Recent advances in breast cancer research impacting clinical diagnostic practice

Should low-risk DCIS lose the cancer label? An evidence review

BACKGROUND

Population mammographic screening for breast cancer has led to large increases in the diagnosis and treatment of ductal carcinoma in situ (DCIS). Active surveillance has been proposed as a management strategy for low-risk DCIS to mitigate against potential overdiagnosis and overtreatment. However, clinicians and patients remain reluctant to choose active surveillance, even within a trial setting. Re-calibration of the diagnostic threshold for low-risk DCIS and/or use of a label that does not include the word 'cancer' might encourage the uptake of active surveillance and other conservative treatment options. We aimed to identify and collate relevant epidemiological evidence to inform further discussion on these ideas.

METHODS

We searched PubMed and EMBASE databases for low-risk DCIS studies in four categories: (1) natural history; (2) subclinical cancer found at autopsy; (3) diagnostic reproducibility (two or more pathologist interpretations at a single time point); and (4) diagnostic drift (two or more pathologist interpretations at different time points). Where we identified a pre-existing systematic review, the search was restricted to studies published after the inclusion period of the review. Two authors screened records, extracted data, and performed risk of bias assessment. We undertook a narrative synthesis of the included evidence within each category.

RESULTS

Natural History (n = 11): one systematic review and nine primary studies were included, but only five provided evidence on the prognosis of women with low-risk DCIS. These studies reported that women with low-risk DCIS had comparable outcomes whether or not they had surgery. The risk of invasive breast cancer in patients with low-risk DCIS ranged from 6.5% (7.5 years) to 10.8% (10 years). The risk of dying from breast cancer in patients with low-risk DCIS ranged from 1.2 to 2.2% (10 years). Subclinical cancer at autopsy (n = 1): one systematic review of 13 studies estimated the mean prevalence of subclinical in situ breast cancer to be 8.9%. Diagnostic reproducibility (n = 13): two systematic reviews and 11 primary studies found at most moderate agreement in differentiating low-grade DCIS from other diagnoses. Diagnostic drift: no studies found.

CONCLUSION

Epidemiological evidence supports consideration of relabelling and/or recalibrating diagnostic thresholds for low-risk DCIS. Such diagnostic changes would need agreement on the definition of low-risk DCIS and improved diagnostic reproducibility.

A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype

Background

Immune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB.

Methods

Open datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB.

Results

A total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/.

Conclusion

TNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed.

CXCL8 Facilitates the Survival and Paclitaxel-Resistance of Triple-Negative Breast Cancers

BACKGROUND

This study aimed to demonstrate CXCL8 expression in TNBC tissues and cells, and elucidate the functional mechanism of CXCL8 in paclitaxel (PTX)-resistant TNBC.

METHODS

Bioinformatics analysis was performed to identify differentially expressed genes (DEGs) in PTX-resistant TNBC using publicly available data from the GEO, TCGA and METABRIC databases. STRING was used to identify the interacting partners of CXCL8. Kaplan-Meier software was used to analyze the relationship between CXCL8 expression and patient survival rate. The protein expression and distribution of CXCL8 were examined by immunohistochemistry, MTT assay and colony formation assay were performed to determine cell viability of TNBC cells treated with PTX. Western blotting was used to assess the levels of drug resistance and apoptosis-related proteins. GO-KEGG analysis was conducted on the DEGs to identify enriched signaling pathways.

RESULTS

The results of bioinformatics analysis demonstrated a high expression of CXCL8 in TNBC tissues and cells. Kaplan-Meier analysis revealed that the expression of CXCL8 is associated with poor prognosis. CXCL8 was upregulated in PTX-resistant TNBC cells. Knockdown of CXCL8 increased the sensitivity of TNBC cells to PTX. Mechanically, CXCL8 deficiency regulated PTX resistance in TNBC cells via cell apoptosis signaling pathway.

CONCLUSION

Our work demonstrated that CXCL8 may be a potential molecule to be targeted for the treatment of PTX-resistant TNBC.

The interplay of interleukin-17A and breast cancer tumor microenvironment as a novel immunotherapeutic approach to increase tumor immunogenicity

Based on its known role in mediating tumor progression and the correlation with poor response to chemotherapy, we hypothesized that blocking interleukin-17A (IL-17A) by anti-IL-17 monoclonal antibodies might have the ability to suppress programmed death-ligand-1 (PD-L1) and to modulate the expression and function of myeloid-derived suppressor cells (MDSCs) in BC microenvironment. We also compared the apoptotic activity of anti-IL-17 with those acquired from our previous work on monoclonal antibodies against IL-6. The influence of anti-IL-17 was investigated in two doses on localized freshly resected tissues from 50 patients with BC. Results revealed increased IL-17A in BC tumor tissues versus surrounding tissues. Additionally, PD-L1 expression was inhibited in cultures treated with both doses of anti-IL-17. Frequencies of MDSCs were reduced in those cultures with anti-IL-17 with reduced suppressive activity. The induced apoptosis in the tumor cells was significantly increased. Anti-IL-17 antibodies effect was related to late stages, vascular metastasis, and hormonal status. Results of the current work suggest a promising role for anti-IL-17 monoclonal antibodies in enhancement of anti-tumor immunological activity in BC, potentially involving suppression of immune checkpoint PD-L1 and MDSCs inhibition with a marked response in aggressive disease.

Impact of Commercialized Genomic Tests on Adjuvant Treatment Decisions in Early Stage Breast Cancer Patients

Introduction

Advances in genomic techniques have been valuable in guiding decisions regarding the treatment of early breast cancer (EBC) patients. These multigene assays include Oncotype DX, Prosigna, and Endopredict. There has generally been a tendency to overtreat or undertreat patients, and having reliable prognostic factors could significantly improve rates of appropriate treatment administration. In this study, we showcase the impact of genomic tests on adjuvant treatment decisions in EBC patients.

Materials and Methods

This is a retrospective study that includes EBC patients treated between December 2016 and February 2018. The physician's choice of treatment was recorded before and after obtaining the results of the genomics tests. Baseline demographics and pathological data were collected from medical records.

Results

A total of 75 patients were included. Fifty patients underwent Oncotype DX genomic analysis, 11 patients underwent Prosigna analysis, and 14 patients underwent Endopredict analysis. A total of 21 physicians' plans (28%) were initially undecided and then carried out after obtaining genomic test results. 13 patients were planned to undergo endocrine therapy alone, while 8 were planned to undergo both endocrine therapy and chemotherapy. Treatment was changed in 26 patients (34.67%). The decision to deescalate therapy was taken in 19 patients (25.33%). The decision to escalate treatment was made in 7 patients (9.33%).

Conclusion

Our study demonstrates the importance of genomics testing, as it assisted physicians in avoiding unnecessary adjuvant chemotherapy in 25.33% of patients, thus reducing side effects of chemotherapy and the financial burden on patients.

Development and Validation of the Gene Expression Predictor of High-grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE)

PURPOSE

Gene expression-based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features.

EXPERIMENTAL DESIGN

Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting.

RESULTS

Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations.

CONCLUSIONS

We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications.See related commentary by McMullen et al., p. 5271.

Prognostic Value and Influence of Receptor Conversion on Treatment Regimen in Metastatic Breast Cancer at the First Time of Recurrence

PURPOSE

At the first time of metastatic breast cancer recurrence, conversion of the receptors status may occur between primary lesions and metastatic lesions, including the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Whether the decision of the treatment regimen is based on the primary receptor status or that of metastatic lesions is still unclear.

METHODS

This study enrolled 411 female patients with a diagnosis of metastatic breast cancer at the first time of recurrence to explore the influence of receptor conversion on prognosis prediction and treatment regimen of patients with metastatic breast cancer.

RESULTS

ER and PR changes from negative to positive are both prognostic factors for patients with breast cancer. Patients receiving endocrine therapy showed a better survival after recurrence than those using chemotherapy alone in the ER or PR Prim- Met+ subgroup. Patients in the HER2 Prim- Met+ subgroup using HER2-targeted therapy in multilines showed a post-recurrence survival advantage. In the bone re-biopsy subgroup, the PR change from positive to negative appeared to be more frequent than at other re-biopsy sites.

CONCLUSIONS

Patients with metastatic breast cancer should perform re-biopsy to clarify the receptor status of the first metastatic lesions, which may provide clinicians valuable evidence to conduct treatments with higher precision.

Hormone-Independent Mouse Mammary Adenocarcinomas with Different Metastatic Potential Exhibit Different Metabolic Signatures

The metabolic characteristics of metastatic and non-metastatic breast carcinomas remain poorly studied. In this work, untargeted Nuclear Magnetic Resonance (NMR) metabolomics was used to compare two medroxyprogesterone acetate (MPA)-induced mammary carcinomas lines with different metastatic abilities. Different metabolic signatures distinguished the non-metastatic (59-2-HI) and the metastatic (C7-2-HI) lines, with glucose, amino acid metabolism, nucleotide metabolism and lipid metabolism as the major affected pathways. Non-metastatic tumours appeared to be characterised by: (a) reduced glycolysis and tricarboxylic acid cycle (TCA) activities, possibly resulting in slower NADH biosynthesis and reduced mitochondrial transport chain activity and ATP synthesis; (b) glutamate accumulation possibly related to reduced glutathione activity and reduced mTORC1 activity; and (c) a clear shift to lower phosphoscholine/glycerophosphocholine ratios and sphingomyelin levels. Within each tumour line, metabolic profiles also differed significantly between tumours (i.e., mice). Metastatic tumours exhibited marked inter-tumour changes in polar compounds, some suggesting different glycolytic capacities. Such tumours also showed larger intra-tumour variations in metabolites involved in nucleotide and cholesterol/fatty acid metabolism, in tandem with less changes in TCA and phospholipid metabolism, compared to non-metastatic tumours. This study shows the valuable contribution of untargeted NMR metabolomics to characterise tumour metabolism, thus opening enticing opportunities to find metabolic markers related to metastatic ability in endocrine breast cancer.

Resistance to PD-1/PD-L1 blockade cancer immunotherapy: mechanisms, predictive factors, and future perspectives

PD-1/PD-L1 blockade therapy is a promising cancer treatment strategy, which has revolutionized the treatment landscape of malignancies. Over the last decade, PD-1/PD-L1 blockade therapy has been trialed in a broad range of malignancies and achieved clinical success. Despite the potentially cure-like survival benefit, only a minority of patients are estimated to experience a positive response to PD-1/PD-L1 blockade therapy, and the primary or acquired resistance might eventually lead to cancer progression in patients with clinical responses. Accordingly, the resistance to PD-1/PD-L1 blockade remains a significant challenge hindering its further application. To overcome the limitation in therapy resistance, substantial effort has been made to improve or develop novel anti-PD-1/PD-L1 based immunotherapy strategies with better clinical response and reduced immune-mediated toxicity. In this review, we provide an overview on the resistance to PD-1/PD-L1 blockade and briefly introduce the mechanisms underlying therapy resistance. Moreover, we summarize potential predictive factors for the resistance to PD-1/PD-L1 blockade. Furthermore, we give an insight into the possible solutions to improve efficacy and clinical response. In the following research, combined efforts of basic researchers and clinicians are required to address the limitation of therapy resistance.

Deep feature-based automatic classification of mammograms

Breast cancer has the second highest frequency of death rate among women worldwide. Early-stage prevention becomes complex due to reasons unknown. However, some typical signatures like masses and micro-calcifications upon investigating mammograms can help diagnose women better. Manual diagnosis is a hard task the radiologists carry out frequently. For their assistance, many computer-aided diagnosis (CADx) approaches have been developed. To improve upon the state of the art, we proposed a deep ensemble transfer learning and neural network classifier for automatic feature extraction and classification. In computer-assisted mammography, deep learning-based architectures are generally not trained on mammogram images directly. Instead, the images are pre-processed beforehand, and then they are adopted to be given as input to the ensemble model proposed. The robust features extracted from the ensemble model are optimized into a feature vector which are further classified using the neural network (nntraintool). The network was trained and tested to separate out benign and malignant tumors, thus achieving an accuracy of 0.88 with an area under curve (AUC) of 0.88. The attained results show that the proposed methodology is a promising and robust CADx system for breast cancer classification. Graphical Abstract Flow diagram of the proposed approach. Figure depicts the deep ensemble extracting the robust features with the final classification using neural networks.

An Overview of Vasculogenic Mimicry in Breast Cancer

Vasculogenic mimicry (VM) is the formation of vascular channels lacking endothelial cells. These channels are lined by tumor cells with cancer stem cell features, positive for periodic acid-Schiff, and negative for CD31 staining. The term VM was introduced by Maniotis et al. (1), who reported this phenomenon in highly aggressive uveal melanomas; since then, VM has been associated with poor prognosis, tumor aggressiveness, metastasis, and drug resistance in several tumors, including breast cancer. It is proposed that VM and angiogenesis (the de novo formation of blood vessels from the established vasculature by endothelial cells, which is observed in several tumors) rely on some common mechanisms. Furthermore, it is also suggested that VM could constitute a means to circumvent anti-angiogenic treatment in cancer. Therefore, it is important to determinant the factors that dictate the onset of VM. In this review, we describe the current understanding of VM formation in breast cancer, including specific signaling pathways, and cancer stem cells. In addition, we discuss the clinical significance of VM in prognosis and new opportunities of VM as a target for breast cancer therapy.

A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival

BACKGROUND

Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic feature, even when controlled for by standard clinicopathological features such as age, grade, and nodal status, yet the molecular testing required to elucidate these subtypes is not routinely performed. Furthermore, when such bulk assays as RNA sequencing are performed, intratumoral heterogeneity that may affect prognosis and therapeutic decision-making can be missed.

METHODS

As a more facile and readily available method for determining IMS in breast cancer, we developed a deep learning approach for approximating PAM50 intrinsic subtyping using only whole-slide images of H&E-stained breast biopsy tissue sections. This algorithm was trained on images from 443 tumors that had previously undergone PAM50 subtyping to classify small patches of the images into four major molecular subtypes-Basal-like, HER2-enriched, Luminal A, and Luminal B-as well as Basal vs. non-Basal. The algorithm was subsequently used for subtype classification of a held-out set of 222 tumors.

RESULTS

This deep learning image-based classifier correctly subtyped the majority of samples in the held-out set of tumors. However, in many cases, significant heterogeneity was observed in assigned subtypes across patches from within a single whole-slide image. We performed further analysis of heterogeneity, focusing on contrasting Luminal A and Basal-like subtypes because classifications from our deep learning algorithm-similar to PAM50-are associated with significant differences in survival between these two subtypes. Patients with tumors classified as heterogeneous were found to have survival intermediate between Luminal A and Basal patients, as well as more varied levels of hormone receptor expression patterns.

CONCLUSIONS

Here, we present a method for minimizing manual work required to identify cancer-rich patches among all multiscale patches in H&E-stained WSIs that can be generalized to any indication. These results suggest that advanced deep machine learning methods that use only routinely collected whole-slide images can approximate RNA-seq-based molecular tests such as PAM50 and, importantly, may increase detection of heterogeneous tumors that may require more detailed subtype analysis.

PD-L1 status in breast cancer: Current view and perspectives

Breast cancer was traditionally not considered a particularly immunogenic tumor. However, recent developments have shown that some aggressive triple-negative breast cancers are immunogenic, exhibit a resistance to chemotherapy and have a poor prognosis. These cancers have been shown to express molecules identified as targets for immunotherapy. Despite the advances, the challenges are many, and include identifying the patients that may benefit from immunotherapy. The best methods to analyze these samples and to evaluate immunogenicity are also major challenges. Therefore, the most accurate and reliable assessment of immune cells as potential targets is one of the most important aims in the current research in breast immunotherapy. In the present review, we briefly discuss the mechanisms of the regulation of checkpoint inhibitors (PD-1/PD-L1) in breast cancer and explore the predictive aspects in the PD-L1 testing.

Recent Advances in Pathology: the 2019 Annual Review Issue of The Journal of Pathology

In this Annual Review Issue of The Journal of Pathology, we present 15 invited reviews on topical aspects of pathology, ranging from the impacts of the microbiome in human disease through mechanisms of cell death and autophagy to recent advances in immunity and the uses of genomics for understanding, classifying and treating human cancers. Each of the reviews is authored by experts in their fields and our intention is to provide comprehensive updates in specific areas of pathology in which there has been considerable recent progress. Copyright © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

∆Np63/p40 correlates with the location and phenotype of basal/mesenchymal cancer stem-like cells in human ER+ and HER2+ breast cancers

ΔNp63, also known as p40, regulates stemness of normal mammary gland epithelium and provides stem cell characteristics in basal and HER2‐driven murine breast cancer models. Whilst ΔNp63/p40 is a characteristic feature of normal basal cells and basal‐type triple‐negative breast cancer, some receptor‐positive breast cancers express ΔNp63/p40 and its overexpression imparts cancer stem cell‐like properties in ER⁺ cell lines. However, the incidence of ER⁺ and HER2⁺ tumours that express ΔNp63/p40 is unclear and the phenotype of ΔNp63/p40⁺ cells in these tumours remains uncertain. Using immunohistochemistry with p63 isoform‐specific antibodies, we identified a ΔNp63/p40⁺ tumour cell subpopulation in 100 of 173 (58%) non‐triple negative breast cancers and the presence of this population associated with improved survival in patients with ER⁻/HER2⁺ tumours (p = 0.006). Furthermore, 41% of ER⁺/PR⁺ and/or HER2⁺ locally metastatic breast cancers expressed ΔNp63/p40, and these cells commonly accounted for <1% of the metastatic tumour cell population that localised to the tumour/stroma interface, exhibited an undifferentiated phenotype and were CD44⁺/ALDH⁻. In vitro studies revealed that MCF7 and T47D (ER⁺) and BT‐474 (HER2⁺) breast cancer cell lines similarly contained a small subpopulation of ΔNp63/p40⁺ cells that increased in mammospheres. In vivo, MCF7 xenografts contained ΔNp63/p40⁺ cells with a similar phenotype to primary ER⁺ cancers. Consistent with tumour samples, these cells also showed a distinct location at the tumour/stroma interface, suggesting a role for paracrine factors in the induction or maintenance of ΔNp63/p40. Thus, ΔNp63/p40 is commonly present in a small population of tumour cells with a distinct phenotype and location in ER⁺ and/or HER2⁺ human breast cancers.

Acquisition of Invasiveness by Breast Adenocarcinoma Cells Engages Established Hallmarks and Novel Regulatory Mechanisms

BACKGROUND/AIM

Proteomics of invasiveness opens a window on the complexity of the metastasis-engaged mechanisms. The extend and types of this complexity require elucidation.

MATERIALS AND METHODS

Proteomics, immunohistochemistry, immunoblotting, network analysis and systems cancer biology were used to analyse acquisition of invasiveness by human breast adenocarcinoma cells.

RESULTS

We report here that invasiveness network highlighted the involvement of hallmarks such as cell proliferation, migration, cell death, genome stability, immune system regulation and metabolism. Identified involvement of cell-virus interaction and gene silencing are potentially novel cancer mechanisms. Identified 6,113 nodes with 11,055 edges affecting 1,085 biological processes show extensive re-arrangements in cell physiology. These high numbers are in line with a similar broadness of networks built with diagnostic signatures approved for clinical use.

CONCLUSION

Our data emphasize a broad systemic regulation of invasiveness, and describe the network of this regulation.

Aurora kinase A stabilizes FOXM1 to enhance paclitaxel resistance in triple-negative breast cancer

Triple-negative breast cancer (TNBC) has a relatively poor outcome. Acquired chemoresistance is a major clinical challenge for TNBC patients. Previously, we reported that kinase-dead Aurora kinase A (Aurora-A) could effectively transactivate the FOXM1 promoter. Here, we demonstrate an additional pathway through which Aurora-A stabilizes FOXM1 by attenuating its ubiquitin in TNBC. Specifically, Aurora-A stabilizes FOXM1 in late M phase and early G1 phase of the cell cycle, which promotes proliferation of TNBC cells. Knock-down of Aurora-A significantly suppresses cell proliferation in TNBC cell lines and can be rescued by FOXM1 overexpression. We observe that paclitaxel-resistant TNBC cells exhibit high expression of Aurora-A and FOXM1. Overexpression of Aurora-A offers TNBC cells an additional growth advantage and protection against paclitaxel. Moreover, Aurora-A and FOXM1 could be simultaneously targeted by thiostrepton. Combination of thiostrepton and paclitaxel treatment reverses paclitaxel resistance and significantly inhibits cell proliferation. In conclusion, our study reveals additional mechanism through which Aurora-A regulates FOXM1 and provides a new therapeutic strategy to treat paclitaxel-resistant triple-negative breast cancer.

Breast Cancer Diagnosis by Surface-Enhanced Raman Scattering (SERS) of Urine

Background: There is an ongoing research for breast cancer diagnostic tools that are cheaper, more accurate and more convenient than mammography. Methods: In this study, we employed surface-enhanced Raman scattering (SERS) for analysing urine from n = 53 breast cancer patients and n = 22 controls, with the aim of discriminating between the two groups using multivariate data analysis techniques such as principal component analysis—linear discriminant analysis (PCA-LDA). The SERS spectra were acquired using silver nanoparticles synthesized by reduction with hydroxylamine hydrochloride, which were additionally activated with Ca2+ 10−4 M. Results: The addition of Ca(NO3)2 10−4 M promoted the specific adsorption to the metal surface of the anionic purine metabolites such as uric acid, xanthine and hypoxanthine. Moreover, the SERS spectra of urine were acquired without any filtering or processing step for removing protein traces and other contaminants. Using PCA-LDA, the SERS spectra of urine from breast cancer patients were classified with a sensitivity of 81%, a specificity of 95% and an overall accuracy of 88%. Conclusion: The results of this preliminary study contribute to the translation of SERS in the clinical setting and highlight the potential of SERS as a novel screening strategy for breast cancer.

Deregulation of VEGFR-2 and PDGFR Expression and Microvascular Density in a Triple-Negative Model of Canine Malignant Mammary Tumors with Lymph Node or Lung Metastasis

Canine mammary tumors (CMT) represent the most common cancer in noncastrated female dogs. Interestingly, triple-negative tumors are the most common molecular subtype in female dogs. In this study, we proposed to evaluate the expression of vascular endothelial growth factor receptor 2 (VEGFR-2), Platelet-derived growth factor receptor (PDGFR), and microvascular density (MVD) in a group of metastatic and nonmetastatic triple-negative CMT and compare the expression based on clinical parameters. Twenty-six female dogs with triple-negative mammary tumors were divided into three groups: nonmetastatic tumors (NMT) (n = 11), tumors with lymph node metastasis (LNM) (n = 10), and tumors with lung metastasis (LM) (n = 5). We observed increased VEGFR-2 expression in LNM compared with NMT and a positive correlation between tumor grade and VEGFR-2 expression. A positive correlation was noted between VEGFR-2 and PDGFR expression. Regarding microvascular density (MVD), we identified a higher number of vessels in primary tumors with lymph node metastasis and lung metastasis compared with tumors with no metastasis. The primary tumors with lung metastasis exhibited an increased MVD compared with carcinoma with lymph node metastasis. Overall, our results suggest a deregulation of VEGFR-2 and PDGFR and high MVD in metastatic tumors, indicating a role for angiogenesis in tumor progression.