Breast cancer classification and prognosis based on gene expression profiles from a population-based study

Regression-based heterogeneity analysis to identify overlapping subgroup structure in high-dimensional data

Heterogeneity is a hallmark of complex diseases. Regression-based heterogeneity analysis, which is directly concerned with outcome-feature relationships, has led to a deeper understanding of disease biology. Such an analysis identifies the underlying subgroup structure and estimates the subgroup-specific regression coefficients. However, most of the existing regression-based heterogeneity analyses can only address disjoint subgroups; that is, each sample is assigned to only one subgroup. In reality, some samples have multiple labels, for example, many genes have several biological functions, and some cells of pure cell types transition into other types over time, which suggest that their outcome-feature relationships (regression coefficients) can be a mixture of relationships in more than one subgroups, and as a result, the disjoint subgrouping results can be unsatisfactory. To this end, we develop a novel approach to regression-based heterogeneity analysis, which takes into account possible overlaps between subgroups and high data dimensions. A subgroup membership vector is introduced for each sample, which is combined with a loss function. Considering the lack of information arising from small sample sizes, an l 2 $l_2$ norm penalty is developed for each membership vector to encourage similarity in its elements. A sparse penalization is also applied for regularized estimation and feature selection. Extensive simulations demonstrate its superiority over direct competitors. The analysis of Cancer Cell Line Encyclopedia data and lung cancer data from The Cancer Genome Atlas show that the proposed approach can identify an overlapping subgroup structure with favorable performance in prediction and stability.

Targeting Squalene Epoxidase Interrupts Homologous Recombination via the ER Stress Response and Promotes Radiotherapy Efficacy

Over 50% of all patients with cancer are treated with radiotherapy. However, radiotherapy is often insufficient as a monotherapy and requires a nontoxic radiosensitizer. Squalene epoxidase (SQLE) controls cholesterol biosynthesis by converting squalene to 2,3-oxidosqualene. Given that SQLE is frequently overexpressed in human cancer, this study investigated the importance of SQLE in breast cancer and non-small cell lung cancer (NSCLC), two cancers often treated with radiotherapy. SQLE-positive IHC staining was observed in 68% of breast cancer and 56% of NSCLC specimens versus 15% and 25% in normal breast and lung tissue, respectively. Importantly, SQLE expression was an independent predictor of poor prognosis, and pharmacologic inhibition of SQLE enhanced breast and lung cancer cell radiosensitivity. In addition, SQLE inhibition enhanced sensitivity to PARP inhibition. Inhibition of SQLE interrupted homologous recombination by suppressing ataxia-telangiectasia mutated (ATM) activity via the translational upregulation of wild-type p53-induced phosphatase (WIP1), regardless of the p53 status. SQLE inhibition and subsequent squalene accumulation promoted this upregulation by triggering the endoplasmic reticulum (ER) stress response. Collectively, these results identify a novel tumor-specific radiosensitizer by revealing unrecognized cross-talk between squalene metabolites, ER stress, and the DNA damage response. Although SQLE inhibitors have been used as antifungal agents in the clinic, they have not yet been used as antitumor agents. Repurposing existing SQLE-inhibiting drugs may provide new cancer treatments.

SIGNIFICANCE

Squalene epoxidase inhibitors are novel tumor-specific radiosensitizers that promote ER stress and suppress homologous recombination, providing a new potential therapeutic approach to enhance radiotherapy efficacy.

R code and downstream analysis objects for the scRNA-seq atlas of normal and tumorigenic human breast tissue

Breast cancer is a common and highly heterogeneous disease. Understanding cellular diversity in the mammary gland and its surrounding micro-environment across different states can provide insight into cancer development in the human breast. Recently, we published a large-scale single-cell RNA expression atlas of the human breast spanning normal, preneoplastic and tumorigenic states. Single-cell expression profiles of nearly 430,000 cells were obtained from 69 distinct surgical tissue specimens from 55 patients. This article extends the study by providing quality filtering thresholds, downstream processed R data objects, complete cell annotation and R code to reproduce all the analyses. Data quality assessment measures are presented and details are provided for all the bioinformatic analyses that produced results described in the study.

Measurement(s)	gene expression
Technology Type(s)	10x sequencing protocol • mRNA Sequencing
Factor Type(s)	Gender • Menopause status • Parity • Cancer type • Cell population
Sample Characteristic - Organism	Homo sapiens

Karanahan: A Potential New Treatment Option for Human Breast Cancer and Its Validation in a Clinical Setting

Introduction:

Karanahan, a cancer treatment technology aimed at eradicating tumor-initiating stem cells, has already proven effective in 7 tumor models. Karanahan comprises the following procedures: (1) collecting surgical specimens, (2) determining the duration of the DNA repair process in tumor cells exposed to a cross-linking cytostatic agent, and (3) determining the time point, when cells, including tumor-initiating stem cells, are synchronized in the certain phase of the cell cycle after triple exposure to the cytostatic, becoming vulnerable for the terminal treatment, which is supposed to completely eliminate the rest of survived tumor-initiating stem cells. Determining these basic tumor properties allows to design the schedule for the administration of a cross-linking cytostatic and a complex composite DNA preparation. Being conducted in accordance with the schedule designed, Karanahan results in the large-scale apoptosis of tumor cells with elimination of tumor-initiating stem cells.

Methods:

Breast tumor specimens were obtained from patients, and basic tumor properties essential for conducting Karanahan therapy were determined.

Results:

We report the first use of Karanahan in patients diagnosed with breast cancer. Technical details of handling surgical specimens for determining the essential Karanahan parameters (tumor volume, cell number, cell proliferation status, etc) have been worked out. The terminally ill patient, who was undergoing palliative treatment and whose tumor specimen matched the required criteria, received a complete course of Karanahan.

Conclusions:

The results of the treatment conducted indicate that Karanahan technology has a therapeutic potency and can be used as a breast cancer treatment option.

The extended concurrent genes signature for disease-free survival in breast cancer

BACKGROUND/PURPOSE

Previously we had identified concurrent genes, which highlighted the interplay between copy number variation (CNV) and differential gene expression (GE) for Han Chinese breast cancers. The merit of the approach is to discovery biomarkers not identifiable by conventional GE only data, for which phenotype-correlation or gene variability is the criteria of gene selection.

MATERIALS AND METHODS

Thirty-one comparative genomic hybridization (CGH) and 83 GE microarrays were performed, with 29 breast cancers assayed from both platforms. Potential targets were revealed by Genomic Identification of Significant Targets in Cancer (GISTIC) from CGH arrays. Concurrent genes and genes with significant GISTIC scores were used to derive the extended concurrent genes signature, which was consensus from leading edge analysis across all studies and a supervised partial least square (PLS) regression predictive model of disease-free survival was constructed.

RESULTS

There were 1584 concurrent genes from 29 samples with both CGH and GE microarrays. Enriched concurrent genes sets for disease-free survival were identified independently from 83 GE arrays and another one with Han Chinese origin as well as three studies of Western origin. For five studies with disease-free survival follow up, prognostic discrepancy was observed between predicted high-risk and low-risk group patients.

CONCLUSION

We concluded that through parallel analyses of CGH and GE microarrays, the proposed extended concurrent gene expression signature can identify biomarkers with prognostic values.

Clinicopathological characteristics and treatment outcomes of luminal B1 breast cancer in Taiwan

BACKGROUND

Hormone receptor-positive, human epidermal growth factor receptor II (HER2)-negative luminal B1 breast cancer is associated with a higher risk of disease relapse than luminal A breast cancer. Therefore, we assessed and compared the distant metastasis pattern and clinical outcomes associated with luminal B1 and luminal A breast cancer in an Asian population.

METHODS

In this observational study, we assessed patients with estrogen receptor-positive, HER2-negative breast cancer who underwent surgery from 2009 to 2016. Patients were classified into luminal A or luminal B1 subsets via immunohistochemical analysis. Disease-free survival, post-metastasis survival, and overall survival were estimated; time to disease relapse and patterns of distant metastasis were compared. Risk of relapse and mortality were assessed using Cox proportional hazards model.

RESULTS

Patients with luminal B1 breast cancer (n = 677) were significantly younger and had larger tumors and a higher degree of affected axillary lymph nodes, lymphovascular invasion, and tumor necrosis than those with luminal A breast cancer (n = 630). Higher rates of local recurrence and distant metastasis were observed for luminal B1 (both p < 0.05); however, no difference was observed in the specific distant metastatic sites. We observed a significant increase in disease relapse risk in luminal B1 patients compared with that in luminal A (hazard ratio: 2.157, 95% CI: 1.340-3.473, p < 0.05). Patient age, tumor size, stage, lymphovascular invasion, and receiving chemotherapy and hormone therapy were independent risk factors for metastasis and recurrence. Only the luminal B1 subtype (hazard ratio: 5.653, 95% CI: 1.166-27.409, p < 0.05) and stage (hazard ratio: 3.400, 95% CI: 1.512-7.649, p < 0.05) were identified as independent risk factors for post metastatic mortality.

CONCLUSION

Luminal B1 breast cancer has aggressive tumor biology compared with luminal A breast cancer in the follow-up period. However, there was no significant difference in the disease relapse pattern between the groups.

Predicting clinical outcomes of cancer patients with a p53 deficiency gene signature

The tumor suppressor p53, encoded by the TP53 gene, is mutated or nullified in nearly 50% of human cancers. It has long been debated whether TP53 mutations can be utilized as a biomarker to predict clinical outcomes of cancer patients. In this study, we applied computational methods to calculate p53 deficiency scores (PDSs) that reflect the inactivation of the p53 pathway, instead of TP53 mutation status. Compared to TP53 mutation status, the p53 deficiency gene signature is a powerful predictor of overall survival and drug sensitivity in a variety of cancer types and treatments. Interestingly, the PDSs predicted clinical outcomes more accurately than drug sensitivity in cell lines, suggesting that tumor heterogeneity and/or tumor microenvironment may play an important role in predicting clinical outcomes using p53 deficiency gene signatures.

Platinum-based systematic therapy in triple-negative breast cancer

Due to the lack of definitive hormone receptors, triple negative breast cancer (TNBC) patients receive little clinical benefit from endocrine or molecular targeted therapies, leading to a highly aggressive disease with a high recurrence rate and poor prognosis. In the past decades, chemotherapy has been the mainstay of treatment for TNBC, with taxane/anthracyclines as the representative regimen. However, increasing irreversible cardiotoxicity of anthracyclines and drug-resistance had to be noticed. Gradually, platinum-based chemotherapy has become a topic of interest for researchers. Based on the accumulating studies on platinum-containing regimens for TNBC patients, we will summarize the progress of relevant clinical trials focusing on platinum monotherapy (e.g., cisplatin, carboplatin and oxaliplatin) or in combination with other therapeutic modalities (e.g., other chemotherapeutic agents, molecular targeted therapies and immunotherapy). To further evaluate patient response to platinum and screen for the optimal population to benefit from platinum, we will also analyze current potential biomarkers, such as breast cancer susceptibility genes (BRCA1/2), homologous recombination repair deficiency (HRD), tumor infiltrating lymphocytes (TILs), TP53 family and other emerging indicators (e.g., intrinsic subtype, cyclin-dependent kinase 2 (CDK2) expression, vascular endothelial growth factor (VEGF) and matrix metalloproteinase-9 (MMP-9)).

Pathophysiological role of ion channels and transporters in HER2-positive breast cancer

The incidence of breast cancer (BC) has been increasing each year, and BC is now the most common malignant tumor in women. Among the numerous BC subtypes, HER2-positive BC can be treated with a variety of strategies based on targeting HER2. Although there has been great progress in the treatment of HER2-positive BC, recurrence, metastasis and drug resistance remain considerable challenges. The dysfunction of ion channels and transporters can affect the development and progression of HER2-positive BC, so these entities are expected to be new therapeutic targets. This review summarizes various ion channels and transporters associated with HER2-positive BC and suggests potential targets for the development of new and effective therapies.

Pegylated Liposomal Doxorubicin (Caelyx®) as Adjuvant Treatment in Early-Stage Luminal B-like Breast Cancer: A Feasibility Phase II Trial

Background: Adjuvant chemotherapy for Luminal B-like breast cancers usually includes anthracycline-based regimens. However, some patients are reluctant to receive chemotherapy because of side-effects, especially alopecia, and ask for a “less intensive” or personalized approach. Patients and methods: We conducted a phase II feasibility trial to evaluate pegylated liposomal doxorubicin (PLD, Caelyx^®) as adjuvant chemotherapy. Patients who received surgery for pT1–3, any N, and luminal B-like early-stage breast cancer (EBC) candidates for adjuvant chemotherapy were included. PLD was administered intravenously at 20 mg/m² biweekly for eight courses. Endocrine therapy was given according to menopausal status. Trastuzumab was administered in HER2-positive disease. The primary endpoint was to evaluate the feasibility of this regimen, defined as the ability of a patient to achieve a relative dose intensity (RDI) of at least 85% of the eight cycles of treatment. Secondary endpoints included adverse events (AEs), tolerability, breast cancer-free survival, disease-free survival, and overall survival. Results: From March 2016 to July 2018, 63 patients were included in the trial. Median age was 49 years (range: 33–76), with mostly pre- and peri-menopausal (65%) and stage I–II (94%). Only 5% of patients had HER2-positive EBC. Median RDI was 100% (range: 12.5–100%; interquartile range, IQR: 87.5–100%). The proportion of patients meeting the primary endpoint was 84% (95% confidence interval, CI: 73–92%). Overall, 55 out of 63 enrolled patients completed treatment (87%, 95% CI: 77–94%). Most common AEs were palmar-plantar erythrodysesthesia (12.2%), fatigue (10.4%), and mucositis (8.5%). Only 13% of patients had G3 AEs. None had alopecia. After a median follow-up of 3.9 years (range: 0.3–4.7) two distant events were observed, and all patients were alive at the date of last visit. Conclusions: The trial successfully met its primary endpoint: the regimen was feasible and well tolerated and could be considered for further evaluation as a treatment option for patients with contraindications to standard anthracyclines or requiring a personalized, less intensive approach.

Bayesian tensor factorization-drive breast cancer subtyping by integrating multi-omics data

Breast cancer is a highly heterogeneous disease. Subtyping the disease and identifying the genomic features driving these subtypes are critical for precision oncology for breast cancer. This study focuses on developing a new computational approach for breast cancer subtyping. We proposed to use Bayesian tensor factorization (BTF) to integrate multi-omics data of breast cancer, which include expression profiles of RNA-sequencing, copy number variation, and DNA methylation measured on 762 breast cancer patients from The Cancer Genome Atlas. We applied a consensus clustering approach to identify breast cancer subtypes using the factorized latent features by BTF. Subtype-specific survival patterns of the breast cancer patients were evaluated using Kaplan-Meier (KM) estimators. The proposed approach was compared with other state-of-the-art approaches for cancer subtyping. The BTF-subtyping analysis identified 17 optimized latent components, which were used to reveal six major breast cancer subtypes. Out of all different approaches, only the proposed approach showed distinct survival patterns (p < 0.05). Statistical tests also showed that the identified clusters have statistically significant distributions. Our results showed that the proposed approach is a promising strategy to efficiently use publicly available multi-omics data to identify breast cancer subtypes.

A curcumin analog CA-5f inhibits urokinase-type plasminogen activator and invasive phenotype of triple-negative breast cancer cells

Triple-negative breast cancer (TNBC) is one of the most aggressive types of breast cancer with poor outcomes. Patients with TNBC cannot benefit from targeted therapies such as Tamoxifen and Herceptin. The aim of the present study was to seek a preventive or therapeutic agent with a potential inhibitory effect on aggressive progression of TNBC. Anticancer effect of a natural compound curcumin have been demonstrated, however, development of more effective curcumin analogs with better bioavailability is needed. We investigated if a curcumin analog CA-5f could inhibit the invasive phenotype of TNBC cell lines in the present study. Treatment with CA-5f inhibited the viability of MDA‑MB‑231 and Hs578T TNBC cells, possible by inducing apoptosis. The invasive phenotypes of these cells were inhibited by CA-5f in a concentration-dependent manner. Protein expression of urokinase-type plasminogen activator (uPA), a serine protease known to degrade the extracellular matrix and lead to invasion, was markedly decreased by CA-5f in Hs578T cells. However, mRNA level of uPA was not altered by CA-5f, implicating that the effect of CA-5f was not through transcriptional regulation. Of note, CA-5f upregulated plasminogen activator inhibitor type (PAI)-1, which is known to inhibit uPA by interacting with urokinase-type plasminogen receptor, in TNBC cells. Taken together, these results demonstrated that CA-5f significantly inhibited the invasive phenotype of TNBC cells, possibly by decreasing the protein level of uPA through upregulating PAI-1. Our results may provide useful information on developing CA-5f as a potential therapeutic agent against malignant progression of TNBC.

Epsin 3 potentiates the NF‑κB signaling pathway to regulate apoptosis in breast cancer

Endocrine drug resistance is common in some patients with estrogen receptor (ER)-positive breast cancer, so it is necessary to identify potential therapeutic targets. The aim of the present study was to investigate the regulatory effect and mechanism of epsin 3 (EPN3) expression level changes on the proliferation and apoptosis of ER-positive breast cancer. Online GEPIA was used to analyze the expression level of EPN3 in breast cancer. The online Kaplan-Meier plotter tool was used to analyze the relationship between EPN3 expression and the prognosis of patients with breast cancer. Reverse transcription-quantitative PCR, immunohistochemistry and western blotting were performed to detect the mRNA and protein expression levels of EPN3 in breast cancer tissues and cells. A lentiviral infection system was used to knockdown the expression of EPN3 in breast cancer cell lines. Cell Counting Kit-8 and flow cytometry assays were conducted to detect the effect of EPN3 knockdown on breast cancer cell proliferation and apoptosis. Western blotting was used to detect the regulation of EPN3 expression on NF-κB, and immunofluorescence was performed to detect the effect of EPN3 expression on NF-κB nuclear translocation. The results demonstrated that the expression level of EPN3 in breast cancer tissues was higher compared with that in adjacent tissues (P<0.05). The expression level of EPN3 in the ER-positive breast cancer cell line, MCF7, was higher compared with that in the other cell lines (MCF10A, ZR75-1, MDA-MB-231, BT549 and SK-BR-3). After knocking down the expression of EPN3 in MCF7 cells, the proliferative ability of the cells was decreased, and the apoptosis rate was increased (P<0.05). After EPN3 knockdown in MCF7 cells, the phosphorylation of NF-κB was decreased (P<0.05), and the nuclear translocation signal was weakened. Thus, it was suggested that EPN3 promoted cell proliferation and inhibited cell apoptosis by regulating the NF-κB signaling pathway in ER-positive breast cancer.

Are There Limits in Explainability of Prognostic Biomarkers? Scrutinizing Biological Utility of Established Signatures

Prognostic biomarkers can have an important role in the clinical practice because they allow stratification of patients in terms of predicting the outcome of a disorder. Obstacles for developing such markers include lack of robustness when using different data sets and limited concordance among similar signatures. In this paper, we highlight a new problem that relates to the biological meaning of already established prognostic gene expression signatures. Specifically, it is commonly assumed that prognostic markers provide sensible biological information and molecular explanations about the underlying disorder. However, recent studies on prognostic biomarkers investigating 80 established signatures of breast and prostate cancer demonstrated that this is not the case. We will show that this surprising result is related to the distinction between causal models and predictive models and the obfuscating usage of these models in the biomedical literature. Furthermore, we suggest a falsification procedure for studies aiming to establish a prognostic signature to safeguard against false expectations with respect to biological utility.

Analysis of breast cancer subtypes and their correlations with receptors and ultrasound

The study aim was to evaluate the ultrasound (US) signs of the mammary lesions classified in the Breast Imaging-Reporting and Data System (BI-RADS) score category 3, 4, and 5, corresponding to US BI-RADS. It also followed the correlation between US changes of lesions suggestive for malignancy with the histopathological results and evaluated the proper management of those lesions. There were correlations of breast cancer (BC) subtypes with the receptors [estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2)], and Ki67 index, and the signs of conventional ultrasonography and US elastography. We selected 108 female patients examined with US, mammography and fine-needle biopsy who presented suspicions for malignancy lesions. Following the immunohistochemical analysis, they were classified in one of the BC subtypes. According to chi-squared analysis of molecular cancer subtypes correlation to receptors and Ki67 index, we found significant associations between both luminal A and luminal B HER2-negative subtypes and hormone receptors (ER, PR). These have an inverse relationship with Ki67 index elevated values; luminal B HER2-positive subtype has a direct association with HER2 presence; HER2-enriched subtype was statistically significant associated to HER2 presence and elevated Ki67 index values but had an inverse relationship to hormone receptors (ER, PR); triple-negative subtype was strongly associated to Ki67 index values and inversely correlated to ER and PR. We found luminal A subtype as being the most common and luminal B HER2-positive subtype as having the fewer cases.

MONTI: A Multi-Omics Non-negative Tensor Decomposition Framework for Gene-Level Integrative Analysis

Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data. Here, we propose a multi-omics analysis method called MONTI (Multi-Omics Non-negative Tensor decomposition for Integrative analysis), which goal is to select multi-omics features that are able to represent trait specific characteristics. Here, we demonstrate the strength of multi-omics integrated analysis in terms of cancer subtyping. The multi-omics data are first integrated in a biologically meaningful manner to form a three dimensional tensor, which is then decomposed using a non-negative tensor decomposition method. From the result, MONTI selects highly informative subtype specific multi-omics features. MONTI was applied to three case studies of 597 breast cancer, 314 colon cancer, and 305 stomach cancer cohorts. For all the case studies, we found that the subtype classification accuracy significantly improved when utilizing all available multi-omics data. MONTI was able to detect subtype specific gene sets that showed to be strongly regulated by certain omics, from which correlation between omics types could be inferred. Furthermore, various clinical attributes of nine cancer types were analyzed using MONTI, which showed that some clinical attributes could be well explained using multi-omics data. We demonstrated that integrating multi-omics data in a gene centric manner improves detecting cancer subtype specific features and other clinical features, which may be used to further understand the molecular characteristics of interest. The software and data used in this study are available at: https://github.com/inukj/MONTI.

Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data

Ultrahigh-dimensional gene features are often collected in modern cancer studies in which the number of gene features p is extremely larger than sample size n. While gene expression patterns have been shown to be related to patients' survival in microarray-based gene expression studies, one has to deal with the challenges of ultrahigh-dimensional genetic predictors for survival predicting and genetic understanding of the disease in precision medicine. The problem becomes more complicated when two types of survival endpoints, distant metastasis-free survival and overall survival, are of interest in the study and outcome data can be subject to semi-competing risks due to the fact that distant metastasis-free survival is possibly censored by overall survival but not vice versa. Our focus in this paper is to extract important features, which have great impacts on both distant metastasis-free survival and overall survival jointly, from massive gene expression data in the semi-competing risks setting. We propose a model-free screening method based on the ranking of the correlation between gene features and the joint survival function of two endpoints. The method accounts for the relationship between two endpoints in a simply defined utility measure that is easy to understand and calculate. We show its favorable theoretical properties such as the sure screening and ranking consistency, and evaluate its finite sample performance through extensive simulation studies. Finally, an application to classifying breast cancer data clearly demonstrates the utility of the proposed method in practice.

Neoadjuvant Therapy of Cyclin-Dependent Kinase 4/6 Inhibitors Combined with Endocrine Therapy in HR+/HER2- Breast Cancer: A Systematic Review and Meta-Analysis

BACKGROUND

Cyclin-dependent kinase (CDK) 4/6 inhibitors have been advocated for adjuvant therapy of metastatic hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)- breast cancer (BC). However, the efficiency of adding CDK 4/6 inhibitors to neoadjuvant therapy was not unequivocal.

OBJECTIVE

The aim of the study was to evaluate the efficiency and toxicity of neoadjuvant CDK 4/6 inhibitors + endocrine therapy (ET) versus neoadjuvant endocrine monotherapy or standard neoadjuvant chemotherapy in HR+/HER2- BC.

METHOD

We searched PubMed, the Cochrane Library, Web of Science, and Embase online databases for randomized controlled trials and single-arm studies written in English until April 2021.

RESULTS

Five studies comparing CDK 4/6 inhibitors + ET as neoadjuvant treatments to ET alone and 2 studies comparing neoadjuvant CDK 4/6 inhibitors + ET to neoadjuvant chemotherapy were analysed. Neoadjuvant CDK 4/6 inhibitors + ET improved the rate of complete cell cycle arrest (CCCA: central Ki67 < 2.7%, odds ratio [OR] = 7.91, 95% confidence interval [CI] = 4.81-13.03, p < 0.001), increased the risk of adverse events (AEs; especially ≥3 AEs; AEs of all grades: OR = 9.10, 95% CI = 2.39-34.58, p = 0.001; AEs ≥3: OR = 12.24, 95% CI = 4.17-35.88, p < 0.001), led to no significant differences in pathological complete response (pCR) in patients with BC (OR = 0.34, 95% CI = 0.04-2.85, p = 0.318) compared to endocrine monotherapy. Moreover, subgroup analysis showed that the 3 types of CDK 4/6 inhibitors all improved the rate of CCCA (ribociclib: OR = 10.31, 95% CI = 3.59-29.61, p < 0.001; palbociclib: OR = 7.39, 95% CI = 1.26-43.40, p = 0.027, and abemaciclib: OR = 8.28, 95% CI = 3.41-20.11, p < 0.001). Compared to neoadjuvant chemotherapy, neoadjuvant CDK 4/6 inhibitors plus ET decreased the risk of AEs ≥3 (OR = 0.50, 95% CI = 0.29-0.87, p = 0.015) and showed similar ability to reach pCR (OR = 0.50, 95% CI = 0.12-2.07, p = 0.342) and reduce the residual cancer burden (RCB, RCB 0-1: OR = 0.47, 95% CI = 0.18-1.22, p = 0.121; RCB 2-3: OR = 2.30, 95% CI = 0.89-5.91, p = 0.084).

CONCLUSIONS

The results suggested that combination therapy had increased efficacy and toxicity compared to endocrine monotherapy and showed similar efficacy to and better safety than neoadjuvant chemotherapy.

Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel Treatments

Breast cancer (BC) is characterized by high disease heterogeneity and represents the most frequently diagnosed cancer among women worldwide. Complex and subtype-specific gene expression alterations participate in disease development and progression, with BC cells known to rewire their cellular metabolism to survive, proliferate, and invade. Hence, as an emerging cancer hallmark, metabolic reprogramming holds great promise for cancer diagnosis, prognosis, and treatment. Multi-omics approaches (the combined analysis of various types of omics data) offer opportunities to advance our understanding of the molecular changes underlying metabolic rewiring in complex diseases such as BC. Recent studies focusing on the combined analysis of genomics, epigenomics, transcriptomics, proteomics, and/or metabolomics in different BC subtypes have provided novel insights into the specificities of metabolic rewiring and the vulnerabilities that may guide therapeutic development and improve patient outcomes. This review summarizes the findings of multi-omics studies focused on the characterization of the specific metabolic phenotypes of BC and discusses how they may improve clinical BC diagnosis, subtyping, and treatment.

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.

A molecular link between diabetes and breast cancer: Therapeutic potential of repurposing incretin-based therapies for breast cancer

Female breast cancer recently surpassed lung cancer and became the most commonly diagnosed cancer worldwide. As per the recent data from WHO, breast cancer accounts for one out of every 8 cancer cases diagnosed among an estimated 2.3 million new cancer cases. Breast cancer is the most prevailing cancer type among women causing the highest number of cancer-related mortality. It has been estimated that in 2020, 68,5000 women died due to this disease. Breast cancers have varying degrees of molecular heterogeneity; therefore, they are divided into various molecular clinical sub types. Recent reports suggest that type 2 diabetes (one of the common chronic diseases worldwide) is linked to the higher incidence, accelerated progression, and aggressiveness of different cancers; especially breast cancer. Breast cancer is hormone-dependent in nature and has a cross-talk with metabolism. A number of antidiabetic therapies are known to exert beneficial effects on various types of cancers, including breast cancer. However, only a few reports are available on the role of incretin-based antidiabetic therapies in cancer as a whole and in breast cancer in particular. The present review sheds light on the potential of incretin based therapies on breast cancer and explores the plausible underlying mechanisms. Additionally, we have also discussed the sub types of breast cancer as well as the intricate relationship between diabetes and breast cancer.

Molecular subtyping of breast cancer intrinsic taxonomy with oligonucleotide microarray and NanoString nCounter

Breast cancer intrinsic subtypes have been identified based on the transcription of a predefined gene expression (GE) profiles and algorithm (PAM50). This study compared molecular subtyping with oligonucleotide microarray and NanoString nCounter assay. A total of 109 Taiwanese breast cancers (24 with adjacent normal breast tissues) were assayed with Affymetrix Human Genome U133 plus 2.0 microarrays and 144 were assayed with the NanoString nCounter while 64 patients were assayed for both platforms. Subtyping with the nearest centroid (single sample prediction) was performed, and 16 out of 24 (67%) matched normal breasts were categorized as the normal breast-like subtype. For 64 breast cancers assayed for both platforms, 41 (65%, one unclassified by microarray) were predicted with an identical subtype, resulting in a fair Kappa statistic of 0.60. Taking nCounter subtyping as the gold standard, prediction accuracy was 43% (3/7), 81% (13/16), 25% (5/20), and 100% (20/20) for basal-like, HER2-enriched, luminal A and luminal B subtype predicted from microarray GE profiles. Microarray identified more luminal B cases from luminal A subtype predicted by nCounter. It's not uncommon to use microarray for breast cancer molecular subtyping for research. Our study showed that fundamental discrepancy existed between distinct GE assays, and cross platform equivalence should be carefully appraised when molecular subtyping was conducted with oligonucleotide microarray.

An insight into the cancer stem cell survival pathways involved in chemoresistance in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most complex, aggressive and fatal subtype of breast cancer. Owing to the lack of targeted therapy and heterogenic nature of TNBC, chemotherapy remains the sole treatment option for TNBC, with taxanes and anthracyclines representing the general chemotherapeutic regimen in TNBC therapy. But unfortunately, patients develop resistance to the existing chemotherapeutic regimen, resulting in approximately 90% treatment failure. Breast cancer stem cells (BCSCs) are one of the major causes for the development of chemoresistance in TNBC patients. After surviving the chemotherapy damage, the presence of BCSCs results in relapse and recurrence of TNBC. Several pathways are known to regulate BCSCs' survival, such as the Wnt/β-catenin, Hedgehog, JAK/STAT and HIPPO pathways. Therefore it is imperative to target these pathways in the context of eliminating chemoresistance. In this review we will discuss the novel strategies and various preclinical and clinical studies to give an insight into overcoming TNBC chemoresistance. We present a detailed account of recent studies carried out that open an exciting perspective in relation to the mechanisms of chemoresistance.

Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer

Biomarker discovery is at the heart of personalized treatment planning and cancer precision therapeutics, encompassing disease classification and prognosis, prediction of treatment response, and therapeutic targeting. However, many biomarkers represent passenger rather than driver alterations, limiting their utilization as functional units for therapeutic targeting. We suggest that identification of driver biomarkers through mechanism-centric approaches, which take into account upstream and downstream regulatory mechanisms, is fundamental to the discovery of functionally meaningful markers. Here, we examine computational approaches that identify mechanism-centric biomarkers elucidated from gene co-expression networks, regulatory networks (e.g., transcriptional regulation), protein-protein interaction (PPI) networks, and molecular pathways. We discuss their objectives, advantages over gene-centric approaches, and known limitations. Future directions highlight the importance of input and model interpretability, method and data integration, and the role of recently introduced technological advantages, such as single-cell sequencing, which are central for effective biomarker discovery and time-cautious precision therapeutics.

Society for Immunotherapy of Cancer (SITC) clinical practice guideline on immunotherapy for the treatment of breast cancer

Breast cancer has historically been a disease for which immunotherapy was largely unavailable. Recently, the use of immune checkpoint inhibitors (ICIs) in combination with chemotherapy for the treatment of advanced/metastatic triple-negative breast cancer (TNBC) has demonstrated efficacy, including longer progression-free survival and increased overall survival in subsets of patients. Based on clinical benefit in randomized trials, ICIs in combination with chemotherapy for the treatment of some patients with advanced/metastatic TNBC have been approved by the United States (US) Food and Drug Administration (FDA), expanding options for patients. Ongoing questions remain, however, about the optimal chemotherapy backbone for immunotherapy, appropriate biomarker-based selection of patients for treatment, the optimal strategy for immunotherapy treatment in earlier stage disease, and potential use in histological subtypes other than TNBC. To provide guidance to the oncology community on these and other important concerns, the Society for Immunotherapy of Cancer (SITC) convened a multidisciplinary panel of experts to develop a clinical practice guideline (CPG). The expert panel drew upon the published literature as well as their clinical experience to develop recommendations for healthcare professionals on these important aspects of immunotherapeutic treatment for breast cancer, including diagnostic testing, treatment planning, immune-related adverse events (irAEs), and patient quality of life (QOL) considerations. The evidence-based and consensus-based recommendations in this CPG are intended to give guidance to cancer care providers treating patients with breast cancer.

Biomarker-guided heterogeneity analysis of genetic regulations via multivariate sparse fusion

Heterogeneity is a hallmark of many complex diseases. There are multiple ways of defining heterogeneity, among which the heterogeneity in genetic regulations, for example, gene expressions (GEs) by copy number variations (CNVs), and methylation, has been suggested but little investigated. Heterogeneity in genetic regulations can be linked with disease severity, progression, and other traits and is biologically important. However, the analysis can be very challenging with the high dimensionality of both sides of regulation as well as sparse and weak signals. In this article, we consider the scenario where subjects form unknown subgroups, and each subgroup has unique genetic regulation relationships. Further, such heterogeneity is "guided" by a known biomarker. We develop a multivariate sparse fusion (MSF) approach, which innovatively applies the penalized fusion technique to simultaneously determine the number and structure of subgroups and regulation relationships within each subgroup. An effective computational algorithm is developed, and extensive simulations are conducted. The analysis of heterogeneity in the GE-CNV regulations in melanoma and GE-methylation regulations in stomach cancer using the TCGA data leads to interesting findings.

Epigenetic Regulation of Breast Cancer Stem Cells Contributing to Carcinogenesis and Therapeutic Implications

Globally, breast cancer has remained the most commonly diagnosed cancer and the leading cause of cancer death among women. Breast cancer is a highly heterogeneous and phenotypically diverse group of diseases, which require different selection of treatments. Breast cancer stem cells (BCSCs), a small subset of cancer cells with stem cell-like properties, play essential roles in breast cancer progression, recurrence, metastasis, chemoresistance and treatments. Epigenetics is defined as inheritable changes in gene expression without alteration in DNA sequence. Epigenetic regulation includes DNA methylation and demethylation, as well as histone modifications. Aberrant epigenetic regulation results in carcinogenesis. In this review, the mechanism of epigenetic regulation involved in carcinogenesis, therapeutic resistance and metastasis of BCSCs will be discussed, and finally, the therapies targeting these biomarkers will be presented.

Mechanisms of Drug Resistance and Use of Nanoparticle Delivery to Overcome Resistance in Breast Cancers

Breast cancer is the leading cancer type diagnosed among women in the world. Unfortunately, drug resistance to current breast cancer chemotherapeutics remains the main challenge for a higher survival rate. The recent progress in the nanoparticle platforms and distinct features of nanoparticles that enhance the efficacy of therapeutic agents, such as improved delivery efficacy, increased intracellular cytotoxicity, and reduced side effects, hold great promise to overcome the observed drug resistance. Currently, multifaceted investigations are probing the resistance mechanisms associated with clinical drugs, and identifying new breast cancer-associated molecular targets that may lead to improved therapeutic approaches with the nanoparticle platforms. Nanoparticle platforms including siRNA, antibody-specific targeting and the role of nanoparticles in cellular processes and their effect on breast cancer were discussed in this article.

Estrogen receptor-low breast cancer: Biology chaos and treatment paradox

Hormone receptor testing mainly serves the purpose of guiding treatment choices for breast cancer patients. Patients with estrogen receptor (ER)‐positive breast cancers show significant response to endocrine therapy. However, the methods to define ER status and eligibility for treatment remain controversial. Despite recent guidelines considering staining ≥1% of tumor nuclei by immunohistology as ER‐positive, it has raised concerns on the benefit of endocrine therapy for tumors with ER 1%‐10% expression, termed “ER‐low positive”. This subgroup accounts for 3% to 9% of all patients and is likely to have unique molecular features, and therefore distinct therapeutic response to endocrine therapy compared with ER‐high positive tumors. The latest guidelines did not provide detailed descriptions for those patients, resulting in inconsistent treatment strategies. Consequently, we aimed to resolve this dilemma comprehensively. This review discusses molecular traits and recent ER‐low positive breast cancer innovations, highlighting molecular‐targeted treatment rather than traditional unified endocrine therapy for future basic and clinical research.

Prognostic Relevance of Neutrophil to Lymphocyte Ratio (NLR) in Luminal Breast Cancer: A Retrospective Analysis in the Neoadjuvant Setting

The neutrophil to lymphocyte ratio (NLR) is a promising predictive and prognostic factor in breast cancer. We investigated its ability to predict disease-free survival (DFS) and overall survival (OS) in patients with luminal A- or luminal B-HER2-negative breast cancer who received neoadjuvant chemotherapy (NACT). Pre-treatment complete blood cell counts from 168 consecutive patients with luminal breast cancer were evaluated to assess NLR. The study population was stratified into NLR^low or NLR^high according to a cut-off value established by receiving operator curve (ROC) analysis. Data on additional pre- and post-treatment clinical-pathological characteristics were also collected. Kaplan–Meier curves, log-rank tests, and Cox proportional hazards models were used for statistical analyses. Patients with pre-treatment NLR^low showed a significantly shorter DFS (HR: 6.97, 95% CI: 1.65–10.55, p = 0.002) and OS (HR: 7.79, 95% CI: 1.25–15.07, p = 0.021) compared to those with NLR^high. Non-ductal histology, luminal B subtype, and post-treatment Ki67 ≥ 14% were also associated with worse DFS (p = 0.016, p = 0.002, and p = 0.001, respectively). In a multivariate analysis, luminal B subtype, post-treatment Ki67 ≥ 14%, and NLR^low remained independent prognostic factors for DFS, while only post-treatment Ki67 ≥ 14% and NLR^low affected OS. The present study provides evidence that pre-treatment NLR^low helps identify women at higher risk of recurrence and death among patients affected by luminal breast cancer treated with NACT.

Invasive lobular carcinoma: clinicopathological features and subtypes

Objective

To analyze the characteristics of invasive lobular carcinoma (ILC) compared with invasive ductal carcinoma (IDC) and to investigate the impact of histology on axillary lymph node (ALN) involvement in luminal A subtype tumors.

Methods

We retrospectively analyzed patients diagnosed with ILC or IDC from 2012 to 2016 who underwent surgery. Patients constituted 493 primary early breast cancer cases (82 ILC; 411 IDC).

Results

Compared with IDC, ILC tumors were significantly more likely to be grade 2, estrogen receptor- (ER) positive (+), have a lower proliferation rate (Ki67 <14%), and a higher pathological T stage (pT2–4). The luminal A subtype was significantly more common in ILC compared with IDC. In a multivariate regression model, grade 2, ER+, progesterone receptor-positive, pT2, and pT3 were significantly associated with ILC. Additionally, with the luminal A subtype, ALN involvement (pathological node stage (pN)1–3) was significantly more frequent with ILC versus IDC.

Conclusions

Our data suggest that grade 2, positive hormone receptor status, and higher pathological T stage are associated with ILC. With the luminal A subtype, ALN involvement was more frequent with ILC versus IDC.

Revealing nuclear receptor hub modules from Basal-like breast cancer expression networks

Nuclear receptors are a class of transcriptional factors. Together with their co-regulators, they regulate development, homeostasis, and metabolism in a ligand-dependent manner. Their ability to respond to environmental stimuli rapidly makes them versatile cellular components. Their coordinated activities regulate essential pathways in normal physiology and in disease. Due to their complexity, the challenge remains in understanding their direct associations in cancer development. Basal-like breast cancer is an aggressive form of breast cancer that often lacks ER, PR and Her2. The absence of these receptors limits the treatment for patients to the non-selective cytotoxic and cytostatic drugs. To identify potential drug targets it is essential to identify the most important nuclear receptor association network motifs in Basal-like subtype progression. This research aimed to reveal the transcriptional network patterns, in the hope to capture the underlying molecular state driving Basal-like oncogenesis. In this work, we illustrate a multidisciplinary approach of integrating an unsupervised machine learning clustering method with network modelling to reveal unique transcriptional patterns (network motifs) underlying Basal-like breast cancer. The unsupervised clustering method provides a natural stratification of breast cancer patients, revealing the underlying heterogeneity in Basal-like. Identification of gene correlation networks (GCNs) from Basal-like patients in both the TCGA and METABRIC databases revealed three critical transcriptional regulatory constellations that are enriched in Basal-like. These represent critical NR components implicated in Basal-like breast cancer transcription. This approach is easily adaptable and applicable to reveal critical signalling relationships in other diseases.

Tissue mechanics in stem cell fate, development, and cancer

Cells in tissues experience a plethora of forces that regulate their fate and modulate development and homeostasis. Cells sense mechanical cues through localized mechanoreceptors or by influencing cytoskeletal or plasma membrane organization. Cells translate force and modulate their behavior through a process termed mechanotransduction. Cells tune their tension upon exposure to chronic force by engaging cellular machinery that modulates actin tension, which in turn stimulates matrix remodeling and stiffening and alters cell-cell adhesions until cells achieve a state of tensional homeostasis. Loss of tensional homeostasis can be induced through oncogene activity and/or tissue fibrosis, accompanies tumor progression, and is associated with increased cancer risk. The mechanical stresses that develop in tumors can also foster the mesenchymal-like transdifferentiation of cells to induce a stem-like phenotype that contributes to their aggression, metastatic dissemination, and treatment resistance. Thus, strategies that ameliorate tumor mechanics may comprise an effective strategy to prevent aggressive tumor behavior.

Circulating immune cell populations related to primary breast cancer, surgical removal, and radiotherapy revealed by flow cytometry analysis

BACKGROUND

Advanced breast cancer (BC) impact immune cells in the blood but whether such effects may reflect the presence of early BC and its therapeutic management remains elusive.

METHODS

To address this question, we used multiparametric flow cytometry to analyze circulating leukocytes in patients with early BC (n = 13) at the time of diagnosis, after surgery, and after adjuvant radiotherapy, compared to healthy individuals. Data were analyzed using a minimally supervised approach based on FlowSOM algorithm and validated manually.

RESULTS

At the time of diagnosis, BC patients have an increased frequency of CD117⁺CD11b⁺ granulocytes, which was significantly reduced after tumor removal. Adjuvant radiotherapy increased the frequency of CD45RO⁺ memory CD4⁺ T cells and CD4⁺ regulatory T cells. FlowSOM algorithm analysis revealed several unanticipated populations, including cells negative for all markers tested, CD11b⁺CD15^low, CD3⁺CD4^-CD8^-, CD3⁺CD4⁺CD8⁺, and CD3⁺CD8⁺CD127⁺CD45RO⁺ cells, associated with BC or radiotherapy.

CONCLUSIONS

This study revealed changes in blood leukocytes associated with primary BC, surgical removal, and adjuvant radiotherapy. Specifically, it identified increased levels of CD117⁺ granulocytes, memory, and regulatory CD4⁺ T cells as potential biomarkers of BC and radiotherapy, respectively. Importantly, the study demonstrates the value of unsupervised analysis of complex flow cytometry data to unravel new cell populations of potential clinical relevance.

High-risk lesions of the breast: concurrent diagnostic tools and management recommendations

Breast lesions with uncertain malignant behavior, also known as high-risk or B3 lesions, are composed of a variety of pathologies with differing risks of associated malignancy. While open excision was previously preferred to manage all high-risk lesions, tailored management has been increasingly favored to reduce overtreatment and spare patients from unnecessary anxiety or high healthcare costs associated with surgical excision. The purpose of this work is to provide the reader with an accurate overview focused on the main high-risk lesions of the breast: atypical intraductal epithelial proliferation (atypical ductal hyperplasia), lobular neoplasia (including the subcategories lobular carcinoma in situ and atypical lobular hyperplasia), flat epithelial atypia, radial scar and papillary lesions, and phyllodes tumor. Beyond merely presenting the radiological aspects of these lesions and the recent literature, information about their potential upgrade rates is discussed in order to provide a useful guide for appropriate clinical management while avoiding the risks of unnecessary surgical intervention (overtreatment).

imPlatelet classifier: image-converted RNA biomarker profiles enable blood-based cancer diagnostics

Liquid biopsies offer a minimally invasive sample collection, outperforming traditional biopsies employed for cancer evaluation. The widely used material is blood, which is the source of tumor‐educated platelets. Here, we developed the imPlatelet classifier, which converts RNA‐sequenced platelet data into images in which each pixel corresponds to the expression level of a certain gene. Biological knowledge from the Kyoto Encyclopedia of Genes and Genomes was also implemented to improve accuracy. Images obtained from samples can then be compared against standard images for specific cancers to determine a diagnosis. We tested imPlatelet on a cohort of 401 non‐small cell lung cancer patients, 62 sarcoma patients, and 28 ovarian cancer patients. imPlatelet provided excellent discrimination between lung cancer cases and healthy controls, with accuracy equal to 1 in the independent dataset. When discriminating between noncancer cases and sarcoma or ovarian cancer patients, accuracy equaled 0.91 or 0.95, respectively, in the independent datasets. According to our knowledge, this is the first study implementing an image‐based deep‐learning approach combined with biological knowledge to classify human samples. The performance of imPlatelet considerably exceeds previously published methods and our own alternative attempts of sample discrimination. We show that the deep‐learning image‐based classifier accurately identifies cancer, even when a limited number of samples are available.

High-background parenchymal enhancement in the contralateral breast is an imaging biomarker for favorable prognosis in patients with triple-negative breast cancer treated with chemotherapy

This study aimed to analyze the association between background parenchymal enhancement (BPE) in the contralateral breast tissue on magnetic resonance imaging (MRI) and clinicopathologic parameters in patients with unilateral breast carcinoma and to investigate its potential prognostic significance. A total of 467 patients who were pathologically confirmed to have unilateral breast cancer and underwent breast MRI were recruited to participate in this cohort study. BPE was assessed in the healthy contralateral breast. Minimal and mild levels were classified as low BPE, whereas moderate and marked levels were classified as high BPE. The effects of BPE on clinicopathologic parameters, overall survival (OS), and invasive disease-free survival (IDFS) were determined. Among the 467 patients, 327 cases were classified into the low-BPE group, whereas 140 cases were classified into the high-BPE group. The high-BPE pattern markedly correlated with age at diagnosis, menopausal status, histologic grading, and estrogen receptor status. BPE pattern did not correlate with OS and IDFS in the entire breast cancer cohort, regardless of whether adjuvant chemotherapy was received. Notably, BPE in the healthy contralateral breast on MRI is markedly related to OS and IDFS in triple-negative breast cancer (TNBC) cases who received chemotherapy. High BPE is related to chemotherapeutic benefits and can be an independent favorable prognostic factor for TNBC patients. Thus, our observations suggest that high BPE pattern can potentially be used as an imaging biomarker for relatively favorable prognosis in TNBC cases receiving chemotherapy. However, the findings need to be verified in a large-scale study.

Detection of acquired radioresistance in breast cancer cell lines using Raman spectroscopy and machine learning

Radioresistance-a living cell's response to, and development of resistance to ionising radiation-can lead to radiotherapy failure and/or tumour recurrence. We used Raman spectroscopy and machine learning to characterise biochemical changes that occur in acquired radioresistance for breast cancer cells. We were able to distinguish between wild-type and acquired radioresistant cells by changes in chemical composition using Raman spectroscopy and machine learning with 100% accuracy. In studying both hormone receptor positive and negative cells, we found similar changes in chemical composition that occur with the development of acquired radioresistance; these radioresistant cells contained less lipids and proteins compared to their parental counterparts. As well as characterising acquired radioresistance in vitro, this approach has the potential to be translated into a clinical setting, to look for Raman signals of radioresistance in tumours or biopsies; that would lead to tailored clinical treatments.

The impact of progesterone receptor negativity on oncological outcomes in oestrogen-receptor-positive breast cancer

BACKGROUND

Oestrogen receptor (ER) status provides invaluable prognostic and therapeutic information in breast cancer (BC). When clinical decision making is driven by ER status, the value of progesterone receptor (PgR) status is less certain. The aim of this study was to describe clinicopathological features of ER-positive (ER+)/PgR-negative (PgR-) BC and to determine the effect of PgR negativity in ER+ disease.

METHODS

Consecutive female patients with ER+ BC from a single institution were included. Factors associated with PgR- disease were assessed using binary logistic regression. Oncological outcome was assessed using Kaplan-Meier and Cox regression analysis.

RESULTS

In total, 2660 patients were included with a mean(s.d.) age of 59.6(13.3) years (range 21-99 years). Median follow-up was 97.2 months (range 3.0-181.2). Some 2208 cases were PgR+ (83.0 per cent) and 452 were PgR- (17.0 per cent). Being postmenopausal (odds ratio (OR) 1.66, 95 per cent c.i. 1.25 to 2.20, P < 0.001), presenting with symptoms (OR 1.71, 95 per cent c.i. 1.30 to 2.25, P < 0.001), ductal subtype (OR 1.51, 95 per cent c.i. 1.17 to 1.97, P = 0.002) and grade 3 tumours (OR 2.20, 95 per cent c.i. 1.68 to 2.87, P < 0.001) were all associated with PgR negativity. In those receiving neoadjuvant chemotherapy (308 patients), pathological complete response rates were 10.1 per cent (25 of 247 patients) in patients with PgR+ disease versus 18.0 per cent in PgR- disease (11 of 61) (P = 0.050). PgR negativity independently predicted worse disease-free (hazard ratio (HR) 1.632, 95 per cent c.i. 1.209 to 2.204, P = 0.001) and overall survival (HR 1.774, 95 per cent c.i. 1.324 to 2.375, P < 0.001), as well as worse overall survival in ER+/HER2- disease (P = 0.004).

CONCLUSIONS

In ER+ disease, PgR- tumours have more aggressive clinicopathological features and worse oncological outcomes. Neoadjuvant and adjuvant therapeutic strategies should be tailored according to PgR status.

An integrated deep learning and dynamic programming method for predicting tumor suppressor genes, oncogenes, and fusion from PDB structures

Mutations in proto-oncogenes (ONGO) and the loss of regulatory function of tumor suppression genes (TSG) are the common underlying mechanism for uncontrolled tumor growth. While cancer is a heterogeneous complex of distinct diseases, finding the potentiality of the genes related functionality to ONGO or TSG through computational studies can help develop drugs that target the disease. This paper proposes a classification method that starts with a preprocessing stage to extract the feature map sets from the input 3D protein structural information. The next stage is a deep convolutional neural network stage (DCNN) that outputs the probability of functional classification of genes. We explored and tested two approaches: in Approach 1, all filtered and cleaned 3D-protein-structures (PDB) are pooled together, whereas in Approach 2, the primary structures and their corresponding PDBs are separated according to the genes' primary structural information. Following the DCNN stage, a dynamic programming-based method is used to determine the final prediction of the primary structures' functionality. We validated our proposed method using the COSMIC online database. For the ONGO vs TSG classification problem the AUROC of the DCNN stage for Approach 1 and Approach 2 DCNN are 0.978 and 0.765, respectively. The AUROCs of the final genes' primary structure functionality classification for Approach 1 and Approach 2 are 0.989, and 0.879, respectively. For comparison, the current state-of-the-art reported AUROC is 0.924. Our results warrant further study to apply the deep learning models to humans' (GRCh38) genes, for predicting their corresponding probabilities of functionality in the cancer drivers.

A Comparative Analysis of Body Mass Index with Estrogen Receptor, Progesterone Receptor and Human Epidermal Growth Factor Receptor 2 Status in Pre- and Postmenopausal Breast Cancer Patients

Background

Breast cancer is now the most common cancer among Indian women. Recent studies have suggested a possible link between risk factors like high BMI and molecular subtypes of breast cancer. Studies from Western and Asian population have shown varying relationship between post- menopausal obesity and expression of ER, PR, Her2-neu receptors in breast cancer patients.

Aim

This study was done with an aim to explore if overweight or obesity as defined by BMI and status of ER, PR and Her2-neu receptors differ in Indian pre-menopausal and post-menopausal breast cancer patients.

Methods and Material

This is a retrospective analysis of 446 breast cancer patients treated at Mahavir Cancer Sansthan, Patna from July to December 2019. Their case records were evaluated and data regarding age, menopausal status, height, weight and ER, PR & HER2-neu receptor status were extracted for analyses.

Statistical Analysis

Chi-square test was used to compare categorical variables between the pre-menopausal and post-menopausal group.

Results

The prevalence of obesity in the post-menopausal group was 2.3% more than the pre-menopausal group (P-value = 0.24). As compared to the pre-menopausal group, there was an increase in the ER/PR positivity in the postmenopausal group by 3.41% (P-value = 0.47) and in the Her2-neu positivity by 6.38% (P-value = 0.15). As compared to the pre-menopausal group, there was further increase in the ER/PR positivity in the post-menopausal group by 6.85% (P-value = 0.40) in sub-group of patients with BMI ≥ 25kg/m².

Conclusions

Our study showed slightly increased incidence of obesity in post-menopausal breast cancer patients. Overweight post-menopausal patients also had a higher percentage of ER/PR receptor positivity and lower percentage of Triple negative breast cancer. The percentage of Her2-neu receptor positivity was more in post-menopausal patients. A high BMI was found to be associated with a lower Her2neu positivity.

A single-cell atlas of the healthy breast tissues reveals clinically relevant clusters of breast epithelial cells

Single-cell RNA sequencing (scRNA-seq) is an evolving technology used to elucidate the cellular architecture of adult organs. Previous scRNA-seq on breast tissue utilized reduction mammoplasty samples, which are often histologically abnormal. We report a rapid tissue collection/processing protocol to perform scRNA-seq of breast biopsies of healthy women and identify 23 breast epithelial cell clusters. Putative cell-of-origin signatures derived from these clusters are applied to analyze transcriptomes of ~3,000 breast cancers. Gene signatures derived from mature luminal cell clusters are enriched in ~68% of breast cancers, whereas a signature from a luminal progenitor cluster is enriched in ~20% of breast cancers. Overexpression of luminal progenitor cluster-derived signatures in HER2+, but not in other subtypes, is associated with unfavorable outcome. We identify TBX3 and PDK4 as genes co-expressed with estrogen receptor (ER) in the normal breasts, and their expression analyses in >550 breast cancers enable prognostically relevant subclassification of ER+ breast cancers.

[Management of breast cancer in Abidjan: A single center experience]

AIM

To present the results of the personalized care of Ivorian women suffering from breast cancer since the advent of immunohistochemistry in Côte d'Ivoire.

METHODS

We carried out a single-center retrospective study at the Yopougon university hospital from January 2014 to December 2018. All women's breast cancer with complementary immunohistochemistry and treated at the Yopougon hospital center were selected. Standard descriptive statistical tests were used to describe patient and tumor characteristics, and univariate and multivariate analyzes were performed with a statistical significance set at a P-value of 0.05 using SPSS version 20.0.

RESULTS

The mean age of women is 48.27 years, SD (11.92). 50.88 % of the tumors were hormone-dependent. The triple negative subgroup was the most represented (43.28 %) followed by luminal A (35.42 %). Conservative treatment represented 18.51 % of cases. In the univariate analysis, the risk of developing a hormone-dependent cancer is statistically significant respectively in women with an education level removed OR=1.98 (P˂0.015) and with a wealthy salary OR=1.85 (P˂0.009). On the other hand, the high level of education (OR=0.44; P˂0.005), and the well-off salary condition (OR=0.59; P˂0.024) would be protective factors for the development of triple negative breast cancer. All these factors are not significant in multivariate analysis, whether for hormone-dependent or triple negative tumors.

CONCLUSION

The personalized care of breast cancer in our African context remains difficult and must take into account several medical and extra-medical parameters.

Molecular classification predicts survival for breast cancer patients in Vietnam: a single institutional retrospective analysis

BACKGROUND

The Bhagarva surrogate molecular subtype definitions classify invasive breast cancer into seven the different subgroups based on immunohistochemical (IHC) criteria according to expression levels of markers as ER, PR, HER2, EGFR and/or basal cytokeratin (CK5/6) which are different in prognosis and responsiveness to adjuvant therapy.

PURPOSE

The present study aimed to classify primary breast cancers and directly compares the prognostic significance of the intrinsic subtypes.

METHODS

The current study was conducted on 522 breast cancer patients who had surgery, but had not received neoadjuvant chemotherapy, from 2011 to 2014. The clinicopathologic characteristics were recorded. IHC staining was performed for ER, PR, HER2, Ki67, CK5/6, EGFR and D2-40 markers. All breast cancer patients were stratified according to Bhagarva criteria. The followed-up patients' survival was analyzed by using Kaplan-Meier and Log-Rank models.

RESULTS

The luminal A (LUMA) was observed at the highest rate (32.5%). Non-basal-like triple negative phenotype (TNB-) and Luminal A HER2-Hybrid (LAHH) were the least common (3.3% in both). LUMA and luminal B (LUMB) were significantly associated with better prognostic features compared to HER2, basal-like triple negative phenotype (TNB+) and TNB-. Statistically significant differences were demonstrated between overall survival (OS), disease-free survival (DFS) and molecular subtypes (P<0.05), of which LUMB and LUMA had the highest rate of OS and DFS being 97.2 and 93.7%; and 97.2 and 90.5%, respectively. Conversely, HER2 revealed the worst prognosis with the lowest prevalence of OS and DFS (72.5 and 69.9%, respectively).

CONCLUSION

The molecular subtypes had a distinct OS and DFS. The intrinsic stratification displayed inversely to clinicopathological features in breast cancer.

NVP-BEZ235 or JAKi Treatment leads to decreased survival of examined GBM and BBC cells

Cancer cells almost universally harbor constitutively active Phosphatidylinositol-3 Kinase (PI3K) Pathway activity via mutation of key signaling components and/or epigenetic mechanisms. Scores of PI3K Pathway inhibitors are currently under investigation as putative chemotherapeutics. However, feedback and stem cell mechanisms induced by PI3K Pathway inhibition can lead to reduced treatment efficacy. To address therapeutic barriers, we examined whether JAKi would reduce stem gene expression in a setting of PI3K Pathway inhibition in order to improve treatment efficacy. We targeted the PI3K Pathway with NVP-BEZ235 (dual PI3K and mTOR inhibitor) in combination with the Janus Kinase inhibitor JAKi in glioblastoma (GBM) and basal-like breast cancer (BBC) cell lines. We examined growth, gene expression, and apoptosis in cells treated with NVP-BEZ235 and/or JAKi. Growth and recovery assays showed no significant impact of dual treatment with NVP-BEZ235/JAKi compared to NVP-BEZ235 treatment alone. Gene expression and flow cytometry revealed that single and dual treatments induced apoptosis. Stem gene expression was retained in dual NVP-BEZ235/JAKi treatment samples. Future in vivo studies may give further insight into the impact of combined NVP-BEZ235/JAKi treatment in GBM and BBC.

Comparison of Methods for Feature Selection in Clustering of High-Dimensional RNA-Sequencing Data to Identify Cancer Subtypes

Cancer subtype identification is important to facilitate cancer diagnosis and select effective treatments. Clustering of cancer patients based on high-dimensional RNA-sequencing data can be used to detect novel subtypes, but only a subset of the features (e.g., genes) contains information related to the cancer subtype. Therefore, it is reasonable to assume that the clustering should be based on a set of carefully selected features rather than all features. Several feature selection methods have been proposed, but how and when to use these methods are still poorly understood. Thirteen feature selection methods were evaluated on four human cancer data sets, all with known subtypes (gold standards), which were only used for evaluation. The methods were characterized by considering mean expression and standard deviation (SD) of the selected genes, the overlap with other methods and their clustering performance, obtained comparing the clustering result with the gold standard using the adjusted Rand index (ARI). The results were compared to a supervised approach as a positive control and two negative controls in which either a random selection of genes or all genes were included. For all data sets, the best feature selection approach outperformed the negative control and for two data sets the gain was substantial with ARI increasing from (-0.01, 0.39) to (0.66, 0.72), respectively. No feature selection method completely outperformed the others but using the dip-rest statistic to select 1000 genes was overall a good choice. The commonly used approach, where genes with the highest SDs are selected, did not perform well in our study.

Screening and Identification of Key Common and Specific Genes and Their Prognostic Roles in Different Molecular Subtypes of Breast Cancer

Breast cancer is one of the most common cancers. Although the present molecular classification improves the treatment effect and prognosis of breast cancer, the heterogeneity of the molecular subtype remains very complex, and the applicability and effectiveness of treatment methods are still limited leading to poorer patient prognosis than expected. Further identification of more refined molecular typing based on gene expression profile will yield better understanding of the heterogeneity, improving treatment effects and prolonging prognosis of patients. Here, we downloaded the mRNA expression profiles and corresponding clinical data of patients with breast cancer from public databases and performed typical molecular typing using PAM50 (Prediction Analysis of Microarray 50) method. Comparative analyses were performed to screen the common and specific differentially expressed genes (DEGs) between cancer and corresponding para-cancerous tissues in each breast cancer subtype. The GO and KEGG analyses of the DEGs were performed to enrich the common and specific functional progress and signaling pathway involved in breast cancer subtypes. A total of 38 key common and specific DEGs were identified and selected based on the validated results, GO/KEGG enrichments, and the priority of expression, including four common DEGs and 34 specific DEGs in different subtypes. The prognostic value of these key common and specific DEGs was further analyzed to obtain useful potential markers in clinic. Finally, the potential roles and the specific prognostic values of the common and specific DEGs were speculated and summarized in total breast cancer and different subtype breast cancer based on the results of these analyses. The findings of our study provide the basis of more refined molecular typing of breast cancer, potential new therapeutic targets and prognostic markers for different breast cancer subtypes