Assessing the role of tumour-associated macrophage subsets in breast cancer subtypes using digital image analysis.

PURPOSE: The number of M1-like and M2-like tumour-associated macrophages (TAMs) and their ratio can play a role in breast cancer development and progression. Early clinical trials using macrophage targeting compounds are currently ongoing. However, the most optimal detection method of M1-like and M2-like macrophage subsets and their clinical relevance in breast cancer is still unclear. We aimed to optimize the assessment of TAM subsets in different breast cancer subtypes, and therefore related TAM subset numbers and ratio to clinicopathological characteristics and clinical outcome. METHODS: Tissue microarrays of 347 consecutive primary Luminal-A, Luminal-B, HER2-positive and triple-negative tumours of patients with early-stage breast cancer were serially sectioned and immunohistochemically stained for the pan-macrophage marker CD68 and the M2-like macrophage markers CD163, CSF-1R and CD206. TAM numbers were quantified using a digital image analysis algorithm. M1-like macrophage numbers were calculated by subtracting M2-like TAM numbers from the total TAM number. RESULTS: M2-like markers CD163 and CSF-1R showed a moderate positive association with each other and with CD68 (r ≥ 0.47), but only weakly with CD206 (r ≤ 0.06). CD68 + , CD163 + and CSF-1R + macrophages correlated with tumour grade in Luminal-B tumours (P < 0.001). Total or subset TAM numbers did not correlate with disease outcome in any breast cancer subtype. CONCLUSION: In conclusion, macrophages and their subsets can be detected by means of a panel of TAM markers and are related to unfavourable clinicopathological characteristics in Luminal-B breast cancer. However, their impact on outcome remains unclear. Preferably, this should be determined in prospective series.

High hepatocyte growth factor expression in primary tumor predicts better overall survival in male breast cancer.

BACKGROUND: Breast cancer is rare in men, but management is focused on tumor characteristics commonly found in female breast cancer. The tumor microenvironment of male breast cancer is less well understood, and insight may improve male breast cancer management. The hepatocyte growth factor (HGF)/c-MET axis and the stromal cell-derived factor-1 (CXCL12)/C-X-C chemokine receptor type 4 (CXCR4) axis are prognostic in women with breast cancer. We aimed to investigate these factors in male breast cancer and correlate them with patient survival. METHODS: From 841 Dutch males with breast cancer who were enrolled in the EORTC 10085/TBCRC/BIG/NABCG International Male Breast Cancer Program (NCT01101425) and diagnosed between 1990 and 2010, archival primary tumor samples were collected. Tissue microarrays were constructed with 3 cores per sample and used for immunohistochemical analysis of HGF, c-MET, CXCL12, and CXCR4. Overall survival (OS) of the patients without metastases (M0) was analyzed using the Kaplan-Meier method. The value of the markers regarding OS was determined using univariable and multivariable Cox regression analyses, providing hazard ratios (HRs) and 95% confidence intervals (95% CIs). RESULTS: Of 720 out of 841 patients, sufficient tissue was available for analysis; 487 out of 720 patients had M0 disease. Patients with high HGF expression and high CXCL12 expression had a superior OS (low vs high expression of both markers, 7.5 vs 13.0 years, hazard ratio [HR] 0.64, 95% CI 0.49-0.84, P = 0.001 [HGF]; 9.1 vs 15.3 years, HR 0.63, 95% CI 0.45-0.87, P = 0.005 [CXCL12]). Multivariate analysis identified HGF as an independent predictor for OS (HR 0.64, 95% CI 0.47-0.88, P = 0.001). CONCLUSIONS: HGF and CXCL12 tumor expression appear to identify male breast cancer patients with a relatively good prognosis. Possibly, this could support male breast cancer-specific management strategies in the future.

An Integrative Framework for Online Prognostic and Health Management Using Internet of Things and Convolutional Neural Network.

With the development of the internet of things (IoTs), big data, smart sensing technology, and cloud technology, the industry has entered a new stage of revolution. Traditional manufacturing enterprises are transforming into service-oriented manufacturing based on prognostic and health management (PHM). However, there is a lack of a systematic and comprehensive framework of PHM to create more added value. In this paper, the authors proposed an integrative framework to systematically solve the problem from three levels: Strategic level of PHM to create added value, tactical level of PHM to make the implementation route, and operational level of PHM in a detailed application. At the strategic level, the authors provided the innovative business model to create added value through the big data. Moreover, to monitor the equipment status, the health index (HI) based on a condition-based maintenance (CBM) method was proposed. At the tactical level, the authors provided the implementation route in application integration, analysis service, and visual management to satisfy the different stakeholders' functional requirements through a convolutional neural network (CNN). At the operational level, the authors constructed a self-sensing network based on anti-inference and self-organizing Zigbee to capture the real-time data from the equipment group. Finally, the authors verified the feasibility of the framework in a real case from China.

Self-Supervised Image Denoising of Third Harmonic Generation Microscopic Images of Human Glioma Tissue by Transformer-based Blind Spot (TBS) Network.

Third harmonic generation (THG) microscopy shows great potential for instant pathology of brain tumor tissue during surgery. However, due to the maximal permitted exposure of laser intensity and inherent noise of the imaging system, the noise level of THG images is relatively high, which affects subsequent feature extraction analysis. Denoising THG images is challenging for modern deep-learning based methods because of the rich morphologies contained and the difficulty in obtaining the noise-free counterparts. To address this, in this work, we propose an unsupervised deep-learning network for denoising of THG images which combines a self-supervised blind spot method and a U-shape Transformer using a dynamic sparse attention mechanism. The experimental results on THG images of human glioma tissue show that our approach exhibits superior denoising performance qualitatively and quantitatively compared with previous methods. Our model achieves an improvement of 2.47-9.50 dB in SNR and 0.37-7.40 dB in CNR, compared to six recent state-of-the-art unsupervised learning models including Neighbor2Neighbor, Blind2Unblind, Self2Self+, ZS-N2N, Noise2Info and SDAP. To achieve an objective evaluation of our model, we also validate our model on public datasets including natural and microscopic images, and our model shows a better denoising performance than several recent unsupervised models such as Neighbor2Neighbor, Blind2Unblind and ZS-N2N. In addition, our model is nearly instant in denoising a THG image, which has the potential for real-time applications of THG microscopy.

Systolic blood pressure and early neurological deterioration in minor stroke: A post hoc analysis of ARAMIS trial.

BACKGROUND: Systolic blood pressure (SBP) was a predictor of early neurological deterioration (END) in stroke. We performed a secondary analysis of ARAMIS trial to investigate whether baseline SBP affects the effect of dual antiplatelet versus intravenous alteplase on END. METHODS: This post hoc analysis included patients in the as-treated analysis set. According to SBP at admission, patients were divided into SBP ≥140 mmHg and SBP <140 mmHg subgroups. In each subgroup, patients were further classified into dual antiplatelet and intravenous alteplase treatment groups based on study drug actually received. Primary outcome was END, defined as an increase of ≥2 in the NIHSS score from baseline within 24 h. We investigated effect of dual antiplatelet vs intravenous alteplase on END in SBP subgroups and their interaction effect with subgroups. RESULTS: A total of 723 patients from as-treated analysis set were included: 344 were assigned into dual antiplatelet group and 379 into intravenous alteplase group. For primary outcome, there was more treatment effect of dual antiplatelet in SBP ≥140 mmHg subgroup (adjusted RD, -5.2%; 95% CI, -8.2% to -2.3%; p < 0.001) and no effect in SBP <140 mmHg subgroup (adjusted RD, -0.1%; 95% CI, -8.0% to 7.7%; p = 0.97), but no significant interaction between subgroups was found (adjusted p = 0.20). CONCLUSIONS: Among patients with minor nondisabling acute ischemic stroke, dual antiplatelet may be better than alteplase with respect to preventing END within 24 h when baseline SBP ≥140 mmHg.

Deep learning-based predictive model for pathological complete response to neoadjuvant chemotherapy in breast cancer from biopsy pathological images: a multicenter study.

Introduction: Early predictive pathological complete response (pCR) is beneficial for optimizing neoadjuvant chemotherapy (NAC) strategies for breast cancer. The hematoxylin and eosin (HE)-stained slices of biopsy tissues contain a large amount of information on tumor epithelial cells and stromal. The fusion of pathological image features and clinicopathological features is expected to build a model to predict pCR of NAC in breast cancer. Methods: We retrospectively collected a total of 440 breast cancer patients from three hospitals who underwent NAC. HE-stained slices of biopsy tissues were scanned to form whole-slide images (WSIs), and pathological images of representative regions of interest (ROI) of each WSI were selected at different magnifications. Based on several different deep learning models, we propose a novel feature extraction method on pathological images with different magnifications. Further, fused with clinicopathological features, a multimodal breast cancer NAC pCR prediction model based on a support vector machine (SVM) classifier was developed and validated with two additional validation cohorts (VCs). Results: Through experimental validation of several different deep learning models, we found that the breast cancer pCR prediction model based on the SVM classifier, which uses the VGG16 model for feature extraction of pathological images at ×20 magnification, has the best prediction efficacy. The area under the curve (AUC) of deep learning pathological model (DPM) were 0.79, 0.73, and 0.71 for TC, VC1, and VC2, respectively, all of which exceeded 0.70. The AUCs of clinical model (CM), a clinical prediction model established by using clinicopathological features, were 0.79 for TC, 0.73 for VC1, and 0.71 for VC2, respectively. The multimodal deep learning clinicopathological model (DPCM) established by fusing pathological images and clinicopathological features improved the AUC of TC from 0.79 to 0.84. The AUC of VC2 improved from 0.71 to 0.78. Conclusion: Our study reveals that pathological images of HE-stained slices of pre-NAC biopsy tissues can be used to build a pCR prediction model. Combining pathological images and clinicopathological features can further enhance the predictive efficacy of the model.

Molecular and functional imaging in cancer-targeted therapy: current applications and future directions.

Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather than harming all rapidly dividing cells as in cytotoxic chemotherapy. The Response Evaluation Criteria in Solid Tumor (RECIST) system has been used to assess tumor response to therapy via changes in the size of target lesions as measured by calipers, conventional anatomically based imaging modalities such as computed tomography (CT), and magnetic resonance imaging (MRI), and other imaging methods. However, RECIST is sometimes inaccurate in assessing the efficacy of targeted therapy drugs because of the poor correlation between tumor size and treatment-induced tumor necrosis or shrinkage. This approach might also result in delayed identification of response when the therapy does confer a reduction in tumor size. Innovative molecular imaging techniques have rapidly gained importance in the dawning era of targeted therapy as they can visualize, characterize, and quantify biological processes at the cellular, subcellular, or even molecular level rather than at the anatomical level. This review summarizes different targeted cell signaling pathways, various molecular imaging techniques, and developed probes. Moreover, the application of molecular imaging for evaluating treatment response and related clinical outcome is also systematically outlined. In the future, more attention should be paid to promoting the clinical translation of molecular imaging in evaluating the sensitivity to targeted therapy with biocompatible probes. In particular, multimodal imaging technologies incorporating advanced artificial intelligence should be developed to comprehensively and accurately assess cancer-targeted therapy, in addition to RECIST-based methods.

Simulation of Internal Defects in TKX-50 Crystals.

1,1'-Dihydroxy-5,5'-bi-tetrazolium dihydroxylamine salt (TKX-50) is a new type of high-energy low-sense explosive with great application value, but TKX-50 made directly from the reaction has problems such as irregular crystal morphology and relatively large length-diameter, and these factors seriously affect the sensitivity of TKX-50 and limit its large-scale application. The internal defects of TKX-50 crystals have a great influence on their weakness, and studying its related properties is of great theoretical significance and application value. To further investigate the microscopic properties of TKX-50 crystals and to explore the connection between microscopic parameters and macroscopic susceptibility, this paper reports the use of molecular dynamics simulations to construct TKX-50 crystal scaling models with three types of defects-vacancy, dislocation and doping-and conducts molecular dynamics simulations. The influence of TKX-50 crystal defects on the initiation bond length, density, bonding diatomic interaction energy, and cohesive energy density of the crystal was obtained. The simulation results show that the models with a higher bond length of the initiator bond and higher percentage activated the initiator's N-N bond and lowered the bond-linked diatomic energy, cohesive energy density, and density corresponding to higher crystal sensitivities. This led to a preliminary connection between TKX-50 microscopic model parameters and macroscopic susceptibility. The results of the study can provide a reference for the design of subsequent experiments, and the research method can be extended to the research work on other energy-containing materials.

Near-infrared dye IRDye800CW-NHS coupled to Trastuzumab for near-infrared II fluorescence imaging in tumor xenograft models of HER-2-positive breast cancer.

Near-infrared II fluorescent probes targeting tumors for diagnostic purposes have received much attention in recent years. In this study, a fluorescent probe for the NIR-II was constructed by using IRDye800CW-NHS fluorescent dye with Trastuzumab, which was investigated for its ability to target HER-2-positive breast cancer in xenograft mice models. This probe was compared with Trastuzumab-ICG which was synthesized using a similar structure, ICG-NHS. The results demonstrated that the IRDye800CW-NHS had significantly stronger fluorescence in the NIR-I and NIR-II than ICG-NHS in the aqueous phase. And the different metabolic modes of IRDye800CW-NHS and ICG-NHS were revealed in bioimaging experiments. IRDye800CW-NHS was mainly metabolised by the kidneys, while ICG-NHS was mainly metabolised by the liver. After coupling with Trastuzumab, Trastuzumab-800CW (TMR = 5.35 ± 0.39) not only had a stronger tumor targeting ability than Trastuzumab-ICG (TMR = 4.42 ± 0.10) based on the calculated maximum tumor muscle ratio (TMR), but also had a comparatively lower hepatic uptake and faster metabolism. Histopathology analysis proved that both fluorescent probes were non-toxic to various organ tissues. These results reveal the excellent optical properties of IRDye800CW-NHS, and the great potential of coupling with antibodies to develop fluorescent probes that will hopefully be applied to intraoperative breast cancer navigation in humans.

Slow Cook-Off Experiment and Numerical Simulation of Spherical NQ-Based Melt-Cast Explosive.

In order to analyze the influence of nitroguanidine (NQ) spheroidization on the corresponding characteristics of slow cook-off molten cast explosives, experiments and simulation calculations were carried out. A calculation method was established, based on a multiphase flow model to simulate the response process of spherical NQ-based molten cast explosives under slow cook-off conditions, to analyze the temperature distribution and liquid phase distribution during the reaction process, and to discuss the reaction temperature, reaction time and reaction location with the change of solid content. The study found that the slow cook-off response level of spherical NQ-based molten cast explosives is deflagration; the phase change cloud diagram can be used to determine the ignition time to obtain more accurate slow cook-off response data; when the solid content is 50%, the ignition temperature of ordinary NQ-based molten cast explosives is 454.3 K, and the ignition time is 50.0 h, while the slow-baking ignition temperature of spherical NQ-based fused-cast explosives is up to 464 K, which is an increase of 2.14%, and the ignition time is 51.8 h, which is a relative increase of 3.55%; it can be seen that the spheroidization of NQ improves the thermal safety of molten-cast explosives has a significant effect.

Two Polysaccharides from Liupao Tea Exert Beneficial Effects in Simulated Digestion and Fermentation Model In Vitro.

Liupao tea is an important dark tea, but few studies on purified Liupao tea polysaccharide (TPS) are reported in the literature. In this study, two TPSs, named TPS2 and TPS5, with molecular weights of 70.5 and 133.9 kDa, respectively, were purified from Liupao tea. TPS2 contained total sugar content (53.73% ± 1.55%) and uronic acid content (35.18% ± 0.96%), while TPS5 was made up of total sugar (51.71% ± 1.1%), uronic acid (40.95% ± 3.12%), polyphenols (0.43% ± 0.03%), and proteins (0.11% ± 0.07%). TPS2 and TPS5 were composed of Man, Rha, GlcA, Glc, Gal, and Ara in the molar ratios of 0.12:0.69:0.20:0.088:1.60:0.37 and 0.090:0.36:0.42:0.07:1.10:0.16, respectively. The effects of TPS2 and TPS5 on digestion and regulation of gut microbiota in hyperlipidemic rats were compared. In simulated digestion, TPS5 was degraded and had good antioxidant effect, whereas TPS2 was not affected. The bile acids binding capacities of TPS2 and TPS5 were 42.79% ± 1.56% and 33.78% ± 0.45%, respectively. During in vitro fermentation, TPS2 could more effectively reduce pH, promote the production of acetic acid and propionic acid, and reduce the ratio of Firmicutes to Bacteroidetes. TPS5 could more effectively promote the production of butyric acid and increase the abundance of genus Bacteroides. Results indicate that polysaccharides without polyphenols and proteins have better antidigestibility and bile acid binding. Meanwhile, polysaccharides with polyphenols and proteins have a better antioxidant property. Both have different effects on the gut microbiota.

Markers Associated With Tumor Recurrence in Patients With Breast Cancer Achieving a Pathologic Complete Response After Neoadjuvant Chemotherapy.

Background: Patients who achieve a tumor pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) have better outcomes than patients with residual tumor. However, tumors still recur in the pCR patients. Therefore, we aim to explore factors associated with tumor recurrence in this patient population. Methods: A total of 1,913 patients diagnosed with breast cancer between 1995 and 2020 and received NAC were included in this analysis. Clinicopathological data of the patients were retrospectively collected. We used Cox regression analysis to assess the associations of clinicopathological factors with patients' outcome. Proteomic study of tumors was applied to identify differentially expressed proteins (DEPs) between tumors from the pCR patients with tumor recurrence and tumors from those without tumor recurrence. PPI network analysis of the corresponding genes of DEPs was used to identify the hub genes. The prognostic value of the corresponding genes of DEPs was evaluated using two online databases, Kaplan-Meier Plotter and bc-GenExMiner. The genes that were significantly associated with patients' survival in both databases, as well as being identified as hub genes, were considered as potential prognostic markers for pCR patients. Publicly available data from Gene Expression Omnibus (GEO) was used to verify the prognostic value of the identified marker. Results: Among the 1,913 included patients, 420 had tumor pCR. The median follow-up for the pCR patients was 32.6 months (IQR, 16.3-55.5). Overall estimated 5-year risk of tumor recurrence for the pCR patients was 11%. Multivariable analysis showed that a higher pre-NAC clinical T stage and N stage were independent predictors for increased risk of tumor recurrence (hazard ratio [HR] 2.57, 95% confidence interval [CI] 1.01-6.51, P=0.047 for clinical T stage and HR 3.48, 95%CI 1.37-8.83, P=0.009 for clinical N stage). NAC regimens, the type of breast and axillary surgery, and adjuvant chemotherapy were not associated with tumor recurrence. Finally, aldehyde dehydrogenase (ALDH) 3A2 was identified by the proteomic study and was verified as a potential predictor for tumor recurrence in the pCR patients (with a median follow up of 3.78 years for dataset GSE32603 and 2.74 years for dataset GSE25066 from GEO, tumor recurrence rate: low versus high expression, 20.7% versus 4.5% [data from GSE32603]; 10.9% versus 0% [data from GSE25066]). Conclusions: Clinical T stage, clinical N stage and tumor expression of ALDH3A2 were potential markers for predicting tumor recurrence in the pCR patients after NAC.

CXCL9 Is a Potential Biomarker of Immune Infiltration Associated With Favorable Prognosis in ER-Negative Breast Cancer.

The chemokine CXCL9 (C-X-C motif chemokine ligand 9) has been reported to be required for antitumour immune responses following immune checkpoint blockade. In this study, we sought to investigate the potential value of CXCL9 according to immune responses in patients with breast cancer (BC). A variety of open-source databases and online tools were used to explore the expression features and prognostic significance of CXCL9 in BC and its correlation with immune-related biomarkers followed by subsequent verification with immunohistochemistry experiments. The CXCL9 mRNA level was found to be significantly higher in BC than in normal tissue and was associated with better survival outcomes in patients with ER-negative tumours. Moreover, CXCL9 is significantly correlated with immune cell infiltration and immune-related biomarkers, including CTLA4, GZMB, LAG3, PDCD1 and HAVCR2. Finally, we performed immunohistochemistry with breast cancer tissue samples and observed that CXCL9 is highly expressed in the ER-negative subgroup and positively correlated with the immune-related factors LAG3, PD1, PDL1 and CTLA4 to varying degrees. These findings suggest that CXCL9 is an underlying biomarker for predicting the status of immune infiltration in ER-negative breast cancer.

Fluorescent image-guided surgery in breast cancer by intravenous application of a quenched fluorescence activity-based probe for cysteine cathepsins in a syngeneic mouse model.

PURPOSE: The reoperation rate for breast-conserving surgery is as high as 15-30% due to residual tumor in the surgical cavity after surgery. In vivo tumor-targeted optical molecular imaging may serve as a red-flag technique to improve intraoperative surgical margin assessment and to reduce reoperation rates. Cysteine cathepsins are overexpressed in most solid tumor types, including breast cancer. We developed a cathepsin-targeted, quenched fluorescent activity-based probe, VGT-309, and evaluated whether it could be used for tumor detection and image-guided surgery in syngeneic tumor-bearing mice. METHODS: Binding specificity of the developed probe was evaluated in vitro. Next, fluorescent imaging in BALB/c mice bearing a murine breast tumor was performed at different time points after VGT-309 administration. Biodistribution of VGT-309 after 24 h in tumor-bearing mice was compared to control mice. Image-guided surgery was performed at multiple time points tumors with different clinical fluorescent camera systems and followed by ex vivo analysis. RESULTS: The probe was specifically activated by cathepsins X, B/L, and S. Fluorescent imaging revealed an increased tumor-to-background contrast over time up to 15.1 24 h post probe injection. In addition, VGT-309 delineated tumor tissue during image-guided surgery with different optical fluorescent imaging camera systems. CONCLUSION: These results indicate that optical fluorescent molecular imaging using the cathepsin-targeted probe, VGT-309, may improve intraoperative tumor detection, which could translate to more complete tumor resection when coupled with commercially available surgical tools and techniques.

Consideration of breast cancer subtype in targeting the androgen receptor.

The androgen receptor (AR) is a drug target in breast cancer, and AR-targeted therapies have induced tumor responses in breast cancer patients. In this review, we summarized the role of AR in breast cancer based on preclinical and clinical data. Response to AR-targeted therapies in unselected breast cancer populations is relatively low. Preclinical and clinical data show that AR antagonists might have a role in estrogen receptor (ER)-negative/AR-positive tumors. The prognostic value of AR for patients remains uncertain due to the use of various antibodies and cut-off values for immunohistochemical assessment. To get more insight into the role of AR in breast cancer, we additionally performed a retrospective pooled analysis to determine the prognostic value of the AR using mRNA profiles of 7270 primary breast tumors. Our analysis shows that a higher AR mRNA level is associated with improved disease outcome in patients with ER-positive/human epidermal growth factor receptor 2 (HER2)-negative tumors, but with worse disease outcome in HER2-positive subgroups. In conclusion, next to AR expression, incorporation of additional tumor characteristics will potentially make AR targeting a more valuable therapeutic strategy in breast cancer.

Toward Support-free 3D Printing: A Skeletal Approach for Partitioning Models.

Minimizing support structures is crucial in reducing 3D printing material and time. Partition-based methods are efficient means in realizing this objective. Although some algorithms exist for support-free fabrication of solid models, no algorithm ever considers the problem of support-free fabrication for shell models (i.e., hollowed meshes). In this paper, we present a skeleton-based algorithm for partitioning a 3D surface model into the least number of parts for 3D printing without using any support structure. To achieve support-free fabrication while minimizing the effect of the seams and cracks that are inevitably induced by the partition, which affect the aesthetics and strength of the final assembled surface, we put forward an optimization system with the minimization of the number of partitions and the total length of the cuts, under the constraints of support-free printing angle. Our approach is particularly tailored for shell models, and it can be applicable to solid models as well. We first rigorously show that the optimization problem is NP-hard and then propose a stochastic method to find an optimal solution to the objectives. We propose a polynomial-time algorithm for a special case when the skeleton graph satisfies the requirement that the number of partitioned parts and the degree of each node are bounded by a small constant. We evaluate our partition method on a number of 3D models and validate our method by 3D printing experiments.

Considering the biology of late recurrences in selecting patients for extended endocrine therapy in breast cancer.

Extended endocrine therapy can reduce recurrences occurring more than 5 years after diagnosis (late recurrences) in estrogen receptor (ER)-positive breast cancer. Given the side effects of endocrine therapy, optimal patient selection for extended treatment is crucial. Enhanced understanding of late recurrence biology could optimize patient selection in this setting. We therefore summarized the current knowledge of late recurrence biology, clinical trials on extended endocrine therapy, and tools for predicting late recurrence and benefit from treatment extension. Extending 5 years of tamoxifen therapy with 5 years of tamoxifen or an aromatase inhibitor (AI) reduces late recurrence risk by 2-5%, but results of extending AI-based therapy are inconsistent. Although several clinicopathological parameters and multigene assays are prognostic for late recurrence, selection tools predicting benefit from extended endocrine therapy are sparse. Therefore, we additionally performed a pooled analysis using 2231 mRNA profiles of patients with ER-positive/human epidermal growth factor receptor 2-negative breast cancer. Gene Set Enrichment Analysis was applied on genes ranked according to their association with early and late recurrence risk. Higher expression of estrogen-responsive genes was associated with a high recurrence risk beyond 5 years after diagnosis when patients had received no systemic therapy. Although 5 years of endocrine therapy reduced this risk, this effect disappeared after treatment cessation. This suggests that late recurrences of tumors with high expression of estrogen-responsive genes are likely ER-driven. Long-term intervention in this pathway by means of extended endocrine therapy might reduce late recurrences in patients with tumors showing high expression of estrogen-responsive genes.

Tumor-associated macrophages in breast cancer: Innocent bystander or important player?

Tumor-associated macrophages (TAMs) are important tumor-promoting cells in the breast tumor microenvironment. Preclinically TAMs stimulate breast tumor progression, including tumor cell growth, invasion and metastasis. TAMs also induce resistance to multiple types of treatment in breast cancer models. The underlying mechanisms include: induction and maintenance of tumor-promoting phenotype in TAMs, inhibition of CD8+ T cell function, degradation of extracellular matrix, stimulation of angiogenesis and inhibition of phagocytosis. Several studies reported that high TAM infiltration of breast tumors is correlated with a worse patient prognosis. Based on these findings, macrophage-targeted treatment strategies have been developed and are currently being evaluated in clinical breast cancer trials. These strategies include: inhibition of macrophage recruitment, repolarization of TAMs to an antitumor phenotype, and enhancement of macrophage-mediated tumor cell killing or phagocytosis. This review summarizes the functional aspects of TAMs and the rationale and current evidence for TAMs as a therapeutic target in breast cancer.

Androgen receptor expression inversely correlates with immune cell infiltration in human epidermal growth factor receptor 2-positive breast cancer.

INTRODUCTION: Although targeting human epidermal growth factor receptor 2 (HER2) is a meaningful treatment in HER2-positive breast cancer, ultimately resistance develops. Androgen receptor (AR) expression and immune cell infiltration are thought to be involved in trastuzumab response and may, therefore, be of interest as additional targets for therapy in HER2-positive breast cancer. AIM: To improve insights into the presence among AR expression, immune cell infiltration and HER2, we analysed HER2-positive breast tumours. METHODS: Primary tumours of 221 patients treated with trastuzumab for metastatic disease were selected. HER2 status was centrally confirmed. AR, T-cells (CD3 and CD8), programmed cell death protein 1 (PD-1) and PD-1 ligand 1 immunohistochemical staining and M2 tumour-associated macrophages (TAMs; CD68 and CD163) immunofluorescence were performed. Tumour-infiltrating lymphocytes were evaluated by haematoxylin and eosin staining. RESULTS: Sufficient tumour material was available for 150 patients. Oestrogen receptor was expressed in 51.3% of the tumours and AR in 81.3% of the tumours. AR expression was inversely correlated with M2 TAM (Pearson's r = -0.361, P < 0.001), CD3+ (r = -0.199, P < 0.030) and CD8+ (r = -0.212, P < 0.021) T-cell infiltration. Clustering analysis showed high immune cell infiltration in AR low-expressing tumours, and low immune cell infiltration in AR-high expressing tumours. CONCLUSION: AR expression inversely correlates with immune cell infiltration in HER2-positive breast cancer.