Immune Molecular Subtypes Research Articles

New Classification of Rheumatoid Arthritis Based on Immune Cells and Clinical Characteristics.

Rheumatoid arthritis (RA) is a chronic systemic immune disease characterized by joint synovitis, but there are differences in clinical manifestations and serum test results among different patients. This is a bioinformatics study. We first obtained the gene expression profile of RA and normal synovium from the database, and screened the differentially expressed immune related genes for enrichment analysis. Subsequently, we classified RA into three subtypes by unsupervised clustering of serum gene expression profiles based on immune enrichment scores. Then, the enrichment and clinical characteristics of different subtypes were analyzed. Finally, according to the infiltration of different subtypes of immune cells, diagnostic markers were screened and verified by qRT-PCR. C1 subtype is related to the increase of neutrophils, C-reactive protein and erythrocyte sedimentation rate, and joint pain is more significant in patients. C2 subtype is related to the expression of CD8+T cells and Tregs, and patients have mild joint pain symptoms. The RF value of C3 subtype is higher, and the expression of various immune cells is increased. CD4 T cells, NK cells activated, macrophages M1 and neutrophils are immune cells significantly infiltrated in synovium and serum of RA patients. IFNGR1, TRAC, IFITM1 can be used as diagnostic markers of different subtypes. In this study, RA patients were divided into different immune molecular subtypes based on gene expression profile, and immune diagnostic markers were screened, which provided a new idea for the diagnosis and treatment of RA.

Integration of CD4+ T cells and molecular subtype predicts benefit from PD-L1 blockade in muscle-invasive bladder cancer.

Muscle-invasive bladder cancer (MIBC) is a disease characterized by molecular and clinical heterogeneity, posing challenges in selecting the most appropriate treatment in clinical settings. Considering the significant role of CD4+ T cells, there is an emerging need to integrate CD4+ T cells with molecular subtypes to refine classification. We conducted a comprehensive study involving 895 MIBC patients from four independent cohorts. The Zhongshan Hospital (ZSHS) and The Cancer Genome Atlas (TCGA) cohorts were included to investigate chemotherapeutic response. The IMvigor210 cohort was included to assess the immunotherapeutic response. NCT03179943 was used to evaluate the clinical response to a combination of immune checkpoint blockade (ICB) and chemotherapy. Additionally, we evaluated genomic characteristics and the immune microenvironment to gain deeper insights into the distinctive features of each subtype. We unveiled four immune-molecular subtypes, each exhibiting distinct clinical outcomes and molecular characteristics. These subtypes include luminal CD4+ Thigh, which demonstrated benefits from both immunotherapy and chemotherapy; luminal CD4+ Tlow, characterized by the highest level of fibroblast growth factor receptor 3 (FGFR3) mutation, thus indicating potential responsiveness to FGFR inhibitors; basal CD4+ Thigh, which could benefit from a combination of ICB and chemotherapy; and basal CD4+ Tlow, characterized by an immune suppression microenvironment and likely to benefit from transforming growth factor-β (TGF-β) inhibition. This immune-molecular classification offers new possibilities for optimizing therapeutic interventions in MIBC.

NOP2/Sun RNA methyltransferase 2 is a potential pan-cancer prognostic biomarker and is related to immunity.

NOP2/Sun RNA methyltransferase 2 (NSUN2), an important methyltransferase of m5C, has been poorly studied in cancers, and the relationship between NSUN2 and immunity remains largely unclear. Therefore, the purpose of this study was to explore the expression and prognostic value of NSUN2 and the role of NSUN2 in immunity in cancers. The TIMER, CPTAC and other databases were used to analyze the expression of NSUN2, its correlation with clinical stage and its prognostic value across cancers. Moreover, the TISIDB, TIMER2.0 and Sangerbox platform were used to depict the relationships between NSUN2 and immune molecular subtypes, tumor-infiltrating lymphocytes (TILs), immune checkpoints (ICPs) and immunoregulatory genes. Furthermore, the NSUN2-interacting proteins and related genes as well as the coexpression networks of NSUN2 in LIHC, LUAD and HNSC were explored with the STRING, DAVID, GEPIA2 and LinkedOmics databases. Finally, the subcellular location and function of NSUN2 in HepG2, A549 and 5-8F cells were investigated by performing immunofluorescence, CCK-8 and wound healing assays. Overall, NSUN2 was highly expressed and related to a poor prognosis in most types of cancers and was also significantly associated with immune molecular subtypes in some cancer types. Furthermore, NSUN2 was significantly associated with the levels of ICPs and immunoregulatory genes. In addition, NSUN2 was found to be involved in a series of immune-related biological processes, such as the humoral immune response in LIHC and LUAD and T-cell activation and B-cell activation in HNSC. Immunofluorescence and CCK-8 assays also confirmed that NSUN2 was widely expressed in the nucleus and cytoplasm, and overexpression of NSUN2 promoted the proliferation and migration of HepG2, A549 and 5-8F cells. NSUN2 was also confirmed to positively regulate the expression of PD-L1. NSUN2 serves as a pan-cancer prognostic biomarker and is correlated with the immune infiltration of tumors.

Genomic analysis and filtration of novel prognostic biomarkers based on metabolic and immune subtypes in pancreatic cancer.

Patients with pancreatic cancer (PC) can be classified into various molecular subtypes and benefit from some precise therapy. Nevertheless, the interaction between metabolic and immune subtypes in the tumor microenvironment (TME) remains unknown. We hope to identify molecular subtypes related to metabolism and immunity in pancreatic cancer METHODS: Unsupervised consensus clustering and ssGSEA analysis were utilized to construct molecular subtypes related to metabolism and immunity. Diverse metabolic and immune subtypes were characterized by distinct prognoses and TME. Afterward, we filtrated the overlapped genes based on the differentially expressed genes (DEGs) between the metabolic and immune subtypes by lasso regression and Cox regression, and used them to build risk score signature which led to PC patients was categorized into high- and low-risk groups. Nomogram were built to predict the survival rates of each PC patient. RT-PCR, in vitro cell proliferation assay, PC organoid, immunohistochemistry staining were used to identify key oncogenes related to PC RESULTS: High-risk patients have a better response for various chemotherapeutic drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. We built a nomogram with the risk group, age, and the number of positive lymph nodes to predict the survival rates of each PC patient with average 1-year, 2-year, and 3-year areas under the curve (AUCs) equal to 0.792, 0.752, and 0.751. FAM83A, KLF5, LIPH, MYEOV were up-regulated in the PC cell line and PC tissues. Knockdown of FAM83A, KLF5, LIPH, MYEOV could reduce the proliferation in the PC cell line and PC organoids CONCLUSION: The risk score signature based on the metabolism and immune molecular subtypes can accurately predict the prognosis and guide treatments of PC, meanwhile, the metabolism-immune biomarkers may provide novel target therapy for PC.

Early Stage Finding of an Immune-Enhanced Genetic Subtype of Nonsmall Cell Lung Cancer Related with T-Cell Depletion.

Background Molecular categorization of lung cancer in medical care is becoming increasingly important on a regular basis. One of the molecular classifications of NSCLC (early-stage NSCLC) supports that tumors of different biological varieties differ in terms of their genomes and clinical characteristics. Methods Based on published immune cell signatures and early-stage NSCLC gene expression data including cancer genome maps, we used consensus cluster analysis to identify immune molecular subtypes associated with early-stage NSCLC expression subtypes. These subtypes were correlated with early-stage NSCLC expression subtypes. Next, applying a wide range of statistical techniques, we evaluated the link between molecular subtypes and clinical features, immunological microenvironment, and immunotherapy reactivity. Molecular subtypes were defined as a classification of cancerous cells. Results Multiple RNAseq cross-platform datasets of immune genes were used to identify two molecular subtypes (C1 and C2) of NSCLC, with C1 showing a more favorable prognosis than C2. The results were validated on the CSE datasets. In terms of clinical characteristics, C2 subtype samples with a worse prognosis showed a more advanced tumor stage and higher mortality. C2 showed immuno-infiltrative characteristics but had depletion of T-cells. Biological functions such as EMT were enriched on C2. A low TIDE score in C1 indicated that C1 samples could benefit from taking immunotherapy. C2 were more susceptible to standard chemotherapeutic treatments such paclitaxel, cisplatin, sorafenib, crizotinib, and erlotinib. Conclusion According to our findings, early-stage NSCLC patients may benefit from receiving treatment with immune checkpoint blockade therapy.

Definition of immune molecular subtypes with distinct immune microenvironment, recurrence, and PANoptosis features to aid clinical therapeutic decision-making.

Objective: Cervical cancer poses a remarkable health burden to females globally. Despite major advances in early detection and treatment modalities, some patients still relapse. The present study proposed a novel immune molecular classification that reflected distinct recurrent risk and therapeutic responses in cervical cancer. Methods: We retrospectively collected two cervical cancer cohorts: TCGA and GSE44001. Consensus clustering approach was conducted based on expression profiling of recurrence- and immune-related genes. The abundance of immune cells was inferred via five algorithms. Immune functions and signatures were quantified through ssGSEA. Genetic mutations were analyzed by maftools package. Immunotherapeutic response was inferred via tumor mutation burden (TMB), Tumor Immune Dysfunction and Exclusion (TIDE), and Submap methods. Finally, we developed a LASSO model for recurrence prediction. Results: Cervical cancer samples were categorized into two immune subtypes (IC1, and IC2). IC2 exhibited better disease free survival (DFS), increased immune cell infiltration within the immune microenvironment, higher expression of immune checkpoints, higher activity of immune-relevant pathways (APC co-inhibition and co-stimulation, inflammation-promoting, MHC class I, IFN response, leukocyte and stromal fractions, macrophage regulation, and TCR Shannon), and higher frequencies of genetic mutations. This molecular classification exhibited a remarkable difference with existing immune subtypes, with diverse PANoptosis (pyroptosis, apoptosis and necroptosis) features. Patients in IC2 were more likely to respond to immunotherapy and targeted, and chemotherapeutic agents. The immune subtype-relevant signature was quantified to predict patients’ recurrence risk. Conclusion: Altogether, we developed an immune molecular classification, which can be utilized in clinical practice to aid decision-making on recurrence management.

Identification and Validation of Immune Molecular Subtypes and Immune Landscape Based on Colon Cancer Cohort.

BackgroundThe incidence and mortality rates of colon adenocarcinoma (COAD), which is the fourth most diagnosed cancer worldwide, are high. A subset of patients with COAD has shown promising responses to immunotherapy. However, the percentage of patients with COAD benefiting from immunotherapy is unclear. Therefore, gaining a better understanding of the immune milieu of colon cancer could aid in the development of immunotherapy and suitable combination strategies.MethodsIn this study, gene expression profiles and clinical follow-up data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and molecular subtypes were identified using the ConsensusClusterPlus package in R. Univariate and multivariate Cox regression analyses were performed to evaluate the prognostic value of immune subtypes. The graph structure learning method was used to reduce the dimension to reveal the internal structure of the immune system. Weighted correlation network analysis (WGCNA) was performed to identify immune-related gene modules. Finally, western blotting was performed to verify the gene expression patterns in COAD samples.ResultsThe results showed that 424 COAD samples could be divided into three subtypes based on 1921 immune cell-related genes, with significant differences in prognosis between subtypes. Furthermore, immune-related genes could be divided into five functional modules, each with a different distribution pattern of immune subtypes. Immune subtypes and gene modules were highly reproducible across many data sets. There were significant differences in the distribution of immune checkpoints, molecular markers, and immune characteristics among immune subtypes. Four core genes, namely, CD2, FGL2, LAT2, and SLAMF1, with prognostic significance were identified by WGCNA and univariate Cox analysis.ConclusionOverall, this study provides a conceptual framework for understanding the tumor immune microenvironment of colon cancer.

Identification of molecular subtypes premised on the characteristics of immune infiltration of endometrial cancer.

BackgroundTumor-infiltrating immune cells are instrumental in the spread, resurgence, metastasis, and immunotherapy responsiveness of endometrial cancer (EC), which is a prevalent, malignant gynecologic tumor with a dismal prognosis. Many questions remain about the safety and effectiveness of immunotherapy, so we investigated both the patients’ immunological condition and the molecular subtypes of EC.MethodsRelevant data sets were collected from the The Cancer Genome Atlas (TCGA), and the CIBERSORT and ESTIMATE methods were used to assess the immune microenvironment and immune cell infiltration (ICI) in uterine corpus EC (UCEC) tumors. Using the immunophenoscore (IPS), the differentially expressed genes between low and high IPS were identified, and the immune gene subtypes of UCEC were further explored. Based on the main effect genes among the immune molecular subtypes, a tumor ICI scoring model was constructed.ResultsThere was individual heterogeneity in the characteristics of ICI in the tumor samples, and these characteristics were associated with overall survival (OS). The IPS was quantified by the pattern of gene differential expression, and five immune molecular subtypes were constructed. The levels of PD1/PD-L1 expression in these subtypes showed significant differences, and there were also differences in gene mutation profiles between low- and high-ICI scores.ConclusionsThe ICI score of UCEC samples can be used to predict the benefit of immunotherapy.

Prognostic characterization of immune molecular subtypes in non-small cell lung cancer to immunotherapy

BackgroundNon-small cell lung cancer (NSCLC) was usually associated with poor prognosis and invalid therapeutical response to immunotherapy due to biological heterogeneity. It is urgent to screen reliable biomarkers, especially immunotherapy-associated biomarkers, that can predict outcomes of these patients.MethodsGene expression profiles of 1026 NSCLC patients were collected from The Cancer Genome Atlas (TCGA) datasets with their corresponding clinical and somatic mutation data. Based on immune infiltration scores, molecular clustering classification was performed to identify immune subtypes in NSCLC. After the functional enrichment analysis of subtypes, hub genes were further screened using univariate Cox, Lasso, and multivariate Cox regression analysis, and the risk score was defined to construct the prognostic model. Other microarray data and corresponding clinical information of 603 NSCLC patients from the GEO datasets were applied to conduct random forest models for the prognosis of NSCLC with 100 runs of cross-validation. Finally, external datasets with immunotherapy and chemotherapy were further applied to explore the significance of risk-scores in clinical immunotherapy response for NSCLC patients.ResultsCompared with Subtype-B, the Subtype-A, associated with better outcomes, was characterized by significantly higher stromal and immune scores, T lymphocytes infiltration scores and up-regulation of immunotherapy markers. In addition, we found and validated an eleven -gene signatures for better application of distinguishing high- and low-risk NSCLC patients and predict patients’ prognosis and therapeutical response to immunotherapy. Furthermore, combined with other clinical characteristics based on multivariate Cox regression analysis, we successfully constructed and validated a nomogram to effectively predict the survival rate of NSCLC patients. External immunotherapy and chemotherapy cohorts validated the patients with higher risk-scores exhibited significant therapeutic response and clinical benefits.ConclusionThese results demonstrated the immunological and prognostic heterogeneity within NSCLC and provided a new clinical application in predicting the prognosis and benefits of immunotherapy for the disease.

Identification of three immune molecular subtypes associated with immune profiles, immune checkpoints, and clinical outcome in multiple myeloma.

PurposeTo identify the immune molecular subtype for MM to help achieve individualized and precise targeted therapy.MethodsThe GDC API was used to download the TCGA‐MM profile dataset, which contains 859 samples in total, all of which were anterior to the standard treatment after diagnosis. Moreover, 282, 298, and 258 samples were stage I, stage II, and stage III separately. We used the immune gene expression profile for consistent clustering; and used the R software package ConsensusClusterPlus to sort the immune molecular subtypes. Correlation between subtypes and clinical features, immunity, and prognosis was then analyzed.ResultsA total of 859 tumor samples were separated into these three subtypes, which were not meaningfully related to age or sex but showed a remarkable association with stage. The results suggested that obvious differences in immune metagene expression and expression of 10 immune checkpoint genes appeared among the three subtypes.ConclusionThe three subtypes are distinctly different in terms of immune metagenes, immune checkpoint molecules, and clinical prognosis. The discovery of the immune microenvironment of MM could further reveal the strategy for immunotherapy in MM and provide a promising candidate prognostic tool for survival.

Identification and Validation of Immune Molecular Subtypes in Pancreatic Ductal Adenocarcinoma: Implications for Prognosis and Immunotherapy.

BackgroundPancreatic ductal adenocarcinoma (PDAC) remains treatment refractory. Immunotherapy has achieved success in the treatment of multiple malignancies. However, the efficacy of immunotherapy in PDAC is limited by a lack of promising biomarkers. In this research, we aimed to identify robust immune molecular subtypes of PDAC to facilitate prognosis prediction and patient selection for immunotherapy.MethodsA training cohort of 149 PDAC samples from The Cancer Genome Atlas (TCGA) with mRNA expression data was analyzed. By means of non-negative matrix factorization (NMF), we virtually dissected the immune-related signals from bulk gene expression data. Detailed immunogenomic and survival analyses of the immune molecular subtypes were conducted to determine their biological and clinical relevance. Validation was performed in five independent datasets on a total of 615 samples.ResultsApproximately 31% of PDAC samples (46/149) had higher immune cell infiltration, more active immune cytolytic activity, higher activation of the interferon pathway, a higher tumor mutational burden (TMB), and fewer copy number alterations (CNAs) than the other samples (all P < 0.001). This new molecular subtype was named Immune Class, which served as an independent favorable prognostic factor for overall survival (hazard ratio, 0.56; 95% confidence interval, 0.33-0.97). Immune Class in cooperation with previously reported tumor and stroma classifications had a cumulative effect on PDAC prognostic stratification. Moreover, programmed cell death-1 (PD-1) inhibitors showed potential efficacy for Immune Class (P = 0.04). The robustness of our immune molecular subtypes was further verified in the validation cohort.ConclusionsBy capturing immune-related signals in the PDAC tumor microenvironment, we reveal a novel molecular subtype, Immune Class. Immune Class serves as an independent favorable prognostic factor for overall survival in PDAC patients.

Integrative analysis of immune molecular subtypes and microenvironment characteristics of bladder cancer.

The emergence of immunotherapy has provided an option of treatment methods for bladder cancer (BC). However, the beneficiaries of immunotherapy are still limited to small‐scale patients, and immunotherapy‐related adverse events often occur. It is a major challenge for clinical work to study the immune subtypes of BC and the molecular mechanism of immune escape, and identify the immune responders accurately. Here, we explore the immune molecular subtypes of bladder cancer and potential escape mechanisms. First, we screened the expression profiles of 303 differentially expressed immune‐related genes in BC patients from the Cancer Genome Atlas (TCGA) database, and successfully identified 4 molecular subtypes of BC. By comparing the clinical characteristics, immune cells infiltration, the expression of checkpoint genes, human leukocyte antigen (HLA) genes, and gene mutation status of different subtypes, we identified different clinical and immunological characteristics of 4 subtypes. Among 4 subtypes, Cluster 2 met the general characteristics of immunotherapy responders and responded well to immunotherapy, while Cluster 4 had the highest expression of immune characteristics, and is similar to the immune environment of normal bladder tissue. Then, the weighted gene co‐expression network analysis (WGCNA) of immune‐related genes revealed that brown module was positively correlated with subtypes. Pathway enrichment analysis explored the major pathways associated with subtypes, which are also associated with immune escape mechanisms. Moreover, the decision tree model, which was constructed by the principle of random forest screening factors, was also validated in internal validation set and external validation set from the Gene Expression Omnibus (GEO) cohort (GSE133624), and could achieve accurate subtypes prediction for BC patients with high‐throughput sequencing. Taken together, we explored the immune molecular subtypes and their mechanisms of BC, and these results may provide guidance for the development of new BC immunotherapy strategies.

Abstract PO006: Heating up the fight against cancer using 3D bioprinting

Abstract Tumor growth is regulated by complex interactions of tumor cells with the tumor microenvironment (TME) which consists of cancer associated fibroblasts (CAFs), various immune cells including tumor infiltrating lymphocytes (TILs) and extracellular matrix components (1). Accurately modeling the TME in vitro is challenging as there is significant genetic (2, 3) and spatial (4) heterogeneity within TME components. Triple-negative breast cancer (TNBC) patients that have tumors with TILs restricted to the margins are called immune “cold” and have poor prognosis (5, 6). Interfering with the mechanisms that exclude TILs from these tumors may improve immunotherapy response and TNBC patient outcome. A major barrier to anti-cancer drug development is the availability of in vitro models that accurately reflect the in vivo TME. Our goal is to establish and validate a 3D bioprinted tumor model that reproduces the spatial relationship between tumor, CAFs and TILs. We have generated a biobank of TNBC patient-derived organoids and tissue-matched TME components. The tumor immune microenvironment molecular subtypes were assigned to each tumor sample using transcriptomic data and immunohistochemistry analysis. We have used microfluidic 3D bioprinting (RX1™ bioprinter, Aspect Biosystems) to print a core-shell fibre design that recapitulates the immune “cold” TME and permits monitoring of immune cell infiltration into the cancer cell core of the fibre. Clinically proven checkpoint inhibitor therapy will be applied to our model to validate its biological function. This innovative bioprinted tumor model will enable phenotypic screening to efficiently identify new targets that transform hard-to-treat immune “cold” tumors into a state where immune cells can infiltrate the tumor. Using patient-derived tissue-matched TME components increases the clinical relevance of the bioprinted tumor model and enables personalized anti-cancer drug screening in vitro to accurately predict patient response to therapy. 1. Balkwill FR, Capasso M, Hagemann T. The tumor microenvironment at a glance. J Cell Sci. 2012; 125: 5591-5596. 2. Finak G, Bertos N, Pepin F, et al. Stromal gene expression predicts clinical outcome in breast cancer. Nat Med. 2008; 14: 518-527. 3. Lehmann BD, Bauer JA, Chen X, et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 2011; 121: 2750-2767. 4. Gruosso T, Gigoux M, Manem VSK, et al. Spatially distinct tumor immune microenvironments stratify triple-negative breast cancers. J Clin Invest. 2019;129(4):1785-1800. 5. Ali HR, Provenzano E, Dawson SJ, et al. Association between CD8+ T-cell infiltration and breast cancer survival in 12,439 patients. Ann Oncol. 2014; 25: 1536-1543. 6. Salgado R, Denkert C, Demaria S, et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. Ann Oncol. 2015; 26: 259-271. Citation Format: Anne-Marie Fortier, Erin Bedford, Salvador Flores Torres, Spiro Getsios, J. Matt Kinsella, Sam Wadsworth, Morag Park. Heating up the fight against cancer using 3D bioprinting [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr PO006.

Correlation study between the magnetic resonance imaging features of breast cancer and expression of immune molecular subtypes.

To investigate the correlation between breast cancer magnetic resonance imaging features and immune molecular subtypes. A total of 129 breast cancer patients were selected as the research object. All the patients were diagnosed by histopathology. All of them had breast magnetic resonance imaging and examination data of immunohistochemical (IHC) ER, PR, HER-2, and Ki-67. The correlation of breast cancer magnetic resonance imaging features with different immune molecular subtypes was retrospectively analyzed. Breast cancer is divided into different molecular subtypes. There were 72 cases with Luminal A type (55.81%), 20 cases with Luminal B type (15.50%), 14 cases with HER-2+ type (HER-2 type for over-expression) (10.85%), 23 cases with TNBC type (ER, PR and HER-2 were negative) (17.84%). The magnetic resonance imaging features of breast cancer were included, the post-enhanced morphology, margins, internal enhancement features, time-signal intensity curve (TIC) and molecular subtype expression of lesions were significantly correlated with the immune molecular subtypes (C=0.602, 0.439, 0.350 and 0.407, p=0.000, 0.000, 0.006 and 0.000). Lesion morphology: Luminal A type was mainly oval, accounting for 76.39% (55/76). Luminal B type and HER-2+ type was mainly irregular, accounting for 75.00% (15/20) and 64.29% (9/14) respectively. TNBC type was mainly shown as lobulation, accounting for 60.87% (14/23). Margin of the lesion: Luminal A type was mainly smooth margin, accounting for 73.61% (53/72). Luminal B type and TNBC type were mainly irregular margin, accounting for 70.00% (14/20) and 56.52% (13/23) respectively. The margin of HER-2+ type was mainly spiculation, accounting for 64.29% (9/14). The internal enhancement features: Luminal A type was mainly even enhancement, accounting for 62.50% (45/72). Luminal B type and HER-2+ type were mainly heterogeneous enhancement, accounting for 65.00% (13/20) and 64.29% (9/14) respectively. TNBC type was mainly annular enhancement, accounting for 73.91% (17/23). TIC type: Luminal A type was mainly Type II, accounting for 66.67% (48/72). Luminal B, HER-2+ type and TNBC type was mainly Type III, accounting for 70.00% (14/20), 64.29% (9/14) and 60.87% (14/23) respectively. The clinical signs include painless breast lumps, bloody breast discharge, and orange peel-like skin changes, nipple retraction and nipple elevation. There is no significant correlation between the above signs and the expression of molecular subtypes (C=0.014, 0.129, 0.154, 0.097 and 0.057, p=0.999, 0.533, 0.447, 0.747 and 0.935 respectively), the difference is not statistically significant (p>0.05). The characteristics of breast cancer magnetic resonance imaging was certainly correlated with the expression of immune molecular subtypes. The breast cancer molecular subtypes can be predicted by the imaging signs, which can provide valuable information for preoperative neoadjuvant treatment of breast cancer.

Classification of gastric cancers based on immunogenomic profiling

BackgroundExtensive evidence showed that gastric cancer (GC) is heterogeneous, and many studies have been focused on identifying GC subtypes based on genomic profiles. However, few studies have specifically explored the GC classification and predicted the classification accuracy that may help facilitate the optimal stratification of GC patients responsive to immunotherapy. MethodsUsing two publicly available GC genomics datasets, we classified GC on the basis of 797 immune related genes. Unsupervised and supervised machine learning methods were used to predict the classification. ResultsWe identified two GC subtypes that we named as Immunity-High (IM-H) and Immunity- Low (IM-L), and demonstrated that this classification was duplicable and predictable by analyzing other datasets. IM-H subtype was characterized by greater immune cell infiltration, stronger immune activities, lower tumor purity, as well as worse survival prognosis compared to IM-L subtype. Besides the immune signatures, some cancer-associated pathways were hyperactivated in IM-H, including TGF-beta signaling pathway, Focal adhesion, Cell adhesion molecules (CAMs), Calcium signaling pathway, mTOR signaling pathway, MAPK signaling pathway and Wnt signaling pathway. In contrast, IM-L presented depressed immune signatures and increased activation of base excision repair, DNA replication, homologous recombination, non-homologous end-joining and nucleotide excision repair pathways. Furthermore, we identified subtype-specific genomic or clinical features, and subtype-specific gene ontology and networks in IM-H and IM-L subtype. ConclusionsWe proposed and validated two reproducible immune molecular subtypes of GC, which has potential clinical implications for GC patient selection of immunotherapy.

Identification and validation of tumour microenvironment-based immune molecular subgroups for gastric cancer: immunotherapeutic implications.

Immunotherapy could trigger durable response in advanced gastric cancer, but it only benefits a minority of patients. We aimed to propose a robust molecular classification of gastric cancer microenvironment to identify ideal candidates for tailoring effective immunotherapy. A training cohort of 375 gastric cancer samples with RNA sequencing data was analysed. We virtually microdissected tumour, stromal, and immune cell gene expression patterns employing a non-negative matrix factorization algorithm. These expression patterns were annotated using immune- and stromal-related gene signatures. Validation of immunogenomic classification was performed across six microarray datasets of 1406 samples. We found approximately half of gastric cancer samples to have higher immune cell infiltrates, PD-L1 expression, markers of cytolytic activity, and fewer copy number aberrations (all P < 0.05). We termed this group of tumours the Immune Class, which incorporated two components, namely Immune Activation and Immunosuppressive Subtype, according to immunosuppressive or activated microenvironment. Immune Activation Subtype was associated with improved survival in multivariate survival analysis and shared similar genomic characteristics with responders of anti-PD-1 therapy. Immunosuppressive Subtype featured high immune infiltration, stromal enrichment, and transforming growth factor (TGF)-β signalling pathway activation and correlated with non-responsiveness signature of checkpoint blockade therapy, which might be suitable for anti-PD-L1 and anti-TGF-β combined therapy. We proposed and independently validated three reproducible immune molecular subtypes of gastric cancer, which may provide implications for patient selection of immunotherapy.

Association of an immune gene signature with pathologic response and outcome after neoadjuvant pembrolizumab (pembro), compared to neoadjuvant chemotherapy (NAC), in muscle-invasive bladder cancer (MIBC).

533 Background: In the PURE01 study (NCT02736266), neoadjuvant pembro resulted in 42% pathologic complete responses (pT0) in patients (pts) with MIBC. In this study, we investigated immune transcriptome signatures and molecular subtyping as potential predictors of pembro efficacy. Methods: Pts enrolled in the PURE01, which is still recruiting pts in its amended design, had predominant urothelial carcinoma histology and stage cT≤4N0 MIBC. Biomarker analyses included transcriptome profiling for 78 pts. A cisplatin-based NAC cohort of 140 cT≤4N0 MIBC pts was used for comparison. The association of Immune190, immune hallmark RNA signatures and molecular subtypes (TCGA, Consensus classifier, Genomic subtyping classifier [GSC]) was evaluated with respect to pT0 and recurrence-free survival (RFS). Multivariable logistic regression analyses (MVA) were used adjusting for the clinical T-stage and gender. Results: The Immune190 signature was significant for pT0 on MVA (OR: 1.54, 95%CI: 1.1-2.3, p=0.02) in the PURE01 cohort, but not in NAC cohort (OR: 0.96, 95%CI: 0.8-1.2, p=0.73). The hallmarks for IFN-γ (OR: 1.10, 95%CI: 1-1.2, p=0.007) and IFN-α response (OR: 1.07, 95%CI: 1-1.1, p=0.009) were also associated with pT0 for PURE01, but not for NAC (IFN-γ: OR: 0.99, 95%CI: 0.9-1.1, p=0.846 and INF-α: OR: 0.99, 95%CI: 0.95-1.04, p=0.806). In PURE-01, patients with Immune190 scores &gt;0.35 had 100% 2-y RFS vs 80% of those with ≤0.35; no difference was observed in NAC pts, as well as for the other hallmarks in both groups. The neuroendocrine (NE)-like subtype had the worst 2-y RFS in all three subtyping models (33%, p&lt;0.01) whereas the GSC Claudin-low subtype had the best outcome with no recurrences within 24 months. The other subtypes had RFS ranging from 75-86% in TCGA, 53-89% in Consensus, and 74-92% in the GSC. Conclusions: We found RNA Immune190 signature was reliably associated with response and outcome after neoadjuvant pembro. Molecular subtyping revealed special subtypes with outlier outcomes. These data suggest RNA profiling may provide a potential tool for personalizing the neoadjuvant therapy approach. Clinical trial information: NCT02736266.

Development and validation of novel microenvironment-based immune molecular subtypes of breast cancer: Implications for immunotherapy.

1094 Background: Breast cancer treatment with immunotherapy can improve clinical benefits, but the majority of patients did not respond to the treatment. To understand tumor–immune interactions in breast cancer, we identified novel microenvironment-based immune molecular subtypes. Methods: A training cohort of 1,394 breast cancer patients from the Molecular Taxonomy of Breast Cancer International Consortium profiled by RNA and DNA sequencing data were analyzed to calculate immune-related gene biomarkers and to assign prognostic categories using LASSO Cox regression model. Additionally, 969 patients from The Cancer Genome Atlas data set was used as an independent validation cohort. We further compared tumor mutation burden (TMB) and cytolytic activity (CYT) levels between different immune molecular subtypes. Results: Using the LASSO model, we established an immune molecular classifier based on following 5 features: IFN-γ signature, ICOSLG, TNFRSF14, Mast.cells.resting, and T.cells. CD4.memory.resting. Then we found that it contained two distinct microenvironment-based subtypes (immune class and non-immune class), characterized by significant differences in overall survival in the training cohort (hazard ratio [HR] 0.71; 95% confidence interval [CI] 0.61 to 0.81; P &lt; 0.001) and in the validation cohort (HR 0.34; 95% CI 0.22 to 0.54; P &lt; 0.001). We found an inverse association between immune gene expression and TMB levels (ρ = 0.096, P &lt; 0.001). Immune class subtype patients with good prognosis had significantly lower TMB and higher CYT than did non-immune class subtype patients with poor prognosis (all, P &lt; 0.05). The clinical use of the immune molecular subtypes showed a closer association with survival than did IFN-γ signature or PD-L1 expression (all, P &lt; 0.05). The robustness of the immune molecular subtypes was confirmed in the validation cohort. Conclusions: We revealed novel immune molecular subtypes, which represented better utility in predicting breast cancer patients’ survival compared with IFN-γ signature or PD-L1, and could be an important guide for precision immunotherapy.

An immunophenotyping of renal clear cell carcinoma with characteristics and a potential therapeutic target for patients insensitive to immune checkpoint blockade.

Renal clear cell carcinoma (RCC) patients who do not achieve optimal control of progression with immune checkpoint blockade (ICB) should be further studied. Unsupervised consensus clustering was used to group 525 RCC patients based on two typical ICB pathways, CTLA-4 and pogrammed death 1 (PD-1)/programmed death-ligand 1 (PD-L1), as well as two new discovered regulators, CMTM6 and CMTM4. Three immune molecular subtypes (IMMSs) with different clinical and immunological characteristics were identified (type I, II, and III), among which there were more stage I and low-grade tumors in type I RCC than in type II and III. The proportion of males was highest in type II RCC. Overall survival of type II and III was similar (5.2 and 6 years) and statistically shorter than that of type I (7.6 years) before and after adjusting for age and gender. When conducting stratified analysis, our IMMSs were able to identify high-risk patients among middle-aged patients, males, and stage IV patients. Among the differentially expressed genes, approximately 84% were highly expressed in type II and III RCC. Genes related to ICB (CTLA-4, CD274, and PDCD1LG2) and cytotoxic lymphocytes (CD8A, GZMA, and PRF1) were all highly expressed in type II and III RCC. These results documented that patients with type II and III cancer may be more sensitive to anti-CTLA-4 therapy, anti-PD-1/PD-L1 therapy, and a combination of immunotherapies. High expression of CMTM4 in type I RCC (69%) and a statistically significant interaction of CD274 and CMTM6 indicated that CMTM4/6 might be new therapy targets for type I, who are resistant to ICB.

Identification and Validation of a Tumour Microenvironment-Based Immune Molecular Subgroups for Gastric Cancer: Immunotherapeutic Implications

Background: Immunotherapy could trigger durable response in advanced gastric cancer, but it only benefits a minority of patients. Aims: To propose a robust molecular classification of gastric cancer microenvironment to identify ideal candidates for tailoring effective immunotherapy. Methods: A training cohort of 375 gastric cancer samples with RNAsequencing data were analysed. We virtually microdissected tumour, stromal, and immune cell gene expression patterns employing a nonnegative matrix factorization algorithm. These expression patterns were annotated using immune- and stromal-related gene signatures. Validation of immunogenomic classification was performed across six microarray datasets of 1,406 samples. Results: We found approximately half of gastric cancer samples to have higher immune cell infiltrates, PD-L1 expression, markers of cytolytic activity, and fewer copy number aberrations (all P < 0.05). We termed this group of tumours the Immune Class, which incorporated two components, namely Immune Activation and Immunosuppressive Subtype, according to immunosuppressive or activated microenvironment. Immune Activation Subtype was associated with improved survival in multivariate survival analysis and shared similar genomic characteristics with responders of anti-PD-1 therapy. Immunosuppressive Subtype featured high immune infiltration, stromal enrichment, and transforming growth factor (TGF)-β signalling pathway activation, which might be suitable for antiPD-L1 and anti-TGF-β combined therapy. Conclusions: We proposed and independently validated three reproducible immune molecular subtypes of gastric cancer, which may provide implications for patient selection of immunotherapy. Funding Statement: This work was supported by grants from the Incubating Program for Clinical Research and Innovation of Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University (Grant No. PYMDT-001), Science and Technology Commission of Shanghai Municipality (Grant No. 16441906903), Three-year action plan for Shin Kang of Shanghai (16CR4005A). Declaration of Interests: The authors declare no potential conflicts of interest. Ethics Approval Statement: Not required.