Molecular signatures in breast cancer

Novel immunotherapies for breast cancer: Focus on 2023 findings

Immunotherapy has emerged as a revolutionary approach in cancer therapy, and recent advancements hold significant promise for breast cancer (BCa) management. Employing the patient's immune system to combat BCa has become a focal point in immunotherapeutic investigations. Strategies such as immune checkpoint inhibitors (ICIs), adoptive cell transfer (ACT), and targeting the tumor microenvironment (TME) have disclosed encouraging clinical outcomes. ICIs, particularly programmed cell death protein 1 (PD-1)/PD-L1 inhibitors, exhibit efficacy in specific BCa subtypes, including triple-negative BCa (TNBC) and human epidermal growth factor receptor 2 (HER2)-positive cancers. ACT approaches, including tumor-infiltrating lymphocytes (TILs) and chimeric antigen receptor (CAR) T-cell therapy, showed promising clinical outcomes in enhancing tumor recognition and elimination. Targeting the TME through immune agonists and oncolytic viruses signifies a burgeoning field of research. While challenges persist in patient selection, resistance mechanisms, and combination therapy optimization, these novel immunotherapies hold transformative potential for BCa treatment. Continued research and clinical trials are imperative to refine and implement these innovative approaches, paving the way for improved outcomes and revolutionizing the management of BCa. This review provides a concise overview of the latest immunotherapies (2023 studies) in BCa, highlighting their potential and current status.

LC-MS/MS platform-based serum untargeted screening reveals the diagnostic biomarker panel and molecular mechanism of breast cancer

BACKGROUND

Breast cancer (BC), the most common form of malignant cancer affecting women worldwide, was characterized by heterogeneous metabolic disorder and lack of effective biomarkers for diagnosis. The purpose of this study is to search for reliable metabolite biomarkers of BC as well as triple-negative breast cancer (TNBC) using serum metabolomics approach.

METHODS

In this study, an untargeted metabolomics technique based on ultra-high performance liquid chromatography combined with mass spectrometry (UHPLC-MS) was utilized to investigate the differences in serum metabolic profile between the BC group (n = 53) and non-BC group (n = 57), as well as between TNBC patients (n = 23) and non-TNBC subjects (n = 30). The multivariate data analysis, determination of the fold change and the Mann-Whitney U test were used to screen out the differential metabolites. Additionally, machine learning methods including receiver operating curve analysis and logistic regression analysis were conducted to establish diagnostic biomarker panels.

RESULTS

There were 36 metabolites found to be significantly different between BC and non-BC groups, and 12 metabolites discovered to be significantly different between TNBC and non-TNBC patients. Results also showed that four metabolites, including N-acetyl-D-tryptophan, 2-arachidonoylglycerol, pipecolic acid and oxoglutaric acid, were considered as vital biomarkers for the diagnosis of BC and non-BC with an area under the curve (AUC) of 0.995. Another two-metabolite panel of N-acetyl-D-tryptophan and 2-arachidonoylglycerol was discovered to discriminate TNBC from non-TNBC and produced an AUC of 0.965.

CONCLUSION

This study demonstrated that serum metabolomics can be used to identify BC specifically and identified promising serum metabolic markers for TNBC diagnosis.

Analysis of the association of HER-2 low carcinomas and PAM50 assay in hormone receptor positive early-stage breast cancer

BACKGROUND

HER2-low has emerged as a new predictive biomarker in metastatic breast cancer. However, its prognostic value in early-stage carcinomas needs to be revisited. We aimed to evaluate the association of HER2-low carcinomas with PAM50 risk groups combined with clinicopathological variables in early breast cancer.

METHODS

We conducted a retrospective analysis of 332 patients with early-stage breast cancer that underwent PAM50 signature analysis between 2015 and 2021at Hospital Universitario 12 de Octubre (Madrid, Spain). Clinical and pathological variables were collected from medical records. After adjusting for potential confounders, we estimated Odds Ratio (OR) and 95% confidence interval for high-risk PAM50 subgroup, comparing HER2-low versus HER2-zero carcinomas by multivariable logistic regression. P values below 0.05 were deemed statistically significant.

RESULTS

192 (57%) patients were classified as HER2-low carcinomas. Median follow-up was 34 months. Adjusted OR for high-risk PAM50 when comparing HER2-low versus HER2-zero carcinomas was 1.31 (95% CI: 0.75-2.30, p = 0.33). The multivariable model detected significant associations for Ki-67% (≥20% vs. <20%: OR = 4.03, 95% CI: 2.15-7.56, p < 0.001), T staging category (T2/T3 vs. T1: OR = 3.44, 95% CI: 1.96-6.04, p < 0.001), progesterone receptor (PR ≥ 20% vs. <20%: OR = 0.44, 95% CI: 0.23-0.83, p = 0.01), nodal staging category (N+ vs. N0: OR = 3.8, 95% CI: 1.89-7.62, p < 0.001) and histological grade (grade 2 vs. 1: OR = 2.41, 95% CI: 1.01-5.73, p = 0.04; grade 3 vs 1: OR = 5.40, 95%CI: 1.98-14.60, p = 0.001).

CONCLUSIONS

In this early-stage breast cancer cohort, HER2-low was not associated with a high-risk PAM50 compared to HER2-zero carcinomas. Ki-67 ≥ 20%, T2/T3, histological grade 2/3, N+ and PR<20% were significantly associated to a high-risk PAM50.

Delineating intra-tumoral heterogeneity and tumor evolution in breast cancer using precision-based approaches

The burden of breast cancer continues to increase worldwide as it remains the most diagnosed tumor in females and the second leading cause of cancer-related deaths. Breast cancer is a heterogeneous disease characterized by different subtypes which are driven by aberrations in key genes such as BRCA1 and BRCA2, and hormone receptors. However, even within each subtype, heterogeneity that is driven by underlying evolutionary mechanisms is suggested to underlie poor response to therapy, variance in disease progression, recurrence, and relapse. Intratumoral heterogeneity highlights that the evolvability of tumor cells depends on interactions with cells of the tumor microenvironment. The complexity of the tumor microenvironment is being unraveled by recent advances in screening technologies such as high throughput sequencing; however, there remain challenges that impede the practical use of these approaches, considering the underlying biology of the tumor microenvironment and the impact of selective pressures on the evolvability of tumor cells. In this review, we will highlight the advances made thus far in defining the molecular heterogeneity in breast cancer and the implications thereof in diagnosis, the design and application of targeted therapies for improved clinical outcomes. We describe the different precision-based approaches to diagnosis and treatment and their prospects. We further propose that effective cancer diagnosis and treatment are dependent on unpacking the tumor microenvironment and its role in driving intratumoral heterogeneity. Underwriting such heterogeneity are Darwinian concepts of natural selection that we suggest need to be taken into account to ensure evolutionarily informed therapeutic decisions.

Transforming Diagnosis and Therapeutics Using Cancer Genomics

In past quarter of the century, much has been understood about the genetic variation and abnormal genes that activate cancer in humans. All the cancers somehow possess alterations in the DNA sequence of cancer cell's genome. In present, we are heading toward the era where it is possible to obtain complete genome of the cancer cells for their better diagnosis, categorization and to explore treatment options.

Prognosis Prediction Through an Integrated Analysis of Single-Cell and Bulk RNA-Sequencing Data in Triple-Negative Breast Cancer

Background: Genomic and antigenic heterogeneity pose challenges in the precise assessment of outcomes of triple-negative breast cancer (TNBC) patients. Thus, this study was designed to investigate the cardinal genes related to cell differentiation and tumor malignant grade to advance the prognosis prediction in TNBC patients through an integrated analysis of single-cell and bulk RNA-sequencing (RNA-seq) data.

Methods: We collected RNA-seq and microarray data of TNBC from two public datasets. Using single-cell pseudotime analysis, differentially expressed genes (DEGs) among trajectories from 1534 cells of 6 TNBC patients were identified as the potential genes crucial for cell differentiation. Furthermore, the grade- and tumor mutational burden (TMB)-related DEGs were explored via a weighted correlation network analysis using the Molecular Taxonomy of Breast Cancer International Consortium dataset. Subsequently, we utilized the DEGs to construct a prognostic signature, which was validated using another independent dataset. Moreover, as gene set variation analysis indicated the differences in immune-related pathways between different risk groups, we explored the immune differences between the two groups.

Results: A signature including 10 genes related to grade and TMB was developed to assess the outcomes of TNBC patients, and its prognostic efficacy was prominent in two cohorts. The low-risk group generally harbored lower immune infiltration compared to the high-risk group.

Conclusion: Cell differentiation and grade- and TMB-related DEGs were identified using single-cell and bulk RNA-seq data. A 10-gene signature for prognosis prediction in TNBC patients was constructed, and its performance was excellent. Interestingly, the signature was found to be closely related to tumor immune infiltration, which might provide evidence for the crucial roles of immune cells in malignant initiation and progression in TNBC.

‘Breast Cancer Resistance Likelihood and Personalized Treatment Through Integrated Multiomics’

In recent times, enormous progress has been made in improving the diagnosis and therapeutic strategies for breast carcinoma, yet it remains the most prevalent cancer and second highest contributor to cancer-related deaths in women. Breast cancer (BC) affects one in eight females globally. In 2018 alone, 1.4 million cases were identified worldwide in postmenopausal women and 645,000 cases in premenopausal females, and this burden is constantly increasing. This shows that still a lot of efforts are required to discover therapeutic remedies for this disease. One of the major clinical complications associated with the treatment of breast carcinoma is the development of therapeutic resistance. Multidrug resistance (MDR) and consequent relapse on therapy are prevalent issues related to breast carcinoma; it is due to our incomplete understanding of the molecular mechanisms of breast carcinoma disease. Therefore, elucidating the molecular mechanisms involved in drug resistance is critical. For management of breast carcinoma, the treatment decision not only depends on the assessment of prognosis factors but also on the evaluation of pathological and clinical factors. Integrated data assessments of these multiple factors of breast carcinoma through multiomics can provide significant insight and hope for making therapeutic decisions. This omics approach is particularly helpful since it identifies the biomarkers of disease progression and treatment progress by collective characterization and quantification of pools of biological molecules within and among the cancerous cells. The scrupulous understanding of cancer and its treatment at the molecular level led to the concept of a personalized approach, which is one of the most significant advancements in modern oncology. Likewise, there are certain genetic and non-genetic tests available for BC which can help in personalized therapy. Genetically inherited risks can be screened for personal predisposition to BC, and genetic changes or variations (mutations) can also be identified to decide on the best treatment. Ultimately, further understanding of BC at the molecular level (multiomics) will define more precise choices in personalized medicine. In this review, we have summarized therapeutic resistance associated with BC and the techniques used for its management.

Bioinformatics Approach for Data Capturing: The Case of Breast Cancer

Background:

With the rapid evolution in advanced computer systems and various statistical algorithms, it is now a days possible to analyze complex biological data. Bioinformatics is an interface between computational and biological assemblies. It is applied in various fields of biological as well as medical sciences.

Aim:

The manuscript aims to summarize the developments in the field of breast cancer research through the applications of bioinformatics.

Methods:

Various search engines like google, science direct, Scopus, PubMed, etc., were used for the literature survey.

Results:

It describes the bioinformatics analysis tools and models, which include mainly artificial neural network models.

Conclusion:

Bioinformatics is the evolutionary approach that is used for the capturing of data from the various case studies related to breast cancer.

miRNome Profiling Reveals Shared Features in Breast Cancer Subtypes and Highlights miRNAs That Potentially Regulate MYB and EZH2 Expression

Breast cancer (BC) has been extensively studied, as it is one of the more commonly diagnosed cancer types worldwide. The study of miRNAs has increased what is known about the complexity of pathways and signaling and has identified potential biomarkers and therapeutic targets. Thus, miRNome profiling could provide important information regarding the molecular mechanisms involved in BC. On average, more than 430 miRNAs were identified as differentially expressed between BC cell lines and normal breast HMEC cells. From these, 110 miRNAs were common to BC subtypes. The miRNome enrichment analysis and interaction maps highlighted epigenetic-related pathways shared by all BC cell lines and revealed potential miRNA targets. Quantitative evaluation of BC patient samples and GETx/TCGA-BRCA datasets confirmed MYB and EZH2 as potential targets from BC miRNome. Moreover, overall survival was impacted by EZH2 expression. The expression of 15 miRNAs, selected according to aggressiveness of BC subtypes, was confirmed in TCGA-BRCA dataset. Of these miRNAs, miRNA-mRNA interaction prediction revealed 7 novel or underexplored miRNAs in BC: miR-1271-5p, miR-130a-5p, and miR-134 as MYB regulators and miR-138-5p, miR-455-3p, miR-487a, and miR-487b as EZH2 regulators. Herein, we report a novel molecular miRNA signature for BC and identify potential miRNA/mRNAs involved in disease subtypes.

Recent advances in biosensing approaches for point-of-care breast cancer diagnostics: challenges and future prospects

Timely and accurate diagnosis of breast cancer is essential for efficient treatment and the best possible survival rates. Biosensors have emerged as a smart diagnostic platform for the detection of biomarkers specific to the onset, recurrence, and therapeutic drug monitoring of breast cancer. There have been exciting recent developments, including significant improvements in the validation, sensitivity, specificity, and integration of sample processing steps to develop point-of-care (POC) integrated micro-total analysis systems for clinical settings. The present review highlights various biosensing modalities (electrical, optical, piezoelectric, mass, and acoustic sensing). It provides deep insights into their design principles, signal amplification strategies, and comparative performance analysis. Finally, this review emphasizes the status of existing integrated micro-total analysis systems (μ-TAS) for personalized breast cancer therapeutics and associated challenges and outlines the approach required to realize their successful translation into clinical settings.

Tumor-Associated Stromal Cellular Density as a Predictor of Recurrence and Mortality in Breast Cancer: Results from Ethnically Diverse Study Populations

PURPOSE

Tumor-associated stroma is comprised of fibroblasts, tumor-infiltrating lymphocytes (TIL), macrophages, endothelial cells, and other cells that interactively influence tumor progression through inflammation and wound repair. Although gene-expression signatures reflecting wound repair predict breast cancer survival, it is unclear whether combined density of tumor-associated stromal cells, a morphologic proxy for inflammation and wound repair signatures on routine hematoxylin and eosin (H&E)-stained sections, is of prognostic relevance.

METHODS

By applying machine learning to digitized H&E-stained sections for 2,084 breast cancer patients from China (n = 596; 24-55 years), Poland (n = 810; 31-75 years), and the United States (n = 678; 55-78 years), we characterized tumor-associated stromal cellular density (SCD) as the percentage of tumor-stroma that is occupied by nucleated cells. Hazard ratios (HR) and 95% confidence intervals (CI) for associations between SCD and clinical outcomes [recurrence (China) and mortality (Poland and the United States)] were estimated using Cox proportional hazard regression, adjusted for clinical variables.

RESULTS

SCD was independently predictive of poor clinical outcomes in hormone receptor-positive (luminal) tumors from China [multivariable HR (95% CI)_{fourth(Q4) vs. first(Q1) quartile} = 1.86 (1.06-3.26); P _trend = 0.03], Poland [HR (95% CI)_{Q4 vs. Q1} = 1.80 (1.12-2.89); P _trend = 0.01], and the United States [HR (95% CI)_{Q4 vs. Q1} = 2.42 (1.33-4.42); P _trend = 0.002]. In general, SCD provided more prognostic information than most classic clinicopathologic factors, including grade, size, PR, HER2, IHC4, and TILs, predicting clinical outcomes irrespective of menopausal or lymph nodal status. SCD was not predictive of outcomes in hormone receptor-negative tumors.

CONCLUSIONS

Our findings support the independent prognostic value of tumor-associated SCD among ethnically diverse luminal breast cancer patients.

IMPACT

Assessment of tumor-associated SCD on standard H&E could help refine prognostic assessment and therapeutic decision making in luminal breast cancer.

Correlation of expression of Akt1 and E2F1 and their phosphorylated forms in breast cancer patients with clinicopathological parameters

Breast cancer is the leading cancer worldwide among women. Traditional clinicopathological prognostic and predictive markers need refining to improve clinical outcomes. This study explored the association between traditional clinicopathological factors and the expression of Akt1 and E2F1 transduction proteins and their phosphorylated forms in breast cancer, to determine their value as novel biomarkers and potential therapeutic targets. Tumor tissues from 94 female breast cancer patients were examined for immunophenotypic expression of total Akt1, pAkt1 (Serine 473), pAkt1 (Threonine 308), total E2F1, pE2F1 (Thr433) and pE2F1 (Ser337). The expression of pAkt1 (Ser473) was significantly associated with ER/PR positive status and total E2F1 with older age (> 50), lymph node involvement and HER2 positivity. There was a significant association between triple negative cancers and total and pAkt1 (Thr308). pAkt1 (Ser473) showed an inverse relationship with Luminal B cancers and pE2F1 (Thr433) showed an inverse association with triple negative cancers. Higher expression of pE2F1 (Ser337) was associated with better OS. Both pAkt1 (Ser473 and Thr308) proteins showed significant association with poorer patient outcomes. E2F1 (Ser337) showed a significant positive correlation with response to chemotherapy. The study suggests that a pAkt1^-/pE2F1⁺ phenotype could indicate an opportunity to minimize chemotherapeutic options in older women; conversely a pAkt1⁺/pE2F1^- phenotype could prompt a more aggressive regimen. Further exploration of this phenotype in younger women with breast cancer and triple-negative breast cancers is warranted.

Targeted RNAseq assay incorporating unique molecular identifiers for improved quantification of gene expression signatures and transcribed mutation fraction in fixed tumor samples

BACKGROUND

Our objective was to assess whether modifications to a customized targeted RNA sequencing (RNAseq) assay to include unique molecular identifiers (UMIs) that collapse read counts to their source mRNA counts would improve quantification of transcripts from formalin-fixed paraffin-embedded (FFPE) tumor tissue samples. The assay (SET4) includes signatures that measure hormone receptor and PI3-kinase related transcriptional activity (SETER/PR and PI3Kges), and measures expression of selected activating point mutations and key breast cancer genes.

METHODS

Modifications included steps to introduce eight nucleotides-long UMIs during reverse transcription (RT) in bulk solution, followed by polymerase chain reaction (PCR) of labeled cDNA in droplets, with optimization of the polymerase enzyme and reaction conditions. We used Lin's concordance correlation coefficient (CCC) to measure concordance, including precision (Rho) and accuracy (Bias), and nonparametric tests (Wilcoxon, Levene's) to compare the modified (NEW) SET4 assay to the original (OLD) SET4 assay and to whole transcriptome RNAseq using RNA from matched fresh frozen (FF) and FFPE samples from 12 primary breast cancers.

RESULTS

The modified (NEW) SET4 assay measured single transcripts (p< 0.001) and SETER/PR (p=0.002) more reproducibly in technical replicates from FFPE samples. The modified SET4 assay was more precise for measuring single transcripts (Rho 0.966 vs 0.888, p< 0.01) but not multigene expression signatures SETER/PR (Rho 0.985 vs 0.968) or PI3Kges (Rho 0.985 vs 0.946) in FFPE, compared to FF samples. It was also more precise than wtRNAseq of FFPE for measuring transcripts (Rho 0.986 vs 0.934, p< 0.001) and SETER/PR (Rho 0.993 vs 0.915, p=0.004), but not PI3Kges (Rho 0.988 vs 0.945, p=0.051). Accuracy (Bias) was comparable between protocols. Two samples carried a PIK3CA mutation, and measurements of transcribed mutant allele fraction was similar in FF and FFPE samples and appeared more precise with the modified SET4 assay. Amplification efficiency (reads per UMI) was consistent in FF and FFPE samples, and close to the theoretically expected value, when the library size exceeded 400,000 aligned reads.

CONCLUSIONS

Modifications to the targeted RNAseq protocol for SET4 assay significantly increased the precision of UMI-based and reads-based measurements of individual transcripts, multi-gene signatures, and mutant transcript fraction, particularly with FFPE samples.

Invasive lobular carcinoma of the breast: the increasing importance of this special subtype

Invasive lobular carcinoma (ILC) is the most common of the breast cancer special types, accounting for up to 15% of all breast cancer cases. ILCs are noted for their lack of E-cadherin function, which underpins their characteristic discohesive growth pattern, with cells arranged in single file and dispersed throughout the stroma. Typically, tumours are luminal in molecular subtype, being oestrogen and progesterone receptor positive, and HER2 negative. Since last reviewing the lobular literature (McCart Reed et al., Breast Cancer Res 17:12, 2015), there has been a considerable increase in research output focused on this tumour type, including studies into the pathology and management of disease, a high-resolution definition of the genomic landscape of tumours as well as the evolution of several potential therapeutic avenues. There abounds a huge amount of new data, which we will review herein.

Network Simulations Reveal Molecular Signatures of Vulnerability to Age-Dependent Stress and Tau Accumulation

Alzheimer’s disease (AD) is the leading cause of dementia and one of the most common causes of death worldwide. As an age-dependent multifactorial disease, the causative triggers of AD are rooted in spontaneous declines in cellular function and metabolic capacity with increases in protein stressors such as the tau protein. This multitude of age-related processes that cause neurons to change from healthy states to ones vulnerable to the damage seen in AD are difficult to simultaneously investigate and even more difficult to quantify. Here we aimed to diminish these gaps in our understanding of neuronal vulnerability in AD development by using simulation methods to theoretically quantify an array of cellular stress responses and signaling molecules. This temporally-descriptive molecular signature was produced using a novel multimethod simulation approach pioneered by our laboratory for biological research; this methodology combines hierarchical agent-based processes and continuous equation-based modeling in the same interface, all while maintaining intrinsic distributions that emulate natural biological stochasticity. The molecular signature was validated for a normal organismal aging trajectory using experimental longitudinal data from Caenorhabditis elegans and rodent studies. In addition, we have further predicted this aging molecular signature for cells impacted by the pathogenic tau protein, giving rise to distinct stress response conditions needed for cytoprotective aging. Interestingly, our simulation experiments showed that oxidative stress signaling (via daf-16 and skn-1 activities) does not substantially protect cells from all the early stressors of aging, but that it is essential in preventing a late-life degenerative cellular phenotype. Together, our simulation experiments aid in elucidating neurodegenerative triggers in the onset of AD for different genetic conditions. The long-term goal of this work is to provide more detailed diagnostic and prognostic tools for AD development and progression, and to provide more comprehensive preventative measures for this disease.

Personalised medicine and the decision to withhold chemotherapy in early breast cancer with intermediate risk of recurrence - a systematic review and meta-analysis

Purpose

To assess the evidence for decision making, at the health care and the patient levels, regarding the use of gene expression assays to inform chemotherapy decisions in breast cancer patients with intermediate clinical risk of recurrence.

Methods

Systematic literature searches were performed (January 2002–April 2020) in Medline, Embase, PubMed, Cochrane Library, PsycINFO and HTA databases. Inclusion criteria: patients (P) were individuals with post-surgical breast cancer at intermediate clinical risk of recurrence; intervention (I)/comparison (C) was (i) use of, versus no use of, a gene expression assay and (ii) withholding versus providing chemotherapy; outcomes (O) were overall survival (OS), health-related quality of life (HRQL), and recurrence. Randomised controlled trials (RCTs) and non-RCTs were included. Random-effects meta-analyses were performed where possible.

Results

Three inconclusive non-RCTs, respectively, compared OS and recurrence with and without a gene expression assay. No studies investigated HRQL. Regarding the comparison withholding versus providing chemotherapy based on a gene expression assay, one RCT and four non-RCTs evaluated OS. In the RCT, 93.9% (I) versus 93.8% (C) were alive at 9 years. Three RCTs and seven non-RCTs evaluated recurrence. Three RCTs could be pooled regarding distant recurrence; 4.29% versus 3.88% had such an event (risk ratio: 1.12 (95% confidence interval: 0.90 to 1.39).

Conclusion

Regarding the use of gene expression assays in breast cancer, evidence on patient effects, informing patient-level chemotherapy decision making, is available. However, evidence for prioritisation at the overall health care level, i.e. use of, versus no use of, such assays, is largely lacking.

Electronic supplementary material

The online version of this article (10.1007/s00228-020-02914-z) contains supplementary material, which is available to authorized users.

A Novel Prognostic Signature of mRNA-lncRNA in Breast Cancer

Comprehensive genomic testing will be required to identify appropriate targets for the precision therapy of breast cancer. Although RNA sequencing (RNA-seq) is an unparalleled platform for this purpose, existing molecular-based prognostic signatures are not optimal for RNA-seq data. In this study, we analyzed RNA-seq datasets to generate a novel prognostic gene signature for breast cancer patients. RNA-seq and clinical datasets from breast cancer patients were obtained from The Cancer Genome Atlas and randomly assigned to training (n = 379) and test (n = 378) cohorts. Using the training cohort, sequential univariate Cox analysis, robust likelihood-based survival analysis, and stepwise multivariable Cox analysis identified a five-gene signature composed of one long noncoding RNA gene and four protein-coding genes. The five-gene signature was then used to dichotomize patients into risk groups and validated using Kaplan-Meier and multivariable Cox analyses. In the full test cohort, the high-risk group had worse overall survival (hazard ratio [HR] = 4.74, 95% confidence interval [CI] = 2.33-9.64, p < 0.0001) and worse relapse-free survival (HR = 2.26, 95% CI = 1.11-4.61, p = 0.024) than the low-risk group. Similarly, overall survival was worse in the high-risk group within nearly all clinically important subsets, including early stage disease (I/II) (HR = 7.87, 95% CI = 3.69-16.77, p < 0.0001), and luminal A (HR = 4.23, 95% CI = 1.11-16.12, p = 0.034), luminal B (HR = 12.79, 95% CI = 2.74-59.69, p = 0.001), and basal (HR = 18.11, 95% CI = 3.21-102.05, p = 0.001) subtypes. Notably, the five-gene signature exhibited superior prognostic performance compared with the Oncotype DX 21-gene signature. This novel five-gene signature may therefore be a powerful prognostic tool for personalized treatment of breast cancer patients as part of an integrated RNA-seq clinical sequencing program.

The impact of RNA extraction method on accurate RNA sequencing from formalin-fixed paraffin-embedded tissues

Background

Utilization of RNA sequencing methods to measure gene expression from archival formalin-fixed paraffin-embedded (FFPE) tumor samples in translational research and clinical trials requires reliable interpretation of the impact of pre-analytical variables on the data obtained, particularly the methods used to preserve samples and to purify RNA.

Methods

Matched tissue samples from 12 breast cancers were fresh frozen (FF) and preserved in RNAlater or fixed in formalin and processed as FFPE tissue. Total RNA was extracted and purified from FF samples using the Qiagen RNeasy kit, and in duplicate from FFPE tissue sections using three different kits (Norgen, Qiagen and Roche). All RNA samples underwent whole transcriptome RNA sequencing (wtRNAseq) and targeted RNA sequencing for 31 transcripts included in a signature of sensitivity to endocrine therapy. We assessed the effect of RNA extraction kit on the reliability of gene expression levels using linear mixed-effects model analysis, concordance correlation coefficient (CCC) and differential analysis. All protein-coding genes in the wtRNAseq and three gene expression signatures for breast cancer were assessed for concordance.

Results

Despite variable quality of the RNA extracted from FFPE samples by different kits, all had similar concordance of overall gene expression from wtRNAseq between matched FF and FFPE samples (median CCC 0.63–0.66) and between technical replicates (median expression difference 0.13–0.22). More than half of genes were differentially expressed between FF and FFPE, but with low fold change (median |LFC| 0.31–0.34). Two out of three breast cancer signatures studied were highly robust in all samples using any kit, whereas the third signature was similarly discordant irrespective of the kit used. The targeted RNAseq assay was concordant between FFPE and FF samples using any of the kits (CCC 0.91–0.96).

Conclusions

The selection of kit to purify RNA from FFPE did not influence the overall quality of results from wtRNAseq, thus variable reproducibility of gene signatures probably relates to the reliability of individual gene selected and possibly to the algorithm. Targeted RNAseq showed promising performance for clinical deployment of quantitative assays in breast cancer from FFPE samples, although numerical scores were not identical to those from wtRNAseq and would require calibration.

Dual and multi-targeted nanoparticles for site-specific brain drug delivery

In recent years, nanomedicines have emerged as a promising method for central nervous system drug delivery, enabling the drugs to overcome the blood-brain barrier and accumulate preferentially in the brain. Despite the current success of brain-targeted nanomedicines, limitations still exist in terms of the targeting specificity. Based on the molecular mechanism, the exact cell populations and subcellular organelles where the injury occurs and the drugs take effect have been increasingly accepted as a more specific target for the next generation of nanomedicines. Dual and multi-targeted nanoparticles integrate different targeting functionalities and have provided a paradigm for precisely delivering the drug to the pathological site inside the brain. The targeting process often involves the sequential or synchronized navigation of the targeting moieties, which allows highly controlled drug delivery compared to conventional targeting strategies. Herein, we focus on the up-to-date design of pathological site-specific nanoparticles for brain drug delivery, highlighting the dual and multi-targeting strategies that were employed and their impact on improving targeting specificity and therapeutic effects. Furthermore, the background discussion of the basic properties of a brain-targeted nanoparticle and the common lesion features classified by neurological pathology are systematically summarized.

The Complex Interaction between the Tumor Micro-Environment and Immune Checkpoints in Breast Cancer

The progression of breast cancer and its association with clinical outcome and treatment remain largely unexplored. Accumulating data has highlighted the interaction between cells of the immune system and the tumor microenvironment in cancer progression, and although studies have identified multiple facets of cancer progression within the development of the tumor microenvironment (TME) and its constituents, there is lack of research into the associations between breast cancer subtype and staging. Current literature has provided insight into the cells and pathways associated with breast cancer progression through expression analysis. However, there is lack of co-expression studies between immune pathways and cells of the TME that form pro-tumorigenic relationships contributing to immune-evasion. We focus on the immune checkpoint and TME elements that influence cancer progression, particularly studies in molecular subtypes of breast cancer.

Genomics applied to the treatment of breast cancer

Breast cancer remains a major health issue in the world with 1.7 million new cases in 2012 worldwide. It is the second cause of death from cancer in western countries. Genomics have started to modify the treatment of breast cancer, and the developments should become more and more significant, especially in the present era of treatment personalization and with the implementation of new technologies. With molecular signatures, genomics enabled a de-escalation of chemotherapy and personalized treatments of localized forms of estrogen-dependent breast cancers. Genomics can also make a real contribution to constitutional genetics, so as to identify mutations in a panel of candidate genes. In this review, we will discuss the contributions of genomics applied to the treatment of breast cancer, whether already validated contributions or possible future applications linked to research data.

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.

The Advent of Salivary Breast Cancer Biomarker Detection Using Affinity Sensors

Breast Cancer is one of the world's most notorious diseases affecting two million women in 2018 worldwide. It is a highly heterogeneous disease, making it difficult to treat. However, its linear progression makes it a candidate for early screening programs, and the earlier its detection the higher the chance of recovery. However, one key hurdle for breast cancer screening is the fact that most screening techniques are expensive, time-consuming, and cumbersome, making them impractical for use in several parts of the world. One current trend in breast cancer detection has pointed to a possible solution, the use of salivary breast cancer biomarkers. Saliva is an attractive medium for diagnosis because it is readily available in large quantities, easy to obtain at low cost, and contains all the biomarkers present in blood, albeit in lower quantities. Affinity sensors are devices that detect molecules through their interactions with biological recognition molecules. Their low cost, high sensitivity, and selectivity, as well as rapid detection time make them an attractive alternative to traditional means of detection. In this review article, we discuss the current status of breast cancer diagnosis, its salivary biomarkers, as well as the current trends in the development of affinity sensors for their detection.

The role of photodynamic therapy on multidrug resistant breast cancer

Breast cancer heterogeneity allows cells with different phenotypes to co-exist, contributing to treatment failure and development of drug resistance. In addition, abnormal signal transduction and dysfunctional DNA repair genes are common features with breast cancer resistance. Chemo-resistance of breast cancer associated with multidrug resistance events utilizes ATP-binding cassette (ABC) efflux transporters to decrease drug intracellular concentration. Photodynamic therapy (PDT) is the treatment that involves a combination of a photosensitizer (PS), light and molecular oxygen to induce cell death. This treatment modality has been considered as a possible approach in combatting multidrug resistance phenomenon although its therapeutic potential towards chemo-resistance is still unclear. Attempts to minimize the impact of efflux transporters on drug resistance suggested concurrent use of chemotherapy agents, nanotechnology, endolysosomal release of drug by photochemical internalization and the use of structurally related compound inhibitors to block the transport function of the multidrug resistant transporters. In this review, we briefly summarize the role of membrane ABC efflux transporters in therapeutic outcomes and highlight research findings related to PDT and its applications on breast cancer with multidrug resistance phenotype. With the development of an ideal PS for photodynamic cancer treatment, it is possible that light activation may be used not only to sensitize the tumour but also to enable release of PS into the cytosol and as such bypass efflux membrane proteins and inhibit escape pathways that may lead to resistance.

The Interleukin-17 Family of Cytokines in Breast Cancer

Breast cancer (BC) is the most common cancer in women worldwide and remains a major cause of mortality with an expected 137,000 death this year in Europe. Standard management of metastatic BC comprises hormonotherapy, chemotherapy, and targeted therapies. Cyclin dependent kinase (CDK) and mammalian target of rapamycin (mTOR) inhibitors have recently proved their efficiency in hormonal receptor expressing BC. Checkpoint proteins inhibition is being evaluated in phase 3 studies. Since inflammation is constantly present in cancers, research teams have focused their attention on the interleukin-17 (IL-17) family of proinflammatory cytokines. Preclinical experiments have reported both pro and antitumor effects depending on the conditions. In the present article, we review the accumulating evidences about the roles of IL-17 in BC and discuss whether this family of cytokines could be a new target in anticancer treatments.

Impact of the hypoxic phenotype on the uptake and efflux of nanoparticles by human breast cancer cells

Breast cancer cells adapt to the hypoxic tumoral environment by undergoing changes in metabolism, cell signalling, endo-lysosomal receptor uptake and recycling. The resulting hypoxic cell phenotype has the potential to undermine the therapeutic efficacy of nanomedicines designed for endocytic uptake and specific intracellular trafficking. The aim of this study was to examine the impact of hypoxia and simulated reperfusion on the in vitro uptake and release of nanomedicines by human breast cancer cells. Cells were exposed to a hypoxic preconditioning treatment in 1% oxygen for 6 and 24 hours to induce temporal changes in the hypoxic circuit (e.g. HIF-1α expression). The preconditioned cells were then dosed with nanoparticles for 45 or 180 minutes emulating nanomedicine access following tumor reperfusion. Hypoxic preconditioning significantly increased nanoparticle retention by up to 10% when compared to normoxic cultures, with the greatest relative difference between normoxic and hypoxic cultures occurring with a 45 minute dosing interval. Exocytosis studies indicated that the preconditioned cells had a significantly increased nanoparticle efflux (up to 9%) when compared to normoxic cells. Overall, we were able to show that hypoxic preconditioning regulates both the endocytosis and exocytosis of nanomedicines in human breast cancer cells.

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.

Polyphenols as Promising Drugs against Main Breast Cancer Signatures

Breast cancer is one of the most common neoplasms worldwide, and in spite of clinical and pharmacological advances, it is still a clinical problem, causing morbidity and mortality. On the one hand, breast cancer shares with other neoplasms some molecular signatures such as an imbalanced redox state, cell cycle alterations, increased proliferation and an inflammatory status. On the other hand, breast cancer shows differential molecular subtypes that determine its prognosis and treatment. These are characterized mainly by hormone receptors especially estrogen receptors (ERs) and epidermal growth factor receptor 2 (HER2). Tumors with none of these receptors are classified as triple negative breast cancer (TNBC) and are associated with a worse prognosis. The success of treatments partially depends on their specificity and the adequate molecular classification of tumors. New advances in anticancer drug discovery using natural compounds have been made in the last few decades, and polyphenols have emerged as promising molecules. They may act on various molecular targets because of their promiscuous behavior, presenting several physiological effects, some of which confer antitumor activity. This review analyzes the accumulated evidence of the antitumor effects of plant polyphenols on breast cancer, with special attention to their activity on ERs and HER2 targets and also covering different aspects such as redox balance, uncontrolled proliferation and chronic inflammation.

Identification of differentially expressed genes regulated by molecular signature in breast cancer-associated fibroblasts by bioinformatics analysis

OBJECTIVE

Breast cancer is a severe risk to public health and has adequately convoluted pathogenesis. Therefore, the description of key molecular markers and pathways is of much importance for clarifying the molecular mechanism of breast cancer-associated fibroblasts initiation and progression. Breast cancer-associated fibroblasts gene expression dataset was downloaded from Gene Expression Omnibus database.

METHODS

A total of nine samples, including three normal fibroblasts, three granulin-stimulated fibroblasts and three cancer-associated fibroblasts samples, were used to identify differentially expressed genes (DEGs) between normal fibroblasts, granulin-stimulated fibroblasts and cancer-associated fibroblasts samples. The gene ontology (GO) and pathway enrichment analysis was performed, and protein-protein interaction (PPI) network of the DEGs was constructed by NetworkAnalyst software.

RESULTS

Totally, 190 DEGs were identified, including 66 up-regulated and 124 down-regulated genes. GO analysis results showed that up-regulated DEGs were significantly enriched in biological processes (BP), including cell-cell signalling and negative regulation of cell proliferation; molecular function (MF), including insulin-like growth factor II binding and insulin-like growth factor I binding; cellular component (CC), including insulin-like growth factor binding protein complex and integral component of plasma membrane; the down-regulated DEGs were significantly enriched in BP, including cell adhesion and extracellular matrix organization; MF, including N-acetylgalactosamine 4-sulfate 6-O-sulfotransferase activity and calcium ion binding; CC, including extracellular space and extracellular matrix. WIKIPATHWAYS analysis showed the up-regulated DEGs were enriched in myometrial relaxation and contraction pathways. WIKIPATHWAYS, REACTOME, PID_NCI and KEGG pathway analysis showed the down-regulated DEGs were enriched endochondral ossification, TGF beta signalling pathway, integrin cell surface interactions, beta1 integrin cell surface interactions, malaria and glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulphate. The top 5 up-regulated hub genes, CDKN2A, MME, PBX1, IGFBP3, and TFAP2C and top 5 down-regulated hub genes VCAM1, KRT18, TGM2, ACTA2, and STAMBP were identified from the PPI network, and subnetworks revealed these genes were involved in significant pathways, including myometrial relaxation and contraction pathways, integrin cell surface interactions, beta1 integrin cell surface interaction. Besides, the target hsa-mirs for DEGs were identified. hsa-mir-759, hsa-mir-4446-5p, hsa-mir-219a-1-3p and hsa-mir-26a-5p were important miRNAs in this study.

CONCLUSIONS

We pinpoint important key genes and pathways closely related with breast cancer-associated fibroblasts initiation and progression by a series of bioinformatics analysis on DEGs. These screened genes and pathways provided for a more detailed molecular mechanism underlying breast cancer-associated fibroblasts occurrence and progression, holding promise for acting as molecular markers and probable therapeutic targets.