Baseline microvessel density predicts response to neoadjuvant bevacizumab treatment of locally advanced breast cancer

DDR2 expression in breast cancer is associated with blood vessel invasion, basal-like tumors, tumor associated macrophages, regulatory T cells, detection mode and prognosis

Discoidin Domain Receptor 2 (DDR2) is a receptor tyrosine kinase for collagen, stimulating epithelial-mesenchymal transition and stiffness in breast cancer. Here, we investigated levels of DDR2 in breast tumor cells in relation to vascular invasion, TIL subsets, macrophages, molecular tumor subtypes, modes of detection and prognosis. This retrospective, population-based series of invasive breast carcinomas from the Norwegian Screening Program in Vestfold County (Norway), period 2004-2009, included 200 screening patients and 82 cases detected in screening intervals. DDR2 was examined on core needle biopsies using a semi-quantitative, immunohistochemical staining index and dichotomized as low or high DDR2 expression. Counts of macrophages and TIL subsets were dichotomized based on immunohistochemistry using TMA. We also recorded blood or lymphatic vessel invasion (BVI or LVI) as present or absent by immunohistochemistry. High expression of DDR2 in tumor cells showed significant relation with high counts of CD163+ macrophages (p < 0.001) and FOXP3 TILs (p = 0.011), presence of BVI (p = 0.028), high tumor cell proliferation by Ki67 (p = 0.033), ER negativity (p = 0.001), triple-negative cases (p = 0.038), basal-like features (p < 0.001) as well as interval detection (p < 0.001). By multivariate analysis, high DDR2 expression was related to reduced recurrence-free survival (HR, 2.3, p = 0.017), when examined together with histologic grading, lymph node assessment, tumor diameter, BVI, and molecular tumor subtype. This study supports a link between high DDR2 expression, high counts of macrophages by CD163 (tumor associated) and regulatory T cells by FOXP3 together with the presence of BVI, possibly indicating increased tumor motility and intravasation in aggressive breast tumors.

Exosomal lncRNA SNHG12 promotes angiogenesis and breast cancer progression

OBJECTIVE

Breast cancer is one of the most prevalent malignancies in women. Exosomes are important mediators of intercellular communication; however, their regulatory mechanisms in human umbilical vein endothelial cells (HUVECs) angiogenesis in breast cancer remain unknown.

METHODS

We isolated and characterized breast cancer cell-derived exosomes and investigated their functions. Exosomal sequencing and the TCGA database were used to screen long non-coding RNA (lncRNA). In vitro and in vivo experiments were performed to investigate the role of exosomal lncRNA in HUVEC angiogenesis and tumor growth. Molecular methods were used to demonstrate the molecular mechanism of lncRNA.

RESULTS

We demonstrated that breast cancer cell-derived exosomes promoted HUVEC proliferation, tube formation, and migration. Combining exosomal sequencing results with The Cancer Genome Atlas Breast Cancer database, we screened lncRNA small nucleolar RNA host gene 12 (SNHG12), which was highly expressed in breast cancer cells. SNHG12 was also upregulated in HUVECs co-cultured with exosome-overexpressed SNHG12. Moreover, overexpression of SNHG12 in exosomes increased HUVEC proliferation and migration, whereas deletion of SNHG12 in exosomes showed the opposite effects. In vivo experiments showed that SNHG12 knockdown in exosomes inhibited breast cancer tumor growth. Transcriptome sequencing identified MMP10 as the target gene of SNHG12. Functional experiments revealed that MMP10 overexpression promoted HUVEC angiogenesis. Mechanistically, SNHG12 blocked the interaction between PBRM1 and MMP10 by directly binding to PBRM1. Moreover, exosomal SNHG12 promoted HUVEC angiogenesis via PBRM1 and MMP10.

CONCLUSIONS

In summary, our findings confirmed that exosomal SNHG12 promoted HUVEC angiogenesis via the PBRM1-MMP10 axis, leading to enhanced malignancy of breast cancer. Exosomal SNHG12 may be a novel therapeutic target for breast cancer.

Package delivered: folate receptor-mediated transporters in cancer therapy and diagnosis

Neoplasias pose a significant threat to aging society, underscoring the urgent need to overcome the limitations of traditional chemotherapy through pioneering strategies. Targeted drug delivery is an evolving frontier in cancer therapy, aiming to enhance treatment efficacy while mitigating undesirable side effects. One promising avenue utilizes cell membrane receptors like the folate receptor to guide drug transporters precisely to malignant cells. Based on the cellular folate receptor as a cancer cell hallmark, targeted nanocarriers and small molecule-drug conjugates have been developed that comprise different (bio) chemistries and/or mechanical properties with individual advantages and challenges. Such modern folic acid-conjugated stimuli-responsive drug transporters provide systemic drug delivery and controlled release, enabling reduced dosages, circumvention of drug resistance, and diminished adverse effects. Since the drug transporters' structure-based de novo design is increasingly relevant for precision cancer remediation and diagnosis, this review seeks to collect and debate the recent approaches to deliver therapeutics or diagnostics based on folic acid conjugated Trojan Horses and to facilitate the understanding of the relevant chemistry and biochemical pathways. Focusing exemplarily on brain and breast cancer, recent advances spanning 2017 to 2023 in conjugated nanocarriers and small molecule drug conjugates were considered, evaluating the chemical and biological aspects in order to improve accessibility to the field and to bridge chemical and biomedical points of view ultimately guiding future research in FR-targeted cancer therapy and diagnosis.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

BACKGROUND

Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.

METHODS

H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.

RESULTS

The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.

CONCLUSION

Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

Therapeutic targeting of the pituitary tumor microenvironment

The tumor microenvironment (TME), the complex environment in which tumors develop, has been increasingly targeted for cancer treatment in recent years. Aggressive pituitary tumors and pituitary carcinomas have been so far targeted with immune-checkpoint inhibitors (28 cases, including a large cohort), and anti-angiogenic drugs (34 cases), specifically bevacizumab (30 cases), sunitinib (three cases), and apatinib (one case). Here, we reviewed all these cases, reporting tumor response, potential predictors of response, as well as adverse events. Given that the histological type could potentially influence treatment response, we present the existing data separately for each type. Briefly, under ICIs, complete response was noted in one case, partial response in a third of cases, stable disease in 10% of cases, while 54% of tumors progressed. Under BVZ monotherapy, most cases (57%) showed stable disease, while 36% of tumors progressed; partial response was reported in only one case. The three cases treated with sunitinib monotherapy progressed. Regarding predictive factors of response, the tumor type (aggressive pituitary tumor versus pituitary carcinoma) appears as the strongest predictor of response to ICIs. To date, no predictor of response to anti-angiogenic drugs in the treatment of pituitary carcinomas and aggressive pituitary tumors has been identified. The interest of BZV add-on to first- or second-line chemotherapy warrants further investigation. In addition, we discuss perspectives regarding the TME-targeting in aggressive pituitary tumors and pituitary carcinomas, including perspectives on immunotherapy, anti-angiogenic drugs, as well as on other TME components, namely stromal cells, extracellular matrix, and secreted molecules.

Heterogeneity of the Tumor Microenvironment Across Molecular Subtypes of Breast Cancer

Breast cancer is a heterogenous disease at the molecular level thus, it can be hypothesized that different molecular subtypes differ in their tumor microenvironment (TME) also. Understanding the TME heterogeneity may provide new prognostic biomarkers and new targets for cancer therapy. For deciphering heterogeneity in the TME, immunohistochemistry for immune markers (CD3, CD4, CD8, CD68, CD163, and programmed death-ligand 1), Cancer-associated fibroblast markers [anti-fibroblast activating protein α (FAP-α), platelet-derived growth factor receptor α (PDGFR-α), S100A4, Neuron-glial antigen 2, and Caveolin-1], and angiogenesis (CD31) was performed on tissue microarrays of different molecular subtypes of breast cancer. High CD3 + T cells were noted in the Luminal B subtype ( P =0.002) of which the majority were CD8 + cytotoxic T cells. Programmed death-ligand 1 expression in immune cells was highest in the human epidermal growth factor receptor 2 (Her-2)-positive and Luminal B subtypes compared with the triple-negative breast cancer (TNBC) subtype ( P =0.003). Her-2 subtype is rich in M2 tumor-associated macrophages ( P =0.000) compared with TNBC and Luminal B subtypes. M2 immune microenvironment correlated with high tumor grade and high Ki-67. Her-2 and TNBC subtypes are rich in extracellular matrix remodeling (FAP-α, P =0.003), angiogenesis-promoting (PDGFR-α; P =0.000) and invasion markers (Neuron-glial antigen 2, P =0.000; S100A4, P =0.07) compared with Luminal subtypes. Mean Microvessel density showed an increasing trend: Luminal A>Luminal B>Her-2 positive>TNBC; however, this difference was not statistically significant. The cancer-associated fibroblasts (FAP-α, PDGFR-α, and Neuron-glial antigen 2) showed a positive correlation with lymph node metastasis in specific subtypes. Immune cells, tumor-associated macrophage, and cancer-associated fibroblast-related s tromal markers showed higher expression in Luminal B, Her-2 positive, and TNBC respectively. This differential expression of different components of TME indicates heterogeneity of the TME across molecular subtypes of breast cancer.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

Background

Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment (TME) in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.

Methods

H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) were separated through a stratified 8-fold cross validation strategy for the first step and leave one out cross validation strategy for the second step. A tile-level histology label prediction pipeline and four machine learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.

Results

The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.

Conclusion

Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

Calcium Homeostasis in the Development of Resistant Breast Tumors

Cancer is one of the main health problems worldwide. Only in 2020, this disease caused more than 19 million new cases and almost 10 million deaths, with breast cancer being the most diagnosed worldwide. Today, despite recent advances in breast cancer treatment, a significant percentage of patients will either not respond to therapy or will eventually experience lethal progressive disease. Recent studies highlighted the involvement of calcium in the proliferation or evasion of apoptosis in breast carcinoma cells. In this review, we provide an overview of intracellular calcium signaling and breast cancer biology. We also discuss the existing knowledge on how altered calcium homeostasis is implicated in breast cancer development, highlighting the potential utility of Ca2+ as a predictive and prognostic biomarker, as well as its potential for the development of new pharmacological treatments to treat the disease.

Ultrasound super-resolution imaging for differential diagnosis of breast masses

OBJECTIVE

Ultrasound imaging has been widely used in breast cancer screening. Recently, ultrasound super-resolution imaging (SRI) has shown the capability to break the diffraction limit to display microvasculature. However, the application of SRI on differential diagnosis of breast masses remains unknown. Therefore, this study aims to evaluate the feasibility and clinical value of SRI for visualizing microvasculature and differential diagnosis of breast masses.

METHODS

B mode, color-Doppler flow imaging (CDFI) and contrast-enhanced ultrasound (CEUS) images of 46 patients were collected respectively. SRI were generated by localizations of each possible contrast signals. Micro-vessel density (MVD) and microvascular flow rate (MFR) were calculated from SRI and time to peak (TTP), peak intensity (PI) and area under the curve (AUC) were obtained by quantitative analysis of CEUS images respectively. Pathological results were considered as the gold standard. Independent chi-square test and multivariate logistic regression analysis were performed using these parameters to examine the correlation.

RESULTS

The results showed that SRI technique could be successfully applied on breast masses and display microvasculature at a significantly higher resolution than the conventional CDFI and CEUS images. The results showed that the PI, AUC, MVD and MFR of malignant breast masses were significantly higher than those of benign breast masses, while TTP was significantly lower than that of benign breast masses. Among all five parameters, MVD showed the highest positive correlation with the malignancy of breast masses.

CONCLUSIONS

SRI is able to successfully display the microvasculature of breast masses. Compared with CDFI and CEUS, SRI can provide additional morphological and functional information for breast masses. MVD has a great potential in assisting the differential diagnosis of breast masses as an important imaging marker.

Molecular perspective on targeted therapy in breast cancer: a review of current status

Breast cancer is categorized at the molecular level according to the status of certain hormone and growth factor receptors, and this classification forms the basis of current diagnosis and treatment. The development of resistance to treatment and recurrence of the disease have led researchers to develop new therapies. In recent years, most of the research in the field of oncology has focused on the development of targeted therapies, which are treatment methods developed directly against molecular abnormalities. Promising advances have been made in clinical trials investigating the effect of these new treatment modalities and their combinations with existing therapeutic treatments in the treatment of breast cancer. Monoclonal antibodies, tyrosine kinase inhibitors, antibody–drug conjugates, PI3K/Akt/mTOR pathway inhibitors, cyclin-dependent kinase 4/6 inhibitors, anti-angiogenic drugs, PARP inhibitors are among the targeted therapies used in breast cancer treatment. In this review, we aim to present a molecular view of recently approved target agents used in breast cancer.

Lymphatic Endothelial Markers and Tumor Lymphangiogenesis Assessment in Human Breast Cancer

Metastasis via lymphatic vessels or blood vessels is the leading cause of death for breast cancer, and lymphangiogenesis and angiogenesis are critical prerequisites for the tumor invasion–metastasis cascade. The research progress for tumor lymphangiogenesis has tended to lag behind that for angiogenesis due to the lack of specific markers. With the discovery of lymphatic endothelial cell (LEC) markers, growing evidence demonstrates that the LEC plays an active role in lymphatic formation and remodeling, tumor cell growth, invasion and intravasation, tumor–microenvironment remodeling, and antitumor immunity. However, some studies have drawn controversial conclusions due to the variation in the LEC markers and lymphangiogenesis assessments used. In this study, we review recent findings on tumor lymphangiogenesis, the most commonly used LEC markers, and parameters for lymphangiogenesis assessments, such as the lymphatic vessel density and lymphatic vessel invasion in human breast cancer. An in-depth understanding of tumor lymphangiogenesis and LEC markers can help to illustrate the mechanisms and distinct roles of lymphangiogenesis in breast cancer progression, which will help in exploring novel potential predictive biomarkers and therapeutic targets for breast cancer.

Alternative Vascularization Mechanisms in Tumor Resistance to Therapy

Simple Summary

Tumors rely on blood vessels to grow and metastasize. Malignant tumors can employ different strategies to create a functional vascular network. Tumor cells can use normal processes of vessel formation but can also employ cancer-specific mechanisms, by co-opting normal vessels present in tissues or by turning themselves into vascular cells. These different types of tumor vessels have specific molecular and functional characteristics that profoundly affect tumor behavior and response to therapies, including drugs targeting the tumor vasculature (antiangiogenic therapies). In this review, we discuss how vessels formed by different mechanisms affect the intrinsic sensitivity of tumors to therapy and, on the other hand, how therapies can affect tumor vessel formation, leading to resistance to drugs, cancer recurrence, and treatment failure. Potential strategies to avoid vessel-mediated resistance to antineoplastic therapies will be discussed.

Abstract

Blood vessels in tumors are formed through a variety of different mechanisms, each generating vessels with peculiar structural, molecular, and functional properties. This heterogeneity has a major impact on tumor response or resistance to antineoplastic therapies and is now emerging as a promising target for strategies to prevent drug resistance and improve the distribution and efficacy of antineoplastic treatments. This review presents evidence of how different mechanisms of tumor vessel formation (vasculogenesis, glomeruloid proliferation, intussusceptive angiogenesis, vasculogenic mimicry, and vessel co-option) affect tumor responses to antiangiogenic and antineoplastic therapies, but also how therapies can promote alternative mechanisms of vessel formation, contributing to tumor recurrence, malignant progression, and acquired drug resistance. We discuss the possibility of tailoring treatment strategies to overcome vasculature-mediated drug resistance or to improve drug distribution and efficacy.