Signatures In Triple Negative Breast Cancer Research Articles

Identification of new subtypes and potential genetic signatures in triple-negative breast cancer using weighted gene co-expression network analysis.

Triple-negative breast cancer (TNBC) is a heterogeneous disease with aggressive behavior and poor prognosis. Here, we used gene expression profiling to define new subtypes of TNBC, which may improve prevention and treatment through personalized medicine. Gene expression profiles from the public datasets GSE76250, GSE61724, GSE61723, and GES76275 were subjected to co-expression analysis to identify differentially expressed genes (DEGs) between TNBC and non-TNBC tissues. Consistency clustering was used to define TNBC subtypes, whose correlation with gene modules was analyzed. Enrichment analysis was used to identify module genes' biological functions and pathways. Single-sample gene set enrichment analysis was used to assess immune cell infiltration in the different TNBC subtypes, and the ChAMP package was used to examine methylation sites in TNBC. A total of 4,958 DEGs in TNBC were identified, which showed the same expression differences across all datasets as in the dataset GSE76250 and clustered into 9 co-expression modules. TNBC samples clustered into two subtypes based on nine hub genes from the modules. Class I showed the most significant correlation with module 1, whose genes were related mainly to interleukin-1 response, while class II showed the most significant correlation with module 6, whose genes were related mainly to the transforming growth factor-β pathway. Class I was significantly enriched in cell cycle and DNA replication, and tumors of this subtype showed lower immune cell infiltration than class II tumors. Tumor infiltration by Th2 cells correlated positively with the expression of MCM10 and negatively with the expression of PREX2. A greater methylation of CIDEC, DLC1, EDNRB, EGR2 and SRPK1 correlated with better prognosis. Class I TNBC, for which a useful biomarker is MCM10, may be associated with a worse prognosis than class II TNBC, for which PREX2 may serve as a biomarker.

Identification of immunosuppressive signature subtypes and prognostic risk signatures in triple-negative breast cancer.

Immune checkpoint blockade (ICB) therapy has transformed the treatment of triple-negative breast cancer (TNBC) in recent years. However, some TNBC patients with high programmed death-ligand 1 (PD-L1) expression levels develop immune checkpoint resistance. Hence, there is an urgent need to characterize the immunosuppressive tumor microenvironment and identify biomarkers to construct prognostic models of patient survival outcomes in order to understand biological mechanisms operating within the tumor microenvironment. RNA sequence (RNA-seq) data from 303 TNBC samples were analyzed using an unsupervised cluster analysis approach to reveal distinctive cellular gene expression patterns within the TNBC tumor microenvironment (TME). A panel of T cell exhaustion signatures, immunosuppressive cell subtypes and clinical features were correlated with the immunotherapeutic response, as assessed according to gene expression patterns. The test dataset was then used to confirm the occurrence of immune depletion status and prognostic features and to formulate clinical treatment recommendations. Concurrently, a reliable risk prediction model and clinical treatment strategy were proposed based on TME immunosuppressive signature differences between TNBC patients with good versus poor survival status and other clinical prognostic factors. Significantly enriched TNBC microenvironment T cell depletion signatures were detected in the analyzed RNA-seq data. A high proportion of certain immunosuppressive cell subtypes, 9 inhibitory checkpoints and enhanced anti-inflammatory cytokine expression profiles were noted in 21.4% of TNBC patients that led to the designation of this group of immunosuppressed patients as the immune depletion class (IDC). Although IDC group TNBC samples contained tumor-infiltrating lymphocytes present at high densities, IDC patient prognosis was poor. Notably, PD-L1 expression was relatively elevated in IDC patients that indicated their cancers were resistant to ICB treatment. Based on these findings, a set of gene expression signatures predicting IDC group PD-L1 resistance was identified then used to develop risk models for use in predicting clinical therapeutic outcomes. A novel TNBC immunosuppressive tumor microenvironment subtype associated with strong PD-L1 expression and possible resistance to ICB treatment was identified. This comprehensive gene expression pattern may provide fresh insights into drug resistance mechanisms for use in optimizing immunotherapeutic approaches for TNBC patients.

Abstract 4852: Metabolomic rewiring in endocrine therapy resistant estrogen receptor positive breast cancer

Abstract Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide and the most common in women in the United States. The majority of the BC are estrogen receptor alpha positive (ER+) and are likely to respond to endocrine therapy. Tamoxifen and fulvestrant are the most widely used hormonal treatments for ER+ BC. However, nearly half of patients receiving endocrine therapy suffer from the risk of recurrence due to either intrinsic or acquired resistance. Thus, it is vital to understand the mechanisms of resistance to endocrine therapy in ER+ BC. Metabolic reprogramming is one of the significant hallmarks of cancer and it is now known that many tumors utilize mitochondrial oxidative phosphorylation (OXPHOS) for their energy needs. Our lab previously reported that metastatic triple-negative breast cancer (TNBC), which does not express ER, has a high energy dependency on mitochondrial fatty acid beta-oxidation (FAO). In this project, we developed a gene signature by integrating the transcriptomic, metabolomic, and lipidomic data from FAO rate-limiting gene (CPT1) modulated TNBC cells. Our signature represented the FAO-regulated gene set relevant to metabolic or lipidomic changes. We discovered a significant reliance on this TNBC signature in endocrine-resistant ER+ BC. Clinical data suggests that our gene signature predicts the survival of endocrine-treated ER+ BC patients. Though increased fatty acid consumption is reported in tamoxifen-resistant cells, the role of FAO in endocrine treatment resistance is largely unclear. We then analyzed the metabolic reprogramming in ER+ BC cell lines after tamoxifen or fulvestrant therapy. Molecular, genetic, and metabolic analyses suggested that endocrine therapy-induce AMPK-FAO-OXPHOS signal activation in ER+ BC cells. Furthermore, the knockdown of CPT1, the rate-limiting enzyme of FAO, or treatment with FAO inhibitors significantly enhanced the response to endocrine therapies. We have also reported that FAO induces the autophosphorylation of the c-Src proto-oncogene in TNBC tumors. Thus, we analyzed this phenomenon in the endocrine-resistant ER+ BC cells. As expected, endocrine therapy-induced FAO activated the Src pathway in ER+ BC also. Moreover, in vitro, and in vivo studies confirmed that endocrine-resistant ER+ BC cells have increased sensitivity to FAO, OXPHOS, and Src inhibitors. Finally, analysis of clinical data suggests that low expression of FAO rate-limiting genes in the ER+ primary tumors have better recurrence-free and distant metastasis-free survival after endocrine therapy. Overall, our findings suggest that the gene signature generated from FAO-modulated TNBC cells predicts response to endocrine therapy in ER+ BC. Moreover, metabolic reprogramming in endocrine-resistant ER+ tumors induce FAO, OXPHOS, and Src pathways, providing potential targets to overcome endocrine-resistance ER+ BC patients. Citation Format: Songyeon Ahn, Junhyoung Park, Sandra L. Grimm, Badrajee WB Piyarathna, Nagireddy Putluri, Gokul Das, Cristian Coarfa, Benny A. Kaipparettu. Metabolomic rewiring in endocrine therapy resistant estrogen receptor positive breast cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4852.

Abstract 2478: The interplay between Apurinic/apyrimidinic Endonuclease 1 (APE1) and DNA G-quadruplex (G4) in regulating triple-negative breast cancer (TNBC) metastasis

Abstract Triple-negative breast cancer (TNBC) is the most aggressive breast cancer (BC) among all the BC subtypes due to its exceptionally high metastasis rate. TNBC patients with metastasis have a short overall survival as the endocrine therapy and chemotherapy are inefficient in TNBC treatment. Hence, efforts are still needed to figure out the molecular mechanism driving TNBC metastasis and find promising therapeutic targets for TNBC treatment. A newly published study revealed that a secondary DNA structure G-quadruplex (G4) is highly enriched in TNBC signature genes’ promoters, and our lab was the first one to uncover that DNA repair protein Apurinic/apyrimidinic endonuclease 1 (APE1) binds to G4 and regulates its stability in cells, so we are aimed to investigate the interaction between APE1 and G4 in TNBC and their roles in regulating TNBC metastasis. We found that knocking out or knocking down APE1 in TNBC cell lines, MDA-MB-231 and BT-549, significantly impaired the cell migration ability. RNA-seq analysis and Gene Ontology analysis revealed that APE1 knockout downregulated expression of genes such as CXCL1, VEGFA, PIK3R3, BMI1, etc. which are mostly involved in promoting cell migration. Further, APE1 ChIP-seq analysis showed that APE1 was highly enriched in CXCL1, VEGFA, PIK3R3 and BMI1 gene promoters, and these regions contained potential G4 folding sequences, suggesting a potential interaction between APE1 and G4 in these gene promoters. Consisted with this, our study with the enzyme linked immunosorbent assays (ELISA) showed that recombinant APE1 bound with high affinity to CXCL1 and VEGFA G4 secondary structures as compared to double-strand DNA oligos in vitro, suggesting that APE1’s enrichment to the metastasis-related gene promoters attributes to the G4 structures in those promoter regions. Promoter-directed ChIP assay further revealed that downregulating APE1 inhibited transcription factor (TF) c-JUN’s occupancy to VEGFA promoters. Interestingly, we discovered that the G4-targeted small molecule TMPyP4 blocked APE1-G4 binding in vitro and reduced APE1’s occupancy at gene promoters in cells. Treatment of cells with TMPyP4 significantly downregulated CXCL1, VEGFA, PIK3R3, and BMI1 gene expression as well as inhibiting cell migration. In orthotopic TNBC metastasis mouse model, mice treating with TMPyP4 significantly reduced the frequency and volume of metastatic tumors in lung tissues. Overall, our study revealed that G4 in gene promoters recruited APE1 which further promoted transcription factors’ binding to promoters and enhanced metastasis-related gene expression. Blocking APE1-G4 interaction by TMPyP4 is a potential therapeutic approach to inhibit TNBC metastasis. Citation Format: Yingling Chen, Suravi Pramanik, Mason Tarpley, Achyuth Kalluchi, Bhopal Mohapatra, Kyle Hewitt, Michael J. Rowley, Vimla Band, Kishor Bhakat. The interplay between Apurinic/apyrimidinic Endonuclease 1 (APE1) and DNA G-quadruplex (G4) in regulating triple-negative breast cancer (TNBC) metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2478.

Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures

Drug-induced gene expression profiling provides a lot of useful information covering various aspects of drug discovery and development. Most importantly, this knowledge can be used to discover drugs' mechanisms of action. Recently, deep learning-based drug design methods are in the spotlight due to their ability to explore huge chemical space and design property-optimized target-specific drug molecules. Recent advances in accessibility of open-source drug-induced transcriptomic data along with the ability of deep learning algorithms to understand hidden patterns have opened opportunities for designing drug molecules based on desired gene expression signatures. In this study, we propose a deep learning model, Gex2SGen (Gene Expression 2 SMILES Generation), to generate novel drug-like molecules based on desired gene expression profiles. The model accepts desired gene expression profiles in a cell-specific manner as input and designs drug-like molecules which can elicit the required transcriptomic profile. The model was first tested against individual gene-knocked-out transcriptomic profiles, where the newly designed molecules showed high similarity with known inhibitors of the knocked-out target genes. The model was next applied on a triple negative breast cancer signature profile, where it could generate novel molecules, highly similar to known anti-breast cancer drugs. Overall, this work provides a generalized method, where the method first learned the molecular signature of a given cell due to a specific condition, and designs new small molecules with drug-like properties.

Abstract PD4-02: PD4-02 Spatial and temporal heterogeneity of predictive and prognostic signatures in triple-negative breast cancer treated with neoadjuvant combination immune-chemotherapy

Abstract Background: It is well known that immunological pathways are relevant for response to classical neoadjuvant chemotherapy as well as combined chemo-immunotherapy. In addition, it has been shown that combined chemo-immunotherapy significantly improves survival, even in the context of only moderate effects on pCR. Due to the window therapy with durvalumab-alone and the option to analyze multiple consecutive biopsies, the GeparNuevo trial offers the opportunity to 1) determine gene expression patterns for pCR and DDFS endpoints 2) identify pathways most relevant for pCR and DDFS 3) identify genes specifically regulated by immunotherapy (comparison of samples pre-and post-window) 4) identify genes specifically regulated by chemotherapy (comparison of samples pre-Tx and after 4 cycles of chemotherapy 5) identify longitudinal patterns of gene expression by comparison of up to four time points and 6) identify changes in the tumor microenvironment by spatial sequencing of tumor cell and stroma areas. Methods: 292 tumor samples were evaluated by gene expression analysis: 162 pretherapeutic core biopsies, 79 post-window biopsies, 32 biopsies during chemotherapy and 19 biopsies of the residual tumor after therapy. These samples were analyzed by HTG OBP panel targeting 2549 genes which are assigned to 25 different biological mechanisms or cellular pathways. In addition, spatial profiling was compared in a subset of pre-and post-window samples using Nanostring GeoMx spatial profiling system. Endpoints were pCR and DDFS. Results: A total of more than 600 genes were significantly associated with either the pCR or the DDFS endpoint in either the complete GeparNuevo cohort or one of the two therapy arms. Interestingly, there was a large number of predictive or prognostic genes (n=247 for pCR and n=179 for DDFS) in the durvalumab arm, while the number of genes in the placebo arm was considerably lower (n=113 for pCR and n=61 for DDFS). We used existing pathway information for HTG OBP panel to analyze the contribution of different cellular processes to pCR and DDFS signatures in different therapy arms. Immune pathways were particularly relevant for durvalumab signatures (pCR and DDFS), while cell cycle related gene expression patterns were particularly involved in signatures predictive of pCR in both therapy arms. To further assign genes to the cellular response to durvalumab-alone or chemotherapy-alone, we compared gene expression patterns in durvalumab arm before and after the window phase (gene expression patterns induced by one dose of durvalumab) with gene expression patterns in placebo arm before and after 4 cycles of chemotherapy. Further longitudinal alterations were analyzed by comparison of longitudinal samples for 4 different time-points (a: before NACT, n=162; b: after window phase, n=79; c: after 4 cycles, n=31 and d: at surgery, n=19). Using the Nanostring GeoMx spatial RNA profiling system guided by cytokeratine immunofluorescence, we compared areas with high tumor cell content with stromal areas with or without TILs. In combination with the HTG gene expression data, we were able allocate the changes induced by durvalumab vs chemotherapy to the stromal cell and tumor cell compartment, indicating a re-organization of the tumor-microenvironment. Conclusions: In our analysis, we show that immune gene signatures are particularly relevant for neoadjuvant response to durvalumab as well as prognosis after durvalumab treatment, while proliferation signatures are involved in pCR-signatures after durvalumab as well as chemotherapy. The spatial analysis showed that relevant changes occur in the stromal compartment, indicating a re-organization of the tumor microenvironment. The parallel targeting of immune- and proliferation pathways might explain why a combined immunotherapy-chemotherapy approach is more successful than each single therapy strategy alone. Citation Format: Carsten Denkert, Andreas Schneeweiss, Julia Rey, Akira Hattesohl, Thomas Karn, Michael Braun, Paul Jank, Jens Huober, Hans-Peter Sinn, Dirk-Michael Zahm, Claus Hanusch, Frederik Marmé, Jenny Furlanetto, Jörg Thomalla, Jens-Uwe Blohmer, Marion van Mackelenbergh, Thorsten Stiewe, Peter Staib, Christian Jackisch, Julia Teply-Szymanski, Peter A. Fasching, Bruno V. Sinn, Michael Untch, Karsten Weber, Sibylle Loibl. PD4-02 Spatial and temporal heterogeneity of predictive and prognostic signatures in triple-negative breast cancer treated with neoadjuvant combination immune-chemotherapy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD4-02.

Abstract PD5-08: PD5-08 Drug target activation mapping of matched triple negative primary and axillary lymph node metastases identifies and links the ER-AR-GR steroid hormone receptor axis with downstream AKT activation

Abstract Title: Drug target activation mapping of matched triple negative primary and axillary LN metastases identifies and links the ER-AR-GR steroid hormone receptor axis with downstream AKT activation Background: Triple negative breast cancer (TNBC) is comprised of heterogeneous subtypes that arise from various molecular alterations that result in TNBC having the poorest overall prognosis. There is growing interest in more sensitive ways to measure estrogen receptor (ER) and HER2 levels in the ER “negative” and HER2 “low/negative” settings, as there are patients with TNBC whose tumors are actually ER and/or HER2 “expressing/active”, and who may benefit from ER- and/or HER2-targeted therapies. Given the limitations of IHC in reliably quantitating ER and HER2 in low (0-1+) expressing tumors, we utilized highly sensitive reverse phase protein array (RPPA) to quantitate expression of these therapeutic targets along with downstream signaling activation mapping in a unique pilot set of patient-matched primary (P) and axillary lymph node (LN) metastases obtained synchronously. The quantitative expression of steroid hormone receptor expression/activation (ER, glucocorticoid receptor (GR), androgen receptor (AR)) along with their signaling architecture and dynamics were analyzed. Methods: A 12 P-LN paired tumor set was chosen for analysis (N=24). Manual macrodissection was used to enrich for tumor epithelium prior to RPPA analysis that quantitatively measured 155 proteins/phosphoprotein drug targets in key signaling pathways known to be involved in steroid hormone receptor biology and tumorigenesis. Statistical analysis using paired sample t-testing or Wilcoxon rank testing was performed (p< 0.05 uncorrected). Results: Protein pathway activation mapping and unsupervised clustering analysis revealed a subset of TNBCs from 3 of the 12 (25%) patients (N=4: 1 LN and 1 P, and one patient-matched LN and P) that was characterized by high relative levels of total ER. Interestingly, these same tumors also had amongst the highest relative levels of both total AR and activated (phosphorylated) GR compared to the rest of the 20 tumor samples. Downstream signaling analysis found significant (p< 0.05) activation/phosphorylation of AKT (S473 and/or T308) associated with the ER-AR-GR/steroid hormone activated/”high” TNBC signature compared to the 20 tumors that were steroid hormone “low”. Conclusions: Functional protein pathway activation mapping of a unique study set of synchronously obtained TNBC primary and LN metastasis revealed a steroid hormone receptor expression/activation signature of co-incidentally activated/expressed ER, GR and AR, that was also characterized by increased AKT activation (p< 0.05). This steroid hormone receptor “activated” signature was not observed to be significantly differentially expressed in LN compared to P tissues in cohort or matched pair analysis, suggesting that this signature may be present as an intrinsic event in the P, and not acquired in LN metastases. Recent data from stage I-III TNBC patients treated in the neoadjuvant setting with an AKT inhibitor1 revealed that responding TNBC patients had pre-treatment tumor biopsies that were enriched for both high AR expression and high phosphoAKT (S473 and/or T308) levels. If these observations that a subset of TNBCs are steroid hormone receptor “activated” despite being ER “negative” are confirmed in larger study sets, we hypothesize that this subgroup, identified by RPPA analysis of both P and LN metastases, could be potentially sensitive to AKT inhibition in combination with agents that target steroid hormone receptors. 1Shi Z, et al. Functional Mapping of AKT Signaling and Biomarkers of Response from the FAIRLANE Trial of Neoadjuvant Ipatasertib plus Paclitaxel for Triple-Negative Breast Cancer. Clin Cancer Res. 2022 Mar 1;28(5):993-1003 Citation Format: Julia Wulfkuhle, Page Blas, Heather Williams, Claudius Mueller, Rosa I. Gallagher, Mariaelena Pierobon, Joyce O’Shaughnessy, Emanuel F. Petricoin. PD5-08 Drug target activation mapping of matched triple negative primary and axillary lymph node metastases identifies and links the ER-AR-GR steroid hormone receptor axis with downstream AKT activation [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD5-08.

Triple negative breast cancer: Immunogenicity, tumor microenvironment, and immunotherapy.

Triple negative breast cancer (TNBC) is a biologically diverse subtype of breast cancer characterized by genomic and transcriptional heterogeneity and exhibiting aggressive clinical behaviour and poor prognosis. In recent years, emphasis has been placed on the identification of mechanisms underlying the complex genomic and biological profile of TNBC, aiming to tailor treatment strategies. High immunogenicity, specific immune activation signatures, higher expression of immunosuppressive genes and higher levels of stromal Tumor Infiltrating Lymphocytes, constitute some of the key elements of the immune driven landscape associated with TNBC. The unprecedented response of TNBC to immunotherapy has undoubtedly changed the standard of care in this disease both in the early and the metastatic setting. However, the extent of interplay between immune infiltration and mutational signatures in TNBC is yet to be fully unravelled. In the present review, we present clinical evidence on the immunogenicity and tumour microenvironment influence on TNBC progression and the current treatment paradigms in TNBC based on immunotherapy.

Identification of ubiquitination-related gene classification and a novel ubiquitination-related gene signature for patients with triple-negative breast cancer.

Background: Ubiquitination-related genes (URGs) are important biomarkers and therapeutic targets in cancer. However, URG prognostic prediction models have not been established in triple-negative breast cancer (TNBC) before. Our study aimed to explore the roles of URGs in TNBC. Methods: The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and the Gene Expression Omnibus (GEO) databases were used to identify URG expression patterns in TNBC. Non-negative matrix factorization (NMF) analysis was used to cluster TNBC patients. The least absolute shrinkage and selection operator (LASSO) analysis was used to construct the multi-URG signature in the training set (METABRIC). Next, we evaluated and validated the signature in the test set (GSE58812). Finally, we evaluated the immune-related characteristics to explore the mechanism. Results: We identified four clusters with significantly different immune signatures in TNBC based on URGs. Then, we developed an 11-URG signature with good performance for patients with TNBC. According to the 11-URG signature, TNBC patients can be classified into a high-risk group and a low-risk group with significantly different overall survival. The predictive ability of this 11-URG signature was favorable in the test set. Moreover, we constructed a nomogram comprising the risk score and clinicopathological characteristics with favorable predictive ability. All of the immune cells and immune-related pathways were higher in the low-risk group than in the high-risk group. Conclusion: Our study indicated URGs might interact with the immune phenotype to influence the development of TNBC, which contributes to a further understanding of molecular mechanisms and the development of novel therapeutic targets for TNBC.

Identification of m6A modification patterns and development of m6A-hypoxia prognostic signature to characterize tumor microenvironment in triple-negative breast cancer.

BackgroundN6-methylation (m6A) modification of RNA has been found to have essential effects on aspects of the tumor microenvironment (TME) including hypoxia status and mobilization of immune cells. However, there are no studies to explore the combined effect of m6A modification and hypoxia on molecular heterogeneity and TME of triple-negative breast cancer (TNBC).MethodsWe collected The Cancer Genome Atlas (TCGA-TNBC, N=139), the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC-TNBC, N=297), the GSE103091, GSE21653, and GSE135565 series from the Gene Expression Omnibus (GEO-TNBC, N=247), and FUSCCTNBC (N=245) for our study. The non-negative matrix factorization algorithm was used to cluster TNBC samples. Immune cell infiltration was analyzed by the CIBERSORT algorithm. The enrichment scores were calculated by single-sample gene set enrichment analysis(ssGSEA) to characterize TME in TNBC samples. Immunohistochemistry (IHC) and qRT-PCR were performed to detect the gene expression.ResultsBased on the expression of m6A-related genes, we identified three distinct m6A clusters (denoted A, B, and C) in TNBC samples. Comparing the TME characteristics among the three clusters, we observed that cluster C was strongly related to hypoxia status and immune suppression, whereas clusters A and B displayed more immune cell infiltration. Therefore, we combine m6A and hypoxia related genes to classify two m6A-hypoxia clusters of TNBC and screened six prognostic genes by LASSO-Cox regression to construct a m6A-hypoxia signature(MHPS), which divided TNBC samples into high- and low-risk groups. We identified different TME features, immune cell infiltration between the two groups, and a better immunotherapy response was observed in the low-risk group. A nomogram was constructed with tumor size, lymph node, and risk score to improve clinical application of MHPS.ConclusionWe identified distinct TME characteristics of TNBC based on three different m6A modification patterns. Then, we constructed a specific m6A–hypoxia signature for TNBC to evaluate risk and predict immunotherapy response of patients, to enable more accurate treatment in the future.

Lipidomic and Membrane Mechanical Signatures in Triple-Negative Breast Cancer: Scope for Membrane-Based Theranostics.

Triple-negative breast cancer (TNBC) is a highly aggressive form of breast cancer associated with poor prognosis, higher grade, and a high rate of metastatic occurrence. Limited therapeutic interventions and the compounding issue of drug resistance in triple-negative breast cancer warrants the discovery of novel therapeutic targets and diagnostic modules. To this view, in addition to proteins, lipids also regulate cellular functions via the formation of membranes that modulate membrane protein function, diffusion, and their localization; thus, orchestrating signaling hot spots enriched in specific lipids/proteins on cell membranes. Lipid deregulation in cancer leads to reprogramming of the membrane dynamics and functions impacting cell proliferation, metabolism, and metastasis, providing exciting starting points for developing lipid-based approaches for treating TNBC. In this review, we provide a detailed account of specific lipidic changes in breast cancer, link the altered lipidome with membrane structure and mechanical properties, and describe how these are linked to subsequent downstream functions implicit in cancer progression, metastasis, and chemoresistance. At the fundamental level, we discuss how the lipid-centric findings in TNBC are providing cues for developing lipid-inspired theranostic strategies while bridging existing gaps in our understanding of the functional involvement of lipid membranes in cancer.

Identification of a five genes prognosis signature for triple-negative breast cancer using multi-omics methods and bioinformatics analysis.

Triple-negative breast cancer (TNBC) has a high degree of malignancy, lack of effective diagnosis and treatment, and poor prognosis. Bioinformatics methods are used to screen the hub genes and signal pathways involved in the progress of TNBC to provide reliable biomarkers for the diagnosis and treatment of TNBC. Download the raw data of four TNBC-related datasets from the Gene Expression Omnibus (GEO) database and use them for bioinformatics analysis. GEO2R tool was used to analyze and identify differentially expressed (DE) mRNAs. DAVID database was used to carry out gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genome Pathways (KEGG) signal pathway enrichment analysis for DE mRNAs. STRING database and Cytoscape were used to build DE mRNAs protein-protein interaction (PPI) network diagram and visualize PPI network, respectively. Through cytoHubba, cBioPortal database, Kaplan-Meier mapper database, Gene Expression Profiling Interactive Analysis (GEPIA) Database, UALCAN Database, The Cancer Genome Atlas (TCGA) database, Tumor Immunity Estimation Resource identify hub genes. Perform qRT-PCR, Human Protein Atlas analysis, mutation analysis, survival analysis, clinical-pathological characteristics, and infiltrating immune cell analysis. 22 DE mRNAs were identified from the four datasets, including 16 upregulated DE mRNAs and six downregulated DE mRNAs. Enrichment analysis of the KEGG showed that DE mRNAs were principally enriched in pathways in cancer, mismatch repair, cell cycle, platinum drug resistance, breast cancer. Six hub genes were screened based on the PPI network diagram of DE mRNAs. Survival analysis found that TOP2A, CCNA2, PCNA, MSH2, CDK6 are related to the prognosis of TNBC. In addition, mutations, clinical indicators, and immune infiltration analysis show that these five hub genes play an important role in the progress of TNBC and immune monitoring. Compared with MCF-10A, MCF-7, and SKBR-3 cells, TOP2A, PCNA, MSH2, and CDK6 were significantly upregulated in MDA-MB-321 cells. Compared with normal, luminal, and Her-2 positive tissues, CCNA2, MSH2, and CDK6 were significantly upregulated in TNBC. Through comparative analysis of GEO datasets related to colorectal cancer and lung adenocarcinoma, it was determined that these five hub genes were unique differentially expressed genes of TNBC. At last, the hub genes related to the progression, prognosis, and immunity of TNBC have been successfully screened. They are indeed specific to TNBC as prognostic features. They can be used as potential markers for the prognosis of TNBC and provide potential therapeutic targets.

MCF-7 Drug Resistant Cell Lines Switch Their Lipid Metabolism to Triple Negative Breast Cancer Signature

Obesity and adipose tissue have been closely related to a poor cancer prognosis, especially in prostate and breast cancer patients. The ability of transferring lipids from the adipose tissue to the tumor cells is actively linked to tumor progression. However, different types of breast tumor seem to use these lipids in different ways and metabolize them in different pathways. In this study we have tracked by mass spectrometry how palmitic acid from the adipocytes is released to media being later incorporated in different breast cancer cell lines (MDA-MB-231, SKBR3, BT474, MCF-7 and its resistant MCF-7 EPIR and MCF-7 TAXR). We have observed that different lines metabolize the palmitic acid in a different way and use their carbons in the synthesis of different new lipid families. Furthermore, we have observed that the lipid synthesis pattern varied according to the cell line. Surprisingly, the metabolic pattern of the resistant cells was more related to the TNBC cell line compared to their sensitive cell line MCF-7. These results allow us to determine a specific lipid pattern in different cell lines that later might be used in breast cancer diagnosis and to find a better treatment according to the cancer molecular type.

Four-lncRNA immune prognostic signature for triple-negative breast cancer Running title: Immune lncRNAs predict prognosis of TNBC.

We aimed to explore key immune-related long non-coding RNAs (lncRNAs) and their effect in predicting of prognosis of triple-negative breast cancer (TNBC). Four datasets of TNBC were downloaded from TCGA and GEO databases. ImmPort database was utilized to acquire immune-related mRNAs. Single sample gene set enrichment analysis (ssGSEA) and correlation analysis were utilized to screen immune-related lncRNAs. Univariate and multivariate Cox regression analyses were utilized to screen independent prognostic lncRNAs to establish prognostic risk model, and the model was evaluated by survival analysis and nomogram. Differential functions and immune cells infiltration in high and low risk group were analyzed by Gene set variation analysis and ssGSEA. Finally, competitive endogenous RNAs was constructed. We revealed 62 immune-related lncRNAs, of which four lncRNAs (RP11-890B15.3, RP11-1024P17.1, MFI2-AS1 and RP11-180N14.1) had independent prognostic value. These four lncRNAs-based prognostic risk model could stratify the TNBC patients into high and low risk groups, and patients with high risk displayed unfavorable outcomes. Nomogram indicated that the prognostic model could indicate TNBC patients survival very well. We further found that high risk group showed significantly enriched immune response to tumor cell, humoral immune response and high infiltrating abundance of regulatory T cell, Type 2 T helper cell, eosinophil, etc. LncRNAs RP11-180N14.1, RP11-1024P17.1 and RP11-890B15.3 regulated more mRNAs by targeting various miRNAs. While MFI2-AS1 regulated three mRNAs by sponging miR-3150a-3p. These four lncRNAs were prognostic biomarkers and could be possible therapeutic targets in TNBC.

Prediction of prognostic signatures in triple-negative breast cancer based on the differential expression analysis via NanoString nCounter immune panel

BackgroundTriple-Negative Breast Cancer (TNBC) is an aggressive and complex subtype of breast cancer. The current biomarkers used in the context of breast cancer treatment are highly dependent on the targeting of oestrogen receptor, progesterone receptor, or HER2, resulting in treatment failure and disease recurrence and creating clinical challenges. Thus, there is still a crucial need for the improvement of TNBC treatment; the discovery of effective biomarkers that can be easily translated to the clinics is essential.MethodsWe report an approach for the discovery of biomarkers that can predict tumour relapse and pathologic complete response (pCR) in TNBC on the basis of mRNA expression quantified using the NanoString nCounter Immunology Panel. To overcome the limited sample size, prediction models based on random Forest were constructed using the differentially expressed genes (DEGs) as selected features. We also evaluated the differences between pre- and post-treatment groups aiming for the combinatorial assessment of pCR and relapse using additive models in edgeR.ResultsWe identify nine and 13 DEGs strongly associated with pCR and relapse, respectively, from 579 immune genes in a small number of samples (n = 55) using edgeR. An additive model for the comparison of pre- and post-treatment groups via the adjustment of the independent subject in the relapse group revealed associations for 41 genes. Comprehensive analysis indicated that our prediction models outperformed those constructed using features extracted from the existing feature selection model Elastic Net in terms of accuracy. The prediction models were assessed using a randomization test to validate the robustness (empirical P for the model of pCR = 0.015 and empirical P for the model of relapse = 0.018). Furthermore, three DEGs (FCER1A, EDNRB, and TGFBI) in the model of relapse showed prognostic significance for predicting the survival of patients with cancer through Cox proportional hazards regression model-based survival analysis.ConclusionGene expression quantified via the NanoString nCounter Immunology Panel can be seamlessly analysed using edgeR, even considering small sample sizes. Our approach provides a scalable framework that can easily be applied for the discovery of biomarkers based on the NanoString nCounter Immunology Panel.Data availabilityThe source code will be available from github at https://github.com/sungheep/nanostring.

Vascular endothelial growth factor encoded by Parapoxviruses can regulate metabolism and survival of triple negative breast cancer cells

Dysbiotic microbiomes are linked to many pathological outcomes including different metabolic disorders like diabetes, atherosclerosis and even cancer. Breast cancer is the second leading cause of cancer associated death in women, and triple negative breast cancer (TNBC) is the most aggressive type with major challenges for intervention. Previous reports suggested that Parapoxvirus signatures are one of the predominant dysbiotic viral signatures in TNBC. These viruses encode several genes that are homologs of human genes. In this study, we show that the VEGF homolog encoded by Parapoxviruses, can induce cell proliferation, and alter metabolism of breast cancer and normal breast cells, through alteration of MAPK-ERK and PI3K-AKT signaling. In addition, the activity of the transcription factor FoxO1 was altered by viral-encoded VEGF through activation of the PI3K-AKT pathway, leading to reprogramming of cellular metabolic gene expression. Therefore, this study provides new insights into the function of viral-encoded VEGFs, which promoted the growth of the breast cancer cells and imparted proliferative phenotype with altered metabolism in normal breast cells.

Abstract 1004: Quantitative digital spatial profiling reveals novel prognostic intratumoral immune signatures in triple-negative breast cancer

Abstract Background: The intrinsic immune response in triple negative breast cancer (TNBC) has both prognostic and predictive utility, and immune-related biomarkers are now actionable targets in TNBC. Current modalities for immune profiling are limited by lack of spatial context and precise quantitation. We used quantitative digital spatial profiling (DSP) to 1) characterize the immune architecture of tumoral and stromal regions in TNBC and 2) identify prognostic immune signatures. Methods: From the FinXX trial of adjuvant capecitabine therapy, tumor sections (FFPE) matched for patient characteristics, treatment arm and RFS-based outcome (N=44) were assessed with Nanostring GeoMxTM DSP. Briefly, with 3-plex immunofluorescence (pan-cytokeratin, CD45, CD68 with SYTO-13 dye for nuclei), spatially-defined CD45-enriched or CD68-enriched stromal segments and adjacent tumor segments (N=950) were selected for digital quantitation (NanoString nCounter) of 39 immune proteins (e.g. immune cell profiling proteins: CD3, CD4, CD8, CD11c, CD20, CD56, CD68, HLA-DR, PD-1, PD-L1, Granzyme B), immune drug targets (e.g. VISTA, STING, IDO1), and activation status-related proteins (e.g. ICOS, PD-L2, CD40, CD40L, CD44). Normalized, differentially-expressed (DE) proteins were evaluated, and weighted gene co-expression network analysis (WGCNA) was performed to identify modules of highly co-expressing immune proteins, and association with RFS as a categorical variable was determined. Results: 20 DE proteins in tumor segments were associated with RFS. Among them, those with the highest RFS-associated fold changes (FC) included CD20, CD40, CD56, HLA-DR, Granzyme B, and PD-L2 (FC): 2.1-4.4) (p < 0.05). In contrast, only 4 DE proteins were associated with RFS in stromal segments (Granzyme B, IDO-1, PD-L1, and PD-L2 (FC: 1.5-3.3) (p < 0.05). By WGCNA, 2 tumor-based immune modules [IM1 (lymphocyte infiltration) and IM2 (lymphocyte activation)] were identified based on their co-expression. The top three co-expressed proteins in IM1 were CD4, CD40, and CD45 (r >0.9). The three top co-expressed proteins in IM2 were PD-L1, PD-L2, and Granzyme B (r > 0.85). Together with CD56 expression, IM1 and IM2 comprised a set of 3 intratumoral immune features that associated with RFS. For recurring tumors, all 3 immune features clustered with low scores . Among non-recurrent tumors, two distinct, non-overlapping clusters with inverse characteristics were identified: high IM2, low IM1, low CD56 vs. low IM2, high IM1, and high CD56. Overall, across all 3 immune features, PDL2, CD40 and CD56 expression most strongly associated with RFS. Conclusion: With spatially-defined, precise quantification of immune proteins, we identified 3 immune features, including two distinct immune modules, IM1 and IM2 that correlate with durable RFS. These immune modules of highly co-expressing proteins were associated with RFS only when present in the tumor compartment but not tumor-adjacent, immune cell-rich stroma. Citation Format: Jodi M. Carter, Saranya Chumsri, Yaohua Ma, Xue Wang, Jennifer M. Kachergus, Douglas Hinerfeld, Heather Ann Brauer, Sarah Warren, Heikki Joensuu, Edith A. Perez, Aubrey Thompson. Quantitative digital spatial profiling reveals novel prognostic intratumoral immune signatures in triple-negative breast cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1004.

ABCC9, NKAPL, and TMEM132C are potential diagnostic and prognostic markers in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease. The aim of this study is to identify the diagnostic and poor prognostic signatures in TNBC by exploring the aberrant DNA methylation and gene expression. Differential expression and methylation analysis of the TNBC and paracancer samples from The Cancer Genome Atlas were performed. Gene set enrichment and protein-protein interaction (PPI) network analysis was used to explore the mechanisms of TNBC. Methylation-gene expression correlation analysis was performed, and multivariate Cox analysis and receiver operating characteristics analysis were used to further screen the hub genes for TNBC. We identified 1,525 differentially expressed genes and 150 differentially methylated genes between TNBC and paracancer samples. About 96.64% of the methylation sites were located on the CpG island. A total of 17 Gene Ontology biological process terms and 18 signal pathways were significantly enriched. GNG4, GNG11, PENK, MAOA, and AOX1 were identified as the core genes of the PPI network. Methylation-expression correlations revealed that ABCC9 (cg06951626), NKAPL (cg18675097, cg01031101, and cg17384889), and TMEM132C (cg03530754) showed promise as diagnostic and prognostic markers in TNBC. ABCC9 (cg06951626), NKAPL (cg18675097, cg01031101, and cg17384889), and TMEM132C (cg03530754) were potential diagnostic and prognostic markers in TNBC.

Coupled Genome-Wide DNA Methylation and Transcription Analysis Identified Rich Biomarkers and Drug Targets in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) has poor clinical prognosis. Lack of TNBC-specific biomarkers prevents active clinical intervention. We reasoned that TNBC must have its specific signature due to the lack of three key receptors to distinguish TNBC from other types of breast cancer. We also reasoned that coupling methylation and gene expression as a single unit may increase the specificity for the detected TNBC signatures. We further reasoned that choosing the proper controls may be critical to increasing the sensitivity to identify TNBC-specific signatures. Furthermore, we also considered that specific drugs could target the detected TNBC-specific signatures. We developed a system to identify potential TNBC signatures. It consisted of (1) coupling methylation and expression changes in TNBC to identify the methylation-regulated signature genes for TNBC; (2) using TPBC (triple-positive breast cancer) as the control to detect TNBC-specific signature genes; (3) searching in the drug database to identify those targeting TNBC signature genes. Using this system, we identified 114 genes with both altered methylation and expression, and 356 existing drugs targeting 10 of the 114 genes. Through docking and molecular dynamics simulation, we determined the structural basis between sapropterin, a drug used in the treatment of tetrahydrobiopterin deficiency, and PTGS2, a TNBC signature gene involved in the conversion of arachidonic acid to prostaglandins. Our study reveals the existence of rich TNBC-specific signatures, and many can be drug target and biomarker candidates for clinical applications.

The identification of a TNBC liver metastasis gene signature by sequential CTC-xenograft modeling.

Triple‐negative breast cancer (TNBC) liver metastasis is associated with poor prognosis and low patient survival. It occurs when tumor cells disseminate from primary tumors, circulate in blood/lymph [circulating tumor cells (CTCs)], and acquire distinct characteristics during disease progression toward the metastatic phenotype. The purpose of this study was to decipher the genomic/transcriptomic properties of TNBC liver metastasis and its recurrence for potential therapeutic targeting. We employed a negative depletion strategy to isolate and interrogate CTCs from the blood of patients with TNBC, and to establish sequential generations of CTC‐derived xenografts (CDXs) through injection of patient CTCs in immunodeficient mice. The isolation and validation of CDX‐derived cell populations [analyses of CTCs were paired with bone marrow‐resident cells (BMRTCs) and liver tissue cells obtained from the same animal] were performed by multiparametric flow cytometry, immune phenotyping, and genomic sequencing of putative CTCs. Comprehensive characterization of gene expression arrays from sequentially generated CDX‐derived cell populations, online gene expression arrays, and TCGA databases were employed to discover a CTC‐driven, liver metastasis‐associated TNBC signature. We discovered a distinct transcriptomic signature of TNBC patient‐isolated CTCs from primary TNBCs, which was consistent throughout sequential CDX modeling. We established a novel TNBC liver metastasis‐specific CDX model that selectively recapitulates CTC biology for four sequential generations of mice. The evaluation of online databases and CDX‐derived populations revealed 597 genes specific to the TNBC liver metastasis signatures. Further investigation of the TNBC liver metastasis signature predicted 16 hub genes, 6 biomarkers with clinically available drugs, and 22 survival genes. The sequential interrogation of CDX‐CTCs is an innovative liquid biopsy‐based approach for the discovery of organ metastasis‐specific signatures of CTCs. This represents the first step for mechanistic and analytical validation in their application as prognostic indicators and therapeutic targets. Targeting CTC drug candidate biomarkers along with combination therapy can improve the clinical outcome of TNBC patients in general and recurrence of liver metastasis in particular.