Breast cancer complexity: implications of intratumoral heterogeneity in clinical management

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.

MEF2D Functions as a Tumor Suppressor in Breast Cancer

The myocyte enhancer factor 2 (MEF2) gene family play fundamental roles in the genetic programs that control cell differentiation, morphogenesis, proliferation, and survival in a wide range of cell types. More recently, these genes have also been implicated as drivers of carcinogenesis, by acting as oncogenes or tumor suppressors depending on the biological context. Nonetheless, the molecular programs they regulate and their roles in tumor development and progression remain incompletely understood. The present study evaluated whether the MEF2D transcription factor functions as a tumor suppressor in breast cancer. The knockout of the MEF2D gene in mouse mammary epithelial cells resulted in phenotypic changes characteristic of neoplastic transformation. These changes included enhanced cell proliferation, a loss of contact inhibition, and anchorage-independent growth in soft agar, as well as the capacity for tumor development in mice. Mechanistically, the knockout of MEF2D induced the epithelial-to-mesenchymal transition (EMT) and activated several oncogenic signaling pathways, including AKT, ERK, and Hippo-YAP. Correspondingly, a reduced expression of MEF2D was observed in human triple-negative breast cancer cell lines, and a low MEF2D expression in tissue samples was found to be correlated with a worse overall survival and relapse-free survival in breast cancer patients. MEF2D may, thus, be a putative tumor suppressor, acting through selective gene regulatory programs that have clinical and therapeutic significance.

The therapeutic potential of circular RNA in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is among the most aggressive subtypes of the disease that does not express estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Circular RNAs (circRNAs) are a type of non-coding RNA with a circular shape formed by non-standard splicing or reverse splicing. Numerous circRNAs exhibit abnormal expression in various malignancies, showing their critical role in the emergence and growth of tumors. Recent studies have shown evidence supporting the idea that certain circRNAs regulate the proliferation and metastasis of TNBC. In addition, circRNAs alter metabolism and the immune microenvironment to promote or inhibit the development of TNBC. Notably, circRNAs may affect the efficacy of clinical drug therapy, serve as therapeutic targets, and be used as molecular biomarkers in the future. Herein, we will first summarize the biogenesis and function of circRNAs. Then, we will explain current research on circRNAs related to TNBC and their potential to serve as therapeutic targets or biomarkers for future drug development, providing a new direction and idea for TNBC therapy.

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.

Current Role and Future Prospects of Positron Emission Tomography (PET)/Computed Tomography (CT) in the Management of Breast Cancer

Breast cancer has become the most diagnosed cancer in women globally, with 2.3 million new diagnoses each year. Accurate early staging is essential for improving survival rates with metastatic spread from loco regional to distant metastasis, decreasing mortality rates by 50%. Current guidelines do not advice the routine use of positron emission tomography (PET)-computed tomography (CT) in the staging of early breast cancer in the absence of symptoms. However, there is a growing body of evidence to suggest that the use of PET-CT in this early stage can benefit the patient by improving staging and as a result treatment and outcomes, as well as psychological burden, without increasing costs to the health service. Ongoing research in PET radiomics and artificial intelligence is showing promising future prospects in its use in diagnosis, staging, prognostication, and assessment of responses to the treatment of breast cancer. Furthermore, ongoing research to address current limitations of PET-CT by improving techniques and tracers is encouraging. In this narrative review, we aim to evaluate the current evidence of the usefulness of PET-CT in the management of breast cancer in different settings along with its future prospects, including the use of artificial intelligence (AI), radiomics, and novel tracers.

Circular RNA and its potential diagnostic and therapeutic values in breast cancer

Breast cancer (BC) is one of the most common malignant tumors in women and still poses a significant threat to women worldwide. Recurrence of BC in situ, metastasis to distant organs, and resistance to chemotherapy are all attached to high mortality in patients with BC. Non-coding RNA (ncRNA) of the type known as "circRNA" links together from one end to another to create a covalently closed, single-stranded circular molecule. With characteristics including plurality, evolutionary conservation, stability, and particularity, they are extensively prevalent in various species and a range of human cells. CircRNAs are new and significant contributors to several kinds of disorders, including cardiovascular disease, multiple organ inflammatory responses and malignancies. Recent studies have shown that circRNAs play crucial roles in the occurrence of breast cancer by interacting with miRNAs to regulate gene expression at the transcriptional or post-transcriptional levels. CircRNAs offer the potential to be therapeutic targets for breast cancer treatment as well as prospective biomarkers for early diagnosis and prognosis of BC. Here, we are about to present an overview of the functions of circRNAs in the proliferation, invasion, migration, and resistance to medicines of breast cancer cells and serve as a promising resource for future investigations on the pathogenesis and therapeutic strategies.

Non-Contrast-Enhanced Multiparametric MRI of the Hypoxic Tumor Microenvironment Allows Molecular Subtyping of Breast Cancer: A Pilot Study

Tumor neoangiogenesis is an important hallmark of cancer progression, triggered by alternating selective pressures from the hypoxic tumor microenvironment. Non-invasive, non-contrast-enhanced multiparametric MRI combining blood-oxygen-level-dependent (BOLD) MRI, which depicts blood oxygen saturation, and intravoxel-incoherent-motion (IVIM) MRI, which captures intravascular and extravascular diffusion, can provide insights into tumor oxygenation and neovascularization simultaneously. Our objective was to identify imaging markers that can predict hypoxia-induced angiogenesis and to validate our findings using multiplexed immunohistochemical analyses. We present an in vivo study involving 36 female athymic nude mice inoculated with luminal A, Her2+, and triple-negative breast cancer cells. We used a high-field 9.4-tesla MRI system for imaging and subsequently analyzed the tumors using multiplex immunohistochemistry for CD-31, PDGFR-β, and Hif1-α. We found that the hyperoxic-BOLD-MRI-derived parameter ΔR2* discriminated luminal A from Her2+ and triple-negative breast cancers, while the IVIM-derived parameter fIVIM discriminated luminal A and Her2+ from triple-negative breast cancers. A comprehensive analysis using principal-component analysis of both multiparametric MRI- and mpIHC-derived data highlighted the differences between triple-negative and luminal A breast cancers. We conclude that multiparametric MRI combining hyperoxic BOLD MRI and IVIM MRI, without the need for contrast agents, offers promising non-invasive markers for evaluating hypoxia-induced angiogenesis.

Breast Cancer Growth on Serial MRI: Volume Doubling Time Based on 3-Dimensional Tumor Volume Assessment

BACKGROUND

The volume doubling time (VDT) of breast cancer was most frequently calculated using the two-dimensional (2D) diameter, which is not reliable for irregular tumors. It was rarely investigated using three-dimensional (3D) imaging with tumor volume on serial magnetic resonance imaging (MRI).

PURPOSE

To investigate the VDT of breast cancer using 3D tumor volume assessment on serial breast MRIs.

STUDY TYPE

Retrospective.

SUBJECTS

Sixty women (age at diagnosis: 57 ± 10 years) with breast cancer, assessed by two or more breast MRI examinations. The median interval time was 791 days (range: 70-3654 days).

FIELD STRENGTH/SEQUENCE

3-T, fast spin-echo T2-weighted imaging (T2WI), single-shot echo-planar diffusion-weighted imaging (DWI), and gradient echo dynamic contrast-enhanced imaging.

ASSESSMENT

Three radiologists independently reviewed the morphological, DWI, and T2WI features of lesions. The whole tumor was segmented to measure the volume on contrast-enhanced images. The exponential growth model was fitted in the 11 patients with at least three MRI examinations. The VDT of breast cancer was calculated using the modified Schwartz equation.

STATISTICAL TESTS

Mann-Whitney U test, Kruskal-Wallis test, Chi-squared test, intraclass correlation coefficients, and Fleiss kappa coefficients. A P-value <0.05 was considered statistically significant. The exponential growth model was evaluated using the adjusted R2 and root mean square error (RMSE).

RESULTS

The median tumor diameter was 9.7 mm and 15.2 mm on the initial and final MRI, respectively. The median adjusted R2 and RMSE of the 11 exponential models were 0.97 and 15.8, respectively. The median VDT was 540 days (range: 68-2424 days). For invasive ductal carcinoma (N = 33), the median VDT of the non-luminal type was shorter than that of the luminal type (178 days vs. 478 days). On initial MRI, breast cancer manifesting as a focus or mass lesion showed a shorter VDT than that of a non-mass enhancement (NME) lesion (median VDT: 426 days vs. 665 days).

DATA CONCLUSION

A shorter VDT was observed in breast cancer manifesting as focus or mass as compared to an NME lesion.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Hyperoxic BOLD-MRI-Based Characterization of Breast Cancer Molecular Subtypes Is Independent of the Supplied Amount of Oxygen: A Preclinical Study

Hyperoxic BOLD-MRI targeting tumor hypoxia may provide imaging biomarkers that represent breast cancer molecular subtypes without the use of injected contrast agents. However, the diagnostic performance of hyperoxic BOLD-MRI using different levels of oxygen remains unclear. We hypothesized that molecular subtype characterization with hyperoxic BOLD-MRI is feasible independently of the amount of oxygen. Twenty-three nude mice that were inoculated into the flank with luminal A (n = 9), Her2+ (n = 5), and triple-negative (n = 9) human breast cancer cells were imaged using a 9.4 T Bruker BioSpin system. During BOLD-MRI, anesthesia was supplemented with four different levels of oxygen (normoxic: 21%; hyperoxic: 41%, 71%, 100%). The change in the spin-spin relaxation rate in relation to the normoxic state, ΔR2*, dependent on the amount of erythrocyte-bound oxygen, was calculated using in-house MATLAB code. ΔR2* was significantly different between luminal A and Her2+ as well as between luminal A and triple-negative breast cancer, reflective of the less aggressive luminal A breast cancer's ability to better deliver oxygen-rich hemoglobin to its tissue. Differences in ΔR2* between subtypes were independent of the amount of oxygen, with robust distinction already achieved with 41% oxygen. In conclusion, hyperoxic BOLD-MRI may be used as a biomarker for luminal A breast cancer identification without the use of exogenous contrast agents.

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

PURPOSE

To 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.

METHOD

This 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.

RESULTS

All 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).

CONCLUSIONS

The CTRW parameters could be used as non-invasive quantitative imaging markers to evaluate breast lesions.

The Breast Cancer Single-Cell Atlas: Defining cellular heterogeneity within model cell lines and primary tumors to inform disease subtype, stemness, and treatment options

PURPOSE

Breast Cancer (BC) is the most diagnosed cancer in women; however, through significant research, relative survival rates have significantly improved. Despite progress, there remains a gap in our understanding of BC subtypes and personalized treatments. This manuscript characterized cellular heterogeneity in BC cell lines through scRNAseq to resolve variability in subtyping, disease modeling potential, and therapeutic targeting predictions.

METHODS

We generated a Breast Cancer Single-Cell Cell Line Atlas (BSCLA) to help inform future BC research. We sequenced over 36,195 cells composed of 13 cell lines spanning the spectrum of clinical BC subtypes and leveraged publicly available data comprising 39,214 cells from 26 primary tumors.

RESULTS

Unsupervised clustering identified 49 subpopulations within the cell line dataset. We resolve ambiguity in subtype annotation comparing expression of Estrogen Receptor, Progesterone Receptor, and Human Epidermal Growth Factor Receptor 2 genes. Gene correlations with disease subtype highlighted S100A7 and MUCL1 overexpression in HER2 + cells as possible cell motility and localization drivers. We also present genes driving populational drifts to generate novel gene vectors characterizing each subpopulation. A global Cancer Stem Cell (CSC) scoring vector was used to identify stemness potential for subpopulations and model multi-potency. Finally, we overlay the BSCLA dataset with FDA-approved targets to identify to predict the efficacy of subpopulation-specific therapies.

CONCLUSION

The BSCLA defines the heterogeneity within BC cell lines, enhancing our overall understanding of BC cellular diversity to guide future BC research, including model cell line selection, unintended sample source effects, stemness factors between cell lines, and cell type-specific treatment response.

Multimodal ultrasound features of breast cancers: correlation with molecular subtypes

OBJECTIVES

To investigate whether multimodal intratumour and peritumour ultrasound features correlate with specific breast cancer molecular subtypes.

METHODS

From March to November 2021, a total of 85 patients with histologically proven breast cancer underwent B-mode, real-time elastography (RTE), colour Doppler flow imaging (CDFI) and contrast-enhanced ultrasound (CEUS). The time intensity curve (TIC) of CEUS was obtained, and the peak and time to peak (TTP) were analysed. Chi-square and binary logistic regression were used to analyse the connection between multimodal ultrasound imaging features and breast cancer molecular subtype.

RESULTS

Among 85 breast cancers, the subtypes were as follows: luminal A (36 cases, 42.4%), luminal B (20 cases, 23.5%), human epidermal growth factor receptor-2 positive (HER2+) (16 cases, 18.8%), and triple negative breast cancer (TNBC) (13 cases, 15.3%). Binary logistic regression models showed that RTE (P < 0.001) and CDFI (P = 0.036) were associated with the luminal A cancer subtype (C-index: 0.741), RTE (P = 0.016) and the peak ratio between intratumour and corpus mamma (P = 0.036) were related to the luminal B cancer subtype (C-index: 0.788). The peak ratio between peritumour and intratumour (P = 0.039) was related to the HER2 + cancer subtype (C-index: 0.859), and CDFI (P = 0.002) was associated with the TNBC subtype (C-index: 0.847).

CONCLUSIONS

Multimodal ultrasound features could be powerful predictors of specific breast cancer molecular subtypes. The intra- and peritumour CEUS features play assignable roles in separating luminal B and HER2 + breast cancer subtypes.

Nuclear Receptor Imaging In Vivo-Clinical and Research Advances

Nuclear receptors are transcription factors that function in normal physiology and play important roles in diseases such as cancer, inflammation, and diabetes. Noninvasive imaging of nuclear receptors can be achieved using radiolabeled ligands and positron emission tomography (PET). This quantitative imaging approach can be viewed as an in vivo equivalent of the classic radioligand binding assay. A main clinical application of nuclear receptor imaging in oncology is to identify metastatic sites expressing nuclear receptors that are targets for approved drug therapies and are capable of binding ligands to improve treatment decision-making. Research applications of nuclear receptor imaging include novel synthetic ligand and drug development by quantifying target drug engagement with the receptor for optimal therapeutic drug dosing and for fundamental research into nuclear receptor function in cells and animal models. This mini-review provides an overview of PET imaging of nuclear receptors with a focus on radioligands for estrogen receptor, progesterone receptor, and androgen receptor and their use in breast and prostate cancer.

PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review

Breast cancer (BC) is a heterogeneous malignancy that still represents the second cause of cancer-related death among women worldwide. Due to the heterogeneity of BC, the correct identification of valuable biomarkers able to predict tumor biology and the best treatment approaches are still far from clear. Although molecular imaging with positron emission tomography/computed tomography (PET/CT) has improved the characterization of BC, these methods are not free from drawbacks. In recent years, radiomics and artificial intelligence (AI) have been playing an important role in the detection of several features normally unseen by the human eye in medical images. The present review provides a summary of the current status of radiomics and AI in different clinical settings of BC. A systematic search of PubMed, Web of Science and Scopus was conducted, including all articles published in English that explored radiomics and AI analyses of PET/CT images in BC. Several studies have demonstrated the potential role of such new features for the staging and prognosis as well as the assessment of biological characteristics. Radiomics and AI features appear to be promising in different clinical settings of BC, although larger prospective trials are needed to confirm and to standardize this evidence.

Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning

BACKGROUND

To evaluate the performances of multiparametric MRI-based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes.

METHODS

A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast-enhanced T₁ -weighted imaging (T₁ C), Apparent diffusion coefficient (ADC), and T₂ -weighted imaging (T₂ W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance.

RESULTS

For the separation of each subtype from other subtypes on the testing set, the T₁ C-based models yielded AUCs from 0.762 to 0.920; the ADC-based models yielded AUCs from 0.686 to 0.851; and the T₂ W-based models achieved AUCs from 0.639 to 0.697.

CONCLUSION

T₁ C-based models performed better than ADC-based models and T₂ W-based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2-enriched subtypes were better than that of luminal A and luminal B subtypes.

Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine

Pathological complete response (pCR) after neoadjuvant chemotherapy in patients with early breast cancer is correlated with better survival. Meanwhile, an expanding arsenal of post-neoadjuvant treatment strategies have proven beneficial in the absence of pCR, leading to an increased use of neoadjuvant systemic therapy in patients with early breast cancer and the search for predictive biomarkers of response. The better prediction of response to neoadjuvant chemotherapy could enable the escalation or de-escalation of neoadjuvant treatment strategies, with the ultimate goal of improving the clinical management of early breast cancer. Clinico-pathological prognostic factors are currently used to estimate the potential benefit of neoadjuvant systemic treatment but are not accurate enough to allow for personalized response prediction. Other factors have recently been proposed but are not yet implementable in daily clinical practice or remain of limited utility due to the intertumoral heterogeneity of breast cancer. In this review, we describe the current knowledge about predictive factors for response to neoadjuvant chemotherapy in breast cancer patients and highlight the future perspectives that could lead to the better prediction of response, focusing on the current biomarkers used for clinical decision making and the different gene signatures that have recently been proposed for patient stratification and the prediction of response to therapies. We also discuss the intratumoral phenotypic heterogeneity in breast cancers as well as the emerging techniques and relevant pre-clinical models that could integrate this biological factor currently limiting the reliable prediction of response to neoadjuvant systemic therapy.

Association of Molecular Biomarker Heterogeneity With Treatment Pattern and Disease Outcomes in Multifocal or Multicentric Breast Cancer

Purpose

This study aimed to evaluate the rates of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki67 heterogeneity in multifocal or multicentric breast cancer (MMBC) and its association with treatment pattern and disease outcomes.

Methods

MMBC patients with ER, PR, HER2, and Ki67 results for each tumor focus were retrospectively analyzed using Kappa test and categorized into the homogeneous group (Homo group) and the heterogeneous group (Hetero group). Chi-square tests were performed to compare the clinical features and treatment options between the groups. Disease-free survival (DFS) and overall survival (OS) rates were estimated from Kaplan–Meier curves and compared between two groups.

Results

A total of 387 patients were included, and 93 (24.0%) were classified into the Hetero group. Adjuvant endocrine therapy was more frequently assigned for patients in the Hetero group than in the Homo group (84.9% vs. 71.7%, p = 0.046). There was no difference in terms of adjuvant anti-HER2 therapy (28.3% vs. 19.6%, p = 0.196) and chemotherapy (69.9% vs. 69.8%, p = 0.987) usage between the two groups. At a median follow-up of 36 months, DFS rates were 81.2% for the Hetero group and 96.5% for the Homo group (p = 0.041; adjusted HR, 2.95; 95% CI, 1.04–8.37). The estimated 3-year OS rates for the groups were 95.8% and 99.5%, respectively (p = 0.059; adjusted HR, 5.36; 95% CI, 0.97–29.69).

Conclusion

Heterogeneity of ER, PR, HER2, or Ki67 was present in 24.0% patients with MMBC. Biomarkers heterogeneity influenced adjuvant endocrine therapy usage and was associated with worse disease outcomes, indicating further clinical evaluation.

Using an Improved Residual Network to Identify PIK3CA Mutation Status in Breast Cancer on Ultrasound Image

Background

The detection of phosphatidylinositol-3 kinase catalytic alpha (PIK3CA) gene mutations in breast cancer is a key step to design personalizing an optimal treatment strategy. Traditional genetic testing methods are invasive and time-consuming. It is urgent to find a non-invasive method to estimate the PIK3CA mutation status. Ultrasound (US), one of the most common methods for breast cancer screening, has the advantages of being non-invasive, fast imaging, and inexpensive. In this study, we propose to develop a deep convolutional neural network (DCNN) to identify PIK3CA mutations in breast cancer based on US images.

Materials and Methods

We retrospectively collected 312 patients with pathologically confirmed breast cancer who underwent genetic testing. All US images (n=800) of breast cancer patients were collected and divided into the training set (n=600) and test set (n=200). A DCNN-Improved Residual Network (ImResNet) was designed to identify the PIK3CA mutations. We also compared the ImResNet model with the original ResNet50 model, classical machine learning models, and other deep learning models.

Results

The proposed ImResNet model has the ability to identify PIK3CA mutations in breast cancer based on US images. Notably, our ImResNet model outperforms the original ResNet50, DenseNet201, Xception, MobileNetv2, and two machine learning models (SVM and KNN), with an average area under the curve (AUC) of 0.775. Moreover, the overall accuracy, average precision, recall rate, and F1-score of the ImResNet model achieved 74.50%, 74.17%, 73.35%, and 73.76%, respectively. All of these measures were significantly higher than other models.

Conclusion

The ImResNet model gives an encouraging performance in predicting PIK3CA mutations based on breast US images, providing a new method for noninvasive gene prediction. In addition, this model could provide the basis for clinical adjustments and precision treatment.

Is it sufficient to evaluate metastatic bone involvement in breast cancer using SPECT/CT? A new approach of SPECT/CT-guided targeted bone marrow biopsy

Objective

To investigate the feasibility, safety, and clinical application value of single photon emission computed tomography/computed tomography (SPECT/CT)-guided bone marrow biopsy (BMB) in breast cancer (BC) patients with suspected bone metastases (BM) and compare its diagnostic performance for detection of BM with SPECT/CT.

Methods

The records of breast cancer patients referred for bone scintigraphy (BS), SPECT/CT and SPECT/CT-guided BMB from January of 2018 to June of 2021 in our hospital were retrospectively reviewed. 49 Patients were consecutively included in this study, all 49 specimens were analyzed by pathological and immunohistochemical studies.The biopsy success rate, total examination time, biopsy operation time, complications, CT radiation dose, and pathological and immunohistochemical results were recorded. The diagnostic performance based on SPECT/CT and SPECT/CT-guided BMB were compared with pathological, immunohistochemical examinations and the results of subsequent follow-up.

Results

Bone samples of the sites with high uptake were obtained in all 49 patients under BMB. No severe postoperative complications occurred. Among all 49 cases, 34 specimens were positive for metastatic breast cancer (69%, 34/49), and positive for benign tissue in 15 cases (31%, 15/49). 1 case of 15 cases was subsequently diagnosed as metastatic breast cancer according to the follow-up result. SPECT/CT-guided BMB demonstrated significantly higher negative predictive value (NPV) when compared to SPECT/CT (p = 0.021 < 0.05). Patients with differential expression of ER, PR, and HER-2 between primary lesions and metastatic lesions accounted for 12, 17, and 5 cases, respectively, and the changing rates were 35.2% (12/34), 50% (17/34), and 14.7% (5/34), respectively. Molecular subtype changes occurred in 7 patients, accounting for 47% (16/34) of metastatic patients.

Conclusion

It is insufficient to evaluate BM in BC patients using SPECT/CT imaging. SPECT/CT-guided BMB provided significantly higher sensitivity and NPV than SPECT/CT for detection of BM in BC patients. Our research redefines a new approach which can confirm diagnosis and potential molecular subtype changes for suspected bone metastatic lesions in BC patients, which can offer important opportunities for precision treatment and improved quality of life of BC patients with BM.

Application of machine learning with multiparametric dual-energy computed tomography of the breast to differentiate between benign and malignant lesions

BACKGROUND

Multiparametric dual-energy computed tomography (mpDECT) is widely used to differentiate various kinds of tumors; however, the data regarding its diagnostic performance with machine learning to diagnose breast tumors is limited. We evaluated univariate analysis and machine learning performance with mpDECT to distinguish between benign and malignant breast lesions.

METHODS

In total, 172 patients with 214 breast lesions (55 benign and 159 malignant) who underwent preoperative dual-phase contrast-enhanced DECT were included in this retrospective study. Twelve quantitative features were extracted for each lesion, including CT attenuation (precontrast, arterial, and venous phases), the arterial-venous phase difference in normalized effective atomic number (nZ_eff), normalized iodine concentration (NIC), and slope of the spectral Hounsfield unit (HU) curve (λ_Hu). Predictive models were developed using univariate analysis and eight machine learning methods [logistic regression, extreme gradient boosting (XGBoost), stochastic gradient descent (SGD), linear discriminant analysis (LDA), adaptive boosting (AdaBoost), random forest (RF), decision tree, and linear support vector machine (SVM)]. Classification performances were assessed based on the area under the receiver operating characteristic curve (AUROC). The best performances of the conventional univariate analysis and machine learning methods were compared using the Delong test.

RESULTS

The univariate analysis showed that the venous phase λ_Hu had the highest AUROC (0.88). Machine learning with mpDECT achieved an excellent and stable diagnostic performance, as shown by the mean classification performances in the training dataset (AUROC, 0.88-0.99) and testing (AUROC, 0.83-0.96) datasets. The performance of the AdaBoost model based on mpDECT was more stable than the other machine learning models and superior to the univariate analysis (AUROC, 0.96 vs. 0.88; P<0.001).

CONCLUSIONS

The performance of the AdaBoost classifier based on mpDECT data achieved the highest mean accuracy compared to the other machine learning models and univariate analysis in differentiating between benign and malignant breast lesions.

Transcriptome analysis of heterogeneity in mouse model of metastatic breast cancer

Background

Cancer metastasis is a complex process involving the spread of malignant cells from a primary tumor to distal organs. Understanding this cascade at a mechanistic level could provide critical new insights into the disease and potentially reveal new avenues for treatment. Transcriptome profiling of spontaneous cancer models is an attractive method to examine the dynamic changes accompanying tumor cell spread. However, such studies are complicated by the underlying heterogeneity of the cell types involved. The purpose of this study was to examine the transcriptomes of metastatic breast cancer cells using the well-established MMTV-PyMT mouse model.

Methods

Organ-derived metastatic cell lines were harvested from 10 female MMTV-PyMT mice. Cancer cells were isolated and sorted based on the expression of CD44^low/EpCAM^high or CD44^high/EpCAM^high surface markers. RNA from each cell line was extracted and sequenced using the NextSeq 500 Illumina platform. Tissue-specific genes were compared across the different metastatic and primary tumor samples. Reads were mapped to the mouse genome using STAR, and gene expression was quantified using RSEM. Single-cell RNA-seq (scRNA-seq) was performed on select samples using the ddSeq platform by BioRad and analyzed using Seurat v3.2.3. Monocle2 was used to infer pseudo-time progression.

Results

Comparison of RNA sequencing data across all cell populations produced distinct gene clusters. Differential gene expression patterns related to CD44 expression, organ tropism, and immunomodulatory signatures were observed. scRNA-seq identified expression profiles based on tissue-dependent niches and clonal heterogeneity. These cohorts of data were narrowed down to identify subsets of genes with high expression and known metastatic propensity. Dot plot analyses further revealed clusters expressing cancer stem cell and cancer dormancy markers. Changes in relevant genes were investigated across pseudo-time and tissue origin using Monocle2. These data revealed transcriptomes that may contribute to sub-clonal evolution and treatment evasion during cancer progression.

Conclusions

We performed a comprehensive transcriptome analysis of tumor heterogeneity and organ tropism during breast cancer metastasis. These data add to our understanding of metastatic progression and highlight targets for breast cancer treatment. These markers could also be used to image the impact of tumor heterogeneity on metastases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-021-01468-x.

Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer

PURPOSE

To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could be used to differentiate triple-negative breast cancer (TNBC) and nontriple-negative breast cancer (non-TNBC).

MATERIALS AND METHODS

This retrospective study included DCE-MRI images of 81 breast cancer patients (44 TNBC and 37 non-TNBC) from August 2018 to October 2019. The MR scans were achieved at a 1.5 T MR scanner. For each patient, the largest tumor mass was selected to analyze. Three-dimensional (3D) images of the regions of interest (ROIs) were automatically segmented on the third DCE phase by a deep learning segmentation model; then, the ROIs were checked and revised by 2 radiologists. DCE-MRI radiomics features were extracted from the 3D tumor volume. The patients were randomly divided into training (N = 57) and test (N = 24) cohorts. The machine learning classifier was built in the training dataset, and 5-fold cross-validation was performed on the training cohort to train and validate. The data of the test cohort were used to investigate the predictive power of the radiomics model in predicting TNBC and non-TNBC. The performance of the model was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

RESULTS

The radiomics model based on 15 features got the best performance. The AUC achieved 0.741 for the cross-validation, and 0.867 for the independent testing cohort.

CONCLUSION

The radiomics model based on automatic image segmentation of DCE-MRI can be used to distinguish TNBC and non-TNBC.

Superparamagnetic Iron Oxide for Identifying Sentinel Lymph Node in Breast Cancer after Neoadjuvant Chemotherapy: Feasibility Study

Sentinel lymph node biopsy (SLNB) is a well-established procedure for staging clinically node-negative early breast cancer (BC). Superparamagnetic iron oxide (SPIO) demonstrated efficacy for nodal identification using a magnetic probe after local retroaeroal interstitial injection. Its benefits lie in its flexibility, which is an essential property in the global setting, where access to the isotope is difficult. To the best of our knowledge, this is the first study to evaluate the feasibility and safety of the SPIO for SLNB in BC patients treated with neoadjuvant chemotherapy (NAC). Seventy-four female patients were included. The median time of lymph node retrieval was 20 min. The median number of resected sentinel nodes (SNs) was 4. SN was detected in all patients. No serious adverse event was observed. SPIO in identifying SN in BC patients after NAC is feasible and oncologically safe.

Superparamagnetic Iron Oxide for Identifying Sentinel Lymph Node in Breast Cancer after Neoadjuvant Chemotherapy: Feasibility Study

Sentinel lymph node biopsy (SLNB) is a well-established procedure for staging clinically node-negative early breast cancer (BC). Superparamagnetic iron oxide (SPIO) demonstrated efficacy for nodal identification using a magnetic probe after local retroaeroal interstitial injection. Its benefits lie in its flexibility, which is an essential property in the global setting, where access to the isotope is difficult. To the best of our knowledge, this is the first study to evaluate the feasibility and safety of the SPIO for SLNB in BC patients treated with neoadjuvant chemotherapy (NAC). Seventy-four female patients were included. The median time of lymph node retrieval was 20 min. The median number of resected sentinel nodes (SNs) was 4. SN was detected in all patients. No serious adverse event was observed. SPIO in identifying SN in BC patients after NAC is feasible and oncologically safe.

Nicotinamide N-Methyltransferase in Acquisition of Stem Cell Properties and Therapy Resistance in Cancer

The activity of nicotinamide N-methyltransferase (NNMT) is tightly linked to the maintenance of the nicotinamide adenine dinucleotide (NAD⁺) level. This enzyme catalyzes methylation of nicotinamide (NAM) into methyl nicotinamide (MNAM), which is either excreted or further metabolized to N1-methyl-2-pyridone-5-carboxamide (2-PY) and H₂O₂. Enzymatic activity of NNMT is important for the prevention of NAM-mediated inhibition of NAD⁺-consuming enzymes poly-adenosine -diphosphate (ADP), ribose polymerases (PARPs), and sirtuins (SIRTs). Inappropriately high expression and activity of NNMT, commonly present in various types of cancer, has the potential to disrupt NAD⁺ homeostasis and cellular methylation potential. Largely overlooked, in the context of cancer, is the inhibitory effect of 2-PY on PARP-1 activity, which abrogates NNMT's positive effect on cellular NAD⁺ flux by stalling liberation of NAM and reducing NAD⁺ synthesis in the salvage pathway. This review describes, and discusses, the mechanisms by which NNMT promotes NAD⁺ depletion and epigenetic reprogramming, leading to the development of metabolic plasticity, evasion of a major tumor suppressive process of cellular senescence, and acquisition of stem cell properties. All these phenomena are related to therapy resistance and worse clinical outcomes.

Value of 18F-FES PET in Solving Clinical Dilemmas in Breast Cancer Patients: A Retrospective Study

Breast cancer (BC) is a heterogeneous disease in which estrogen receptor (ER) expression plays an important role in most tumors. A clinical dilemma may arise when a metastasis biopsy to determine the ER status cannot be performed safely or when ER heterogeneity is suspected between tumor lesions. Whole-body ER imaging, such as 16α-¹⁸F-fluoro-17β-estradiol (¹⁸F-FES) PET, may have added value in these situations. However, the role of this imaging technique in routine clinical practice remains to be further determined. Therefore, we assessed whether the physician's remaining clinical dilemma after the standard workup was solved by the ¹⁸F-FES PET scan. Methods: This retrospective study included ¹⁸F-FES PET scans of patients who had (or were suspected to have) ER-positive metastatic BC and for whom a clinical dilemma remained after the standard workup. The scans were performed at the University Medical Center of Groningen between November 2009 and January 2019. We investigated whether the physician's clinical dilemma was solved, defined either as solving the clinical dilemma through the ¹⁸F-FES PET results or as basing a treatment decision directly on the ¹⁸F-FES PET results. In addition, the category of the clinical dilemma was reported, as well as the rate of ¹⁸F-FES-positive or -negative PET scans, and any correlation to the frequency of solved dilemmas was determined. Results: One hundred ¹⁸F-FES PET scans were performed on 83 patients. The clinical dilemma categories were inability to determine the extent of metastatic disease or suspected metastatic disease with the standard workup (n = 52), unclear ER status of the tumor (n = 31), and inability to determine which primary tumor caused the metastases (n = 17). The dilemmas were solved by ¹⁸F-FES PET in 87 of 100 scans (87%). In 81 of 87 scans, a treatment decision was based directly on ¹⁸F-FES PET results (treatment change, 51 scans; continuance, 30 scans). The frequency of solved dilemmas was not related to the clinical dilemma category (P = 0.334). However, the frequency of solved dilemmas was related to whether scans were ¹⁸F-FES-positive (n = 63) or ¹⁸F-FES-negative (n = 37; P < 0.001). Conclusion: For various indications, the ¹⁸F-FES PET scan can help to solve most clinical dilemmas that may remain after the standard workup. Therefore, the ¹⁸F-FES PET scan has added value in BC patients who present the physician with a clinical dilemma.

Dual-energy CT quantitative parameters for the differentiation of benign from malignant lesions and the prediction of histopathological and molecular subtypes in breast cancer

Background

Dual-energy computed tomography (DECT) is widely used to characterize and differentiate tumors. However, data regarding its diagnostic performance for the characterization of breast tumors are limited. In this study, we assessed the diagnostic performance of quantitative parameters derived from DECT in differentiating benign from malignant lesions and predicting histopathological and molecular subtypes in patients with breast cancer.

Methods

Dual-phase contrast-enhanced DECT of the thorax was performed on participants with breast tumors. Conventional CT attenuation and DECT quantitative parameters, including normalized iodine concentration (NIC), the slope of the spectral Hounsfield unit curve (λ_Hu), and normalized effective atomic number (nZ_eff), were obtained and compared between benign and malignant lesions, invasive non-special carcinoma, and ductal carcinoma in situ (DCIS), and among the four molecular subtypes of breast cancer. The diagnostic performance of the quantitative parameters was analyzed using receiver operating characteristic (ROC) curves.

Results

This study included 130 participants with 161 breast lesions (44 benign and 117 malignant). In the arterial and venous phase, NICs, λ_Hu, nZ_eff, and attenuation were higher in malignant lesions than benign lesions (all P<0.001). The venous phase λ_Hu had the best differential diagnostic capability, with an area under the curve (AUC) of 0.90, a sensitivity of 84.1% (37 of 44), a specificity of 86.3% (101 of 117), and an accuracy of 85.7% (138 of 161). The NICs in the arterial and venous phases were higher in invasive non-special carcinoma than DCIS (both P<0.001). In terms of diagnostic performance, NIC in the venous phase had an AUC of 0.77, a sensitivity of 75.0% (12 of 16), a specificity of 81.2% (82 of 101), and an accuracy of 80.3% (94 of 117). The luminal A subtype produced a lower venous phase NIC, and arterial and venous phase nZ_eff than the non-luminal A subtype (AUC of 0.91 for the combination of these three parameters).

Conclusions

Dual-energy CT quantitative parameters are a feasible and valuable noninvasive means of differentiating between benign and malignant lesions, and predicting histopathological and molecular subtypes in patients with breast cancer.

Breast Tumor Characterization Using [18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics

Simple Summary

Breast cancer is the second most common diagnosed malignancy in women worldwide. In this study, we examine the feasibility of breast tumor characterization based on [¹⁸F]FDG-PET/CT images using machine learning (ML) approaches in combination with data-preprocessing techniques. ML prediction models for breast cancer detection and the identification of breast cancer receptor status, proliferation rate, and molecular subtypes were established and evaluated. Furthermore, the importance of most repeatable features was investigated. Results displayed high performance of malignant/benign tumor differentiation and triple negative tumor subtype ML models. We observed high repeatability of radiomic features for both high performing predictive models.

Abstract

Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [¹⁸F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were compared with conventional data analysis using standard uptake value lesion classification. Methods: A cohort of 170 patients with 173 breast cancer tumors (132 malignant, 38 benign) was examined with [¹⁸F]FDG-PET/CT. Breast tumors were segmented and radiomic features were extracted following the imaging biomarker standardization initiative (IBSI) guidelines combined with optimized feature extraction. Ensemble learning including five supervised ML algorithms was utilized in a 100-fold Monte Carlo (MC) cross-validation scheme. Data pre-processing methods were incorporated prior to machine learning, including outlier and borderline noisy sample detection, feature selection, and class imbalance correction. Feature importance in each model was assessed by calculating feature occurrence by the R-squared method across MC folds. Results: Cross validation demonstrated high performance of the cancer detection model (80% sensitivity, 78% specificity, 80% accuracy, 0.81 area under the curve (AUC)), and of the triple negative tumor identification model (85% sensitivity, 78% specificity, 82% accuracy, 0.82 AUC). The individual receptor status and luminal A/B subtype models yielded low performance (0.46–0.68 AUC). SUV_max model yielded 0.76 AUC in cancer detection and 0.70 AUC in predicting triple negative subtype. Conclusions: Predictive models based on [¹⁸F]FDG-PET/CT images in combination with advanced data pre-processing steps aid in breast cancer diagnosis and in ML-based prediction of the aggressive triple negative breast cancer subtype.

The Biogenesis and Functions of circRNAs and Their Roles in Breast Cancer

Recent statistics show that breast cancer is among the most frequent cancers in clinical practice. It is also the second-leading cause of cancer-related deaths among women worldwide. CircRNAs are a new class of endogenous regulatory RNA molecules whose 5’ end and 3’ end are connected together to form a covalently closed single-stranded loop by back-splicing. CircRNAs present the advantages of disease-specific expression and excellent expression stability, and they can modulate gene expression at posttranscriptional and transcriptional levels. CircRNAs are abnormally expressed in multiple cancers, such as breast cancer, and drive the initiation and progression of cancer. In this review, we describe current knowledge about the functions of circRNAs and generalize their roles in various aspects of breast cancer, including cell proliferation, cell cycle, apoptosis, invasion and metastasis, autophagy, angiogenesis, drug resistance, and tumor immunity, and their prognostic and diagnostic value. This may add to a better understanding of the functions and roles of circRNAs in breast cancer, which may become new diagnostic and predictive biomarkers of breast cancer.

Systemic Multi-Omics Analysis Reveals Amplified P4HA1 Gene Associated With Prognostic and Hypoxic Regulation in Breast Cancer

Breast cancer (BC) is a common malignant tumor in females around the world. While multimodality therapies exist, the mortality rate remains high. The hypoxic condition was one of the potent determinants in BC progression. The molecular mechanisms underpinning hypoxia and their association with BC can contribute to a better understanding of tailored therapies. In this study, two hypoxic induced BC transcriptomic cohorts (GSE27813 and GSE47533) were assessed from the GEO database. The P4HA1 gene was identified as a putative candidate and significantly regulated in hypoxic BC cells compared to normal BC cells at different time intervals (6 h, 9 h, 16 h, 32 h, and 48 h). In patients with Luminal (p < 1E-12), triple-negative subclasses (p = 1.35059E-10), Stage 1 (p = 8.8817E-16), lymph node N1 (p = 1.62436E-12), and in the 40–80 age group (p = 1.62447E-12), the expression of P4HA1 was closely associated with the clinical subtypes of BC. Furthermore, at the 10q22.1 chromosomal band, the P4HA1 gene displayed a high copy number elevation and was associated with a poor clinical regimen with overall survival, relapse-free survival, and distant metastases-free survival in BC patients. In addition, using BioGRID, the protein–protein interaction (PPI) network was built and the cellular metabolic processes, and hedgehog pathways are functionally enriched with GO and KEGG terms. This tentative result provides insight into the molecular function of the P4HA1 gene, which is likely to promote hypoxic-mediated carcinogenesis, which may favor early detection of BC and therapeutic stratification.

Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study

OBJECTIVES

To evaluate the prediction performance of deep convolutional neural network (DCNN) based on ultrasound (US) images for the assessment of breast cancer molecular subtypes.

METHODS

A dataset of 4828 US images from 1275 patients with primary breast cancer were used as the training samples. DCNN models were constructed primarily to predict the four St. Gallen molecular subtypes and secondarily to identify luminal disease from non-luminal disease based on the ground truth from immunohistochemical of whole tumor surgical specimen. US images from two other institutions were retained as independent test sets to validate the system. The models' performance was analyzed using per-class accuracy, positive predictive value (PPV), and Matthews correlation coefficient (MCC).

RESULTS

The model achieved good performance in identifying the four breast cancer molecular subtypes in the two test sets, with accuracy ranging from 80.07% (95% CI, 76.49-83.23%) to 97.02% (95% CI, 95.22-98.16%) and 87.94% (95% CI, 85.08-90.31%) to 98.83% (95% CI, 97.60-99.43) for the two test cohorts for each sub-category, respectively. In terms of 4-class weighted average MCC, the model achieved 0.59 for test cohort A and 0.79 for test cohort B. Specifically, the DCNN also yielded good diagnostic performance in discriminating luminal disease from non-luminal disease, with a PPV of 93.29% (95% CI, 90.63-95.23%) and 88.21% (95% CI, 85.12-90.73%) for the two test cohorts, respectively.

CONCLUSION

Using pretreatment US images of the breast cancer, deep learning model enables the assessment of molecular subtypes with high diagnostic accuracy.

TRIAL REGISTRATION

Clinical trial number: ChiCTR1900027676 KEY POINTS: • Deep convolutional neural network (DCNN) helps clinicians assess tumor features with accuracy. • Multicenter retrospective study shows that DCNN derived from pretreatment ultrasound imagine improves the prediction of breast cancer molecular subtypes. • Management of patients becomes more precise based on the DCNN model.

Rising Circulating Tumor DNA As a Molecular Biomarker of Early Disease Progression in Metastatic Breast Cancer

PURPOSE

Accurate monitoring of therapeutic response remains an important unmet need for patients with metastatic breast cancer (MBC). Analysis of tumor genomics obtained via circulating tumor DNA (ctDNA) can provide a comprehensive overview of tumor evolution. Here, we evaluated ctDNA change as an early prognostic biomarker of subsequent radiologic progression and survival in MBC.

PATIENTS AND METHODS

Paired blood samples from patients with MBC were analyzed for levels of ctDNA, carcinoembryonic antigen, and cancer antigen 15-3 at baseline and during treatment. A Clinical Laboratory Improvement Amendments–certified sequencing panel of 73 genes was used to quantify tumor-specific point mutations in ctDNA. Multivariable logistic regression analysis was conducted to evaluate the association between ctDNA rise from baseline to during-treatment (genomic progression) and subsequent radiologic progression and progression-free survival (PFS).

RESULTS

Somatic mutations were detected in 76 baseline samples (90.5%) and 71 during-treatment samples (84.5%). Patients with genomic progression were more than twice as likely to have subsequent radiologic progression (odds ratio, 2.04; 95% CI, 1.74 to 2.41; P < .0001), with a mean lead time of 5.8 weeks. Genomic assessment provided a high positive predictive value of 81.8% and a negative predictive value of 89.7%. The subset of patients with genomic progression also had shorter PFS (median, 4.2 v 8.3 months; hazard ratio, 2.97; 95% CI, 1.75 to 5.04; log-rank P < .0001) compared with those without genomic progression.

CONCLUSION

Genomic progression, as assessed by early rise in ctDNA, is an independent biomarker of disease progression before overt radiologic or clinical progression becomes evident in patients with MBC.

Non-Invasive Assessment of Hypoxia and Neovascularization with MRI for Identification of Aggressive Breast Cancer

The aim of this study was to investigate the potential of magnetic resonance imaging (MRI) for a non-invasive synergistic assessment of tumor microenvironment (TME) hypoxia and induced neovascularization for the identification of aggressive breast cancer. Fifty-three female patients with breast cancer underwent multiparametric breast MRI including quantitative blood-oxygen-level-dependent (qBOLD) imaging for hypoxia and vascular architecture mapping for neovascularization. Quantitative MRI biomarker maps of oxygen extraction fraction (OEF), metabolic rate of oxygen (MRO2), mitochondrial oxygen tension (mitoPO2), microvessel radius (VSI), microvessel density (MVD), and microvessel type indicator (MTI) were calculated. Histopathology was the standard of reference. Histopathological markers (vascular endothelial growth factor receptor 1 (FLT1), podoplanin, hypoxia-inducible factor 1-alpha (HIF-1alpha), carbonic anhydrase 9 (CA IX), vascular endothelial growth factor C (VEGF-C)) were used to confirm imaging biomarker findings. Univariate and multivariate regression analyses were performed to differentiate less aggressive luminal from aggressive non-luminal (HER2-positive, triple negative) malignancies and assess the interplay between hypoxia and neoangiogenesis markers. Aggressive non-luminal cancers (n = 40) presented with significantly higher MRO2 (i.e., oxygen consumption), lower mitoPO2 values (i.e., hypoxia), lower MTI, and higher MVD than less aggressive cancers (n = 13). Data suggest that a model derived from OEF, mitoPO2, and MVD can predict tumor proliferation rate. This novel MRI approach, which can be easily implemented in routine breast MRI exams, aids in the non-invasive identification of aggressive breast cancer.

Senescence-Associated Pro-inflammatory Cytokines and Tumor Cell Plasticity

The well-recognized cell phenotypic heterogeneity in tumors is a great challenge for cancer treatment. Dynamic interconversion and movement within a spectrum of different cell phenotypes (cellular plasticity) with the acquisition of specific cell functions is a fascinating biological puzzle, that represent an additional difficulty for cancer treatment and novel therapies development. The understanding of the molecular mechanisms responsible for moving or stabilizing tumor cells within this spectrum of variable states constitutes a valuable tool to overcome these challenges. In particular, cell transitions between epithelial and mesenchymal phenotypes (EMT-MET) and de-and trans-differentiation processes are relevant, since it has been shown that they confer invasiveness, drug resistance, and metastatic ability, due to the simultaneous acquisition of stem-like cell properties. Multiple drivers participate in these cell conversions events. In particular, cellular senescence and senescence-associated soluble factors have been shown to unveil stem-like cell properties and cell plasticity. By modulating gradually the composition of their secretome and the time of exposure, senescent cells may have differential effect not only on tumor cells but also on surrounding cells. Intriguingly, tumor cells that scape from senescence acquire stem-like cell properties and aggressiveness. The reinforcement of senescence and inflammation by soluble factors and the participation of immune cells may provide a dynamic milieu having varied effects on cell transitions, reprogramming, plasticity, stemness and therefore heterogeneity. This will confer different epithelial/mesenchymal traits (hybrid phenotype) and stem-like cell properties, combinations of which, in a particular cell context, could be responsible for different cellular functions during cancer progression (survival, migration, invasion, colonization or proliferation). Additionally, cooperative behavior between cell subpopulations with different phenotypes/stemness functions could also modulate their cellular plasticity. Here, we will discuss the role of senescence and senescence-associated pro-inflammatory cytokines on the induction of cellular plasticity, their effect role in establishing particular states within this spectrum of cell phenotypes and how this is accompanied by stem-like cell properties that, as the epithelial transitions, may also have a continuum of characteristics providing tumor cells with functional adaptability specifically useful in the different stages of carcinogenesis.

Development of a Non-invasive Assessment of Hypoxia and Neovascularization with Magnetic Resonance Imaging in Benign and Malignant Breast Tumors: Initial Results

PURPOSE

To develop a novel magnetic resonance imaging (MRI) approach for the noninvasive assessment of hypoxia and neovascularization in breast tumors.

PROCEDURES

In this IRB-approved prospective study, 20 patients with suspicious breast lesions (BI-RADS 4/5) underwent multiparametric breast MRI including quantitative BOLD (qBOLD) and vascular architecture mapping (VAM). Custom-made in-house MatLab software was used for qBOLD and VAM data postprocessing and calculation of quantitative MRI biomarker maps of oxygen extraction fraction (OEF), metabolic rate of oxygen (MRO₂), and mitochondrial oxygen tension (mitoPO₂) to measure tissue hypoxia and neovascularization including vascular architecture including microvessel radius (VSI), density (MVD), and type (MTI). Histopathology was used as standard of reference. Appropriate statistics were performed to assess and compare correlations between MRI biomarkers for hypoxia and neovascularization.

RESULTS

qBOLD and VAM data with good quality were obtained from all patients with 13 invasive ductal carcinoma (IDC) and 7 benign breast tumors with a lesion diameter of at least 10 mm in all spatial directions. MRI biomarker maps of oxygen metabolism and neovascularization demonstrated intratumoral spatial heterogeneity with a broad range of biomarker values. Bulk tumor neovasculature consisted of draining venous microvasculature with slow flowing blood. High OEF and low mitoPO₂ were associated with low MVD and vice versa. The heterogeneous pattern of MRO₂ values showed spatial congruence with VSI. IDCs showed significantly higher MRO₂ (P = 0.007), lower mitoPO₂ (P = 0.021), higher MVD (P = 0.005), and lower (i.e., more pathologic) MTI (P = 0.001) compared with benign breast tumors. These results indicate that IDCs consume more oxygen and are more hypoxic and neovascularized than benign tumors.

CONCLUSIONS

We developed a novel MRI approach for the noninvasive assessment of hypoxia and neovascularization in benign and malignant breast tumors that can be easily integrated in a diagnostic MRI protocol and provides insight into intratumoral heterogeneity.

Bioprofiling TS/A Murine Mammary Cancer for a Functional Precision Experimental Model

The TS/A cell line was established in 1983 from a spontaneous mammary tumor arisen in an inbred BALB/c female mouse. Its features (heterogeneity, low immunogenicity and metastatic ability) rendered the TS/A cell line suitable as a preclinical model for studies on tumor-host interactions and for gene therapy approaches. The integrated biological profile of TS/A resulting from the review of the literature could be a path towards the description of a precision experimental model of mammary cancer.

Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results

BACKGROUND

To evaluate the diagnostic performance of radiomic signatures extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) for the assessment of breast cancer receptor status and molecular subtypes.

METHODS

One hundred and forty-three patients with biopsy-proven breast cancer who underwent CE-MRI at 3 T were included in this IRB-approved HIPAA-compliant retrospective study. The training dataset comprised 91 patients (luminal A, n = 49; luminal B, n = 8; HER2-enriched, n = 11; triple negative, n = 23), while the validation dataset comprised 52 patients from a second institution (luminal A, n = 17; luminal B, n = 17; triple negative, n = 18). Radiomic analysis of manually segmented tumors included calculation of features derived from the first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient (GRA), autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry (GEO). Fisher, probability of error and average correlation (POE + ACC), and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise radiomic-based separation of receptor status and molecular subtypes. Histopathology served as the standard of reference.

RESULTS

In the training dataset, radiomic signatures yielded the following accuracies > 80%: luminal B vs. luminal A, 84.2% (mainly based on COM features); luminal B vs. triple negative, 83.9% (mainly based on GEO features); luminal B vs. all others, 89% (mainly based on COM features); and HER2-enriched vs. all others, 81.3% (mainly based on COM features). Radiomic signatures were successfully validated in the separate validation dataset for luminal A vs. luminal B (79.4%) and luminal B vs. triple negative (77.1%).

CONCLUSIONS

In this preliminary study, radiomic signatures with CE-MRI enable the assessment of breast cancer receptor status and molecular subtypes with high diagnostic accuracy. These results need to be confirmed in future larger studies.

Phenotypic, structural, and ultrastructural analysis of triple-negative breast cancer cell lines and breast cancer stem cell subpopulation

Triple negative breast cancer (TNBC) is a highly heterogeneous disease, which influences the therapeutic response and makes difficult the discovery of effective targets. This heterogeneity is attributed to the presence of breast cancer stem cells (BCSCs), which determines resistance to chemotherapy and subsequently disease recurrence and metastasis. In this context, this work aimed to evaluate the morphological and phenotypic cellular heterogeneity of two TNBC cell lines cultured in monolayer and tumorsphere (TS) models by fluorescence and electron microscopy and flow cytometry. The BT-549 and Hs 578T analyses demonstrated large phenotypic and morphological heterogeneity between these cell lines, as well as between the cell subpopulations that compose them. BT-549 and Hs 578T are heterogeneous considering the cell surface marker CD44 and CD24 expression, characterizing BCSC mesenchymal-like cells (CD44⁺/CD24^-), epithelial cells (CD44^-/CD24⁺), hybrid cells with mesenchymal and epithelial features (CD44⁺/CD24⁺), and CD44^-/CD24^- cells. BCSC epithelial-like cells (ALDH⁺) were found in BT-549, BT-549 TS, and Hs 578T TS; however, only BT-549 TS showed a high ALDH activity. Ultrastructural characterization showed the heterogeneity within and among BT-549 and Hs 578T in monolayer and TS models being formed by more than one cellular type. Further, the mesenchymal characteristic of these cells is demonstrated by E-cadherin absence and filopodia. It is well known that tumor cell heterogeneity can influence survival, therapy responses, and the rate of tumor growth. Thus, molecular understanding of this heterogeneity is essential for the identification of potential therapeutic options and vulnerabilities of oncological patients.

PD-L1 expression is a predictive biomarker for CIK cell-based immunotherapy in postoperative patients with breast cancer

Background

A sequential combination of radiochemotherapy/endocrinotherapy and cytokine-induced killer cell (CIK) infusion has been shown to be an effective therapy for post-mastectomy breast cancer based on statistical analysis of the patient population. However, whether an individual could obtain an improved prognosis from CIK cell-based treatment remains unknown. In the present study, we focused on immune microenvironment regulation and specifically investigated the relationship between PD-L1 expression and survival benefit from CIK immunotherapy in breast cancer.

Methods

A total of 310 postoperative breast cancer patients who received comprehensive treatment were enrolled in this retrospective study, including 160 patients in the control group (received chemotherapy/radiotherapy/endocrinotherapy) and 150 patients in the CIK cell treatment group (received chemotherapy/radiotherapy/ endocrinotherapy and subsequent CIK infusion).

Results

We found that overall survival (OS) and recurrence-free survival (RFS) were significantly better in the CIK group than that in the control group. PD-L1 expression in tumor tissue sections was showed to be an independent prognostic factor for patients in the CIK treatment group using multivariate survival analysis. Further survival analysis in the CIK group showed that patients with PD-L1 tumor expression exhibited longer OS and RFS. In addition, among all patients who were enrolled in this study, only the patients with PD-L1 expression experienced survival benefits from CIK treatment.

Conclusions

Our study showed the relationship between PD-L1 expression and CIK therapy and revealed that PD-L1 expression in the tumor is as an indicator of adjuvant CIK therapy for postoperative breast cancer.

Electronic supplementary material

The online version of this article (10.1186/s40425-019-0696-8) contains supplementary material, which is available to authorized users.

miRNA profile obtained by next‑generation sequencing in metastatic breast cancer patients is able to predict the response to systemic treatments

Metastatic breast cancer (MBC) is a challenge for oncologists, and public efforts should focus on identifying additional molecular markers and therapeutic management to improve clinical outcomes. Among all diagnosed cases of breast cancer (BC; approximately 10%) involve metastatic disease; notably, approximately 40% of patients with early-stage BC develop metastasis within 5 years. The management of MBC consists of systemic therapy. Despite different treatment options, the 5-year survival rate is <20%, which may be due to a lack of response with de novo or acquired resistance. MicroRNAs (miRNAs or miRs) are promising biomarkers as they are readily detectable and have a broad spectrum and potential clinical applications. The aim of this study was to identify a miRNA profile for distinguishing patients with MBC who respond to systemic treatment. Patients with MBC were treated according to the National Comprehensive Cancer Network guidelines. We performed miRNA-Seq on 9 primary tumors using the Thermo Fisher Scientific Ion S5 system. To obtain global miRNA profiles, we carried out differentially expressed gene elimination strategy (DEGES) analysis between the responsive and non-responsive patients. The results identified a profile of 12 miRNAs associated with the response to systemic treatment. The data were validated in an independent cohort (TCGA database). Based on the results, the upregulation of miR-342-3p and miR-187-3p was associated with the response to systemic treatment, and with an increased progression-free survival (PFS) and overall survival (OS); by contrast, the downregulation of miR-301a-3p was associated with a higher PFS and OS. On the whole, the findings of this study indicate that these miRNAs may serve as biomarkers for the response to systemic treatment or the prognosis of patients with MBC. However, these data should be validated experimentally in other robust cohorts and using different specimens before implementing these miRNAs as biomarkers in clinical practice to benefit this group of patients.

Patterns of Genomic Instability in Breast Cancer

Breast cancer is one of the most common cancers affecting women. Despite significant improvements in overall survival, it remains a significant cause of death worldwide. Genomic instability (GI) is a hallmark of cancer and plays a pivotal role in breast cancer development and progression. In the past decade, high-throughput technologies have provided a wealth of information that has facilitated the identification of a diverse repertoire of mutated genes and mutational processes operative across cancers. Here, we review recent findings on genomic alterations and mutational processes in breast cancer pathogenesis. Most importantly, we summarize the clinical challenges and opportunities to utilize omics-based signatures for better management of breast cancer patients and treatment decision-making.

Sequential [18F]FDG-[18F]FMISO PET and Multiparametric MRI at 3T for Insights into Breast Cancer Heterogeneity and Correlation with Patient Outcomes: First Clinical Experience

The aim of this study was to assess whether sequential multiparametric ¹⁸[F]fluoro-desoxy-glucose (¹⁸[F]FDG)/[¹⁸F]fluoromisonidazole ([¹⁸F]FMISO) PET-MRI in breast cancer patients is possible, facilitates information on tumor heterogeneity, and correlates with prognostic indicators. In this pilot study, IRB-approved, prospective study, nine patients with ten suspicious breast lesions (BIRADS 5) and subsequent breast cancer diagnosis underwent sequential combined [¹⁸F]FDG/[18F]FMISO PET-MRI. [¹⁸F]FDG was used to assess increased glycolysis, while [¹⁸F]FMISO was used to detect tumor hypoxia. MRI protocol included dynamic breast contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). Qualitative and quantitative multiparametric imaging findings were compared with pathological features (grading, proliferation, and receptor status) and clinical endpoints (recurrence/metastases and disease-specific death) using multiple correlation analysis. Histopathology was the standard of reference. There were several intermediate to strong correlations identified between quantitative bioimaging markers, histopathologic tumor characteristics, and clinical endpoints. Based on correlation analysis, multiparametric criteria provided independent information. The prognostic indicators proliferation rate, death, and presence/development of recurrence/metastasis correlated positively, whereas the prognostic indicator estrogen receptor status correlated negatively with PET parameters. The strongest correlations were found between disease-specific death and [¹⁸F]FDG_mean (R=0.83, p < 0.01) and between the presence/development of metastasis and [¹⁸F]FDG_max (R=0.79, p < 0.01), respectively. This pilot study indicates that multiparametric [¹⁸F]FDG/[¹⁸F]FMISO PET-MRI might provide complementary quantitative prognostic information on breast tumors including clinical endpoints and thus might be used to tailor treatment for precision medicine in breast cancer.

Long-term exposure to carcinoma-associated fibroblasts makes breast cancer cells addictive to integrin β1

We studied the long-term effect of stromal factors on the development of fulvestrant-resistance (FR) and fulvestrant-induced dormancy (D). Sublines established from stroma-treated FR-cells (C-FR cells) and D-cells (C-D cells) show permanently high expression of integrin β1 as well as Bcl-3 and P-STAT3 (C-FR) or IGF1R (C-D). Yet, cells fail to withstand fulvestrant better and do not migrate or grow faster than control cells. Instead, C-D cells rely on stromal factors to perform as well as control cells. In addition, C-FR cells adapted to integrin β1 for growth in 3D cultures. These data suggest that long-term exposure to stromal factors leads to addiction rather than better performance in cellular activities. We also found that morphologically distinct breast cancer cell line subpopulations share key responses to stromal factors suggesting that intratumoral heterogeneity may play a minor role in the interaction between breast cancer and stromal cells.

Kaempferol Suppresses Proliferation and Induces Cell Cycle Arrest, Apoptosis, and DNA Damage in Breast Cancer Cells

Kaempferol is a flavonoid that has been extensively investigated owing to its antitumor effects. Nevertheless, little is known about its underlying mechanisms of action. We aimed to explore the role of kaempferol in breast cancer (BC), and thus we investigated how kaempferol suppresses the growth of BC cells. The cells were treated with kaempferol, and the effects on multiple cancer-associated pathways were evaluated. The MTS assay was used to study the cell growth inhibition induced by kaempferol. The cell cycle was analyzed by flow cytometry. Western blotting was used to analyze cellular apoptosis and DNA damage. We found that the proliferation of the triple-negative BC (TNBC) MDA-MB-231 cells was suppressed effectively by kaempferol. Interestingly, the suppressive effect of kaempferol on cell proliferation was stronger in MDA-MB-231 cells than in the estrogen receptor-positive BT474 cell line. Furthermore, after the treatment with kaempferol for 48 h, the population of cells in the G₁ phase was significantly reduced, from 85.48% to 51.35%, and the population of cells in the G₂ phase increased markedly from 9.27% to 37.5%, which indicated that kaempferol contributed to the induction of G₂/M arrest. Kaempferol also induced apoptosis and DNA damage in MDA-MB-231 cells. Kaempferol increased the expression levels of γH2AX, cleaved caspase 9, cleaved caspase 3, and p-ATM compared to those of the control group. Collectively, these results showed that kaempferol may be a potential drug for the effective treatment of TNBC.