Stromal Signature Research Articles

Identification of Breast Cancer Subtypes Based on Gene Expression Profiles in Breast Cancer Stroma

BackgroundTumor stroma is a heterogeneous cellular component in the tumor microenvironment of breast cancer. However, very few studies have explored the identification of breast cancer subtypes based on highly heterogeneous tumor stromal signatures. Materials and MethodsUsing a combined dataset composed of 8 gene expression profiling datasets for breast tumor stroma, we clustered breast cancers based on the expression levels of 100 genes whose expression values were most variable across all samples. Furthermore, we investigated the molecular features of the breast cancer subtypes identified. ResultsWe identified 2 breast cancer subtypes, termed SBCS-1 and SBCS-2. We found that the contents of stroma and immune cells were lower in SBCS-1 than in SBCS-2, while the proportion of tumor cells was higher in SBCS-1. SBCS-1 was enriched in cancer-associated pathways, including ribosomes, cell cycle, RNA degradation, RNA polymerase, DNA replication, oxidative phosphorylation, proteasome, spliceosome, and glycolysis/gluconeogenesis. SBCS-2 was enriched in pathways of graft versus host disease, type 1 diabetes mellitus, intestinal immune network for IgA production, allograft rejection, and steroid hormone biosynthesis. Moreover, many oncogenic biological processes were highly activated in SBCS-1, including proliferation, stemness, epithelial-to-mesenchymal transition (EMT), and angiogenesis. Gene co-expression network analysis identified prognostic hub genes, transcription factor encoding genes (PFDN5 and EZH2), and protein kinase encoding gene (AURKA) in a gene module highly enriched in SBCS-1. ConclusionBased on the gene expression profiles in breast cancer stroma, breast cancer can be divided into 2 subtypes, which have significantly different molecular, and clinical characteristics. The identification of new subtypes of breast cancer has clinical implications for the management of this disease.

Cancer-Associated Fibroblasts: Mechanisms of Tumor Progression and Novel Therapeutic Targets.

Simple SummaryThe tumor microenvironment plays an important role in determining the biological behavior of several of the more aggressive malignancies. Among the various cell types evident in the tumor “field”, cancer-associated fibroblasts (CAFs) are a heterogenous collection of activated fibroblasts secreting a wide repertoire of factors that regulate tumor development and progression, inflammation, drug resistance, metastasis and recurrence. Insensitivity to chemotherapeutics and metastatic spread are the major contributors to cancer patient mortality. This review discusses the complex interactions between CAFs and the various populations of normal and neoplastic cells that interact within the dynamic confines of the tumor microenvironment with a focus on the involved pathways and genes.Cancer-associated fibroblasts (CAFs) are a heterogenous population of stromal cells found in solid malignancies that coexist with the growing tumor mass and other immune/nonimmune cellular elements. In certain neoplasms (e.g., desmoplastic tumors), CAFs are the prominent mesenchymal cell type in the tumor microenvironment, where their presence and abundance signal a poor prognosis in multiple cancers. CAFs play a major role in the progression of various malignancies by remodeling the supporting stromal matrix into a dense, fibrotic structure while secreting factors that lead to the acquisition of cancer stem-like characteristics and promoting tumor cell survival, reduced sensitivity to chemotherapeutics, aggressive growth and metastasis. Tumors with high stromal fibrotic signatures are more likely to be associated with drug resistance and eventual relapse. Clarifying the molecular basis for such multidirectional crosstalk among the various normal and neoplastic cell types present in the tumor microenvironment may yield novel targets and new opportunities for therapeutic intervention. This review highlights the most recent concepts regarding the complexity of CAF biology including CAF heterogeneity, functionality in drug resistance, contribution to a progressively fibrotic tumor stroma, the involved signaling pathways and the participating genes.

Upper tract urothelial carcinoma transcriptome profiling and immune microenvironment characterization.

564 Background: Upper tract urothelial carcinoma (UTUC) is an aggressive disease that is risk-stratified by clinicopathological factors due to an incomplete understanding of its molecular features. Thus, we performed transcriptomic profiling of UTUC tumors from radical nephroureterectomy specimens and compared their molecular characterization to survival outcomes. Methods: 100 UTUC tumors from 100 patients were subject to RNA sequencing and a hybridization capture-based assay for deep sequencing of cancer-associated genes, followed by unsupervised nonnegative matrix factorization clustering based on the top 10% of variant genes. Gene Set Enrichment and immune deconvolution analyses assessed for differences in the tumor microenvironments (TME) between clusters. Results: Consensus clustering analysis identified 5 biologically distinct clusters (Cluster 1 (C1) = 17, C2 = 18, C3 = 30, C4 = 11, and C5 = 24 patients), which were associated with significant differences in disease-free (DFS) (p < 0.01) and overall survival (OS) (p = 0.03). C1 and C2 were associated with pT3/4 stages and worse DFS and OS, while C5 was associated with pTa/1 stages and better DFS and OS. In terms of somatic mutation frequency differences, C3 and C4 had overall higher tumor mutation burden and mutations in epigenetic modulators, which corresponds with the transcriptomic finding of higher microsatellite instability expression signatures in these two clusters as well. Of note, all Lynch patients (N = 4) were in C3. C3 was enriched for the presence of FGFR3 driver mutations in 93% of tumors, and TP53 mutations were frequent in C2 and C4 in 47 and 55% of tumors, respectively. Differentially expressed genes and Gene Set Enrichment analyses revealed that C1 and C2 were enriched with several Hallmark inflammation signatures, such as TNF-α signaling via NF-kB, allograft rejection, inflammatory response, IL6 JAK/STAT3 signaling, and IL2 STAT5 signaling. C1 demonstrated a particularly inflammatory phenotype enriched with INF-γ and INF-α response gene sets. Lastly, in the TME deconvolution analysis, C1 and C2 had higher expression of PDL-1, immune checkpoint, immune suppression, cancer-associated fibroblasts, and myeloid inflammation surrogate signatures. C2 and C3 were enriched with CD8 T-cells, while C1 was enriched for INF-γ and hypoxia signatures. C2 had the least hypoxic TME, which may be related to stronger stromal, EMT, and angiogenesis signature signals seen. These results were then validated using an outside institution’s published cohort. Conclusions: Several differences in transcriptomic features indicate heterogeneity among UTUC tumors. Two clusters with high rates of recurrence and worse prognosis are associated with higher immune and myeloid cell infiltration. In addition to clinicopathologic factors, tumor microenvironment immune features may have potential use for disease prognostication.

Subtyping of sarcomas based on pathway enrichment scores in bulk and single cell transcriptomes

BackgroundSarcomas are highly heterogeneous in molecular, pathologic, and clinical features. However, a classification of sarcomas by integrating different types of pathways remains mostly unexplored.MethodsWe performed hierarchical clustering analysis of sarcomas based on the enrichment scores of 14 pathways involved in immune, stromal, DNA damage repair (DDR), and oncogenic signatures in three bulk tumor transcriptome datasets.ResultsConsistently in the three datasets, sarcomas were classified into three subtypes: Immune Class (Imm-C), Stromal Class (Str-C), and DDR Class (DDR-C). Imm-C had the strongest anti-tumor immune signatures and the lowest intratumor heterogeneity (ITH); Str-C showed the strongest stromal signatures, the highest genomic stability and global methylation levels, and the lowest proliferation potential; DDR-C had the highest DDR activity, expression of the cell cycle pathway, tumor purity, stemness scores, proliferation potential, and ITH, the most frequent TP53 mutations, and the worst survival. We further validated the stability and reliability of our classification method by analyzing a single cell RNA-Seq (scRNA-seq) dataset. Based on the expression levels of five genes in the pathways of T cell receptor signaling, cell cycle, mismatch repair, focal adhesion, and calcium signaling, we built a linear risk scoring model (ICMScore) for sarcomas. We demonstrated that ICMScore was an adverse prognostic factor for sarcomas and many other cancers.ConclusionsOur classification method provides novel insights into tumor biology and clinical implications for sarcomas.

Stromal computational signatures predict upgrade to invasive carcinoma in mass-forming DCIS: A brief report of 44 cases

Mass-forming ductal carcinoma in situ (DCIS) detected on core needle biopsy (CNB) is often a radiology-pathology discordance and thought to represent missed invasive carcinoma. This brief report applied supervised machine learning (ML) for image segmentation to investigate a series of 44 mass-forming DCIS cases, with the primary focus being stromal computational signatures. The area under the curve (AUC) for receiver operator curves (ROC) in relation to upgrade to invasive carcinoma from DCIS were as follows: high myxoid stromal ratio (MSR): 0.923, P = <0.001; low collagenous stromal percentage (CSP): 0.875, P = <0.001; and low proportionated stromal area (PSA): 0.682, P = 0.039. The use of ML in mass-forming DCIS could predict upgraded to invasive carcinoma with high sensitivity and specificity. The findings from this brief report are clinically useful and should be further validated by future studies.

Identification of key tumor stroma-associated transcriptional signatures correlated with survival prognosis and tumor progression in breast cancer.

The aberrant expression of stromal gene signatures in breast cancer has been widely studied. However, the association of stromal gene signatures with tumor immunity, progression, and clinical outcomes remains lacking. Based on eight breast tumor stroma (BTS) transcriptomics datasets, we identified differentially expressed genes (DEGs) between BTS and normal breast stroma. Based on the DEGs, we identified dysregulated pathways and prognostic hub genes, hub oncogenes, hub protein kinases, and other key marker genes associated with breast cancer. Moreover, we compared the enrichment levels of stromal and immune signatures between breast cancer patients with bad and good clinical outcomes. We also investigated the association between tumor stroma-related genes and breast cancer progression. The DEGs included 782 upregulated and 276 downregulated genes in BTS versus normal breast stroma. The pathways significantly associated with the DEGs included cytokine-cytokine receptor interaction, chemokine signaling, T cell receptor signaling, cell adhesion molecules, focal adhesion, and extracellular matrix-receptor interaction. Protein-protein interaction network analysis identified the stromal hub genes with prognostic value in breast cancer, including two oncogenes (COL1A1 and IL21R), two protein kinases encoding genes (PRKACA and CSK), and a growth factor encoding gene (PLAU). Moreover, we observed that the patients with bad clinical outcomes were less enriched in stromal and antitumor immune signatures (CD8 + T cells and tumor-infiltrating lymphocytes) but more enriched in tumor cells and immunosuppressive signatures (MDSCs and CD4 + regulatory T cells) compared with the patients with good clinical outcomes. The ratios of CD8 + /CD4 + regulatory T cells were lower in the patients with bad clinical outcomes. Furthermore, we identified the tumor stroma-related genes, including MCM4, SPECC1, IMPA2, and AGO2, which were gradually upregulated through grade I, II, and III breast cancers. In contrast, COL14A1, ESR1, SLIT2, IGF1, CH25H, PRR5L, ABCA6, CEP126, IGDCC4, LHFP, MFAP3, PCSK5, RAB37, RBMS3, SETBP1, and TSPAN11 were gradually downregulated through grade I, II, and III breast cancers. It suggests that the expression of these stromal genes has an association with the progression of breast cancers. These progression-associated genes also displayed an expression association with recurrence-free survival in breast cancer patients. This study identified tumor stroma-associated biomarkers correlated with deregulated pathways, tumor immunity, tumor progression, and clinical outcomes in breast cancer. Our findings provide new insights into the pathogenesis of breast cancer.

Pan-Cancer Analysis Shows Enrichment of Macrophages, Overexpression of Checkpoint Molecules, Inhibitory Cytokines, and Immune Exhaustion Signatures in EMT-High Tumors.

Epithelial–mesenchymal transition (EMT) is a highly dynamic process that occurs under normal circumstances; however, EMT is also known to play a central role in tumor progression and metastasis. Furthermore, role of tumor immune microenvironment (TIME) in shaping anticancer immunity and inducing the EMT is also well recognized. Understanding the key features of EMT is critical for the development of effective therapeutic interventions. Given the central role of EMT in immune escape and cancer progression and treatment, we have carried out a pan-cancer TIME analysis of The Cancer Genome Atlas (TCGA) dataset in context to EMT. We have analyzed infiltration of various immune cells, expression of multiple checkpoint molecules and cytokines, and inflammatory and immune exhaustion gene signatures in 22 cancer types from TCGA dataset. A total of 16 cancer types showed a significantly increased (p < 0.001) infiltration of macrophages in EMT-high tumors (mesenchymal samples). Furthermore, out of the 17 checkpoint molecules we analyzed, 11 showed a significant overexpression (p < 0.001) in EMT-high samples of at least 10 cancer types. Analysis of cytokines showed significant enrichment of immunosuppressive cytokines—TGFB1 and IL10—in the EMT-high group of almost all cancer types. Analysis of various gene signatures showed enrichment of inflammation, exhausted CD8+ T cells, and activated stroma signatures in EMT-high tumors. In summary, our pan-cancer EMT analysis of TCGA dataset shows that the TIME of EMT-high tumors is highly immunosuppressive compared to the EMT-low (epithelial) tumors. The distinctive features of EMT-high tumors are as follows: (i) the enrichment of tumor-associated macrophages, (ii) overexpression of immune checkpoint molecules, (iii) upregulation of immune inhibitory cytokines TGFB1 and IL10, and (iv) enrichment of inflammatory and exhausted CD8+ T-cell signatures. Our study shows that TIMEs of different EMT groups differ significantly, and this would pave the way for future studies analyzing and targeting the TIME regulators for anticancer immunotherapy.

Tumor-infiltrating lymphocytes are functionally inactivated by CD90+ stromal cells and reactivated by combined Ibrutinib and Rapamycin in human pleural mesothelioma

Rationale: Despite evidence suggesting that the tumor microenvironment (TME) in malignant pleural mesothelioma (MPM) is linked with poor prognosis, there is a lack of studies that functionally characterize stromal cells and tumor-infiltrating lymphocytes (TILs). Here, we aim to characterize the stromal subsets within MPM, investigate their relationship to TILs, and explore the potential therapeutic targets.Methods: We curated a core set of genes defining stromal/immune signatures expressed by mesenchymal cells within the TME using molecular analysis of The Cancer Genome Atlas (TCGA) MPM cohort. Stromal and immune profiles were molecularly characterized using flow cytometry, immunohistochemistry, microarray, and functionally evaluated using T cell-activation/expansion, coculture assays and drug compounds treatment, based on samples from an independent MPM cohort.Results: We found that a high extracellular matrix (ECM)/stromal gene signature, a high ECM score, or the ratio of ECM to an immune activation gene signature are significantly associated with poor survival in the MPM cohort in TCGA. Analysis of an independent MPM cohort (n = 12) revealed that CD8+ and CD4+ TILs were characterized by PD1 overexpression and concomitant downregulation in degranulation and CD127. This coincided with an increase in CD90+ cells that overexpressed PD-L1 and were enriched for ECM/stromal genes, activated PI3K-mTOR signaling and suppressed T cells. Protein array data demonstrated that MPM samples with high PD-L1 expression were most associated with activation of the mTOR pathway. Further, to reactivate functionally indolent TILs, we reprogrammed ex vivo TILs with Ibrutinib plus Rapamycin to block interleukin-2-inducible kinase (ITK) and mTOR pathways, respectively. The combination treatment shifted effector memory (TEM) CD8+ and CD4+ TILs towards T cells that re-expressed CD45RA (TEMRA) while concomitantly downregulating exhaustion markers. Gene expression analysis confirmed that Ibrutinib plus Rapamycin downregulated coinhibitory and T cell signature pathways while upregulating pathways involved in DNA damage and repair and immune cell adhesion and migration.Conclusions: Our results suggest that targeting the TME may represent a novel strategy to redirect the fate of endogenous TILs with the goal of restoring anti-tumor immunity and control of tumor growth in MPM.

16P Stromal compartment specific gene signatures dissect immunotherapy response groups in non-small cell lung cancer

Immunotherapies, such as immune checkpoint inhibitors (ICI) have shown durable benefit in a subset of non-small cell lung cancer (NSCLC) patients. The composition and activation status of the cellular milieu contained within the tumour microenvironment (TME) is becomingly increasingly recognised as a driving factor in treatment-refractory disease.

Molecular and Genetic Characterization of Tumor Samples from Patients with Relapsed or Refractory Follicular Lymphoma Identifies Factors Influencing Response to Tazemetostat

Molecular and Genetic Characterization of Tumor Samples from Patients with Relapsed or Refractory Follicular Lymphoma Identifies Factors Influencing Response to Tazemetostat

Multiphoton Microscopy for Identifying Collagen Signatures Associated with Biochemical Recurrence in Prostate Cancer Patients.

Prostate cancer is a heterogeneous disease that remains dormant for long periods or acts aggressively with poor clinical outcomes. Identifying aggressive prostate tumor behavior using current glandular-focused histopathological criteria is challenging. Recent evidence has implicated the stroma in modulating prostate tumor behavior and in predicting post-surgical outcomes. However, the emergence of stromal signatures has been limited, due in part to the lack of adoption of imaging modalities for stromal-specific profiling. Herein, label-free multiphoton microscopy (MPM), with its ability to image tissue with stromal-specific contrast, is used to identify prostate stromal features associated with aggressive tumor behavior and clinical outcome. MPM was performed on unstained prostatectomy specimens from 59 patients and on biopsy specimens from 17 patients with known post-surgery recurrence status. MPM-identified collagen content, organization, and morphological tumor signatures were extracted for each patient and screened for association with recurrent disease. Compared to tumors from patients whose disease did not recur, tumors from patients with recurrent disease exhibited higher MPM-identified collagen amount and collagen fiber intensity signal and width. Our study shows an association between MPM-identified stromal collagen features of prostate tumors and post-surgical disease recurrence, suggesting their potential for prostate cancer risk assessment.

A gene expression–based model predicts outcome in children with intermediate-risk classical Hodgkin lymphoma

AbstractClassical Hodgkin lymphoma (cHL) is a common malignancy in children and adolescents. Although cHL is highly curable, treatment with chemotherapy and radiation often come at the cost of long-term toxicity and morbidity. Effective risk-stratification tools are needed to tailor therapy. Here, we used gene expression profiling (GEP) to investigate tumor microenvironment (TME) biology, to determine molecular correlates of treatment failure, and to develop an outcome model prognostic for pediatric cHL. A total of 246 formalin-fixed, paraffin-embedded tissue biopsies from patients enrolled in the Children's Oncology Group trial AHOD0031 were used for GEP and compared with adult cHL data. Eosinophil, B-cell, and mast cell signatures were enriched in children, whereas macrophage and stromal signatures were more prominent in adults. Concordantly, a previously published model for overall survival prediction in adult cHL did not validate in pediatric cHL. Therefore, we developed a 9-cellular component model reflecting TME composition to predict event-free survival (EFS). In an independent validation cohort, we observed a significant difference in weighted 5-year EFS between high-risk and low-risk groups (75.2% vs 90.3%; log-rank P = .0138) independent of interim response, stage, fever, and albumin. We demonstrate unique disease biology in children and adolescents that can be harnessed for risk-stratification at diagnosis. This trial was registered at www.clinicaltrials.gov as #NCT00025259.

A quantitative polymerase chain reaction based method for molecular subtype classification of urinary bladder cancer-Stromal gene expressions show higher prognostic values than intrinsic tumor genes.

Transcriptome-based molecular subtypes of muscle-invasive bladder cancer (MIBC) have been shown to be both prognostic and predictive, but are not used in routine clinical practice. We aimed to develop a feasible, reverse transcription quantitative polymerase chain reaction (RT-qPCR)-based method for molecular subtyping. First, we defined a 68-gene set covering tumor intrinsic (luminal, basal, squamous, neuronal, epithelial-to-mesenchymal, in situ carcinoma) and stromal (immune, extracellular matrix, p53-like) signatures. Then, classifier methods with this 68-gene panel were developed in silico and validated on public data sets with available subtype class information (MD Anderson [MDA], The Cancer Genome Atlas [TCGA], Lund, Consensus). Finally, expression of the selected 68 genes was determined in 104 frozen tissue samples of our MIBC cohort by RT-qPCR using the TaqMan Array Card platform and samples were classified by our newly developed classifiers. The prognostic value of each subtype classification system and molecular signature scores were assessed. We found that the reduced marker set combined with the developed classifiers were able to reproduce the TCGA II, MDA, Lund and Consensus subtype classification systems with an overlap of 79%, 76%, 69% and 64%, respectively. Importantly, we could successfully classify 96% (100/104) of our MIBC samples by using RT-qPCR. Neuronal and luminal subtypes and low stromal gene expressions were associated with poor survival. In conclusion, we developed a robust and feasible method for the molecular subtyping according to the TCGA II, MDA, Lund and Consensus classifications. Our results suggest that stromal signatures have a superior prognostic value compared to tumor intrinsic signatures and therefore underline the importance of tumor-stroma interaction during the progression of MIBC.

Analysis of Immune-Stromal Score-Based Gene Signature and Molecular Subtypes in Osteosarcoma: Implications for Prognosis and Tumor Immune Microenvironment.

Objective: Infiltrating immune and stromal cells are essential for osteosarcoma progression. This study set out to analyze immune–stromal score-based gene signature and molecular subtypes in osteosarcoma. Methods: The immune and stromal scores of osteosarcoma specimens from the TARGET cohort were determined by the ESTIMATE algorithm. Then, immune-stromal score-based differentially expressed genes (DEGs) were screened, followed by univariate Cox regression analysis. A LASSO regression analysis was applied for establishing a prognostic model. The predictive efficacy was verified in the GSE21257 dataset. Associations between the risk scores and chemotherapy drug sensitivity, immune/stromal scores, PD-1/PD-L1 expression, immune cell infiltrations were assessed in the TARGET cohort. NMF clustering analysis was employed for characterizing distinct molecular subtypes based on immune-stromal score-based DEGs. Results: High immune/stromal scores exhibited the prolonged survival duration of osteosarcoma patients. Based on 85 prognosis-related stromal–immune score-based DEGs, a nine-gene signature was established. High-risk scores indicated undesirable prognosis of osteosarcoma patients. The AUCs of overall survival were 0.881 and 0.849 in the TARGET cohort and GSE21257 dataset, confirming the well predictive performance of this signature. High-risk patients were more sensitive to doxorubicin and low-risk patients exhibited higher immune/stromal scores, PD-L1 expression, and immune cell infiltrations. Three molecular subtypes were characterized, with distinct clinical outcomes and tumor immune microenvironment. Conclusion: This study developed a robust prognostic gene signature as a risk stratification tool and characterized three distinct molecular subtypes for osteosarcoma patients based on immune–stromal score-based DEGs, which may assist decision-making concerning individualized therapy and follow-up project.

DNA methylation landscapes of 1538 breast cancers reveal a replication-linked clock, epigenomic instability and cis-regulation

DNA methylation is aberrant in cancer, but the dynamics, regulatory role and clinical implications of such epigenetic changes are still poorly understood. Here, reduced representation bisulfite sequencing (RRBS) profiles of 1538 breast tumors and 244 normal breast tissues from the METABRIC cohort are reported, facilitating detailed analysis of DNA methylation within a rich context of genomic, transcriptional, and clinical data. Tumor methylation from immune and stromal signatures are deconvoluted leading to the discovery of a tumor replication-linked clock with genome-wide methylation loss in non-CpG island sites. Unexpectedly, methylation in most tumor CpG islands follows two replication-independent processes of gain (MG) or loss (ML) that we term epigenomic instability. Epigenomic instability is correlated with tumor grade and stage, TP53 mutations and poorer prognosis. After controlling for these global trans-acting trends, as well as for X-linked dosage compensation effects, cis-specific methylation and expression correlations are uncovered at hundreds of promoters and over a thousand distal elements. Some of these targeted known tumor suppressors and oncogenes. In conclusion, this study demonstrates that global epigenetic instability can erode cancer methylomes and expose them to localized methylation aberrations in-cis resulting in transcriptional changes seen in tumors.

Activation of epithelial-mesenchymal transition process during breast cancer progression - the impact of molecular subtype and stromal composition.

Breast cancer (BC) is a heterogeneous disease with different molecular subtypes, which can be defined by oestrogen (ER), progesterone (PR) and human epidermal growth factor (HER2) receptors' status as luminal, HER2+ and triple negative (TNBC). Molecular subtypes also differ in their epithelial-mesenchymal phenotype, which might be related to their aggressiveness, as activation of the epithelial-mesenchymal transition (EMT) is linked with increased ability of cancer cells to survive andmetastasize. Nevertheless, the reverse process of mesenchymal-epithelial transition was shown tobe required to sustain metastatic colonization. In this study we aimed to analyse activation of the EMT process in primary tumours (PT), which have (N+) or have not (N-) colonized the lymph nodes, as well as the lymph nodes metastases (LNM) themselves in 88 BC patients. We showed that luminal N- PT have the lowest activation of the EMT process (27%), in comparison to N+ PT (48%,p=0.06). On the other hand, TNBC do not show statistically significant EMT activation at the stage before lymph colonization (N-, 83%) and after colonization of the lymph nodes (N+, 63%, p=0.58). TNBC are also the least plastic (unable to change the EMT phenotype) in terms of turning EMT on or off between matched PT and LNM (0% EMT plasticity in TNBC vs 36% plasticity in luminal tumours). Moreover, in TNBC activation of EMT was correlated with increased cell division rate of the PT- inmesenchymal TNBC PT median Ki-67 was 45% in comparison to 10% in epithelial TNBC PT (p=0.002), whereas in PT of luminal subtypes Ki-67 did not differ between epithelial andmesenchymal phenotypes. Profiling of immunotranscriptome of epithelial and mesenchymal luminal BC with Nanostring technology revealed that N- PT with epithelial phenotype were enriched ininflammatory response signatures, whereas N+ mesenchymal cancers showed elevated MHC class II antigen presentation. Overall, activation of EMT changes during cancer progression and metastatic colonization of the lymph nodes depending on the PT molecular subtype and is related to differences in stromal signatures. Activation of EMT is associated with colonizing phenotype in luminal PT andproliferative phenotype of TNBC.

Antibiotic-induced disturbances of the gut microbiota result in accelerated breast tumor growth

SummaryThe gut microbiota’s function in regulating health has seen it linked to disease progression in several cancers. However, there is limited research detailing its influence in breast cancer (BrCa). This study found that antibiotic-induced perturbation of the gut microbiota significantly increases tumor progression in multiple BrCa mouse models. Metagenomics highlights the common loss of several bacterial species following antibiotic administration. One such bacteria, Faecalibaculum rodentium, rescued this increased tumor growth. Single-cell transcriptomics identified an increased number of cells with a stromal signature in tumors, and subsequent histology revealed an increased abundance of mast cells in the tumor stromal regions. We show that administration of a mast cell stabilizer, cromolyn, rescues increased tumor growth in antibiotic treated animals but has no influence on tumors from control cohorts. These findings highlight that BrCa-microbiota interactions are different from other cancers studied to date and suggest new research avenues for therapy development.

Computational analysis of TMPRSS2 expression in normal and SARS-CoV-2-infected human tissues

The transmembrane serine protease 2 (TMPRSS2) is a key molecule for SARS-CoV-2 invading human host cells. To provide insights into SARS-CoV-2 infection of various human tissues and understand the potential mechanism of SARS-CoV-2 infection, we investigated TMPRSS2 expression in various normal human tissues and SARS-CoV-2-infected human tissues. Using publicly available datasets, we performed computational analyses of TMPRSS2 expression levels in 30 normal human tissues, and compared them between males and females and between younger (ages ≤ 49 years) and older (ages > 49 years) populations in these tissues. We found that TMPRSS2 expression levels were the highest in the prostate, stomach, pancreas, lungs, small intestine, and salivary gland. The TMPRSS2 protein had relatively high expression levels in the parathyroid gland, stomach, small intestine, pancreas, kidneys, seminal vesicle, epididymis, and prostate. However, TMPRSS2 expression levels were not significantly different between females and males or between younger and older populations in these tissues. The pathways enriched in TMPRSS2-upregulated pan-tissue were mainly involved in immune, metabolism, cell growth and proliferation, stromal signatures, and cancer and other diseases. Many cytokine genes displayed positive expression correlations with TMPRSS2 in pan-tissue, including TNF-α, IL-1, IL-2, IL-4, IL-7, IL-8, IL-12, IL-18, IFN-α, MCP-1, G-CSF, and IP-10. We further analyzed TMPRSS2 expression levels in nasopharyngeal swabs from SARS-CoV-2-infected patients. TMPRSS2 expression levels showed no significant difference between males and females or between younger and older patients. However, they were significantly lower in SARS-CoV-2-infected than in healthy individuals and patients with other viral acute respiratory illnesses. Interestingly, TMPRSS2 expression levels were positively correlated with the enrichment levels of four immune signatures (B cells, CD8+ T cells, NK cells, and interferon response) in SARS-CoV-2-infected patients but likely to be negatively correlated with them in the normal lung tissue. Our data may provide insights into the mechanism of SARS-CoV-2 infection.

Comprehensive analysis of circRNA expression pattern and circRNA-miRNA-mRNA network in oral squamous cell carcinoma

ObjectiveCircRNAs are critical gene modulators in tumor initiation and progression. However, the expression pattern and molecular pathogenesis of circRNAs in oral squamous cell carcinoma (OSCC) are still poorly characterized. MethodsRNA sequencing with CIRCexplorer2 pipeline was performed to identify circRNAs in 46 tumor-normal paired tissues from OSCC patients. Another set of 48 head and neck squamous cell carcinoma samples from the MiOncoCirc database were utilized as an independent validation. ResultsOf the 1276 identified high-confidence circRNAs, 154 were differentially expressed between tumor and normal tissues (log2|Fold Change|≥1 and false discovery rate < 0.05). CircRNAs expression was globally down-regulated in tumors compared to normal tissues (P = 9.44 × 10−14). Correlation analysis demonstrated that the global expression of circRNAs was positively related to tumor infiltrating lymphocyte (P = 1.10 × 10−4) and stromal signature (P = 2.70 × 10−3) whereas negatively associated with cell proliferation markers (P = 4.32 × 10−2). CircRNAs-miRNAs-mRNAs regulatory network revealed 6574 interactions, and the target genes were enriched in extracellular matrix and immune-related pathways. Survival analysis were performed on target genes in immune-related pathways, and 20 genes were significantly associated with the prognostic status of OSCC in The Cancer Genome Atlas cohort. The risk model constructed with above 20 genes was associated with the prognosis status of OSCC (HR = 3.28, P = 5.06 × 10−11), and the result was validated in an independent study (GSE41613) (HR = 2.06, P = 1.73 × 10−2). ConclusionCircRNAs showed a global down-regulation pattern in OSCC tissues, and genes regulated by circRNAs primarily involved in immune and extracellular matrix pathways, which could also affect the OSCC prognosis, indicating that they may serve as potential prognostic biomarkers.

Abstract 2702: Prostate stromal transcriptome as biomarker of aggressive prostate cancer

Abstract Background: Prostate cancer has a heterogeneous prognosis. Discovery of molecular biomarkers that improve identification of potentially lethal prostate cancer could enhance the ability to determine which patients would benefit most from immediate treatment. Most prognostic biomarkers have focused on those in prostate tumors. However, it is increasingly recognized that the tumor microenvironment contributes to cancer aggressiveness and progression. We therefore examined whole transcriptome expression of the prostate stroma and associations with clinically significant, high-grade prostate cancer. Methods: We performed RNA-sequencing (Illumina TruSeq RNA Exome library preparation) of macrodissected stromal samples (adjacent to and distant from tumor) from 363 radical prostatectomy specimens from Health Professionals Follow-up Study and Physicians' Health Study participants. We performed a differential expression analysis comparing the gene expression and pathways of stroma adjacent to and distant from tumor. We also performed a cluster analysis and computed a single sample gene set enrichment analysis (ssGSEA) score to assess the association of our previously reported 25 stromal signature genes (Tyekucheva et al. 2017) to determine the association with Gleason score. Results: Eighty-eight genes as well as eight Gene Ontology pathways and fourteen Canonical pathways were differentially expressed (FDR&amp;lt;0.05) between tumor adjacent to and distant from tumor. Pathways included cell-substrate adhesion, muscle development, and cardiomyopathy. 30% of men had high-grade (Gleason score 8-10) tumors. We observed higher expression of the individual signature genes in the cluster enriched for high Gleason score tumors, and a higher stromal signature ssGSEA score was associated with high Gleason score (p=0.04). Conclusions: Pathways differentially expressed between stroma adjacent to and distant from tumor suggest known differences in cell types by location or a potential field effect. We observed that the direction of the association of the stromal signature with Gleason score agreed with our previous report. We are currently analyzing the full expression dataset to identify additional stromal genes associated with Gleason score and lethal prostate cancer, and performing multi-plex immunohistochemistry to assess stromal cell-type heterogeneity in regions of tumor and normal prostate across tissue microarrays. Citation Format: Yinglu Zhou, Chaoran Ma, Giuseppe Nicoló Fanelli, Lavinia Stefanizzi, Elizaveta Gazeeva, Lorelei A. Mucci, Massimo Loda, Svitlana Tyekucheva, Kathryn L. Penney. Prostate stromal transcriptome as biomarker of aggressive prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2702.