Molecular subclasses of breast cancer: how do we define them? The IMPAKT 2012 Working Group Statement

Multiparametric MRI model to predict molecular subtypes of breast cancer using Shapley additive explanations interpretability analysis

PURPOSE

The purpose of this study was to assess the predictive performance of multiparametric magnetic resonance imaging (MRI) for molecular subtypes and interpret features using SHapley Additive exPlanations (SHAP) analysis.

MATERIAL AND METHODS

Patients with breast cancer who underwent pre-treatment MRI (including ultrafast dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, diffusion kurtosis imaging and intravoxel incoherent motion) were recruited between February 2019 and January 2022. Thirteen semantic and thirteen multiparametric features were collected and the key features were selected to develop machine-learning models for predicting molecular subtypes of breast cancers (luminal A, luminal B, triple-negative and HER2-enriched) by using stepwise logistic regression. Semantic model and multiparametric model were built and compared based on five machine-learning classifiers. Model decision-making was interpreted using SHAP analysis.

RESULTS

A total of 188 women (mean age, 53 ± 11 [standard deviation] years; age range: 25-75 years) were enrolled and further divided into training cohort (131 women) and validation cohort (57 women). XGBoost demonstrated good predictive performance among five machine-learning classifiers. Within the validation cohort, the areas under the receiver operating characteristic curves (AUCs) for the semantic models ranged from 0.693 (95% confidence interval [CI]: 0.478-0.839) for HER2-enriched subtype to 0.764 (95% CI: 0.681-0.908) for luminal A subtype, inferior to multiparametric models that yielded AUCs ranging from 0.771 (95% CI: 0.630-0.888) for HER2-enriched subtype to 0.857 (95% CI: 0.717-0.957) for triple-negative subtype. The AUCs between the semantic and the multiparametric models did not show significant differences (P range: 0.217-0.640). SHAP analysis revealed that lower iAUC, higher kurtosis, lower D*, and lower kurtosis were distinctive features for luminal A, luminal B, triple-negative breast cancer, and HER2-enriched subtypes, respectively.

CONCLUSION

Multiparametric MRI is superior to semantic models to effectively predict the molecular subtypes of breast cancer.

Survival, treatment patterns, and costs of HER2+ metastatic breast cancer patients in Ontario between 2005 to 2020

BACKGROUND

To enable the integration of novel therapies, it is critical to understand current long-term outcomes in HER2-positive metastatic breast cancer (mBC), including survival, treatment patterns, and costs. We sought to define these outcomes among patients with mBC in Ontario.

METHODS

We conducted a retrospective population-level study in Ontario women diagnosed with breast cancer of any stage between January 1, 2005 and December 31, 2019, with follow-up until December 31, 2020. HER2-positivity was based on receipt of a HER2-targeted therapy (HER2-TT) in the first line (1L) metastatic setting. Administrative databases at ICES were used to assess outcomes.

RESULTS

In Ontario, 2557 patients were diagnosed with mBC and received a HER2-TT, and of these 1606 were diagnosed with early-stage (stage I-III) that became metastatic (recurrent), while 951 were diagnosed with late stage/de novo mBC (stage IV). The average age of all patients was 54.8 years ± 12.7 years. Treatment regimens that included pertuzumab and trastuzumab (cohort name: pert_tras) were the most frequently used HER2-TT for 1L mBC (51.4%), while T-DM1 was the most frequent therapy (87.5%) in second line (2L). The median overall survival (mOS) from initiation of 1L pert_tras was not reached, whereas mOS from initiation of T-DM1 in 2L was 18.7 months. The overall mean cost per patient on pert_tras during 1L was $267,282. The main cost drivers were the cost of systemic therapy, followed by cancer clinic visits, with a mean cost per patient at $158,961 and $73,882, respectively.

CONCLUSION

The baseline characteristics and treatment patterns for patients who received HER2-TT in our study align with previously reported results. However, the mOS observed for 2L T-DM1 was shorter than that found in pivotal, clinical trial literature. As expected, anti-cancer systemic therapy costs were the main contributor to the over quarter-million dollar mean cost per patient on pert_tras in 1L.

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

BACKGROUND

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.

PURPOSE

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.

STUDY TYPE

Prospective.

SUBJECTS

486 female breast cancer patients (training/validation/test: 64%/16%/20%).

FIELD STRENGTH/SEQUENCE

3.0 T/DW-MRI (13 b-values) and DCE-MRI (one precontrast and five postcontrast phases).

ASSESSMENT

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.

STATISTICAL TESTS

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.

RESULTS

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.

DATA CONCLUSION

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.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 1.

Combination of DCE-MRI and NME-DWI via Deep Neural Network for Predicting Breast Cancer Molecular Subtypes

BACKGROUND

To explore whether the combination of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and nonmono-exponential (NME) model-based diffusion-weighted imaging (DWI) via deep neural network (DNN) can improve the prediction of breast cancer molecular subtypes compared to either imaging technique used alone.

PATIENTS AND METHODS

This prospective study examined 480 breast cancers in 475 patients undergoing DCE-MRI and NME-DWI at 3.0 T. Breast cancers were classified as follows: human epidermal growth factor receptor 2 enriched (HER2-enriched), luminal A, luminal B (HER2-), luminal B (HER2+), and triple-negative subtypes. A total of 20% cases were withheld as an independent test dataset, and the remaining cases were used to train DNN with an 80% to 20% training-validation split and 5-fold cross-validation. The diagnostic accuracies of DNN in 5-way subtype classification between the DCE-MRI, NME-DWI, and their combined multiparametric-MRI datasets were compared using analysis of variance with least significant difference posthoc test. Areas under the receiver-operating characteristic curves were calculated to assess the performances of DNN in binary subtype classification between the 3 datasets.

RESULTS

The 5-way classification accuracies of DNN on both DCE-MRI (0.71) and NME-DWI (0.64) were significantly lower (P < .05) than on multiparametric-MRI (0.76), while on DCE-MRI was significantly higher (P < .05) than on NME-DWI. The comparative results of binary classification between the 3 datasets were consistent with the 5-way classification.

CONCLUSION

The combination of DCE-MRI and NME-DWI via DNN achieved a significant improvement in breast cancer molecular subtype prediction compared to either imaging technique used alone. Additionally, DCE-MRI outperformed NME-DWI in differentiating subtypes.

FEATURES OF COL1A1 EXPRESSION IN BREAST CANCER TISSUE OF YOUNG PATIENTS

BACKGROUND

In the last decades, the incidence of breast cancer (BCa) in young women has been increasing steadily. The quantitative indicators of expression of collagen, which play important role in stromal microenvironment, and their association with the age and survival rates of BCa patients have not been yet definitively clarified.

AIM

To investigate the relationship between the COL1A1 gene expression at the mRNA and protein levels in BCa tissue and the clinicopatological features and survival rates of BCa patients of different age groups.

MATERIALS AND METHODS

The study was conducted on the clinical material of 50 patients with stage I-III BCa. COL1A1 gene expression at the mRNA and protein levels in BCa tissue were studied using the real-time PCR and immunohistochemical methods, as well as the bioinformatic analysis (UALCAN and Kaplan - Meier Plotter databases).

RESULTS

The bioinformatic analysis showed that BCa tissue is characterized by 6.0 times (p < 0.05) higher level of COL1A1 mRNA compared to normal breast tissue. The correlation of COL1A1 expression at the mRNA and protein levels with the molecular subtype of neoplasms was demonstrated. According to Kaplan - Meier Plotter database, a low level of expression of COL1A1 protein level in BCa tissue is associated with lower rates of relapse-free survival of patients. The ex vivo study of the clinical material revealed a decrease in COL1A1 protein expression in tumor tissue of young patients with BCa of T3 category (p < 0.0374), low differentiation grade (p < 0.0163) and basal molecular subtype (p < 0.0001). A correlation between the expression of COL1A1 at the mRNA and protein levels and the expression status of estrogen receptors (p < 0.0001) and progesterone receptors (p < 0.0040) was established. The relapse-free 3-year survival rate of young BCa patients is significantly lower in the presence of a low COL1A1 optical density index in the tumor tissue.

CONCLUSIONS

The identified relationship between COL1A1 expression and such indicators of BCa malignancy as tumor size, differentiation grade, molecular subtype, receptor status, and the recurrencefree survival of patients indicates the prospects of its use to predict the aggressiveness of the BCa course in young patients.

Update on the Use of Molecular Subtyping in Breast Cancer

The standard of care for invasive cancers of the breast has been and continues to be to evaluate them for breast prognostic markers: estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 by immunohistochemistry. Over 2 decades ago, a study was the first to report on the molecular subtypes of breast cancer. Four main subtypes were reported. Since then there have been some changes in the molecular subtype classification, but overall many studies have shown that this subtyping has clinical prognostic and predictive value. More recently, molecular assays have been developed and studies have shown similar clinical prognostic and predictive value. We reviewed the literature for studies evaluating the clinical significance of all 3 of these methods of evaluation and the follow-up findings of that review are presented below.

The Prediction Analysis of Microarray 50 (PAM50) Gene Expression Classifier Utilized in Indeterminate-Risk Breast Cancer Patients in Hungary: A Consecutive 5-Year Experience

BACKGROUND

Breast cancer has been categorized into molecular subtypes using immunohistochemical staining (IHC) and fluorescence in situ hybridization (FISH) since the early 2000s. However, recent research suggests that gene expression testing, specifically Prosigna® Prediction Analysis of Microarray 50 (PAM50), provides more accurate classification methods. In this retrospective study, we compared the results of IHC/FISH and PAM50 testing. We also examined the impact of various PAM50 parameters on overall survival (OS) and progression-free survival (PFS).

RESULTS

We analyzed 42 unilateral breast cancer samples, with 18 classified as luminal A, 10 as luminal B, 8 as Human epidermal growth factor receptor 2 (HER2)-positive, and 6 as basal-like using PAM50. Interestingly, 17 out of the 42 samples (40.47%) showed discordant results between histopathological assessment and the PAM50 classifier. While routine IHC/FISH resulted in classification differences for a quarter to a third of samples within each subtype, all basal-like tumors were misclassified. Hormone receptor-positive tumors (hazard rate: 8.7803; p = 0.0085) and patients who had higher 10-year recurrence risk scores (hazard rate: 1.0539; p = 0.0201) had shorter OS and PFS.

CONCLUSIONS

Our study supports the existing understanding of molecular subtypes in breast cancer and emphasizes the overlap between clinical characteristics and molecular subtyping. These findings underscore the value of gene expression profiling, such as PAM50, in improving treatment decisions for breast cancer patients.

Overrepresentation of human epidermal growth factor receptor 2 positive- and Luminal B breast cancer metastases in the eyes and orbit

BACKGROUND

Breast cancer is the most common cancer to spread to the choroid and orbit. Depending on a set of prognostic and predictive biomarkers, breast cancer can be divided into at least four distinct subtypes with separate treatment and clinical course.

SUBJECTS

Thirty-two patients with metastases to the eye and periocular area diagnosed between 2005 and 2020, of which 11 also had primary tumour tissue available. Expression levels of oestrogen- (ER) and progesterone receptors (PR), Human epidermal growth factor receptor 2 (HER2) and the proliferation marker Ki67 were analysed.

RESULTS

Twenty-five of 32 patients (78%) had a history of primary breast cancer, whereas the remaining 7 (22%) presented with metastatic disease. Of available metastases, 83% were positive for ER, 37% for PR, 54% for HER2, and 50% for Ki67. Metastases had significantly lower proportions of PR-positive cells than primary tumours, and the distribution of the Luminal A, Luminal B, HER2 enriched and triple-negative subtypes differed between primary tumours and metastases (P = 0.012): Six of 9 patients with a full set of biomarkers on both primary tumours and metastases switched subtype (67%), and 23 of 32 metastases (77%) were of the Luminal B subtype.

CONCLUSIONS

Nearly 4 in 5 breast cancer metastases in the eyes and orbit are of the Luminal B subtype, and a majority are HER2 positive. The breast cancer subtype frequently switches between primary tumours and metastases. Future studies should evaluate these results in larger cohorts.

Female breast cancer subtypes in the Romagna Unit of the Emilia-Romagna cancer registry, and estimated incident cases by subtypes and age in Italy in 2020

PURPOSE

The aim of this study was to estimate the Italian burden of incident breast cancer (BC) by subtypes, according to the distribution of hormonal receptor (HR) status and expression of human epidermal growth factor 2 (HER2).

METHODS

Female breast cancers incidence in the Romagna Unit of the Emilia-Romagna registry (n. 10,711) were grouped into: HR+ /HER2-, HR+ /HER2+ , HR-/HER2+ , HR-/HER2- and missing, and by age: < 50, 50-69 and 70+ years. Data were compared with other published Italian population-bases series before using them for national estimates. We used national and regional numbers of expected breast cancers published by the Italian network of cancer registries considering the age- and geographic-specific variation of the Italian population.

RESULTS

Overall, 70.7% of incident BC cases are expected to be HR+ /HER2-, 8.5% HR+ /HER2+ , 7.5% HR-/HER2-, 4.1% HR-/HER2+ and 9.3% missing. The global ranking is similar across age-groups but with age-specific differences. The proportion of missing was around 3-times lower than in the other Italian published population-based series and similar to the SEER one. In Italy, are estimated 38,841 HR+ /HER2- breast cancer cases, 4665 HR+ /HER2+ , 4098 HR-/HER2-, 2281 HR-/HER2+ , and 5092 not specified. Numbers by age-group were provided.

CONCLUSIONS

The present estimates relied on high-quality population-based data and provide a clinically relevant information on the burden of breast cancer subtypes. These data will support the planning of therapy needs for oncologists, decision-makers, and all other stakeholders.

Survivin Expression in Luminal Breast Cancer and Adjacent Normal Tissue for Immuno-Oncology Applications

Survivin (BIRC5) is a tumor-associated antigen (TAA) overexpressed in various tumors but present at low to undetectable levels in normal tissue. Survivin is known to have a high expression in breast cancer (e.g., Ductal Carcinoma in situ (DCIS) and triple negative breast cancer). Previous studies have not compared survivin expression levels in DCIS tumor samples to levels in adjacent, normal breast tissue from the same patient. To ensure the effective use of survivin as a target for T cell immunotherapy of breast cancer, it is essential to ascertain the varying levels of survivin expression between DCIS tumor tissue samples and the adjacent normal breast tissue taken from the same patient simultaneously. Next-generation sequencing of RNA (RNA-seq) in normal breast tissue and tumor breast tissue from five women presenting with DCIS for lumpectomy was used to identify sequence variation and expression levels of survivin. The identity of both tumor and adjacent normal tissue samples were corroborated by histopathology. Survivin was overexpressed in human breast tissue tumor samples relative to the corresponding adjacent human normal breast tissue. Wild-type survivin transcripts were the predominant species identified in all tumor tissue sequenced. This study demonstrates upregulated expression of wild type survivin in DCIS tumor tissue versus normal breast tissue taken from the same patient at the same time, and provides evidence that developing selective cytotoxic T lymphocyte (CTL) immunotherapy for DCIS targeting survivin warrants further study.

A3SOM, abstained explainable semi-supervised neural network based on self-organizing map

In the sea of data generated daily, unlabeled samples greatly outnumber labeled ones. This is due to the fact that, in many application areas, labels are scarce or hard to obtain. In addition, unlabeled samples might belong to new classes that are not available in the label set associated with data. In this context, we propose A3SOM, an abstained explainable semi-supervised neural network that associates a self-organizing map to dense layers in order to classify samples. Abstained classification enables the detection of new classes and class overlaps. The use of a self-organizing map in A3SOM allows integrated visualization and makes the model explainable. Along with describing our approach, this paper shows that the method is competitive with other classifiers and demonstrates the benefits of including abstention rules. A use case is presented on breast cancer subtype classification and discovery to show the relevance of our method in real-world medical problems.

Identification of novel interacts partners of ADAR1 enzyme mediating the oncogenic process in aggressive breast cancer

Triple-negative breast cancer (TNBC) subtype is characterized by aggressive clinical behavior and poor prognosis patient outcomes. Here, we show that ADAR1 is more abundantly expressed in infiltrating breast cancer (BC) tumors than in benign tumors. Further, ADAR1 protein expression is higher in aggressive BC cells (MDA-MB-231). Moreover, we identify a novel interacting partners proteins list with ADAR1 in MDA-MB-231, using immunoprecipitation assay and mass spectrometry. Using iLoop, a protein-protein interaction prediction server based on structural features, five proteins with high iloop scores were discovered: Histone H2A.V, Kynureninase (KYNU), 40S ribosomal protein SA, Complement C4-A, and Nebulin (ranged between 0.6 and 0.8). In silico analysis showed that invasive ductal carcinomas had the highest level of KYNU gene expression than the other classifications (p < 0.0001). Moreover, KYNU mRNA expression was shown to be considerably higher in TNBC patients (p < 0.0001) and associated with poor patient outcomes with a high-risk value. Importantly, we found an interaction between ADAR1 and KYNU in the more aggressive BC cells. Altogether, these results propose a new ADAR-KYNU interaction as potential therapeutic targeted therapy in aggressive BC.

Multimodal analysis of genome-wide methylation, copy number aberrations, and end motif signatures enhances detection of early-stage breast cancer

Introduction

Breast cancer causes the most cancer-related death in women and is the costliest cancer in the US regarding medical service and prescription drug expenses. Breast cancer screening is recommended by health authorities in the US, but current screening efforts are often compromised by high false positive rates. Liquid biopsy based on circulating tumor DNA (ctDNA) has emerged as a potential approach to screen for cancer. However, the detection of breast cancer, particularly in early stages, is challenging due to the low amount of ctDNA and heterogeneity of molecular subtypes.

Methods

Here, we employed a multimodal approach, namely Screen for the Presence of Tumor by DNA Methylation and Size (SPOT-MAS), to simultaneously analyze multiple signatures of cell free DNA (cfDNA) in plasma samples of 239 nonmetastatic breast cancer patients and 278 healthy subjects.

Results

We identified distinct profiles of genome-wide methylation changes (GWM), copy number alterations (CNA), and 4-nucleotide oligomer (4-mer) end motifs (EM) in cfDNA of breast cancer patients. We further used all three signatures to construct a multi-featured machine learning model and showed that the combination model outperformed base models built from individual features, achieving an AUC of 0.91 (95% CI: 0.87-0.95), a sensitivity of 65% at 96% specificity.

Discussion

Our findings showed that a multimodal liquid biopsy assay based on analysis of cfDNA methylation, CNA and EM could enhance the accuracy for the detection of early- stage breast cancer.

DNA aneuploidy identifies a subset of Luminal subtype breast carcinoma patients with worse clinical outcome

AIM

In breast carcinoma (BC), the relationship between the ploidy pattern and molecular subtyping is still unknown. We aim to investigate the prognostic impact of DNA ploidy within molecular subtypes of a large cohort of BC patients.

METHODS

The series involved 520 BC patients with no neoadjuvant therapy and a median follow-up of 115.5 months. Immunohistochemical assessment of hormonal receptors, ERBB2 (HER2) status and Ki67 proliferative activity was the basis of the surrogate molecular subtyping. Ploidy was evaluated by DNA flow cytometry in fresh/frozen tumour samples. Kaplan-Meier (K-M) survival estimation was used for prognostic statistical analysis.

RESULTS

Luminal A subtype had the lowest prevalence of disease recurrences (23.6 %) and deaths from disease (18.3 %), while Luminal B (42.2 %/37.9 %), Triple-negative (46.4 %/40.6 %), and HER2-positive (55.9 %/50.0 %) subtypes had the highest. The Triple-positive subtype shows an intermediate/low frequency of adverse events (29.4 % of disease recurrences and 17.6 % of deaths from disease). Luminal A tumours were mostly diploid (55.3 %), while Triple-negative and HER2-positive tumours showed a high incidence of aneuploidy (82.6 % and 88.2 %, respectively). Luminal B and Triple-positive tumours had an intermediate percentage of aneuploidy (63.8 % and 70.6 %, respectively). K-M survival curves showed that DNA aneuploidy is significantly associated with shorter disease-free survival and overall survival in Luminal A and Luminal B molecular subtypes.

CONCLUSION

DNA aneuploidy identifies a subset of Luminal BC patients with worse clinical outcome, potentially eligible for more aggressive adjuvant therapy.

β-Caryophyllene Counteracts Chemoresistance Induced by Cigarette Smoke in Triple-Negative Breast Cancer MDA-MB-468 Cells

Exposure to cigarette smoke (CS) has been associated with an increased risk of fatal breast cancers and recurrence, along with chemoresistance and chemotherapy impairment. This strengthens the interest in chemopreventive agents to be exploited both in healthy and oncological subjects to prevent or repair CS damage. In the present study, we evaluated the chemopreventive properties of the natural sesquiterpene β-caryophyllene towards the damage induced by cigarette smoke condensate (CSC) in triple negative breast cancer MDA-MB-468 cells. Particularly, we assessed the ability of the sesquiterpene to interfere with the mechanisms exploited by CSC to promote cell survival and chemoresistance, including genomic instability, cell cycle progress, autophagy/apoptosis, cell migration and related pathways. β-Caryophyllene was found to be able to increase the CSC-induced death of MDA-MB-468 cells, likely triggering oxidative stress, cell cycle arrest and apoptosis; moreover, it hindered cell recovery, autophagy activation and cell migration; at last, a marked inhibition of the signal transducer and activator of transcription 3 (STAT3) activation was highlighted: this could represent a key mechanism of the chemoprevention by β-caryophyllene. Although further studies are required to confirm the in vivo efficacy of β-caryophyllene, the present results suggest a novel strategy to reduce the harmful effect of smoke in cancer patients and to improve the survival expectations in breast cancer women.

Design and Validation of Nanofibers Made of Self-Assembled Peptides to Become Multifunctional Stimuli-Sensitive Nanovectors of Anticancer Drug Doxorubicin

Self-assembled peptides possess remarkable potential as targeted drug delivery systems and key applications dwell anti-cancer therapy. Peptides can self-assemble into nanostructures of diverse sizes and shapes in response to changing environmental conditions (pH, temperature, ionic strength). Herein, we investigated the development of self-assembled peptide-based nanofibers (NFs) with the inclusion of a cell-penetrating peptide (namely gH625) and a matrix metalloproteinase-9 (MMP-9) responsive sequence, which proved to enhance respectively the penetration and tumor-triggered cleavage to release Doxorubicin in Triple Negative Breast Cancer cells where MMP-9 levels are elevated. The NFs formulation has been optimized via critical micelle concentration measurements, fluorescence, and circular dichroism. The final nanovectors were characterized for morphology (TEM), size (hydrodynamic diameter), and surface charge (zeta potential). The Doxo loading and release kinetics were studied in situ, by optical microspectroscopy (fluorescence and surface-enhanced Raman scattering–SERS). Confocal spectral imaging of the Doxo fluorescence was used to study the TNBC models in vitro, in cells with various MMP-9 levels, the drug delivery to cells as well as the resulting cytotoxicity profiles. The results confirm that these NFs are a promising platform to develop novel nanovectors of Doxo, namely in the framework of TNBC treatment.

Prognostic significance of different molecular typing methods and immune status based on RNA sequencing in HR-positive and HER2-negative early-stage breast cancer

BACKGROUND

This study was conducted to evaluate the prognostic significance of different molecular typing methods and immune status based on RNA sequencing (RNA-seq) in hormone receptor (HR)-positive and human epidermal growth factor receptor 2 (HER2)-negative (HR + /HER2-) early-stage breast cancer and develop a modified immunohistochemistry (IHC)-based surrogate for intrinsic subtype analysis.

METHODS

The gene expression profiles of samples from 87 HR + /HER2- early-stage breast cancer patients were evaluated using the RNA-seq of Oncotype Dx recurrence score (RS), PAM50 risk of recurrence (ROR), and immune score. Intrinsic tumor subtypes were determined using both PAM50- and IHC-based detection of estrogen receptor, progesterone receptor, Ki-67, epidermal growth factor receptor, and cytokeratins 14 and 5/6. Prognostic variables were analyzed through Cox regression analysis of disease-free survival (DFS) and distant metastasis-free survival (DMFS).

RESULTS

Survival analysis showed that ROR better predicted recurrence and distant metastasis compared to RS (for DFS: ROR, P = 0.000; RS, P = 0.027; for DMFS, ROR, P = 0.047; RS, P = 0.621). Patients with HR + /HER2- early-stage breast cancer was classified into the luminal A, luminal B, HER2-enriched, and basal-like subtypes by PAM50. Basal-like subgroups showed the shortest DFS and DMFS. A modified IHC-based surrogate for intrinsic subtype analysis improved the concordance with PAM50 from 66.7% to 73.6%, particularly for basal-like subtype identification. High level of TILs and high expression of immune genes predicted poor prognosis. Multi-factor Cox analysis showed that IHC-based basal-like markers were the only independent factors affecting DMFS.

CONCLUSIONS

Prognosis is better evaluated by PAM50 ROR in early-stage HR + /HER2- breast cancer and significantly differs among intrinsic subtypes. The modified IHC-based subtype can improve the basal-like subtype identification of PAM50. High immunity status and IHC-based basal-like markers are negative prognostic factors.

Progress on deep learning in digital pathology of breast cancer: a narrative review

BACKGROUND AND OBJECTIVE

Pathology is the gold standard criteria for breast cancer diagnosis and has important guiding value in formulating the clinical treatment plan and predicting the prognosis. However, traditional microscopic examinations of tissue sections are time consuming and labor intensive, with unavoidable subjective variations. Deep learning (DL) can evaluate and extract the most important information from images with less need for human instruction, providing a promising approach to assist in the pathological diagnosis of breast cancer. To provide an informative and up-to-date summary on the topic of DL-based diagnostic systems for breast cancer pathology image analysis and discuss the advantages and challenges to the routine clinical application of digital pathology.

METHODS

A PubMed search with keywords ("breast neoplasm" or "breast cancer") and ("pathology" or "histopathology") and ("artificial intelligence" or "deep learning") was conducted. Relevant publications in English published from January 2000 to October 2021 were screened manually for their title, abstract, and even full text to determine their true relevance. References from the searched articles and other supplementary articles were also studied.

KEY CONTENT AND FINDINGS

DL-based computerized image analysis has obtained impressive achievements in breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further researches.

CONCLUSIONS

Having searched PubMed and other databases and summarized the application of DL-based AI models in breast cancer pathology, we conclude that DL is undoubtedly a promising tool for assisting pathologists in routines, but further studies are needed to realize the digitization and automation of clinical pathology.

Identification of Differentially Expressed Hub Genes Associated With Immune Cell Recruitment in Claudin-Low Breast Cancer

Breast cancer (BCa) is the most common malignancy in women and claudin-low breast cancer (CL-BCa) is a newly identified BCa subtype characterized by low expression of claudin 3&4&7. However, the hub genes associated with the recruitment of immune cells into CL-BCa were rarely described. This study aimed at exploring the differentially expressed hub genes associated with tumor-infiltrating immune cells in CL-BCa by a multi-approach bioinformatics analysis. The top 200 genes associated with CL-BCa were screened in the METABRIC dataset; the PPI network was constructed using STRING and Cytoscape; tumor-infiltrating immune cells were analyzed by TIMER 2.0; and the correlation of feature cytokines and claudins on survival was examined in METABRIC and TCGA datasets. Consequently, we found that the fraction of tumor-infiltrating immune cells, especially CD8+T cells and macrophages, increased in the CL-BCa. Differentially expressed cytokines (CCL5, CCL19, CXCL9 and CXCL10) and claudins (CLDN8, CLDN11 and CLDN19) were related to the overall survival, and their expression levels were also examined both in tumor tissues of CL-BCa patients by IHC and in typical CL-BCa cell lines by qPCR. Finally, the BCa patients with high expression of these DEGs (CCL5, CCL19, CXCL9, CLDN8 and CLDN11) showed a better overall survival. This study sheds light on molecular features of CL-BCa on immune microenvironments and contributes to identification of prognosis biomarkers for the CL-BCa patients.

Pathology of Tumors Associated With Pathogenic Germline Variants in 9 Breast Cancer Susceptibility Genes

IMPORTANCE

Rare germline genetic variants in several genes are associated with increased breast cancer (BC) risk, but their precise contributions to different disease subtypes are unclear. This information is relevant to guidelines for gene panel testing and risk prediction.

OBJECTIVE

To characterize tumors associated with BC susceptibility genes in large-scale population- or hospital-based studies.

DESIGN, SETTING, AND PARTICIPANTS

The multicenter, international case-control analysis of the BRIDGES study included 42 680 patients and 46 387 control participants, comprising women aged 18 to 79 years who were sampled independently of family history from 38 studies. Studies were conducted between 1991 and 2016. Sequencing and analysis took place between 2016 and 2021.

EXPOSURES

Protein-truncating variants and likely pathogenic missense variants in ATM, BARD1, BRCA1, BRCA2, CHEK2, PALB2, RAD51C, RAD51D, and TP53.

MAIN OUTCOMES AND MEASURES

The intrinsic-like BC subtypes as defined by estrogen receptor, progesterone receptor, and ERBB2 (formerly known as HER2) status, and tumor grade; morphology; size; stage; lymph node involvement; subtype-specific odds ratios (ORs) for carrying protein-truncating variants and pathogenic missense variants in the 9 BC susceptibility genes.

RESULTS

The mean (SD) ages at interview (control participants) and diagnosis (cases) were 55.1 (11.9) and 55.8 (10.6) years, respectively; all participants were of European or East Asian ethnicity. There was substantial heterogeneity in the distribution of intrinsic subtypes by gene. RAD51C, RAD51D, and BARD1 variants were associated mainly with triple-negative disease (OR, 6.19 [95% CI, 3.17-12.12]; OR, 6.19 [95% CI, 2.99-12.79]; and OR, 10.05 [95% CI, 5.27-19.19], respectively). CHEK2 variants were associated with all subtypes (with ORs ranging from 2.21-3.17) except for triple-negative disease. For ATM variants, the association was strongest for the hormone receptor (HR)+ERBB2- high-grade subtype (OR, 4.99; 95% CI, 3.68-6.76). BRCA1 was associated with increased risk of all subtypes, but the ORs varied widely, being highest for triple-negative disease (OR, 55.32; 95% CI, 40.51-75.55). BRCA2 and PALB2 variants were also associated with triple-negative disease. TP53 variants were most strongly associated with HR+ERBB2+ and HR-ERBB2+ subtypes. Tumors occurring in pathogenic variant carriers were of higher grade. For most genes and subtypes, a decline in ORs was observed with increasing age. Together, the 9 genes were associated with 27.3% of all triple-negative tumors in women 40 years or younger.

CONCLUSIONS AND RELEVANCE

The results of this case-control study suggest that variants in the 9 BC risk genes differ substantially in their associated pathology but are generally associated with triple-negative and/or high-grade disease. Knowing the age and tumor subtype distributions associated with individual BC genes can potentially aid guidelines for gene panel testing, risk prediction, and variant classification and guide targeted screening strategies.

Rule-Based Information Extraction from Free-Text Pathology Reports Reveals Trends in South African Female Breast Cancer Molecular Subtypes and Ki67 Expression

Clinical information on molecular subtypes and the Ki67 index is critical for breast cancer (BC) prognosis and personalised treatment plan. Extracting such information into structured data is essential for research, auditing, and cancer incidence reporting and underpins the potential for automated decision support. Herewith, we developed a rule-based natural language processing algorithm that retrieved and extracted important BC parameters from free-text pathology reports towards exploring molecular subtypes and Ki67-proliferation trends. We considered malignant BC pathology reports with different free-text narrative attributes from the South African National Health Laboratory Service. The reports were preprocessed and parsed through the algorithm. Parameters extracted by the algorithm were validated against manually extracted parameters. For all parameters extracted, we obtained accurate annotations of 83-100%, 93-100%, 91-100%, and 92-100% precision, recall, F₁-score, and kappa, respectively. There was a significant trend in the proportion of each molecular subtype by patient age, histologic type, grade, Ki67, and race. The findings also showed significant association in the Ki67 trend with hormone receptors, human epidermal growth factors, age, grade, and race. Our approach bridges the gap between data availability and actionable knowledge and provides a framework that could be adapted and reused in other cancers and beyond cancer studies. Information extracted from these reports showed interesting trends that may be exploited for BC screening and treatment resources in South Africa. Finally, this study strongly encourages the implementation of a synoptic style pathology report in South Africa.

Impact of Skeletal Muscle Loss and Visceral Obesity Measured Using Serial CT on the Prognosis of Operable Breast Cancers in Asian Patients

Objective

This study aimed to investigate the impact of baseline values and temporal changes in body composition parameters, including skeletal muscle index (SMI) and visceral adipose tissue area (VAT), measured using serial computed tomography (CT) imaging on the prognosis of operable breast cancers in Asian patients.

Materials and Methods

This study retrospectively included 627 Asian female (mean age ± standard deviation [SD], 53.6 ± 8.3 years) who underwent surgery for stage I–III breast cancer between January 2011 and September 2012. Body composition parameters, including SMI and VAT, were semi-automatically calculated on baseline abdominal CT at the time of diagnosis and follow-up CT for post-treatment surveillance. Serial changes in SMI and VAT were calculated as the delta values. Multivariable Cox regression analysis was used to evaluate the association of baseline and delta SMI and VAT values with disease-free survival.

Results

Among 627 patients, 56 patients (9.2%) had breast cancer recurrence after a median of 40.5 months. The mean value ± SD of the baseline SMI and baseline VAT were 43.7 ± 5.8 cm²/m² and 72.0 ± 46.0 cm², respectively. The mean value of the delta SMI was -0.9 cm²/m² and the delta VAT was 0.5 cm². The baseline SMI and VAT were not significantly associated with disease-free survival (adjusted hazard ratio [HR], 0.983; 95% confidence interval [CI], 0.937–1.031; p = 0.475 and adjusted HR, 1.001; 95% CI, 0.995–1.006; p = 0.751, respectively). The delta SMI and VAT were also not significantly associated with disease-free survival (adjusted HR, 0.894; 95% CI, 0.766–1.043; p = 0.155 and adjusted HR, 1.001; 95% CI, 0.989–1.014; p = 0.848, respectively).

Conclusion

Our study revealed that baseline and early temporal changes in SMI and VAT were not independent prognostic factors regarding disease-free survival in Asian patients undergoing surgery for breast cancer.

Disruption of the pentraxin 3/CD44 interaction as an efficient therapy for triple-negative breast cancers

Due to the heterogeneity and high frequency of genome mutations in cancer cells, targeting vital protumour factors found in stromal cells in the tumour microenvironment may represent an ideal strategy in cancer therapy. However, the regulation and mechanisms of potential targetable therapeutic candidates need to be investigated. An in vivo study demonstrated that loss of pentraxin 3 (PTX3) in stromal cells significantly decreased the metastasis and growth of cancer cells. Clinically, our results indicate that stromal PTX3 expression correlates with adverse prognostic features and is associated with worse survival outcomes in triple-negative breast cancer (TNBC). We also found that transforming growth factor beta 1 (TGF-β1) induces PTX3 expression by activating the transcription factor CCAAT/enhancer binding protein delta (CEBPD) in stromal fibroblasts. Following PTX3 stimulation, CD44, a PTX3 receptor, activates the downstream ERK1/2, AKT and NF-κB pathways to specifically contribute to the metastasis/invasion and stemness of TNBC MDA-MB-231 cells. Two types of PTX3 inhibitors were developed to disrupt the PTX3/CD44 interaction and they showed a significant effect on attenuating growth and restricting the metastasis/invasion of MDA-MB-231 cells, suggesting that targeting the PTX3/CD44 interaction could be a new strategy for future TNBC therapies.

Immunohistochemistry scoring of breast tumor tissue microarrays: A comparison study across three software applications

Digital pathology can efficiently assess immunohistochemistry (IHC) data on tissue microarrays (TMAs). Yet, it remains important to evaluate the comparability of the data acquired by different software applications and validate it against pathologist manual interpretation. In this study, we compared the IHC quantification of 5 clinical breast cancer biomarkers-estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), and cytokeratin 5/6 (CK5/6)-across 3 software applications (Definiens Tissue Studio, inForm, and QuPath) and benchmarked the results to pathologist manual scores. IHC expression for each marker was evaluated across 4 TMAs consisting of 935 breast tumor tissue cores from 367 women within the Nurses' Health Studies; each women contributing three 0.6-mm cores. The correlation and agreement between manual and software-derived results were primarily assessed using Spearman's ρ, percentage agreement, and area under the curve (AUC). At the TMA core-level, the correlations between manual and software-derived scores were the highest for HER2 (ρ ranging from 0.75 to 0.79), followed by ER (0.69-0.71), PR (0.67-0.72), CK5/6 (0.43-0.47), and EGFR (0.38-0.45). At the case-level, there were good correlations between manual and software-derived scores for all 5 markers (ρ ranging from 0.43 to 0.82), where QuPath had the highest correlations. Software-derived scores were highly comparable to each other (ρ ranging from 0.80 to 0.99). The average percentage agreements between manual and software-derived scores were excellent for ER (90.8%-94.5%) and PR (78.2%-85.2%), moderate for HER2 (65.4%-77.0%), highly variable for EGFR (48.2%-82.8%), and poor for CK5/6 (22.4%-45.0%). All AUCs across markers and software applications were ≥0.83. The 3 software applications were highly comparable to each other and to manual scores in quantifying these 5 markers. QuPath consistently produced the best performance, indicating this open-source software is an excellent alternative for future use.

Drug-Resistant Breast Cancer: Dwelling the Hippo Pathway to Manage the Treatment

Breast cancer can be categorized as a commonly occurring cancer among women with a high mortality rate. Due to the repetitive treatment cycles, it has been noted that the patients develop resistance towards the chemotherapeutic drugs and remain unresponsive towards them. Therefore, many researchers are studying various signaling pathways involved in drug resistance for cancer treatment to overcome the obstacle. Hippo signaling is a widely studied pathway involved in tumor progression and controlling cell proliferation. Hence, understanding the aspects of the gene involved Hippo pathway would provide an insight into the mechanism behind the resistance and result in the development of new treatments. Here, we review the Hippo signaling pathway in humans and how the expression of different components leads to the regulation of resistance against some of the common chemo-drugs used in breast cancer treatment. The article will also discuss the chemotherapeutics that became ineffective due to the resistance and the mechanism following the process.

Correlation between molecular prognostic factors and magnetic resonance imaging intravoxel incoherent motion histogram parameters in breast cancer

OBJECTIVE

To explore the efficacy of the quantitative parameter histogram analysis of intravoxel incoherent motion (IVIM) for different molecular prognostic factors of breast cancer.

MATERIALS AND METHODS

A total of 72 patients with breast cancer who were confirmed by surgical pathology and underwent preoperative magnetic resonance imaging (MRI) were analyzed retrospectively. A region of interest (ROI) was drawn in each slice of the IVIM images. Whole-tumor histogram parameters were obtained with Firevoxel's software by accumulating all ROIs. Next, Kolmogorov-Smirnov test, Student's t-test, Mann-Whitney U test, receiver operating characteristic curve analysis and spearman rank correlation analysis were used to assess the relationship between histogram parameters and molecular prognostic factors of breast cancer.

RESULTS

Among estrogen receptor (ER)-negative ROCs, the apparent diffusion coefficient (ADC) 10th percentile had the highest ROC of 0.792, with a cut-off value of 0.788 × 10^-3 mm²/s, and sensitivity and specificity of 0.714 and 0.867, respectively. The negative correlation between lymph node metastasis status and ADC standard deviation was significant (ρ = -0.44, the correlation coefficients was represented by ρ). Positive correlations were observed between hormonal expression of ER and progesterone receptor (PR) with heterogeneity metrics of ADC or perfusion fraction (f), such as ADC inhomogeneity (ρ = 0.37, ρ = 0.29) and f skewness (ρ = 0.32, ρ = 0.28). Negative correlations were observed with numerical metrics, such as the ADC median (ρ = -0.31, ρ = -0.34) and f 45th percentile (ρ = -0.35, ρ = -0.28). The positive correlations between human epidermal receptor factor-2 (HER2) and pseudo-diffusivity (Dp) numerical metrics, Ki-67 expression, and heterogeneity metrics of Dp were high.

CONCLUSIONS

The ADC 10th percentile had the largest area under the curve in the ER-negative ROC analysis, and the ADC standard deviation was the most valuable in the correlation analysis of lymph node metastasis. Whole-lesion quantitative histogram parameters of IVIM could, therefore, provide a scientific basis for radiomics to further guide clinical practice in the prognosis of breast cancer.

Patient-Derived Organoids Can Guide Personalized-Therapies for Patients with Advanced Breast Cancer

Most breast cancers at an advanced stage exhibit an aggressive nature, and there is a lack of effective anticancer options. Herein, the development of patient-derived organoids (PDOs) is described as a real-time platform to explore the feasibility of tailored treatment for refractory breast cancers. PDOs are successfully generated from breast cancer tissues, including heavily treated specimens. The microtubule-targeting drug-sensitive response signatures of PDOs predict improved distant relapse-free survival for invasive breast cancers treated with adjuvant chemotherapy. It is further demonstrated that PDO pharmaco-phenotyping reflects the previous treatment responses of the corresponding patients. Finally, as clinical case studies, all patients who receive at least one drug predicate to be sensitive by PDOs achieve good responses. Altogether, the PDO model is developed as an effective platform for evaluating patient-specific drug sensitivity in vitro, which can guide personal treatment decisions for breast cancer patients at terminal stage.

Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer

OBJECTIVES

To develop and validate an ultrasound elastography radiomics nomogram for preoperative evaluation of the axillary lymph node (ALN) burden in early-stage breast cancer.

METHODS

Data of 303 patients from hospital #1 (training cohort) and 130 cases from hospital #2 (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomics features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. The minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select ALN status-related features. Proportional odds ordinal logistic regression was performed using the radiomics signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated its performance using C-index and calibration.

RESULTS

SWE signature, US-reported LN status, and molecular subtype were independent risk factors associated with ALN status. The nomogram based on these variables showed good discrimination in the training (overall C-index: 0.842; 95%CI, 0.773-0.879) and the validation set (overall C-index: 0.822; 95%CI, 0.765-0.838). For discriminating between disease-free axilla (N0) and any axillary metastasis (N + (≥ 1)), it achieved a C-index of 0.845 (95%CI, 0.777-0.914) for the training cohort and 0.817 (95%CI, 0.769-0.865) for the validation cohort. The tool could also discriminate between low (N + (1-2)) and heavy metastatic ALN burden (N + (≥ 3)), with a C-index of 0.827 (95%CI, 0.742-0.913) in the training cohort and 0.810 (95%CI, 0.755-0.864) in the validation cohort.

CONCLUSION

The radiomics model shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for decision-making.

KEY POINTS

• Radiomics analysis helps radiologists to evaluate the axillary lymph node status of breast cancer with accuracy. • This multicentre retrospective study showed that radiomics nomogram based on shear-wave elastography provides incremental information for risk stratification. • Treatment can be given with more precision based on the model.

Evaluation of early post-natal pig mammary gland development and human breast cancer gene expression

Breast cancer is the second leading cause of death in women after lung cancer, and only 5% of patients with metastatic breast cancer survive beyond ten years of diagnosis. Considering the heterogeneous subclasses of breast cancer, current cancer models have shortfalls due to copy number variants, and genetic differences of humans and immunocompromised animal models. Preclinical studies indicate stem cell activity in early post-natal mammary development may be reactivated in the human adult as a trigger to initiate cell proliferation leading to breast cancer. The goal of the work reported herein was to compare genetic expression of early development, post-natal pig mammary glands to the literature reported genes implicated in different subclasses of human breast cancer. Differentially expressed genes associated with breast cancer and present in early developing pig samples include NUCB2, ANGPTL4 and ACE. Histological staining confirmed E-cadherin, Vimentin, N-cadherin, and Claudin-1, which are all implicated in malignant cancer. Due to the homology of gene expression patterns in the developing pig mammary gland and reported genes in human breast cancer profiles, this research is worthy of further study to address a potential model using mammary development cues to unravel breast cancer biology.

Metabolic Syndrome in Breast Cancer Patients: An Observational Study

Background:

The reported association between metabolic syndrome (MetS) and breast cancer may have a significant impact on the incidence and mortality related to breast cancer. We undertook this study to find if the disease is different in patients with MetS.

Materials and Methods:

Patients with biopsy-proven breast cancer were divided into groups based on the presence or absence of MetS (according to the IDF definition of 2006) and also based on menopausal status. The presence of known risk and prognostic factors were also recorded, and the groups were compared.

Results:

A total of 305 patients were recruited, of which 191 (62.6%) had MetS. Patients with MetS were older than those without (52.1 versus 48.3 years, P = .014) and had a lower incidence of nulliparity (4.1% vs 12.8%, P = .005) and dense breasts (2.9% in MetS vs 10.8% in no MetS, P = .009). On further dividing into premenopausal and postmenopausal, these differences persisted only in premenopausal patients. MetS group had a lower number of HER2-positive tumours (14.3% for MetS, 23.9% for no MetS; P = .036). After dividing into premenopausal and postmenopausal, significant differences were observed in distant metastases (5.4% in MetS vs 16.1% in no MetS, P = .045) and in grade (higher grade in MetS, P = .05) in premenopausal patients. In postmenopausal patients, difference was observed in HER2 positivity (12.3% in MetS vs 28.8% in no MetS, P = .008).

Conclusions:

Breast cancer in patients with MetS may not be significantly different from breast cancer in patients without MetS.

Intrinsic Subtypes and Androgen Receptor Gene Expression in Primary Breast Cancer. A Meta-Analysis

The androgen receptor (AR) is frequently expressed in breast cancer (BC), but its association with clinical and biological parameters of BC patients remains unclear. Here, we investigated the association of AR gene expression according to intrinsic BC subtypes by meta-analysis of large-scale microarray transcriptomic datasets. Sixty-two datasets including 10315 BC patients were used in the meta-analyses. Interestingly, AR mRNA level is significantly increased in patients categorized with less aggressive intrinsic molecular subtypes including, Luminal A compared to Basal-like (standardized mean difference, SMD: 2.12; 95% confidence interval, CI: 1.88 to 2.35; p < 0.001) or when comparing Luminal B to Basal-like (SMD: 1.53; CI: 1.33 to 1.72; p < 0.001). The same trend was observed when analyses were performed using immunohistochemistry-based surrogate subtypes. Consistently, the AR mRNA expression was higher in patients with low histological grade (p < 0.001). Furthermore, our data revealed higher levels of AR mRNA in BC patients expressing either estrogen or progesterone receptors (p < 0.001). Together, our findings indicate that high mRNA levels of AR are associated with BC subgroups with the less aggressive clinical features.

High expression of ERBB2 is an independent risk factor for reduced recurrence-free survival in patients with stage T1 non-muscle-invasive bladder cancer

INTRODUCTION

Molecular markers associated with breast cancer are assumed to be associated with outcome in non-muscle-invasive bladder cancer (NMIBC).

MATERIALS AND METHODS

We retrospectively investigated the association of the mRNA expression of estrogen receptor 1 (ESR1) and 2 (ESR2), progesterone receptor (PGR), MKI67, and HER2 (ERBB2) with recurrence-free (RFS), cancer-specific (CSS), and overall survival (OS) in 80 patients with stage T1 NMIBC.

RESULTS

High expression of ESR2 (P = 0.003), ERBB2 (P < 0.001), and MKI67 (P = 0.029) was associated with shorter RFS. Only high ERBB2 was an independent prognostic factor for reduced RFS (HR = 2.98; P = 0.009). When sub stratifying the cohort, high ESR2 was associated with reduced RFS (P < 0.001), CSS (P = 0.037) and OS (P = 0.006) in patients without instillation therapy. High ESR2 was associated with reduced CSS (P = 0.018) and OS (P = 0.029) in females and with shorter RFS in both sexes (males: P = 0.035; females: P = 0.010). Patients with high ERBB2 showed reduced CSS (P = 0.011) and OS (P = 0.042) in females and reduced CSS (P = 0.012) in those without instillation, while RFS was significantly reduced irrespective of sex or instillation.

CONCLUSION

High mRNA expression of ERBB2 is an independent predictor of reduced RFS in patients with stage T1 NMIBC. High ERBB2 and ESR2 are associated with reduced outcomes, especially in females and patients without instillation therapy.

Contrast-Enhanced Mammography and Radiomics Analysis for Noninvasive Breast Cancer Characterization: Initial Results

PURPOSE

To investigate the potential of contrast-enhanced mammography (CEM) and radiomics analysis for the noninvasive differentiation of breast cancer invasiveness, hormone receptor status, and tumor grade.

PROCEDURES

This retrospective study included 100 patients with 103 breast cancers who underwent pretreatment CEM. Radiomics analysis was performed using MAZDA software. Lesions were manually segmented. Radiomic features were derived from first-order histogram (HIS), co-occurrence matrix (COM), run length matrix (RLM), absolute gradient, autoregressive model, the discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation (POE+ACC), and mutual information (MI) coefficients informed feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise texture-based separation of tumor invasiveness and hormone receptor status using histopathology as the standard of reference.

RESULTS

Radiomics analysis achieved the highest accuracies of 87.4 % for differentiating invasive from noninvasive cancers based on COM+HIS/MI, 78.4 % for differentiating HR positive from HR negative cancers based on COM+HIS/Fisher, 97.2 % for differentiating human epidermal growth factor receptor 2 (HER2)-positive/HR-negative from HER2-negative/HR-positive cancers based on RLM+WAV/MI, 100 % for differentiating triple-negative from triple-positive breast cancers mainly based on COM+WAV+HIS/POE+ACC, and 82.1 % for differentiating triple-negative from HR-positive cancers mainly based on WAV+HIS/Fisher. Accuracies for differentiating grade 1 vs. grades 2 and 3 cancers were 90 % for invasive cancers (based on COM/MI) and 100 % for noninvasive cancers (almost entirely based on COM/MI).

CONCLUSIONS

Radiomics analysis with CEM has potential for noninvasive differentiation of tumors with different degrees of invasiveness, hormone receptor status, and tumor grade.

Actin-Like Protein 8 Promotes the Progression of Triple-Negative Breast Cancer via Activating PI3K/AKT/mTOR Pathway

Objective

The purpose of this study was to investigate the function of actin-like protein 8 (ACTL8) on triple-negative breast cancer (TNBC) and its potential mechanisms.

Methods

In our study, ACTL8 expression and the prognostic values of ACTL8 were evaluated via the dataset from the Cancer Genome Atlas (TCGA). At the same time, the expression of ACTL8 in TNBC cells was measured by Western blot and qRT-PCR. Then, the effects of ACTL8 on the growth and metastasis of TNBC were investigated by using 5-ethynyl-20-deoxyuridine (EdU), colony formation, flow cytometry, wound healing and transwell assays. Mechanistically, Western blot was performed to confirm the interaction between ACTL8 and phosphatidylinositol 3′-kinase/protein kinase B/mammalian target of rapamycin (PI3K/Akt/mTOR) signaling pathway in TNBC.

Results

ACTL8 expression was upregulated in TNBC and associated with the poor prognosis of TNBC. Silencing ACTL8 suppressed the proliferation, migration and invasion, also promoted the apoptosis in MDA-MB-231 and BT-549 cells. Moreover, we found that silencing ACTL8 could inhibit the activation of PI3K/AKT/mTOR signaling pathway in MDA-MB-231 and BT-549 cells. Meanwhile, the impact of silencing ACTL8 on the proliferation, apoptosis, migration and invasion was enhanced by PI3K/AKT/mTOR pathway inhibitor (Wortmannin) and reversed by PI3K/AKT/mTOR pathway activator (740Y-P).

Conclusion

Our data demonstrated that ACTL8 may facilitate the proliferation, migration and invasion, while inhibiting apoptosis through activating PI3K/Akt/mTOR signaling pathway in TNBC.

Understanding breast cancer heterogeneity through non-genetic heterogeneity

Intricacy in treatment and diagnosis of breast cancer has been an obstacle due to genotype and phenotype heterogeneity. Understanding of non-genetic heterogeneity mechanisms along with considering role of genetic heterogeneity may fill the gaps in landscape painting of heterogeneity. The main factors contribute to non-genetic heterogeneity including: transcriptional pulsing/bursting or discontinuous transcriptions, stochastic partitioning of components at cell division and various signal transduction from tumor ecosystem. Throughout this review, we desired to provide a conceptual framework focused on non-genetic heterogeneity, which has been intended to offer insight into prediction, diagnosis and treatment of breast cancer.

Prognostic significance of histopathological factors in survival and recurrence of atypical carcinoid tumours

OBJECTIVES

Atypical carcinoids are neuroendocrine neoplasms of intermediate degree and low frequency. The aim of this study is to analyse their clinical characteristics and the importance of different histopathological factors in their prognosis.

METHODS

Multicentre cooperative group EMETNE prospectively reviewed 153 patients operated on between 1998 and 2016 with diagnosis of atypical carcinoids. Clinical variables and histopathological features were assessed.

RESULTS

Mean age was 54.36 years, similar for both genders. Concerning pathological study, mean tumour size was 31.7 mm. Rosettes were presented in 17% of the cases and tumoural necrosis in 23.3%. The cell proliferation factor Ki-67 index was 10.7%. The 2- and 5-year overall survival rates were 95.8% and 88.9%, respectively. In the univariate study, statistically significant differences in survival were found for each of the categories of T, N and M factors. Mitotic index and quantification of expression of Ki-67 showed influence in overall survival, although without statistical significance. In the multivariate analysis, factors N, M and mitotic index behaved as independent prognostic factors related to survival. Median disease-free interval in the series was 163.35 months. In cases with loco-regional recurrence, 53% had positive hiliar or mediastinal nodal involvement at the time of the surgery. In the univariate analysis, we observed statistically significant differences in disease-free interval in patients with nodal involvement (P = 0.024) and non-anatomical resections (P = 0.04). Histological characteristics showed no statistically significant differences in disease-free interval.

CONCLUSIONS

Lymph node involvement, the development of distant metastasis and mitotic index, more than Ki-67 determination, were shown as independent prognostic factors related to survival of these patients.

Omission of Chemotherapy in HR+/HER2- Early Invasive Breast Cancer Based on Combined 6-IHC Score?

PURPOSE

Current methods of judging whether HR+/HER2- breast cancer (BC) require adjuvant therapy, such as Ki67 and multigene prognostic tests, cannot balance accuracy with the price most patients can afford.

METHODS

A retrospective analysis of 330 HR+/HER2- BC patients was conducted. Six BC-related genes (Cathepsin L2, MMP11, CyclinB1, Aurora A, Survivin, and Ki67) were screened using univariate and multivariate COX regression, and correlate clinical follow-up with immunohistochemical expression (designated as 6-IHC). All the included patients were divided randomly at a 7:3 ratio into training and testing cohorts. The cutoff prognosis index (PI) of 6-IHC was determined by multivariate Cox risk regression analysis after calculating the PI of each patient in training cohort and confirmed in testing cohort. Kaplan-Meier (KM) method was used to analyze Disease-free survival (DFS) and overall survival (OS). Six-IHC score and other factors associated with survival benefit of adjuvant chemotherapy were compared with Ki67 index.

RESULTS

The receiver operating characteristic curve analysis showed that the patients can be divided into 6-IHC score "High" and "Low" risk groups. The 8-year DFS and OS of the KM curves showed that chemotherapy did not significantly improve the DFS in the 6-IHC score "Low" risk group (P= 0.830), but significantly improved the DFS in the 6-IHC score "High" risk group (P = 0.012).

CONCLUSIONS

Combined 6-IHC score could be a reliable tool in predicting cancer-specific recurrences and survival in HR+/HER2-breast cancer patients, with additional advantages over using immunohistochemical expression of Ki67.

[Multimodal, multiparametric and genetic breast imaging]

CLINICAL/METHODOLOGICAL ISSUE

Multiparametric magnetic resonance imaging (MRI) aims to visualize and quantify biological, physiological and pathological processes at the cellular and molecular level and provides valuable information about key processes in cancer development and progression. "Omics" strategies (genomics, transcriptomics, proteomics, metabolomics) have many uses in oncology.

STANDARD RADIOLOGICAL METHODS

Multiparametric MRI of the breast currently includes T2-weighted, diffusion-weighted and dynamic contrast-enhanced MRI (DCE-MRI) METHODOLOGICAL INNOVATIONS: Additional parameters such as proton magetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), blood oxygen level-dependent (BOLD), hyperpolarized (HP) MRI or lipid MRS are currently being developed and are being evaluated in breast cancer diagnostics.

ACHIEVEMENTS

Radiogenomics is a new direction in medical science that has been made possible by significant advances in imaging and image analysis methods, as well as the development of techniques to extract and correlate various imaging parameters with "omics" data. The aim of radiogenomics is to correlate imaging characteristics (phenotypes) with gene expression patterns, gene mutations and other genome-associated properties and is the evolution of the correlation between radiology and pathology from the anatomical-histological to the molecular level. Quantitative and qualitative imaging biomarkers provide insights into the complex tumor biology. Initial results suggest that radiogemics will play an important role in the diagnosis, prognosis, and treatment of breast cancer.

PRACTICAL RECOMMENDATIONS

This article provides an overview of the current state of radiogenomics of the breast and future applications and challenges.

Prolactin receptor-driven combined luminal and epithelial differentiation in breast cancer restricts plasticity, stemness, tumorigenesis and metastasis

Dedifferentiation increased cellular plasticity and stemness are established derivers of tumor heterogeneity, metastasis and therapeutic failure resulting in incurable cancers. Therefore, it is essential to decipher pro/forward-differentiation mechanisms in cancer that may serve as therapeutic targets. We found that interfering with expression of the receptor for the lactogenic hormone prolactin (PRLR) in breast cancer cells representative of the luminal and epithelial breast cancer subtypes (hormone receptor positive (HR+) and HER2-enriched (HER2-E) resulted in loss of their differentiation state, enriched for stem-like cell subpopulations, and increased their tumorigenic capacity in a subtype-specific manner. Loss of PRLR expression in HR+ breast cancer cells caused their dedifferentiation generating a mesenchymal-basal-like phenotype enriched in CD44+ breast cancer stem-like cells (BCSCs) showing high tumorigenic and metastatic capacities and resistance to anti-hormonal therapy. Whereas loss of PRLR expression in HER2-E breast cancer cells resulted in loss of their luminal differentiation yet enriched for epithelial ALDH+ BCSC population showing elevated HER2-driven tumorigenic, multi-organ metastatic spread, and resistance to anti-HER2 therapy. Collectively, this study defines PRLR as a driver of precise luminal and epithelial differentiation limiting cellular plasticity, stemness, and tumorigenesis and emphasizing the function of pro/forward-differentiation pathways as a foundation for the discovery of anti-cancer therapeutic targets.

A Critical Review on the Synthesis of Natural Sodium Alginate Based Composite Materials: An Innovative Biological Polymer for Biomedical Delivery Applications

Sodium alginate (Na-Alg) is water-soluble, neutral, and linear polysaccharide. It is the derivative of alginic acid which comprises 1,4-β-d-mannuronic (M) and α-l-guluronic (G) acids and has the chemical formula (NaC6H7O6). It shows water-soluble, non-toxic, biocompatible, biodegradable, and non-immunogenic properties. It had been used for various biomedical applications, among which the most promising are drug delivery, gene delivery, wound dressing, and wound healing. For different biomedical applications, it is used in different forms with the help of new techniques. That is the reason it had been blended with different polymers. In this review article, we present a comprehensive overview of the combinations of sodium alginate with natural and synthetic polymers and their biomedical applications involving delivery systems. All the scientific/technical issues have been addressed, and we have highlighted the recent advancements.

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

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

Molecular subtypes of triple-negative breast cancer: understanding of subtype categories and clinical implication

BACKGROUND

Triple-negative breast cancer (TNBC) is a heterogeneous entity that encompasses several subtypes with distinct molecular characteristics. The patients with TNBCs show unpredictable response to the chemotherapy, and further there is the lack of effective agents. Thus, many studies have been underway to discover targeted therapy suitable for patients with specific genetic alterations in each molecular subtypes. TNBCs are classified as four major molecular subtypes according to the gene expression patterns. These are luminal androgen receptor (LAR), mesenchymal-like, immunomodulatory (IM), and basal-like types.

CONCLUSION

Here, we discuss the unique molecular features of each subtype as well as promising targets for anti-cancer therapy.

BioPerine Encapsulated Nanoformulation for Overcoming Drug-Resistant Breast Cancers

The evolving dynamics of drug resistance due to tumor heterogeneity often creates impediments to traditional therapies making it a challenging issue for cancer cure. Breast cancer often faces challenges of current therapeutic interventions owing to its multiple complexities and high drug resistivity， for example against drugs like trastuzumab and tamoxifen. Drug resistance in the majority of breast cancer is often aided by the overtly expressed P-glycoprotein (P-gp) that guides in the rapid drug efflux of chemotherapy drugs. Despite continuous endeavors and ground-breaking achievements in the pursuit of finding better cancer therapeutic avenues, drug resistance is still a menace to hold back. Among newer therapeutic approaches, the application of phytonutrients such as alkaloids to suppress P-gp activity in drug-resistant cancers has found an exciting niche in the arena of alternative cancer therapies. In this work, we would like to present a black pepper alkaloid derivative known as BioPerine-loaded chitosan (CS)-polyethylene glycol (PEG) coated polylactic acid (PLA) hybrid polymeric nanoparticle to improve the bioavailability of BioPerine and its therapeutic efficacy in suppressing P-gp expression in MDA-MB 453 breast cancer cell line. Our findings revealed that the CS-PEG-BioPerine-PLA nanoparticles demonstrated a smooth spherical morphology with an average size of 316 nm, with improved aqueous solubility, and provided sustained BioPerine release. The nanoparticles also enhanced in vitro cytotoxicity and downregulation of P-gp expression in MDA-MB 453 cells compared to the commercial inhibitor verapamil hydrochloride, thus promising a piece of exciting evidence for the development of BioPerine based nano-drug delivery system in combination with traditional therapies as a crucial approach to tackling multi-drug resistance in cancers.

A case-control study on the SNP309T → G and 40-bp Del1518 of the MDM2 gene and a systematic review for MDM2 polymorphisms in the patients with breast cancer

Objective

The current research was conducted to study the association between the SNP309 and del1518 polymorphisms with the breast cancer in the patients with the Kurdish ethnic background from western Iran. Also, a systematic review of the relevant case‐control studies on the MDM2 polymorphisms in the patients with breast cancer was performed.

Methodology

Two mL of peripheral blood was taken from 100 patients with breast cancer and 100 healthy individuals. The frequencies of MDM2 SNP309 and del1518 genotypes and alleles were determined using the PCR‐RFLP and PCR methods, respectively.

Results

The frequency of the TT, TG, and GG of MDM2‐SNP309 genotypes in the patients was obtained as 23%, 52%, and 25%, and they were equal to 22%, 40%, and 38% in the control group, respectively. Also, considering the MDM2‐del1518 polymorphism, the frequencies of ins/ins, ins/del, and del/del genotypes were equal to 52%, 41%, and 7% in the breast cancer group and they were equal to 62, 30, and 8% in the control group, respectively. Analysis of the results indicated that the GG genotype plays a protective role for the breast cancer in the recessive model (GG vs TT + TG) of SNP309 (χ² = 3.916, P = .048, and OR = 0.54).

Conclusion

Our findings revealed that the GG genotype of MDM2‐SNP309 can play a protective role in the breast cancer disease. Also, our systematic review indicated that the SNP309, SNP285, and del1518 of MDM2 gene in different populations mostly did not have a significant association with the risk of breast cancer.

Screening detects a myriad of breast disease - refining practice will increase effectiveness and reduce harm

For many individuals, the term 'cancer' equates to a disease that if untreated will progress, spread from the area initially affected and ultimately cause death. 'Breast cancer', however, is a diverse of range of pathological entities, incorporating indolent to fast-growing and aggressive lesions, with varying histological patterns, clinical presentations, treatment responses and outcomes. Screening for malignancy is based on the assumption that cancer has a gradual, orderly progression and that detecting lesions earlier in their natural history, and intervening, will reduce mortality. The natural history of epithelial atypia, ductal carcinoma in situ and even invasive breast cancer is poorly understood, but widely variable. We believe that population breast screening methodology needs to change to focus on diagnosis of lesions of greatest clinical relevance.

Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm

To develop a convolutional neural network (CNN) algorithm that can predict the molecular subtype of a breast cancer based on MRI features. An IRB-approved study was performed in 216 patients with available pre-treatment MRIs and immunohistochemical staining pathology data. First post-contrast MRI images were used for 3D segmentation using 3D slicer. A CNN architecture was designed with 14 layers. Residual connections were used in the earlier layers to allow stabilization of gradients during backpropagation. Inception style layers were utilized deeper in the network to allow learned segregation of more complex feature mappings. Extensive regularization was utilized including dropout, L2, feature map dropout, and transition layers. The class imbalance was addressed by doubling the input of underrepresented classes and utilizing a class sensitive cost function. Parameters were tuned based on a 20% validation group. A class balanced holdout set of 40 patients was utilized as the testing set. Software code was written in Python using the TensorFlow module on a Linux workstation with one NVidia Titan X GPU. Seventy-four luminal A, 106 luminal B, 13 HER2+, and 23 basal breast tumors were evaluated. Testing set accuracy was measured at 70%. The class normalized macro area under receiver operating curve (ROC) was measured at 0.853. Non-normalized micro-aggregated AUC was measured at 0.871, representing improved discriminatory power for the highly represented Luminal A and Luminal B subtypes. Aggregate sensitivity and specificity was measured at 0.603 and 0.958. MRI analysis of breast cancers utilizing a novel CNN can predict the molecular subtype of breast cancers. Larger data sets will likely improve our model.

Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging

The aim of our intra-individual comparison study was to investigate and compare the potential of radiomics analysis of contrast-enhanced mammography (CEM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status, and tumor grade in patients with primary breast cancer. This retrospective study included 48 female patients with 49 biopsy-proven breast cancers who underwent pretreatment breast CEM and MRI. Radiomics analysis was performed by using MaZda software. Radiomics parameters were correlated with tumor histology (invasive vs. non-invasive), hormonal status (HR+ vs. HR-), and grading (low grade G1 + G2 vs. high grade G3). CEM radiomics analysis yielded classification accuracies of up to 92% for invasive vs. non-invasive breast cancers, 95.6% for HR+ vs. HR- breast cancers, and 77.8% for G1 + G2 vs. G3 invasive cancers. MRI radiomics analysis yielded classification accuracies of up to 90% for invasive vs. non-invasive breast cancers, 82.6% for HR+ vs. HR- breast cancers, and 77.8% for G1+G2 vs. G3 cancers. Preliminary results indicate a potential of both radiomics analysis of DCE-MRI and CEM for non-invasive assessment of tumor-invasiveness, hormone receptor status, and tumor grade. CEM may serve as an alternative to MRI if MRI is not available or contraindicated.