Gene Signature In Breast Cancer Research Articles

Machine learning unveils key Redox signatures for enhanced breast Cancer therapy

BackgroundBreast cancer remains a leading cause of mortality among women worldwide, necessitating innovative prognostic models to enhance treatment strategies.MethodsOur study retrospectively enrolled 9,439 breast cancer patients from 12 independent datasets and single-cell data from 12 patients (64,308 cells). Moverover, 30 in-house clinical cohort were collected for validation. We employed a comprehensive approach by combining ten distinct machine learning algorithms across 108 different combinations to scrutinize 88 pre-existing signatures of breast cancer. To affirm the efficacy of our developed model, immunohistochemistry assays were performed. Additionally, we investigated various potential immunotherapeutic and chemotherapeutic interventions.ResultsThis study introduces an Artificial Intelligence-aided Redox Signature (AIARS) as a novel prognostic tool, leveraging machine learning to identify critical redox-related gene signatures in breast cancer. Our results demonstrate that AIARS significantly outperforms existing prognostic models in predicting breast cancer outcomes, offering a robust tool for personalized treatment planning. Validation through immunohistochemistry assays on samples from 30 patients corroborated our results, underscoring the model’s applicability on a wider scale. Furthermore, the analysis revealed that patients with low AIARS expression levels are more responsive to immunotherapy. Conversely, those exhibiting high AIARS were found to be more susceptible to certain chemotherapeutic agents, including vincristine.ConclusionsOur study underscores the importance of redox biology in breast cancer prognosis and introduces a powerful machine learning-based tool, the AIARS, for personalized treatment strategies. By providing a more nuanced understanding of the redox landscape in breast cancer, the AIARS paves the way for the development of redox-targeted therapies, promising to enhance patient outcomes significantly. Future work will focus on clinical validation and exploring the mechanistic roles of identified genes in cancer biology.

Identification of shared gene signatures in major depressive disorder and triple-negative breast cancer.

Patients with major depressive disorder (MDD) have an increased risk of breast cancer (BC), implying that these two diseases share similar pathological mechanisms. This study aimed to identify the key pathogenic genes that lead to the occurrence of both triple-negative breast cancer (TNBC) and MDD. Public datasets GSE65194 and GSE98793 were analyzed to identify differentially expressed genes (DEGs) shared by both datasets. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape to identify key PPI genes using cytoHubba. Hub DEGs were obtained from the intersection of hub genes from a PPI network with genes in the disease associated modules of the Weighed Gene Co-expression Network Analysis (WGCNA). Independent datasets (TCGA and GSE76826) and RT-qPCR validated hub gene expression. A total of 113 overlapping DEGs were identified between TNBC and MDD. The PPI network was constructed, and 35 hub DEGs were identified. Through WGCNA, the blue, brown, and turquoise modules were recognized as highly correlated with TNBC, while the brown, turquoise, and yellow modules were similarly correlated with MDD. Notably, G3BP1, MAF, NCEH1, and TMEM45A emerged as hub DEGs as they appeared both in modules and PPI hub DEGs. Within the GSE65194 and GSE98793 datasets, G3BP1 and MAF exhibited a significant downregulation in TNBC and MDD groups compared to the control, whereas NCEH1 and TMEM45A demonstrated a significant upregulation. These findings were further substantiated by TCGA and GSE76826, as well as through RT-qPCR validation. This study identified G3BP1, MAF, NCEH1 and TMEM45A as key pathological genes in both TNBC and MDD.

Abstract PO5-02-09: Discordance of the PAM50 intrinsic subtypes with the immunohistochemistry-based subtypes in HER2-negative early breast cancer treated with neoadjuvant chemotherapy

Abstract Background The PAM50 (Prosigna Breast Cancer Gene Signature Assay) can be used to assess the expression levels of 50 genes in early breast cancer biopsies, including formalin-fixed paraffin-embedded (FFPE) tissue from human epidermal growth factor receptor 2 (HER2)-negative patients. However, there is currently no practical molecular assay for intrinsic subtype in real-world practice that addresses the problems of cost and run-time. Methods In the phase 2 HER2E-PAM/PAMILIA study (NCT04817540), we prospectively analyzed molecular subtyping through the PAM50 test in low HER2 (HER2 IHC 1+ or 2+ SISH-) breast cancer patients. PAM50 intrinsic subtypes were determined according to 50 cancer genes using the NanoString nCounter Analysis System. This study was originally designed to determine whether adding HER2-targeted treatment in HER2 enriched molecular subtype increases the pathologic complete rate (pCR). We aimed to analyze the discordance between immunohistochemistry (IHC)-based surrogate subtyping of pre- & post-operative tissues and PAM50 intrinsic subtypes, and to assess the pCR according to the discordance. Results In a total 82 patients, the proportions of HR+/HER2- and triple negative breast cancer (TNBC) in the preoperative tissue were 85.4% (n=70) and 14.6% (n=12), respectively. According to PAM50 intrinsic subtypes, 11.0% (n=9) were basal, 8.5% (n=7) were HER2-enriched, 34.1% (n=28) were luminal A, 36.6% (n=30) were luminal B, and 3.7% (n=3) were normal-like type. In total, 32 patients (41.5%) were discordant between IHC-based preoperative subtype and PAM50 intrinsic subtype. Among the 70 patients with HR+/HER2-, non-luminal A, B type was found in 12.9% with basal-like, 8.6% with HER2-enriched, and 4.3% with normal-like type, respectively. Of 12 TNBC patients, 83.3% were luminal A, B type, and 8.3% were HER2-enriched. In the other hands, 6 patients (13.0%) were discordant between IHC-based post-operative subtype and PAM50 intrinsic subtype. Among the 40 patients with HR+/HER2- postoperative subtype, non-luminal A, B type was found in 2.5% with basal-like, 2.5% with HER2-enriched, and 5.0% with normal-like type, respectively. Of 6 TNBC postoperative patients, 16.7% were normal-like. Most received anthracycline- and taxane-based neoadjuvant chemotherapy. During data analysis (June 2023), 54 cases underwent surgery after neoadjuvant chemotherapy. 4 of 54 patients (7.4%) achieved a pCR, of which one was HER2-enriched, one was luminal B-like, and two were basal-like PAM50 intrinsic subtype. However, discordance of IHC based subtype with intrinsic subtype was not considerable and was not correlated with pCR. Conclusion A substantial portion of patients showed discrepancy between preoperative and postoperative IHC subtype and PAM50 intrinsic subtype in our study. Citation Format: Jee Hung Kim, Soong June Bae, Sung Gwe Ahn, Jeonghee Lim, Min Hwan Kim, Gun Min Kim, Joo Hyuk Sohn, Joon Jeong. Discordance of the PAM50 intrinsic subtypes with the immunohistochemistry-based subtypes in HER2-negative early breast cancer treated with neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-02-09.

Identification and Validation of Novel Metastasis-Related Immune Gene Signature in Breast Cancer.

Distant metastasis remains the leading cause of death among patients with breast cancer (BRCA). The process of cancer metastasis involves multiple mechanisms, including compromised immune system. However, not all genes involved in immune function have been comprehensively identified. Firstly 1623 BRCA samples, including transcriptome sequencing and clinical information, were acquired from Gene Expression Omnibus (GSE102818, GSE45255, GSE86166) and The Cancer Genome Atlas-BRCA (TCGA-BRCA) dataset. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed using the GSE102818 dataset to identify the most relevant module to the metastasis of BRCA. Besides, ConsensusClusterPlus was applied to divide TCGA-BRCA patients into two subgroups (G1 and G2). In the meantime, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a metastasis-related immune genes (MRIGs)_score to predict the metastasis and progression of cancer. Importantly, the expression of vital genes was validated through reverse transcription quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC). The expression pattern of 76 MRIGs screened by WGCNA divided TCGA-BRCA patients into two subgroups (G1 and G2), and the prognosis of G1 group was worse. Also, G1 exhibited a higher mRNA expression level based on stemness index score and Tumor Immune Dysfunction and Exclusion score. In addition, higher MRIGs_score represented the higher probability of progression in BRCA patients. It was worth mentioning that the patients in the G1 group had a high MRIGs_score than those in the G2 group. Importantly, the results of RT-qPCR and IHC demonstrated that fasciculation and elongation protein zeta 1 (FEZ1) and insulin-like growth factor 2 receptor (IGF2R) were risk factors, while interleukin (IL)-1 receptor antagonist (IL1RN) was a protective factor. Our study revealed a prognostic model composed of eight immune related genes that could predict the metastasis and progression of BRCA. Higher score represented higher metastasis probability. Besides, the consistency of key genes in BRCA tissue and bioinformatics analysis results from mRNA and protein levels was verified.

Abstract A004: Identification of genetic susceptibility loci for ErbB2-driven mammary tumor development and metastasis using Collaborative Cross mice and human relevant validation

Abstract Introduction Epidermal growth factor receptor 2 (HER2)-targeted therapies are effective treatments for HER2+ breast cancers (BC), but intrinsic and acquired resistance hampers their clinical benefit. Therefore, new biological insights into HER2-driven cancers are needed to conquer drug resistance. Here, we used a genetically diverse and population-based Collaborative Cross (CC) mouse model to identify genetic susceptibility to tumor development and metastasis driven by HER2. Materials and methods 732 F1 hybrid mice were generated from a cross between FVB/N MMTV-ErbB2 and 30 CC strains and were monitored for mammary tumor development within two years. Tumor onset, multiplicity and metastasis were assessed. Genome wide association study (GWAS) was used to identify genetic variations controlling different tumor phenotypes. Polygenetic risk score was created for each phenotype using multivariate analysis. We systematically evaluated the clinical value regarding prognosis and therapeutic responses of mouse tumor susceptibility gene signature (mTSGS) in human breast cancer (BC) using publicly available cohorts including I-SPY2 clinical trial cohorts. Results We observed huge differences in tumor onset, multiplicities, and metastasis across 30 CC strains. In addition to lung metastasis, we found liver/kidney metastasis in some CC strains. GWAS identified 1,525 SNPs significantly associated with tumor onset (p<1.00E-30) corresponding to 287 known genes, 800 SNPs significantly associated with number of tumors (p<1.00E-15) corresponding to 203 known genes, 568 SNPs significantly associated with lung metastasis (p<1.00E-4) corresponding to 189 known genes, and 23 SNPs significantly associated with liver metastasis (p<1.00E-4) corresponding to 12 known genes. Multivariate analyses identified the SNPs in 8 genes (Stx6, Ramp1, Traf3ip1, Nckap5, Pfkfb2, Trmt1l, Rprd1b and Rer1) independently associated with age onset, in 11 genes (Stx6, Sepsecs, Rhobtb1, Tsen15, Abcc3, Arid5b, Tnr, Dock2, Tti1, Fam81a and Oxr1) independently associated with number of tumors, and in 2 genes (Plxna2 and Tbc1d31) independently associated with tumor metastasis, which were pooled together as mTSGS. To evaluate the impact of mTSGS on human BC, we established mTSGS score (mTSGSS) based on their transcriptional levels in BC. We found that mTSGSS is significantly and independently associated with overall, disease free and progression free survival compared to clinical factors and PAM50 molecular subtype in different cohorts. Additionally, patients with low mTSGSS have a higher pathological complete response (pCR) rate in comparison to these with high mTSGSS for 6 of 13 treatment regimens in I-SPY2 cohort. Multivariate logistic regression analysis showed that predictive value of mTSGSS in pCR is independent of MammaPrint (MP) score in these treatment regimens. Conclusion Our findings lead significantly to biological insights into ErbB2-driven cancers and indicate that mTSGSS can serve as a biomarker for tailoring treatment to BC patients. Citation Format: Hang Chang, Jamie L Inman, Antoine M Snijders, Jian-Hua Mao. Identification of genetic susceptibility loci for ErbB2-driven mammary tumor development and metastasis using Collaborative Cross mice and human relevant validation [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A004.

Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis.

Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis. A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan-Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP. We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC. The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.

An EZH2-NF-κB regulatory axis drives expression of pro-oncogenic gene signatures in triple negative breast cancer

The histone methyltransferase EZH2 has been studied most extensively in the context of PRC2-dependent gene repression. Accumulating evidence indicates non-canonical functions for EZH2 in cancer contexts including promoting paradoxical gene expression through interactions with transcription factors, including NF-κB in triple negative breast cancer (TNBC). We profile EZH2 and NF-κB factor co-localization and positive gene regulation genome-wide, and define a subset of NF-κB targets and genes associated with oncogenic functions in TNBC that is enriched in patient datasets. We demonstrate interaction between EZH2 and RelA requiring the recently identified transactivation domain (TAD) which mediates EZH2 recruitment to, and activation of certain NF-κB-dependent genes, and supports downstream migration and stemness phenotypes in TNBC cells. Interestingly, EZH2-NF-κB positive regulation of genes and stemness does not require PRC2. This study provides new insight into pro-oncogenic regulatory functions for EZH2 in breast cancer through PRC2-independent, and NF-κB-dependent regulatory mechanisms.

A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods

The robustness of a breast cancer gene signature, the super-proliferation set (SPS), is initially tested and investigated on breast cancer cell lines from the Cancer Cell Line Encyclopaedia (CCLE). Previously, SPS was derived via a meta-analysis of 47 independent breast cancer gene signatures, benchmarked on survival information from clinical data in the NKI dataset. Here, relying on the stability of cell line data and associative prior knowledge, we first demonstrate through Principal Component Analysis (PCA) that SPS prioritizes survival information over secondary subtype information, surpassing both PAM50 and Boruta, an artificial intelligence-based feature-selection algorithm, in this regard. We can also extract higher resolution ‘progression’ information using SPS, dividing survival outcomes into several clinically relevant stages (‘good’, ‘intermediate’, and ‘bad) based on different quadrants of the PCA scatterplot. Furthermore, by transferring these ‘progression’ annotations onto independent clinical datasets, we demonstrate the generalisability of our method on actual patient data. Finally, via the characteristic genetic profiles of each quadrant/stage, we identified efficacious drugs using their gene reversal scores that can shift signatures across quadrants/stages, in a process known as gene signature reversal. This confirms the power of meta-analytical approaches for gene signature inference in breast cancer, as well as the clinical benefit in translating these inferences onto real-world patient data for more targeted therapies.

Abstract P6-01-12: Predictive value of immune genomic signatures from breast cancer cohorts containing data for both response to neoadjuvant chemotherapy and prognosis after surgery

Abstract Abstract Background Previous studies of immune-related gene signatures (IGSs) in breast cancer have attempted to predict the response to chemotherapy or prognosis and were performed using different patient cohorts. The purpose of this study was to evaluate the predictive functions of various IGSs using the same patient cohort that included data for response to chemotherapy as well as the prognosis after surgery. Methods We applied five previously described IGS models in a public dataset of 508 breast cancer patients treated with neoadjuvant chemotherapy. The prognostic and predictive values of each model were evaluated, and their correlations were compared. Results We observed a high proportion of expression concordance among the IGS models (r: 0.56-1). Higher gene expression scores of IGSs were detected in aggressive breast cancer subtypes (basal and HER2-enriched) (P < 0.001). Four of the five IGSs could predict chemotherapy responses and two could predict 5-year relapse-free survival in cases with hormone receptor-positive (HR+) tumors. However, the models showed no significant differences in their predictive abilities for hormone receptor-negative (HR-) tumors. Conclusions IGSs are, to some extent, useful for predicting prognosis and chemotherapy response; moreover, they show substantial agreement for specific breast cancer subtypes. However, it is necessary to identify more compelling biomarkers for both prognosis and response to chemotherapy in HR- and HER2+ cases. Citation Format: Yidan Zhu. Predictive value of immune genomic signatures from breast cancer cohorts containing data for both response to neoadjuvant chemotherapy and prognosis after surgery [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 P6-01-12.

Cytotoxic Lymphocyte-Related Gene Signature in Triple-Negative Breast Cancer.

To curate the signature genes of cytotoxic lymphocytes (CLs) and explore the heterogeneity based on the CL-related (CLR) gene signature, we analyzed the gene expression of 592 patients with histologically diagnosed triple-negative breast cancer. Based on the 13-gene panel, CLR signatures were curated and associated with the stage of tumor size. Patients in the CLR-low group exhibited the worse overall survival (OS) (median OS, 75.23 months vs. 292.66 months, p < 0.0001) and were characterized by the upregulation of the NF-κB, Wnt, and p53 pathways, the positive regulation of angiogenesis, and a higher expression of immune checkpoints including CTLA4, LAG3, CD86, ICOS, ICOSLG, and TNFSF9. In cancer immunotherapy cohorts (GSE157284, GSE35640, IMvigor210), a higher CLR signature score was remarkably associated with greater tumor shrinkage and immune characteristics consisting of higher PD-L1 and neoantigen expression, as well as an inflamed tumor microenvironment. In the pan-cancer atlas, the CLR signature was notably associated with patient survival and revealed a profound heterogeneity across the malignancy types. In sum, the CLR signature is a promising indicator for immune characteristics, tumor shrinkage, and survival outcomes following cancer immunotherapy in addition to the prognostic heterogeneity in the pan-cancer atlas.

Development and Verification of a Prognostic Stemness-Related Gene Signature in Triple-Negative Breast Cancer

Background. It is well known that cancer stem cells can induce cancer metastasis, which causes the majority of cancer-related death, especially in triple-negative breast cancer (TNBC). TNBC features a high metastatic rate and low metastasis-free survival and is regarded as the most malignant subtype of breast cancer. The purpose of this study is to explore prognostic biomarkers that can predict metastasis of triple-negative breast cancer. Methods. The human triple-negative breast cancerSUM149PT cells were used for the study. The cancer stem cell spheres (sum149-Stem) and paired adherent cancer cells (sum149-Tumor) were collected to extract total RNAs. RNA-seq was used to analysis the mRNA expression of cancer stem cells and paired adherent cancer cells. Two different gene expression omnibus datasets (https://www.ncbi.nlm.nih.gov/gds), GSE58812 and GSE33926, were used to explore the mechanism of different expression genes between stem cells and adherent cancer cells. Seven genes showed prognostic function in all datasets. The STITCH database (https://www.stitchdata.com/) was used to explore the possible metastasis-inhibiting drugs that can target the seven genes. Each gene expression was compared by Pearson analysis. The receiver operating characteristic curve (ROC) and Kaplan–Meier survival curve were performed to assess the metastasis prognostic ability of the seven-gene modeling two different GEO datasets. Results. A subset of 7 stemness-related genes (SRGs) containing UCN, ST3GAL5, FDPS, HK2, MALL, LMTK3, and CRHR2 were identified in three independent cohorts. Univariate Cox analysis showed that ST3GAL5 plays an antitumor role in TNBC metastasis, and the other 6 genes promote the metastatic progression of TNBC. The ability of the 7-SRGs gene Cox model to predict TNBC metastasis was constructed with the GSE58812 dataset. Most of the genes showed significant expression in patients with different risk levels. Additionally, the model showed predictive value in another GEO dataset of TNBC patients. ROC curves indicated that the seven-gene model has a significant predictive value of TNBC metastasis. Conclusions. Expression analysis of the 7-SRGs signature model at diagnosis has predictive value for metastasis in TNBC patients.

Prognostic implication and immunotherapy response prediction of a ubiquitination-related gene signature in breast cancer.

Breast cancer (BC) is one of the most common tumor types and has poor outcomes. In this study, a ubiquitination-related prognostic signature was constructed, and its association with immunotherapy response in BC was explored. A list of ubiquitination-related genes was obtained from the molecular signatures database, and a ubiquitination-related gene signature was obtained by least absolute shrinkage and selection operator Cox regression. The genes, TCN1, DIRAS3, and IZUMO4, had significant influence on BC outcomes. Patients were categorized into two clusters-a high-risk group with poor survival and a low-risk group with greater chances of controlling BC progression. Univariate and multivariate Cox regression analyses revealed that the risk signature was an independent prognostic factor for BC. Gene set enrichment analysis suggested that the high-risk group was enriched in cell cycle and DNA replication pathways. The risk score was positively linked to the tumor microenvironment and negatively correlated with the immunotherapy response. The IC50 values for rapamycin were higher in the low-risk group, whereas those for axitinib, AZD6244, erlotinib, GDC0941, GSK650394, GSK269962A, lapatinib, and PD0325901 were higher in the high-risk group. Therefore, the ubiquitination-related signature is considered a promising tool for predicting a BC patient's immunotherapy response.

Construction of an immunogenic cell death-based risk score prognosis model in breast cancer.

Immunogenic cell death (ICD) is a form of regulated cell death that elicits immune response. Common inducers of ICD include cancer chemotherapy and radiation therapy. A better understanding of ICD might contribute to modify the current regimens of anti-cancer therapy, especially immunotherapy. This study aimed to identify ICD-related prognostic gene signatures in breast cancer (BC). An ICD-based gene prognostic signature was developed using Lasso-cox regression and Kaplan-Meier survival analysis based on datasets acquired from the Cancer Genome Atlas and Gene Expression Omnibus. A nomogram model was developed to predict the prognosis of BC patients. Gene Set Enrichment Analysis (GESA) and Gene Set Variation Analysis (GSVA) were used to explore the differentially expressed signaling pathways in high and low-risk groups. CIBERSORT and ESTIMATE algorithms were performed to investigate the difference of immune status in tumor microenvironment of different risk groups. Six genes (CALR, CLEC9A, BAX, TLR4, CXCR3, and PIK3CA) were selected for construction and validation of the prognosis model of BC based on public data. GSEA and GSVA analysis found that immune-related gene sets were enriched in low-risk group. Moreover, immune cell infiltration analysis showed that the immune features of the high-risk group were characterized by higher infiltration of tumor-associated macrophages and a lower proportion of CD8+ T cells, suggesting an immune evasive tumor microenvironment. We constructed and validated an ICD-based gene signature for predicting prognosis of breast cancer patients. Our model provides a tool with good discrimination and calibration abilities to predict the prognosis of BC, especially triple-negative breast cancer (TNBC).

Prognostic Significance of Cuproptosis-Related Gene Signatures in Breast Cancer Based on Transcriptomic Data Analysis

Breast cancer (BRCA) remains a serious threat to women's health, with the rapidly increasing morbidity and mortality being possibly due to a lack of a sophisticated classification system. To date, no reliable biomarker is available to predict prognosis. Cuproptosis has been recently identified as a new form of programmed cell death, characterized by the accumulation of copper in cells. However, little is known about the role of cuproptosis in breast cancer. In this study, a cuproptosis-related genes (CRGs) risk model was constructed, based on transcriptomic data with corresponding clinical information relating to breast cancer obtained from both the TCGA and GEO databases, to assess the prognosis of breast cancer by comprehensive bioinformatics analyses. The CRGs risk model was constructed and validated based on the expression of four genes (NLRP3, LIPT1, PDHA1 and DLST). BRCA patients were then divided into two subtypes according to the CRGs risk model. Furthermore, our analyses revealed that the application of this risk model was significantly associated with clinical outcome, immune infiltrates and tumor mutation burden (TMB) in breast cancer patients. Additionally, a new clinical nomogram model based on risk score was established and showed great performance in overall survival (OS) prediction, confirming the potential clinical significance of the CRGs risk model. Collectively, our findings revealed that the CRGs risk model can be a useful tool to stratify subtypes and that the cuproptosis-related signature plays an important role in predicting prognosis in BRCA patients.

Genome engineering for estrogen receptor mutations reveals differential responses to anti-estrogens and new prognostic gene signatures for breast cancer

Mutations in the estrogen receptor (ESR1) gene are common in ER-positive breast cancer patients who progress on endocrine therapies. Most mutations localise to just three residues at, or near, the C-terminal helix 12 of the hormone binding domain, at leucine-536, tyrosine-537 and aspartate-538. To investigate these mutations, we have used CRISPR-Cas9 mediated genome engineering to generate a comprehensive set of isogenic mutant breast cancer cell lines. Our results confirm that L536R, Y537C, Y537N, Y537S and D538G mutations confer estrogen-independent growth in breast cancer cells. Growth assays show mutation-specific reductions in sensitivities to drugs representing three classes of clinical anti-estrogens. These differential mutation- and drug-selectivity profiles have implications for treatment choices following clinical emergence of ER mutations. Our results further suggest that mutant expression levels may be determinants of the degree of resistance to some anti-estrogens. Differential gene expression analysis demonstrates up-regulation of estrogen-responsive genes, as expected, but also reveals that enrichment for interferon-regulated gene expression is a common feature of all mutations. Finally, a new gene signature developed from the gene expression profiles in ER mutant cells predicts clinical response in breast cancer patients with ER mutations.

Deep View of HCC Gene Expression Signatures and Their Comparison with Other Cancers.

Simple SummaryThere is a huge gap between the numerous HCC gene signatures and the fact that no one HCC signature has successfully entered clinical practice. The purpose of this study is to explore the specificity of public signatures to HCC as this may be critical for clinical application. For this, we evaluated public HCC signatures using a comparative transcriptomics profiling approach and showed that specificity of current HCC signatures remains challenging, demonstrating the need of standards in gene signature generation and tissue/RNA preparation.Background: Gene expression signatures correlate genetic alterations with specific clinical features, providing the potential for clinical usage. A plethora of HCC-dependent gene signatures have been developed in the last two decades. However, none of them has made its way into clinical practice. Thus, we investigated the specificity of public gene signatures to HCC by establishing a comparative transcriptomic analysis, as this may be essential for clinical applications. Methods: We collected 10 public HCC gene signatures and evaluated them by utilizing four different (commercial and non-commercial) gene expression profile comparison tools: Oncomine Premium, SigCom LINCS, ProfileChaser (modified version), and GENEVA, which can assign similar pre-analyzed profiles of patients with tumors or cancer cell lines to our gene signatures of interests. Among the query results of each tool, different cancer entities were screened. In addition, seven breast and colorectal cancer gene signatures were included in order to further challenge tumor specificity of gene expression signatures. Results: Although the specificity of the evaluated HCC gene signatures varied considerably, none of the gene signatures showed strict specificity to HCC. All gene signatures exhibited potential significant specificity to other cancers, particularly for colorectal and breast cancer. Since signature specificity proved challenging, we furthermore investigated common core genes and overlapping enriched pathways among all gene signatures, which, however, showed no or only very little overlap, respectively. Conclusion: Our study demonstrates that specificity, independent validation, and clinical use of HCC genetic signatures solely relying on gene expression remains challenging. Furthermore, our work made clear that standards in signature generation and statistical methods but potentially also in tissue preparation are urgently needed.

A Signature of Three Apoptosis-Related Genes Predicts Overall Survival in Breast Cancer.

BackgroundThe commonest malignancy in women is known as breast cancer (BC). Numerous studies demonstrated that apoptosis appears to be critical to the management and clinical outcome of BC patients. The purpose of this study is to explore the potential connection between apoptosis and BC and establish the apoptosis-associated gene signature in BC.MethodsThe data of BC patient transcripts and related clinical information comes from the Cancer Genome Atlas Database (TCGA), and the genes related to apoptosis come from the Molecular Characterization Database (MSigDB). We identified the abnormally expressed apoptosis-related genes in BC samples. The optimal apoptosis-related genes screened by Cox regression analysis were designed to construct a prognostic model for predicting BC patients. Using the Nom Chart to Predict 1-Year, 3-Year, and 5-Year overall survival for BC patients. The gene signature-related functional pathways were explored by gene set enrichment analysis (GSEA).ResultsThree genes [alpha subunit of the interleukin 3 receptor (IL3RA), apoptosis-inducing factor mitochondrial-associated 1 (AIFM1), and phosphatidylinositol-3 kinase catalytic alpha (PIK3CA)] correlated with apoptosis were shown to be strongly linked to the overall survival of BC. Survival analysis shows that the risk score is directly proportional to the poor prognosis of BC patients. Risk assessment based on three genetic characteristics (age, pathological stage N, and pathological stage M) can independently predict the prognosis of patients with BC. The Nom chart is most suitable for assessing the long-term survival rate of BC patients. The results of GSEA demonstrated that numerous cell cycle-related pathways were abundant in the high-risk group.ConclusionWe constructed an apoptosis-associated gene signature in BC, which had a potential clinical application prospect for BC patients.

Tumor Microenvironment-Mediated Immune Profiles Characterized by Distinct Survival Outcome and Immunotherapeutic Efficacy in Breast Cancer

Background: Numerous reports have highlighted that the tumor microenvironment (TME) is closely linked to survival outcome and therapeutic efficacy. However, a comprehensive investigation of the TME feature in breast cancer (BC) has not been performed. Methods: Here, we performed consensus clustering analysis based on TME cell expression profiles to construct TME pattern clusters and TME-related gene signature in BC. GSVA combined with CIBERSORT and ssGSEA algorithms were applied to evaluate the differences in biological pathway and immune cell infiltration level, respectively. The PCA method was employed to construct TME-score to quantify the TME-mediated pattern level in individual BC patients. Results: We determined two distinct TME gene clusters among 3,738 BC samples, which exhibited distinct survival outcome and enriched biological processes. The TME features demonstrated that these two clusters corresponded to the established immune profiles: hot and cold tumor phenotypes, respectively. Based on TME-related signature genes, we constructed the TME-score and stratified BC patients into low and high TME-score groups. Patients with high TME-score exhibited favorable outcome and increased infiltration of immune cells. Further investigation revealed that high TME-score was also related with high expression of immunosuppressive molecules, decreased tumor mutation burden (TMB), and high rate of mutation in significantly mutated genes (SMGs) (e.g., PIK3CA and CDH1). Conclusion: Assessing the TME-mediated pattern level of individual BC patients will assist us in better understanding the responses of BC patients to immunotherapies and directing more effective immunotherapeutic approaches.

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.

Pyroptosis-Mediated Molecular Subtypes are Characterized by Distinct Tumor Microenvironment Infiltration Characteristics in Breast Cancer.

BackgroundNumerous reports have highlighted that pyroptosis is closely linked to tumorigenesis and drug resistance of tumors. However, the potential role of pyroptosis in regulating immune cell infiltration in tumor microenvironment (TME) remains unclear.MethodsHere, we performed consensus clustering analysis based on the expression of 10 typical pyroptosis-related regulators (PRRs) to construct pyroptosis-mediated tumor pattern clusters and pyroptosis-related gene signature in breast cancer (BC). GSVA combined with ssGSEA methods were applied to evaluate the differences in biological pathway and immune cell infiltration level, respectively. The PCA method was employed to construct the pyro-score to quantify the pyroptosis pattern level of individual BC patient.ResultsWe determined three distinct pyro-clusters among 1852 BC samples, which exhibited different survival outcomes and enriched biological processes. The TME features demonstrated that these three clusters corresponded to three established immune profiles: immune-desert, immune-excluded and immune-inflamed phenotype, respectively. Based on pyroptosis-related signature genes, we constructed the pyro-score and stratified BC patients into high and low pyro-score group. Patients with high pyro-score exhibited favorable outcome and increased infiltration of immune cells. Further investigation revealed that high pyro-score was also related to high expression of immunosuppressive molecules, decreased tumor mutation burden (TMB) and high rate of mutation in significantly mutated genes (SMGs) (eg, PIK3CA and CDH1).ConclusionThis research emphasizes the indispensable role of pyroptosis in TME complexity and diversity. Assessing the pyroptosis pattern level of individual BC patient will assist us in better understanding TME features and directing more effective immunotherapeutic approaches.