The molecular portraits of breast tumors are conserved across microarray platforms

Mutational signatures and mutable motifs in cancer genomes

Cancer is a genetic disorder, meaning that a plethora of different mutations, whether somatic or germ line, underlie the etiology of the 'Emperor of Maladies'. Point mutations, chromosomal rearrangements and copy number changes, whether they have occurred spontaneously in predisposed individuals or have been induced by intrinsic or extrinsic (environmental) mutagens, lead to the activation of oncogenes and inactivation of tumor suppressor genes, thereby promoting malignancy. This scenario has now been recognized and experimentally confirmed in a wide range of different contexts. Over the past decade, a surge in available sequencing technologies has allowed the sequencing of whole genomes from liquid malignancies and solid tumors belonging to different types and stages of cancer, giving birth to the new field of cancer genomics. One of the most striking discoveries has been that cancer genomes are highly enriched with mutations of specific kinds. It has been suggested that these mutations can be classified into 'families' based on their mutational signatures. A mutational signature may be regarded as a type of base substitution (e.g. C:G to T:A) within a particular context of neighboring nucleotide sequence (the bases upstream and/or downstream of the mutation). These mutational signatures, supplemented by mutable motifs (a wider mutational context), promise to help us to understand the nature of the mutational processes that operate during tumor evolution because they represent the footprints of interactions between DNA, mutagens and the enzymes of the repair/replication/modification pathways.

LobSig is a multigene predictor of outcome in invasive lobular carcinoma

Invasive lobular carcinoma (ILC) is the most common special type of breast cancer, and is characterized by functional loss of E-cadherin, resulting in cellular adhesion defects. ILC typically present as estrogen receptor positive, grade 2 breast cancers, with a good short-term prognosis. Several large-scale molecular profiling studies have now dissected the unique genomics of ILC. We have undertaken an integrative analysis of gene expression and DNA copy number to identify novel drivers and prognostic biomarkers, using in-house (n = 25), METABRIC (n = 125) and TCGA (n = 146) samples. Using in silico integrative analyses, a 194-gene set was derived that is highly prognostic in ILC (P = 1.20 × 10-5)-we named this metagene 'LobSig'. Assessing a 10-year follow-up period, LobSig outperformed the Nottingham Prognostic Index, PAM50 risk-of-recurrence (Prosigna), OncotypeDx, and Genomic Grade Index (MapQuantDx) in a stepwise, multivariate Cox proportional hazards model, particularly in grade 2 ILC cases (χ 2, P = 9.0 × 10-6), which are difficult to prognosticate clinically. Importantly, LobSig status predicted outcome with 94.6% accuracy amongst cases classified as 'moderate-risk' according to Nottingham Prognostic Index in the METABRIC cohort. Network analysis identified few candidate pathways, though genesets related to proliferation were identified, and a LobSig-high phenotype was associated with the TCGA proliferative subtype (χ 2, P < 8.86 × 10-4). ILC with a poor outcome as predicted by LobSig were enriched with mutations in ERBB2, ERBB3, TP53, AKT1 and ROS1. LobSig has the potential to be a clinically relevant prognostic signature and warrants further development.

Breast cancer PAM50 signature: correlation and concordance between RNA-Seq and digital multiplexed gene expression technologies in a triple negative breast cancer series

BACKGROUND

Full RNA-Seq is a fundamental research tool for whole transcriptome analysis. However, it is too costly and time consuming to be used in routine clinical practice. We evaluated the transcript quantification agreement between RNA-Seq and a digital multiplexed gene expression platform, and the subtype call after running the PAM50 assay in a series of breast cancer patients classified as triple negative by IHC/FISH. The goal of this study is to analyze the concordance between both expression platforms overall, and for calling PAM50 triple negative breast cancer intrinsic subtypes in particular.

RESULTS

The analyses were performed in paraffin-embedded tissues from 96 patients recruited in a multicenter, prospective, non-randomized neoadjuvant triple negative breast cancer trial (NCT01560663). Pre-treatment core biopsies were obtained following clinical practice guidelines and conserved as FFPE for further RNA extraction. PAM50 was performed on both digital multiplexed gene expression and RNA-Seq platforms. Subtype assignment was based on the nearest centroid classification following this procedure for both platforms and it was concordant on 96% of the cases (N = 96). In four cases, digital multiplexed gene expression analysis and RNA-Seq were discordant. The Spearman correlation to each of the centroids and the risk of recurrence were above 0.89 in both platforms while the agreement on Proliferation Score reached up to 0.97. In addition, 82% of the individual PAM50 genes showed a correlation coefficient > 0.80.

CONCLUSIONS

In our analysis, the subtype calling in most of the samples was concordant in both platforms and the potential discordances had reduced clinical implications in terms of prognosis. If speed and cost are the main driving forces then the preferred technique is the digital multiplexed platform, while if whole genome patterns and subtype are the driving forces, then RNA-Seq is the preferred method.

Array comparative genomic hybridization analysis discloses chromosome copy number alterations as indicators of patient outcome in lymph node-negative breast cancer

BACKGROUND

Patients with lymph node metastasis-negative (pN0) invasive breast cancer have favorable outcomes following initial treatment. However, false negatives which occur during routine histologic examination of lymph nodes are reported to underestimate the clinical stage of disease. To identify a high-risk group in pN0 invasive breast cancer, we examined copy number alterations (CNAs) of 800 cancer-related genes.

METHODS

Using array-based comparative genomic hybridization (CGH) in 51 pN0 cases (19 relapsed and 32 non-relapsed cases), the positivities of specific gene CNAs in the relapsed and non-relapsed groups were compared. An unsupervised hierarchical cluster analysis was then performed to identify case groups that were correlated with patient outcomes.

RESULTS

The cluster analysis identified three distinct clusters of cases: groups 1, 2, and 3. The major component was triple-negative cases (69%, 9 of 13) in group 1, luminal B-like (57%, 13 of 23) and HER2-overexpressing (26%, 6 of 23) subtypes in group 2, and luminal A-like subtype (60%, 9 of 15) in group 3. Among all 51 cases, those in group 1 showed significantly worse overall survival (OS) than group 2 (p = 0.014), and 5q15 loss was correlated with worse OS (p = 0.017). Among 19 relapsed cases, both OS and relapse-free survival (RFS) rates were significantly lower in group 1 than in group 2 (p = 0.0083 and 0.0018, respectively), and 5q15 loss, 12p13.31 gain, and absence of 16p13.3 gain were significantly correlated with worse OS and RFS (p = 0.019 and 0.0027, respectively).

CONCLUSIONS

As the target genes in these loci, NR2F1 (5q15), TNFRSF1A (12p13.31), and ABCA3 (16p13.3) were examined. 5q15 loss, 12p13.31 gain, and absence of 16q13.3 gain were potential indicators of high-risk recurrence and aggressive clinical behavior of pN0 invasive breast cancers.

Exploring Collagen Parameters in Pure Special Types of Invasive Breast Cancer

One of the promising tools to evaluate collagen in the extracellular matrix is the second-harmonic generation microscopy (SHG). This approach may shed light on the biological behavior of cancers and their taxonomy, but has not yet been applied to characterize collagen fibers in cases diagnosed as invasive breast carcinoma (BC) of histological special types (IBC-ST). Tissue sections from 99 patients with IBC-ST and 21 of invasive breast carcinoma of no special type (IBC-NST) were submitted to evaluation of collagen parameters by SHG. Tissue microarray was performed to evaluate immunohistochemical-based molecular subtype. In intratumoral areas, fSHG and bSHG (forward-SHG and backward-SHG) collagen parameters achieved their lowest values in mucinous, papillary and medullary carcinomas, whereas the highest values were found in classic invasive lobular and tubular carcinomas. Unsupervised hierarchical cluster analysis and minimal spanning tree using intratumoral collagen parameters allowed the identification of three main groups of breast cancer: group A (classic invasive lobular and tubular carcinomas); group B (IBC-NST, metaplastic, invasive apocrine and micropapillary carcinomas); and group C (medullary, mucinous and papillary carcinomas). Our findings provide further characterization of the tumor microenvironment of IBC-ST. This understanding may add information to build more consistent tumor categorization and to refine prognostication.

Integrated omics-based pathway analyses uncover CYP epoxygenase-associated networks as theranostic targets for metastatic triple negative breast cancer

Background

Current prognostic tools and targeted therapeutic approaches have limited value for metastatic triple negative breast cancer (TNBC). Building upon current knowledge, we hypothesized that epoxyeicosatrienoic acids (EETs) and related CYP450 epoxygenases may have differential roles in breast cancer signaling, and better understanding of which may uncover potential directions for molecular stratification and personalized therapy for TNBC patients.

Methods

We analyzed the oxylipin metabolome of paired tumors and adjacent normal mammary tissues from patients with pathologically confirmed breast cancer (N = 62). We used multivariate statistical analysis to identify important metabolite contributors and to determine the predictive power of tumor tissue metabolite clustering. In vitro functional assays using a panel of breast cancer cell lines were carried out to further confirm the crucial roles of endogenous and exogenous EETs in the metastasis transformation of TNBC cells. Deregulation of associated downstream signaling networks associated with EETs/CYPs was established using transcriptomics datasets from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). Comparative TNBC proteomics using the same tissue specimens subjected to oxylipin metabolomics analysis was used as validation set.

Results

Metabolite-by-metabolite comparison, tumor immunoreactivity, and gene expression analyses showed that CYP epoxygenases and arachidonic acid-epoxygenation products, EET metabolites, are strongly associated with TNBC metastasis. Notably, all the 4 EET isomers (5,6-, 8,9-, 11,12-, and 14,15-EET) was observed to profoundly drive the metastasis transformation of mesenchymal-like TNBC cells among the TNBC (basal- and mesenchymal-like), HER2-overexpressing and luminal breast cancer cell lines examined. Our pathway analysis revealed that, in hormone-positive breast cancer subtype, CYP epoxygenase overexpression is more related to immune cell-associated signaling, while EET-mediated Myc, Ras, MAPK, EGFR, HIF-1α, and NOD1/2 signaling are the molecular vulnerabilities of metastatic CYP epoxygenase-overexpressing TNBC tumors.

Conclusions

This study suggests that categorizing breast tumors according to their EET metabolite ratio classifiers and CYP epoxygenase profiles may be useful for prognostic and therapeutic assessment. Modulation of CYP epoxygenase and EET-mediated signaling networks may offer an effective approach for personalized treatment of breast cancer, and may be an effective intervention option for metastatic TNBC patients.

Electronic supplementary material

The online version of this article (10.1186/s13046-019-1187-y) contains supplementary material, which is available to authorized users.

Overexpression of ASPM, CDC20, and TTK Confer a Poorer Prognosis in Breast Cancer Identified by Gene Co-expression Network Analysis

Breast cancer is one of the most common malignancies among females, and its prognosis is affected by a complex network of gene interactions. In this study, we constructed free-scale gene co-expression networks using weighted gene co-expression network analysis (WGCNA). The gene expression profiles of GSE25055 were downloaded from the Gene Expression Omnibus (GEO) database to identify potential biomarkers associated with breast cancer progression. GSE42568 was downloaded for validation. A total of 9 modules were established via the average linkage hierarchical clustering. We identified 3 hub genes (ASPM, CDC20, and TTK) in the significant module (R 2 = 0.52), which were significantly correlated with poor prognosis both in test and validation datasets. In the datasets GSE25055 and GSE42568, higher expression levels of ASPM, CDC20, and TTK correlated with advanced tumor grades. Immunohistochemistry data from the Human Protein Atlas also demonstrated that their protein levels were higher in tumor samples. According to gene set enrichment analysis, 4 commonly enriched pathways were identified: cell cycle pathway, DNA replication pathway, homologous recombination pathway, and P53 signaling pathway. In addition, strong correlations were found among their expression levels. In conclusion, our WGCNA analysis identified candidate prognostic biomarkers for further basic and clinical researches.

PAM50 Molecular Intrinsic Subtypes in the Nurses' Health Study Cohorts

BACKGROUND

Modified median and subgroup-specific gene centering are two essential preprocessing methods to assign breast cancer molecular subtypes by PAM50. We evaluated the PAM50 subtypes derived from both methods in a subset of Nurses' Health Study (NHS) and NHSII participants; correlated tumor subtypes by PAM50 with IHC surrogates; and characterized the PAM50 subtype distribution, proliferation scores, and risk of relapse with proliferation and tumor size weighted (ROR-PT) scores in the NHS/NHSII.

METHODS

PAM50 subtypes, proliferation scores, and ROR-PT scores were calculated for 882 invasive breast tumors and 695 histologically normal tumor-adjacent tissues. Cox proportional hazards models evaluated the relationship between PAM50 subtypes or ROR-PT scores/groups with recurrence-free survival (RFS) or distant RFS.

RESULTS

PAM50 subtypes were highly comparable between the two methods. The agreement between tumor subtypes by PAM50 and IHC surrogates improved to fair when Luminal subtypes were grouped together. Using the modified median method, our study consisted of 46% Luminal A, 18% Luminal B, 14% HER2-enriched, 15% Basal-like, and 8% Normal-like subtypes; 53% of tumor-adjacent tissues were Normal-like. Women with the Basal-like subtype had a higher rate of relapse within 5 years. HER2-enriched subtypes had poorer outcomes prior to 1999.

CONCLUSIONS

Either preprocessing method may be utilized to derive PAM50 subtypes for future studies. The majority of NHS/NHSII tumor and tumor-adjacent tissues were classified as Luminal A and Normal-like, respectively.

IMPACT

Preprocessing methods are important for the accurate assignment of PAM50 subtypes. These data provide evidence that either preprocessing method can be used in epidemiologic studies.

Elevated signature of a gene module coexpressed with CDC20 marks genomic instability in glioma

Genomic instability (GI) drives tumor heterogeneity and promotes tumor progression and therapy resistance. However, causative factors underlying GI and means for clinical detection of GI in glioma are inadequately identified. We describe here that elevated expression of a gene module coexpressed with CDC20 (CDC20-M), the activator of the anaphase-promoting complex in the cell cycle, marks GI in glioma. The CDC20-M, containing 139 members involved in cell proliferation, DNA damage response, and chromosome segregation, was found to be consistently coexpressed in glioma transcriptomes. The coexpression of these genes was conserved across multiple species and organ systems, particularly in human neural stem and progenitor cells. CDC20-M expression was not correlated with the morphological subtypes, nor with the recently defined molecular subtypes of glioma. CDC20-M signature was an independent and robust predictor for poorer prognosis in over 1,000 patients from four large databases. Elevated CDC20-M signature enabled the identification of individual glioma samples with severe chromosome instability and mutation burden and of primary glioma cell lines with extensive mitotic errors leading to chromosome mis-segregation. AURKA, a core member of CDC20-M, was amplified in one-third of CDC20-M-high gliomas with gene-dosage-dependent expression. MLN8237, a Food and Drug Administration-approved AURKA inhibitor, selectively killed temozolomide-resistant primary glioma cells in vitro and prolonged the survival of a patient-derived xenograft mouse model with a high-CDC20-M signature. Our findings suggest that application of the CDC20-M signature may permit more selective use of adjuvant therapies for glioma patients and that dysregulated CDC20-M members may provide a therapeutic vulnerability in glioma.

circTADA2As suppress breast cancer progression and metastasis via targeting miR-203a-3p/SOCS3 axis

More and more evidence indicates that circular RNAs (circRNAs) have important roles in several diseases, especially in cancers. However, their involvement remains to be investigated in breast cancer. Through screening circRNA profile, we identified 235 differentially expressed circRNAs in breast cancer. Subsequently, we explored the clinical significance of two circTADA2As in a large cohort of triple-negative breast cancer (TNBC), and performed functional analysis of circTADA2A-E6 in vitro and in vivo to support clinical findings. Finally, we evaluated the effect of circTADA2A-E6 on miR-203a-3p and its target gene SOCS3. We detected two circRNAs, circTADA2A-E6 and circTADA2A-E5/E6, which were among the top five differentially expressed circRNAs in breast cancer. They were consistently and significantly decreased in a large cohort of breast cancer patients, and their downregulation was associated with poor patient survival for TNBC. Especially, circTADA2A-E6 suppressed in vitro cell proliferation, migration, invasion, and clonogenicity and possessed tumor-suppressor capability. circTADA2A-E6 preferentially acted as a miR-203a-3p sponge to restore the expression of miRNA target gene SOCS3, resulting in a less aggressive oncogenic phenotype. circTADA2As as promising prognostic biomarkers in TNBC patients, and therapeutic targeting of circTADA2As/miRNA/mRNA network may be a potential strategy for the treatment of breast cancer.

Breast Carcinoma: Updates in Molecular Profiling 2018

The most significant contribution of molecular subtyping of breast carcinomas has been the identification of estrogen-positive and estrogen-negative tumor subtypes. Knowledge of genetic alterations in these tumors will help clinicians identify novel therapeutic targets. Understanding the progression pathways involved in the transition of in situ carcinoma to invasive carcinoma might lead to efficient risk stratification in these patients. The Cancer Genome Analysis Network has collected genomic and epigenomic data to provide comprehensive information regarding carcinogenesis and pathway interactions. Such information improves understanding of the disease process and also provides more accurate information toward identifying targetable mutations for treatment.

Gene co-expression network approach for predicting prognostic microRNA biomarkers in different subtypes of breast cancer

New diagnostic miRNA biomarkers for different types of cancer have been studied extensively, particularly for breast cancer (BC), which is a leading cause of death among women and has many different subtypes. In the present study, a systems biology approach was used to find remarkable and novel miRNA biomarkers for five molecular subtypes of BC: luminal A, luminal B, ERBB2, basal-like and normal-like. The mRNA expression data from the five BC subtypes was used to reconstruct co-expression networks. The important mRNA-miRNA interactions were considered when reconstructing the bipartite networks from which the five bipartite sub-networks were reconstructed for further analysis. The novel biomarkers detected for each subtype are as follows: miRNAs 26b-5p and 124-3p for basal-like, 26b-5p, 124-3p and 5011-5p for ERBB2, 26b-5p and 5011-5p for LumA, 124-3p, 26b-5p and 7-5p for LumB and 26b-5p, 124-3p and 193b-3p for normal-like. The roles of the identified miRNAs in the occurrence or development of each subtype of BC remain unclear and should be investigated in future studies. In addition, the target genes of these miRNAs may be critical to the mechanisms underlying each subtype and should be analyzed as therapeutic targets in future studies.

Re-interpretation of PAM50 gene expression as quantitative tumor dimensions shows utility for clinical trials: application to prognosis and response to paclitaxel in breast cancer

Background

We recently showed PAM50 gene expression data can be represented by five quantitative, orthogonal, multi-gene breast tumor traits. These novel tumor ‘dimensions’ were superior to categorical intrinsic subtypes for clustering in high-risk breast cancer pedigrees, indicating potential to represent underlying genetic susceptibilities and biological pathways. Here we explore the prognostic and predictive utility of these dimensions in a sub-study of GEICAM/9906, a Phase III randomized prospective clinical trial of paclitaxel in breast cancer.

Methods

Tumor dimensions, PC1–PC5, were calculated using pre-defined coefficients. Univariable and multivariable Cox proportional hazards (PH) models for disease-free survival (DFS) were used to identify associations between quantitative dimensions and prognosis or response to the addition of paclitaxel. Results were illustrated using Kaplan–Meier curves.

Results

Dimensions PC1 and PC5 were associated with DFS (Cox PH p = 6.7 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× 10⁻⁷ and p = 0.036), remaining significant after correction for standard clinical–pathological prognostic characteristics. Both dimensions were selected in the optimal multivariable model, together with nodal status and tumor size (Cox PH p = 1.4 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× 10⁻¹²). Interactions with treatment were identified for PC3 and PC4. Response to paclitaxel was restricted to tumors with low PC3 and PC4 (log-rank p = 0.0021). Women with tumors high for PC3 or PC4 showed no survival advantage.

Conclusions

Our proof-of-concept application of quantitative dimensions illustrated novel findings and clinical utility beyond standard clinical–pathological characteristics and categorical intrinsic subtypes for prognosis and predicting chemotherapy response. Consideration of expression data as quantitative tumor dimensions offers new potential to identify clinically important patient subsets in clinical trials and advance precision medicine.

Electronic supplementary material

The online version of this article (10.1007/s10549-018-05097-5) contains supplementary material, which is available to authorized users.

Exploiting single-cell RNA sequencing data to link alternative splicing and cancer heterogeneity: A computational approach

Cell heterogeneity studies using single-cell sequencing are gaining great significance in the era of personalized medicine. In particular, characterization of tumor heterogeneity is an emergent issue to improve clinical oncology, since both inter- and intra-tumor level heterogeneity influence the utility and application of molecular classifications through specific biomarkers. Majority of studies have exploited gene expression to discriminate cell types. However, to provide a more nuanced view of the underlying differences, isoform expression and alternative splicing events have to be analyzed in detail. In this study, we utilize publicly available single cell and bulk RNA sequencing datasets of breast cancer cells from primary tumors and immortalized cell lines. Breast cancer is very heterogeneous with well defined molecular subtypes and was therefore chosen for this study. RNA-seq data were explored in terms of genes, isoforms abundance and splicing events. The study was conducted from an average based approach (gene level expression) to detailed and deeper ones (isoforms abundance/splicing events) to perform a comparative analysis, and, thus, highlight the importance of the splicing machinery in defining the tumor heterogeneity. Moreover, here we demonstrate how the investigation of gene isoforms expression can help to identify the appropriate in vitro models. We furthermore extracted marker isoforms, and alternatively spliced genes between and within the different single cell populations to improve the classification of the breast cancer subtypes.

Single Cell Omics of Breast Cancer: An Update on Characterization and Diagnosis

Breast cancer is recognized for its different clinical behaviors and patient outcomes, regardless of common histopathological features at diagnosis. The heterogeneity and dynamics of breast cancer undergoing clonal evolution produces cells with distinct degrees of drug resistance and metastatic potential. Presently, single cell analysis have made outstanding advancements, overshadowing the hurdles of heterogeneity linked with vast populations. The speedy progression in sequencing analysis now allow unbiased, high-output and high-resolution elucidation of the heterogeneity from individual cell within a population. Classical therapeutics strategies for individual patients are governed by the presence and absence of expression pattern of the estrogen and progesterone receptors and human epidermal growth factor receptor 2. However, such tactics for clinical classification have fruitfulness in selection of targeted therapies, short-term patient responses but unable to predict the long-term survival. In any phenotypic alterations, like breast cancer disease, molecular signature have proven its implication, as we aware that individual cell's state is regulated at diverse levels, such as DNA, RNA and protein, by multifaceted interplay of intrinsic biomolecules pathways existing in the organism and extrinsic stimuli such as ambient environment. Thus for complete understanding, complete profiling of single cell requires a synchronous investigations from different levels (multi-omics) to avoid incomplete information produced from single cell. In this article, initially we briefed on novel updates of various methods available to explore omics and then we finally pinpointed on various omics (i.e. genomics, transcriptomics, epigenomics, proteomics and metabolomics) data and few special aspects of circulating tumor cells, disseminated tumor cells and cancer stem cells, so far available from various studies that can be used for better management of breast cancer patients.

Long-Term Outcomes of Immunohistochemically Defined Subtypes of Breast Cancer Less Than or Equal to 2 cm After Breast-Conserving Surgery

BACKGROUND

Molecular subtype predicts the prognosis of early-stage breast cancer patients. We assessed the long-term outcomes of breast cancer ≤2 cm treated with breast-conserving surgery (BCS) and stratified according to an immunohistochemically (IHC)-based subtype definition.

METHODS

This retrospective study was conducted from a prospectively collected database. Included patients had pT1, any N, M0 breast cancer after BCS (without anti-HER2 therapy) and available information on estrogen receptor (ER), progesterone receptor (PR), HER2 status, Ki-67 index. Five IHC-defined subtypes were identified: luminal A-like (ER and/or PR-positive/HER2-negative/Ki-67 < 20%), luminal B-like/HER2-negative (ER and/or PR-positive/HER2-negative/Ki-67 ≥ 20%), luminal B-like/HER2-positive (ER and/or PR-positive/HER2-positive/any Ki-67 value), HER2-positive/nonluminal (ER and PR-negative/HER2-positive), and triple-negative (ER and PR-negative/HER2-negative).

RESULTS

We analyzed 184 (65%) luminal A-like, 57 (20%) luminal B-like/HER2-negative, 17 (6%) luminal B-like/HER2-positive, 6 (2%) HER2-positive/nonluminal, and 18 (7%) triple-negative patients. Median follow-up was 112 (interquartile range 94-125) mo. The cumulative 5- and 10-y local recurrence (LR) rates were 1.5% and 4%, respectively. The cumulative 5- and 10-y distant recurrence (DR) rates were 3% and 8%, respectively. The Cox regression revealed that HER2-positive/nonluminal subtypes had the highest risk of LR (P = 0.0025). The luminal B-like/HER2-positive subtypes had the highest risk of DR (P = 0.0019). HER2 positivity carried a higher risk of DR in women with luminal breast cancer who completed 5 y of adjuvant hormonal therapy (P = 0.02).

CONCLUSIONS

The IHC-defined subtype impacts on the prognosis of breast cancer ≤2 cm after BCS, determining significant differences in LR and DR rates. In the pre-"anti-HER2 therapy" era, patients with HER2-positive/nonluminal or luminal B-like/HER2-positive subtype had worse long-term outcomes than those with luminal A-like subtype.

Integrated analysis of dysregulated long non-coding RNAs/microRNAs/mRNAs in metastasis of lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD), largely remains a primary cause of cancer-related death worldwide. The molecular mechanisms in LUAD metastasis have not been completely uncovered.

METHODS

In this study, we identified differentially expressed genes (DEGs), miRNAs (DEMs) and lncRNAs (DELs) underlying metastasis of LUAD from The Cancer Genome Atlas database. Intersection mRNAs were used to perform gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and co-expression network analysis. In addition, survival analyses of intersection mRNAs were conducted. Finally, intersection mRNAs, miRNAs and lncRNAs were subjected to construct miRNA-mRNA-lncRNA network.

RESULTS

A total of 1015 DEGs, 54 DEMs and 22 DELs were identified in LUAD metastasis and non-metastasis samples. GO and KEGG pathway analysis had proven that the functions of intersection mRNAs were closely related with many important processes in cancer pathogenesis. Among the co-expression interactions network, 22 genes in the co-expression network were over the degree 20. These genes imply that they have connections with many other gene nodes. In addition, 14 target genes (ARHGAP11A, ASPM, HELLS, PRC1, TMPO, ARHGAP30, CD52, IL16, IRF8, P2RY13, PRKCB, PTPRC, SASH3 and TRAF3IP3) were found to be associated with survival in patients with LUAD significantly (log-rank P < 0.05). Two lncRNAs (LOC96610 and ADAM6) acting as ceRNAs were identified based on the miRNA-mRNA-lncRNA network.

CONCLUSIONS

Taken together, the results may provide a novel perspective to develop a multiple gene diagnostic tool for LUAD prognosis, which might also provide potential biomarkers or therapeutic targets for LUAD.

MicroRNA dysregulational synergistic network: discovering microRNA dysregulatory modules across subtypes in non-small cell lung cancers

Background

The majority of cancer-related deaths are due to lung cancer, and there is a need for reliable diagnostic biomarkers to predict stages in non-small cell lung cancer cases. Recently, microRNAs were found to have potential as both biomarkers and therapeutic targets for lung cancer. However, some of the microRNA’s functions are unknown, and their roles in cancer stage progression have been mostly undiscovered in this clinically and genetically heterogeneous disease. As evidence suggests that microRNA dysregulations are implicated in many diseases, it is essential to consider the changes in microRNA-target regulation across different lung cancer subtypes.

Results

We proposed a pipeline to identify microRNA synergistic modules with similar dysregulation patterns across multiple subtypes by constructing the MicroRNA Dysregulational Synergistic Network. From the network, we extracted microRNA modules and incorporated them as prior knowledge to the Sparse Group Lasso classifier. This leads to a more relevant selection of microRNA biomarkers, thereby improving the cancer stage classification accuracy. We applied our method to the TCGA Lung Adenocarcinoma and the Lung Squamous Cell Carcinoma datasets. In cross-validation tests, the area under ROC curve rate for the cancer stages prediction has increased considerably when incorporating the learned microRNA dysregulation modules. The extracted modules from multiple independent subtypes differential analyses were found to have high agreement with microRNA family annotations, and they can also be used to identify mutual biomarkers between different subtypes. Among the top-ranked candidate microRNAs selected by the model, 87% were reported to be related to Lung Adenocarcinoma. The overall result demonstrates that clustering microRNAs from the dysregulation pattern between microRNAs and their targets leads to biomarkers with high precision and recall rate to known differentially expressed disease-associated microRNAs.

Conclusions

The results indicated that our method improves microRNA biomarker selection by detecting similar microRNA dysregulational synergistic patterns across the multiple subtypes. Since microRNA-target dysregulations are implicated in many cancers, we believe this tool can have broad applications for discovery of novel microRNA biomarkers in heterogeneous cancer diseases.

Tamoxifen therapy benefit predictive signature combining with prognostic signature in surgical-only ER-positive breast cancer

Tamoxifen treatment is important assistant for estrogen-receptor-positive breast cancer (BRCA) after resection. This study aimed to identify signatures for predicting the prognosis of patients with BRCA after tamoxifen treatment. Data of gene-specific DNA methylation (DM), as well as the corresponding clinical data for the patients with BRCA, were obtained from The Cancer Genome Atlas and followed by systematic bioinformatics analyses. After mapping these DM CPG sites onto genes, we finally obtained 352 relapse-free survival (RFS) associated DM genes, with which 61,776 gene pairs were combined, including 1,614 gene pairs related to RFS. An 11 gene-pair signature was identified to cluster the 189 patients with BRCA into the surgical low-risk group (136 patients) and high-risk group (53 patients). Then, we further identified a tamoxifen-predictive signature that could classify surgical high-risk patients with significant differences on RFS. Combining surgical-only prognostic signature and tamoxifen-predictive signature, patients were clustered into surgical-only low-risk group, tamoxifen nonbenefit group, and tamoxifen benefit group. In conclusion, we identified that the gene pair PDHA2-APRT could serve as a potential prognostic biomarker for patients with BRCA after tamoxifen treatment.

Identification of Jun loss promotes resistance to histone deacetylase inhibitor entinostat through Myc signaling in luminal breast cancer

BACKGROUND

Based on promising phase II data, the histone deacetylase inhibitor entinostat is in phase III trials for patients with metastatic estrogen receptor-positive breast cancer. Predictors of sensitivity and resistance, however, remain unknown.

METHODS

A total of eight cell lines and nine mouse models of breast cancer were treated with entinostat. Luminal cell lines were treated with or without entinostat at their IC₅₀ doses, and MMTV/Neu luminal mouse tumors were untreated or treated with entinostat until progression. We investigated these models using their gene expression profiling by microarray and copy number by arrayCGH. We also utilized the network-based DawnRank algorithm that integrates DNA and RNA data to identify driver genes of resistance. The impact of candidate drivers was investigated in The Cancer Genome Atlas and METABRIC breast cancer datasets.

RESULTS

Luminal models displayed enhanced sensitivity to entinostat as compared to basal-like or claudin-low models. Both in vitro and in vivo luminal models showed significant downregulation of Myc gene signatures following entinostat treatment. Myc gene signatures became upregulated on tumor progression in vivo and overexpression of Myc conferred resistance to entinostat in vitro. Further examination of resistance mechanisms in MMTV/Neu tumors identified a portion of mouse chromosome 4 that had DNA copy number loss and low gene expression. Within this region, Jun was computationally identified to be a driver gene of resistance. Jun knockdown in cell lines resulted in upregulation of Myc signatures and made these lines more resistant to entinostat. Jun-deleted samples, found in 17-23% of luminal patients, had significantly higher Myc signature scores that predicted worse survival.

CONCLUSIONS

Entinostat inhibited luminal breast cancer through Myc signaling, which was upregulated by Jun DNA loss to promote resistance to entinostat in our models. Jun DNA copy number loss, and/or high MYC signatures, might represent biomarkers for entinostat responsiveness in luminal breast cancer.

An assessment of prognostic immunity markers in breast cancer

Tumor-infiltrating lymphocytes (TIL) and immunity gene signatures have been reported to be significantly prognostic in breast cancer but have not yet been applied for calculation of risk of recurrence in clinical assays. A compact set of 17 immunity genes was derived herein from an Affymetrix-derived gene expression dataset including 1951 patients (AFFY1951). The 17 immunity genes demonstrated significant prognostic stratification of estrogen receptor (ER)-negative breast cancer patients with high proliferation gene expression. Further analysis of blood and breast cancer single-cell RNA-seq datasets revealed that the 17 immunity genes were derived from TIL that were inactive in the blood and became active in tumor tissue. Expression of the 17 immunity genes was significantly (p < 2.2E-16, n = 91) correlated with TILs percentage on H&E in triple negative breast cancer. To demonstrate the impact of tumor immunity genes on prognosis, we built a Cox model to incorporate breast cancer subtypes, proliferation score and immunity score (72 gene panel) with significant prediction of outcomes (p < 0.0001, n = 1951). The 72 gene panel and its risk evaluation model were validated in two other published gene expression datasets including Illumina beads array data METABRIC (p < 0.0001, n = 1997) and whole transcriptomic mRNA-seq data TCGA (p = 0.00019, n = 996) and in our own targeted RNA-seq data TARGETSEQ (p < 0.0001, n = 303). Further examination of the 72 gene panel in single cell RNA-seq of tumors demonstrated tumor heterogeneity with more than two subtypes observed in each tumor. In conclusion, immunity gene expression was an important parameter for prognosis and should be incorporated into current multi-gene assays to improve assessment of risk of distant metastasis in breast cancer.

The elevated expression of 17 immunity-related genes is associated with better outcomes among women with aggressive forms of estrogen receptor–negative breast cancer. Zhiyuan Hu from the University of North Carolina at Chapel Hill, USA, and colleagues identified the 17-gene set by analyzing a larger expression dataset from close to 2,000 patients. Single-cell sequencing revealed that the genes were turned on in a group of cancer-fighting immune cells known as tumor-infiltrating lymphocytes, but were inactive in circulating blood cells. The researchers incorporated the immunity-related genes into a larger panel of genes involved in proliferation, invasion and other relevant biological processes. The resulting 72-gene test was an accurate predictor of the risk for developing distant metastases. The findings suggest that immunity-related genes should be incorporated into current multi-gene prognostic assays for women with breast cancer.

Ancillary Prognostic and Predictive Testing in Breast Cancer: Focus on Discordant, Unusual, and Borderline Results

Ancillary testing in breast cancer has become standard of care to determine what therapies may be most effective for individual patients with breast cancer. Single-marker tests are required on all newly diagnosed and newly metastatic breast cancers. Markers of proliferation are also used, and include both single-marker tests like Ki67 as well as panel-based gene expression tests, which have made more recent contributions to prognostic and predictive testing in breast cancers. This review focuses on pathologist interpretation of these ancillary test results, with a focus on expected versus unexpected results and troubleshooting borderline, unusual, or discordant results.

Development of parallel reaction monitoring (PRM)-based quantitative proteomics applied to HER2-Positive breast cancer

Introduction

treatments targeting the Human Epidermal Growth Factor Receptor 2 (HER2/ERBB2) have improved the natural history of HER2-positive breast cancer. However, except HER2 protein expression and gene amplification, there is no predictive biomarker to guide the HER2-targeted therapies. We developed Parallel reaction monitoring (PRM) a powerful approach, to quantify and evaluate key proteins involved in the HER2 pathway and/or anti-HER2 treatment sensitivity.

Results

in BCLs, PRM measurements correlated with western blot immunocytochemistry and transcriptomic data. At baseline, higher expression of HER2, EGFR, PTEN and HER3 but lower expression of phospho-HER2 correlated with trastuzumab sensitivity. Under trastuzumab, PRM demonstrated a decrease in HER2 and an increase in phospho-HER2, which correlated with drug sensitivity. The opposite was observed under lapatinib. HER2 quantification was also correlated with immunohistochemistry in PDXs and clinical breast cancer samples.

Discussion

in conclusion, PRM-based assay, developed to quantify proteins of the HER2 pathway in breast cancer samples revealed a large magnitude of expression, which may have relevance in terms of treatment sensitivity.

Materials and Methods

we first evaluated PRM in term of sensitivity, linearity and reproducibility. PRM was then applied to breast cancer cell lines (BCLs) including BCLs exposed to anti-HER2 agents, patient-derived xenografts (PDXs) and frozen breast cancer samples.

Predictive markers of endocrine response in breast cancer

Ongoing clinical and research efforts seek to optimise the use of endocrine therapy in the treatment of breast cancer. Accurate biomarkers are needed that predict response for individual patients. The presence of the estrogen receptor (ER) as the direct (for tamoxifen and fulvestrant) or indirect (for aromatase inhibitors) target molecule for endocrine therapy remains the foremost biomarker and determinant of response. However, ER expression only poorly predicts outcome and further indicators of response or resistance are required. The development and application of molecular signature assays such as Oncotype Dx, Prosigna, Mammaprint and Endopredict have provided valuable information on prognosis and these are being used to support clinical decision making on whether endocrine therapy alone alongside surgery is sufficient for ER-positive early stage breast cancers or whether combination of endocrine with chemotherapy are also warranted. Ki67, the proliferation marker, has been widely used in the neo-adjuvant (pre-operative) setting to help predict response and long term outcome. Gene expression studies within the same setting have allowed monitoring of changes of potential predictive markers. These have identified frequent changes in estrogen-regulated and proliferation genes. Specific molecules such as mutant ER may also prove helpful biomarkers in predicting outcome and monitoring response to treatment.

Role of activating transcription factor 3 and its interacting proteins under physiological and pathological conditions

Activating transcription factor 3 (ATF3) is a stress-responsive factor that belongs to the activator protein 1 (AP-1) family of transcription factors. ATF3 expression is stimulated by various factors such as hypoxia, cytokines, and chemotherapeutic and DNA damaging agents. Upon stimulation, ATF3 can form homodimers or heterodimers with other members of the AP-1 family to repress or activate transcription. Under physiological conditions, ATF3 expression is transient and plays a pivotal role in controlling the expression of cell-cycle regulators and tumor suppressor, DNA repair, and apoptosis genes. However, under pathological conditions such as those during breast cancer, a sustained and prolonged expression of ATF3 has been observed. In this review, the structure and function of ATF3, its posttranslational modifications (PTM), and its interacting proteins are discussed with a special emphasis on breast cancer metastasis.

A combined approach with gene-wise normalization improves the analysis of RNA-seq data in human breast cancer subtypes

Breast cancer (BC) is increasing in incidence and resistance to treatment worldwide. The challenges in limited therapeutic options and poor survival outcomes in BC subtypes persist because of its molecular heterogeneity and resistance to standard endocrine therapy. Recently, high throughput RNA sequencing (RNA-seq) has been used to identify biomarkers of disease progression and signaling pathways that could be amenable to specific therapies according to the BC subtype. However, there is no single generally accepted pipeline for the analysis of RNA-seq data in biomarker discovery due, in part, to the needs of simultaneously satisfying constraints of sensitivity and specificity. We proposed a combined approach using gene-wise normalization, UQ-pgQ2, followed by a Wald test from DESeq2. Our approach improved the analysis based on within-group comparisons in terms of the specificity when applied to publicly available RNA-seq BC datasets. In terms of identifying differentially expressed genes (DEGs), we combined an optimized log2 fold change cutoff with a nominal false discovery rate of 0.05 to further minimize false positives. Using this method in the analysis of two GEO BC datasets, we identified 797 DEGs uniquely expressed in triple negative BC (TNBC) and significantly associated with T cell and immune-related signaling, contributing to the immunotherapeutic efficacy in TNBC patients. In contrast, we identified 1403 DEGs uniquely expressed in estrogen positive and HER2 negative BC (ER+HER2-BC) and significantly associated with eicosanoid, notching and FAK signaling while a common set of genes was associated with cellular growth and proliferation. Thus, our approach to control for false positives identified two distinct gene expression profiles associated with these two subtypes of BC which are distinguishable by their molecular and functional attributes.

Centrosome aberrations and chromosome instability contribute to tumorigenesis and intra-tumor heterogeneity

Centrosomes serve as the major microtubule organizing centers in cells and thereby contribute to cell shape, polarity, and motility. Also, centrosomes ensure equal chromosome segregation during mitosis. Centrosome aberrations arise when the centrosome cycle is deregulated, or as a result of cytokinesis failure. A long-standing postulate is that centrosome aberrations are involved in the initiation and progression of cancer. However, this notion has been a subject of controversy because until recently the relationship has been correlative. Recently, it was shown that numerical or structural centrosome aberrations can initiate tumors in certain tissues in mice, as well as invasion. Particularly, we will focus on centrosome amplification and chromosome instability as drivers of intra-tumor heterogeneity and their consequences in cancer. We will also discuss briefly the controversies surrounding this theory to highlight the fact that the role of both centrosome amplification and chromosome instability in cancer is highly context-dependent. Further, we will discuss single-cell sequencing as a novel technique to understand intra-tumor heterogeneity and some therapeutic approaches to target chromosome instability.

Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment

Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. ^© RSNA, 2018.

Molecular subtypes of screen-detected breast cancer

BACKGROUND

Detection of breast cancers by mammographic screening confers a survival advantage of 20-50% compared to symptomatic presentations. The improved prognosis is only partly explained by stage migration. The distribution of the molecular subtypes of screen-detected breast cancer (SDBC) or their HER2 status has not been studied extensively. We wished to address these issues through the study of a large series of SDBC, with other presentations serving as controls.

DESIGN

Deidentified cases of female invasive cancer, diagnosed in Australia and New Zealand during 2005-2015, were retrieved from the BreastSurgANZ Quality Audit (BQA). Method of detection and selected patient, tumour and treatment data were assessed. Immunohistochemical surrogates for molecular subtypes were defined as Luminal A (ER+ and/or PR+, HER2-), Luminal B (ER+ and/or PR+, HER2+), HER2-enriched (ER-, PR- and HER2+) and basal-like (triple negative). Results were compared with the findings of controls and previous studies.

RESULT

100983 invasive cancers were diagnosed, including 32493 (32.7%) SDBC and 66907 (67.3%) with other presentations. The biomarker profile for SDBC versus other presentations in the same population was ER 89.3 versus 80.3%, PR 78.8 versus 69.8% and for HER2 11 versus 15.6%. The distribution of molecular subtypes was Luminal A 81.9 versus 70.74%, Luminal B 7.39 versus 9.52%, HER2-enriched 3.63 versus 6.06% and Basal-like 7.08 versus 13.68%. These differences were significant (p < 0.0001).

CONCLUSION

Molecular profiles of SDBC are significantly different from those of symptomatic cancers, with over-representation of the Luminal A and proportionately lower rates of all other subtypes. We have shown, for the first time, significantly lower rates of HER2 positivity in SDBC. These differences may contribute to the better survival of SDBC and have implications for prognostication, targeted therapy decisions and for laboratory quality assurance programs in setting target ranges for proportions of ER-positive and HER2 results in heavily screened populations.

Hypoxia promotes breast cancer cell invasion through HIF-1α-mediated up-regulation of the invadopodial actin bundling protein CSRP2

Hypoxia is a common feature of solid tumours that promotes invasion and metastatic dissemination. Invadopodia are actin-rich membrane protrusions that direct extracellular matrix proteolysis and facilitate tumour cell invasion. Here, we show that CSRP2, an invadopodial actin bundling protein, is upregulated by hypoxia in various breast cancer cell lines, as well as in pre-clinical and clinical breast tumour specimens. We functionally characterized two hypoxia responsive elements within the proximal promoter of CSRP2 gene which are targeted by hypoxia-inducible factor-1 (HIF-1) and required for promoter transactivation in response to hypoxia. Remarkably, CSRP2 knockdown significantly inhibits hypoxia-stimulated invadopodium formation, ECM degradation and invasion in MDA-MB-231 cells, while CSRP2 forced expression was sufficient to enhance the invasive capacity of HIF-1α-depleted cells under hypoxia. In MCF-7 cells, CSRP2 upregulation was required for hypoxia-induced formation of invadopodium precursors that were unable to promote ECM degradation. Collectively, our data support that CSRP2 is a novel and direct cytoskeletal target of HIF-1 which facilitates hypoxia-induced breast cancer cell invasion by promoting invadopodia formation.

Inference of patient-specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer

Breast cancer is one of the most deadly forms of cancer in women worldwide. Better prediction of breast cancer prognosis is essential for more personalized treatment. In this study, we aimed to infer patient-specific subpathway activities to reveal a functional signature associated with the prognosis of patients with breast cancer. We integrated pathway structure with gene expression data to construct patient-specific subpathway activity profiles using a greedy search algorithm. A four-subpathway prognostic signature was developed in the training set using a random forest supervised classification algorithm and a prognostic score model with the activity profiles. According to the signature, patients were classified into high-risk and low-risk groups with significantly different overall survival in the training set (median survival of 65 vs 106 months, P = 1.82e-13) and test set (median survival of 75 vs 101 months, P = 4.17e-5). Our signature was then applied to five independent breast cancer data sets and showed similar prognostic values, confirming the accuracy and robustness of the subpathway signature. Stratified analysis suggested that the four-subpathway signature had prognostic value within subtypes of breast cancer. Our results suggest that the four-subpathway signature may be a useful biomarker for breast cancer prognosis.

Expression of ID4 protein in breast cancer cells induces reprogramming of tumour-associated macrophages

Background

As crucial regulators of the immune response against pathogens, macrophages have been extensively shown also to be important players in several diseases, including cancer. Specifically, breast cancer macrophages tightly control the angiogenic switch and progression to malignancy. ID4, a member of the ID (inhibitors of differentiation) family of proteins, is associated with a stem-like phenotype and poor prognosis in basal-like breast cancer. Moreover, ID4 favours angiogenesis by enhancing the expression of pro-angiogenic cytokines interleukin-8, CXCL1 and vascular endothelial growth factor. In the present study, we investigated whether ID4 protein exerts its pro-angiogenic function while also modulating the activity of tumour-associated macrophages in breast cancer.

Methods

We performed IHC analysis of ID4 protein and macrophage marker CD68 in a triple-negative breast cancer series. Next, we used cell migration assays to evaluate the effect of ID4 expression modulation in breast cancer cells on the motility of co-cultured macrophages. The analysis of breast cancer gene expression data repositories allowed us to evaluate the ability of ID4 to predict survival in subsets of tumours showing high or low macrophage infiltration. By culturing macrophages in conditioned media obtained from breast cancer cells in which ID4 expression was modulated by overexpression or depletion, we identified changes in the expression of ID4-dependent angiogenesis-related transcripts and microRNAs (miRNAs, miRs) in macrophages by RT-qPCR.

Results

We determined that ID4 and macrophage marker CD68 protein expression were significantly associated in a series of triple-negative breast tumours. Interestingly, ID4 messenger RNA (mRNA) levels robustly predicted survival, specifically in the subset of tumours showing high macrophage infiltration. In vitro and in vivo migration assays demonstrated that expression of ID4 in breast cancer cells stimulates macrophage motility. At the molecular level, ID4 protein expression in breast cancer cells controls, through paracrine signalling, the activation of an angiogenic programme in macrophages. This programme includes both the increase of angiogenesis-related mRNAs and the decrease of members of the anti-angiogenic miR-15b/107 group. Intriguingly, these miRNAs control the expression of the cytokine granulin, whose enhanced expression in macrophages confers increased angiogenic potential.

Conclusions

These results uncover a key role for ID4 in dictating the behaviour of tumour-associated macrophages in breast cancer.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-0990-2) contains supplementary material, which is available to authorized users.

Genome-wide multi-omics profiling of the 8p11-p12 amplicon in breast carcinoma

Genomic instability contributes to the neoplastic phenotype by deregulating key cancer-related genes, which in turn can have a detrimental effect on patient outcome. DNA amplification of the 8p11-p12 genomic region has clinical and biological implications in multiple malignancies, including breast carcinoma where the amplicon has been associated with tumor progression and poor prognosis. However, oncogenes driving increased cancer-related death and recurrent genetic features associated with the 8p11-p12 amplicon remain to be identified. In this study, DNA copy number and transcriptome profiling data for 229 primary invasive breast carcinomas (corresponding to 185 patients) were evaluated in conjunction with clinicopathological features to identify putative oncogenes in 8p11-p12 amplified samples. Illumina paired-end whole transcriptome sequencing and whole-genome SNP genotyping were subsequently performed on 23 samples showing high-level regional 8p11-p12 amplification to characterize recurrent genetic variants (SNPs and indels), expressed gene fusions, gene expression profiles and allelic imbalances. We now show previously undescribed chromothripsis-like patterns spanning the 8p11-p12 genomic region and allele-specific DNA amplification events. In addition, recurrent amplification-specific genetic features were identified, including genetic variants in the HIST1H1E and UQCRHL genes and fusion transcripts containing MALAT1 non-coding RNA, which is known to be a prognostic indicator for breast cancer and stimulated by estrogen. In summary, these findings highlight novel candidate targets for improved treatment of 8p11-p12 amplified breast carcinomas.

Genes and functions from breast cancer signatures

BACKGROUND

Breast cancer is a heterogeneous disease and personalized medicine is the hope for the improvement of the clinical outcome. Multi-gene signatures for breast cancer stratification have been extensively studied in the past decades and more than 30 different signatures have been reported. A major concern is the minimal overlap of genes among the reported signatures. We investigated the breast cancer signature genes to address our hypothesis that the genes of different signature may share common functions, as well as to use these previously reported signature genes to build better prognostic models.

METHODS

A total of 33 signatures and the corresponding gene lists were investigated. We first examined the gene frequency and the gene overlap in these signatures. Then the gene functions of each signature gene list were analysed and compared by the KEGG pathways and gene ontology (GO) terms. A classifier built using the common genes was tested using the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) data. The common genes were also tested for building the Yin Yang gene mean expression ratio (YMR) signature using public datasets (GSE1456 and GSE2034).

RESULTS

Among a total of 2239 genes collected from the 33 breast cancer signatures, only 238 genes overlapped in at least two signatures; while from a total of 1979 function terms enriched in the 33 signature gene lists, 429 terms were common in at least two signatures. Most of the common function terms were involved in cell cycle processes. While there is almost no common overlapping genes between signatures developed for ER-positive (e.g. 21-gene signature) and those developed for ER-negative (e.g. basal signatures) tumours, they have common function terms such as cell death, regulation of cell proliferation. We used the 62 genes that were common in at least three signatures as a classifier and subtyped 1141 METABRIC cases including 144 normal samples into nine subgroups. These subgroups showed different clinical outcome. Among the 238 common genes, we selected those genes that are more highly expressed in normal breast tissue than in tumours as Yang genes and those more highly expressed in tumours than in normal as Yin genes and built a YMR model signature. This YMR showed significance in risk stratification in two datasets (GSE1456 and GSE2034).

CONCLUSIONS

The lack of significant numbers of overlapping genes among most breast cancer signatures can be partially explained by our discovery that these signature genes represent groups with similar functions. The genes collected from these previously reported signatures are valuable resources for new model development. The subtype classifier and YMR signature built from the common genes showed promising results.

Fatty acid metabolism in breast cancer subtypes

Dysregulation of fatty acid metabolism is recognized as a component of malignant transformation in many different cancers, including breast; yet the potential for targeting this pathway for prevention and/or treatment of cancer remains unrealized. Evidence indicates that proteins involved in both synthesis and oxidation of fatty acids play a pivotal role in the proliferation, migration and invasion of breast cancer cells. The following essay summarizes data implicating specific fatty acid metabolic enzymes in the genesis and progression of breast cancer, and further categorizes the relevance of specific metabolic pathways to individual intrinsic molecular subtypes of breast cancer. Based on mRNA expression data, the less aggressive luminal subtypes appear to rely on a balance between de novo fatty acid synthesis and oxidation as sources for both biomass and energy requirements, while basal-like, receptor negative subtypes overexpress genes involved in the utilization of exogenous fatty acids. With these differences in mind, treatments may need to be tailored to individual subtypes.

Digital image analysis in breast pathology-from image processing techniques to artificial intelligence

Breast cancer is the most common malignant disease in women worldwide. In recent decades, earlier diagnosis and better adjuvant therapy have substantially improved patient outcome. Diagnosis by histopathology has proven to be instrumental to guide breast cancer treatment, but new challenges have emerged as our increasing understanding of cancer over the years has revealed its complex nature. As patient demand for personalized breast cancer therapy grows, we face an urgent need for more precise biomarker assessment and more accurate histopathologic breast cancer diagnosis to make better therapy decisions. The digitization of pathology data has opened the door to faster, more reproducible, and more precise diagnoses through computerized image analysis. Software to assist diagnostic breast pathology through image processing techniques have been around for years. But recent breakthroughs in artificial intelligence (AI) promise to fundamentally change the way we detect and treat breast cancer in the near future. Machine learning, a subfield of AI that applies statistical methods to learn from data, has seen an explosion of interest in recent years because of its ability to recognize patterns in data with less need for human instruction. One technique in particular, known as deep learning, has produced groundbreaking results in many important problems including image classification and speech recognition. In this review, we will cover the use of AI and deep learning in diagnostic breast pathology, and other recent developments in digital image analysis.

Applications of RNA Indexes for Precision Oncology in Breast Cancer

Precision oncology aims to offer the most appropriate treatments to cancer patients mainly based on their individual genetic information. Genomics has provided numerous valuable data on driver mutations and risk loci; however, it remains a formidable challenge to transform these data into therapeutic agents. Transcriptomics describes the multifarious expression patterns of both mRNAs and non-coding RNAs (ncRNAs), which facilitates the deciphering of genomic codes. In this review, we take breast cancer as an example to demonstrate the applications of these rich RNA resources in precision medicine exploration. These include the use of mRNA profiles in triple-negative breast cancer (TNBC) subtyping to inform corresponding candidate targeted therapies; current advancements and achievements of high-throughput RNA interference (RNAi) screening technologies in breast cancer; and microRNAs as functional signatures for defining cell identities and regulating the biological activities of breast cancer cells. We summarize the benefits of transcriptomic analyses in breast cancer management and propose that unscrambling the core signaling networks of cancer may be an important task of multiple-omic data integration for precision oncology.

An Effect of Culture Media on Epithelial Differentiation Markers in Breast Cancer Cell Lines MCF7, MDA-MB-436 and SkBr3

Background and objectives: Cell culture is one of the mainstays in the research of breast cancer biology, although the extent to which this approach allows to preserve the original characteristics of originating tumor and implications of cell culture findings to real life situations have been widely debated in the literature. The aim of this study was to determine the role of three cell culture media on transcriptional expression of breast cancer markers in three breast cancer reference cell lines (MCF7, SkBr3 and MDA-MB-436). Materials and methods: Cell lines were conditioned in three studied media (all containing 5% fetal bovine serum (FBS) + hormones/growth factors; different composition of basal media) for four passages. Population growth was characterized by cumulative population doubling levels, average generation time, cell yield and viability at the fourth passage. Transcriptional expression of breast cancer differentiation markers and regulatory transcriptional programs was measured by qPCR. Results: Differences in the composition of growth media significantly influenced the growth of studied cell lines and the expression of mammary lineage governing transcriptional programs and luminal/basal markers. Effects of media on transcriptional expression were more pronounced in luminal cell lines (MCF7, SkBr3), than in the basal cell line (MDA-MB-436). Changes in growth media in terms of supplementation and basal medium delayed growth of cells, but improved cell yields. Conclusions: The expression of breast cancer cell differentiation phenotypic markers depends on the composition of cell growth medium, therefore cell culture as a tool in phenotypic studies should be used considering this effect. The findings of such studies should always be interpreted with caution. The formulation of cell growth media has greater effect on the expression of phenotypic markers in luminal, rather than basal cell lines. Media containing mitogens and higher vitamin content improved efficacy of cell culture in terms of cell yields, although greatly increased growth times.

Cysteine and glycine-rich protein 2 (CSRP2) transcript levels correlate with leukemia relapse and leukemia-free survival in adults with B-cell acute lymphoblastic leukemia and normal cytogenetics

Relapse is the major cause of treatment-failure in adults with B-cell acute lymphoblastic leukemia (ALL) achieving complete remission after induction chemotherapy. Greater precision identifying persons likely to relapse is important. We did bio-informatics analyses of transcriptomic data to identify mRNA transcripts aberrantly-expressed in B-cell ALL. We selected 9 candidate genes for validation 7 of which proved significantly-associated with B-cell ALL. We next focused on function and clinical correlations of the cysteine and glycine-rich protein 2 (CSRP2). Quantitative real-time polymerase chain reaction (RT-qPCR) was used to examine gene transcript levels in bone marrow samples from 236 adults with B-cell ALL compared with samples from normals. CSRP2 was over-expressed in 228 out of 236 adults (97%) with newly-diagnosed B-cell ALL. A prognostic value was assessed in 168 subjects. In subjects with normal cytogenetics those with high CSRP2 transcript levels had a higher 5-year cumulative incidence of relapse (CIR) and worse relapse-free survival (RFS) compared with subjects with low transcript levels (56% [95% confidence interval, 53, 59%] vs. 19% [18, 20%]; P = 0.011 and 41% [17, 65%] vs. 80% [66–95%]; P = 0.007). In multivariate analyses a high CSRP2 transcript level was independently-associated with CIR (HR = 5.32 [1.64–17.28]; P = 0.005) and RFS (HR = 5.56 [1.87, 16.53]; P = 0.002). Functional analyses indicated CSRP2 promoted cell proliferation, cell-cycle progression, in vitro colony formation and cell migration ability. Abnormal CSRP2 expression was associated with resistance to chemotherapy; sensitivity was restored by down-regulating CSRP2 expression.

Comprehensive landscape of subtype-specific coding and non-coding RNA transcripts in breast cancer

Molecular classification of breast cancer into clinically relevant subtypes helps improve prognosis and adjuvant-treatment decisions. The aim of this study is to provide a better characterization of the molecular subtypes by providing a comprehensive landscape of subtype-specific isoforms including coding, long non-coding RNA and microRNA transcripts. Isoform-level expression of all coding and non-coding RNAs is estimated from RNA-sequence data of 1168 breast samples obtained from The Cancer Genome Atlas (TCGA) project. We then search the whole transcriptome systematically for subtype-specific isoforms using a novel algorithm based on a robust quasi-Poisson model. We discover 5451 isoforms specific to single subtypes. A total of 27% of the subtype-specific isoforms have better accuracy in classifying the intrinsic subtypes than that of their corresponding genes. We find three subtype-specific miRNA and 707 subtype-specific long non-coding RNAs. The isoforms from long non-coding RNAs also show high performance for separation between Luminal A and Luminal B subtypes with an AUC of 0.97 in the discovery set and 0.90 in the validation set. In addition, we discover 1500 isoforms preferentially co-expressed in two subtypes, including 369 isoforms co-expressed in both Normal-like and Basal subtypes, which are commonly considered to have distinct ER-receptor status. Finally, analyses at protein level reveal four subtype-specific proteins and two subtype co-expression proteins that successfully validate results from the isoform level.

Associations between genomic stratification of breast cancer and centrally reviewed tumour pathology in the METABRIC cohort

The integration of genomic and transcriptomic profiles of 2000 breast tumours from the METABRIC [Molecular Taxonomy of Breast Cancer International Consortium] cohort revealed ten subtypes, termed integrative clusters (IntClust/s), characterised by distinct genomic drivers. Central histopathology (N = 1643) review was undertaken to explore the relationship between these ten molecular subtypes and traditional clinicopathological features. IntClust subtypes were significantly associated with histological type, tumour grade, receptor status, and lymphocytic infiltration (p < 0.0001). Lymph node status and Nottingham Prognostic Index [NPI] categories were also significantly associated with IntClust subtype. IntClust 3 was enriched for tubular and lobular carcinomas, the latter largely accounting for the association with CDH1 mutations in this cluster. Mucinous carcinomas were not present in IntClusts 5 or 10, but did not show an association with any of the remaining IntClusts. In contrast, medullary-like cancers were associated with IntClust 10 (15/26). Hormone receptor-positive tumours were scattered across all IntClusts. IntClust 5 was dominated by HER2 positivity (127/151), including both hormone receptor-positive (60/72) and hormone receptor-negative tumours (67/77). Triple-negative tumours comprised the majority of IntClust 10 (132/159) and around a quarter of IntClust 4 (52/217). Whilst the ten IntClust subtypes of breast cancer show characteristic patterns of association with traditional clinicopathological variables, no IntClust can be adequately identified by these variables alone. Hence, the addition of genomic stratification has the potential to enhance the biological relevance of the current clinical evaluation and facilitate genome-guided therapeutic strategies.

Common profiles of Notch signaling differentiate disease-free survival in luminal type A and triple negative breast cancer

Breast cancer (BC) is characterized by high heterogeneity regarding its biology and clinical characteristics. The Notch pathway regulates such processes as organ modeling and epithelial-to-mesenchymal transition (EMT).

The aim of the study was to determine the effect of differential expression of Notch members on disease-free survival (DFS) in luminal type A (lumA) and triple negative (TN) BC.

The differential expression of 19 Notch members was examined in a TCGA BC cohort. DFS analysis was performed using the log-rank test (p<0.05). Biological differences between DFS groups were determined with Gene Set Enrichment Analysis (GSEA) (tTest, FDR<0.25). Common expression profiles according to Notch signaling were examined using ExpressCluster (K-means, mean centered, Euclidean distance metric).

The overexpression of HES1, LFNG and PSEN1 was found to be favorable for DFS in lumA, and lowered expression favorable for DFS in TN.

GSEA analysis showed that differential Notch signaling is associated with cell cycle, tissue architecture and remodeling. Particularly, targets of E2F, early stage S phase transcription factor, were upregulated in the lumA unfavorable group and the TN favorable group differentiated on a basis of HES1 and PSEN1 expression.

Summarizing, our analysis show significance of Notch signaling in BRCA progression through triggering EMT. Moreover, identification of numerous genes which overexpression is associated with disease recurrence may serve as a source of potential targets for a new anticancer therapy.

Molecular Mechanisms and Emerging Therapeutic Targets of Triple-Negative Breast Cancer Metastasis

Breast cancer represents a highly heterogeneous disease comprised by several subtypes with distinct histological features, underlying molecular etiology and clinical behaviors. It is widely accepted that triple-negative breast cancer (TNBC) is one of the most aggressive subtypes, often associated with poor patient outcome due to the development of metastases in secondary organs, such as the lungs, brain, and bone. The molecular complexity of the metastatic process in combination with the lack of effective targeted therapies for TNBC metastasis have fostered significant research efforts during the past few years to identify molecular “drivers” of this lethal cascade. In this review, the most current and important findings on TNBC metastasis, as well as its closely associated basal-like subtype, including metastasis-promoting or suppressor genes and aberrantly regulated signaling pathways at specific stages of the metastatic cascade are being discussed. Finally, the most promising therapeutic approaches and novel strategies emerging from these molecular targets that could potentially be clinically applied in the near future are being highlighted.

Polo-like kinase 4 inhibition produces polyploidy and apoptotic death of lung cancers

Polo-like kinase 4 (PLK4) is a serine/threonine kinase regulating centriole duplication. CFI-400945 is a highly selective PLK4 inhibitor that deregulates centriole duplication, causing mitotic defects and death of aneuploid cancers. Prior work was substantially extended by showing CFI-400945 causes polyploidy, growth inhibition, and apoptotic death of murine and human lung cancer cells, despite expression of mutated KRAS or p53. Analysis of DNA content by propidium iodide (PI) staining revealed cells with >4N DNA content (polyploidy) markedly increased after CFI-400945 treatment. Centrosome numbers and mitotic spindles were scored. CFI-400945 treatment produced supernumerary centrosomes and mitotic defects in lung cancer cells. In vivo antineoplastic activity of CFI-400945 was established in mice with syngeneic lung cancer xenografts. Lung tumor growth was significantly inhibited at well-tolerated dosages. Phosphohistone H3 staining of resected lung cancers following CFI-400945 treatment confirmed the presence of aberrant mitosis. PLK4 expression profiles in human lung cancers were explored using The Cancer Genome Atlas (TCGA) and RNA in situ hybridization (RNA ISH) of microarrays containing normal and malignant lung tissues. PLK4 expression was significantly higher in the malignant versus normal lung and conferred an unfavorable survival (P < 0.05). Intriguingly, cyclin dependent kinase 2 (CDK2) antagonism cooperated with PLK4 inhibition. Taken together, PLK4 inhibition alone or as part of a combination regimen is a promising way to combat lung cancer.