The changing role of pathology in breast cancer diagnosis and treatment

Folic acid modified precision nanocarriers: charting new frontiers in breast cancer management beyond conventional therapies

Breast cancer presents a significant global health challenge, ranking highest incidence rate among all types of cancers. Functionalised nanocarriers offer a promising solution for precise drug delivery by actively targeting cancer cells through specific receptors, notably folate receptors. By overcoming the limitations of passive targeting in conventional therapies, this approach holds the potential for enhanced treatment efficacy through combination therapy. Encouraging outcomes from studies like in vitro and in vivo, underscore the promise of this innovative approach. This review explores the therapeutic potential of FA (Folic acid) functionalised nanocarriers tailored for breast cancer management, discussing various chemical modification techniques for functionalization. It examines FA-conjugated nanocarriers containing chemotherapeutics to enhance treatment efficacy and addresses the pharmacokinetic aspect of these functionalised nanocarriers. Additionally, the review integrates active targeting via folic acid with theranostics, photothermal therapy, and photodynamic therapy, offering a comprehensive management strategy. Emphasising rigorous experimental validation for practical utility, the review underscores the need to bridge laboratory research to clinical application. While these functionalised nanocarriers show promise, their credibility and applicability in real-world settings necessitate thorough validation for effective clinical use.

Tissue Expression of Growth Differentiation Factor 11 in Patients with Breast Cancer

Protein growth differentiation factor 11 (GDF11) plays crucial roles in cellular processes, including differentiation and development; however, its clinical relevance in breast cancer patients is poorly understood. We enrolled 68 breast cancer patients who underwent surgery at our hospital and assessed the expression of GDF11 in tumorous, ductal carcinoma in situ (DCIS), and non-tumorous tissues using immunohistochemical staining, with interpretation based on histochemical scoring (H-score). Our results indicated higher GDF11 expressions in DCIS and normal tissues compared to tumorous tissues. In addition, the GDF11 H-score was lower in the patients with a tumor size ≥ 2 cm, pathologic T3 + T4 stages, AJCC III-IV stages, Ki67 ≥ 14% status, HER2-negative, and specific molecular tumor subtypes. Notably, the patients with triple-negative breast cancer exhibited a loss of GDF11 expression. Spearman correlation analysis revealed associations between GDF11 expression and various clinicopathological characteristics, including tumor size, stage, Ki67, and molecular subtypes. Furthermore, GDF11 expression was positively correlated with mean corpuscular hemoglobin concentration and negatively correlated with neutrophil count, as well as standard deviation and coefficient of variation of red cell distribution width. These findings suggest that a decreased GDF11 expression may play a role in breast cancer pathogenesis.

Unravelling heterogeneous effects of cancer‑associated fibroblasts on poor prognosis markers in breast cancer EM‑G3 cell line: In vitro‑targeted treatment (anti‑IL-6, anti‑VEGF-A, anti‑MFGE8) based on transcriptomic profiling

Breast cancer is the most frequently diagnosed cancer in women worldwide. Although dramatically increased survival rates of early diagnosed cases have been observed, late diagnosed patients and metastatic cancer may still be considered fatal. The present study's main focus was on cancer‑associated fibroblasts (CAFs) which is an active component of the tumor microenvironment (TME) regulating the breast cancer ecosystem. Transcriptomic profiling and analysis of CAFs isolated from breast cancer skin metastasis, cutaneous basal cell carcinoma, and squamous cell carcinoma unravelled major gene candidates such as IL6, VEGFA and MFGE8 that induced co‑expression of keratins‑8/‑14 in the EM‑G3 cell line derived from infiltrating ductal breast carcinoma. Western blot analysis of selected keratins (keratin‑8, ‑14, ‑18, ‑19) and epithelial‑mesenchymal transition‑associated markers (SLUG, SNAIL, ZEB1, E‑/N‑cadherin, vimentin) revealed specific responses pointing to certain heterogeneity of the studied CAF populations. Experimental in vitro treatment using neutralizing antibodies against IL-6, VEGF‑A and MFGE8 attenuated the modulatory effect of CAFs on EM‑G3 cells. The present study provided novel data in characterizing and understanding the interactions between CAFs and EM‑G3 cells in vitro. CAFs of different origins support the pro‑inflammatory microenvironment and influence the biology of breast cancer cells. This observation potentially holds significant interest for the development of novel, clinically relevant approaches targeting the TME in breast cancer. Furthermore, its implications extend beyond breast cancer and have the potential to impact a wide range of other cancer types.

Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance

The pathology is decisive for disease diagnosis but relies heavily on experienced pathologists. In recent years, there has been growing interest in the use of artificial intelligence in pathology (AIP) to enhance diagnostic accuracy and efficiency. However, the impressive performance of deep learning-based AIP in laboratory settings often proves challenging to replicate in clinical practice. As the data preparation is important for AIP, the paper has reviewed AIP-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. An in-depth analysis of data preparation methods is conducted, encompassing the acquisition of pathological tissue slides, data cleaning, screening, and subsequent digitization. Expert review, image annotation, dataset division for model training and validation are also discussed. Furthermore, we delve into the reasons behind the challenges in reproducing the high performance of AIP in clinical settings and present effective strategies to enhance AIP's clinical performance. The robustness of AIP depends on a randomized collection of representative disease slides, incorporating rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and sufficient data volume. Digital pathology is fundamental in clinical-grade AIP, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. The key to performance reproducibility lies in having representative data, an adequate amount of labeling, and ensuring consistency across multiple centers. Digital pathology for clinical diagnosis, data standardization and the technique of WSI-based weakly supervised learning will hopefully build clinical-grade AIP.

Identification of tumor tissue in thin pathological samples via femtosecond laser-induced breakdown spectroscopy and machine learning

In the treatment of most newly discovered solid cancerous tumors, surgery remains the first treatment option. An important factor in the success of these operations is the precise identification of oncological safety margins to ensure the complete removal of the tumor without affecting much of the neighboring healthy tissue. Here we report on the possibility of applying femtosecond Laser-Induced Breakdown Spectroscopy (LIBS) combined with Machine Learning algorithms as an alternative discrimination technique to differentiate cancerous tissue. The emission spectra following the ablation on thin fixed liver and breast postoperative samples were recorded with high spatial resolution; adjacent stained sections served as a reference for tissue identification by classical pathological analysis. In a proof of principle test performed on liver tissue, Artificial Neural Networks and Random Forest algorithms were able to differentiate both healthy and tumor tissue with a very high Classification Accuracy of around 0.95. The ability to identify unknown tissue was performed on breast samples from different patients, also providing a high level of discrimination. Our results show that LIBS with femtosecond lasers is a technique with potential to be used in clinical applications for rapid identification of tissue type in the intraoperative surgical field.

The Association Between Meningioma and Breast Cancer: A Systematic Review and Meta-analysis

Importance

A potential relationship between meningioma and breast cancer was suggested 70 years ago. However, to date, no conclusive evidence is available on this topic.

Objective

To provide a comprehensive review of the literature on the association of meningioma with breast cancer, supported by a meta-analysis.

Data Sources

A systematic PubMed search was performed up to April 2023 to identify articles on the association of meningioma with breast cancer. The following key words were used strategically: meningioma, breast cancer, breast carcinoma, association, relation.

Study Selection

All studies reporting women diagnosed with meningioma and breast cancer were identified. The search strategy was not limited by study design or publication date but only included articles in English. Additional articles were identified via citation searching. Studies reporting a complete population of meningiomas or breast cancer patients throughout a specific study period and a proportion of patients with a second pathology could be used for the meta-analysis.

Data Extraction and Synthesis

Data extraction was performed by 2 authors in accordance with the Preferred Reporting Items for Systematic Reviews (PRISMA) statement. Meta-analyses regarding both populations were performed using a random-effects model. Risk of bias was assessed.

Main Outcomes and Measures

The main measures were whether there was an increased prevalence of breast cancer in female patients with meningioma and whether there was an increased prevalence of meningioma in female patients with breast cancer.

Results

A total of 51 retrospective studies (case reports, case series, and cancer registry reports) describing 2238 patients with both diseases were identified; 18 studies qualified for prevalence analyses and meta-analysis. The random-effects meta-analysis (13 studies) revealed a significantly greater prevalence of breast cancer in female patients with meningioma than in the overall population (odds ratio [OR], 9.87; 95% CI, 7.31-13.32). Meningioma incidence in patients with breast cancer (11 studies) was greater than that in the baseline population; however, the difference according to the random-effects model was not statistically significant (OR, 1.41; 95% CI, 0.99-2.02).

Conclusions and Relevance

This large systematic review and the meta-analysis on the association between meningioma and breast cancer found nearly 10-fold higher odds of breast cancer in female patients with meningioma compared with the general female population. These findings suggest that female patients with meningioma should be screened more intensively for breast cancer. Further research is required to identify the factors causing this association.

Functionalized liposomes for targeted breast cancer drug delivery

Despite the exceptional progress in breast cancer pathogenesis, prognosis, diagnosis, and treatment strategies, it remains a prominent cause of female mortality worldwide. Additionally, although chemotherapies are effective, they are associated with critical limitations, most notably their lack of specificity resulting in systemic toxicity and the eventual development of multi-drug resistance (MDR) cancer cells. Liposomes have proven to be an invaluable drug delivery system but of the multitudes of liposomal systems developed every year only a few have been approved for clinical use, none of which employ active targeting. In this review, we summarize the most recent strategies in development for actively targeted liposomal drug delivery systems for surface, transmembrane and internal cell receptors, enzymes, direct cell targeting and dual-targeting of breast cancer and breast cancer-associated cells, e.g., cancer stem cells, cells associated with the tumor microenvironment, etc.

Multimodality Imaging Review of HER2-positive Breast Cancer and Response to Neoadjuvant Chemotherapy

Human epidermal growth factor receptor 2 (HER2/neu or ErbB2)-positive breast cancers comprise 15%-20% of all breast cancers. The most common manifestation of HER2-positive breast cancer at mammography or US is an irregular mass with spiculated margins that often contains calcifications; at MRI, HER2-positive breast cancer may appear as a mass or as nonmass enhancement. HER2-positive breast cancers are often of intermediate to high nuclear grade at histopathologic analysis, with increased risk of local recurrence and metastases and poorer overall prognosis. However, treatment with targeted monoclonal antibody therapies such as trastuzumab and pertuzumab provides better local-regional control and leads to improved survival outcome. With neoadjuvant treatments, including monoclonal antibodies, taxanes, and anthracyclines, women are now potentially able to undergo breast conservation therapy and sentinel lymph node biopsy versus mastectomy and axillary lymph node dissection. Thus, the radiologist's role in assessing the extent of local-regional disease and response to neoadjuvant treatment at imaging is important to inform surgical planning and adjuvant treatment. However, assessment of treatment response remains difficult, with the potential for different imaging modalities to result in underestimation or overestimation of disease to varying degrees when compared with surgical pathologic analysis. In particular, the presence of calcifications at mammography is especially difficult to correlate with the results of pathologic analysis after chemotherapy. Breast MRI findings remain the best predictor of pathologic response. The authors review the initial manifestations of HER2-positive tumors, the varied responses to neoadjuvant chemotherapy, and the challenges in assessing residual cancer burden through a multimodality imaging review with pathologic correlation. ^© RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.

Rapid Diagnosis of Ductal Carcinoma In Situ and Breast Cancer Based on Raman Spectroscopy of Serum Combined with Convolutional Neural Network

Ductal carcinoma in situ (DCIS) and breast cancer are common female breast diseases and pose a serious health threat to women. Early diagnosis of breast cancer and DCIS can help to develop targeted treatment plans in time. In this paper, we investigated the feasibility of using Raman spectroscopy combined with convolutional neural network (CNN) to discriminate between healthy volunteers, breast cancer and DCIS patients. Raman spectra were collected from the sera of 241 healthy volunteers, 463 breast cancer and 100 DCIS patients, and a total of 804 spectra were recorded. The pre-processed Raman spectra were used as the input of CNN to establish a model to classify the three different spectra. After using cross-validation to optimize its hyperparameters, the model's final classification performance was assessed using an unknown test set. For comparison with other machine learning algorithms, we additionally built models using support vector machine (SVM), random forest (RF) and k-nearest neighbor (KNN) methods. The final accuracies for CNN, SVM, RF and KNN were 98.76%, 94.63%, 80.99% and 78.93%, respectively. The values for area under curve (AUC) were 0.999, 0.994, 0.931 and 0.900, respectively. Therefore, our study results demonstrate that CNN outperforms three traditional algorithms in terms of classification performance for Raman spectral data and can be a useful auxiliary diagnostic tool of breast cancer and DCIS.

The Potential of Antibody Technology and Silver Nanoparticles for Enhancing Photodynamic Therapy for Melanoma

Melanoma is highly aggressive and is known to be efficient at resisting drug-induced apoptotic signals. Resection is currently the gold standard for melanoma management, but it only offers local control of the early stage of the disease. Metastatic melanoma is prone to recurrence, and has a poor prognosis and treatment response. Thus, the need for advanced theranostic alternatives is evident. Photodynamic therapy has been increasingly studied for melanoma treatment; however, it relies on passive drug accumulation, leading to off-target effects. Nanoparticles enhance drug biodistribution, uptake and intra-tumoural concentration and can be functionalised with monoclonal antibodies that offer selective biorecognition. Antibody–drug conjugates reduce passive drug accumulation and off-target effects. Nonetheless, one limitation of monoclonal antibodies and antibody–drug conjugates is their lack of versatility, given cancer’s heterogeneity. Monoclonal antibodies suffer several additional limitations that make recombinant antibody fragments more desirable. SNAP-tag is a modified version of the human DNA-repair enzyme, O6-alkylguanine-DNA alkyltransferase. It reacts in an autocatalytic and covalent manner with benzylguanine-modified substrates, providing a simple protein labelling system. SNAP-tag can be genetically fused with antibody fragments, creating fusion proteins that can be easily labelled with benzylguanine-modified payloads for site-directed delivery. This review aims to highlight the benefits and limitations of the abovementioned approaches and to outline how their combination could enhance photodynamic therapy for melanoma.

Breast Cancer-Epidemiology, Classification, Pathogenesis and Treatment (Review of Literature)

Breast cancer is the most-commonly diagnosed malignant tumor in women in the world, as well as the first cause of death from malignant tumors. The incidence of breast cancer is constantly increasing in all regions of the world. For this reason, despite the progress in its detection and treatment, which translates into improved mortality rates, it seems necessary to look for new therapeutic methods, and predictive and prognostic factors. Treatment strategies vary depending on the molecular subtype. Breast cancer treatment is multidisciplinary; it includes approaches to locoregional therapy (surgery and radiation therapy) and systemic therapy. Systemic therapies include hormone therapy for hormone-positive disease, chemotherapy, anti-HER2 therapy for HER2-positive disease, and quite recently, immunotherapy. Triple negative breast cancer is responsible for more than 15-20% of all breast cancers. It is of particular research interest as it presents a therapeutic challenge, mainly due to its low response to treatment and its highly invasive nature. Future therapeutic concepts for breast cancer aim to individualize therapy and de-escalate and escalate treatment based on cancer biology and early response to therapy. The article presents a review of the literature on breast carcinoma-a disease affecting women in the world.

Correlation of prognostic factors of carcinoma breast with Ki 67 proliferation assay

Prognostic factors are important for the diagnosis of breast cancer as it helps in identification of high risk patients.  The objective of the study is to assess the proliferation index, Ki-67 and correlate it with other markers. The present study was a cohort study conducted in the Department of General Surgery at Tertiary Care Teaching Hospital over a period of 1 year with a sample size of 98. All the patients meeting the inclusion and exclusion criteria are recruited sequentially by convenient sampling until the sample size is attained, with the agreement of the institutional ethics committee. A total of 98 patients with a mean age of 53.61 ± 12.48 years were studied in the final analysis. The mean duration of lump was 4.62 ± 2.18 months and only 6.12% had the complaint of pain.  Majority of them had stage IIIB carcinoma at 43.88%, followed by stage IIA at 27.55%, 15.31% stage IIB, 13.27% stage IIIA.  At cut off 20, 69(70.40%) had ki67 proliferation index ≥20 and 29(29.59%) had<20. Correlation of Ki-67 Index with expression of estrogen receptor status had a p value of 0.019 and with progesterone receptor status, p 0.003 which was significant. 

A deep matrix completion method for imputing missing histological data in breast cancer by integrating DCE-MRI radiomics

PURPOSE

Clinical indicators of histological information are important for breast cancer treatment and operational decision making, but these histological data suffer from frequent missing values due to various experimental/clinical reasons. The limited amount of histological information from breast cancer samples impedes the accuracy of data imputation. The purpose of this study was to impute missing histological data, including Ki-67 expression level, luminal A subtype, and histological grade, by integrating tumor radiomics.

METHODS

To this end, a deep matrix completion (DMC) method was proposed for imputing missing histological data using nonmissing features composed of histological and tumor radiomics (termed radiohistological features). DMC finds a latent nonlinear association between radiohistological features across all samples and samples for all the features. Radiomic features of morphologic, statistical, and texture were extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) inside the tumor. Experiments on missing histological data imputation were performed with a variable number of features and missing data rates. The performance of the DMC method was compared with those of the nonnegative matrix factorization (NMF) and collaborative filtering (MCF)-based data imputation methods. The area under the curve (AUC) was used to assess the performance of missing histological data imputation.

RESULTS

By integrating radiomics from DCE-MRI, the DMC method showed significantly better performance in terms of AUC than that using only histological data. Additionally, DMC using 120 radiomic features showed an optimal prediction performance (AUC = 0.793), which was better than the NMF (AUC = 0.756) and MCF methods (AUC = 0.706; corrected p = 0.001). The DMC method consistently performed better than the NMF and MCF methods with a variable number of radiomic features and missing data rates.

CONCLUSIONS

DMC improves imputation performance by integrating tumor histological and radiomics data. This study transforms latent imaging-scale patterns for interactions with molecular-scale histological information and is promising in the tumor characterization and management of patients.

Genome profiles of pathologist-defined cell clusters by multiregional LCM and G&T-seq in one triple-negative breast cancer patient

Pathological examination is the gold standard for cancer diagnosis, and breast tumor cells are often found in clusters. We report a case study on one triple-negative breast cancer (TNBC) patient, analyzing tumor development, metastasis, and prognosis with simultaneous DNA and RNA sequencing of pathologist-defined cell clusters from multiregional frozen sections. The cell clusters are isolated by laser capture microdissection (LCM) from primary tumor tissue, lymphatic vessels, and axillary lymph nodes. Data are reported for a total of 97 cell clusters. A combination of tumor cell-cluster clonality and phylogeny reveals 3 evolutionarily distinct pathways for this patient, each associated with a unique mRNA signature, and each correlated with disparate survival outcomes. Hub gene analysis indicates that extensive downregulation of ribosomal protein mRNA is a potential marker of poor prognosis in breast cancer.

Pathologically diverse cell clusters share genomic and transcriptomic profiles

Transcriptome-defined clones are more complex than genome-defined clones

Three distinct pathways were inferred, each with disparate survival outcomes

Lower expression of ribosomal proteins may be an indicator of poor prognosis

Assessment of proliferation in breast cancer: cell cycle or mitosis? An observational study

BACKGROUND AND AIMS

Proliferation is an important indicator of breast cancer (BC) prognosis, but is assessed using different approaches. Not all cells in the cell cycle are committed to division. This study aimed to characterise quantitative differences between BC cells in the cell cycle and those in mitosis and assess their relationship with other pathological parameters.

METHODS AND RESULTS

A cohort of BC sections (n = 621) was stained with haematoxylin and eosin and immunohistochemistry for Ki-67. The proportion of mitotic cells and Ki-67-positive cells was assessed in the same areas. The Cancer Genome Atlas (TCGA) BC cohort was used to assess MKI-67 transcriptome level and its association with the mitotic counts. The mean proportion of BC cells in the cell cycle was 24% (range = 1-90%), while the mean proportion of BC cells in mitosis was 5% (range = 0-73%). A low proportion of mitoses to whole cycling cells was associated with low histological grade tumours and the luminal A molecular subtype, while tumours with a high proportion of mitoses to the overall cycling cells were associated with triple-negative subtype, larger tumour size, grade 3 tumours and lymph node metastasis. The high mitosis/low Ki-67-positive cells tumours showed a significant association with variables of poor prognosis, including high-grade and triple-negative subtypes.

CONCLUSION

The proportion of BC cells in the cell cycle and mitosis is variable. We show that not only the number of cells in the cell cycle or mitosis, but also the difference between them, provides valuable information on tumour aggressiveness.

Application of a deep-learning technique to non-linear images from human tissue biopsies for shedding new light on breast cancer diagnosis

The development of label-free non-invasive techniques to be used as diagnostic tools in cancer research is of great importance for improving the quality of life for millions of patients. Previous studies have demonstrated that Third Harmonic Generation (THG) imaging could differentiate malignant from benign unlabeled human breast biopsies and distinguish the different grades of cancer. Towards the application of such technologies to clinic, in the present report, a deep learning technique was applied to THG images recorded from breast cancer tissues of grades 0, I, II and III. By the implementation of a convolutional neural network (CNN) model, the differentiation of malignant from benign breast tissue samples and the discrimination of the different grades of cancer in a fast and accurate way were achieved. The obtained results provide a step ahead towards the use of optical diagnostic tools in conjunction with the CNN image classifier for the reliable and rapid malignancy diagnosis in clinic.

Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy

BACKGROUND

The successful recognition of benign and malignant breast nodules using ultrasound images is based mainly on supervised learning that requires a large number of labeled images. However, because high-quality labeling is expensive and time-consuming, we hypothesized that semi-supervised learning could provide a low-cost and powerful alternative approach. This study aimed to develop an accurate semi-supervised recognition method and compared its performance with supervised methods and sonographers.

METHODS

The faster region-based convolutional neural network was used for nodule detection from ultrasound images. A semi-supervised classifier based on the mean teacher model was proposed to recognize benign and malignant nodule images. The general performance of the proposed method on two datasets (8,966 nodules) was reported.

RESULTS

The detection accuracy was 0.88±0.03 and 0.86±0.02, respectively, on two testing sets (1,350 and 2,220 nodules). When 800 labeled training nodules were available, the proposed semi-supervised model plus 4,396 unlabeled nodules performed better than the supervised learning model (area under the curve (AUC): 0.934±0.026 vs. 0.83±0.050; 0.916±0.022 vs. 0.815±0.049). The performance of the semi-supervised model trained on 800 labeled and 4,396 unlabeled nodules was close to that of the supervised learning model trained on a massive number of labeled nodules (n=5,196) (AUC: 0.934±0.026 vs. 0.952±0.027; 0.916±0.022 vs. 0.918±0.017). Moreover, the semi-supervised model was better than the average accuracy of five human sonographers (AUC: 0.922 vs. 0.889).

CONCLUSIONS

The semi-supervised model can achieve excellent performance for nodule recognition and be useful for medical sciences. The method reduced the number of labeled images required for training, thus significantly alleviating the difficulty in data preparation of medical artificial intelligence.

Precision Oncology Beyond Genomics: The Future Is Here-It Is Just Not Evenly Distributed

Cancer is a multifactorial disease with increasing incidence. There are more than 100 different cancer types, defined by location, cell of origin, and genomic alterations that influence oncogenesis and therapeutic response. This heterogeneity between tumors of different patients and also the heterogeneity within the same patient's tumor pose an enormous challenge to cancer treatment. In this review, we explore tumor heterogeneity on the longitudinal and the latitudinal axis, reviewing current and future approaches to study this heterogeneity and their potential to support oncologists in tailoring a patient's treatment regimen. We highlight how the ideal of precision oncology is reaching far beyond the knowledge of genetic variants to inform clinical practice and discuss the technologies and strategies already available to improve our understanding and management of heterogeneity in cancer treatment. We will focus on integrating multi-omics technologies with suitable in vitro models and their proficiency in mimicking endogenous tumor heterogeneity.

Development of near-infrared imaging agents for detection of junction adhesion molecule-A protein

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Anti-junctional adhesion molecule-A (JAM-A) monoclonal antibodies (mAb) conjugated with near infra-red fluorescent dye, IR700 – as a JAM-A mAb/IR700 agent was developed.

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An in vivo JAM-A mAb/IR700-specific near infra-red imaging of human-derived prostate and breast cancer xenograft is presented.

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A single injection of the agent is diminished number of mitotic cells in cancerous tissue of mice bearing heterotopic tumors.

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Since, our agent depicts the specific accumulation within the targeted tumors, this agent may be adapted to solid tumor targeted photoimmunotherapy.

Introduction

Prostate and breast cancer are the most prevalent primary malignant human tumors globally. Prostatectomy and breast conservative surgery remain the most common definitive treatment option for the >500,000 men and women newly diagnosed with localized prostate and breast cancer each year only in the US. Morphological examination is the mainstay of diagnosis but margin under-sampling of the excised cancer tissue may lead to local recurrence. In despite of the progress of non-invasive optical imaging, there is still a clinical need for targeted optical imaging probes that could rapidly and globally visualize cancerous tissues.

Methods

Elevated expression of junctional adhesion molecule-A (JAM-A) on tumor cells and its multiple pro-tumorigenic activity make the JAM-A a candidate for molecular imaging. Near-infrared imaging probe, which employed anti-JAM-A monoclonal antibody (mAb) phthalocyanine dye IR700 conjugates (JAM-A mAb/IR700), was synthesized and used to identify and visualize heterotopic human prostate and breast tumor mouse xenografts in vivo.

Results

The intravenously injected JAM-A mAb/IR700 conjugates enabled the non-invasive detection of prostate and breast cancerous tissue by fluorescence imaging. A single dose of JAM-A mAb/IR700 reduced number of mitotic cancer cells in vivo, indicating theranostic ability of this imaging agent. The JAM-A mAb/IR700 conjugates allowed us to image a specific receptor expression in prostate and breast tumors without post-image processing.

Conclusion

This agent demonstrates promise as a method to image the extent of prostate and breast cancer in vivo and could assist with real-time visualization of extracapsular extension of cancerous tissue.

Prognostic and predictive parameters in breast pathology: a pathologist's primer

The pathologist's role in the breast cancer treatment team has evolved from rendering a diagnosis of breast cancer, to providing a growing list of prognostic and predictive parameters such that individualized treatment decisions can be made based on likelihood of benefit from additional treatments and potential benefit from specific therapies. In all stages, ER and HER2 status help segregate breast cancers into treatment groups with similar outcomes and treatment response rates, however, traditional pathologic parameters such as favorable histologic subtype, size, lymph node status, and Nottingham grade also have remained clinically relevant in early stage disease decision-making. This is especially true for the most common subtype of breast cancer; ER positive, HER2 negative disease. For this same group of breast cancers, an ever-expanding list of gene-expression panels also can provide prediction and prognostication about potential chemotherapy benefit beyond standard endocrine therapies, with the 21-gene Recurrence Score, currently the only prospectively validated predictive test for this purpose. In the more aggressive ER-negative cancer subtypes, response to neoadjuvant therapy and` the extent of tumor infiltrating lymphocytes (TILs) are more recently recognized powerful prognostic parameters, and clinical guidelines now offer additional treatment options for those high-risk patients with residual cancer after standard neoadjuvant therapy. In stage four disease, predictive tests like germline BRCA status, tumor PIK3CA mutation status (in ER+ metastatic disease) and PDL-1 status (in triple negative metastatic disease) are now used to determine additional new treatment options. The objective of this review is to describe the latest in prognostic and predictive parameters in breast cancer as they are relevant to standard pathology reporting and how they are used in breast cancer clinical treatment decisions.

An end-to-end breast tumour classification model using context-based patch modelling - A BiLSTM approach for image classification

Researchers working on computational analysis of Whole Slide Images (WSIs) in histopathology have primarily resorted to patch-based modelling due to large resolution of each WSI. The large resolution makes WSIs infeasible to be fed directly into the machine learning models due to computational constraints. However, due to patch-based analysis, most of the current methods fail to exploit the underlying spatial relationship among the patches. In our work, we have tried to integrate this relationship along with feature-based correlation among the extracted patches from the particular tumorous region. The tumour regions extracted from WSI have arbitrary dimensions having the range 20,570 to 195 pixels across width and 17,290 to 226 pixels across height. For the given task of classification, we have used BiLSTMs to model both forward and backward contextual relationship. Also, using RNN based model, the limitation of sequence size is eliminated which allows the modelling of variable size images within a deep learning model. We have also incorporated the effect of spatial continuity by exploring different scanning techniques used to sample patches. To establish the efficiency of our approach, we trained and tested our model on two datasets, microscopy images and WSI tumour regions. Both datasets were published by ICIAR BACH Challenge 2018. Finally, we compared our results with top 5 teams who participated in the BACH challenge and achieved the top accuracy of 90% for microscopy image dataset. For WSI tumour region dataset, we compared the classification results with state of the art deep learning networks such as ResNet, DenseNet, and InceptionV3 using maximum voting technique. We achieved the highest performance accuracy of 84%. We found out that BiLSTMs with CNN features have performed much better in modelling patches into an end-to-end Image classification network. Additionally, the variable dimensions of WSI tumour regions were used for classification without the need for resizing. This suggests that our method is independent of tumour image size and can process large dimensional images without losing the resolution details.

Assessment of breast cancer immunohistochemistry and tumor characteristics in Nigeria

BACKGROUND

Female breast cancer is the leading type of cancer worldwide with an incidence of approximately 2.1 million in 2018. Hormone receptor status plays a vital role in its management.

AIM

To determine the molecular expression pattern of biomarkers in breast cancer and their correlation with tumor variables.

METHODS

This prospective study was designed to analyze expression patterns of estrogen receptor(ER), progesterone receptor (PR) and human epidermal growth factor receptor(HER2/neu) in breast cancer patients. The dataset has been taken from the Department of Radiotherapy and Oncology of Usmanu Danfodiyo University Teaching Hospital Sokoto, Nigeria from 1 January 2015 to 2 December 2019. The dataset had 259 records and 7 attributes. SPSS version 23.0 for statistical analysis was used. The data analyzed demographic and other clinicopathological characteristics as categorical variables. The mean and standard deviation were determined for the quantitative variable.

RESULTS

A total of 259 breast cancer cases were included in the study. The mean age was 48.3 ± 11.0, with an age range of 26-80 years and a median age of 46 years. The morphological categories were invasive ductal carcinoma 258 (99.6%) and invasive lobular carcinoma 1 (0.4%). ER, positivity increased in 73 patients (50%) under the age of 50 years, as well as PR positivity increased in 34 patients (23.6%) under the age of 50 years. HER/2neupositivity decreased in 8 patients (5.6%) under the age of 50 years. Hormonal receptors were statistically significant with clinicopathological characteristics (P < 0.05).

CONCLUSION

Our study showed that ER, PR and HER2/neuexpression had a strong correlation with age, tumor grade, tumor size and lymph node status. Hence, hormone receptor assessment is highly recommended because of its significance in clinical management and prognostication.

Prediction of Overall Survival Among Female Patients With Breast Cancer Using a Prognostic Signature Based on 8 DNA Repair-Related Genes

IMPORTANCE

Breast cancer (BC), a common malignant tumor, ranks first among cancers in terms of morbidity and mortality among female patients. Currently, identifying effective prognostic models has a significant association with the prediction of the overall survival of patients with BC and guidance of clinicians in early diagnosis and treatment.

OBJECTIVES

To identify a potential DNA repair-related prognostic signature through a comprehensive evaluation and to further improve the accuracy of prediction of the overall survival of patients with BC.

DESIGN, SETTING, AND PARTICIPANTS

In this prognostic study, conducted from October 9, 2019, to February 3, 2020, the gene expression profiles and clinical data of patients with BC were collected from The Cancer Genome Atlas database. This study consisted of a training set from The Cancer Genome Atlas database and 2 validation cohorts from the Gene Expression Omnibus, which included 1096 patients with BC. A prognostic signature based on 8 DNA repair-related genes (DRGs) was developed to predict overall survival among female patients with BC.

MAIN OUTCOMES AND MEASURES

Primary screening prognostic biomarkers were analyzed using univariate Cox proportional hazards regression analysis and the least absolute shrinkage and selection operator Cox proportional hazards regression. A risk model was completely established through multivariate Cox proportional hazards regression analysis. Finally, a prognostic nomogram, combining the DRG signature and clinical characteristics of patients, was constructed. To examine the potential mechanisms of the DRGs, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed.

RESULTS

In this prognostic study based on samples from 1096 women with BC (mean [SD] age, 59.6 [13.1] years), 8 DRGs (MDC1, RPA3, MED17, DDB2, SFPQ, XRCC4, CYP19A1, and PARP3) were identified as prognostic biomarkers. The time-dependent receiver operating characteristic curve analysis suggested that the 8-gene signature had a good predictive accuracy. In the training cohort, the areas under the curve were 0.708 for 3-year survival and 0.704 for 5-year survival. In the validation cohort, the areas under the curve were 0.717 for 3-year survival and 0.772 for 5-year survival in the GSE9893 data set and 0.691 for 3-year survival and 0.718 for 5-year survival in the GSE42568 data set. This DRG signature mainly involved some regulation pathways of vascular endothelial cell proliferation.

CONCLUSIONS AND RELEVANCE

In this study, a prognostic signature using 8 DRGs was developed that successfully predicted overall survival among female patients with BC. This risk model provides new clinical evidence for the diagnostic accuracy and targeted treatment of BC.

All-optical label-free human breast tissue block histology using photoacoustic remote sensing

The direct imaging of tissue preserved in formalin-fixed paraffin-embedded (FFPE) blocks remains a challenge. There are presently millions of tissues preserved as FFPE blocks whose assessment via bright-field microscopes requires them to be sectioned and subsequently stained. These processes are laborious, resource-intensive, and time consuming. In this Letter, we utilize an ultraviolet laser with photoacoustic remote sensing to provide a novel method that enables direct label-free pathological assessment of FFPE blocks. We demonstrate the efficacy of this technique by imaging human breast tissue, highlighting salient features such as ducts, adipocytes, and ductal hyperplasia. This direct imaging of FFPE blocks facilitates pathological assessment much earlier in the histopathological workflow, saving valuable time in clinical and research settings. The presented non-contact label-free reflection-mode device enables augmentation of existing histopathological workflows and aims to expand the arsenal of imaging technologies available to clinicians.

99mTc Tamoxifen for Imaging Estrogen Receptor Expression in Metastatic Breast Cancer Patient

Estrogen receptor-expressing breast cancer exhibits better prognosis due to responsiveness to antiestrogen treatment. Therefore, knowledge of the estrogen receptor status of a tumor is an important prognostic and predictive indicator in breast cancer. We present noninvasive imaging of estrogen receptors with Tc tamoxifen that can identify the active tumor and approachable sites for biopsy. It may help in selection of patients for hormone replacement therapy and in assessment of receptor status in recurrent disease and also in response evaluation.

Vascular Mimicry Expression in Invasive Ductal Carcinoma; A New Technique for Prospect of Aggressiveness

Background & Objective:

In vascular (vasculogenic) mimicry (VM), tumoral cells mimic the endothelial cells and form the extracellular matrix-rich tubular networks. It has been proposed that VM is more extensive in aggressive tumors. This study was designed to investigate the rate of VM expression in the stromal cells of invasive ductal carcinoma (IDC) and to find its relationship with other clinicopathological factors.

Methods:

In this cross-sectional study, 120 patients with histopathologic diagnosis of IDC who received mastectomy were included. The VM expression was determined by immunohistochemistry (IHC). The clinicopathologic data including age, tumor size, histological grade, clinical stage, axillary lymph node metastasis, hormonal receptors, and survival were documented.

Results:

The mean (±SD) age of the patients was 51 (±13.83) years old. The stromal VM expression was detected in 16 of 120 patients (13.3%). Twelve specimens (75%) of positive VM expression group had grade 3 which was higher than negative VM expression group (9 cases, 8.65%; P<0.001). The VM expression showed statistically significant relationship with higher histologic grade higher clinical stage (stage 3) of the tumor (62.5% vs. 87%; P=0.003), the presence of axillary lymph node metastasis (95.6% vs. 55.8%; P<0.001), and positive HER-2 (100% vs. 31.1%; P<0.001); but not estrogen receptor (ER) or progesterone receptor (PR). However, age, tumor size and mortality rate were not significantly different among the patients with and without VM expression.

Conclusion:

The stromal VM expression showed significant relationship with higher stage and grade of the tumor and the presence of nodal metastasis. The VM expression in IDC can be used as a marker for tumor aggressiveness.

Fourier transform infrared spectroscopy based spectral biomarkers of metastasized breast cancer progression

Breast cancer is a global health issue and the second leading cause of cancer death in women. Breast cancer tends to migrate to bone and causes bone metastases which is ultimately the cause of death. Here, we report the use of FTIR to identify spectral biomarkers of cancer progression on 3D in vitro model of breast cancer bone metastasis. Our results indicate that the following spectral biomarkers can monitor cancer progression, for example, lipids (CH2 asymmetric/CH2 symmetric stretch), Amide I/Amide II, and RNA/DNA. Principal component analysis also confirmed the involvement of protein, lipids and nucleic acids in cancer progression on sequential culture. The collective observations from this study suggest successful application of FTIR as a non-invasive and accurate method to identify biochemical changes in cancer cells during the progression of breast cancer bone metastasis.

HOXA1 upregulation is associated with poor prognosis and tumor progression in breast cancer

Breast cancer (BC) is the most commonly diagnosed cancer and the second leading cause of cancer-associated mortality among females worldwide. As a member of the homeobox (HOX) gene family, HOXA1 is involved in tumor progression and prognosis in several types of human cancer. However, the clinical significance and biological functions of HOXA1 in BC remains unknown. The current study assessed the expression of HOXA1 in BC tissues and cells via western blotting and reverse transcription-quantitative polymerase chain reaction. The association between HOXA1 expression and the clinicopathological features of patients with BC was analyzed using the Chi-square test. The overall survival of patients was calculated using the Kaplan-Meier method and examined using the log-rank test. Cell proliferation was examined via an MTT assay. Cell cycle distribution and cell apoptosis were analyzed using flow cytometry. The current study demonstrated that HOXA1 mRNA and protein expression was upregulated in BC. In addition, HOXA1 overexpression was associated with poor prognosis and advanced clinicopathological features in patients with BC. Furthermore, knockdown of HOXA1 significantly inhibited cell proliferation by enhancing cell apoptosis and cell cycle arrest in BC cells, which was accompanied with aberrant expression of cell cycle and apoptosis-associated proteins, cyclin D1, B-cell lymphoma 2 (Bcl-2) and Bcl-2-like protein 4. Taken together, the results suggested that HOXA1 may serve as a novel prognostic marker and therapeutic target in BC.

Automated diagnosis of breast ultrasonography images using deep neural networks

Ultrasonography images of breast mass aid in the detection and diagnosis of breast cancer. Manually analyzing ultrasonography images is time-consuming, exhausting and subjective. Automated analyzing such images is desired. In this study, we develop an automated breast cancer diagnosis model for ultrasonography images. Traditional methods of automated ultrasonography images analysis employ hand-crafted features to classify images, and lack robustness to the variation in the shapes, size and texture of breast lesions, leading to low sensitivity in clinical applications. To overcome these shortcomings, we propose a method to diagnose breast ultrasonography images using deep convolutional neural networks with multi-scale kernels and skip connections. Our method consists of two components: the first one is to determine whether there are malignant tumors in the image, and the second one is to recognize solid nodules. In order to let the two networks work in a collaborative way, a region enhance mechanism based on class activation maps is proposed. The mechanism helps to improve classification accuracy and sensitivity for both networks. A cross training algorithm is introduced to train the networks. We construct a large annotated dataset containing a total of 8145 breast ultrasonography images to train and evaluate the models. All of the annotations are proven by pathological records. The proposed method is compared with two state-of-the-art approaches, and outperforms both of them by a large margin. Experimental results show that our approach achieves a performance comparable to human sonographers and can be applied to clinical scenarios.

The clinical heterogeneity of preeclampsia is related to both placental gene expression and placental histopathology

BACKGROUND

Preeclampsia is a life-threatening disorder of pregnancy, demonstrating a high degree of heterogeneity in clinical features such as presentation, disease severity, and outcomes. This heterogeneity suggests distinct pathophysiological mechanisms may be driving the placental disease underlying this disorder. Our group recently reported distinct clusters of placental gene expression in preeclampsia and control pregnancies, allowing for the identification of at least 3 clinically relevant gene expression-based subtypes of preeclampsia. Histopathological examination of a small number of samples from 2 of the gene expression-based subtypes revealed placental lesions consistent with their gene expression phenotype, suggesting that detailed placental histopathology may provide further insight into the pathophysiology underlying these distinct gene expression-based subtypes.

OBJECTIVES

The objective of the study was to assess histopathological lesions in the placentas of patients belonging to each identified gene expression-based subtype of preeclampsia, characterized in our previous study. Our goal was to further understand the pathophysiologies defining these gene expression-based subtypes by integrating gene expression with histopathological findings, possibly identifying additional subgroups of preeclampsia patients.

STUDY DESIGN

Paraffin-embedded placental biopsies from patients included in the gene expression profiling study (n = 142 of 157, 90.4%) were sectioned, hematoxylin and eosin stained, and imaged. An experienced perinatal pathologist, blinded to gene expression findings and clinical information, assessed the presence and severity of histological lesions using a comprehensive, standardized data collection form. The frequency and severity scores of observed histopathological lesions were compared among gene expression-based subtypes as well as within each subtype using using Fisher exact tests, Kruskal-Wallis tests, and hierarchical clustering. The histological findings of the placental samples were visualized using t-distributed stochastic neighbor embedding and phylogenetic trees. Concordance and discordance between gene expression findings and histopathology were also investigated and visualized using principal component analysis.

RESULTS

Several histological lesions were found to be characteristic of each gene expression-based preeclampsia subtype. The overall concordance between gene expression and histopathology for all samples was 65% (93 of 142), with characteristic placental lesions for each gene expression-based subtype complementing prior gene enrichment findings (ie, placentas with enrichment of hypoxia-associated genes showed severe lesions of maternal vascular malperfusion). Concordant samples were located in the central area of each gene expression-based cluster when viewed on a principal component analysis plot. Interestingly, discordant samples (gene expression and histopathology not reflective of one another) were generally found to lie at the periphery of the gene expression-based clusters and tended to border the group of patients with phenotypically similar histopathology.

CONCLUSION

Our findings demonstrates a high degree of concordance between placental lesions and gene expression across subtypes of preeclampsia. Additionally, novel integrative analysis of scored placental histopathology severity and gene expression findings allowed for the identification of patients with intermediate phenotypes of preeclampsia not apparent through gene expression profiling alone. Future investigations should examine the temporal relationship between these 2 modalities as well as consider the maternal and fetal contributions to these subtypes of disease.

Ancillary Prognostic and Predictive Testing in Breast Cancer: Focus on Discordant, Unusual, and Borderline Results

Ancillary testing in breast cancer has become standard of care to determine what therapies may be most effective for individual patients with breast cancer. Single-marker tests are required on all newly diagnosed and newly metastatic breast cancers. Markers of proliferation are also used, and include both single-marker tests like Ki67 as well as panel-based gene expression tests, which have made more recent contributions to prognostic and predictive testing in breast cancers. This review focuses on pathologist interpretation of these ancillary test results, with a focus on expected versus unexpected results and troubleshooting borderline, unusual, or discordant results.

Updates in the Evaluation and Management of Breast Cancer

Breast cancer is the most commonly diagnosed cancer worldwide. More than 200,000 new cases of invasive breast cancer are diagnosed annually in the United States; approximately 40,000 patients die of the disease. The etiology of most breast cancer cases is unknown, although multiple factors predisposing to the disease have been identified. Apart from increasing age and female sex, these other factors account for only a minority of breast cancer diagnoses. This article provides an overview of the management of noninvasive and invasive breast cancer, which is often complex and varies according to patient factors, disease stage, and breast cancer subtype. Although much progress has been made, continued research endeavors are ongoing; enrollment of eligible patients in prospective clinical trials is an essential way to improve patient outcomes.

Bim may be a poor prognostic biomarker in breast cancer patients especially in those with luminal A tumors

BACKGROUND

Bcl-2 interacting mediator of cell death (Bim) appears to have contradictory roles in cancer. It is uncertain whether Bim show prognostic significance in patients with breast cancer.

OBJECTIVE

To investigate the correlation between Bim expression and clinicopathological characteristics of breast cancer and to evaluate Bim's effect on overall survival (OS).

METHODS

We used immunohistochemistry (IHC) technique to detect the expression of Bim via tissue microarray in 275 breast cancer samples, Kaplan-Meier analysis to perform survival analysis, and Cox proportional hazards regression model to explore the risk factors of breast cancer.

RESULTS

The results revealed that Bim expression was significantly correlated with age, estrogen receptor (ER) and/or progesterone receptor (PR), human epidermal growth factor receptor (HER2) and Ki67 expression (P< 0.05). Bim expression was significantly different in the four molecular subtypes (P= 0.000). Survival analysis showed that Bim positive expression contributed to a shorter OS (P= 0.034), especially in patients with luminal A tumors (P= 0.039). Univariate and multivariate regression analysis showed that Bim was an independent prognostic factor for breast cancer (P< 0.05).

CONCLUSION

Bim may serve as an effective predictive factor for lower OS in breast cancer patients, especially in those with luminal A tumors.

Downgrading of Breast Masses Suspicious for Cancer by Using Optoacoustic Breast Imaging

Purpose To assess the ability of optoacoustic (OA) ultrasonography (US) to help correctly downgrade benign masses classified as Breast Imaging Reporting and Data System (BI-RADS) 4a and 4b to BI-RADS 3 or 2. Materials and Methods OA/US technology uses laser light to detect relative amounts of oxygenated and deoxygenated hemoglobin in and around suspicious breast masses. In this prospective, multicenter study, results of 209 patients with 215 breast masses classified as BI-RADS 4a or 4b at US are reported. Patients were enrolled between 2015 and 2016. Masses were first evaluated with US with knowledge of previous clinical information and imaging results, and from this information a US imaging-based probability of malignancy (POM) and BI-RADS category were assigned to each mass. The same masses were then re-evaluated at OA/US. During the OA/US evaluation, radiologists scored five OA/US features, and then reassigned an OA/US-based POM and BI-RADS category for each mass. BI-RADS downgrade and upgrade percentages at OA/US were assessed by using a weighted sum of the five OA feature scores. Results At OA/US, 47.9% (57 of 119; 95% CI: 0.39, 0.57) of benign masses classified as BI-RADS 4a and 11.1% (three of 27; 95% CI: 0.03, 0.28) of masses classified as BI-RADS 4b were correctly downgraded to BI-RADS 3 or 2. Two of seven malignant masses classified as BI-RADS 4a at US were incorrectly downgraded, and one of 60 malignant masses classified as BI-RADS 4b at US was incorrectly downgraded for a total of 4.5% (three of 67; 95% CI: 0.01, 0.13) false-negative findings. Conclusion At OA/US, benign masses classified as BI-RADS 4a could be downgraded in BI-RADS category, which would potentially decrease biopsies negative for cancer and short-interval follow-up examinations, with the limitation that a few masses may be inappropriately downgraded.

Modulation of Molecular Biomarker Expression in Response to Chemotherapy in Invasive Ductal Carcinoma

Breast cancer (BC) has varied morphological and biological features and is classified based on molecular and morphological examinations. Molecular classification of BC is based on biological gene-expression profiling. In this study, biomarker modulation was assessed during BC treatment in 30 previously untreated patients. Heterogeneity among patients was pathologically diagnosed and classified into luminal and basal-like immunohistochemical profiles based on estrogen, progesterone, and human epidermal growth factor receptor (ER/PR/HER2) status. Marker heterogeneity was compared with mRNA biomarker expression in patients with BC before and after therapy. Reverse transcription-polymerase chain reaction was performed for molecular characterization. Expression and modulation of biological markers, CK19, hMAM, CEA, MUC, Myc, Ki-67, HER2/neu, ErbB2, and ER, were assessed after treatment, where the expression of the biomarkers CK19, Ki-67, Myc, and CEA was noted to be significantly decreased. Marker expression modulation was determined according to different stages and pathological characteristics of patients; coexpression of three markers (CK19, Ki-67, and Myc) was specifically modulated after therapy. In the histopathologically classified basal-like group, two markers (CK19 and Ki-67) were downregulated and could be considered as diagnostic biomarkers. In conclusion, pathological characteristics and marker variation levels can be evaluated to decide a personalized treatment for patients.

[Impact of pathological complete response to neoadjuvant chemotherapy in invasive breast cancer according to molecular subtype]

OBJECTIVES

The aim of this study was to evaluate the impact of pathological complete response (pCR) on overall survival (OS) and recurrence-free survival (RFS) according to molecular subtypes in women treated for an invasive breast cancer after neoadjuvant chemotherapy (NAC).

METHODS

All women (n=225) managed with a neoadjuvant chemotherapy for an invasive breast cancer in our institution between January 2007 and December 2013 were included. The characteristics of patients with pCR (pCR-1), breast pCR and axillary pCR were compared to those without pCR (pCR-0) according to the molecular subtypes: luminal A (n=62), luminal B (n=77), Her-2 (n=31) and triple negative (n=55).

RESULTS

NAC concerned 225 patients of whom 36 (16%) had pCR. Achievement of pCR led to significantly better overall survival in women with Her-2 tumors (35% versus 100%, P=0.035) and also to significantly better locoregional survival in women treated for triple negative tumors (P=0.026). Predictive factors of pCR were a high pathologic grade: OR=2.39, IC 95% (1.19-4.83), P=0.008; Her-2 molecular subtype (P=0.008); positive estrogenic hormonal receptors (P=0.006), a positive Her-2 receptor: OR=2.58, IC 95% (1.20-5.54), P=0.01.

CONCLUSION

Achievement of pCR is an intermediate marker of survival in women managed with NAC for breast cancer.

The ever-evolving role of pathologists in the management of breast cancer with neoadjuvant treatment: recommendations based on the Spanish clinical experience

PURPOSE

To compare the current international standards for neoadjuvant systemic therapy (NAST) protocols, and establish consensus recommendations by Spanish breast pathologists; and to look into the Spanish reality of defining pathological complete response in daily practice.

MATERIALS AND METHODS

A modified Delphi technique was used to gain consensus among a panel of 46 experts with regard to important issues about NAST specimens, with the objective of standardize handling and analysis of these breast cancer specimens. In addition, a survey was conducted among 174 pathologists to explore the Spanish reality of post-NAST breast cancer specimens handling.

RESULTS

Our survey shows that pathologists in Spain follow the same guidelines as their international colleagues and face the same problems and controversies. Among the experts, 94.1% agreed on the recommendation for a pre-treatment evaluation with a core needle biopsy, and 100% of experts agreed on the need of having properly indicated information for the post-NAST surgical specimens. However, only 82.7% of them receive properly labelled specimens and even less receive specimens where markers are identified and the degree of clinical/radiological response is mentioned. Among participants 59.9% were familiar with the residual cancer burden system for post-NAST response quantification, but only 16.1% used it regularly.

CONCLUSIONS

Active participation on breast cancer multidisciplinary teams, optimal usage of core needle biopsy for timely and standardized procedures for the diagnostic analysis, and accurate diagnosis of pathological complete response and complete evaluation of the response to NAST need to become the standard practice when handling breast cancer specimens in Spain.

Omics-based molecular techniques in oral pathology centred cancer: prospect and challenges in Africa

BACKGROUND

The completion of the human genome project and the accomplished milestones in the human proteome project; as well as the progress made so far in computational bioinformatics and "big data" processing have contributed immensely to individualized/personalized medicine in the developed world.

MAIN BODY

At the dawn of precision medicine, various omics-based therapies and bioengineering can now be applied accurately for the diagnosis, prognosis, treatment, and risk stratification of cancer in a manner that was hitherto not thought possible. The widespread introduction of genomics and other omics-based approaches into the postgraduate training curriculum of diverse medical and dental specialties, including pathology has improved the proficiency of practitioners in the use of novel molecular signatures in patient management. In addition, intricate details about disease disparity among different human populations are beginning to emerge. This would facilitate the use of tailor-made novel theranostic methods based on emerging molecular evidences.

CONCLUSION

In this review, we examined the challenges and prospects of using currently available omics-based technologies vis-à-vis oral pathology as well as prompt cancer diagnosis and treatment in a resource limited setting.

(1S,2E,4S,7E,11E)-2,7,11-Cembratriene-4,6-diol semisynthetic analogs as novel c-Met inhibitors for the control of c-Met-dependent breast malignancies

(1S,2E,4S,6R,7E,11E)-2,7,11-Cembratriene-4,6-diol (1) and its 4-epi-analog (2) are the cembranoid precursors to several key flavor ingredients in most Nicotiana (tobacco) species. Nearly 40-60% of 1 and 2 are purposely degraded during the commercial tobacco fermentation. However, 1 and 2 display promising bioactivities, including anticancer. Breast cancer is the most diagnosed cancer in women and ranked second female disease killer. The receptor tyrosine kinase c-Met correlates with aggressiveness of certain breast cancer phenotypes and thus considered a valid therapeutic target. This study reports the discovery and optimization of the tobacco-based cembranoid 1 as a novel c-Met inhibitory scaffold using combined structure- and ligand-based approaches. 1 displayed antiproliferative, anti-migratory and anti-invasive effects against the c-Met overexpressing MDA-MB-231 breast cancer cells at moderate μM concentrations. The Z'-LYTE kinase platform and Western blot analysis identified c-Met as a potential macromolecular target. Rationally designed carbamate analogs were proposed to probe additional targeted c-Met interactions and improve the cellular potency. The 6-phenyl carbamate 3 showed enhanced c-Met inhibitory activity. Structure-activity relationships of different substituents on the 3's phenyl moiety were studied. The most active analog 20 showed potent in vitro anticancer activity against the MDA-MB-231 breast cancer cells at low μM concentrations, with minimal toxicity on the non-tumorigenic MCF-10A mammary epithelial cells. Cembranoid 20 potently inhibited the c-Met catalytic activity in Z'-LYTE kinase assay and various cellular c-Met-driven signaling pathways. Furthermore, 20 displayed a robust antitumor activity in a breast cancer xenograft athymic mouse model and thus promoted to the lead rank. Cembranoids are novel c-Met inhibitors appropriate for future use to control c-Met dependent malignancies.

The role of ctDNA detection and the potential of the liquid biopsy for breast cancer monitoring

INTRODUCTION

Recent advances in deep amplicon sequencing have enabled rapid assessment of somatic mutations and structural changes in multiple cancer genes in DNA isolated from tumour tissues and circulating cell-free DNA (cfDNA). This cfDNA is under investigation as a 'liquid biopsy' for the real time monitoring of patients with cancer in a growing number of research studies and clinical trials.

AREAS COVERED

Here we will provide a brief overview of the potential clinical utility of cfDNA profiling for detection and monitoring of patients with breast cancer. The review was conducted in English using PubMed and search terms including 'breast cancer', 'plasma DNA', 'circulating cell free DNA' and 'circulating tumour DNA'. Expert commentary: Liquid biopsies through circulating tumor DNA (ctDNA) enable monitoring of patients with breast cancer. The challenge ahead will be to incorporate cfDNA mutation profiling into routine clinical practice to provide patients with the most appropriate and timely treatment.

Ras protein expression as a marker for breast cancer

Breast cancer, the most common neoplasm in women of all ages, is the leading cause of cancer-related mortality in women worldwide. Markers to help to predict the risk of progression and ultimately provide non-surgical treatment options would be of great benefit. At present, there are no available molecular markers to predict the risk of carcinoma in situ progression to invasive cancer; therefore, all women diagnosed with this type of malignancy must undergo surgery. Breast cancer is a heterogeneous complex disease, and different patients respond differently to different treatments. In breast cancer, analysis using immunohistochemical markers remains an essential component of routine pathological examinations, and plays an import role in the management of the disease by providing diagnostic and prognostic strategies. The aim of the present study was to identify a marker that can be used as a prognostic tool for breast cancer. For this purpose, we firstly used an established breast cancer model. MCF-10F, a spontaneously immortalized breast epithelial cell line was transformed by exposure to estrogen and radiation. MCF-10F cells were exposed to low doses of high linear energy transfer (LET) α particles (150 keV/μm) of radiation, and subsequently cultured in the presence of 17β-estradiol. Three cell lines were used: i) MCF-10F cells as a control; ii) Alpha5 cells, a malignant and tumorigenic cell line; and iii) Tumor2 cells derived from Alpha5 cells injected into nude mice. Secondly, we also used normal, benign and malignant breast specimens obtained from biopsies. The results revealed that the MCF-10F cells were negative for c-Ha-Ras protein expression; however, the Alpha5 and Tumor2 cell lines were positive for c-Ha-Ras protein expression. The malignant breast samples were also strongly positive for c-Ha-Ras expression. The findings of our study indicate that c-Ha-Ras protein expression may be used as a marker to predict the progression of breast cancer; this marker may also ultimately provide non-surgical treatment options for patients who are at a lower risk.

Molecular Testing and the Pathologist's Role in Clinical Trials of Breast Cancer

Molecular characterization of breast cancer is pivotal for identifying new molecular targets and determining the appropriate treatment choices. Advances in molecular profiling technology have given greater insight into this heterogeneous disease, over and above hormone receptor and human epidermal growth factor receptor 2 status. Agents targeting recently characterized molecular biomarkers are under clinical development; the success of these targeted agents is likely to depend on identifying the patient population most likely to benefit. Therefore, clinical trials of breast cancer often require prescreening for, or stratification by, relevant molecular markers or exploratory analyses of biomarkers that can predict or monitor the response to treatment. Consequently, the role of the pathologist has become increasingly important. The key considerations for pathologists include tissue availability, ownership of archival tissue, type of diagnostic/biomarker test required, method of sample processing, concordance between different tests and testing centers, and tumor heterogeneity. In the present review, we explore how pathology is used in current clinical trials of breast cancer and describe the various technologies available for molecular testing. Furthermore, the factors required for the successful application of pathology in clinical trials of breast cancer and the issues that can arise and how these can be circumvented are discussed.

Implications of Overdiagnosis: Impact on Screening Mammography Practices

This review article explores the issue of overdiagnosis in screening mammography. Overdiagnosis is the screen detection of a breast cancer, histologically confirmed, that might not otherwise become clinically apparent during the lifetime of the patient. While screening mammography is an imperfect tool, it remains the best tool we have to diagnose breast cancer early, before a patient is symptomatic and at a time when chances of survival and options for treatment are most favorable. In 2015, an estimated 231,840 new cases of breast cancer (excluding ductal carcinoma in situ) will be diagnosed in the United States, and some 40,290 women will die. Despite these data, screening mammography for women ages 40-69 has contributed to a substantial reduction in breast cancer mortality, and organized screening programs have led to a shift from late-stage diagnosis to early-stage detection. Current estimates of overdiagnosis in screening mammography vary widely, from 0% to upwards of 30% of diagnosed cancers. This range reflects the fact that measuring overdiagnosis is not a straightforward calculation, but usually one based on different sets of assumptions and often biased by methodological flaws. The recent development of tomosynthesis, which creates high-resolution, three-dimensional images, has increased breast cancer detection while reducing false recalls. Because the greatest harm of overdiagnosis is overtreatment, the key goal should not be less diagnosis but better treatment decision tools. (Population Health Management 2015;18:S3-S11).