Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen

Aberrant expression of collagen type X in solid tumor stroma is associated with EMT, immunosuppressive and pro-metastatic pathways, bone marrow stromal cell signatures, and poor survival prognosis

BACKGROUND

Collagen type X (ColXα1, encoded by COL10A1) is expressed specifically in the cartilage-to-bone transition, in bone marrow cells, and in osteoarthritic (OA) cartilage. We have previously shown that ColXα1 is expressed in breast tumor stroma, correlates with tumor-infiltrating lymphocytes, and predicts poor adjuvant therapy outcomes in ER+/HER2+ breast cancer. However, the underlying molecular mechanisms for these effects are unknown. In this study, we performed bioinformatic analysis of COL10A1-associated gene modules in breast and pancreatic cancer as well as in cells from bone marrow and OA cartilage. These findings provide important insights into the mechanisms of transcriptional and extracellular matrix changes which impact the local stromal microenvironment and tumor progression.

METHODS

Immunohistochemistry was performed to examine collagen type X expression in solid tumors. WGCNA was used to generate COL10A1-associated gene networks in breast and pancreatic tumor cohorts using RNA-Seq data from The Cancer Genome Atlas. Computational analysis was employed to assess the impact of these gene networks on development and progression of cancer and OA. Data processing and statistical analysis was performed using R and various publicly-available computational tools.

RESULTS

Expression of COL10A1 and its associated gene networks highlights inflammatory and immunosuppressive microenvironments, which identify aggressive breast and pancreatic tumors and contribute to metastatic potential in a sex-dependent manner. Both cancer types are enriched in stroma, and COL10A1 implicates bone marrow-derived fibroblasts as contributors to the epithelial-to-mesenchymal transition (EMT) in these tumors. Heightened expression of COL10A1 and its associated gene networks is correlated with poorer patient outcomes in both breast and pancreatic cancer. Common transcriptional changes and chondrogenic activity are shared between cancer and OA cartilage, suggesting that similar microenvironmental alterations may underlie both diseases.

CONCLUSIONS

COL10A1-associated gene networks may hold substantial value as regulators and biomarkers of aggressive tumor phenotypes with implications for therapy development and clinical outcomes. Identification of tumors which exhibit high expression of COL10A1 and its associated genes may reveal the presence of bone marrow-derived stromal microenvironments with heightened EMT capacity and metastatic potential. Our analysis may enable more effective risk assessment and more precise treatment of patients with breast and pancreatic cancer.

Organoid models of breast cancer in precision medicine and translational research

One of the most famous and heterogeneous cancers worldwide is breast cancer (BC). Owing to differences in the gene expression profiles and clinical features of distinct BC subtypes, different treatments are prescribed for patients. However, even with more thorough pathological evaluations of tumors than in the past, available treatments do not perform equally well for all individuals. Precision medicine is a new approach that considers the effects of patients' genes, lifestyle, and environment to choose the right treatment for an individual patient. As a powerful tool, the organoid culture system can maintain the morphological and genetic characteristics of patients' tumors. Evidence also shows that organoids have high predictive value for patient treatment. In this review, a variety of BC studies performed on organoid culture systems are evaluated. Additionally, the potential of using organoid models in BC translational research, especially in immunotherapy, drug screening, and precision medicine, has been reported.

Integrated single-cell analysis reveals distinct epigenetic-regulated cancer cell states and a heterogeneity-guided core signature in tamoxifen-resistant breast cancer

BACKGROUND

Inter- and intra-tumor heterogeneity is considered a significant factor contributing to the development of endocrine resistance in breast cancer. Recent advances in single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) allow us to explore inter- and intra-tumor heterogeneity at single-cell resolution. However, such integrated single-cell analysis has not yet been demonstrated to characterize the transcriptome and chromatin accessibility in breast cancer endocrine resistance.

METHODS

In this study, we conducted an integrated analysis combining scRNA-seq and scATAC-seq on more than 80,000 breast tissue cells from two normal tissues (NTs), three primary tumors (PTs), and three tamoxifen-treated recurrent tumors (RTs). A variety of cell types among breast tumor tissues were identified, PT- and RT-specific cancer cell states (CSs) were defined, and a heterogeneity-guided core signature (HCS) was derived through such integrated analysis. Functional experiments were performed to validate the oncogenic role of BMP7, a key gene within the core signature.

RESULTS

We observed a striking level of cell-to-cell heterogeneity among six tumor tissues and delineated the primary to recurrent tumor progression, underscoring the significance of these single-cell level tumor cell clusters classified from scRNA-seq data. We defined nine CSs, including five PT-specific, three RT-specific, and one PT-RT-shared CSs, and identified distinct open chromatin regions of CSs, as well as a HCS of 137 genes. In addition, we predicted specific transcription factors (TFs) associated with the core signature and novel biological/metabolism pathways that mediate the communications between CSs and the tumor microenvironment (TME). We finally demonstrated that BMP7 plays an oncogenic role in tamoxifen-resistant breast cancer cells through modulating MAPK signaling pathways.

CONCLUSIONS

Our integrated single-cell analysis provides a comprehensive understanding of the tumor heterogeneity in tamoxifen resistance. We envision this integrated single-cell epigenomic and transcriptomic measure will become a powerful approach to unravel how epigenetic factors and the tumor microenvironment govern the development of tumor heterogeneity and to uncover potential therapeutic targets that circumvent heterogeneity-related failures.

Aberrant expression of collagen type X in solid tumor stroma is associated with EMT, immunosuppressive and pro-metastatic pathways, bone marrow stromal cell signatures, and poor survival prognosis

Background

Collagen type X (ColXα1, encoded by COL10A1) is expressed specifically in the cartilage-to-bone transition, in bone marrow cells, and in osteoarthritic (OA) cartilage. We have previously shown that ColXα1 is expressed in breast tumor stroma, correlates with tumor-infiltrating lymphocytes, and predicts poor adjuvant therapy outcomes in ER+/HER2+ breast cancer. However, the underlying molecular mechanisms for these effects are unknown. In this study, we performed bioinformatic analysis of COL10A1-associated gene modules in breast and pancreatic cancer as well as in cells from bone marrow and OA cartilage. These findings provide important insights into the mechanisms of transcriptional and extracellular matrix changes which impact the local stromal microenvironment and tumor progression.

Methods

Immunohistochemistry was performed to examine collagen type X expression in solid tumors. WGCNA was used to generate COL10A1-associated gene networks in breast and pancreatic tumor cohorts using RNA-Seq data from The Cancer Genome Atlas. Computational analysis was employed to assess the impact of these gene networks on development and progression of cancer and OA. Data processing and statistical analysis was performed using R and various publicly-available computational tools.

Results

Expression of COL10A1 and its associated gene networks highlights inflammatory and immunosuppressive microenvironments, which identify aggressive breast and pancreatic tumors and contribute to metastatic potential in a sex-dependent manner. Both cancer types are enriched in stroma, and COL10A1 implicates bone marrow-derived fibroblasts as drivers of the epithelial-to-mesenchymal transition (EMT) in these tumors. Heightened expression of COL10A1 and its associated gene networks is correlated with poorer patient outcomes in both breast and pancreatic cancer. Common transcriptional changes and chondrogenic activity are shared between cancer and OA cartilage, suggesting that similar microenvironmental alterations may underlie both diseases.

Conclusions

COL10A1-associated gene networks may hold substantial value as regulators and biomarkers of aggressive tumor phenotypes with implications for therapy development and clinical outcomes. Identification of tumors which exhibit high expression of COL10A1 and its associated genes may reveal the presence of bone marrow-derived stromal microenvironments with heightened EMT capacity and metastatic potential. Our analysis may enable more effective risk assessment and more precise treatment of patients with breast and pancreatic cancer.

Development of a polygenic score predicting drug resistance and patient outcome in breast cancer

Gene expression profiles of hundreds of cancer cell-lines and the cell-lines' response to drug treatment were analyzed to identify genes whose expression correlated with drug resistance. In the GDSC dataset of 809 cancer cell lines, expression of 36 genes were associated with drug resistance (increased IC50) to many anti-cancer drugs. This was validated in the CTRP dataset of 860 cell lines. A polygenic score derived from the correlation coefficients of the 36 genes in cancer cell lines, UAB36, predicted resistance of cell lines to Tamoxifen. Although the 36 genes were selected from cell line behaviors, UAB36 successfully predicted survival of breast cancer patients in three different cohorts of patients treated with Tamoxifen. UAB36 outperforms two existing predictive gene signatures and is a predictor of outcome of breast cancer patients independent of the known clinical co-variates that affect outcome. This approach should provide promising polygenic biomarkers for resistance in many cancer types against specific drugs.

Integration of scHi-C and scRNA-seq data defines distinct 3D-regulated and biological-context dependent cell subpopulations

An integration of 3D chromatin structure and gene expression at single-cell resolution has yet been demonstrated. Here, we develop a computational method, a multiomic data integration (MUDI) algorithm, which integrates scHi-C and scRNA-seq data to precisely define the 3D-regulated and biological-context dependent cell subpopulations or topologically integrated subpopulations (TISPs). We demonstrate its algorithmic utility on the publicly available and newly generated scHi-C and scRNA-seq data. We then test and apply MUDI in a breast cancer cell model system to demonstrate its biological-context dependent utility. We find the newly defined topologically conserved associating domain (CAD) is the characteristic single-cell 3D chromatin structure and better characterizes chromatin domains in single-cell resolution. We further identify 20 TISPs uniquely characterizing 3D-regulated breast cancer cellular states. We reveal two of TISPs are remarkably resemble to high cycling breast cancer persister cells and chromatin modifying enzymes might be functional regulators to drive the alteration of the 3D chromatin structures. Our comprehensive integration of scHi-C and scRNA-seq data in cancer cells at single-cell resolution provides mechanistic insights into 3D-regulated heterogeneity of developing drug-tolerant cancer cells.

NOTCH3 as a prognostic biomarker and its correlation with immune infiltration in gastrointestinal cancers

NOTCH receptor 3 (NOTCH3) is known to regulate the transcription of oncogenes or tumor suppressor genes, thereby playing a crucial role in tumor development, invasion, maintenance, and chemotherapy resistance. However, the specific mechanism of how NOTCH3 drives immune infiltration in gastrointestinal cancer remains uncertain. The expression of NOTCH3 was analyzed through Western blot, PCR, Oncomine database, and the Tumor Immune Estimation Resource (TIMER) site. Kaplan-Meier plotter, PrognoScan database, and gene expression profile interactive analysis (GEPIA) were used to assess the impact of NOTCH3 on clinical prognosis. The correlation between NOTCH3 expression and immune infiltration gene markers was investigated using TIMER and GEPIA. NOTCH3 was found to be commonly overexpressed in various types of gastrointestinal tumors and was significantly associated with poor prognosis. Furthermore, the expression level of NOTCH3 showed a significant correlation with the tumor purity of gastrointestinal tumors and the extent of immune infiltration by different immune cells. Our findings suggest that NOTCH3 may act as a crucial regulator of tumor immune cell infiltration and can serve as a valuable prognostic biomarker in gastrointestinal cancers.

An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome

BACKGROUND

Predicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome prediction. However, the application of these methods is still challenged by interpretability. In this study, we proposed a novel multitask deep neural network called UISNet to predict the outcome of breast cancer. The UISNet is able to interpret the importance of features for the prediction model via an uncertainty-based integrated gradients algorithm. UISNet improved the prediction by introducing prior biological pathway knowledge and utilizing patient heterogeneity information.

RESULTS

The model was tested in seven public datasets of breast cancer, and showed better performance (average C-index = 0.691) than the state-of-the-art methods (average C-index = 0.650, ranged from 0.619 to 0.677). Importantly, the UISNet identified 20 genes as associated with breast cancer, among which 11 have been proven to be associated with breast cancer by previous studies, and others are novel findings of this study.

CONCLUSIONS

Our proposed method is accurate and robust in predicting breast cancer outcomes, and it is an effective way to identify breast cancer-associated genes. The method codes are available at: https://github.com/chh171/UISNet .

MCM2 is involved in subtyping and tamoxifen resistance of ERα-positive breast cancer by acting as the downstream factor of ERα

Tamoxifen (TAM) resistance is finally developed in over 40% of patients with estrogen receptor α-positive breast cancer (ERα+ -BC), documenting that discovering new molecular subtype is needed to confer perception to the heterogeneity of ERα+ -BC. We obtained representative gene sets subtyping ERα+ -BC using gene set variation analysis (GSVA), non-negative matrix factorization (NMF), and COX regression methods on the basis of METABRIC, TCGA, and GEO databases. Furthermore, the risk score of ERα+ -BC subtyping was established using least absolute shrinkage and selection operator (LASSO) regression on the basis of genes in the representative gene sets, thereby generating the two subtypes of ERα+ -BC. We further found that minichromosome maintenance complex component 2 (MCM2) functioned as the hub gene subtyping ERα+ -BC using GO, KEGG, and MCODE. MCM2 expression was capable for specifically predicting 1-year overall survival (OS) of ERα+ -BC and correlated with T stage, AJCC stage, and tamoxifen (TAM) sensitivity of ERα+ -BC. The downregulation of MCM2 expression inhibited proliferation, migration, and invasion of TAM-resistant cells and promoted G0/G1 arrest. Altogether, tamoxifen resistance entails that MCM2 is a hub gene subtyping ERα+ -BC, providing a novel dimension for discovering a potential target of TAM-resistant BC.

Unfolding the role of the PI3K/AKT/MTOR pathway in male breast cancer: A pragmatic appraisal

Breast cancer in men is rare, but a relevant public health issue, yielding a 25% higher risk of mortality comparing to female counterparts. The representation of males in clinical trials has been scarce and treatment decisions are based mainly on extrapolations from data in females. In the setting of estrogen-dependent metastatic disease, the use of everolimus has been seldom reported, although the PI3K/AKT/mTOR pathway seems to be a critical oncogenic driver. This paper dissects hallmark biological features of ER+/HER2-advanced male breast cancer, setting a comprehensive basis to promote personalized care, focusing on the potential of targeting the PI3K/AKT/mTOR pathway.

Targeting PEG10 as a novel therapeutic approach to overcome CDK4/6 inhibitor resistance in breast cancer

BACKGROUND

Breast cancer is the global leading cancer burden in women and the hormone receptor-positive (HR+) subtype is a major part of breast cancer. Though cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors are highly effective therapy for HR+ subtype, acquired resistance is inevitable in most cases. Herein, we investigated the paternally expressed gene 10 (PEG10)-associated mechanism of acquired resistance to CDK4/6 inhibitors.

METHODS

Palbociclib-resistant cells were generated by exposing human HR+ breast cancer cell lines to palbociclib for 7-9 months. In vitro mechanistic study and in vivo xenograft assay were performed. For clinical relevance, public mRNA microarray data sets of early breast cancer were analyzed and PEG10 immunohistochemical staining was performed using pre-CDK4/6 inhibitor tumor samples.

RESULTS

We observed that PEG10 was significantly upregulated in palbociclib-resistant cells. Ectopic overexpression of PEG10 in parental cells caused CDK4/6 inhibitor resistance and enhanced epithelial-mesenchymal transition (EMT). On the contrary, PEG10-targeting siRNA or antisense oligonucleotides (ASOs) combined with palbociclib synergistically inhibited proliferation of palbociclib-resistant cells and growth of palbociclib-resistant xenograft in mice and suppressed EMT as well. The mechanistic study confirmed that high PEG10 expression suppressed p21, a natural CDK inhibitor, and SIAH1, a post-translational degrader of ZEB1, augmenting CDK4/6 inhibitor resistance. Then PEG10 siRNA combined with palbociclib suppressed cell cycle progression and EMT via activating p21 and SIAH1, respectively. Consequently, combined PEG10 inhibition and palbociclib overcame CDK4/6 inhibitor resistance. Furthermore, high PEG10 expression was significantly associated with a shorter recurrence-free survival (RFS) based on public mRNA expression data. In pre-CDK4/6 inhibitor treatment tissues, PEG10 positivity by IHC also showed a trend toward a shorter progression-free survival (PFS) with CDK4/6 inhibitor. These results support clinical relevance of PEG10 as a therapeutic target.

CONCLUSIONS

We demonstrated a novel PEG10-associated mechanism of CDK4/6 inhibitor resistance. We propose PEG10 as a promising therapeutic target for overcoming PEG10-associated resistance to CDK4/6 inhibitors.

Integration of scHi-C and scRNA-seq data defines distinct 3D-regulated and biological-context dependent cell subpopulations

An integration of 3D chromatin structure and gene expression at single-cell resolution has yet been demonstrated. Here, we develop a computational method, a multiomic data integration (MUDI) algorithm, which integrates scHi-C and scRNA-seq data to precisely define the 3D-regulated and biological-context dependent cell subpopulations or topologically integrated subpopulations (TISPs). We demonstrate its algorithmic utility on the publicly available and newly generated scHi-C and scRNA-seq data. We then test and apply MUDI in a breast cancer cell model system to demonstrate its biological-context dependent utility. We found the newly defined topologically conserved associating domain (CAD) is the characteristic single-cell 3D chromatin structure and better characterizes chromatin domains in single-cell resolution. We further identify 20 TISPs uniquely characterizing 3D-regulated breast cancer cellular states. We reveal two of TISPs are remarkably resemble to high cycling breast cancer persister cells and chromatin modifying enzymes might be functional regulators to drive the alteration of the 3D chromatin structures. Our comprehensive integration of scHi-C and scRNA-seq data in cancer cells at single-cell resolution provides mechanistic insights into 3D-regulated heterogeneity of developing drug-tolerant cancer cells.

Pan-cancer analysis of the prognostic and immunological role of SNX29: a potential target for survival and immunotherapy

BACKGROUND

There is growing evidence that the SNX family is critical for clinical prognosis, immune infiltration and drug sensitivity in many types of tumors. The relationships between the SNX29 gene and clinical prognosis as well as pan-cancer cell infiltration and drug sensitivity have not been fully elucidated.

METHODS

In the current study, we explored the correlation between SNX29 expression and 33 types of malignancies via TCGA and GTEx. The relationship between SNX29 expression and prognostic outcome in the pan-caner cohort was also analyzed. Immune infiltration, microsatellite instability, tumor mutational burden and potential therapeutic targets of SNX29 were investigated by analyzing public databases.

RESULTS

The expression of SNX29 was found to be significantly upregulated in most tumor tissues compared to normal tissues. SNX29 expression was associated with prognosis and clinical stage. In the immune infiltration analysis, a significant relationship was found between SNX29 expression and the level of immune infiltration. In addition, we found associations between the SNX29 gene and tumor mutation burden, microsatellite instability, immunoinhibition-related genes and autophagy-related genes. Finally, the expression of SNX29 was significantly associated with the sensitivity of various tumor cell lines to 8 antitumor drugs. These results suggest that SNX29 expression is important in determining the progression, immune infiltration and drug sensitivity of various cancers.

CONCLUSION

This study provides novel insights into the potential pan-cancer targets of SNX29.

ELOVL5 and IGFBP6 genes modulate sensitivity of breast cancer cells to ferroptosis

Introduction: Relapse of breast cancer is one of the key obstacles to successful treatment. Previously we have shown that low expression of ELOVL5 and IGFBP6 genes in breast cancer tissue corresponded to poor prognosis. ELOVL5 participates directly in the elongation of polyunsaturated fatty acids (PUFAs) that are considered to play an important role in cancer cell metabolism. Thus, in this work we studied the changes in lipid metabolism in breast cancer cells with reduced expression of either ELOVL5 or IGFBP6 gene. Methods: MDA-MB-231 cells with a stable knockdown of either ELOVL5 or IGFBP6 gene were used in this study. Transcriptomic and proteomic analysis as well as RT-PCR were utilized to assess gene expression. Content of individual fatty acids in the cells was measured with HPLC-MS. HPLC was used for analysis of the kinetics of PUFAs uptake. Cell viability was measured with MTS assay. Flow cytometry was used to measure activation of apoptosis. Fluorescent microscopy was utilized to assess accumulation of ROS and formation of lipid droplets. Glutathione peroxidase activity was measured with a colorimetric assay. Results: We found that the knockdown of IGFBP6 gene led to significant changes in the profile of fatty acids in the cells and in the expression of many genes associated with lipid metabolism. As some PUFAs are known to inhibit proliferation and cause death of cancer cells, we also tested the response of the cells to single PUFAs and to combinations of docosahexaenoic acid (DHA, a n-3 PUFA) with standard chemotherapeutic drugs. Our data suggest that external PUFAs cause cell death by activation of ferroptosis, an iron-dependent mechanism of cell death with excessive lipid peroxidation. Moreover, both knockdowns increased cells' sensitivity to ferroptosis, probably due to a significant decrease in the activity of the antioxidant enzyme GPX4. Addition of DHA to commonly used chemotherapeutic drugs enhanced their effect significantly, especially for the cells with low expression of IGFBP6 gene. Discussion: The results of this study suggest that addition of PUFAs to the treatment regimen for the patients with low expression of IGFBP6 and ELOVL5 genes can be potentially beneficial and is worth testing in a clinically relevant setting.

Novel markers in breast pathology

Breast pathology is an ever-expanding database of information which includes markers, or biomarkers, that detect or help treat the disease as prognostic or predictive information. This review focuses on these aspects of biomarkers which are grounded in immunohistochemistry, liquid biopsies and next-generation sequencing.

Bioinformatics analysis of the clinical significance of HLA class II in breast cancer

Human leukocyte antigen (HLA) class II plays critical roles in antigen presentation and the initiation of immune responses. However, the correlation between the HLA class II gene expression level and the survival of patients with breast cancer is still under investigation. We analyzed microarray and RNA-Seq data of breast cancer from the cancer genome atlas (TCGA), genotype-tissue expression (GTEx) and Oncomine databases by using bioinformatics tools. The expression of the HLA-DQA1, HLA-DQA2, and HLA-DQB2 genes was significantly upregulated in breast cancer. Higher expression levels of HLA class II genes in breast cancer, especially HLA-DOB and HLA-DQB2, were significantly associated with better overall survival. Furthermore, the expression of HLA class II genes was more closely associated with survival in breast cancer than in other cancer types. CD48 coexpressed with both HLA-DOB and HLA-DQB2 was also positively associated with the overall survival of breast cancer patients. The results indicated that HLA class II and CD48 may enhance antitumor immunity, and their expression patterns may serve as potential prognostic biomarkers and therapeutic targets in breast cancer.

Copy number heterogeneity identifies ER+ breast cancer patients that do not benefit from adjuvant endocrine therapy

BACKGROUND

Endocrine therapy forms the backbone of adjuvant treatment for oestrogen-receptor-positive (ER+) breast cancer. However, it remains unclear whether adjuvant treatment improves survival rates in low-risk patients. Low intra-tumour heterogeneity (ITH) has been shown to confer low risk for recurrent disease. Here, it is studied if chromosomal copy-number ITH (CNH) can identify low-risk ER+, lymph-node-negative breast cancer patients who do not benefit from adjuvant endocrine therapy.

METHODS

Lymph-node-negative ER+ patients from the observational METABRIC dataset were retrospectively analysed (n = 708). CNH was determined from a single bulk copy-number measurement for each patient. Survival rates were compared between patients that did or did not receive adjuvant endocrine therapy for CNH-low, middle and high groups with Cox proportional-hazards models, using propensity-score weights to correct for confounders.

RESULTS

Adjuvant endocrine therapy improved the relapse-free survival (RFS) for CNH-high patients treatment (HR = 0.55), but not for CNH-low patients treatment (HR = 0.88). For CNH-low patients adjuvant endocrine therapy was associated with impaired OS (HR = 1.62).

CONCLUSIONS

This retrospective study of lymph-node-negative, ER+ breast cancer finds that patients identified as low risk using CNH do not benefit from adjuvant endocrine therapy.

ExhauFS: exhaustive search-based feature selection for classification and survival regression

Feature selection is one of the main techniques used to prevent overfitting in machine learning applications. The most straightforward approach for feature selection is an exhaustive search: one can go over all possible feature combinations and pick up the model with the highest accuracy. This method together with its optimizations were actively used in biomedical research, however, publicly available implementation is missing. We present ExhauFS-the user-friendly command-line implementation of the exhaustive search approach for classification and survival regression. Aside from tool description, we included three application examples in the manuscript to comprehensively review the implemented functionality. First, we executed ExhauFS on a toy cervical cancer dataset to illustrate basic concepts. Then, multi-cohort microarray breast cancer datasets were used to construct gene signatures for 5-year recurrence classification. The vast majority of signatures constructed by ExhauFS passed 0.65 threshold of sensitivity and specificity on all datasets, including the validation one. Moreover, a number of gene signatures demonstrated reliable performance on independent RNA-seq dataset without any coefficient re-tuning, i.e., turned out to be cross-platform. Finally, Cox survival regression models were used to fit isomiR signatures for overall survival prediction for patients with colorectal cancer. Similarly to the previous example, the major part of models passed the pre-defined concordance index threshold 0.65 on all datasets. In both real-world scenarios (breast and colorectal cancer datasets), ExhauFS was benchmarked against state-of-the-art feature selection models, including L₁-regularized sparse models. In case of breast cancer, we were unable to construct reliable cross-platform classifiers using alternative feature selection approaches. In case of colorectal cancer not a single model passed the same 0.65 threshold. Source codes and documentation of ExhauFS are available on GitHub: https://github.com/s-a-nersisyan/ExhauFS.

A pan-cancer analysis of the prognostic and immunological role of β-actin (ACTB) in human cancers

Beta-actin (ACTB), a highly conserved cytoskeleton structural protein, has been regarded as a common housekeep gene and used as a reference gene for years. However, accumulating evidence indicates that ACTB is abnormally expressed in multiple cancers and hence changes the cytoskeleton to affect the invasiveness and metastasis of tumors. This study aimed to investigate the function and clinical significance of ACTB in pan-cancer. The role of ACTB for prognosis and immune regulation across 33 tumors was explored based on the datasets of gene expression omnibus and the cancer genome atlas. Differential expression of ACTB was found between cancer and adjacent normal tissues, and significant associations was found between ACTB expression and prognosis of tumor patients. In most cancers, ACTB expression was associated with immune cells infiltration, immune checkpoints and other immune modulators. Relevance between ACTB and metastasis and invasion was identified in various types of cancers by CancerSEA. Moreover, focal adhesion and actin regulation-associated pathways were included in the functional mechanisms of ACTB. The expression of ACTB was verified by quantitative real-time polymerase chain reaction. Knockdown of ACTB inhibited head and neck squamous carcinoma cell migration and invasion by NF-κB and Wnt/β-catenin pathways. Our first pan-cancer study of ACTB offers insight into the prognostic and immunological roles of ACTB across different tumors, indicating ACTB may be a potential biomarker for poor prognosis and immune infiltration in cancers, and the role of ACTB as a reference gene in cancers was challenged.

PAN: Personalized Annotation-Based Networks for the Prediction of Breast Cancer Relapse

The classification of clinical samples based on gene expression data is an important part of precision medicine. In this manuscript, we show how transforming gene expression data into a set of personalized (sample-specific) networks can allow us to harness existing graph-based methods to improve classifier performance. Existing approaches to personalized gene networks have the limitation that they depend on other samples in the data and must get re-computed whenever a new sample is introduced. Here, we propose a novel method, called Personalized Annotation-based Networks (PAN), that avoids this limitation by using curated annotation databases to transform gene expression data into a graph. Unlike competing methods, PANs are calculated for each sample independent of the population, making it a more efficient way to obtain single-sample networks. Using three breast cancer datasets as a case study, we show that PAN classifiers not only predict cancer relapse better than gene features alone, but also outperform PPI (protein-protein interactions) and population-level graph-based classifiers. This work demonstrates the practical advantages of graph-based classification for high-dimensional genomic data, while offering a new approach to making sample-specific networks. Supplementary information: PAN and the baselines are implemented in Python. Source code and data are available at https://github.com/thinng/PAN.

A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer

Scholarly requirements have led to a massive increase of transcriptomic data in the public domain, with millions of samples available for secondary research. We identified gene-expression datasets representing 10,214 breast-cancer patients in public databases. We focused on datasets that included patient metadata on race and/or immunohistochemistry (IHC) profiling of the ER, PR, and HER-2 proteins. This review provides a summary of these datasets and describes findings from 32 research articles associated with the datasets. These studies have helped to elucidate relationships between IHC, race, and/or treatment options, as well as relationships between IHC status and the breast-cancer intrinsic subtypes. We have also identified broad themes across the analysis methodologies used in these studies, including breast cancer subtyping, deriving predictive biomarkers, identifying differentially expressed genes, and optimizing data processing. Finally, we discuss limitations of prior work and recommend future directions for reusing these datasets in secondary analyses.

Prognostic and tumor-immune infiltration cell signatures in tamoxifen-resistant breast cancers

BACKGROUND

The cumulative risk of distant recurrence of hormone receptor-positive (HR+) breast cancer in the past 20 years has ranged from 22% to 52% after 5 years of endo-therapy. The TNM stage, histological grade, and age are important clinical factors related to recurrence, however the exact mechanism of tamoxifen resistance is still unclear.

METHODS

Differentially expressed genes (DEGs) were identified in 10 pairs of patients who had relapsed and non-relapsed after tamoxifen treatment based on matching their clinicopathological factors. After analysis of the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, 10 hub genes were identified using Cytoscape software. Next, real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database were used to verify the expression and overall survival (OS) of the 10 hub genes respectively, and GSE96058 and Kaplan-Meier Plotter website were used to further verify the OS of C3, CX3CL1, CXCL2, and SAA1. Finally, Immune Cell Abundance Identifier (ImmuCellAI) and the TIMER database were used to estimate immune cell infiltration and the expression of prognostic genes.

RESULTS

The DEGs were mainly enriched in the inflammatory response and cytokine-receptor interaction. The expression and the survival analysis identified CX3CL1, CXCL2, and SAA1 as prognostic factors, whose overexpression in HR+/human epidermal growth factor receptor 2 (HER-2) negative breast cancer possibly predicted a longer disease-free survival. The expression levels of these 3 genes are positively correlated with immune cell infiltration. Their high expression levels may predict longer disease-free survival in breast cancer after tamoxifen treatment and may be biomarkers for tamoxifen-resistant therapy.

CONCLUSIONS

In conclusion, the high expression of CX3CL1, CXCL2, and SAA1 may predict longer disease-free survival in breast cancer after tamoxifen treatment and may be a biomarker for tamoxifen therapy.

Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis

Background

Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced. However, it is not clear whether using miRNA and/or lncRNA expression data helps improve the performance of gene expression based subtyping and prognosis methods, and this raises challenges as to how and when to use these data and methods in practice.

Results

In this paper, we conduct a comparative study of 35 methods, including 12 breast cancer subtyping methods and 23 breast cancer prognosis methods, on a collection of 19 independent breast cancer datasets. We aim to uncover the roles of miRNAs and lncRNAs in breast cancer subtyping and prognosis from the systematic comparison. In addition, we created an R package, CancerSubtypesPrognosis, including all the 35 methods to facilitate the reproducibility of the methods and streamline the evaluation.

Conclusions

The experimental results show that integrating miRNA expression data helps improve the performance of the mRNA-based cancer subtyping methods. However, miRNA signatures are not as good as mRNA signatures for breast cancer prognosis. In general, lncRNA expression data does not help improve the mRNA-based methods in both cancer subtyping and cancer prognosis. These results suggest that the prognostic roles of miRNA/lncRNA signatures in the improvement of breast cancer prognosis needs to be further verified.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04215-3.

Effect of the Expression of ELOVL5 and IGFBP6 Genes on the Metastatic Potential of Breast Cancer Cells

Breast cancer (BC) is the leading cause of death from malignant neoplasms among women worldwide, and metastatic BC presents the biggest problems for treatment. Previously, it was shown that lower expression of ELOVL5 and IGFBP6 genes is associated with a higher risk of the formation of distant metastases in BC. In this work, we studied the change in phenotypical traits, as well as in the transcriptomic and proteomic profiles of BC cells as a result of the stable knockdown of ELOVL5 and IGFBP6 genes. The knockdown of ELOVL5 and IGFBP6 genes was found to lead to a strong increase in the expression of the matrix metalloproteinase (MMP) MMP1. These results were in good agreement with the correlation analysis of gene expression in tumor samples from patients and were additionally confirmed by zymography. The knockdown of ELOVL5 and IGFBP6 genes was also discovered to change the expression of a group of genes involved in the formation of intercellular contacts. In particular, the expression of the CDH11 gene was markedly reduced, which also complies with the correlation analysis. The spheroid formation assay showed that intercellular adhesion decreased as a result of the knockdown of the ELOVL5 and IGFBP6 genes. Thus, the obtained data indicate that malignant breast tumors with reduced expression of the ELOVL5 and IGFBP6 genes can metastasize with a higher probability due to a more efficient invasion of tumor cells.

miRGTF-net: Integrative miRNA-gene-TF network analysis reveals key drivers of breast cancer recurrence

Analysis of regulatory networks is a powerful framework for identification and quantification of intracellular interactions. We introduce miRGTF-net, a novel tool for construction of miRNA-gene-TF networks. We consider multiple transcriptional and post-transcriptional interaction types, including regulation of gene and miRNA expression by transcription factors, gene silencing by miRNAs, and co-expression of host genes with their intronic miRNAs. The underlying algorithm uses information on experimentally validated interactions as well as integrative miRNA/mRNA expression profiles in a given set of samples. The latter ensures simultaneous tissue-specificity and biological validity of interactions. We applied miRGTF-net to paired miRNA/mRNA-sequencing data of breast cancer samples from The Cancer Genome Atlas (TCGA). Together with topological analysis of the constructed network we showed that considered players can form reliable prognostic gene signatures for ER-positive breast cancer. A number of signatures demonstrated remarkably high accuracy on transcriptomic data obtained by both microarrays and RNA sequencing from several independent patient cohorts. Furthermore, an essential part of prognostic genes were identified as direct targets of transcription factor E2F1. The putative interplay between estrogen receptor alpha and E2F1 was suggested as a potential recurrence factor in patients treated with tamoxifen. Source codes of miRGTF-net are available at GitHub (https://github.com/s-a-nersisyan/miRGTF-net).

NCAPG upregulation mediated by four microRNAs combined with activation of the p53 signaling pathway is a predictor of poor prognosis in patients with breast cancer

The role of non-SMC condensin I complex subunit G (NCAPG) in breast cancer remains unclear. The present study used online databases, reverse transcription-quantitative PCR, flow cytometry and western blotting to determine the expression levels, prognosis and potential molecular mechanisms underlying the role of NCAPG in breast cancer. The association between NCAPG expression and several different clinicopathological parameters in patients with breast cancer was determined, and the results revealed that NCAPG expression was negatively associated with estrogen receptor and progesterone receptor positive status, but was positively associated with HER2 positive status, Nottingham Prognostic Index score and Scarff-Bloom-Richardson grade status. Furthermore, upregulated expression levels of NCAPG resulted in a poor prognosis in patients with breast cancer. A total of 27 microRNAs (miRNAs/miRs) were predicted to target NCAPG, among which four miRNAs (miR-101-3p, miR-195-5p, miR-214-3p and miR-944) were predicted to most likely regulate NCAPG expression in breast cancer. A total of 261 co-expressed genes of NCAPG were identified, including cell division cyclin 25 homolog C (CDC25C), and pathway enrichment analysis indicated that these co-expressed genes were significantly enriched in the p53 signaling pathway. CDC25C expression was downregulated in breast cancer and was associated with a poor prognosis. These findings suggested that upregulated NCAPG expression may be a prognostic biomarker of breast cancer.

Cancer-associated exportin-6 upregulation inhibits the transcriptionally repressive and anticancer effects of nuclear profilin-1

Aberrant expression of nuclear transporters and deregulated subcellular localization of their cargo proteins are emerging as drivers and therapeutic targets of cancer. Here, we present evidence that the nuclear exporter exportin-6 and its cargo profilin-1 constitute a functionally important and frequently deregulated axis in cancer. Exportin-6 upregulation occurs in numerous cancer types and is associated with poor patient survival. Reducing exportin-6 level in breast cancer cells triggers antitumor effects by accumulating nuclear profilin-1. Mechanistically, nuclear profilin-1 interacts with eleven-nineteen-leukemia protein (ENL) within the super elongation complex (SEC) and inhibits the ability of the SEC to drive transcription of numerous pro-cancer genes including MYC. XPO6 and MYC are positively correlated across diverse cancer types including breast cancer. Therapeutically, exportin-6 loss sensitizes breast cancer cells to the bromodomain and extra-terminal (BET) inhibitor JQ1. Thus, exportin-6 upregulation is a previously unrecognized cancer driver event by spatially inhibiting nuclear profilin-1 as a tumor suppressor.

FEN1 is a prognostic biomarker for ER+ breast cancer and associated with tamoxifen resistance through the ERα/cyclin D1/Rb axis

Background

Tamoxifen is an important choice in endocrine therapy for patients with oestrogen receptor-positive (ER+) breast cancer, and disease progression-associated resistance to tamoxifen therapy is still challenging. Flap endonuclease-1 (FEN1) is used as a prognostic biomarker and is considered to participate in proliferation, migration, and drug resistance in multiple cancers, especially breast cancer, but the prognostic function of FEN1 in ER+ breast cancer, and whether FEN1 is related to tamoxifen resistance or not, remain to be explored.

Methods

On-line database Kaplan-Meier (KM) plotter, GEO datasets, and immunohistochemistry were used to analyse the prognostic value of FEN1 in ER+ breast cancer from mRNA and protein levels. Cell viability assay and colony formation assays showed the response of tamoxifen in MCF-7 and T47D cells. Microarray data with FEN1 siRNA versus control group in MCF-7 cells were analysed by Gene Set Enrichment Analysis (GSEA). The protein levels downstream of FEN1 were detected by western blot assay.

Results

ER+ breast cancer patients who received tamoxifen for adjuvant endocrine therapy with poor prognosis showed a high expression of FEN1. MCF-7 and T47D appeared resistant to tamoxifen after FEN1 over-expression and increased sensitivity to tamoxifen after FEN1 knockdown. Importantly, FEN1 over-expression could activate tamoxifen resistance through the ERα/cyclin D1/Rb axis.

Conclusions

As a biomarker of tamoxifen effectiveness, FEN1 participates in tamoxifen resistance through ERα/cyclin D1/Rb axis. In the future, reversing tamoxifen resistance by knocking-down FEN1 or by way of action as a small molecular inhibitor of FEN1 warrants further investigation.

Identifying intracellular signaling modules and exploring pathways associated with breast cancer recurrence

Exploring complex modularization of intracellular signal transduction pathways is critical to understanding aberrant cellular responses during disease development and drug treatment. IMPALA (Inferred Modularization of PAthway LAndscapes) integrates information from high throughput gene expression experiments and genome-scale knowledge databases to identify aberrant pathway modules, thereby providing a powerful sampling strategy to reconstruct and explore pathway landscapes. Here IMPALA identifies pathway modules associated with breast cancer recurrence and Tamoxifen resistance. Focusing on estrogen-receptor (ER) signaling, IMPALA identifies alternative pathways from gene expression data of Tamoxifen treated ER positive breast cancer patient samples. These pathways were often interconnected through cytoplasmic genes such as IRS1/2, JAK1, YWHAZ, CSNK2A1, MAPK1 and HSP90AA1 and significantly enriched with ErbB, MAPK, and JAK-STAT signaling components. Characterization of the pathway landscape revealed key modules associated with ER signaling and with cell cycle and apoptosis signaling. We validated IMPALA-identified pathway modules using data from four different breast cancer cell lines including sensitive and resistant models to Tamoxifen. Results showed that a majority of genes in cell cycle/apoptosis modules that were up-regulated in breast cancer patients with short survivals (< 5 years) were also over-expressed in drug resistant cell lines, whereas the transcription factors JUN, FOS, and STAT3 were down-regulated in both patient and drug resistant cell lines. Hence, IMPALA identified pathways were associated with Tamoxifen resistance and an increased risk of breast cancer recurrence. The IMPALA package is available at https://dlrl.ece.vt.edu/software/ .

Rhosin Suppressed Tumor Cell Metastasis through Inhibition of Rho/YAP Pathway and Expression of RHAMM and CXCR4 in Melanoma and Breast Cancer Cells

The high mortality rate of cancer is strongly correlated with the development of distant metastases at secondary sites. Although Rho GTPases, such as RhoA, RhoB, RhoC, and RhoE, promote tumor metastasis, the main roles of Rho GTPases remain unidentified. It is also unclear whether rhosin, a Rho inhibitor, acts by suppressing metastasis by a downstream inhibition of Rho. In this study, we investigated this mechanism of metastasis in highly metastatic melanoma and breast cancer cells, and the mechanism of inhibition of metastasis by rhosin. We found that rhosin suppressed the RhoA and RhoC activation, the nuclear localization of YAP, but did not affect ERK1/2, Akt, or NF-κB activation in the highly metastatic cell lines B16BL6 and 4T1. High expression of YAP was associated with poor overall and recurrence-free survival in patients with breast cancer or melanoma. Treatment with rhosin inhibited lung metastasis in vivo. Moreover, rhosin inhibited tumor cell adhesion to the extracellular matrix via suppression of RHAMM expression, and inhibited SDF-1-induced cell migration and invasion by decreasing CXCR4 expression in B16BL6 and 4T1 cells. These results suggest that the inhibition of RhoA/C-YAP pathway by rhosin could be an extremely useful therapeutic approach in patients with melanoma and breast cancer.

A 23-gene prognostic classifier for prediction of recurrence and survival for Asian breast cancer patients

We report a 23- gene-classifier profiled from Asian women, with the primary purpose of assessing its clinical utility towards improved risk stratification for relapse for breast cancer patients from Asian cohorts within 10 years’ following mastectomy. Four hundred and twenty-two breast cancer patients underwent mastectomy and were used to train the classifier on a logistic regression model. A subset of 197 patients were chosen to be entered into the follow-up studies post mastectomy who were examined to determine the patterns of recurrence and survival analysis based on gene expression of the gene classifier, age at diagnosis, tumor stage and lymph node status, over a 5 and 10 years follow-up period. Metastasis to lymph node (N2-N3) with N0 as the reference (N2 vs. N0 hazard ratio: 2.02 (1.05–8.70), N3 vs. N0 hazard ratio: 4.32 (1.41–13.22) for 5 years) and gene expression of the 23-gene panel (P=0.06, 5 years and 0.02, 10 years, log-rank test) were found to have significant discriminatory effects on the risk of relapse (HR (95%CI):2.50 (0.95–6.50)). Furthermore, survival curves for subgroup analysis with N0-N1 and T1-T2 predicted patients with higher risk scores. The study provides robust evidence of the effectiveness of the 23-gene-classifier and could be used to determine the risk of relapse event (locoregional and distant recurrence) in Asian patients, leading to a meaningful reduction in chemotherapy recommendations.

Pregnancy-specific glycoprotein 9 acts as both a transcriptional target and a regulator of the canonical TGF-β/Smad signaling to drive breast cancer progression

Pregnancy-specific glycoprotein 9 (PSG9) is a placental glycoprotein essential for the maintenance of normal gestation in mammals. Bioinformatics analysis of multiple publicly available datasets revealed aberrant PSG9 expression in breast tumors, but its functional and mechanistic role in breast cancer remains unexplored. Here, we report that PSG9 expression levels were elevated in tumor tissues and plasma specimens from breast cancer patients, and were associated with poor prognosis. Gain- or loss-of-function studies demonstrated that PSG9 promoted breast cancer cell proliferation, migration, and invasionin vitro, and enhanced tumor growth and lung colonization in vivo. Mechanistically, transforming growth factor-β1 (TGF-β1) transcriptionally activated PSG9 expression through enhancing the enrichment of Smad3 and Smad4 onto PSG9 promoter regions containing two putative Smad-binding elements (SBEs). Mutation of both SBEs in the PSG9 promoter, or knockdown of TGF-β receptor 1 (TGFBR1), TGFBR2, Smad3, or Smad4 impaired the ability of TGF-β1 to induce PSG9 expression. Consequently, PSG9 contributed to TGF-β1-induced epithelial-mesenchymal transition (EMT) and breast cancer cell migration and invasion. Moreover, PSG9 enhanced the stability of Smad2, Smad3, and Smad4 proteins by blocking their proteasomal degradation, and regulated the expression of TGF-β1 target genes involved in EMT and breast cancer progression, thus further amplifying the canonical TGF-β/Smad signaling in breast cancer cells. Collectively, these findings establish PSG9 as a novel player in breast cancer progressionvia hijacking the canonical TGF-β/Smad signaling, and identify PSG9 as a potential plasma biomarker for the early detection of breast cancer.

Combined CDK2 and CDK4/6 Inhibition Overcomes Palbociclib Resistance in Breast Cancer by Enhancing Senescence

Breast cancer represents the number one global cancer burden in women and the hormone receptor (HR)-positive subtype comprises approximately 70% of breast cancers. Unfortunately, acquired resistance ultimately occurs in almost all cases, even though cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors are a highly effective therapy for HR-positive/human epidermal growth factor receptor 2-negative subtype. Here, we investigated mechanisms of resistance to CDK4/6 inhibitor and potential therapeutic strategies using our palbociclib-resistant preclinical model. We observed that cyclin E was significantly overexpressed in palbociclib-resistant cells, and similar association was also confirmed in pleural effusion samples collected from HR-positive breast cancer patients. After confirmation of cyclin E-CDK2 interaction by co-immunoprecipitation, we demonstrated CDK2 inhibition combined with palbociclib synergistically suppressed proliferation of palbociclib-resistant cells and growth of palbociclib-resistant xenograft in mice. We also proved that enhancing C-MYC-mediated senescence is a novel mechanism behind the synergism created by targeting both CDK2 and CDK4/6. Furthermore, the clinical relevance of cyclin E as a therapeutic target was supported by significant association between CCNE1 overexpression and poor prognosis based on large-scale public gene expression data sets in HR-positive breast cancer patients. Therefore, we propose cyclin E-CDK2 signaling as a promising therapeutic target for overcoming cyclin E-associated resistance to CDK4/6 inhibitor.

Genome-wide analysis of therapeutic response uncovers molecular pathways governing tamoxifen resistance in ER+ breast cancer

Background

Prioritization of breast cancer patients based on the risk of resistance to tamoxifen plays a significant role in personalized therapeutic planning and improving disease course and outcomes.

Methods

In this work, we demonstrate that a genome-wide pathway-centric computational framework elucidates molecular pathways as markers of tamoxifen resistance in ER+ breast cancer patients. In particular, we associated activity levels of molecular pathways with a wide spectrum of response to tamoxifen, which defined markers of tamoxifen resistance in patients with ER+ breast cancer.

Findings

We identified five biological pathways as markers of tamoxifen failure and demonstrated their ability to predict the risk of tamoxifen resistance in two independent patient cohorts (Test cohort1: log-rank p-value = 0.02, adjusted HR = 3.11; Test cohort2: log-rank p-value = 0.01, adjusted HR = 4.24). We have shown that these pathways are not markers of aggressiveness and outperform known markers of tamoxifen response. Furthermore, for adoption into clinic, we derived a list of pathway read-out genes and their associated scoring system, which assigns a risk of tamoxifen resistance for new incoming patients.

Interpretation

We propose that the identified pathways and their read-out genes can be utilized to prioritize patients who would benefit from tamoxifen treatment and patients at risk of tamoxifen resistance that should be offered alternative regimens.

Funding

This work was supported by the Rutgers SHP Dean's research grant, Rutgers start-up funds, Libyan Ministry of Higher Education and Scientific Research, and Katrina Kehlet Graduate Award from The NJ Chapter of the Healthcare Information Management Systems Society.

Meta-analysis of gene expression profiling reveals novel basal gene signatures in MCF-10A cells transformed with cadmium

Cadmium (Cd2+) is an environmental toxicant and a human carcinogen. Several studies show an association of Cd2+ exposure to the development of breast cancer. Previously, we have transformed the immortalized non-tumorigenic cell line MCF-10A with Cd2+ and have demonstrated that the transformed cells have anchorage independent growth. In a separate study, we showed that transformation of the immortalized urothelial cells with the environmental carcinogen arsenite (As3+) results in an increase in expression of genes associated with the basal subtype of bladder cancer. In this study, we determined if transformation of the MCF-10A cells with Cd2+ would have a similar effect on the expression of basal genes. The results of our study indicate that there is a decrease in expression of genes associated with keratinization and cornification and this gene signature includes the genes associated with the basal subtype of breast cancer. An analysis of human breast cancer databases indicates an increased expression of this gene signature is associated with a positive correlation to patient survival whereas a reduced expression/absence of this gene signature is associated with poor patient survival. Thus, our study suggests that transformation of the MCF-10A cells with Cd2+ produces a decreased basal gene expression profile that correlates to patient outcome.

TIE2 Induces Breast Cancer Cell Dormancy and Inhibits the Development of Osteolytic Bone Metastases

Breast cancer (BCa) cells disseminating to the bone can remain dormant and resistant to treatments for many years until relapsing as bone metastases. The tyrosine kinase receptor TIE2 induces the dormancy of hematopoietic stem cells, and could also induce the dormancy of BCa cells. However, TIE2 is also a target for anti-angiogenic treatments in ongoing clinical trials, and its inhibition could then restart the proliferation of dormant BCa cells in bone. In this study, we used a combination of patient data, in vitro, and in vivo models to investigate the effect of TIE2 in the dormancy of bone metastases. In BCa patients, we found that a higher TIE2 expression is associated with an increased time to metastases and survival. In vitro, TIE2 decreased cell proliferation as it increased the expression of cyclin-dependent kinase inhibitors CDKN1A and CDKN1B and arrested cells in the G₀/G₁ phase. Expression of TIE2 also increased the resistance to the chemotherapeutic 5-Fluorouracil. In mice, TIE2 expression reduced tumor growth and the formation of osteolytic bone metastasis. Together, these results show that TIE2 is sufficient to induce dormancy in vitro and in vivo, and could be a useful prognostic marker for patients. Our data also suggest being cautious when using TIE2 inhibitors in the clinic, as they could awaken dormant disseminated tumor cells.

Correlation between MR Image-Based Radiomics Features and Risk Scores Associated with Gene Expression Profiles in Breast Cancer

Purpose

To investigate the correlation between magnetic resonance (MR) image-based radiomics features and the genomic features of breast cancer by focusing on biomolecular intrinsic subtypes and gene expression profiles based on risk scores.

Materials and Methods

We used the publicly available datasets from the Cancer Genome Atlas and the Cancer Imaging Archive to extract the radiomics features of 122 breast cancers on MR images. Furthermore, PAM50 intrinsic subtypes were classified and their risk scores were determined from gene expression profiles. The relationship between radiomics features and biomolecular characteristics was analyzed. A penalized generalized regression analysis was performed to build prediction models.

Results

The PAM50 subtype demonstrated a statistically significant association with the maximum 2D diameter (p = 0.0189), degree of correlation (p = 0.0386), and inverse difference moment normalized (p = 0.0337). Among risk score systems, GGI and GENE70 shared 8 correlated radiomic features (p = 0.0008–0.0492) that were statistically significant. Although the maximum 2D diameter was most significantly correlated to both score systems (p = 0.0139, and p = 0.0008), the overall degree of correlation of the prediction models was weak with the highest correlation coefficient of GENE70 being 0.2171.

Conclusion

Maximum 2D diameter, degree of correlation, and inverse difference moment normalized demonstrated significant relationships with the PAM50 intrinsic subtypes along with gene expression profile-based risk scores such as GENE70, despite weak correlations.

OSbrca: A Web Server for Breast Cancer Prognostic Biomarker Investigation With Massive Data From Tens of Cohorts

Potential prognostic mRNA biomarkers are exploited to assist in the clinical management and treatment of breast cancer, which is the first life-threatening tumor in women worldwide. However, it is technically challenging for untrained researchers to process high dimensional profiling data to screen and validate the potential prognostic values of genes of interests in multiple cohorts. Our aim is to develop an easy-to-use web server to facilitate the screening, developing, and evaluating of prognostic biomarkers in breast cancers. Herein, we collected more than 7,400 cases of breast cancer with gene expression profiles and clinical follow-up information from The Cancer Genome Atlas and Gene Expression Omnibus data, and built an Online consensus Survival analysis web server for Breast Cancers, abbreviated OSbrca, to generate the Kaplan–Meier survival plot with a hazard ratio and log rank P-value for given genes in an interactive way. To examine the performance of OSbrca, the prognostic potency of 128 previously published biomarkers of breast cancer was reassessed in OSbrca. In conclusion, it is highly valuable for biologists and clinicians to perform the preliminary assessment and validation of novel or putative prognostic biomarkers for breast cancers. OSbrca could be accessed at http://bioinfo.henu.edu.cn/BRCA/BRCAList.jsp.

FusionLearn: a biomarker selection algorithm on cross-platform data

MOTIVATION

In high dimensional genetic data analysis, the objective is to select important biomarkers which are involved in some biological processes, such as disease progression, immune response, etc. The experimental data are often collected from different platforms including microarray experiments and proteomic experiments. The conventional single-platform approach lacks the capability to learn from multiple platforms, and the resulted lists of biomarkers vary across different platforms. There is a great need to develop an algorithm which can aggregate information across platforms and provide a consolidated list of biomarkers across different platforms.

RESULTS

In this paper, we introduce an R package FusionLearn, which implements a fusion learning algorithm to analyze cross-platform data. The consolidated list of biomarkers is selected by the technique of group penalization. We first apply the algorithm on a collection of breast cancer microarray experiments from the NCBI (National Centre for Biotechnology Information) microarray database and the resulted list of selected genes have higher classification accuracy rate across different datasets than the lists generated from each single dataset. Secondly, we use the software to analyze a combined microarray and proteomic dataset for the study of the growth phase versus the stationary phase in Streptomyces coelicolor. The selected biomarkers demonstrate consistent differential behavior across different platforms.

AVAILABILITY AND IMPLEMENTATION

R package: https://cran.r-project.org/package=FusionLearn.

Differential impact of classical and non-canonical NF-κB pathway-related gene expression on the survival of breast cancer patients

Inflammation is a well-known driver of carcinogenesis and cancer progression, often attributed to the tumor microenvironment. However, tumor cells themselves are capable of secreting a variety of inflammatory molecules, leading to the activation of specific signaling pathways that promote tumor progression. The NF-κB signaling pathway is one of the most important connections between inflammation and tumorigenesis. NF-κB is a superfamily of transcription factors that plays an important role in several types of hematological and solid tumors, including breast cancer. However, the role of the NF-κB pathway in the survival of breast cancer patients is poorly studied. In this study, we analyzed and related the expression of both canonical and alternative NF-κB pathways and selected target genes with the relapse-free and overall survival of breast cancer patients. We used the public database Kaplan-Meier plotter (KMplot) which includes gene expression data and survival information of 3951 breast cancer patients. We found that the expression of IKKα was associated with poor relapse-free survival in patients with ER-positive tumors. Moreover, the expression of IL-8 and MMP-1 was associated with poor relapse-free and overall survival. In contrast, expression of IKKβ, p50, and p65 from the canonical pathway, and NIK and RELB from the alternative pathway correlated with better relapse-free survival also when the patients were classified by their hormonal and nodal status. Our study suggests that the expression of genes of the canonical and alternative NF-κB pathways is ultimately critical for tumor persistence. Understanding the communication between both pathways would help to find better therapeutic and prophylactic targets to prevent breast cancer progression and relapse.

Heterocellular gene signatures reveal luminal-A breast cancer heterogeneity and differential therapeutic responses

Breast cancer is a highly heterogeneous disease. Although differences between intrinsic breast cancer subtypes have been well studied, heterogeneity within each subtype, especially luminal-A cancers, requires further interrogation to personalize disease management. Here, we applied well-characterized and cancer-associated heterocellular signatures representing stem, mesenchymal, stromal, immune, and epithelial cell types to breast cancer. This analysis stratified the luminal-A breast cancer samples into five subtypes with a majority of them enriched for a subtype (stem-like) that has increased stem and stromal cell gene signatures, representing potential luminal progenitor origin. The enrichment of immune checkpoint genes and other immune cell types in two (including stem-like) of the five heterocellular subtypes of luminal-A tumors suggest their potential response to immunotherapy. These immune-enriched subtypes of luminal-A tumors (containing only estrogen receptor positive samples) showed good or intermediate prognosis along with the two other differentiated subtypes as assessed using recurrence-free and distant metastasis-free patient survival outcomes. On the other hand, a partially differentiated subtype of luminal-A breast cancer with transit-amplifying colon-crypt characteristics showed poor prognosis. Furthermore, published luminal-A subtypes associated with specific somatic copy number alterations and mutations shared similar cellular and mutational characteristics to colorectal cancer subtypes where the heterocellular signatures were derived. These heterocellular subtypes reveal transcriptome and cell-type based heterogeneity of luminal-A and other breast cancer subtypes that may be useful for additional understanding of the cancer type and potential patient stratification and personalized medicine.

GENT2: an updated gene expression database for normal and tumor tissues

BACKGROUND

Gene Expression database of Normal and Tumor tissues 2 (GENT2) is an updated version of GENT, which has provided a user-friendly search platform for gene expression patterns across different normal and tumor tissues compiled from public gene expression data sets.

RESULTS

We refactored GENT2 with recent technologies such as Apache Lucene indexing for fast search and Google Web Toolkit (GWT) framework for a user-friendly web interface. Now, GENT2 contains more than 68,000 samples and has several new useful functions. First, GENT2 now provides gene expression across 72 different tissues compared to 57 in GENT. Second, with increasing importance of tumor subtypes, GENT2 provides an option to study the differential expression and its prognostic significance based on tumor subtypes. Third, whenever available, GENT2 provides prognostic information of a gene of interest. Fourth, GENT2 provides a meta-analysis of survival information to provide users more reliable prognostic value of a gene of interest.

CONCLUSIONS

In conclusion, with these significant improvements, GENT2 will continue to be a useful tool to a wide range of researchers. GENT2 is freely available at http://gent2.appex.kr .

Reprogramming of Amino Acid Transporters to Support Aspartate and Glutamate Dependency Sustains Endocrine Resistance in Breast Cancer

Endocrine therapy (ET) is the standard of care for estrogen receptor-positive (ER+) breast cancers. Despite its efficacy, ∼40% of women relapse with ET-resistant (ETR) disease. A global transcription analysis in ETR cells reveals a downregulation of the neutral and basic amino acid transporter SLC6A14 governed by enhanced miR-23b-3p expression, resulting in impaired amino acid metabolism. This altered amino acid metabolism in ETR cells is supported by the activation of autophagy and the enhanced import of acidic amino acids (aspartate and glutamate) mediated by the SLC1A2 transporter. The clinical significance of these findings is validated by multiple orthogonal approaches in a large cohort of ET-treated patients, in patient-derived xenografts, and in in vivo experiments. Targeting these amino acid metabolic dependencies resensitizes ETR cells to therapy and impairs the aggressive features of ETR cells, offering predictive biomarkers and potential targetable pathways to be exploited to combat or delay ETR in ER+ breast cancers.

Identification and clinical validation of a multigene assay that interrogates the biology of cancer stem cells and predicts metastasis in breast cancer: A retrospective consecutive study

Background

Breast cancers show variations in the number and biological aggressiveness of cancer stem cells that correlate with their clinico-prognostic and molecular heterogeneity. Thus, prognostic stratification of breast cancers based on cancer stem cells might help guide patient management.

Methods

We derived a 20-gene stem cell signature from the transcriptional profile of normal mammary stem cells, capable of identifying breast cancers with a homogeneous profile and poor prognosis in in silico analyses. The clinical value of this signature was assessed in a prospective-retrospective cohort of 2, 453 breast cancer patients. Models for predicting individual risk of metastasis were developed from expression data of the 20 genes in patients randomly assigned to a training set, using the ridge-penalized Cox regression, and tested in an independent validation set.

Findings

Analyses revealed that the 20-gene stem cell signature provided prognostic information in Triple-Negative and Luminal breast cancer patients, independently of standard clinicopathological parameters. Through functional studies in individual tumours, we correlated the risk score assigned by the signature with the proliferative and self-renewal potential of the cancer stem cell population. By retraining the 20-gene signature in Luminal patients, we derived the risk model, StemPrintER, which predicted early and late recurrence independently of standard prognostic factors.

Interpretation

Our findings indicate that the 20-gene stem cell signature, by its unique ability to interrogate the biology of cancer stem cells of the primary tumour, provides a reliable estimate of metastatic risk in Triple-Negative and Luminal breast cancer patients independently of standard clinicopathological parameters.

Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups

The rates and routes of lethal systemic spread in breast cancer are poorly understood owing to a lack of molecularly characterized patient cohorts with long-term, detailed follow-up data. Long-term follow-up is especially important for those with oestrogen-receptor (ER)-positive breast cancers, which can recur up to two decades after initial diagnosis1-6. It is therefore essential to identify patients who have a high risk of late relapse7-9. Here we present a statistical framework that models distinct disease stages (locoregional recurrence, distant recurrence, breast-cancer-related death and death from other causes) and competing risks of mortality from breast cancer, while yielding individual risk-of-recurrence predictions. We apply this model to 3,240 patients with breast cancer, including 1,980 for whom molecular data are available, and delineate spatiotemporal patterns of relapse across different categories of molecular information (namely immunohistochemical subtypes; PAM50 subtypes, which are based on gene-expression patterns10,11; and integrative or IntClust subtypes, which are based on patterns of genomic copy-number alterations and gene expression12,13). We identify four late-recurring integrative subtypes, comprising about one quarter (26%) of tumours that are both positive for ER and negative for human epidermal growth factor receptor 2, each with characteristic tumour-driving alterations in genomic copy number and a high risk of recurrence (mean 47-62%) up to 20 years after diagnosis. We also define a subgroup of triple-negative breast cancers in which cancer rarely recurs after five years, and a separate subgroup in which patients remain at risk. Use of the integrative subtypes improves the prediction of late, distant relapse beyond what is possible with clinical covariates (nodal status, tumour size, tumour grade and immunohistochemical subtype). These findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials.

Neoadjuvant Therapy for Breast Cancer as a Model for Translational Research

Neoadjuvant therapy, where patients receive systemic therapy before surgical removal of the tumour, can downstage tumours allowing breast-conserving surgery, rather than mastectomy. In addition to its impact on surgery, the neoadjuvant setting offers a valuable opportunity to monitor individual tumour response. The effectiveness of standard and/or potential new therapies can be tested in the neoadjuvant pre-surgical setting. It can potentially help to identify markers differentiating patients that will potentially benefit from continuing with the same or a different adjuvant treatment enabling personalised treatment. Characterising the molecular response to treatment over time can more accurately identify the significant differences between baseline samples that would not be identified without post-treatment samples. In this review, we discuss the potential and challenges of using the neoadjuvant setting in translational breast cancer research, considering the implications for improving our understanding of response to treatment, predicting therapy benefit, modelling breast cancer dormancy, and the development of drug resistance.