Transcriptome sequencing of HER2-positive breast cancer stem cells identifies potential prognostic marker

Characterization of gene cluster heterogeneity in single-cell transcriptomic data within and across cancer types

Despite the remarkable progress in probing tumor transcriptomic heterogeneity by single-cell RNA sequencing (sc-RNAseq) data, several gaps exist in prior studies. Tumor heterogeneity is frequently mentioned but not quantified. Clustering analyses typically target cells rather than genes, and differential levels of transcriptomic heterogeneity of gene clusters are not characterized. Relations between gene clusters inferred from multiple datasets remain less explored. We provided a series of quantitative methods to analyze cancer sc-RNAseq data. First, we proposed two quantitative measures to assess intra-tumoral heterogeneity/homogeneity. Second, we established a hierarchy of gene clusters from sc-RNAseq data, devised an algorithm to reduce the gene cluster hierarchy to a compact structure, and characterized the gene clusters with functional enrichment and heterogeneity. Third, we developed an algorithm to align the gene cluster hierarchies from multiple datasets to a small number of meta gene clusters. By applying these methods to nine cancer sc-RNAseq datasets, we discovered that cancer cell transcriptomes were more homogeneous within tumors than the accompanying normal cells. Furthermore, many gene clusters from the nine datasets were aligned to two large meta gene clusters, which had high and low heterogeneity and were enriched with distinct functions. Finally, we found the homogeneous meta gene cluster retained stronger expression coherence and associations with survival times in bulk level RNAseq data than the heterogeneous meta gene cluster, yet the combinatorial expression patterns of breast cancer subtypes in bulk level data were not preserved in single-cell data. The inference outcomes derived from nine cancer sc-RNAseq datasets provide insights about the contributing factors for transcriptomic heterogeneity of cancer cells and complex relations between bulk level and single-cell RNAseq data. They demonstrate the utility of our methods to enable a comprehensive characterization of co-expressed gene clusters in a wide range of sc-RNAseq data in cancers and beyond.

Summary: We propose quantitative methods to analyze cancer sc-RNAseq data: measures of intra-tumoral heterogeneity, characterization of a hierarchy of gene clusters, and alignment of gene cluster hierarchies from multiple datasets.

Transcriptome profiles of stem-like cells from primary breast cancers allow identification of ITGA7 as a predictive marker of chemotherapy response

BACKGROUND

Breast cancer stem cells (BCSCs) are drivers of therapy-resistance, therefore are responsible for poor survival. Molecular signatures of BCSCs from primary cancers remain undefined. Here, we identify the consistent transcriptome of primary BCSCs shared across breast cancer subtypes, and we examine the clinical relevance of ITGA7, one of the genes differentially expressed in BCSCs.

METHODS

Primary BCSCs were assessed using immunohistochemistry and fluorescently labelled using Aldefluor (n = 17). Transcriptomes of fluorescently sorted BCSCs and matched non-stem cancer cells were determined using RNA-seq (n = 6). ITGA7 expression was examined in breast cancers using immunohistochemistry (n = 305), and its functional role was tested using siRNA in breast cancer cells.

RESULTS

Proportions of BCSCs varied from 0 to 9.4%. 38 genes were significantly differentially expressed in BCSCs; genes were enriched for functions in vessel morphogenesis, motility, and metabolism. ITGA7 was found to be significantly downregulated in BCSCs, and low expression significantly correlated with reduced survival in patients treated with chemotherapy, and with chemoresistance in breast cancer cells in vitro.

CONCLUSIONS

This study is the first to define the molecular profile of BCSCs from a range of primary breast cancers. ITGA7 acts as a predictive marker for chemotherapy response, in accordance with its downregulation in BCSCs.

Single-cell RNA sequencing in cancer: Applications, advances, and emerging challenges

Cancer has become one of the greatest threats to human health, and new technologies are urgently needed to further clarify the mechanisms of cancer so that better detection and treatment strategies can be developed. At present, extensive genomic analysis and testing of clinical specimens shape the insights into carcinoma. Nevertheless, carcinoma of humans is a complex ecosystem of cells, including carcinoma cells and immunity-related and stroma-related subsets, with accurate characteristics obscured by extensive genome-related approaches. A growing body of research shows that sequencing of single-cell RNA (scRNA-seq) is emerging to be an effective way for dissecting human tumor tissue at single-cell resolution, presenting one prominent way for explaining carcinoma biology. This review summarizes the research progress of scRNA-seq in the field of tumors, focusing on the application of scRNA-seq in tumor circulating cells, tumor stem cells, tumor drug resistance, the tumor microenvironment, and so on, which provides a new perspective for tumor research.

The Role of Breast Cancer Stem Cell-Related Biomarkers as Prognostic Factors

Breast cancer is one of the leading causes of cancer-related deaths in women worldwide, and its incidence is on the rise. A small fraction of cancer stem cells was identified within the tumour bulk, which are regarded as cancer-initiating cells, possess self-renewal and propagation potential, and a key driver for tumour heterogeneity and disease progression. Cancer heterogeneity reduces the overall efficacy of chemotherapy and contributes to treatment failure and relapse. The cell-surface and subcellular biomarkers related to breast cancer stem cell (BCSC) phenotypes are increasingly being recognised. These biomarkers are useful for the isolation of BCSCs and can serve as potential therapeutic targets and prognostic tools to monitor treatment responses. Recently, the role of noncoding microRNAs (miRNAs) has extensively been explored as novel biomarker molecules for breast cancer diagnosis and prognosis with high specificity and sensitivity. An in-depth understanding of the biological roles of miRNA in breast carcinogenesis provides insights into the pathways of cancer development and its utility for disease prognostication. This review gives an overview of stem cells, highlights the biomarkers expressed in BCSCs and describes their potential role as prognostic indicators.

Minichromosome maintenance protein 5 is an important pathogenic factor of oral squamous cell carcinoma

Oral squamous cell carcinoma is one of the most common causes of malignancy-associated death. Early diagnosis of oral squamous cell carcinoma (OSCC) is important in patient treatment and prognostic evaluation. Due to the lack of significant therapeutic benefit, the 5-year survival rate has not improved. Therefore, effective novel markers are needed to improve diagnosis. To determine novel promising diagnostic biomarkers for OSCC, 416 upregulated and 416 downregulated differentially expressed genes were screened from OSCC tissues using an RNA microarray. The results suggested that minichromosome maintenance protein (MCM5) mRNA was significantly overexpressed in OSCC tissues compared with that in adjacent normal tissues. Moreover, silencing of MCM5 expression an OSCC cell line (SCC-15) significantly impaired proliferation and colony formation. Furthermore, negative regulation of the mRNA and protein expression of MCM5 and demonstrated that MCM5 served as a cancer-promoting gene modulating OSCC cell proliferation through induced G₂/M phase arrest. In this process, the mRNA expression of cyclin E and cyclin-dependent kinase 2 was downregulated, while p21 expression was upregulated. These results suggested that MCM5 may be an important pathogenic factor of OSCC. High expression levels of MCM5 may serve as a marker for the early diagnosis of OSCC.

The prognostic association of SPAG5 gene expression in breast cancer patients with systematic therapy

Background

Despite much effort on the treatment of breast cancer over the decades, a great uncertainty regarding the appropriate molecular biomarkers and optimal therapeutic strategy still exists. This research was performed to analyze the association of SPAG5 gene expression with clinicopathological factors and survival outcomes.

Methods

We used a breast cancer database including 5667 patients with a mean follow-up of 69 months. Kaplan-Meier survival analyses for relapse free survival (RFS), overall survival (OS), and distant metastasis-free survival (DMFS) were performed. In addition, ROC analysis was performed to validate SPAG5 as a prognostic candidate gene.

Results

Mean SPAG5 expression value was significantly higher with some clinicopathological factors that resulted in tumor promotion and progression, including poor differentiated type, HER2 positive or TP53 mutated breast cancer. Based on ROC-analysis SPAG 5 is a suitable prognostic marker of poor survival. In patients who received chemotherapy alone, SPAG5 had only a moderate and not significant predictive impact on survival outcomes. However, in hormonal therapy, high SPAG5 expression could strongly predict prognosis with detrimental RFS (HR = 1.57, 95% CI 1.2–2.06, p = 0.001), OS (HR = 2, 95% CI 1.05–3.8, p = 0.03) and DMFS (HR = 2.36, 95% CI 1.57–3.54, p <  0.001), respectively. In addition, SPAG5 could only serve as a survival predictor in ER+, but not ER- breast cancer patients. Patients might also be at an increased risk of relapse despite being diagnosed with a lower grade cancer (well differentiated type).

Conclusions

SPAG5 could be used as an independent prognostic and predictive biomarker that might have clinical utility, especially in ER+ breast cancer patients who received hormonal therapy.

Integrative Analysis Defines Distinct Prognostic Subgroups of Intrahepatic Cholangiocarcinoma

Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver cancer. It is defined by cholangiocytic differentiation and has poor prognosis. Recently, epigenetic processes have been shown to play an important role in cholangiocarcinogenesis. We performed an integrative analysis on 52 iCCAs using both genetic and epigenetic data with a specific focus on DNA methylation components. We found recurrent isocitrate dehydrogenase 1 (IDH1) and IDH2 (28%) gene mutations, recurrent arm-length copy number alterations (CNAs), and focal alterations such as deletion of 3p21 or amplification of 12q15, which affect BRCA1 Associated Protein 1, polybromo 1, and mouse double minute 2 homolog. DNA methylome analysis revealed excessive hypermethylation of iCCA, affecting primarily the bivalent genomic regions marked with both active and repressive histone modifications. Integrative clustering of genetic and epigenetic data identified four iCCA subgroups with prognostic relevance further designated as IDH, high (H), medium (M), and low (L) alteration groups. The IDH group consisted of all samples with IDH1 or IDH2 mutations and showed, together with the H group, a highly disrupted genome, characterized by frequent deletions of chromosome arms 3p and 6q. Both groups showed excessive hypermethylation with distinct patterns. The M group showed intermediate characteristics regarding both genetic and epigenetic marks, whereas the L group exhibited few methylation changes and mutations and a lack of CNAs. Methylation-based latent component analysis of cell-type composition identified differences among these four groups. Prognosis of the H and M groups was significantly worse than that of the L group. Conclusion: Using an integrative genomic and epigenomic analysis approach, we identified four major iCCA subgroups with widespread genomic and epigenomic differences and prognostic implications. Furthermore, our data suggest differences in the cell-of-origin of the iCCA subtypes.

Estradiol induces cell proliferation in MCF‑7 mammospheres through HER2/COX‑2

Cluster of differentiation (CD)44+/CD24- breast cancer cells have stem cell‑like characteristics and are potent initiators of tumorigenesis. Mammosphere cells can partially initiate breast tumorigenesis by inducing estradiol (E2)‑dependent breast cancer cells. However, the mechanisms by which E2 mediates cancer formation in MCF‑7 mammosphere (MS) cells have remained elusive. In the present study, MS cells were isolated by sphere culture. It was possible to maintain these MS cells in culture for long periods of time, while retaining the CD44+/CD24- stem cell marker status. The CD44+/CD24- status was confirmed by flow cytometry. Furthermore, the stem‑cell markers Musashi‑1, cytokeratin (CK)7 and CK19 were identified by immunofluorescence microscopy. It was revealed that treatment of MS cells with E2 increased the expression of CD44, whereas decreased the expression of CD24 on MS cells. In addition, treatment with E2 increased colony formation by MS cells. E2 also induced cyclooxygenase‑2 (COX‑2) expression in MS cells, which promoted their proliferation through the estrogen receptor/human epidermal growth factor receptor 2 (HER2)/mitogen‑activated protein kinase/phosphoinositide‑3 kinase signaling pathway. The results suggested a tumorigenic mechanism by which E2 promotes tumor cell proliferation via HER2/COX‑2 signaling. The present study provided evidence for the molecular impact of E2 on breast tumorigenesis, and suggested possible strategies for preventing and treating human breast cancer.

Single-cell analysis of tumors: Creating new value for molecular biomarker discovery of cancer stem cells and tumor-infiltrating immune cells

Biomarker-driven individualized treatment in oncology has made tremendous progress through technological developments, new therapeutic modalities and a deeper understanding of the molecular biology for tumors, cancer stem cells and tumor-infiltrating immune cells. Recent technical developments have led to the establishment of a variety of cancer-related diagnostic, prognostic and predictive biomarkers. In this regard, different modern OMICs approaches were assessed in order to categorize and classify prognostically different forms of neoplasia. Despite those technical advancements, the extent of molecular heterogeneity at the individual cell level in human tumors remains largely uncharacterized. Each tumor consists of a mixture of heterogeneous cell types. Therefore, it is important to quantify the dynamic cellular variations in order to predict clinical parameters, such as a response to treatment and or potential for disease recurrence. Recently, single-cell based methods have been developed to characterize the heterogeneity in seemingly homogenous cancer cell populations prior to and during treatment. In this review, we highlight the recent advances for single-cell analysis and discuss the challenges and prospects for molecular characterization of cancer cells, cancer stem cells and tumor-infiltrating immune cells.

Ensemble outlier detection and gene selection in triple-negative breast cancer data

Background

Learning accurate models from ‘omics data is bringing many challenges due to their inherent high-dimensionality, e.g. the number of gene expression variables, and comparatively lower sample sizes, which leads to ill-posed inverse problems. Furthermore, the presence of outliers, either experimental errors or interesting abnormal clinical cases, may severely hamper a correct classification of patients and the identification of reliable biomarkers for a particular disease. We propose to address this problem through an ensemble classification setting based on distinct feature selection and modeling strategies, including logistic regression with elastic net regularization, Sparse Partial Least Squares - Discriminant Analysis (SPLS-DA) and Sparse Generalized PLS (SGPLS), coupled with an evaluation of the individuals’ outlierness based on the Cook’s distance. The consensus is achieved with the Rank Product statistics corrected for multiple testing, which gives a final list of sorted observations by their outlierness level.

Results

We applied this strategy for the classification of Triple-Negative Breast Cancer (TNBC) RNA-Seq and clinical data from the Cancer Genome Atlas (TCGA). The detected 24 outliers were identified as putative mislabeled samples, corresponding to individuals with discrepant clinical labels for the HER2 receptor, but also individuals with abnormal expression values of ER, PR and HER2, contradictory with the corresponding clinical labels, which may invalidate the initial TNBC label. Moreover, the model consensus approach leads to the selection of a set of genes that may be linked to the disease. These results are robust to a resampling approach, either by selecting a subset of patients or a subset of genes, with a significant overlap of the outlier patients identified.

Conclusions

The proposed ensemble outlier detection approach constitutes a robust procedure to identify abnormal cases and consensus covariates, which may improve biomarker selection for precision medicine applications. The method can also be easily extended to other regression models and datasets.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2149-7) contains supplementary material, which is available to authorized users.

Single-cell sequencing and tumorigenesis: improved understanding of tumor evolution and metastasis

Extensive genomic and transcriptomic heterogeneity in human cancer often negatively impacts treatment efficacy and survival, thus posing a significant ongoing challenge for modern treatment regimens. State-of-the-art DNA- and RNA-sequencing methods now provide high-resolution genomic and gene expression portraits of individual cells, facilitating the study of complex molecular heterogeneity in cancer. Important developments in single-cell sequencing (SCS) technologies over the past 5 years provide numerous advantages over traditional sequencing methods for understanding the complexity of carcinogenesis, but significant hurdles must be overcome before SCS can be clinically useful. In this review, we: (1) highlight current methodologies and recent technological advances for isolating single cells, single-cell whole-genome and whole-transcriptome amplification using minute amounts of nucleic acids, and SCS, (2) summarize research investigating molecular heterogeneity at the genomic and transcriptomic levels and how this heterogeneity affects clonal evolution and metastasis, and (3) discuss the promise for integrating SCS in the clinical care arena for improved patient care.