throughput proteomics assay (OLINK) and cytotoxic molecules by ELISA. RNA extracted from skin biopsies collected prior treatment was analyzed by Nanostring. RESULTS: Serum inflammatory profiles in SJS/TEN patients were notably characterized by massive upregulation of type 1 immune response and proinflammatory markers. Surprisingly, there was limited overlap between cutaneous and serum immune profiles. Serial serological measurements of immune response markers showed very diverse dynamics between the different treatment groups. IVIG-treated patients showed completely different dynamics and most significant proteomic changes in an early phase (Day 5-7). In all treatment groups, type 1-/inflammatory response markers were dampened at day 21. Clinically, there were no outcome differences. CONCLUSION: Our study demonstrates that BSCO, CSA, and IVIG have very diverse biological effects on the systemic inflammatory response in SJS/TEN, which may not correlate with clinical outcome differences.

Genomic landscape of the immunogenicity regulation in skin melanomas with diverse tumor mutation burden.

Skin melanoma cells are tightly interconnected with their tumor microenvironment (TME), which influences their initiation, progression, and sensitivity/resistance to therapeutic interventions. An immune-active TME favors patient response to immune checkpoint inhibition (ICI), but not all patients respond to therapy. Here, we assessed differential gene expression in primary and metastatic tumors from the TCGA-SKCM dataset, compared to normal skin samples from the GTEx project and validated key findings across 4 independent GEO datasets, as well as using immunohistochemistry in independent patient cohorts. We focused our attention on examining the expression of various immune receptors, immune-cell fractions, immune-related signatures and mutational signatures across cutaneous melanomas with diverse tumor mutation burdens (TMB). Globally, the expression of most immunoreceptors correlated with patient survival, but did not differ between TMBhigh and TMBlow tumors. Melanomas were enriched in "naive T-cell", "effector memory T-cell", "exhausted T-cell", "resting Treg T-cell" and "Th1-like" signatures, irrespective of their BRAF, NF1 or RAS mutational status. Somatic mutations in IDO1 and HLA-DRA were frequent and could be involved in hindering patient response to ICI therapies. We finally analyzed transcriptome profiles of ICI-treated patients and associated their response with high levels of IFNγ, Merck18, CD274, CD8, and low levels of myeloid-derived suppressor cells (MDSCs), cancer-associated fibroblasts (CAFs) and M2 macrophages, irrespective of their TMB status. Overall, our findings highlight the importance of pre-existing T-cell immunity in ICI therapeutic outcomes in skin melanoma and suggest that TMBlow patients could also benefit from such therapies.

Based on different immune responses under the glucose metabolizing type of papillary thyroid cancer and the response to anti-PD-1 therapy.

Glucose metabolism-related genes play an important role in the development and immunotherapy of many tumours, but their role in thyroid cancer is ambiguous. To investigate the role of glucose metabolism-related genes in the development of papillary thyroid cancer (PTC) and their correlation with the clinical outcome of PTC, we collected transcriptomic data from 501 PTC patients in the Cancer Genome Atlas (TCGA). We performed nonnegative matrix decomposition clustering of 2752 glucose metabolism-related genes from transcriptome data and classified PTC patients into three subgroups (C1 for high activation of glucose metabolism, C2 for low activation of glucose metabolism and C3 for moderate activation of glucose metabolism) based on the activation of different glucose metabolism-related genes in 10 glucose metabolism-related pathways. We found a positive correlation between the activation level of glucose metabolism and the tumour mutation burden (TMB), neoantigen number, mRNA stemness index (mRNAsi), age, and tumour stage in PTC patients. Next, we constructed a prognostic prediction model for PTC using six glucose metabolism-related genes (PGBD5, TPO, IGFBPL1, TMEM171, SOD3, TDRD9) and constructed a nomogram based on the risk score and clinical parameters of PTC patients. Both the prognostic risk prediction model and nomogram had high stability and accuracy for predicting the progression-free interval (PFI) in PTC patients. Patients were then divided into high-risk and low-risk groups by risk score. The high-risk group was sensitive to paclitaxel and anti-PD-1 treatment, and the low-risk group was sensitive to sorafenib treatment. We found that the high-risk group was enriched in inflammatory response pathways and associated with high level of immune cell infiltration. To verify the accuracy of the prognostic prediction model, we knocked down PGBD5 in PTC cells and found that the proliferation ability of PTC cells was significantly reduced. This suggests that PGBD5 may be a relatively important oncogene in PTC. Our study constructed a prognostic prediction model and classification of PTC by glucose metabolism-related genes, which provides a new perspective on the role of glucose metabolism in the development and immune microenvironment of PTC and in guiding chemotherapy, targeted therapy and immune checkpoint blockade therapy of PTC.

Corrigendum: Identification of breast cancer immune subtypes by analyzing bulk tumor and single cell transcriptomes.

[This corrects the article DOI: 10.3389/fcell.2021.781848.].

Characterization of MET Exon 14 Skipping Alterations (in NSCLC) and Identification of Potential Therapeutic Targets Using Whole Transcriptome Sequencing.

Introduction: Genomic alterations in the juxtamembrane exon 14 splice sites in NSCLC lead to increased MET stability and oncogenesis. We present the largest cohort study of MET Exon 14 (METex14) using whole transcriptome sequencing. Methods: A total of 21,582 NSCLC tumor samples underwent complete genomic profiling with next-generation sequencing of DNA (592 Gene Panel, NextSeq, whole exome sequencing, NovaSeq) and RNA (NovaSeq, whole transcriptome sequencing). Clinicopathologic information including programmed death-ligand 1 and tumor mutational burden were collected and RNA expression for mutation subtypes and MET amplification were quantified. Immunogenic signatures and potential pathways of invasion were characterized using single-sample gene set enrichment analysis and mRNA gene signatures. Results: A total of 533tumors (2.47%) with METex14 were identified. The most common alterations were point mutations (49.5%) at donor splice sites. Most alterations translated to increased MET expression, with MET co-amplification resulting in synergistic increase in expression (q < 0.05). Common coalterations were amplifications of MDM2 (19.0% versus 1.8% wild-type [WT]), HMGA2 (13.2% versus 0.98% WT), and CDK4 (10.0% versus 1.5% WT) (q < 0.05). High programmed death-ligand 1 > 50% (52.5% versus 27.3% WT, q < 0.0001) and lower proportion of high tumor mutational burden (>10 mutations per megabase, 8.3% versus 36.7% WT, p < 0.0001) were associated with METex14, which were also enriched in both immunogenic signatures and immunosuppressive checkpoints. Pathways associated with METex14 included angiogenesis and apical junction pathways (q < 0.05). Conclusions: METex14 splicing alterations and MET co-amplification translated to higher and synergistic MET expression at the transcriptomic level. High frequencies of MDM2 and CDK4 co-amplifications and association with multiple immunosuppressive checkpoints and angiogenic pathways provide insight into potential actionable targets for combination strategies in METex14 NSCLC.

Characterization of the Immune Cell Infiltration Landscape in Esophageal Squamous Cell Carcinoma.

Background: Immunotherapy has achieved remarkable efficacy in treating oesophageal squamous cell carcinoma (ESCC). However, this treatment has limited efficacy in some patients. An increasing number of evidence suggested that immune cells within the tumour microenvironment (TME) are strongly related to immunotherapy response and patient prognosis. Thus, the landscape of immune cell infiltration (ICI) in ESCC needs to be mapped. Methods: In the study, the ICI pattern in 206 cases of ESCC was characterised by two algorithms, namely, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA). The ICI score of each specimen was calculated by principal component analysis (PCA) according to ICI signature genes A (ICISGA) and B (ICISGB). The prognostic difference was evaluated by using the Kaplan-Meier method. The related pathways of ICI score were investigated by applying gene set enrichment analysis (GSEA). The R packages of 'regplot', 'timeROC' and 'rms' were applied for the construction of nomogram model. Result: Three TME subtypes were identified with no prognostic implication. A total of 333 differentially expressed genes (DEGs) among immune subtypes were determined, among which ICISGA and ICISGB were identified. Finally, ICI scores were constructed, and the patients were grouped into high or low ICI score group. Compared with the low ICI score group, the high ICI score group had better prognosis. GSEA revealed that the high ICI score group referred to multiple signalling pathways, including B cell receptor, Fc gamma R-mediated phagocytosis, NOD-like receptor and TGF-ß signalling pathways. In addition, the nomogram model was constructed to evaluate 1-, 3- and 5-year probability of death in an ESCC patient. The ROC and calibration curves indicated that the model has a good discrimination ability. Conclusion: We depicted a comprehensive ICI landscape in ESCC. ICI score may be used as a predictor of survival rate, which may be helpful for guiding immunotherapy in the future.

eIF6 is potential diagnostic and prognostic biomarker that associated with 18F-FDG PET/CT features and immune signatures in esophageal carcinoma.

BACKGROUND: Although eukaryotic initiation factor 6 (eIF6) is a novel therapeutic target, data on its importance in the development of esophageal carcinoma (ESCA) remains limited. This study evaluated the correlation between eIF6 expression and metabolic analysis using fluorine-18 fluorodeoxyglucose (18F-FDG) -Positron emission tomography (PET) and immune gene signatures in ESCA. METHODS: This study employed The Cancer Genome Atlas (TCGA) to analyze the expression and prognostic value of eIF6, as well as its relationship with the immune gene signatures in ESCA patients. The qRT-PCR and Western blot analyses were used to profile the expression of eIF6 in ESCA tissues and different ESCA cell lines. The expression of tumor eIF6 and glucose transporter 1 (GLUT1) was examined using immunohistochemical tools in fifty-two ESCA patients undergoing routine 18F-FDG PET/CT before surgery. In addition, the cellular responses to eIF6 knockdown in human ESCA cells were assessed via the MTS, EdU, flow cytometry and wound healing assays. RESULTS: Our data demonstrated that compared with the normal esophageal tissues, eIF6 expression was upregulated in ESCA tumor tissues and showed a high diagnostic value with an area under curve of 0.825 for predicting ESCA. High eIF6 expression was significantly correlated with shorter overall survival of patients with esophagus adenocarcinoma (p = 0.038), but not in squamous cell carcinoma of the esophagus (p = 0.078). In addition, tumor eIF6 was significantly associated with 18F-FDG PET/CT parameters: maximal and mean standardized uptake values (SUVmax and SUVmean) and total lesion glycolysis (TLG) (rho = 0.458, 0.460, and 0.300, respectively, p < 0.01) as well as GLUT1 expression (rho = 0.453, p < 0.001). A SUVmax cutoff of 18.2 led to prediction of tumor eIF6 expression with an accuracy of 0.755. Functional analysis studies demonstrated that knockdown of eIF6 inhibited ESCA cell growth and migration, and fueled cell apoptosis. Moreover, the Bulk RNA gene analysis revealed a significant inverse association between eIF6 and the tumor-infiltrating immune cells (macrophages, T cells, or Th1 cells) and immunomodulators in the ESCA microenvironment. CONCLUSION: Our study suggested that eIF6 might serve as a potential prognostic biomarker associated with metabolic variability and immune gene signatures in ESCA tumor microenvironment.

Clinical, Virological and Immunological Subphenotypes in a Cohort of Early Treated HIV-Infected Children.

Background: Identifying subphenotypes within heterogeneous diseases may have an impact in terms of therapeutic options. In this study, we aim to assess different subphenotypes in children living with human immunodeficiency virus (HIV-1), according to the clinical, virological, and immunological characteristics. Methods: We collected clinical and sociodemographic data, baseline viral load (VL), CD4 and CD8 count and percentage, age at initiation of ART, HIV DNA reservoir size in peripheral blood mononuclear cells (PBMCs), cell-associated RNA (CA-RNA), ultrasensitive VL, CD4 subsets (T effector CD25+, activated memory cells, Treg cells), humoral-specific HIV response (T-bet B cells), innate response (CD56dim natural killer (NK) cells, NKp46+, perforin), exhaustion markers (PD-1, PD-L1, DNAM), CD8 senescence, and biomarkers for T-lymphocyte thymic output (TREC) and endothelial activation (VCAM). The most informative variables were selected using an unsupervised lasso-type penalty selection for sparse clustering. Hierarchical clustering was performed using Pearson correlation as the distance metric and WARD.D2 as the clustering method. Internal validation was applied to select the best number of clusters. To compare the characteristics among clusters, boxplot and Kruskal Wallis test were assessed. Results: Three subphenotypes were discovered (cluster1: n=18, 45%; cluster2: n=11, 27.5%; cluster3: n=11, 27.5%). Patients in cluster1 were treated earlier, had higher baseline %CD4, low HIV reservoir size, low western blot score, higher TREC values, and lower VCAM values than the patients in the other clusters. In contrast, cluster3 was the less favorable. Patients were treated later and presented poorer outcomes with lower %CD4, and higher reservoir size, along with a higher percentage of CD8 immunosenescent cells, lower TREC, higher VCAM cytokine, and a higher %CD4 PD-1. Cluster2 was intermediate. Patients were like those of cluster1, but had lower levels of t-bet expression and higher HIV DNA reservoir size. Conclusions: Three HIV pediatric subphenotypes with different virological and immunological features were identified. The most favorable cluster was characterized by a higher rate of immune reconstitution and a slower disease progression, and the less favorable with more senescence and high reservoir size. In the near future therapeutic interventions for a path of a cure might be guided or supported by the different subphenotypes.