PCA-PAM50 improves consistency between breast cancer intrinsic and clinical subtyping reclassifying a subset of luminal A tumors as luminal B

Proteogenomic characterization of difficult-to-treat breast cancer with tumor cells enriched through laser microdissection

BACKGROUND

Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer death among women globally. Despite advances, there is considerable variation in clinical outcomes for patients with non-luminal A tumors, classified as difficult-to-treat breast cancers (DTBC). This study aims to delineate the proteogenomic landscape of DTBC tumors compared to luminal A (LumA) tumors.

METHODS

We retrospectively collected a total of 117 untreated primary breast tumor specimens, focusing on DTBC subtypes. Breast tumors were processed by laser microdissection (LMD) to enrich tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor preparation, followed by whole genome sequencing, paired-end RNA sequencing, global proteomics and phosphoproteomics. Differential feature analysis, pathway analysis and survival analysis were performed to better understand DTBC and investigate biomarkers.

RESULTS

We observed distinct variations in gene mutations, structural variations, and chromosomal alterations between DTBC and LumA breast tumors. DTBC tumors predominantly had more mutations in TP53, PLXNB3, Zinc finger genes, and fewer mutations in SDC2, CDH1, PIK3CA, SVIL, and PTEN. Notably, Cytoband 1q21, which contains numerous cell proliferation-related genes, was significantly amplified in the DTBC tumors. LMD successfully minimized stromal components and increased RNA-protein concordance, as evidenced by stromal score comparisons and proteomic analysis. Distinct DTBC and LumA-enriched clusters were observed by proteomic and phosphoproteomic clustering analysis, some with survival differences. Phosphoproteomics identified two distinct phosphoproteomic profiles for high relapse-risk and low relapse-risk basal-like tumors, involving several genes known to be associated with breast cancer oncogenesis and progression, including KIAA1522, DCK, FOXO3, MYO9B, ARID1A, EPRS, ZC3HAV1, and RBM14. Lastly, an integrated pathway analysis of multi-omics data highlighted a robust enrichment of proliferation pathways in DTBC tumors.

CONCLUSIONS

This study provides an integrated proteogenomic characterization of DTBC vs LumA with tumor cells enriched through laser microdissection. We identified many common features of DTBC tumors and the phosphopeptides that could serve as potential biomarkers for high/low relapse-risk basal-like BC and possibly guide treatment selections.

Clinically relevant gene signatures provide independent prognostic information in older breast cancer patients

BACKGROUND

The clinical utility of gene signatures in older breast cancer patients remains unclear. We aimed to determine signature prognostic capacity in this patient subgroup.

METHODS

Research versions of the genomic grade index (GGI), 70-gene, recurrence score (RS), cell cycle score (CCS), PAM50 risk-of-recurrence proliferation (ROR-P), and PAM50 signatures were applied to 39 breast cancer datasets (N = 9583). After filtering on age ≥ 70 years, and the presence of estrogen receptor (ER) and survival data, 871 patients remained. Signature prognostic capacity was tested in all (n = 871), ER-positive/lymph node-positive (ER + /LN + , n = 335) and ER-positive/lymph node-negative (ER + /LN-, n = 374) patients using Kaplan-Meier and multivariable Cox-proportional hazard (PH) modelling.

RESULTS

All signatures were statistically significant in Kaplan-Meier analysis of all patients (Log-rank P < 0.001). This significance remained in multivariable analysis (Cox-PH, P ≤ 0.05). In ER + /LN + patients all signatures except PAM50 were significant in Kaplan-Meier analysis (Log-rank P ≤ 0.05) and remained so in multivariable analysis (Cox-PH, P ≤ 0.05). In ER + /LN- patients all except RS were significant in Kaplan-Meier analysis (Log-rank P ≤ 0.05) but only the 70-gene, CCS, ROR-P, and PAM50 signatures remained so in multivariable analysis (Cox-PH, P ≤ 0.05).

CONCLUSIONS

We found that gene signatures provide prognostic information in survival analyses of all, ER + /LN + and ER + /LN- older (≥ 70 years) breast cancer patients, suggesting a potential role in aiding treatment decisions in older patients.

The Role of Transcription Factors in the Loss of Inter-Chromosomal Co-Expression for Breast Cancer Subtypes

Breast cancer encompasses a diverse array of subtypes, each exhibiting distinct clinical characteristics and treatment responses. Unraveling the underlying regulatory mechanisms that govern gene expression patterns in these subtypes is essential for advancing our understanding of breast cancer biology. Gene co-expression networks (GCNs) help us identify groups of genes that work in coordination. Previous research has revealed a marked reduction in the interaction of genes located on different chromosomes within GCNs for breast cancer, as well as for lung, kidney, and hematopoietic cancers. However, the reasons behind why genes on the same chromosome often co-express remain unclear. In this study, we investigate the role of transcription factors in shaping gene co-expression networks within the four main breast cancer subtypes: Luminal A, Luminal B, HER2+, and Basal, along with normal breast tissue. We identify communities within each GCN and calculate the transcription factors that may regulate these communities, comparing the results across different phenotypes. Our findings indicate that, in general, regulatory behavior is to a large extent similar among breast cancer molecular subtypes and even in healthy networks. This suggests that transcription factor motif usage does not fully determine long-range co-expression patterns. Specific transcription factor motifs, such as CCGGAAG, appear frequently across all phenotypes, even involving multiple highly connected transcription factors. Additionally, certain transcription factors exhibit unique actions in specific subtypes but with limited influence. Our research demonstrates that the loss of inter-chromosomal co-expression is not solely attributable to transcription factor regulation. Although the exact mechanism responsible for this phenomenon remains elusive, this work contributes to a better understanding of gene expression regulatory programs in breast cancer.

Multiplexed RNA-FISH-guided Laser Capture Microdissection RNA Sequencing Improves Breast Cancer Molecular Subtyping, Prognostic Classification, and Predicts Response to Antibody Drug Conjugates

On a retrospective cohort of 1,082 FFPE breast tumors, we demonstrated the analytical validity of a test using multiplexed RNA-FISH-guided laser capture microdissection (LCM) coupled with RNA-sequencing (mFISHseq), which showed 93% accuracy compared to immunohistochemistry. The combination of these technologies makes strides in i) precisely assessing tumor heterogeneity, ii) obtaining pure tumor samples using LCM to ensure accurate biomarker expression and multigene testing, and iii) providing thorough and granular data from whole transcriptome profiling. We also constructed a 293-gene intrinsic subtype classifier that performed equivalent to the research based PAM50 and AIMS classifiers. By combining three molecular classifiers for consensus subtyping, mFISHseq alleviated single sample discordance, provided near perfect concordance with other classifiers (κ > 0.85), and reclassified 30% of samples into different subtypes with prognostic implications. We also use a consensus approach to combine information from 4 multigene prognostic classifiers and clinical risk to characterize high, low, and ultra-low risk patients that relapse early (< 5 years), late (> 10 years), and rarely, respectively. Lastly, to identify potential patient subpopulations that may be responsive to treatments like antibody drug-conjugates (ADC), we curated a list of 92 genes and 110 gene signatures to interrogate their association with molecular subtype and overall survival. Many genes and gene signatures related to ADC processing (e.g., antigen/payload targets, endocytosis, and lysosome activity) were independent predictors of overall survival in multivariate Cox regression models, thus highlighting potential ADC treatment-responsive subgroups. To test this hypothesis, we constructed a unique 19-feature classifier using multivariate logistic regression with elastic net that predicted response to trastuzumab emtansine (T-DM1; AUC = 0.96) better than either ERBB2 mRNA or Her2 IHC alone in the T-DM1 arm of the I-SPY2 trial. This test was deployed in a research-use only format on 26 patients and revealed clinical insights into patient selection for novel therapies like ADCs and immunotherapies and de-escalation of adjuvant chemotherapy.

Deconvolution of cancer cell states by the XDec-SM method

Proper characterization of cancer cell states within the tumor microenvironment is a key to accurately identifying matching experimental models and the development of precision therapies. To reconstruct this information from bulk RNA-seq profiles, we developed the XDec Simplex Mapping (XDec-SM) reference-optional deconvolution method that maps tumors and the states of constituent cells onto a biologically interpretable low-dimensional space. The method identifies gene sets informative for deconvolution from relevant single-cell profiling data when such profiles are available. When applied to breast tumors in The Cancer Genome Atlas (TCGA), XDec-SM infers the identity of constituent cell types and their proportions. XDec-SM also infers cancer cells states within individual tumors that associate with DNA methylation patterns, driver somatic mutations, pathway activation and metabolic coupling between stromal and breast cancer cells. By projecting tumors, cancer cell lines, and PDX models onto the same map, we identify in vitro and in vivo models with matching cancer cell states. Map position is also predictive of therapy response, thus opening the prospects for precision therapy informed by experiments in model systems matched to tumors in vivo by cancer cell state.

Advances in understanding the role and mechanisms of tumor stem cells in HER2-positive breast cancer treatment resistance (Review)

Approximately 15-20% of breast carcinomas exhibit human epidermal growth factor receptor (HER2) protein overexpression. HER2-positive breast cancer (BC) is a heterogeneous and aggressive subtype with poor prognosis and high relapse risk. Although several anti-HER2 drugs have achieved substantial efficacy, certain patients with HER2-positive BC relapse due to drug resistance after a treatment period. There is increasing evidence that BC stem cells (BCSCs) drive therapeutic resistance and a high rate of BC recurrence. BCSCs may regulate cellular self-renewal and differentiation, as well as invasive metastasis and treatment resistance. Efforts to target BCSCs may yield new methods to improve patient outcomes. In the present review, the roles of BCSCs in the occurrence, development and management of BC treatment resistance were summarized; BCSC-targeted strategies for the treatment of HER2-positive BC were also discussed.

Immature natural killer cells promote progression of triple-negative breast cancer

Natural killer (NK) cells are cytotoxic lymphocytes that accumulate within the tumor microenvironment and are generally considered to be antitumorigenic. Using single-cell RNA sequencing and functional analysis of multiple triple-negative breast cancer (TNBC) and basal tumor samples, we observed a unique subcluster of Socs3highCD11b-CD27- immature NK cells that were present only in TNBC samples. These tumor-infiltrating NK cells expressed a reduced cytotoxic granzyme signature and, in mice, were responsible for activating cancer stem cells through Wnt signaling. NK cell-mediated activation of these cancer stem cells subsequently enhanced tumor progression in mice, whereas depletion of NK cells or Wnt ligand secretion from NK cells by LGK-974 decreased tumor progression. In addition, NK cell depletion or inhibition of their function improved anti-programmed cell death ligand 1 (PD-L1) antibody or chemotherapy response in mice with TNBC. Furthermore, tumor samples from patients with TNBC and non-TNBC revealed that increased numbers of CD56bright NK cells were present in TNBC tumors and were correlated to poor overall survival in patients with TNBC. Together, our findings identify a population of protumorigenic NK cells that may be exploited for both diagnostic and therapeutic strategies to improve outcomes for patients with TNBC.

Glycosylated proteins with abnormal glycosylation changes are potential biomarkers for early diagnosis of breast cancer

Conventional cancer management relies on tumor type and stage for diagnosis and treatment, which leads to recurrence and metastasis and death in young women. Early detection of proteins in the serum aids diagnosis, progression, and clinical outcomes, possibly improving survival rate of breast cancer patients. In this review, we provided an insight into the influence of aberrant glycosylation on breast cancer development and progression. Examined literatures revealed that mechanisms underlying glycosylation moieties alteration could enhance early detection, monitoring, and therapeutic efficacy in breast cancer patients. This would serve as a guide for the development of new serum biomarkers with higher sensitivity and specificity, providing possible serological biomarkers for breast cancer diagnosis, progression, and treatment.

A Simple Method for Robust and Accurate Intrinsic Subtyping of Breast Cancer

Motivation

The PAM50 signature/method is widely used for intrinsic subtyping of breast cancer samples. However, depending on the number and composition of the samples included in a cohort, the method may assign different subtypes to the same sample. This lack of robustness is mainly due to the fact that PAM50 subtracts a reference profile, which is computed using all samples in the cohort, from each sample before classification. In this paper we propose modifications to PAM50 to develop a simple and robust single-sample classifier, called MPAM50, for intrinsic subtyping of breast cancer. Like PAM50, the modified method uses a nearest centroid approach for classification, but the centroids are computed differently, and the distances to the centroids are determined using an alternative method. Additionally, MPAM50 uses unnormalized expression values for classification and does not subtract a reference profile from the samples. In other words, MPAM50 classifies each sample independently, and so avoids the previously mentioned robustness issue.

Results

A training set was employed to find the new MPAM50 centroids. MPAM50 was then tested on 19 independent datasets (obtained using various expression profiling technologies) containing 9637 samples. Overall good agreement was observed between the PAM50- and MPAM50-assigned subtypes with a median accuracy of 0.792, which (we show) is comparable with the median concordance between various implementations of PAM50. Additionally, MPAM50- and PAM50-assigned intrinsic subtypes were found to agree comparably with the reported clinical subtypes. Also, survival analyses indicated that MPAM50 preserves the prognostic value of the intrinsic subtypes. These observations demonstrate that MPAM50 can replace PAM50 without loss of performance. On the other hand, MPAM50 was compared with 2 previously published single-sample classifiers, and with 3 alternative modified PAM50 approaches. The results indicated a superior performance by MPAM50.

Conclusions

MPAM50 is a robust, simple, and accurate single-sample classifier of intrinsic subtypes of breast cancer.

The Transcriptomic Landscape of Pediatric Astrocytoma

Central nervous system tumors are the most common solid neoplasia during childhood and represent one of the leading causes of cancer-related mortality. Tumors arising from astrocytic cells (astrocytomas) are the most frequently diagnosed, and according to their histological and pathological characteristics, they are classified into four categories. However, an additional layer of molecular classification considering the DNA sequence of the tumorigenesis-associated genes IDH1/2 and H3F3A has recently been incorporated into the classification guidelines. Although mutations in H3F3A are found exclusively in a subtype of grade IV pediatric astrocytoma, mutations in IDH1/2 genes are very rare in children under 14 years of age. The transcriptomic profiles of astrocytoma in adults and children have been extensively studied. However, there is scarce information on these profiles in pediatric populations considering the status of tumorigenesis-associated genes. Therefore, here we report the transcriptomic landscape of the four grades of pediatric astrocytoma by RNA sequencing. We found several well-documented biological functions associated with the misregulated genes in the four grades of astrocytoma, as well as additional biological pathways. Among the four grades of astrocytoma, we found shared misregulated genes that could have implications in tumorigenesis. Finally, we identified a transcriptional signature for almost all grades of astrocytoma that could be used as a transcription-based identification method.

ROLE OF ANDROGEN RECEPTOR-TARGETED AGENTS IN LOCALIZED PROSTATE CANCER

Anti-androgen therapy continues to be a basic pilar of treatment for both localized and metastatic prostate cancer. The advent of new generation of androgen receptor targeted agents (ARTA) transformed the care of patients with advanced disease. After such a success, the steps were taken to incorporate a new generation of ARTAs into the treatment landscape of localized prostate cancer. High-risk prostate cancer represents the most aggressive form of localized disease with significant metastatic potential and poor outcome. Here, the impact of novel therapies will likely be profound and transforming. This clinical space has already been a showcase for multidisciplinary treatment where the combination of local therapies with systemic treatment gradually improved patient outcomes and the chances of cure. The most recent step in redefining the treatment of localized disease is the adoption of novel ARTAs moving forward the multidisciplinary platform. In this narrative review, we discuss current clinical evidence supporting the use of novel ARTAs in patients with localized high-risk prostate cancer and cover recent developments in biomarker-driven strategies for treatment individualization in this clinical context.

What is beyond LncRNAs in breast cancer: A special focus on colon cancer-associated Transcript-1 (CCAT-1)

Long non-coding RNAs (LncRNAs) play a vital role in the process of malignant transformation. In breast cancer (BC), lncRNAs field is currently under intensive investigations. Yet, the role of lncRNAs as promising diagnostic and/or prognostic biomarkers and as therapeutic target/tool among BC patients still needs a special focus from the biomedical scientists. In BC, triple negative breast cancer patients (TNBC) are the unlucky group as they are always represented with the worst prognosis and the highest mortality rates. For that reason, a special focus on TNBC and associated lncRNAs was addressed in this review. Colon cancer-associated transcript 1 (CCAT-1) is a newly discovered oncogenic lncRNA that has been emerged as a vital biomarker for diagnosis, prognosis and therapeutic interventions in multiple malignancies and showed differential expression among TNBC patients. In this review, the authors shed the light onto the general role of lncRNAs in BC and the specific functional activities, molecular mechanisms, competing endogenous ncRNA role of CCAT-1 in TNBC.

Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data

As high-throughput genomics assays become more efficient and cost effective, their utilization has become standard in large-scale biomedical projects. These studies are often explorative, in that relationships between samples are not explicitly defined a priori, but rather emerge from data-driven discovery and annotation of molecular subtypes, thereby informing hypotheses and independent evaluation. Here, we present K2Taxonomer, a novel unsupervised recursive partitioning algorithm and associated R package that utilize ensemble learning to identify robust subgroups in a 'taxonomy-like' structure. K2Taxonomer was devised to accommodate different data paradigms, and is suitable for the analysis of both bulk and single-cell transcriptomics, and other '-omics', data. For each of these data types, we demonstrate the power of K2Taxonomer to discover known relationships in both simulated and human tissue data. We conclude with a practical application on breast cancer tumor infiltrating lymphocyte (TIL) single-cell profiles, in which we identified co-expression of translational machinery genes as a dominant transcriptional program shared by T cells subtypes, associated with better prognosis in breast cancer tissue bulk expression data.

Common tumor-suppressive signaling of thyroid hormone receptor beta in breast and thyroid cancer cells

The thyroid hormone receptor beta (TRβ) is a tumor suppressor in multiple types of solid tumors, most prominently in breast and thyroid cancer. An increased understanding of the molecular mechanisms by which TRβ abrogates tumorigenesis will aid in understanding the core tumor-suppressive functions of TRβ. Here, we restored TRβ expression in the MDA-MB-468 basal-like breast cancer cell line and perform RNA-sequencing to determine the TRβ-mediated changes in gene expression and associated signaling pathways. The TRβ expressing MDA-MB-468 cells exhibit a more epithelial character as determined by principle component analysis-based iterative PAM50 subtyping score and through reduced expression of mesenchymal cytokeratins. The epithelial to mesenchymal transition pathway is also significantly reduced. The MDA-MB-468 data set was further compared with RNA sequencing results from TRβ expressing thyroid cancer cell line SW1736 to determine which genes are TRβ correspondingly regulated across both cell types. Several pathways including lipid metabolism and chromatin remodeling processes were observed to be altered in the shared gene set. These data provide novel insights into the molecular mechanisms by which TRβ suppresses breast tumorigenesis.

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

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

Breast Cancer-Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies-An Updated Review

Simple Summary

Breast cancer is the most common cancer among women. It is estimated that 2.3 million new cases of BC are diagnosed globally each year. Based on mRNA gene expression levels, BC can be divided into molecular subtypes that provide insights into new treatment strategies and patient stratifications that impact the management of BC patients. This review addresses the overview on the BC epidemiology, risk factors, classification with an emphasis on molecular types, prognostic biomarkers, as well as possible treatment modalities.

Abstract

Breast cancer (BC) is the most frequently diagnosed cancer in women worldwide with more than 2 million new cases in 2020. Its incidence and death rates have increased over the last three decades due to the change in risk factor profiles, better cancer registration, and cancer detection. The number of risk factors of BC is significant and includes both the modifiable factors and non-modifiable factors. Currently, about 80% of patients with BC are individuals aged >50. Survival depends on both stage and molecular subtype. Invasive BCs comprise wide spectrum tumors that show a variation concerning their clinical presentation, behavior, and morphology. Based on mRNA gene expression levels, BC can be divided into molecular subtypes (Luminal A, Luminal B, HER2-enriched, and basal-like). The molecular subtypes provide insights into new treatment strategies and patient stratifications that impact the management of BC patients. The eighth edition of TNM classification outlines a new staging system for BC that, in addition to anatomical features, acknowledges biological factors. Treatment of breast cancer is complex and involves a combination of different modalities including surgery, radiotherapy, chemotherapy, hormonal therapy, or biological therapies delivered in diverse sequences.

Visualization of Protein-Drug Interactions for the Analysis of Drug Resistance in Lung Cancer

Non-small cell lung cancer (NSCLC) caused by mutation of the epidermal growth factor receptor (EGFR) is a major cause of death worldwide. Tyrosine kinase inhibitors (TKIs) of EGFR have been developed and show promising results at the initial stage of therapy. However, in most cases, their efficacy becomes limited due to the emergence of secondary mutations causing drug resistance after about a year. In this work, we investigated the mechanism of drug resistance due to these mutations. We performed molecular dynamics (MD) simulations of EGFR-drug interactions to obtain Euclidean distance and binding free energy values to analyse drug resistance and visualize drug-protein interactions. A PCA-based method is proposed to find normal, rigid, flexible, and critical residues. We have established a systematic method for the visualization of protein-drug interactions, which provides an effective framework for the analysis of drug resistance in lung cancer at the atomic level.

Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments.

MODEL-BASED FEATURE SELECTION AND CLUSTERING OF RNA-SEQ DATA FOR UNSUPERVISED SUBTYPE DISCOVERY

Clustering is a form of unsupervised learning that aims to uncover latent groups within data based on similarity across a set of features. A common application of this in biomedical research is in delineating novel cancer subtypes from patient gene expression data, given a set of informative genes. However, it is typically unknown a priori what genes may be informative in discriminating between clusters, and what the optimal number of clusters are. Few methods exist for performing unsupervised clustering of RNA-seq samples, and none currently adjust for between-sample global normalization factors, select cluster-discriminatory genes, or account for potential confounding variables during clustering. To address these issues, we propose the Feature Selection and Clustering of RNA-seq (FSCseq): a model-based clustering algorithm that utilizes a finite mixture of regression (FMR) model and the quadratic penalty method with a Smoothly-Clipped Absolute Deviation (SCAD) penalty. The maximization is done by a penalized Classification EM algorithm, allowing us to include normalization factors and confounders in our modeling framework. Given the fitted model, our framework allows for subtype prediction in new patients via posterior probabilities of cluster membership, even in the presence of batch effects. Based on simulations and real data analysis, we show the advantages of our method relative to competing approaches.

In silico transcriptomic mapping of integrins and immune activation in Basal-like and HER2+ breast cancer

PURPOSE

Integrins, transmembrane receptors that mediate cell-extracellular matrix and cell-cell interactions, have been linked to several cancer-associated features. A less explored function of integrins in cancer is their role in leukocyte homing and activation. Understanding their relationship with immune cell infiltrates and immune checkpoints is an area of interest in cancer research.

METHODS

The expression of 33 different integrins was evaluated in relation with breast cancer patient outcome using transcriptomic data (Affymetrix dataset, exploratory cohort) and the METABRIC study (validation cohort). The TIMER online tool was used to assess the association of the identified integrin genes with immune cell infiltration, and the TCGA and METABRIC studies to assess correlations between integrin gene expression and genomic signatures of immune activation.

RESULTS

We identified 7 genes coding for integrin α and β subunits, i.e., ITGA4, ITGB2, ITGAX, ITGB7, ITGAM, ITGAL and ITGA8, which predict a favorable prognosis in Basal-like and HER2+ breast cancers. Their expression positively correlated with the presence of immune cell infiltrates within the tumor (dendritic cells, CD4+ T-cells, neutrophils, CD8+ T-cells and B-cells), with markers of T-cell activation and antigen presentation, and with gene signatures of immune surveillance (cytotoxic T lymphocyte activation and IFN gamma signature). By contrast, we found that genes coding for integrins that predicted a detrimental outcome (IBSP, ITGB3BP, ITGB6, ITGB1 and ITGAV) were not associated with any of these parameters.

CONCLUSIONS

We identified an integrin signature composed of 7 genes with potential to recognize immune infiltrated and activated Basal-like and HER2+ breast cancers with a favorable prognosis.

Molecular intrinsic versus clinical subtyping in breast cancer: A comprehensive review

Breast cancer is a heterogeneous disease manifesting diversity at the molecular, histological and clinical level. The development of breast cancer classification was centered on informing clinical decisions. The current approach to the classification of breast cancer, which categorizes this disease into clinical subtypes based on the detection of estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and proliferation marker Ki67, is not ideal. This is manifested as a heterogeneity of therapeutic responses and outcomes within the clinical subtypes. The newer classification model, based on gene expression profiling (intrinsic subtyping) informs about transcriptional responses downstream from IHC single markers, revealing deeper appreciation for the disease heterogeneity and capturing tumor biology in a more comprehensive way than an expression of a single protein or gene alone. While accumulating evidences suggest that intrinsic subtypes provide clinically relevant information beyond clinical surrogates, it is imperative to establish whether the current conventional immunohistochemistry-based clinical subtyping approach could be improved by gene expression profiling and if this approach has a potential to translate into clinical practice.

Triple-Negative Breast Cancer Comparison With Canine Mammary Tumors From Light Microscopy to Molecular Pathology

Many similar characteristics in human and dog cancers including, spontaneous development, clinical presentation, tumor heterogeneity, disease progression, and response to standard therapies have promoted the approval of this comparative model as an alternative to mice. Breast cancer represents the second most frequent neoplasm in humans after lung cancer. Triple-negative breast cancers (TNBC) constitute around 15% of all cases of breast cancer and do not express estrogen receptor (ER), progesterone receptor (PR), and do not overexpress human epidermal growth factor receptor 2 (HER2). As a result, they do not benefit from hormonal or trastuzumab-based therapy. Patients with TNBC have worse overall survival than patients with non-TNBC. Lehmann and collaborators described six different molecular subtypes of TNBC which further demonstrated its transcriptional heterogeneity. This six TNBC subtype classification has therapeutic implications. Breast cancer is the second most frequent neoplasm in sexually intact female dogs after skin cancer. Canine mammary tumors are a naturally occurring heterogeneous group of cancers that have several features in common with human breast cancer (HBC). These similarities include etiology, signaling pathway activation, and histological classification. Molecularly CMTs are more like TNBCs, and therefore dogs are powerful spontaneous models of cancer to test new therapeutic approaches, particularly for human TNBCs. More malignant tumors of the breast are more often ER and PR negative in both humans and dogs. Promising breast cancer biomarkers in both humans and canines are cancer-associated stroma (CAS), circulating tumor cells and tumor DNA (ctDNA), exosomes and miRNAs, and metabolites.

The FGF/FGFR System in Breast Cancer: Oncogenic Features and Therapeutic Perspectives

One of the major challenges in the treatment of breast cancer is the heterogeneous nature of the disease. With multiple subtypes of breast cancer identified, there is an unmet clinical need for the development of therapies particularly for the less tractable subtypes. Several transduction mechanisms are involved in the progression of breast cancer, therefore making the assessment of the molecular landscape that characterizes each patient intricate. Over the last decade, numerous studies have focused on the development of tyrosine kinase inhibitors (TKIs) to target the main pathways dysregulated in breast cancer, however their effectiveness is often limited either by resistance to treatments or the appearance of adverse effects. In this context, the fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) system represents an emerging transduction pathway and therapeutic target to be fully investigated among the diverse anti-cancer settings in breast cancer. Here, we have recapitulated previous studies dealing with FGFR molecular aberrations, such as the gene amplification, point mutations, and chromosomal translocations that occur in breast cancer. Furthermore, alterations in the FGF/FGFR signaling across the different subtypes of breast cancer have been described. Next, we discussed the functional interplay between the FGF/FGFR axis and important components of the breast tumor microenvironment. Lastly, we pointed out the therapeutic usefulness of FGF/FGFR inhibitors, as revealed by preclinical and clinical models of breast cancer.

EEF1A2 triggers stronger ERK mediated metastatic program in ER negative breast cancer cells than in ER positive cells

AIMS

Ever since EEF1A2's identification as a putative oncogene in breast cancer, it has stimulated curiosity due to its contrasting role in predicting the prognostic values in breast cancer patients. Contradicting reports suggest it to be playing a pro-survival as well as a negative role in the survival of patients. This prompted us to find the association of this protein with molecular subtypes in breast cancer and its effect on EMT in representative cell lines.

MAIN METHODS

Data-mining was carried out to ascertain the correlation of EEF1A2 with molecular subtypes in breast cancer patients. Scratch wound healing and transwell invasion assays were carried out to assess its role in migration and invasion. Western blot, qRT-PCR, and ELISA were carried out to determine key signalling pathways, cytokines, and EMT factors responsible for the observed phenotype.

KEY FINDINGS

EEF1A2 was associated with ER receptor positivity in breast cancer and was involved in its transcriptional regulation. It induced a robust metastatic program in MDA-MB-231 (a triple-negative cell line), and induced significant changes in its invasive and migratory properties via activation of the ERK pathway. This was not the case in MCF7 which is an ER-positive cell line.

SIGNIFICANCE

We highlight the specific tendency of EEF1A2 to enhance invasive properties of cell lines in particular molecular subtype only. This sheds light on its selective role in regulating oncogenic processes in breast cancer and could explain its contradicting association with good survival, despite being an oncogene in a certain cohort of breast cancer patients.

Machine learning for RNA sequencing-based intrinsic subtyping of breast cancer

Stratification of breast cancer (BC) into molecular subtypes by multigene expression assays is of demonstrated clinical utility. In principle, global RNA-sequencing (RNA-seq) should enable reconstructing existing transcriptional classifications of BC samples. Yet, it is not clear whether adaptation to RNA-seq of classifiers originally developed using PCR or microarrays, or reconstruction through machine learning (ML) is preferable. Hence, we focused on robustness and portability of PAM50, a nearest-centroid classifier developed on microarray data to identify five BC "intrinsic subtypes". We found that standard PAM50 is profoundly affected by the composition of the sample cohort used for reference construction, and we propose a strategy, named AWCA, to mitigate this issue, improving classification robustness, with over 90% of concordance, and prognostic ability; we also show that AWCA-based PAM50 can even be applied as single-sample method. Furthermore, we explored five supervised learners to build robust, single-sample intrinsic subtype callers via RNA-seq. From our ML-based survey, regularized multiclass logistic regression (mLR) displayed the best performance, further increased by ad-hoc gene selection on the global transcriptome. On external test sets, mLR classifications reached 90% concordance with PAM50-based calls, without need of reference sample; mLR proven robustness and prognostic ability make it an equally valuable single-sample method to strengthen BC subtyping.

UDP-glucose 6-dehydrogenase knockout impairs migration and decreases in vivo metastatic ability of breast cancer cells

Dysregulated metabolism is a hallmark of cancer that supports tumor growth and metastasis. One understudied aspect of cancer metabolism is altered nucleotide sugar biosynthesis, which drives aberrant cell surface glycosylation known to support various aspects of cancer cell behavior including migration and signaling. We examined clinical association of nucleotide sugar pathway gene expression and found that UGDH, encoding UDP-glucose 6-dehydrogenase which catalyzes production of UDP-glucuronate, is associated with worse breast cancer patient survival. Knocking out the mouse homolog Ugdh in highly-metastatic 6DT1 breast cancer cells impaired migration ability without affecting in vitro proliferation. Further, Ugdh-KO resulted in significantly decreased metastatic capacity in vivo when the cells were orthotopically injected in syngeneic mice. Our experiments show that UDP-glucuronate biosynthesis is critical for metastasis in a mouse model of breast cancer.

Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape

The cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer patients. Based on the expression levels of co-expressed genes, GEP-based receptor-status prediction can classify clinical subtypes more accurately than can immunohistochemistry (IHC). Using data from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets, we identified common predictor genes found in both datasets and performed receptor-status prediction based on these genes. By assessing the survival outcomes of patients classified using GEP- or IHC-based receptor status, we compared the prognostic value of the two methods. We found that GEP-based HR prediction provided higher concordance with the intrinsic subtypes and a stronger association with treatment outcomes than did IHC-based hormone receptor (HR) status. GEP-based prediction improved the identification of patients who could benefit from hormone therapy, even in patients with non-luminal breast cancer. We also confirmed that non-matching subgroup classification affected the survival of breast cancer patients and that this could be largely overcome by GEP-based receptor-status prediction. In conclusion, GEP-based prediction provides more reliable classification of HR status, improving therapeutic decision making for breast cancer patients.