Landscape of the intratumroal microenvironment in bladder cancer: Implications for prognosis and immunotherapy.

Introduction: This study aims to present the landscape of the intratumoral microenvironment and by which establish a classification system that can be used to predict the prognosis of bladder cancer patients and their response to anti-PD-L1 immunotherapy. Methods: The expression profiles of 1554 bladder cancer cases were downloaded from seven public datasets. Single-sample gene set enrichment analysis (ssGSEA), univariate Cox regression analysis, and meta-analysis were employed to establish the bladder cancer immune prognostic index (BCIPI). Extensive analyses were executed to investigate the association between BCIPI and overall survival, tumor-infiltrated immunocytes, immunotherapeutic response, mutation load, etc. Results: The results obtained from seven independent cohorts and meta-analyses suggested that the BCIPI is an effective classification system for estimating bladder cancer patients' overall survival. Patients in the BCIPI-High subgroup revealed different immunophenotypic outcomes from those in the BCIPI-Low subgroup regarding tumor-infiltrated immunocytes and mutated genes. Subsequent analysis suggested that patients in the BCIPI-High subgroup were more sensitive to anti-PD-L1 immunotherapy than those in the BCIPI-Low subgroup. Conclusions: The newly established BCIPI is a valuable tool for predicting overall survival outcomes and immunotherapeutic responses in patients with bladder cancer.

Identification of immunophenotypes in esophageal squamous cell carcinoma based on immune gene sets

PurposeEsophageal squamous cell carcinoma (ESCC) is a malignant tumor with high heterogeneity. Research on molecular mechanisms involved in the process of tumor origination and progression is extremely limited to investigating mechanisms of molecular typing for ESCC.MethodsAfter comprehensively analyzing the gene expression profiles in The Cancer Genome Atlas and Gene Expression Omnibus databases, we identified four immunotypes of ESCC (referred to as C1–C4) based on the gene sets of 28 immune cell subpopulations. The discrepancies in prognostic value, clinical features, drug sensitivity, and tumor components between the immunotypes were individually analyzed.ResultsThe ranking of immune infiltration is C1 > C4 > C3 > C2. These subtypes are characterized by high and low expression of immune checkpoint proteins, enrichment and insufficiency of immune-related pathways, and differential distribution of immune cell subgroups. Poorer survival was observed in the C1 subtype, which we hypothesized could be caused by an immunosuppressive cell population. Fortunately, C1’s susceptibility to anti-PD-1 therapy offers hope for patients with poor prognosis in advanced stages. On the other hand, C4 is sensitive to docetaxel, which may offer novel treatment strategies for ESCC in the future. It is worth noting that immunophenotyping is tightly bound to the abundance of stromal components and stem cells, which could explain the tumor immune escape to some extent. Ultimately, determination of hub genes based on the C1 subtypes provides a reference for the discovery of immunotarget drugs against ESCC.ConclusionThe identification of immunophenotypes in our study provides new therapeutic strategies for patients with ESCC.

Identification of immunophenotypes in esophageal squamous cell carcinoma based on immune gene sets.

PURPOSE: Esophageal squamous cell carcinoma (ESCC) is a malignant tumor with high heterogeneity. Research on molecular mechanisms involved in the process of tumor origination and progression is extremely limited to investigating mechanisms of molecular typing for ESCC. METHODS: After comprehensively analyzing the gene expression profiles in The Cancer Genome Atlas and Gene Expression Omnibus databases, we identified four immunotypes of ESCC (referred to as C1-C4) based on the gene sets of 28 immune cell subpopulations. The discrepancies in prognostic value, clinical features, drug sensitivity, and tumor components between the immunotypes were individually analyzed. RESULTS: The ranking of immune infiltration is C1 > C4 > C3 > C2. These subtypes are characterized by high and low expression of immune checkpoint proteins, enrichment and insufficiency of immune-related pathways, and differential distribution of immune cell subgroups. Poorer survival was observed in the C1 subtype, which we hypothesized could be caused by an immunosuppressive cell population. Fortunately, C1's susceptibility to anti-PD-1 therapy offers hope for patients with poor prognosis in advanced stages. On the other hand, C4 is sensitive to docetaxel, which may offer novel treatment strategies for ESCC in the future. It is worth noting that immunophenotyping is tightly bound to the abundance of stromal components and stem cells, which could explain the tumor immune escape to some extent. Ultimately, determination of hub genes based on the C1 subtypes provides a reference for the discovery of immunotarget drugs against ESCC. CONCLUSION: The identification of immunophenotypes in our study provides new therapeutic strategies for patients with ESCC.

A genetic mouse model with postnatal Nf1 and p53 loss recapitulates the histology and transcriptome of human malignant peripheral nerve sheath tumor.

BACKGROUND: Malignant peripheral nerve sheath tumors (MPNST) are aggressive sarcomas. Somatic inactivation of NF1 and cooperating tumor suppressors, including CDKN2A/B, PRC2, and p53, is found in most MPNST. Inactivation of LATS1/2 of the Hippo pathway was recently shown to cause tumors resembling MPNST histologically, although Hippo pathway mutations are rarely found in MPNST. Because existing genetically engineered mouse (GEM) models of MPNST do not recapitulate some of the key genetic features of human MPNST, we aimed to establish a GEM-MPNST model that recapitulated the human disease genetically, histologically, and molecularly. METHODS: We combined 2 genetically modified alleles, an Nf1;Trp53 cis-conditional allele and an inducible Plp-CreER allele (NP-Plp), to model the somatic, possibly postnatal, mutational events in human MPNST. We also generated conditional Lats1;Lats2 knockout mice. We performed histopathologic analyses of mouse MPNST models and transcriptomic comparison of mouse models and human nerve sheath tumors. RESULTS: Postnatal Nf1;Trp53 cis-deletion resulted in GEM-MPNST that were histologically more similar to human MPNST than the widely used germline Nf1;Trp53 cis-heterozygous (NPcis) model and showed partial loss of H3K27me3. At the transcriptome level, Nf1;p53-driven GEM-MPNST were distinct from Lats-driven GEM-MPNST and resembled human MPNST more closely than do Lats-driven tumors. CONCLUSIONS: The NP-Plp model recapitulates human MPNST genetically, histologically, and molecularly.

CONFIGURE: A pipeline for identifying context specific regulatory modules from gene expression data and its application to breast cancer.

BACKGROUND: Gene expression data is widely used for identifying subtypes of diseases such as cancer. Differentially expressed gene analysis and gene set enrichment analysis are widely used for identifying biological mechanisms at the gene level and gene set level, respectively. However, the results of differentially expressed gene analysis are difficult to interpret and gene set enrichment analysis does not consider the interactions among genes in a gene set. RESULTS: We present CONFIGURE, a pipeline that identifies context specific regulatory modules from gene expression data. First, CONFIGURE takes gene expression data and context label information as inputs and constructs regulatory modules. Then, CONFIGURE makes a regulatory module enrichment score (RMES) matrix of enrichment scores of the regulatory modules on samples using the single-sample GSEA method. CONFIGURE calculates the importance scores of the regulatory modules on each context to rank the regulatory modules. We evaluated CONFIGURE on the Cancer Genome Atlas (TCGA) breast cancer RNA-seq dataset to determine whether it can produce biologically meaningful regulatory modules for breast cancer subtypes. We first evaluated whether RMESs are useful for differentiating breast cancer subtypes using a multi-class classifier and one-vs-rest binary SVM classifiers. The multi-class and one-vs-rest binary classifiers were trained using the RMESs as features and outperformed baseline classifiers. Furthermore, we conducted literature surveys on the basal-like type specific regulatory modules obtained by CONFIGURE and showed that highly ranked modules were associated with the phenotypes of basal-like type breast cancers. CONCLUSIONS: We showed that enrichment scores of regulatory modules are useful for differentiating breast cancer subtypes and validated the basal-like type specific regulatory modules by literature surveys. In doing so, we found regulatory module candidates that have not been reported in previous literature. This demonstrates that CONFIGURE can be used to predict novel regulatory markers which can be validated by downstream wet lab experiments. We validated CONFIGURE on the breast cancer RNA-seq dataset in this work but CONFIGURE can be applied to any gene expression dataset containing context information.