Exome-capture RNA sequencing of decade-old breast cancers and matched decalcified bone metastases

RNA-sequencing predicts a role of androgen receptor and aldehyde dehydrogenase 1A1 in osteosarcoma lung metastases

One-third of pediatric patients with osteosarcoma (OS) develop lung metastases (LM), which is the primary predictor of mortality. While current treatments of patients with localized bone disease have been successful in producing 5-year survival rates of 65-70%, patients with LM experience poor survival rates of only 19-30%. Unacceptably, this situation that has remained unchanged for 30 years. Thus, there is an urgent need to elucidate the mechanisms of metastatic spread in OS and to identify targetable molecular pathways that enable more effective treatments for patients with LM. We aimed to identify OS-specific gene alterations using RNA-sequencing of extremity and LM human tissues. Samples of extremity and LM tumors, including 4 matched sets, were obtained from patients with OS. Our data demonstrate aberrant regulation of the androgen receptor (AR) pathway in LM and predicts aldehyde dehydrogenase 1A1 (ALDH1A1) as a downstream target. Identification of AR pathway upregulation in human LM tissue samples may provide a target for novel therapeutics for patients with LM resistant to conventional chemotherapy.

Clonal expansion of intra-epithelial T cells in breast cancer revealed by spatial transcriptomics

The spatial distribution of tumor-infiltrating lymphocytes (TIL) predicts breast cancer outcome and response to systemic therapy, highlighting the importance of an intact tissue structure for characterizing tumors. Here, we present ST-FFPE, a spatial transcriptomics method for the analysis of formalin-fixed paraffin-embedded samples, which opens the possibility of interrogating archival tissue. The method involves extraction, exome capture and sequencing of RNA from different tumor compartments microdissected by laser-capture, and can be used to study the cellular composition of tumor microenvironment. Focusing on triple-negative breast cancer (TNBC), we characterized T cells, B cells, dendritic cells, fibroblasts and endothelial cells in both stromal and intra-epithelial compartments. We found a highly variable spatial distribution of immune cell subsets among tumors. This analysis revealed that the immune repertoires of intra-epithelial T and B cells were consistently less diverse and more clonal than those of stromal T and B cells. T-cell receptor (TCR) sequencing confirmed a reduced diversity and higher clonality of intra-epithelial T cells relative to the corresponding stromal T cells. Analysis of the top 10 dominant clonotypes in the two compartments showed a majority of shared but also some unique clonotypes both in stromal and intra-epithelial T cells. Hyperexpanded clonotypes were more abundant among intra-epithelial than stromal T cells. These findings validate the ST-FFPE method and suggest an accumulation of antigen-specific T cells within tumor core. Because ST-FFPE is applicable for analysis of previously collected tissue samples, it could be useful for rapid assessment of intratumoral cellular heterogeneity in multiple disease and treatment settings.

Epigenetic markers and therapeutic targets for metastasis

The last few years have seen an increasing number of discoveries which collectively demonstrate that histone and DNA modifying enzyme modulate different stages of metastasis. Moreover, epigenomic alterations can now be measured at multiple scales of analysis and are detectable in human tumors or liquid biopsies. Malignant cell clones with a proclivity for relapse in certain organs may arise in the primary tumor as a consequence of epigenomic alterations which cause a loss in lineage integrity. These alterations may occur due to genetic aberrations acquired during tumor progression or concomitant to therapeutic response. Moreover, evolution of the stroma can also alter the epigenome of cancer cells. In this review, we highlight current knowledge with a particular emphasis on leveraging chromatin and DNA modifying mechanisms as biomarkers of disseminated disease and as therapeutic targets to treat metastatic cancers.

FOXA1 Reprogramming Dictates Retinoid X Receptor Response in ESR1-Mutant Breast Cancer

Estrogen receptor alpha (ER/ESR1) mutations occur in 30% to 40% of endocrine resistant ER-positive (ER+) breast cancer. Forkhead box A1 (FOXA1) is a key pioneer factor mediating ER-chromatin interactions and endocrine response in ER+ breast cancer, but its role in ESR1-mutant breast cancer remains unclear. Our previous FOXA1 chromatin immunoprecipitation sequencing (ChIP-seq) identified a large portion of redistributed binding sites in T47D genome-edited Y537S and D538G ESR1-mutant cells. Here, we further integrated FOXA1 genomic binding profile with the isogenic ER cistrome, accessible genome, and transcriptome data of T47D cell model. FOXA1 redistribution was significantly associated with transcriptomic alterations caused by ESR1 mutations. Furthermore, in ESR1-mutant cells, FOXA1-binding sites less frequently overlapped with ER, and differential gene expression was less associated with the canonical FOXA1-ER axis. Motif analysis revealed a unique enrichment of retinoid X receptor (RXR) motifs in FOXA1-binding sites of ESR1-mutant cells. Consistently, ESR1-mutant cells were more sensitive to growth stimulation with the RXR agonist LG268. The mutant-specific response was dependent on two RXR isoforms, RXR-α and RXR-β, with a stronger dependency on the latter. In addition, T3, the agonist of thyroid receptor (TR) also showed a similar growth-promoting effect in ESR1-mutant cells. Importantly, RXR antagonist HX531 blocked growth of ESR1-mutant cells and a patient-derived xenograft (PDX)-derived organoid with an ESR1 D538G mutation. Collectively, our data support the evidence for a stronger RXR response associated with FOXA1 reprograming in ESR1-mutant cells, suggesting development of therapeutic strategies targeting RXR pathways in breast tumors with ESR1 mutation.

IMPLICATIONS

It provides comprehensive characterization of the role of FOXA1 in ESR1-mutant breast cancer and potential therapeutic strategy through blocking RXR activation.

TOR1B: a predictor of bone metastasis in breast cancer patients

Recent therapeutic advances in breast cancer (BC) have improved survival outcomes; however, the prognosis for patients with bone metastasis (BM) remains poor. Hence, novel clinical biomarkers are needed to accurately predict BC BM as well as to promote personalized medicine. Here, we discovered a novel biomarker, TOR1B, for BM in BC patients via analysis of BC gene expression data and clinical information downloaded from open public databases. In cancer cells, we found high expression levels of TOR1B in the nucleus and endoplasmic reticulum. Regarding gene expression, the level of TOR1B was significantly upregulated in BC patients with BM (p < 0.05), and the result was externally validated. In addition, gene expression clearly demonstrated two distinct types of prognoses in ER- and PR-positive patients. In multivariate regression, the gene could be an independent predictor of BM in BC patients, i.e., a low expression level of TOR1B was associated with delayed metastasis to bone in BC patients (HR, 0.28; 95% CI 0.094-0.84). Conclusively, TOR1B might be a useful biomarker for predicting BM; specifically, patients with ER- and PR-positive subtypes would benefit from the clinical use of this promising prognostic biomarker.

Role of Single Nucleotide Polymorphism-Related Genes in Tumour Immune Cell Infiltration and Prognosis of Cutaneous Melanoma

Background

Advances in cancer research have allowed for early diagnosis and improved treatment of cutaneous melanoma (CM). However, its invasiveness and recurrent metastasis, along with rising resistance to newer therapies, have lent urgency to the search for novel biomarkers and the underlying molecular mechanisms of CM.

Methods

Single nucleotide polymorphism- (SNP-) related genes were obtained from the sequencing data of 428 CM samples in The Cancer Genome Atlas. Functional enrichment of these genes was analysed in clusterProfiler. Additionally, a protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Gene (STRING) database. Gene Expression Profiling Interactive Analysis (GEPIA) was used to identify the expression and prognostic value of mutated genes. Finally, the Tumour Immune Estimation Resource (TIMER) analysed the relationship between gene expression and immune cell infiltration.

Results

We constructed a PPI network from the top 60 SNP-related genes. Mutated genes were mainly involved in calcium and oxytocin signalling pathways, as well as circadian entrainment. In addition, three SNP-related genes, BRAF, FLG, and SORL1, were significantly associated with patient prognosis. BRAF and SORL1 were positively associated with infiltration abundance of B cells, CD8+ T cells, CD4+ T cells, neutrophils, and dendritic cells, whereas FLG expression was negatively associated. Furthermore, higher immune cell infiltration was positively correlated with good prognosis.

Conclusions

Our study provides vital bioinformatic data and a relevant theoretical basis to further explore the molecular pathogenesis of CM and improve patient prognosis.

Isoforms of Neuropilin-2 Denote Unique Tumor-Associated Macrophages in Breast Cancer

Tumor-associated macrophages (TAMs) exert profound influence over breast cancer progression, promoting immunosuppression, angiogenesis, and metastasis. Neuropilin-2 (NRP2), consisting of the NRP2a and NRP2b isoforms, is a co-receptor for heparin-binding growth factors including VEGF-C and Class 3 Semaphorins. Selective upregulation in response to environmental stimuli and independent signaling pathways endow the NRP2 isoforms with unique functionality, with NRP2b promoting increased Akt signaling via receptor tyrosine kinases including VEGFRs, MET, and PDGFR. Although NRP2 has been shown to regulate macrophage/TAM biology, the role of the individual NRP2a/NRP2b isoforms in TAMs has yet to be evaluated. Using transcriptional profiling and spectral flow cytometry, we show that NRP2 isoform expression was significantly higher in TAMs from murine mammary tumors. NRP2a/NRP2b levels in human breast cancer metastasis were dependent upon the anatomic location of the tumor and significantly correlated with TAM infiltration in both primary and metastatic breast cancers. We define distinct phenotypes of NRP2 isoform-expressing TAMs in mouse models of breast cancer and within malignant pleural effusions from breast cancer patients which were exclusive of neuropilin-1 expression. Genetic depletion of either NRP2 isoform in macrophages resulted in a dramatic reduction of LPS-induced IL-10 production, defects in phagosomal processing of apoptotic breast cancer cells, and increase in cancer cell migration following co-culture. By contrast, depletion of NRP2b, but not NRP2a, inhibited production of IL-6. These results suggest that NRP2 isoforms regulate both shared and unique functionality in macrophages and are associated with distinct TAM subsets in breast cancer.

A Six-Gene Risk Model Based on the Immune Score Reveals Prognosis in Intermediate-Risk Acute Myeloid Leukemia

Tumor microenvironment (TME) has been revealed as an important determinant of diagnosis and treatment response in AML patients. The scores of immune and stromal cell scores of AML in the intermediate-risk group from The Cancer Genome Atlas (TCGA) database were calculated using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm. Differentially expressed genes were identified between high and low scores. Gene set enrichment and pathway analyses were performed. A risk score model based on TME for six immune-related genes was established and validated. Patients with a lower immune score had a longer overall survival than those with a higher score (P = 0.044). A total of 805 intersected genes as differentially expressed genes were identified and selected according to the comparison of both immune and stromal scores. The functional enrichment analysis shows that these genes are mainly associated with the immune/inflammatory response. The risk score model based on TME for six immune-related genes (including MEF2C, ENPP2, FAM107A, CD37, TNFAIP8L2, and CASS4) was established and validated in the TCGA database and well validated in the TARGET database (P = 0.005). A key microenvironment-related gene signature was identified that affects the outcomes of AML patients in the intermediate-risk group and might serve as therapeutic targets.

ESR1 mutant breast cancers show elevated basal cytokeratins and immune activation

Estrogen receptor alpha (ER/ESR1) is frequently mutated in endocrine resistant ER-positive (ER+) breast cancer and linked to ligand-independent growth and metastasis. Despite the distinct clinical features of ESR1 mutations, their role in intrinsic subtype switching remains largely unknown. Here we find that ESR1 mutant cells and clinical samples show a significant enrichment of basal subtype markers, and six basal cytokeratins (BCKs) are the most enriched genes. Induction of BCKs is independent of ER binding and instead associated with chromatin reprogramming centered around a progesterone receptor-orchestrated insulated neighborhood. BCK-high ER+ primary breast tumors exhibit a number of enriched immune pathways, shared with ESR1 mutant tumors. S100A8 and S100A9 are among the most induced immune mediators and involve in tumor-stroma paracrine crosstalk inferred by single-cell RNA-seq from metastatic tumors. Collectively, these observations demonstrate that ESR1 mutant tumors gain basal features associated with increased immune activation, encouraging additional studies of immune therapeutic vulnerabilities.

Hotspot ESR1 Mutations Are Multimodal and Contextual Modulators of Breast Cancer Metastasis

Constitutively active estrogen receptor α (ER/ESR1) mutations have been identified in approximately one-third of ER+ metastatic breast cancers. Although these mutations are known as mediators of endocrine resistance, their potential role in promoting metastatic disease has not yet been mechanistically addressed. In this study, we show the presence of ESR1 mutations exclusively in distant but not local recurrences in five independent breast cancer cohorts. In concordance with transcriptomic profiling of ESR1-mutant tumors, genome-edited ESR1 Y537S and D538G-mutant cell models exhibited a reprogrammed cell adhesive gene network via alterations in desmosome/gap junction genes and the TIMP3/MMP axis, which functionally conferred enhanced cell-cell contacts while decreasing cell-extracellular matrix adhesion. In vivo studies showed ESR1-mutant cells were associated with larger multicellular circulating tumor cell (CTC) clusters with increased compactness compared with ESR1 wild-type CTCs. These preclinical findings translated to clinical observations, where CTC clusters were enriched in patients with ESR1-mutated metastatic breast cancer. Conversely, context-dependent migratory phenotypes revealed cotargeting of Wnt and ER as a vulnerability in a D538G cell model. Mechanistically, mutant ESR1 exhibited noncanonical regulation of several metastatic pathways, including secondary transcriptional regulation and de novo FOXA1-driven chromatin remodeling. Collectively, these data provide evidence for ESR1 mutation-modulated metastasis and suggest future therapeutic strategies for targeting ESR1-mutant breast cancer.

SIGNIFICANCE

Context- and allele-dependent transcriptome and cistrome reprogramming in mutant ESR1 cell models elicit diverse metastatic phenotypes related to cell adhesion and migration, which can be pharmacologically targeted in metastatic breast cancer.

Identification of prognostic genes and tumor-infiltrating immune cells in the tumor microenvironment of esophageal squamous cell carcinoma and esophageal adenocarcinoma

BACKGROUND

Esophageal cancer (EC) is a highly aggressive malignancy that is classified as esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). Infiltrating stromal/immune cells, a major component of the tumor immune microenvironment (TIME), have prognostic significance in various cancers.

METHODS

In this study we investigated genes and immune factors in the tumor microenvironment (TME) of ESCC and EAC that can serve as prognostic biomarkers. Stromal and immune scores were calculated using the Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) algorithm based on gene expression profiles of patient-derived tumor tissues in The Cancer Genome Atlas database. The correlation between ESTIMATE scores and survival rates in EC were analyzed. A comparison of high and low stromal/immune score groups revealed multiple differentially expressed genes (DEGs) as candidate prognostic genes; their role in immune-related biological processes was evaluated by functional and protein-protein interaction (PPI) network analyses, and the genes were validated using Gene Expression Omnibus datasets. Additionally, 22 tumor-infiltrating immune cell (TIIC) subsets were analyzed using the CIBERSORT algorithm.

RESULTS

Median stromal score was higher whereas immune score was lower in ESCC than in EAC (both P<0.01). Stromal score was lower in female as compared to male ESCC patients (P<0.05), and was significantly correlated with T stage (P<0.05). In EAC, median immune score was higher in female as compared to male patients (P<0.05) and was correlated with tumor-node-metastasis stage (P<0.05). The identified DEGs were mainly involved in lymphocyte (especially T-lymphocyte) activation and carbohydrate binding. Moreover, the levels of infiltrating resting-stage dendritic cells, CD8+ T cells, naïve B cells, activated mast cells, and resting memory CD4+ T cells were significantly correlated with EC prognosis (P<0.05).

CONCLUSIONS

The immune microenvironment of ESCC and EAC are quite different. We have found genes with prognostic value in multiple tumor databases.

Matrix Gla protein (MGP), GATA3, and TRPS1: a novel diagnostic panel to determine breast origin

Background

Metastatic breast carcinoma is commonly considered during differential diagnosis when metastatic disease is detected in females. In addition to the tumor morphology and documented clinical history, sensitive and specific immunohistochemical (IHC) markers such as GCDFP-15, mammaglobin, and GATA3 are helpful for determining breast origin. However, these markers are reported to show lower sensitivity in certain subtypes, such as triple-negative breast cancer (TNBC).

Materials and methods

Using bioinformatics analyses, we identified a potential diagnostic panel to determine breast origin: matrix Gla protein (MGP), transcriptional repressor GATA binding 1 (TRPS1), and GATA-binding protein 3 (GATA3). We compared MGP, TRPS1, and GATA3 expression in different subtypes of breast carcinoma of (n = 1201) using IHC. As a newly identified marker, MGP expression was also evaluated in solid tumors (n = 2384) and normal tissues (n = 1351) from different organs.

Results

MGP and TRPS1 had comparable positive expression in HER2-positive (91.2% vs. 92.0%, p = 0.79) and TNBC subtypes (87.3% vs. 91.2%, p = 0.18). GATA3 expression was lower than MGP (p < 0.001) or TRPS1 (p < 0.001), especially in HER2-positive (77.0%, p < 0.001) and TNBC (43.3%, p < 0.001) subtypes. TRPS1 had the highest positivity rate (97.9%) in metaplastic TNBCs, followed by MGP (88.6%), while only 47.1% of metaplastic TNBCs were positive for GATA3. When using MGP, GATA3, and TRPS1 as a novel IHC panel, 93.0% of breast carcinomas were positive for at least two markers, and only 9 cases were negative for all three markers. MGP was detected in 36 cases (3.0%) that were negative for both GATA3 and TRPS1. MGP showed mild-to-moderate positive expression in normal hepatocytes, renal tubules, as well as 31.1% (99/318) of hepatocellular carcinomas. Rare cases (0.6–5%) had focal MGP expression in renal, ovarian, lung, urothelial, and cholangiocarcinomas.

Conclusions

Our findings suggest that MGP is a newly identified sensitive IHC marker to support breast origin. MGP, TRPS1, and GATA3 could be applied as a reliable diagnostic panel to determine breast origin in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-022-01569-1.

Exploration of genes and tumor infiltrating lymphocytes in female lung adenocarcinoma microenvironment that predicted prognosis

The tumor microenvironment has an important impact on tumor growth, invasion, metastasis, anti-tumor immune tolerance, and prognosis. The present study aimed to explore female lung adenocarcinoma microenvironment-associated tumor infiltrating lymphocytes (TILs) and genes that predict prognosis in The Cancer Genome Atlas (TCGA) database. Gene expression profiles of female patients with lung adenocarcinoma were downloaded from TCGA. Base on the CIBERSORT algorithm, we determined the fractions of TILs. By applying the ESTIMATE algorithm, immune scores and stromal scores were derived. According to the immune and stromal scores, we categorized the female patients with lung adenocarcinoma into high and low score groups. We also identified the fractions of TILs and differentially expressed genes (DEGs) that were significantly related with prognosis. The proportion of M1 macrophages was significantly negatively related to overall survival in female patients with lung adenocarcinoma. There were 269 upregulated genes and 35 downregulated genes both in immune scores and stromal scores. PTPRC (protein tyrosine phosphatase receptor type C) and GIMAP6 (GTPase, IMAP family member 6) were not only hub genes, but also were significantly related to overall survival in the Kaplan-Meier Plotter online and TCGA databases. In summary, our study provided new insight into the tumor microenvironment-related cellular and molecular mechanisms of women with lung adenocarcinoma. The results will be useful for future clinical studies.

Endocrine resistance in breast cancer: from molecular mechanisms to therapeutic strategies

Estrogen receptor-positive (ER +) breast cancer accounts for approximately 75% of all breast cancers. Endocrine therapies, including selective ER modulators (SERMs), aromatase inhibitors (AIs), and selective ER down-regulators (SERDs) provide substantial clinical benefit by reducing the risk of disease recurrence and mortality. However, resistance to endocrine therapies represents a major challenge, limiting the success of ER + breast cancer treatment. Mechanisms of endocrine resistance involve alterations in ER signaling via modulation of ER (e.g., ER downregulation, ESR1 mutations or fusions); alterations in ER coactivators/corepressors, transcription factors (TFs), nuclear receptors and epigenetic modulators; regulation of signaling pathways; modulation of cell cycle regulators; stress signaling; and alterations in tumor microenvironment, nutrient stress, and metabolic regulation. Current therapeutic strategies to improve outcome of endocrine-resistant patients in clinics include inhibitors against mechanistic target of rapamycin (mTOR), cyclin-dependent kinase (CDK) 4/6, and the phosphoinositide 3-kinase (PI3K) subunit, p110α. Preclinical studies reveal novel therapeutic targets, some of which are currently tested in clinical trials as single agents or in combination with endocrine therapies, such as ER partial agonists, ER proteolysis targeting chimeras (PROTACs), next-generation SERDs, AKT inhibitors, epidermal growth factor receptor 1 and 2 (EGFR/HER2) dual inhibitors, HER2 targeting antibody-drug conjugates (ADCs) and histone deacetylase (HDAC) inhibitors. In this review, we summarize the established and emerging mechanisms of endocrine resistance, alterations during metastatic recurrence, and discuss the approved therapies and ongoing clinical trials testing the combination of novel targeted therapies with endocrine therapy in endocrine-resistant ER + breast cancer patients.

Bioinformatics prediction and experimental verification identify MAD2L1 and CCNB2 as diagnostic biomarkers of rhabdomyosarcoma

Background

Rhabdomyosarcoma (RMS) is a malignant soft-tissue tumour. In recent years, the tumour microenvironment (TME) has been reported to be associated with the development of tumours. However, the relationship between the occurrence and development of RMS and TME is unclear. The purpose of this study is to identify potential tumor microenvironment-related biomarkers in rhabdomyosarcoma and analyze their molecular mechanisms, diagnostic and prognostic significance.

Methods

We first applied bioinformatics method to analyse the tumour samples of 125 patients with rhabdomyosarcoma (RMS) from the Gene Expression Omnibus database (GEO). Differential genes (DEGs) that significantly correlate with TME and the clinical staging of tumors were extracted. Immunohistochemistry (IHC) was applied to validate the expression of mitotic arrest deficient 2 like 1 (MAD2L1) and cyclin B2 (CCNB2) in RMS tissue. Then, we used cell function and molecular biology techniques to study the influence of MAD2L1 and CCNB2 expression levels on the progression of RMS.

Results

Bioinformatics results show that the RMS TME key genes were screened, and a TME-related tumour clinical staging model was constructed. The top 10 hub genes were screened through the establishment of a protein–protein interaction (PPI) network, and then Gene Expression Profiling Interactive Analysis (GEPIA) was conducted to measure the overall survival (OS) of the 10 hub genes in the sarcoma cases in The Cancer Genome Atlas (TCGA). Six DEGs of statistical significance were acquired. The relationship between these six differential genes and the clinical stage of RMS was analysed. Further analysis revealed that the OS of RMS patients with high expression of MAD2L1 and CCNB2 was worse and the expression of MAD2L1 and CCNB2 was related to the clinical stage of RMS patients. Gene set enrichment analysis (GSEA) revealed that the genes in MAD2L1 and CCNB2 groups with high expression were mainly related to the mechanism of tumour metastasis and recurrence. In the low-expression MAD2L1 and CCNB2 groups, the genes were enriched in the metabolic and immune pathways. Immunohistochemical results also confirmed that the expression levels of MAD2L1 (30/33, 87.5%) and CCNB2 (33/33, 100%) were remarkably higher in RMS group than in normal control group (0/11, 0%). Moreover, the expression of CCNB2 was related to tumour size. Downregulation of MAD2L1 and CCNB2 suppressed the growth, invasion, migration, and cell cycling of RMS cells and promoted their apoptosis. The CIBERSORT immune cell fraction analysis indicated that the expression levels of MAD2L1 and CCNB2 affected the immune status in the TME.

Conclusions

The expression levels of MAD2L1 and CCNB2 are potential indicators of TME status changes in RMS, which may help guide the prognosis of patients with RMS and the clinical staging of tumours.

The Development and Implementation of an Autopsy/ Tissue Donation for Breast Cancer Research

There is growing interest in tissue procurement for cancer research through autopsy. Establishing an autopsy/tissue donation programme for breast cancer research within an academic medical centre in the United States requires consideration, planning, multi-departmental collaboration and labour-intensive maintenance. It is the purpose of this paper to outline the necessary considerations in implementing and maintaining a tissue donation and autopsy programme within a breast cancer centre at a comprehensive cancer centre. Considerations of programme planning include: patient engagement, the recruitment of patients and families into the programme, the role and scope of work of the clinical coordinator, regulatory issues and the coordination with both pathology and the research team at time of death and autopsy/tissue donation. All aspects of the tissue donation/rapid autopsy programme development and implementation are discussed and illustrated through case study. An Autopsy/ Tissue Donation for breast cancer research can be successfully developed and implemented.

Establishment and verification of a prognostic tumor microenvironment-based and immune-related gene signature in colon cancer

Background

Gastrointestinal malignant cancers affect many sites in the intestinal tract, including the colon. In this study, we purposed to improve prognostic predictions for colon cancer (CC) patients by establishing a novel biosignature of immune-related genes (IRGs) based on the tumor microenvironment (TME).

Methods

Using the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm, we calculated the stromal and immune scores of every CC patient extracted from The Cancer Genome Atlas (TCGA). We then identified 4 immune-related messenger RNA (mRNA) biosignatures through a Cox and least absolute shrinkage and selection operator (LASSO) univariate analysis, and a Cox multivariate analysis. Relationships between tumor immune infiltration and the risk score were evaluated through the CIBERSORT algorithm and Tumor Immune Estimation Resource (TIMER) database.

Results

Our studies showed that individuals who had a high immune score (P=0.017) and low stromal score (P=0.041) had a favorable overall survival (OS) rate. By comparing high/low scores cohort, 220 differentially expressed genes (DEGs) were determined. Then an immune-related four-mRNA biosignature, including PDIA2, NAFTC1, VEGFC, and CD1B was identified. Kaplan-Meier, calibration, and receiver operating characteristic (ROC) curves verified the model's performance. By using univariate and multivariate Cox analyses, we found each biosignature was an independent risk factor for assessing a CC patient's survival. Three external GEO cohorts validated its good efficiency in estimating OS among individuals with CC. Moreover, the signature was also related to infiltration of several cells of the immune system in the tumor microenvironment.

Conclusions

The resultant model in our study included 4 IRGs associated with the TME. These IRGs can be utilized as an auxiliary variable to estimate and help improve the prognosis of individuals with CC.

Fatal enhanced respiratory syncytial virus disease in toddlers

In 1967, two toddlers immunized with a formalin-inactivated vaccine against respiratory syncytial virus (FIRSV) in the United States died from enhanced RSV disease (ERD), a severe form of illness resulting from aberrant priming of the antiviral immune response during vaccination. Up to 80% of immunized children subsequently exposed to wild-type virus were hospitalized. These events hampered RSV vaccine development for decades. Here, we provide a characterization of the clinical, immunopathological, and transcriptional signature of fatal human ERD, outlining evidence for safety evaluation of RSV vaccines and a framework for understanding disease enhancement for pathogens in general.

A seven-nuclear receptor-based prognostic signature in breast cancer

BACKGROUND

Breast cancer (BRCA) is a malignant cancer that threatened the life of female with unsatisfactory prognosis. The aim of this study was to identify prognostic nuclear receptors (NRs) signature of BRCA.

METHODS

BRCA patient samples were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Consensus clustering analysis, univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were performed to evaluate, select NRs as prognostic factors and build Risk Score model. GSEA analysis was explored to check signaling differences between High- and Low-Risk group. Nomogram model basing on age and Risk Score was established to predict the 1-, 3- and 5-year survival. Model performance was assessed by a time-dependent receiver operating characteristic (ROC) curve and calibration plot. CIBERSORT, ESTIMATE and TIMER algorithm were introduced to evaluate the immune landscape.

RESULTS

NR3C1, NR4A3, THRA, RXRG, NR2F6, NR1D2 and RORB were optimized as a prognostic signature for BRCA. This seven-NR-based Risk Score could effectively predict overall survival status. The area under the curve (AUC) of 1-, 3- and 5-year overall survival are 0.702, 0.734 and 0.722 in TCGA training cohort, and 0.630, 0.721 and 0.823 in GEO validation cohort, respectively. Calibration plot demonstrated satisfactory agreement between predictive and observed outcomes. Nomogram model worked well on predicting survival probabilities. Multiple cancer-related pathways were highly enriched in High-Risk group. High- and Low-Risk groups showed significant differed immune cell infiltration. There exists an obvious connection between Risk Score and immune checkpoints LAG3, PD1 and TIM3.

CONCLUSION

The seven-NR-based Risk Score represents a promising signature for estimating overall survival in patients with BRCA, and is correlated with the immune microenvironment.

Why MUC16 mutations lead to a better prognosis: A study based on The Cancer Genome Atlas gastric cancer cohort

BACKGROUND

MUC16, encoding cancer antigen 125, is a frequently mutated gene in gastric cancer. In addition, MUC16 mutations seem to result in a better prognosis in gastric cancer. However, the mechanisms that lead to a better prognosis by MUC16 mutations have not yet been clarified.

AIM

To delve deeper into the underlying mechanisms that explain why MUC16 mutations signal a better prognosis in gastric cancer.

METHODS

We used multi-omics data, including mRNA, simple nucleotide variation, copy number variation and methylation data from The Cancer Genome Atlas, to explore the relationship between MUC16 mutations and prognosis. Cox regression and random survival forest algorithms were applied to search for hub genes. Gene set enrichment analysis was used to elucidate the molecular mechanisms. Single-sample gene set enrichment analysis and “EpiDISH” were used to assess immune cells infiltration, and “ESTIMATE” for analysis of the tumor microenvironment.

RESULTS

Our study found that compared to the wild-type group, the mutation group had a better prognosis. Additional analysis indicated that the MUC16 mutations appear to activate the DNA repair and p53 pathways to act as an anti-tumor agent. We also identified a key gene, NPY1R (neuropeptide Y receptor Y1), which was significantly more highly expressed in the MUC16 mutations group than in the MUC16 wild-type group. The high expression of NPY1R predicted a poorer prognosis, which was also confirmed in a separate Gene Expression Omnibus cohort. Further susceptibility analysis revealed that NPY1R might be a potential drug target for gastric cancer. Furthermore, in the analysis of the tumor microenvironment, we found that immune cells in the mutation group exhibited higher anti-tumor effects. In addition, the tumor mutation burden and cancer stem cells index were also higher in the mutation group than in the wild-type group.

CONCLUSION

We speculated that the MUC16 mutations might activate the p53 pathway and DNA repair pathway: alternatively, the tumor microenvironment may be involved.

ADAMTS12 acts as a tumor microenvironment related cancer promoter in gastric cancer

The infiltration degree of immune and stromal cells has been shown clinically significant in tumor microenvironment (TME). However, the utility of stromal and immune components in Gastric cancer (GC) has not been investigated in detail. In the present study, ESTIMATE and CIBERSORT algorithms were applied to calculate the immune/stromal scores and the proportion of tumor-infiltrating immune cell (TIC) in GC cohort, including 415 cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were screened by Cox proportional hazard regression analysis and protein-protein interaction (PPI) network construction. Then ADAMTS12 was regarded as one of the most predictive factors. Further analysis showed that ADAMTS12 expression was significantly higher in tumor samples and correlated with poor prognosis. Gene Set Enrichment Analysis (GSEA) indicated that in high ADAMTS12 expression group gene sets were mainly enriched in cancer and immune-related activities. In the low ADAMTS12 expression group, the genes were enriched in the oxidative phosphorylation pathway. CIBERSORT analysis for the proportion of TICs revealed that ADAMTS12 expression was positively correlated with Macrophages M0/M1/M2 and negatively correlated with T cells follicular helper. Therefore, ADAMTS12 might be a tumor promoter and responsible for TME status and tumor energy metabolic conversion.

Development of an Immune-Related Prognostic Index Associated With Glioblastoma

Background: Although the tumor microenvironment (TME) is known to influence the prognosis of glioblastoma (GBM), the underlying mechanisms are not clear. This study aims to identify hub genes in the TME that affect the prognosis of GBM.

Methods: The transcriptome profiles of the central nervous systems of GBM patients were downloaded from The Cancer Genome Atlas (TCGA). The ESTIMATE scoring algorithm was used to calculate immune and stromal scores. The application of these scores in histology classification was tested. Univariate Cox regression analysis was conducted to identify genes with prognostic value. Subsequently, functional enrichment analysis and protein–protein interaction (PPI) network analysis were performed to reveal the pathways and biological functions associated with the genes. Next, these prognosis genes were validated in an independent GBM cohort from the Chinese Glioma Genome Atlas (CGGA). Finally, the efficacy of current antitumor drugs targeting these genes against glioma was evaluated.

Results: Gene expression profiles and clinical data of 309 GBM samples were obtained from TCGA database. Higher immune and stromal scores were found to be significantly correlated with tissue type and poor overall survival (OS) (p = 0.15 and 0.77, respectively). Functional enrichment analysis identified 860 upregulated and 162 downregulated cross genes, which were mainly linked to immune response, inflammatory response, cell membrane, and receptor activity. Survival analysis identified 228 differentially expressed genes associated with the prognosis of GBM (p ≤ 0.05). A total of 48 hub genes were identified by the Cytoscape tool, and pathway enrichment analysis of the genes was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID). The 228 genes were validated in an independent GBM cohort from the CGGA. In total, 10 genes were found to be significantly associated with prognosis of GBM. Finally, 14 antitumor drugs were identified by drug–gene interaction analysis.

Conclusions: Here, 10 TME-related genes and 14 corresponding antitumor agents were found to be associated with the prognosis and OS of GBM.

Stromal Score-Based Gene Signature: A Prognostic Prediction Model for Colon Cancer

BACKGROUND

Growing evidence has revealed the crucial roles of stromal cells in the microenvironment of various malignant tumors. However, efficient prognostic signatures based on stromal characteristics in colon cancer have not been well-established yet. The present study aimed to construct a stromal score-based multigene prognostic prediction model for colon cancer.

METHODS

Stromal scores were calculated based on the expression profiles of a colon cancer cohort from TCGA database applying the ESTIMATE algorithm. Linear models were used to identify differentially expressed genes between low-score and high-score groups by limma R package. Univariate, LASSO, and multivariate Cox regression models were used successively to select the prognostic gene signature. Two independent datasets from GEO were used as external validation cohorts.

RESULTS

Low stromal score was demonstrated to be a favorable factor to the overall survival of colon cancer patients in TCGA cohort (p = 0.0046). Three hundred and seven stromal score-related differentially expressed genes were identified. Through univariate, LASSO, and multivariate Cox regression analyses, a gene signature consisting of LEP, NOG, and SYT3 was recognized to build a prognostic prediction model. Based on the predictive values estimated by the established integrated model, patients were divided into two groups with significantly different overall survival outcomes (p < 0.0001). Time-dependent Receiver operating characteristic curve analyses suggested the satisfactory predictive efficacy for the 5-year overall survival of the model (AUC value = 0.733). A nomogram with great predictive performance combining the multigene prediction model and clinicopathological factors was developed. The established model was validated in an external cohort (AUC value = 0.728). In another independent cohort, the model was verified to be of significant prognostic value for different subgroups, which was demonstrated to be especially accurate for young patients (AUC value = 0.763).

CONCLUSION

The well-established model based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.

A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma

Increasing evidences show the clinical significance of the interaction between hypoxia and immune in clear cell renal cell carcinoma (ccRCC) microenvironment. However, reliable prognostic signatures based on a combination of hypoxia and immune have not been well established. Moreover, many studies have only used RNA-seq profiles to screen the prognosis feature of ccRCC. Presently, there is no comprehensive analysis of multiomics data to mine a better one. Thus, we try and get it. First, t-SNE and ssGSEA analysis were used to establish tumor subtypes related to hypoxia-immune, and we investigated the hypoxia-immune-related differences in three types of genetic or epigenetic characteristics (gene expression profiles, somatic mutation, and DNA methylation) by analyzing the multiomics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step strategy based on lasso regression and Cox regression was used to construct a satisfying prognostic model, with average 1-year, 3-year and 5-year areas under the curve (AUCs) equal to 0.806, 0.776 and 0.837. Comparing it with other nine known prognostic biomarkers and clinical prognostic scoring algorithms, the multiomics-based signature performs better. Then, we verified the gene expression differences in two external databases (ICGC and SYSU cohorts). Next, eight hub genes were singled out and seven hub genes were validated as prognostic genes in SYSU cohort. Furthermore, it was indicated high-risk patients have a better response for immunotherapy in immunophenoscore (IPS) analysis and TIDE algorithm. Meanwhile, estimated by GDSC and cMAP database, the high-risk patients showed sensitive responses to six chemotherapy drugs and six candidate small-molecule drugs. In summary, the signature can accurately predict the prognosis of ccRCC and may shed light on the development of novel hypoxia-immune biomarkers and target therapy of ccRCC.

Identification and immunoprofiling of key prognostic genes in the tumor microenvironment of hepatocellular carcinoma

Tumor microenvironment (TME) is involved in the occurrence and development of hepatocellular carcinoma (HCC), and immune cells in the TME have been implicated in its progression and treatment. However, the association of genes involved in the TME with HCC prognosis remains unclear. Thus, in this study, we obtained transcriptomic and clinicopathological data of patients with HCC from The Cancer Genome Atlas to identify key genes in TME associated with HCC prognosis. Stromal and immune cell scores were calculated using the ESTIMATE method, and differentially expressed genes (DEGs) were determined. We identified 830 DEGs, which were further subjected to survival analyses and functional enrichment analysis. Next, we identified prognostic TME-associated DEGs, established a protein-protein interaction (PPI) network, and performed Cox analysis.Consequently, four key prognostic genes (CXCL5, CXCL8, IL18RAP, and TREM2) associated with TME, were identified, in which CXCL5 and IL18RAP may be potential independent prognostic factors. Age, clinical stage, N stage, and risk score were also determined as significant prognostic variables. CIBERSORT was used to predict the constitution and relative content of the immune cells, wherein M0 macrophages were the most closely related to the key genes. In conclusion, CXCL5, CXCL8, IL18RAP, and TREM2 were associated with HCC prognosis and were important for immune cell invasion into the TME. Additionally, IL18RAP expression may contribute toward favorable prognosis in patients with HCC. Consequently, these genes may serve as potential biomarkers and immunotherapeutic targets for HCC.

Identification of P2RY13 as an immune-related prognostic biomarker in lung adenocarcinoma: A public database-based retrospective study

Background

Lung adenocarcinoma (LUAD) is the leading histological subtype of non-small cell lung cancer (NSCLC).

Methods

In the present study, the gene matrixes of LUAD were downloaded from The Cancer Genome Atlas to infer immune and stromal scores with the ‘Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data’ (ESTIMATE) algorithm and identified immune-related differentially expressed genes (DEGs) between the high- and low-stromal/immune score groups. Next, all DEGs were subjected to univariate Cox regression and survival analyses to screen out prognostic biomarkers in the tumor microenvironment (TME), and were validated in the Gene Expression Omnibus database. Single-sample gene set enrichment analysis (ssGSEA) was performed to assess the level of tumor-infiltrating immune cells (TIICs) and immune functions, and GSEA was used to identified pathways altered by prognostic biomarkers.

Results

Survival analysis showed that LUAD in the high-immune and stromal score group had a better clinical prognosis. A total of 303 immune-related DEGs were detected. Univariate Cox regression and survival analyses revealed that P2Y purinoceptor 13 (P2RY13) was a favorable factor for the prognosis of LUAD. ssGSEA and Spearman correlation analysis demonstrated that P2RY13 was highly correlated with various TIICs and immune functions. Several immune-associated pathways were enriched between the high- and low-expression P2RY13 groups.

Conclusion

P2RY13 may be a potential prognostic indicator and is highly associated with the TME in LUAD. However, further experimental studies are required to validate the present findings.

Mining TCGA database for gene expression in ovarian serous cystadenocarcinoma microenvironment

Background

Ovarian cancer is one of the leading causes of female deaths worldwide. Ovarian serous cystadenocarcinoma occupies about 90% of it. Effective and accurate biomarkers for diagnosis, outcome prediction and personalized treatment are needed urgently

Methods

Gene expression profile for OSC patients was obtained from the TCGA database. The ESTIMATE algorithm was used to calculate immune scores and stromal scores of expression data of ovarian serous cystadenocarcinoma samples. Survival results between high and low groups of immune and stromal score were compared and differentially expressed genes (DEGs) were screened out by limma package. The Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and the protein-protein interaction (PPI) network analysis were performed with the g:Profiler database, the Cytoscape and Search Tool for the Retrieval of Interacting Genes (STRING-DB). Survival results between high and low immune and stromal score groups were compared. Kaplan-Meier plots based on TCGA follow up information were generated to evaluate patients’ overall survival.

Results

Eighty-six upregulated DEGs and one downregulated DEG were identified. Three modules, which included 49 nodes were chosen as important networks. Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were considered to be correlated with poor overall survival.

Conclusion

Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were correlated with poor overall survival in our study. This new set of genes can become strong predictor of survival, individually or combined. Further investigation of these genes is needed to validate the conclusion to provide novel understanding of tumor microenvironment with ovarian serous cystadenocarcinoma prognosis and treatment.

Cyclin B1 acts as a tumor microenvironment-related cancer promoter and prognostic biomarker in hepatocellular carcinoma

OBJECTIVES

The present study aimed to develop a gene signature based on the ESTIMATE algorithm in hepatocellular carcinoma (HCC) and explore possible cancer promoters.

METHODS

The ESTIMATE and CIBERSORT algorithms were applied to calculate the immune/stromal scores and the proportion of tumor-infiltrating immune cells (TICs) in a cohort of HCC patients. The differentially expressed genes (DEGs) were screened by Cox proportional hazards regression analysis and protein-protein interaction (PPI) network construction. Cyclin B1 (CCNB1) function was verified using experiments.

RESULTS

The stromal and immune scores were associated with clinicopathological factors and recurrence-free survival (RFS) in HCC patients. In total, 546 DEGs were up-regulated in low score groups, 127 of which were associated with RFS. CCNB1 was regarded as the most predictive factor closely related to prognosis of HCC and could be a cancer promoter. Gene Set Enrichment Analysis (GSEA) and CIBERSORT analyses indicated that CCNB1 levels influenced HCC tumor microenvironment (TME) immune activity.

CONCLUSIONS

The ESTIMATE signature can be used as a prognosis tool in HCC. CCNB1 is a tumor promoter and contributes to TME status conversion.

The bone microenvironment increases phenotypic plasticity of ER+ breast cancer cells

Estrogen receptor-positive (ER+) breast cancer exhibits a strong bone tropism in metastasis. How the bone microenvironment (BME) impacts ER signaling and endocrine therapy remains poorly understood. Here, we discover that the osteogenic niche transiently and reversibly reduces ER expression and activities specifically in bone micrometastases (BMMs), leading to endocrine resistance. As BMMs progress, the ER reduction and endocrine resistance may partially recover in cancer cells away from the osteogenic niche, creating phenotypic heterogeneity in macrometastases. Using multiple approaches, including an evolving barcoding strategy, we demonstrated that this process is independent of clonal selection, and represents an EZH2-mediated epigenomic reprogramming. EZH2 drives ER+ BMMs toward a basal and stem-like state. EZH2 inhibition reverses endocrine resistance. These data exemplify how epigenomic adaptation to BME promotes phenotypic plasticity of metastatic seeds, fosters intra-metastatic heterogeneity, and alters therapeutic responses. Our study provides insights into the clinical enigma of ER+ metastatic recurrences despite endocrine therapies.

Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune‐related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune‐related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune‐related biomarkers for metastatic melanoma.

Development of a Prognostic Model for Ovarian Cancer Patients Based on Novel Immune Microenvironment Related Genes

Ovarian cancer (OV) has become the most lethal gynecological cancer. However, its treatment methods and staging system are far from ideal. In the present study, taking the advantage of large-scale public cohorts, we extracted a list of immune-related prognostic genes that differentially expressed in tumor and normal ovarian tissues. Importantly, an individualized immune-related gene based prognostic model (IPM) for OV patients were developed. Furthermore, we validated our IPM in Gene Expression Omnibus (GEO) repository and compared the immune landscape and pathways between high-risk and low-risk groups. The results of our study can serve as an important model to identify the immune subset of patients and has potential for use in immune therapeutic selection and patient management.

Influence of Immune Microenvironment on Diagnosis and Prognosis of Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is an immunosuppressive malignancy accompanied by noted alterations in various immune cells and cytokines. Recognition of the immune system's role in contributing to cancer development is an important advancement in our original understanding of carcinoma. We obtained HNSCC gene expression and clinical data from The Cancer Genome Atlas (TCGA) database. We assessed the relative proportion of 22 Infiltrating immune cell types in both HNSCC and adjacent non-cancer tissues using Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) method, identifying the influence of the immune cells content in tumor staging and survival prediction. We further predicted the tumor purity, and the presence of infiltrating stromal/immune cells in HNSCC tissues using Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm, identifying its potential correlation with patient survival. Stromal and immune score-associated differentially expressed genes (DEGs) were subsequently verified and their roles in immune response were displayed by functional enrichment analysis and protein-protein interaction (PPI) network. Our research demonstrated the underlying association between the immune microenvironment and HNSCC, and the results were intended to serve as valuable terms for HNSCC diagnosis, prognosis, and targeted immune therapy.

IGLL5 is correlated with tumor-infiltrating immune cells in clear cell renal cell carcinoma

Renal cell carcinomas (RCCs) account for about 90% of renal tumors, and their major histological subtype is ccRCC (clear cell RCC). Increasing evidence has indicated that the tumor microenvironment plays a significant role in the occurrence and development of ccRCC. In this study, we used ESTIMATE and CIBERSORT computational methods to calculate the proportion of immune and stromal components and the rate of TICs (tumor‐infiltrating immune cells) in 539 ccRCC samples from The Cancer Genome Atlas database. By examining the intersection of the differentially expressed genes obtained by the protein–protein interaction network and Cox regression analysis, we identified only one overlapping gene: IGLL5 (immunoglobulin lambda‐like polypeptide 5). We report that IGLL5 expression is correlated with TICs. Furthermore, our immunoinfiltration analyses revealed that three types of TIC are positively correlated with IGLL5 expression. IGLL5 may have potential as a prognostic biomarker of ccRCC.

Comparative analysis of the AIB1 interactome in breast cancer reveals MTA2 as a repressive partner which silences E-Cadherin to promote EMT and associates with a pro-metastatic phenotype

Steroid regulated cancer cells use nuclear receptors and associated regulatory proteins to orchestrate transcriptional networks to drive disease progression. In primary breast cancer, the coactivator AIB1 promotes estrogen receptor (ER) transcriptional activity to enhance cell proliferation. The function of the coactivator in ER+ metastasis however is not established. Here we describe AIB1 as a survival factor, regulator of pro-metastatic transcriptional pathways and a promising actionable target. Genomic alterations and functional expression of AIB1 associated with reduced disease-free survival in patients and enhanced metastatic capacity in novel CDX and PDX ex-vivo models of ER+ metastatic disease. Comparative analysis of the AIB1 interactome with complementary RNAseq characterized AIB1 as a transcriptional repressor. Specifically, we report that AIB1 interacts with MTA2 to form a repressive complex, inhibiting CDH1 (encoding E-cadherin) to promote EMT and drive progression. We further report that pharmacological and genetic inhibition of AIB1 demonstrates significant anti-proliferative activity in patient-derived models establishing AIB1 as a viable strategy to target endocrine resistant metastasis. This work defines a novel role for AIB1 in the regulation of EMT through transcriptional repression in advanced cancer cells with a considerable implication for prognosis and therapeutic interventions.

Screening the key genes of prognostic value in the microenvironment for head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is the sixth common malignancy worldwide. The tumor microenvironment is highly related to tumor initiation, progression, and prognosis. This study aims to screen the tumor microenvironment related key genes of prognostic value for HNSCC.The gene expression and clinical data for HNSCC were downloaded from the cancer genome atlas (TCGA). The immune/stromal/ESTIMATE scores were downloaded from the website of the MD Anderson Cancer Center. Correlation of patient gender and tumor grade with immune/stromal/ESTIMATE score was tested. Patients were divided into low and high immune/stromal/ESTIMATE score subgroups. Survival analysis was performed to evaluate the prognostic value of the immune/stromal/ESTIMATE score. Tumor microenvironment related differentially expressed genes were determined and applied for functional enrichment analysis and protein-protein interaction network was predicted. The prediction value of the common differentially expressed genes on patient survival was tested.Four hundred eighty samples with complete clinical, expression data, and immune/stromal/ESTIMATE scores were enrolled for analysis. Immune/stromal/ESTIMATE score was higher in female patients than males. A total of 44 common differentially expressed genes were screened in high and low immune/stromal/ESTIMATE score subgroups. Of the 44 genes, 7 genes (ADGRG7, CSN3, CST8, KRT81, MUC7, MYH6, and SEZ6) were found to be closely related to patient survival. Enrichment analysis showed that the differentially expressed genes mainly enriched in the protein-coupled receptor signaling pathway, extracellular region, G-protein coupled receptor activity, salivary secretion, and regulation of lipolysis in adipocytes. Protein-protein interaction analysis revealed that POSTN and OGN were crucial microenvironments related genes.Tumor microenvironment related genes ADGRG7, CSN3, CST8, KRT81, MUC7, MYH6, and SEZ6 are valuable predictors for HNSCC patient survival. POSTN and OGN are crucial in modulating the microenvironment and tumor biology for HNSCC.

Identification of Four Genes as Prognosis Signatures in Lung Adenocarcinoma Microenvironment

Background

Tumor microenvironment (TME) cells constitute a vital element of tumor tissues. Increasing evidence has shown that immune response in the microenvironment plays an active role in tumor invasion, metastasis, and recurrence, and is an important factor affecting tumor prognosis. Our study aimed to identify the gene signatures in lung adenocarcinoma (LUAD) microenvironment for prognosis and immunotherapy.

Methods

In this study, we evaluated, for the first time, the stromal and immune scores of 594 patients from The Cancer Genome Atlas (TCGA) database with LUAD using the ESTIMATE algorithm. Three hundred and sixty-seven dysregulated immune-related genes were identified. Then, we performed functional enrichment analysis of these genes, and found the best gene model and construct the signature through univariate, Lasso and multivariate COX regression analysis. To assess the independently prognostic ability of the signature, the Kaplan–Meier survival analysis and Cox’s proportional hazards model were performed.

Results

Functional enrichment analysis and protein–protein interaction networks showed that the immune-related genes mainly played a role in immune response, activation/proliferation of immune-related cells, and chemokine activity. A prognostic model involving 6 genes was constructed and the signature was identified as an independent prognostic factor and significantly associated with the overall survival (OS) of LUAD. The area under curve (AUC) of the receiver operating characteristic curve (ROC curve) for the 6 genes signature in predicting the 3-year survival rate was 0.708. Finally, four genes (FOXN4, KLHL4, FAM83F and CCR2) can be used as candidate prognostic biomarkers for LUAD.

Conclusion

Our findings will help evaluate the prognosis of LUAD and provide new ideas for exploring the potential relationship between TME and LUAD treatment and prognosis.

Tumor microenvironment related novel signature predict lung adenocarcinoma survival

Background

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer‐related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis.

Method

The information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters.

Result

A total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year.

Conclusion

A series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.

CXCL10 is a potential biomarker and associated with immune infiltration in human papillary thyroid cancer

Background: In recent years, the annual incidence of thyroid cancer (TC) has increased, with papillary thyroid cancer (PTC) identified as the most commonwinwordpathological type accounting for approximately 80% of all thyroid cancer cases. The tumor microenvironment is known to play a vital role in tumor information transmission and immune detection.

Methods: In the present study, we examined gene expression data from 518 patients with PTC. The ESTIMATE algorithm was used to calculate immune and stromal scores of PTC patients. Based on a protein–protein interaction (PPI) network, functional enrichment and overall survival analyses, C-X-C motif chemokine ligand 10 (CXCL10) was identified as a core gene. We further investigated the roles of core genes of PTC in the tumor immune microenvironment using LinkedOmics, GSEA, and TIMER tools.

Results: Immune, stromal and ESTIMATE scores were related to clinicopathological variables of patients with PTC, but not survival outcomes. Eight differentially expressed genes (DEGs) were associated with survival outcome. In addition, immunochemical staining experiments revealed lower expression of CXCL10 in PTC than paracancerous tissues. GSEA pathway enrichment analysis revealed downregulation of CXCL10 in multiple cancer pathways. CXCL10 and related genes were enriched in pathways related to adaptive immune response, cellular defense response and regulation of innate immune response.

Conclusion: The tumor microenvironment plays a critical role in development of PTC and CXCL10 may serve as a novel target of precision therapy for this patient population.

Acquired mutations and transcriptional remodeling in long-term estrogen-deprived locoregional breast cancer recurrences

BACKGROUND

Endocrine therapy resistance is a hallmark of advanced estrogen receptor (ER)-positive breast cancer. In this study, we aimed to determine acquired genomic changes in endocrine-resistant disease.

METHODS

We performed DNA/RNA hybrid-capture sequencing on 12 locoregional recurrences after long-term estrogen deprivation and identified acquired genomic changes versus each tumor's matched primary.

RESULTS

Despite being up to 7 years removed from the primary lesion, most recurrences harbored similar intrinsic transcriptional and copy number profiles. Only two genes, AKAP9 and KMT2C, were found to have single nucleotide variant (SNV) enrichments in more than one recurrence. Enriched mutations in single cases included SNVs within transcriptional regulators such as ARID1A, TP53, FOXO1, BRD1, NCOA1, and NCOR2 with one local recurrence gaining three PIK3CA mutations. In contrast to DNA-level changes, we discovered recurrent outlier mRNA expression alterations were common-including outlier gains in TP63 (n = 5 cases [42%]), NTRK3 (n = 5 [42%]), NTRK2 (n = 4 [33%]), PAX3 (n = 4 [33%]), FGFR4 (n = 3 [25%]), and TERT (n = 3 [25%]). Recurrent losses involved ESR1 (n = 5 [42%]), RELN (n = 5 [42%]), SFRP4 (n = 4 [33%]), and FOSB (n = 4 [33%]). ESR1-depleted recurrences harbored shared transcriptional remodeling events including upregulation of PROM1 and other basal cancer markers.

CONCLUSIONS

Taken together, this study defines acquired genomic changes in long-term, estrogen-deprived disease; highlights the importance of longitudinal RNA profiling; and identifies a common ESR1-depleted endocrine-resistant breast cancer subtype with basal-like transcriptional reprogramming.

Identification of the Prognostic Value of Tumor Microenvironment-Related Genes in Esophageal Squamous Cell Carcinoma

Background: Esophageal squamous cell carcinoma (ESCC) is the most prevalent histological type of esophageal cancer, but there is a lack of definite prognostic markers for this cancer. Methods: We used the ESTIMATE algorithm to access the tumor microenvironment (TME) of ESCC cases deposited in the TCGA database, and identified TME-related prognostic genes using Cox regression analysis. A least absolute shrinkage and selector operation or LASSO algorithm was used to identify key prognostic genes. Risk scores were calculated, and a clinical predictive model was constructed to evaluate the prognostic value of TME-related genes. Results: We found that high immune and stromal scores were significantly associated with poor overall survival (p < 0.05). We identified a total of 1,151 TME-related differently expression genes, among which 67 were prognosis-related genes. Through the LASSO method, 13 key prognostic genes were selected, namely, ADAMTS16, LOC51089, CH25H, CORO2B, DLGAP1, GYS2, HAL, MXRA8, NPTX1, OTX1, RET, SLC24A2, and SPI1, and a 13-gene risk score was constructed. A higher score was indicative of a poorer prognosis than a lower risk score (hazard ratio = 8.21, 95% confidence interval: 2.56-26.31; P < 0.001). The risk score was significantly correlated with immune/stromal scores and various types of infiltrating immune cells, including CD8 cells, regulatory T cells, and resting macrophages. Conclusion: We characterized the tumor microenvironment in ESCC, and identified the key prognosis genes. The risk score based on the expression profiles of these genes is proposed as an indicator of TME status and is instrumental in predicting patient prognosis.

Prognostic Hub Genes in the Immune Microenvironment of Lung Adenocarcinoma by Estimation

BACKGROUND

The mortality of lung adenocarcinoma(LUAD) is high. Recent studies have found that the degree of immune infiltration and stromal cells in the tumour microenvironment or tumours makes a significant contribution to prognosis.

METHODS

During study, we screened differentially expressed genes (DEGs) of TCGA database for prognostic genes in LUAD immune microenvironment. Further, immune and stromal cells were quantified using ESTIMATE algorithm. To study the effects of immune and stromal cell-associated genes on the prognosis of LUAD, LUAD patients were divided into high and low groups according to their immune/ stromal scores. The obtained scores were found to be related to the phenotype and survival rate of LUAD patients. By selecting DEGs with high expression in immune and stromal cells, we performed functional enrichment analysis and found that most genes are associated with pathways of cancer, stimulus response and the MAPK signaling. The functions and enriched pathways of LUAD prognostic genes were shown by a protein-protein interaction (PPI) network. Nonetheless, an external database was used to validate the prognostic genes from the TCGA.

RESULTS

Prognostic genes were listed according to their expression position and protein function.

CONCLUSION

We provided a new targets for immunotherapy of LUAD, which further provides basic knowledge for future clinical research.

The Lung Adenocarcinoma Microenvironment Mining and Its Prognostic Merit

Background:

As a common pathological type of lung cancer, lung adenocarcinoma (LUAD) is mainly treated by surgery, chemotherapy, targeted therapy and radiotherapy. Although a relatively mature treatment system has been established, there are few studies on the microenvironment of LUAD.

Material and Methods:

The immune and stromal scores of patients from the LUAD cohort in the TCGA database were obtained by using ESTIMATE. The relationship of immune and stromal scores with the clinicopathological characteristics and overall survival of LUAD patients was assessed by R. GO, KEGG and Cox regression analyses were employed to analyze intersecting genes and to identify reliable prognostic markers. The identified genes were also analyzed in the GEPIA database to assess their correlations with survival, and these relationships were verified with the Kaplan-Meier Plotter database.

Results:

The immune score was related to the survival time and tumor topography of LUAD patients. There was a significant correlation between stromal score and tumor metastasis. Through multivariate analysis, stage (HR = 1.640, 95% CI = 1.019-2.642, P = 0.042) and risk score (HR = 1.036, 95% CI = 1.026-1.046, P < 0.001). The genes (ARHGAP15, BTLA, CASS4, CLECL1, FAM129C, STAP1, TESPA1, and S100P) showed credible prognostic value in LUAD patients in TCGA through GEPIA database online analysis and verification in the Kaplan-Meier plotter database.

Conclusions:

In the microenvironment of lung adenocarcinoma, the differentially expressed genes screened by immune score and stromal score have certain value in evaluating the survival/prognosis of patients, as well as the invasion and progression of tumors.

ADAM22/LGI1 complex as a new actionable target for breast cancer brain metastasis

BACKGROUND

Metastatic breast cancer is a major cause of cancer-related deaths in woman. Brain metastasis is a common and devastating site of relapse for several breast cancer molecular subtypes, including oestrogen receptor-positive disease, with life expectancy of less than a year. While efforts have been devoted to developing therapeutics for extra-cranial metastasis, drug penetration of blood-brain barrier (BBB) remains a major clinical challenge. Defining molecular alterations in breast cancer brain metastasis enables the identification of novel actionable targets.

METHODS

Global transcriptomic analysis of matched primary and metastatic patient tumours (n = 35 patients, 70 tumour samples) identified a putative new actionable target for advanced breast cancer which was further validated in vivo and in breast cancer patient tumour tissue (n = 843 patients). A peptide mimetic of the target's natural ligand was designed in silico and its efficacy assessed in in vitro, ex vivo and in vivo models of breast cancer metastasis.

RESULTS

Bioinformatic analysis of over-represented pathways in metastatic breast cancer identified ADAM22 as a top ranked member of the ECM-related druggable genome specific to brain metastases. ADAM22 was validated as an actionable target in in vitro, ex vivo and in patient tumour tissue (n = 843 patients). A peptide mimetic of the ADAM22 ligand LGI1, LGI1MIM, was designed in silico. The efficacy of LGI1MIM and its ability to penetrate the BBB were assessed in vitro, ex vivo and in brain metastasis BBB 3D biometric biohybrid models, respectively. Treatment with LGI1MIM in vivo inhibited disease progression, in particular the development of brain metastasis.

CONCLUSION

ADAM22 expression in advanced breast cancer supports development of breast cancer brain metastasis. Targeting ADAM22 with a peptide mimetic LGI1MIM represents a new therapeutic option to treat metastatic brain disease.

TCGA and ESTIMATE data mining to identify potential prognostic biomarkers in HCC patients

Hepatocellular carcinoma (HCC) is an aggressive form of cancer characterized by a high recurrence rate following resection. Studies have implicated stromal and immune cells, which form part of the tumor microenvironment, as significant contributors to the poor prognoses of HCC patients. In the present study, we first downloaded gene expression datasets for HCC patients from The Cancer Genome Atlas database and categorized the patients into low and high stromal or immune score groups. By comparing those groups, we identified differentially expressed genes significantly associated with HCC prognosis. The Gene Ontology database was then used to perform functional enrichment analysis, and the STRING network database was used to construct protein-protein interaction networks. Our results show that most of the differentially expressed genes were involved in immune processes and responses and the plasma membrane. Those results were then validated using another a dataset from a HCC cohort in the Gene Expression Omnibus database and in 10 pairs of HCC tumor tissue and adjacent nontumor tissue. These findings enabled us to identify several tumor microenvironment-related genes that associate with HCC prognosis, and some those appear to have the potential to serve as HCC biomarkers.

An Innovative Immune Score-Based Prognostic Nomogram for Patients with Cervical Cancer

Background

In the past few years, the immune system and tumor immune microenvironment are becoming increasingly popular as more work has been accomplished in this field. However, nomograms based on immune-related characteristics for prognosis prediction of cervical cancer have not been fully explored to our knowledge. We constructed a novel immune score-based nomogram to predict patients with high risk and poor prognosis.

Materials and Methods

198 patients with cervical cancer from The Cancer Genome Atlas (TCGA) database were included in our study. Immune scores were generated with Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm, and clinic-pathological characteristics were also included for subsequent analysis. Cox proportional hazards regression models were performed for univariate and multivariate analyses to screen the significant factors, and a prognostic nomogram was built. Bootstrap resampling analysis was used for internal validation. The calibration curve and concordance index (C-index) were used to assess the predictive performance of the nomogram.

Results

Patients were split into three subgroups based on immune scores. We found that patients with high immune scores conferred significantly better overall survival (OS) compared with those with medium and low immune scores (hazard ratio (HR), 0.305; 95% confidence interval (CI), 0.108-0.869). A nomogram with a C-index of 0.720 had a favorable performance for predicting survival rate for clinical use by combining immune scores with other clinical features. The calibration curves at 3 and 5 years suggested a good consistency between the predicted OS and the actual OS probability.

Conclusions

Our work highlights the potential clinical application significance of immune score-based nomogram in predicting the OS of cervical cancer patients.

Development and validation of an immune and stromal prognostic signature in uveal melanoma to guide clinical therapy

The tumor microenvironment is known to play an important role in uveal melanoma. Reliable prognostic signatures are needed to aid high risk patients and improve prognosis. Uveal melanoma tissues from three public datasets were analyzed. RNA sequence data of uveal melanoma and corresponding clinical features were obtained from The Cancer Genome Atlas database. Immune and stromal scores were calculated by applying the "ESTIMATE" algorithm. The samples were divided into high and low immune or stromal score groups. We constructed prognostic models by using the 'lasso' package and tested them for 500 iterations. The cell signature was validated in another GSE44295 and GSE84976 datasets. We found that the median survival time of the low immune/stromal score group is longer than that of the high-score group. Thirteen immune cells and one stromal cell were concerned significant in predicting poor overall survival rate. Finally, a four-cell model was identified. Further validation revealed that the low-risk group has a significantly better survival than the high-risk group in another two datasets (P < 0.05). Moreover, the high-risk group is more sensitive to immunotherapy and chemotherapy. Summarizing, the proposed immune cells signature is a promising biomarker for estimating overall survival in uveal melanoma.

Mining TCGA database for prognostic genes in head and neck squamous cell carcinoma microenvironment

Background/purpose

Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignant tumors. The aim of this study was to elucidate the effect of tumor microenvironment-related genes on the prognosis of HNSCC and to obtain tumor microenvironment-related genes that can predict poor prognosis in HNSCC patients.

Materials and methods

The ESTIMATE algorithm was applied to the HNSCC transcriptomic data downloaded from the TCGA (The cancer genome atlas), and then the samples were divided into two groups: high and low immune scoring groups, and high and low basal scoring groups to screen for differentially expressed genes (DEGs) associated with poor patient outcomes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to explore the potential functions of DEGs, and then to explore the potential prognostic value of individual DEGs. The results of survival analysis between DEGs and overall survival (OS) to explore tumor microenvironment-related genes relevant to the prognosis of HNSCC patients.

Results

Fifty-nine tumor microenvironment-related genes were screened for association of OS with HNSCC (P < 0.05). The GO and KEGG enrichment analysis showed that the selected DEGs may mediate immune response, extracellular matrix, and immunoglobulin binding via neutrophil activation in HNSCC. Six of these DEGs, GIMAP6, SELL, TIFAB, KCNA3, P2RY8 and CCR4 were most significantly associated with OS (P < 0.001).

Conclusion

We identified six tumor microenvironment-related genes that were significantly associated with poor prognosis in HNSCC. These genes may inspire researchers to discover new targets and approaches for HNSCC treatment.

Prognostic value of immune-related genes and immune cell infiltration analysis in the tumor microenvironment of head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is one of the few malignant tumors that respond well to immunotherapy. We aimed to investigate the immune-related genes and immune cell infiltration of HNSCC and construct a predictive model for its prognosis.

METHODS

We calculated the stromal/immune scores of patients with HNSCC from The Cancer Genome Atlas using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm and investigated the relationship between the scores and patients' prognosis. Three machine learning algorithms (LASSO, Random Forest, and Rbsurv) were performed to screen key immune-related genes and constructed a predictive model. The immune cell infiltrating was calculated by the Tumor Immune Estimation Resource algorithm.

RESULTS

The stromal and immune scores significantly correlated with prognosis. A 6-gene signature was selected and displayed a robust predictive effect. The expressions of key genes were associated with immune infiltrating. GSE65858 validated the results.

CONCLUSION

Our study comprehensively analyzed the tumor microenvironment of HNSCC and constructed a robust predictive model, providing a basis for further investigation of therapy.