Molecular Subtypes Of Breast Cancer Research Articles

RS17817449 FTO gene variation associated with familial disease burden rather than individual risk for breast cancer

Introduction: Breast cancer (BC) is the most common malignancy in the female population globally. Obesity is associated with an increased risk of postmenopausal BC, BC recurrence, and mortality. Fat mass and obesity-associated (FTO) gene polymorphisms have attracted the most attention due to several single-nucleotide polymorphisms (SNPs) that may have an impact on obesity and different types of cancer. The primary goal of our work was to assess the association of the SNP rs17817449 FTO, physical status/metabolic changes, and dietary habits with the occurrence of BC. Methods: We conducted research as a population-genetic study including 93 women with a diagnosis of BC during their lifetime. Genomic DNA was extracted from the swabs of the buccal mucosa. Genotyping was achieved by polymerase chain reaction-restriction fragment length polymorphism. The IBM SPSS Statistics program v. 23.0 was used for statistical analysis. All values of p < 0.05 were considered statistically significant. Results: The risk genotype of the FTO gene (rs17817449) GG was detected in 16 subjects (17.2%), the heterozygous TG in 46 subjects (49.5%), while the normal genotype TT was recorded in 29 subjects (31.2%). We found no statistically significant difference in the body mass index values of the three genotype groups, p = 0.72, χ2 = 2.1 and no significant relationship between the allelic or genotypic frequencies of the rs17817449 FTO gene polymorphism and other variables examined in our study. Analysis of the distribution of hereditary diseases in the family according to the molecular subtype of BC showed statistically significant p-values, p = 0.02. Conclusion: While previous research has suggested a potential link between FTO gene polymorphism, obesity, and BC, our study did not find a statistically significant association between the aforementioned variables. Future studies with a larger number of subjects in different populations should confirm the role of the FTO genotype in the risk of BC.

The Influence of Adipocyte Secretome on Selected Metabolic Fingerprints of Breast Cancer Cell Lines Representing the Four Major Breast Cancer Subtypes.

Molecular subtype (MS) is one of the most used classifications of breast cancer (BC). Four MSs are widely accepted according to receptor expression of estrogen, progesterone, and HER2. The impact of adipose tissue on BC MS metabolic impairment is still unclear. The present work aims to elucidate the metabolic alterations in breast cancer cell lines representing different MSs subjected to adipocyte associated factors. Preadipocytes isolated from human subcutaneous adipose tissue were differentiated into mature adipocytes. MS representative cell lines were exposed to mature adipocyte secretome. Extracellular medium was collected for metabolomics and RNA was extracted to evaluate enzymatic expression by RT-PCR. Adipocyte secretome exposure resulted in a decrease in the Warburg effect rate and an increase in cholesterol release. HER2+ cell lines (BT-474 and SK-BR-3) exhibited a similar metabolic pattern, in contrast to luminal A (MCF-7) and triple negative (TN) (MDA-MB-231), both presenting identical metabolisms. Anaplerosis was found in luminal A and TN representative cells, whereas cataplerotic reactions were likely to occur in HER2+ cell lines. Our results indicate that adipocyte secretome affects the central metabolism distinctly in each BC MS representative cell line.

Evaluation of miRNA-200c and βIII tubulin as promising markers for clinical decision making in locally advanced breast cancer patients receiving taxanes therapy

BackgroundClinical management of locally advanced breast cancer (LABC) is a common clinical problem. Therefore, finding a good predictor for the efficacy of treatment is crucial. The miRNAs are small non-coding RNAs that play an important role in gene regulation of many biological processes. Class III tubulin (βIII tubulin) are dynamic fibrous cytoskeletal proteins that play a significant role in chromosome segregation during mitosis and meiosis and has been identified to participate in cell proliferation and cancer development. AimTo evaluate the role of miRNA-200c and βIII tubulin as promising markers for clinical decision making in locally advanced breast cancer patients receiving taxanes therapy. MethodsA retrospective study of paraffin sections of 143 female breast patients: 22 patients with benign breast diseases and 121 breast cancer patients receiving neoadjuvant taxane therapy divided to: 94 responder patients and 27 non responder patients. Patients were followed up from date of first diagnosis till end of the study. The expression level of miRNA 200c was evaluated using real time PCR and the expression level of βIII tubulin protein was immunohistochemically estimated. Results were correlated with response to neoadjuvant taxanes therapy. ResultsThe miRNA 200 c was significantly down regulated in patients responding to taxanes as compared with non-responders (p = 0.009). The βIII tubulin protein was significantly highly expressed in ER negative and PR negative breast cancer patients (p = 0.011, p = 0.053, respectively). It was significantly up regulated in triple negative and HER2 enriched (non luminal) cases and down regulated in luminal cases (p = 0.023). ConclusionThe results suggest miRNA 200c may be a predictive marker for response to neoadjuvant taxanes therapy in breast cancer patients, while βIII tubulin protein showed association with molecular subtypes of breast cancer namely luminal versus non luminal cases that may serve as an excellent marker for clinical decision making.

Aberrant activation of five embryonic stem cell-specific genes robustly predicts a high risk of relapse in breast cancers

BackgroundIn breast cancer, as in all cancers, genetic and epigenetic deregulations can result in out-of-context expressions of a set of normally silent tissue-specific genes. The activation of some of these genes in various cancers empowers tumours cells with new properties and drives enhanced proliferation and metastatic activity, leading to a poor survival prognosis.ResultsIn this work, we undertook an unprecedented systematic and unbiased analysis of out-of-context activations of a specific set of tissue-specific genes from testis, placenta and embryonic stem cells, not expressed in normal breast tissue as a source of novel prognostic biomarkers. To this end, we combined a strict machine learning framework of transcriptomic data analysis, and successfully created a new robust tool, validated in several independent datasets, which is able to identify patients with a high risk of relapse. This unbiased approach allowed us to identify a panel of five biomarkers, DNMT3B, EXO1, MCM10, CENPF and CENPE, that are robustly and significantly associated with disease-free survival prognosis in breast cancer. Based on these findings, we created a new Gene Expression Classifier (GEC) that stratifies patients. Additionally, thanks to the identified GEC, we were able to paint the specific molecular portraits of the particularly aggressive tumours, which show characteristics of male germ cells, with a particular metabolic gene signature, associated with an enrichment in pro-metastatic and pro-proliferation gene expression.ConclusionsThe GEC classifier is able to reliably identify patients with a high risk of relapse at early stages of the disease. We especially recommend to use the GEC tool for patients with the luminal-A molecular subtype of breast cancer, generally considered of a favourable disease-free survival prognosis, to detect the fraction of patients undergoing a high risk of relapse.

Using administrative healthcare data to evaluate drug repurposing opportunities for cancer: the possibility of using beta-blockers to treat breast cancer.

Introduction: Cancer registries and hospital electronic medical records are commonly used to investigate drug repurposing candidates for cancer. However, administrative data are often more accessible than data from cancer registries and medical records. Therefore, we evaluated if administrative data could be used to evaluate drug repurposing for cancer by conducting an example study on the association between beta-blocker use and breast cancer mortality. Methods: A retrospective cohort study of women aged ≥50years with incident breast cancer was conducted using a linked dataset with statewide hospital admission data and nationwide medication claims data. Women receiving beta blockers and first-line anti-hypertensives prior to and at diagnosis were compared. Breast cancer molecular subtypes and metastasis status were inferred by algorithms from commonly prescribed breast cancer antineoplastics and hospitalization diagnosis codes, respectively. Subdistribution hazard ratios (sHR) and corresponding 95% confidence intervals (CIs) for breast cancer mortality were estimated using Fine and Gray's competing risk models adjusted for age, Charlson comorbidity index, congestive heart failure, myocardial infraction, molecular subtype, presence of metastasis at diagnosis, and breast cancer surgery. Results: 2,758 women were hospitalized for incident breast cancer. 604 received beta-blockers and 1,387 received first-line antihypertensives. In total, 154 breast cancer deaths were identified over a median follow-up time of 2.7years. We found no significant association between use of any beta-blocker and breast-cancer mortality (sHR 0.86, 95%CI 0.58-1.28), or when stratified by beta-blocker type (non-selective, sHR 0.42, 95%CI 0.14-1.25; selective, sHR 0.95, 95%CI 0.63-1.43). Results were not significant when stratified by molecular subtypes (e.g., triple negative breast cancer (TNBC), any beta blocker, sHR 0.16, 95%CI 0.02-1.51). Discussion: It is possible to use administrative data to explore drug repurposing opportunities. Although non-significant, an indication of an association was found for the TNBC subtype, which aligns with previous studies using registry data. Future studies with larger sample size, longer follow-up are required to confirm the association, and linkage to clinical data sources are required to validate our methodologies.

Exploring the human microbiome - A step forward for precision medicine in breast cancer.

The second most frequent cancer in the world and the most common malignancy in women is breast cancer. Breast cancer is a significant health concern in India with a high mortality-to-incidence ratio and presentation at a younger age. Recent studies have identified gut microbiota as a significant factor that can have an influence on the development, treatment, and prognosis of breast cancer. This review article aims to describe the influence of microbial dysbiosis on breast cancer occurrence and the possible interactions between oncobiome and specific breast cancer molecular subtypes. The review further also discusses the role of epigenetics and diet/nutrition in the regulation of the gut and breast microbiome and its association with breast cancer prevention, therapy, and recurrence. Additionally, the recent technological advances in microbiome research, including next-generation sequencing (NGS) technologies, genome sequencing, single-cell sequencing, and microbial metabolomics along with recent advances in artificial intelligence (AI) have also been reviewed. This is an attempt to present a comprehensive status of the microbiome as a key cancer biomarker. We believe that correlating microbiome and carcinogenesis is important as it can provide insights into the mechanisms by which microbial dysbiosis can influence cancer development and progression, leading to the potential use of the microbiome as a tool for prognostication and personalized therapy.

Evaluating chemotherapeutic potential of soya-isoflavonoids against high penetrance genes in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive molecular subtype of breast cancer (BC) associated with a poor prognosis. Owing to the structural similarity with 17-β-estradiol, consumption of soya-isoflavonoids are associated with a reduced rate of hormone-receptive BC incidence, but their role in TNBC is not deciphered in detail. This present study thus aims to investigate the therapeutic binding dynamics of dietary soya-flavonoids with the six high penetrance (HP) receptors in TNBC, viz. BRCA1, BRCA2, PALB2, PTEN, STK11 and TP53. Out of the 14 soya-flavonoids screened based on ADMET descriptors and several other physicochemical, bioavailability, drug and lead-likeness properties, four hits were shortlisted (Daidzein, Genistein, Glycitein and Biochanin A). Docking and molecular dynamics (MD) simulation revealed Genistein as the most potential multi-target inhibitor of the six TNBC HP genes. Additionally, Genistein exhibited excellent binding specificity with PTEN, a potent mediator of the PI3K signaling pathway in TNBC. The binding interaction of PTEN and Genistein was further compared against a standardized FDA-approved chemotherapeutic inhibitor, Olaparib, computed through various MD trajectory analysis, principal component analysis and computation of free energy landscape. This study reveals a comparatively better binding dynamics of PTEN-Genistein than PTEN-Olaparib. With a significant global surge in biomarker-based precision therapeutics in oncology, the results of this exhaustive in-silico study thus encourage the prospect of validating PTEN as a druggable target of Genistein, a unique drug-receptor combination in the future. Communicated by Ramaswamy H. Sarma

Research Progress on Molecular Subtyping and Modern Treatment of Triple-Negative Breast Cancer.

Breast cancer has become the most common malignant tumor worldwide. Triple-negative breast cancer (TNBC) is a type of breast cancer that is negative for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Compared with other molecular subtypes of breast cancer, TNBC is the most aggressive and highly heterogeneous. TNBC is insensitive to endocrine and anti-HER2 therapy, and chemotherapy is currently the main systemic treatment. With the continuous development of detection techniques and deepening research on TNBC molecular subtypes, drugs targeting immune checkpoints and different targets have emerged, such as atezolizumab, pembrolizumab, poly (ADP-ribose) polymerase (PARP) inhibitors, trophoblast cell-surface antigen 2 (TROP-2), and antibody-drug conjugates. These therapies provide new hope for TNBC treatment. Based on the analysis and classification of TNBC, this article summarizes the immunotherapy, targeted therapy, and new treatment combinations, providing references for the precise treatment of TNBC in the future.

Prognostic role of serum soluble ST2 of advanced breast cancer patients: a retrospective cohort study.

The soluble suppression of tumorigenicity 2 receptor (sST2) binds to interleukin-33 (IL-33) and blocks IL-33 and ST2 binding, suggesting that sST2 acts as a "decoy" receptor for IL-33 and is involved in the malignant progression of breast cancer. This paper aimed to investigate the differences in sST2 expression in patients with different molecular subtypes of breast cancer and assess its clinical value in the prognostic evaluation of advanced breast cancer. In this paper, we collected sera from 91 patients firstly diagnosed with advanced breast cancer at the Tianjin Medical University Cancer Institute and Hospital from 2008 to 2021 during their first hospitalization. We detected the expression level of sST2 in the serum of patients with different molecular fractions of breast cancer and analyzed the relationship between serum sST2 levels and breast cancer-related bone metastasis, cardiotoxicity, and overall survival. Bone metastases were detected using the Emission Computed Tomography technique, and chemotherapy drug-induced cardiotoxicity was detected by echocardiography. The Overall Survival was evaluated using the Kaplan-Meier estimator, and multivariate Cox regression analysis was performed to demonstrate the association between variables and sST2 levels. Serum sST2 levels did not vary among the different pathological types, molecular types, and estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and tumor proliferation marker (Ki-67) subgroups, and only differed significantly in the cardiotoxicity group. There were no statistical differences in tissue polypeptide specific antigen (TPSA), carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), carbohydrate antigen 153 (CA153), D-dimer, and inflammatory indexes neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) among different molecular subgroups. However, except for triple-negative breast cancer (TNBC), there was no difference in peripheral blood soluble ST2 concentrations among other molecular subtypes. Meanwhile, the survival of the high sST2 level group among advanced breast cancer patients was significantly lower than that of the normal group, and sST2 expression was closely related to cardiotoxicity and clinical stage. Serum sST2 is not only traditionally applied to the assessment of cardiac injury, but can also be utilized for assessing the prognosis of advanced breast cancer, excluding the influence of clinical stage and cardiotoxicity.

Reassessing Breast Cancer-Associated Fibroblasts (CAFs) Interactions with Other Stromal Components and Clinico-Pathologic Parameters by Using Immunohistochemistry and Digital Image Analysis (DIA).

Breast cancer (BC) stroma has CD34- and αSMA-positive cancer-associated fibroblasts (CAFs) differently distributed. During malignant transformation, CD34-positive fibroblasts decrease while αSMA-positive CAFs increase. The prevalence of αSMA-positive CAFs in BC stroma makes microscopic examination difficult without digital image analysis processing (DIA). DIA was used to compare CD34- and αSMA-positive CAFs among breast cancer molecular subgroups. DIA-derived data were linked to age, survival, tumor stroma vessels, tertiary lymphoid structures (TLS), invasion, and recurrence. Double immunostaining for CD34 and αSMA showed different CAF distribution patterns in normal and BC tissues. Single CD34 immunohistochemistry on supplemental slides quantified tumor stroma CD34_CAFs. Digital image analysis (DIA) data on CAF density, intensity, stromal score, and H-score were correlated with clinico-pathologic factors. CD34/αSMA CAF proportion was significantly related to age in Luminal A (LA), Luminal B (LB), and HER2 subtypes. CD34_CAF influence on survival, invasion, and recurrence of LA, LB-HER2, and TNBC subtypes was found to be significant. The CD34/αSMA-expressing CAFs exhibited a heterogeneous impact on stromal vasculature and TLS. BC stromal CD34_CAFs/αSMA_CAFs have an impact on survival, invasion, and recurrence differently between BC molecular subtypes. The tumor stroma DIA assessment may have predictive potential to prognosis and long-term follow-up of patients with breast cancer.

Assessing the Role of Different Heterogeneous Regions in DCE-MRI for Predicting Molecular Subtypes of Breast Cancer based on Network Architecture Search and Vision Transformer.

Breast cancer, the most common female malignancy, is highly heterogeneous, manifesting as different molecular subtypes. It is clinically important to distinguish between these molecular subtypes due to marked differences in prognosis, treatment and survival outcomes. In this study, we first performed convex analysis of mixtures (CAM) analysis on both intratumoral and peritumoral regions in DCE-MRI to generate multiple heterogeneous regions. Then, we developed a vision transformer (ViT)-based DL model and performed network architecture search (NAS) to evaluate all the combination of different heterogeneous regions for predicting molecular subtypes of breast cancer. Experimental results showed that the input plasma from both peritumoral and intratumoral regions, and the fast-flow kinetics from intratumoral regions were critical for predicting different molecular subtypes, achieving an area under receiver operating characteristic curve (AUROC) value of 0.66-0.68.Clinical Relevance- This study reduces the redundancy in multiple heterogeneous subregions and supports the precise prediction of molecular subtypes, which is of potential importance for the medicine care and treatment planning of patients with breast cancer.

Evaluation of breast cancer malignancy, prognostic factors and molecular subtypes using a continuous-time random-walk MR diffusion model

PurposeTo assess the continuous-time random-walk (CTRW) model’s diagnostic value in breast lesions and to explore the associations between the CTRW parameters and breast cancer pathologic factors. MethodThis retrospective study included 85 patients (70 malignant and 18 benign lesions) who underwent 3.0T MRI examinations. Diffusion-weighted images (DWI) were acquired with 16b-values to fit the CTRW model. Three parameters (Dm, α, and β) derived from CTRW and apparent diffusion coefficient (ADC) from DWI were compared among the benign/malignant lesions, molecular prognostic factors, and molecular subtypes by Mann-Whitney U test. Spearman correlation was used to evaluate the associations between the parameters and prognostic factors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) based on the diffusion parameters. ResultsAll parameters, ADC, Dm, α, and β were significantly lower in the malignant than benign lesions (P < 0.05). The combination of all the CTRW parameters (Dm, α, and β) provided the highest AUC (0.833) and the best sensitivity (94.3%) in differentiating malignant status. And the positive status of estrogen receptor (ER) and progesterone receptor (PR) showed significantly lower β compared with the negative counterparts (P < 0.05). The high Ki-67 expression produced significantly lower Dm and ADC values (P < 0.05). Additionally, combining multiple CTRW parameters improved the performance of diagnosing molecular subtypes of breast cancer. Moreover, Spearman correlations analysis showed that β produced significant correlations with ER, PR and Ki-67 expression (P < 0.05). ConclusionsThe CTRW parameters could be used as non-invasive quantitative imaging markers to evaluate breast lesions.

Poor Outcome in Postpartum Breast Cancer Patients Is Associated with Distinct Molecular and Immunologic Features.

Patients with postpartum breast cancer diagnosed after cessation of breastfeeding (postweaning, PP-BCPW) have a particularly poor prognosis compared with patients diagnosed during lactation (PP-BCDL), or to pregnant (Pr-BC) and nulliparous (NP-BC) patients, regardless of standard prognostic characteristics. Animal studies point to a role of the involution process in stimulation of tumor growth in the mammary gland. However, in women, the molecular mechanisms that underlie this poor prognosis of patients with PP-BCPW remain vastly underexplored, due to of lack of adequate patient numbers and outcome data. We explored whether distinct prognostic features, common to all breast cancer molecular subtypes, exist in postpartum tumor tissue. Using detailed breastfeeding data, we delineated the postweaning period in PP-BC as a surrogate for mammary gland involution and performed whole transcriptome sequencing, immunohistochemical, and (multiplex) immunofluorescent analyses on tumor tissue of patients with PP-BCPW, PP-BCDL, Pr-BC, and NP-BC. We found that patients with PP-BCPW having a low expression level of an immunoglobulin gene signature, but high infiltration of plasma B cells, have an increased risk for metastasis and death. Although PP-BCPW tumor tissue was also characterized by an increase in CD8+ cytotoxic T cells and reduced distance among these cell types, these parameters were not associated with differential clinical outcomes among groups. These data point to the importance of plasma B cells in the postweaning mammary tumor microenvironment regarding the poor prognosis of PP-BCPW patients. Future prospective and in-depth research needs to further explore the role of B-cell immunobiology in this specific group of young patients with breast cancer.

Transcriptome profiling and network enrichment analyses identify subtype-specific therapeutic gene targets for breast cancer and their microRNA regulatory networks

Previous studies have suggested that breast cancer (BC) from the Middle East and North Africa (MENA) is presented at younger age with advanced tumor stage, indicating underlying biological differences. Given the scant transcriptomic data on BC from the MENA region and to better understand the biology of this disease, we performed mRNA and microRNA (miRNA) transcriptomic profiling on a local cohort of BC (n = 96) from Qatar. Our data revealed the differentially expressed genes and miRNAs as function of BC molecular subtypes (HR+, HER2+, HER2+HR+, and TNBC), tumor grade (GIII vs GI-II), patients’ age (young (≤40) vs old (>40)), and ethnicity (MENA vs non-MENA). Our profiling data revealed close similarity between TNBC and HER2+, while the transcriptome of HER2+HR+ tumor was resemblant of that from HR+ tumors. Network analysis identified complex miRNA-mRNA regulatory networks in each BC molecular subtype, in high vs low grade tumors, in tumors from young vs old patients, and in tumors from MENA vs non-MENA, thus implicating miRNA-mediated gene regulation as an essential mechanism in shaping the transcriptome of BC. Integration of our transcriptomic data with CRISPR-Cas9 functional screen data and the OncoKB database identified numerous dependencies and therapeutic vulnerabilities in each BC molecular subtype, while CDC123 was functionally validated as potential therapeutic target for TNBC. Cox regression survival analyses identified mRNA and miRNA-based signatures predicative of worse and better relapse free survival (RFS), which were validated in larger BC cohorts. Our data provides comprehensive transcriptomic profiling and unraveled the miRNA-mRNA regulatory networks in BC patients from the region and identified novel actionable gene targets, employing integrated approach. Findings from the current study have potential implications to improve the current standard-of-care for BC from the MENA as well as patients from other ethnicities.

Interferon-gamma plasma levels and presence of mouse mammary tumor virus-like env gene: Implications on the pathogenesis of breast cancer

Mouse mammary tumor virus (MMTV) is a retrovirus that has been associated with the development of breast cancer (BC) in mice. The identification of a 95% homologous gene sequence to MMTV in human BC samples has increased interest in this hypothesis. This virus in humans received the name of mouse mammary tumor virus-like (MMTV-like). Several cytokines may be involved in the interactions between MMTV and the immune system, such as interferon-gamma (IFN-γ), which can enhance Th1-mediated antitumor immune response but it can also play a protumorigenic role by transmitting antiapoptotic and proliferative signals. Little is known about the antiviral immune response in a microenvironment with the presence of MMTV-like in BC patients. Therefore, the purpose of the present study was to quantify the plasma levels of IFN-γ in the peripheral blood of 123 neoplasia-free donors and 98 BC patients of different molecular subtypes, by enzyme-linked immunosorbent assay (ELISA), and evaluate the association of these plasma levels with the detection of the MMTV-like env gene in tumor tissue. Correlation analyzes involving IFN-γ plasma levels and clinical-pathological parameters were performed by Kendall Tau-c test. In our study, a decrease in IFN-γ levels was observed in the group of BC patients (30.85 ± 57.49 pg/ml) compared to the control group (115.00 ± 176.80 pg/ml) (p < 0.0001). In the analysis by stratified BC molecular subtypes, Luminal-A (30.79 ± 61.04 pg/ml; p < 0.0001), Luminal-B (24.74 ± 25.78 pg/ml; p = 0.0188) and triple-negative (23.95 ± 40.45 pg/ml; p = 0.0005) had a lower plasma level compared to control group. There was no significant difference between IFN-γ plasma levels of MMTV-like DNA positive samples compared to MMTV-negative samples (p = 0.2056). In general BC, patients with larger tumor size had higher IFN-γ plasma levels (Tau-c = 0.202; p = 0.019). By analyzing the MMTV-like env negative samples, we could identify that IFN-γ plasma levels were higher in larger tumor size (Tau-c = 0.222; p = 0.020) and with greater lymph node involvement (Tau-c = 0.258; p = 0.042). Also, higher IFN-γ plasma levels were observed in patients with higher histopathological grades (Tau-c = 0.384; p = 0.019) in MMTV-like env positive samples. For the first time, we assessed the association between plasma levels of IFN-γ and the presence of the MMTV-like env gene in BC samples. However, more studies are needed to clarify whether the high levels of IFN-γ in MMTV-like env positive samples are reflecting a possible antiviral immune response or whether this cytokine is promoting tumor growth.

Association of reproductive risk factors and breast cancer molecular subtypes: a systematic review and meta-analysis

BackgroundAssociations between reproductive factors and breast cancer (BC) risk vary by molecular subtype (i.e., luminal A, luminal B, HER2, and triple negative/basal-like [TNBC]). In this systematic review and meta-analysis, we summarized the associations between reproductive factors and BC subtypes.MethodsStudies from 2000 to 2021 were included if BC subtype was examined in relation to one of 11 reproductive risk factors: age at menarche, age at menopause, age at first birth, menopausal status, parity, breastfeeding, oral contraceptive (OC) use, hormone replacement therapy (HRT), pregnancy, years since last birth and abortion. For each reproductive risk factor, BC subtype, and study design (case–control/cohort or case-case), random-effects models were used to estimate pooled relative risks and 95% confidence intervals.ResultsA total of 75 studies met the inclusion criteria for systematic review. Among the case–control/cohort studies, later age at menarche and breastfeeding were consistently associated with decreased risk of BC across all subtypes, while later age at menopause, later age of first childbirth, and nulliparity/low parity were associated with increased risk of luminal A, luminal B, and HER2 subtypes. In the case-only analysis, compared to luminal A, postmenopausal status increased the risk of HER2 and TNBC. Associations were less consistent across subtypes for OC and HRT use.ConclusionIdentifying common risk factors across BC subtypes can enhance the tailoring of prevention strategies, and risk stratification models can benefit from subtype specificity. Adding breastfeeding status to current BC risk prediction models can enhance predictive ability, given the consistency of the associations across subtypes.

A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI.

Dynamic contrast-enhanced (DCE) MRI commonly outperforms diffusion-weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE-MRI, particularly in patients with chronic kidney disease. To develop a novel deep learning model to fully exploit the potential of overall b-value DW-MRI without the need for a contrast agent in predicting breast cancer molecular subtypes and to evaluate its performance in comparison with DCE-MRI. Prospective. 486 female breast cancer patients (training/validation/test: 64%/16%/20%). 3.0 T/DW-MRI (13 b-values) and DCE-MRI (one precontrast and five postcontrast phases). The breast cancers were divided into four subtypes: luminal A, luminal B, HER2+, and triple negative. A channel-dimensional feature-reconstructed (CDFR) deep neural network (DNN) was proposed to predict these subtypes using pathological diagnosis as the reference standard. Additionally, a non-CDFR DNN (NCDFR-DNN) was built for comparative purposes. A mixture ensemble DNN (ME-DNN) integrating two CDFR-DNNs was constructed to identify subtypes on multiparametric MRI (MP-MRI) combing DW-MRI and DCE-MRI. Model performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Model comparisons were performed using the one-way analysis of variance with least significant difference post hoc test and the DeLong test. P < 0.05 was considered significant. The CDFR-DNN (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.94) demonstrated significantly improved predictive performance than the NCDFR-DNN (accuracies, 0.76 ~ 0.78; AUCs, 0.92 ~ 0.93) on DW-MRI. Utilizing the CDFR-DNN, DW-MRI attained the predictive performance equal (P = 0.065 ~ 1.000) to DCE-MRI (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.95). The predictive performance of the ME-DNN on MP-MRI (accuracies, 0.85 ~ 0.87; AUCs, 0.96 ~ 0.97) was superior to those of both the CDFR-DNN and NCDFR-DNN on either DW-MRI or DCE-MRI. The CDFR-DNN enabled overall b-value DW-MRI to achieve the predictive performance comparable to DCE-MRI. MP-MRI outperformed DW-MRI and DCE-MRI in subtype prediction. 2 TECHNICAL EFFICACY STAGE: 1.

Stromal Tumor-Infiltrating Lymphocytes Associated with Immunohistopathology and Molecular Subtypes of Breast Cancer in Vietnam.

Breast cancer is a heterogeneous disease with varied symptoms and pathogenesis, as well as variable prognosis and therapeutic outcomes. Stromal tumor-infiltrating lymphocytes, one of the tumor microenvironment factors, has been recognized as an important immunological biomarker that reflected the antitumor immune response in breast cancer. We analyzed 207 invasive breast cancer patients who had lumpectomy or mastectomy and have not received any pre-operative treatment. Clinicopathological characteristics, immunohistochemistry characteristics, molecular subtypes classification and stromal TILs evaluation were investigated. Stromal TILs correlated with well-established prognostic markers. Tumor grade showed significantly higher sTILs percentages in high-grade tumors than in low-grade tumors (p<0.001). There was a statistically significant association between intermediate and high levels of sTILs and a high Ki-67 index (p< 0.001). ER/PR negative was significantly related to high sTILs. Mean sTILs score was significantly higher in TNBC (40.1±31.6%) compared to others, statistically significant (p<0.001). In HER2-negative breast cancer, sTILs were significantly associated with histologic grade, ER status, PR status, and Ki67 index. sTILs played an important role, associated with unfavorable factors in breast cancer. Our findings support the use of stromal sTILs to identify a more aggressive phenotype of tumors.

A quantitative heterogeneity analysis approach to molecular subtype recognition of breast cancer in dynamic contrast-enhanced magnetic imaging images from radiomics data.

Breast cancer is a major cause of mortality among women worldwide. Dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) is a good imaging technique that can show temporal information about the kinetics of the contrast agent in suspicious breast lesions as well as acceptable spatial resolution. Computer-aided detection systems assist in the detection of lesions through medical image processing techniques combined with computerized analysis and calculation, which in turn helps radiologists recognize molecular subtypes of breast lesions that will be beneficial for better treatment plan decisions. In this paper, a computer-aided diagnosis method is proposed to automatically locate breast cancer lesions and identify molecular subtypes of breast cancer with heterogeneity analysis from radiomics data. A fast region-based convolutional network (Faster R-CNN) framework is first applied to images to detect breast cancer lesions. Then, the heterogeneous regions of every breast cancer lesion are extracted. Based on the multiple visual and kinetic radiomics features extracted from the heterogeneous regions, a temporal bag of visual word model is proposed, which takes into account the dynamic characteristics of both lesion and heterogeneous regions in images over time. The recognition task of molecular subtypes of breast lesions is realized based on a stacking classification model. At the genetic level, breast cancer is divided into four molecular subtypes, namely, luminal epithelial type A (Luminal A), luminal epithelial type B (Luminal B), HER-2 overexpression and basal cell type. The experimental results show that the precision of the four subtypes is 93%, 94%, 83%, 86%; the recall is 96%, 80%, 91%, 94%; and the F1-score is 95%, 86%, 87%. The experimental results denote the influence of heterogeneous regions on the recognition task. The DCE-MRI-based approach to identify molecular typing of breast cancer for noninvasive diagnosis will contribute to the development of breast cancer treatment, improved outcomes and reduced mortality.

Tumor response to neoadjuvant chemotherapy in molecular breast cancer subtypes in Medellin, Colombia. Retrospective cohort study

To describe the frequency of clinical and pathological response in different molecular subtypes of breast cancer, in patients receiving prior neoadjuvant chemotherapy. Descriptive retrospective cohort. The study population consisted of women 18 years of age and older with a histological diagnosis of invasive breast cancer stages IIA, IIB, IIIA, IIIB and IIIC, with a classification by molecular subtypes, who had received prior neoadjuvant chemotherapy, seen at a high complexity clinic in Medellin (Colombia), between July 1, 2017, and July 30, 2019. We measured age clinical stage, histological characteristics, molecular classification, and complete clinical and pathological responses by molecular subtype. A descriptive analysis was conducted. Overall, 255 patients met the inclusion criteria. Mean age was 55.2 years; the clinical stages with the highest prevalence were IIIB (28.6 %) and IIB (26.3 %), and the most frequent by histologic grading were grades 3 (48.2 %) and 2 (37.3 %). Frequency by molecular types was as follows: luminal A (10.2 %), HER2-negative luminal B (39.6 %), triple-negative (23.1%), HER2-positive luminal B (13.7 %), and pure HER2 (13.3 %). Complete clinical response following chemotherapy, by molecular type, was as follows: luminal A (26.9 %), HER2-negative luminal B (37.6 %), HER2-positive luminal B (48.6 %), pure HER2 (41.2 %), triple-negative (45.8 %). Complete pathological response by molecular subtype was achieved in the luminal A (19.2 %), HER2-negative luminal B (32.7 %), HER2-positive luminal B (54.3 %), pure HER2 (50 %) and triple-negative (42.4 %) subtypes. In clinical practice, breast cancer classification by molecular subtypes is a means to approach the assess the to neoadjuvant chemotherapy. Prospective studies are needed in the region in order to determine the ability to predict overall and disease-free survival based on the complete pathologic response.