Combinatorial biomarker expression in breast cancer. Breast Cancer Res Treat. 2010;120:293-308. [PMID: 20107892 DOI: 10.1007/s10549-010-0746-x]

PSA doubling time 4.65 months as an optimal cut-off of Japanese nonmetastatic castration-resistant prostate cancer

A multicenter study of nonmetastatic castration-resistant prostate cancer (nmCRPC) was conducted to identify the optimal cut-off value of prostate-specific antigen (PSA) doubling time (PSADT) that correlated with the prognosis in Japanese nmCRPC. Of the 515 patients diagnosed and treated for nmCRPC at 25 participating Japanese Urological Oncology Group centers, 450 patients with complete clinical information were included. The prognostic values of clinical factors were evaluated with respect to prostate specific antigen progression-free (PFS), cancer-specific survival (CSS), and overall survival (OS). The optimal cutoff value of PSADT was identified using survival tree analysis by Python. The Median PSA and PSADT at diagnosis of nmCRPC were 3.3 ng/ml, and 5.2 months, respectively. Patients treated with novel hormonal therapy (NHT) showed significantly longer PFS (HR: hazard ratio 0.38, p < 0.0001) and PFS2 (HR 0.45, p < 0.0001) than those treated with vintage nonsteroidal antiandrogen agent (Vintage). The survival tree identified 4.65 months as the most prognostic PSADT cutoff point. Among the clinical and pathological factors PSADT of < 4.65 months remained an independent prognostic factor for OS (HR 2.96, p = 0.0003) and CSS (HR 3.66, p < 0.0001). Current data represented optimal cut-off of PSADT 4.65 months for a Japanese nmCRPC.

Contrast-Enhanced Mammography Radiomics Analysis for Preoperative Prediction of Breast Cancer Molecular Subtypes

BACKGROUND

Predicting breast cancer molecular subtypes can help guide individualised clinical treatment of patients who need the rational preoperative treatment. This study aimed to investigate the efficacy of preoperative prediction of breast cancer molecular subtypes by contrast-enhanced mammography (CEM) radiomic features.

METHODS

This retrospective two-centre study included women with breast cancer who underwent CEM preoperatively between August 2016 and May 2022. We included 356 patients with 386 lesions, which were grouped into training (n = 162), internal test (n = 160) and external test sets (n = 64). Radiomics features were extracted from low-energy (LE) images and recombined (RC) images and selected. Three dichotomous tasks were established according to postoperative immunohistochemical results: Luminal vs. non-Luminal, human epidermal growth factor receptor (HER2)-enriched vs. non-HER2-enriched, and triple-negative breast cancer (TNBC) vs. non-TNBC. For each dichotomous task, the LE, RC, and LE+RC radiomics models were built by the support vector machine classifier. The prediction performance of the models was assessed by the area under the receiver operating characteristic curve (AUC). Then, the accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for the models. DeLong's test was utilised to compare the AUCs.

RESULTS

Radiomics models based on CEM are valuable for predicting breast cancer molecular subtypes. The LE+RC model achieved the best performance in the test set. The LE+RC model predicted Luminal, HER2-enriched, and TNBC subtypes with AUCs of 0.93, 0.89, and 0.87 in the internal test set and 0.82, 0.83, and 0.69 in the external test set, respectively. In addition, the LE model performed more satisfactorily than the RC model.

CONCLUSION

CEM radiomics features can effectively predict breast cancer molecular subtypes preoperatively, and the LE+RC model has the best predictive performance.

Molecular Changes in Breast Cancer Induced by Radiation Therapy

PURPOSE

Breast cancer treatments are based on prognostic clinicopathologic features that form the basis for therapeutic guidelines. Although the utilization of these guidelines has decreased breast cancer-associated mortality rates over the past three decades, they are not adequate for individualized therapy. Radiation therapy (RT) is the backbone of breast cancer treatment. Although a highly successful therapeutic modality clinically, from a biological perspective, preclinical studies have shown RT to have the potential to alter tumor cell phenotype, immunogenicity, and the surrounding microenvironment, potentially changing the behavior of cancer cells and resulting in a significant variation in RT response. This review presents the recent advances in revealing the complex molecular changes induced by RT in the treatment of breast cancer and highlights the complexities of translating this information into clinically relevant tools for improved prognostic insights and the revelation of novel approaches for optimizing RT.

METHODS AND MATERIALS

Current literature was reviewed with a focus on recent advances made in the elucidation of tumor-associated radiation-induced molecular changes across molecular, genetic, and proteomic bases. This review was structured with the aim of providing an up-to-date overview over the very broad and complex subject matter of radiation-induced molecular changes and radioresistance, familiarizing the reader with the broader issue at hand.

RESULTS

The subject of radiation-induced molecular changes in breast cancer has been broached from various physiological focal points including that of the immune system, immunogenicity and the abscopal effect, tumor hypoxia, breast cancer classification and subtyping, molecular heterogeneity, and molecular plasticity. It is becoming increasingly apparent that breast cancer clinical subtyping alone does not adequately account for variation in RT response or radioresistance. Multiple components of the tumor microenvironment and immune system, delivered RT dose and fractionation schedules, radiation-induced bystander effects, and intrinsic tumor physiology and heterogeneity all contribute to the resultant RT outcome.

CONCLUSIONS

Despite recent advances and improvements in anticancer therapies, tumor resistance remains a significant challenge. As new analytical techniques and technologies continue to provide crucial insight into the complex molecular mechanisms of breast cancer and its treatment responses, it is becoming more evident that personalized anticancer treatment regimens may be vital in overcoming radioresistance.

Understanding the mechanistic pathways and clinical aspects associated with protein and gene based biomarkers in breast cancer

Cancer is one of the most widespread and severe diseases with a huge mortality rate. In recent years, the second-leading mortality rate of any cancer globally has been breast cancer, which is one of the most common and deadly cancers found in women. Detecting breast cancer in its initial stages simplifies treatment, decreases death risk, and recovers survival rates for patients. The death rate for breast cancer has risen to 0.024 % in some regions. Sensitive and accurate technologies are required for the preclinical detection of BC at an initial stage. Biomarkers play a very crucial role in the early identification as well as diagnosis of women with breast cancer. Currently, a wide variety of cancer biomarkers have been discovered for the diagnosis of cancer. For the identification of these biomarkers from serum or other body fluids at physiological amounts, many detection methods have been developed. In the case of breast cancer, biomarkers are especially helpful in discovering those who are more likely to develop the disease, determining prognosis at the time of initial diagnosis and choosing the best systemic therapy. In this study we have compiled various clinical aspects and signaling pathways associated with protein-based biomarkers and gene-based biomarkers.

Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels

Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple 'omics' strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer's molecular complexities, but also promotes the development of personalized treatment strategies.

"Diagnostic and Prognostic Biomarkers of Luminal Breast Cancer: Where are We Now?"

Luminal breast cancers are hormone receptor (estrogen and/or progesterone) positive that are further divided into HER2-negative luminal A and HER2-positive luminal B subtypes. According to currently accepted convention, they represent the most common subtypes of breast cancer, accounting for approximately 70% of cases. Biomarkers play a critical role in the functional characterization, prognostication, and therapeutic prediction, rendering them indispensable for the clinical management of invasive breast cancer. Traditional biomarkers include clinicopathological parameters, which are increasingly extended by genetic and other molecular markers, enabling the comprehensive characterization of patients with luminal breast cancer. Liquid biopsies capturing and analyzing circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) are emerging technologies that envision personalized management through precision oncology. This article reviews key biomarkers in luminal breast cancer and ongoing developments.

Estrogen receptor status heterogeneity in breast cancer tumor: role in response to endocrine treatment

Tumor heterogeneity affects diagnosis, prognosis and response to therapy. Heterogeneity is found in both normal and neoplastic human mammary gland. Indeed, luminal ER-negative cells can give rise to various phenotypes, including ER-negative and ER-positive mammary tumors. As a result, the tumor phenotype does not necessarily reflects the cell of origin of cancer. With regard to the ER status, heterogeneity can challenge endocrine therapies, where the elimination of responsive clones could lead to reduced treatment efficacy and tumor relapse through the expansion of the resistant clones. The aim of this study was to investigate breast tumor heterogeneity and its role in endocrine resistance onset. For this purpose, we used ER⁺ (T47D, CAMA1) and triple-negative breast cancer cell lines (TNBC; MDA-MB-231, HCC70), co-cultures using 2D and 3D models. Our results showed that ER status is modulated when ER⁺ cells are cultured in the presence of TNBC cells, leading to a different response to endocrine therapy, demonstrating that the response to treatment can be affected by the influence that different breast cancer cell types exert on each other. In addition, ER⁺ positive cells doubling time was modified after exposure to TNBC cell co-culturing. Further experiments are required to fully elucidate the molecular mechanism of these observations.

Machine-learning predicts time-series prognosis factors in metastatic prostate cancer patients treated with androgen deprivation therapy

Machine learning technology is expected to support diagnosis and prognosis prediction in medicine. We used machine learning to construct a new prognostic prediction model for prostate cancer patients based on longitudinal data obtained from age at diagnosis, peripheral blood and urine tests of 340 prostate cancer patients. Random survival forest (RSF) and survival tree were used for machine learning. In the time-series prognostic prediction model for metastatic prostate cancer patients, the RSF model showed better prediction accuracy than the conventional Cox proportional hazards model for almost all time periods of progression-free survival (PFS), overall survival (OS) and cancer-specific survival (CSS). Based on the RSF model, we created a clinically applicable prognostic prediction model using survival trees for OS and CSS by combining the values of lactate dehydrogenase (LDH) before starting treatment and alkaline phosphatase (ALP) at 120 days after treatment. Machine learning provides useful information for predicting the prognosis of metastatic prostate cancer prior to treatment intervention by considering the nonlinear and combined impacts of multiple features. The addition of data after the start of treatment would allow for more precise prognostic risk assessment of patients and would be beneficial for subsequent treatment selection.

Survival is associated with repressive histone trimethylation markers in both HR-positive HER2-negative and triple-negative breast cancer patients

About 30% of patients with hormone receptor (HR)-positive breast cancers and up to 50% of human epidermal growth factor receptor 2 (HER2)-positive patients develop progression due to treatment resistance, highlighting the need for more differentiated tumor classifications within the breast cancer molecular subtype to optimize the therapies. We aim to examine the roles of histone modification markers. The levels of common repressive histone markers, histone H3 lysine 9 trimethylation (H3K9me3), histone H3 lysine 27 trimethylation (H3K27me3), and histone H4 lysine 20 trimethylation (H4K20me3), in tumors were evaluated by immunohistochemistry for 914 breast cancer patients. The subjects were followed up until December 2021. Hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS) were estimated using Cox regression models. For H3K27me3, patients with the high level had a longer PFS rate (81.3%) than that with the low level (73.9%) within HR-positive/HER2-negative subtype during a follow-up of 85 months only in univariate analysis (P < 0.05). For H3K9me3, the significant association between the high level of it and the longer OS [HR = 0.57, P < 0.05] was found within HR-positive/HER2-negative subtype in multivariate analysis. For H4K20me3, patients with the high level had a longer both OS [HR = 0.38] and PFS [HR = 0.46] within HR-positive/HER2-negative subtype, while had a shorter OS [HR = 3.28] in triple-negative breast cancer (TNBC) in multivariate analysis (all P < 0.05). H3K9me3 and H3K27me3 were the potential prognostic markers for breast cancer patients with HR-positive/HER2-negative subtype. Importantly, H4K20me3 was a robust prognostic marker for both HR-positive/HER2-negative and TNBC patients.

LncRNA TINCR impairs the efficacy of immunotherapy against breast cancer by recruiting DNMT1 and downregulating MiR-199a-5p via the STAT1-TINCR-USP20-PD-L1 axis

Although programmed death-ligand 1 (PD-L1) inhibitors have achieved some therapeutic success in breast cancer, their efficacy is limited by low therapeutic response rates, which is closely related to the immune escape of breast cancer cells. Tissue differentiation inducing non-protein coding RNA (TINCR), a long non-coding RNA, as an oncogenic gene associated with the progression of various malignant tumors, including breast cancer; however, the role of TINCR in tumor immunity, especially in breast cancer, remains unclear. We confirmed that TINCR upregulated PD-L1 expression in vivo and in vitro, and promoted the progression of breast cancer. Next, we revealed that TINCR knockdown can significantly improve the therapeutic effect of PD-L1 inhibitors in breast cancer in vivo. Mechanistically, TINCR recruits DNMT1 to promote the methylation of miR-199a-5p loci and inhibit its transcription. Furthermore, in the cytoplasm, TINCR potentially acts as a molecular sponge of miR-199a-5p and upregulates the stability of USP20 mRNA through a competing endogenous RNA (ceRNA) regulatory mechanism, thus promoting PD-L1 expression by decreasing its ubiquitination level. IFN-γ stimulation activates STAT1 by phosphorylation, which migrates into the nucleus to promote TINCR transcription. This is the first study to describe the regulatory role of TINCR in breast cancer tumor immunity, broadening the current paradigm of the functional diversity of TINCR in tumor biology. In addition, our study provides new research directions and potential therapeutic targets for PD-L1 inhibitors in breast cancer.

Low expression of NR1H3 correlates with macrophage infiltration and indicates worse survival in breast cancer

Background: Nuclear receptor NR1H3 is a key regulator of macrophage function and lipid homeostasis. Here, we aimed to visualize the prognostic value and immunological characterization of NR1H3 in breast cancer. Methods: The expression pattern and prognostic value of NR1H3 were analyzed via multiple databases, including TIMER2, GEPIA2 and Kaplan-Meier Plotter. TISIDB, TIMER2 and immunohistochemical analysis were used to investigate the correlation between NR1H3 expression and immune infiltration. GO enrichment analysis, KEGG analysis, Reactome analysis, ConsensusPathDB and GeneMANIA were used to visualize the functional enrichment of NR1H3 and signaling pathways related to NR1H3. Results: We demonstrated that the expression of NR1H3 was significantly lower in breast cancer compared with adjacent normal tissues. Kaplan-Meier survival curves showed shorter overall survival in basal breast cancer patients with low NR1H3 expression, and poorer prognosis of relapse-free survival in breast cancer patients with low NR1H3 expression. NR1H3 was mainly expressed in immune cells, and its expression was closely related with infiltrating levels of tumor-infiltrating immune cells in breast cancer. Additionally, univariate and multivariate analysis indicated that the expression of NR1H3 and the level of macrophage infiltration were independent prognostic factors for breast cancer. Gene interaction network analysis showed the function of NR1H3 involved in regulating of innate immune response and macrophage activation. Moreover, NR1H3 may function as a predictor of chemoresponsiveness in breast cancer. Conclusion: These findings suggest that NR1H3 serves as a prognostic biomarker and contributes to the regulation of macrophage activation in breast cancer.

An update on the pathological classification of breast cancer

Breast cancer (BC) is a heterogeneous disease, encompassing a diverse spectrum of tumours with varying morphological, biological, and clinical phenotypes. Although tumours may show phenotypic overlap, they often display different biological behaviour and response to therapy. Advances in high-throughput molecular techniques and bioinformatics have contributed to improved understanding of BC biology and refinement of molecular taxonomy with the identification of specific molecular subclasses. Although the traditional pathological morphological classification of BC is of paramount importance and provides diagnostic and prognostic information, current interest focusses on the use of a single gene and multigene assays to stratify BC into distinct groups to guide decisions on systemic therapy. This review considers approaches to the classification of BC, including their limitations, and with particular emphasis on the fundamental role of morphology in establishing an accurate diagnosis of primary invasive carcinoma of breast origin. This forms the basis for further morphological characterization and for all other approaches to BC classification that are used to provide prognostic and therapeutic predictive information.

Transforming Diagnosis and Therapeutics Using Cancer Genomics

In past quarter of the century, much has been understood about the genetic variation and abnormal genes that activate cancer in humans. All the cancers somehow possess alterations in the DNA sequence of cancer cell's genome. In present, we are heading toward the era where it is possible to obtain complete genome of the cancer cells for their better diagnosis, categorization and to explore treatment options.

A controversial ER negative PR positive molecular subtype of breast carcinoma-Report of two cases

ER-/PR+ is a controversial subtype and is not formally recognised as molecular subtype of breast carcinoma. Few studies concluded that this subtype does not exist and is due to technical errors, however, in contrast others consider it to be distinct entity with different response to therapy and clinical outcome. It is also essential to know whether this subtype shows any distinct histomorphological features or prognosis.Therefore, the present two cases of controversial subtype ER-/PR+ breast cancer is being reported with both the cases showing neuroendocrinal differentiation.

Increasing importance of breast cancer in Nepal

Recently, breast cancer cases in Nepal are on the rise, accounting for approximately 16% of all cancer cases, making it the second most common malignancy. Given the dependence of the Nepalese on agriculture, the rampant use of pesticides as well as the presence of arsenic in water supplies might be contributing to this huge rise in cancer cases. Herein, we provide a brief overview of the status of breast cancer, its burden, risk factors, screening and modes of treatment in Nepal.

Multiparametric MRI Features of Breast Cancer Molecular Subtypes

Background and Objectives: Breast cancer (BC) molecular subtypes have unique incidence, survival and response to therapy. There are five BC subtypes described by immunohistochemistry: luminal A, luminal B HER2 positive and HER2 negative, triple negative (TNBC) and HER2-enriched. Multiparametric breast MRI (magnetic resonance imaging) provides morphological and functional characteristics of breast tumours and is nowadays recommended in the preoperative setting. Aim: To evaluate the multiparametric MRI features (T2-WI, ADC values and DCE) of breast tumours along with breast density and background parenchymal enhancement (BPE) features among different BC molecular subtypes. Materials and Methods: This was a retrospective study which included 344 patients. All underwent multiparametric breast MRI (T2WI, ADC and DCE sequences) and features were extracted according to the latest BIRADS lexicon. The inter-reader agreement was assessed using the intraclass coefficient (ICC) between the ROI of ADC obtained from the two breast imagers (experienced and moderately experienced). Results: The study population was divided as follows: 89 (26%) with luminal A, 39 (11.5%) luminal B HER2 positive, 168 (48.5%) luminal B HER2 negative, 41 (12%) triple negative (TNBC) and 7 (2%) with HER2 enriched. Luminal A tumours were associated with special histology type, smallest tumour size and persistent kinetic curve (all p-values < 0.05). Luminal B HER2 negative tumours were associated with lowest ADC value (0.77 × 10−3 mm2/s2), which predicts the BC molecular subtype with an accuracy of 0.583. TNBC were associated with asymmetric and moderate/marked BPE, round/oval masses with circumscribed margins and rim enhancement (all p-values < 0.05). HER2 enriched BC were associated with the largest tumour size (mean 37.28 mm, p-value = 0.02). Conclusions: BC molecular subtypes can be associated with T2WI, ADC and DCE MRI features. ADC can help predict the luminal B HER2 negative cases.

Value of machine learning algorithms for predicting diabetes risk: A subset analysis from a real-world retrospective cohort study

AIMS/INTRODUCTION

To compare the application value of different machine learning (ML) algorithms for diabetes risk prediction.

MATERIALS AND METHODS

This is a 3-year retrospective cohort study with a total of 3,687 participants being included in the data analysis. Modeling variable screening and predictive model building were carried out using logistic regression (LR) analysis and 10-fold cross-validation, respectively. In total, six different ML algorithms, including random forests, light gradient boosting machine, extreme gradient boosting, adaptive boosting (AdaBoost), multi-layer perceptrons and gaussian naive bayes were used for model construction. Model performance was mainly evaluated by the area under the receiver operating characteristic curve. The best performing ML model was selected for comparison with the traditional LR model and visualized using Shapley additive explanations.

RESULTS

A total of eight risk factors most associated with the development of diabetes were identified by univariate and multivariate LR analysis, and they were visualized in the form of a nomogram. Among the six different ML models, the random forests model had the best predictive performance. After 10-fold cross-validation, its optimal model has an area under the receiver operating characteristic value of 0.855 (95% confidence interval [CI] 0.823-0.886) in the training set and 0.835 (95% CI 0.779-0.892) in the test set. In the traditional LR model, its area under the receiver operating characteristic value is 0.840 (95% CI 0.814-0.866) in the training set and 0.834 (95% CI 0.785-0.884) in the test set.

CONCLUSIONS

In the real-world epidemiological research, the combination of traditional variable screening and ML algorithm to construct a diabetes risk prediction model has satisfactory clinical application value.

Assessment of Predictive Biomarkers in Breast Cancer: Challenges and Updates

The management of patients with breast cancer (BC) relies on the assessment of a defined set of well-established prognostic and predictive markers. Despite overlap, prognostic markers are used to assess the risk of recurrence and the likely benefit of systemic therapy, whereas predictive markers are used to determine the type of systemic therapy to be offered to an individual patient. In this review, we provide an update and present some challenges in the assessment of the main BC-specific molecular predictive markers, namely hormone receptors (oestrogen receptor [ER] and progesterone receptor [PR]), human epidermal growth factor receptor 2 (HER2), and KI67. As the main platform for assessing these markers in BC is immunohistochemistry (IHC), we address the cut-off values used to define positivity, the ER-low subgroup, the existence and significance of the ER-/PR+ phenotype, the use of PR in routine practice, and the role of hormone receptors in ductal carcinoma in situ. We discuss the newly introduced HER2-low class of BC and the clinical/biological difference between different HER2 groups (e.g., HER2 IHC score 3+ BCs vs. those with a HER2 IHC score 2+ with HER2 gene amplification). The review concludes with an update on the applications of KI67 assessment in BC and observations on the role of immune checkpoint identification in BC.

Update on prognostic and predictive biomarkers of breast cancer

Breast cancer represents a heterogeneous group of human cancer at both histological and molecular levels. Estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) are the most commonly used biomarkers in clinical practice for making treatment plans for breast cancer patients by oncologists. Recently, PD-L1 testing plays an important role for immunotherapy for triple-negative breast cancer. With the increased understanding of the molecular characterization of breast cancer and the emergence of novel targeted therapies, more potential biomarkers are needed for the development of more personalized treatments. In this review, we summarized several main prognostic and predictive biomarkers in breast cancer at genomic, transcriptomic and proteomic levels, including hormone receptors, HER2, Ki67, multiple gene expression assays, PD-L1 testing, mismatch repair deficiency/microsatellite instability, tumor mutational burden, PIK3CA, ESR1 andNTRK and briefly introduced the roles of digital imaging analysis in breast biomarker evaluation.

‘Breast Cancer Resistance Likelihood and Personalized Treatment Through Integrated Multiomics’

In recent times, enormous progress has been made in improving the diagnosis and therapeutic strategies for breast carcinoma, yet it remains the most prevalent cancer and second highest contributor to cancer-related deaths in women. Breast cancer (BC) affects one in eight females globally. In 2018 alone, 1.4 million cases were identified worldwide in postmenopausal women and 645,000 cases in premenopausal females, and this burden is constantly increasing. This shows that still a lot of efforts are required to discover therapeutic remedies for this disease. One of the major clinical complications associated with the treatment of breast carcinoma is the development of therapeutic resistance. Multidrug resistance (MDR) and consequent relapse on therapy are prevalent issues related to breast carcinoma; it is due to our incomplete understanding of the molecular mechanisms of breast carcinoma disease. Therefore, elucidating the molecular mechanisms involved in drug resistance is critical. For management of breast carcinoma, the treatment decision not only depends on the assessment of prognosis factors but also on the evaluation of pathological and clinical factors. Integrated data assessments of these multiple factors of breast carcinoma through multiomics can provide significant insight and hope for making therapeutic decisions. This omics approach is particularly helpful since it identifies the biomarkers of disease progression and treatment progress by collective characterization and quantification of pools of biological molecules within and among the cancerous cells. The scrupulous understanding of cancer and its treatment at the molecular level led to the concept of a personalized approach, which is one of the most significant advancements in modern oncology. Likewise, there are certain genetic and non-genetic tests available for BC which can help in personalized therapy. Genetically inherited risks can be screened for personal predisposition to BC, and genetic changes or variations (mutations) can also be identified to decide on the best treatment. Ultimately, further understanding of BC at the molecular level (multiomics) will define more precise choices in personalized medicine. In this review, we have summarized therapeutic resistance associated with BC and the techniques used for its management.

Validation of the Prognostic Stage from the American Joint Committee on Cancer 8th Staging Manual in Luminal B-Like Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer

Purpose

The 8th edition American Joint Committee on Cancer (AJCC) prognostic staging system (PS) has been validated numerous times; however, the prognostic value of PS for breast cancer based on molecular subtype has rarely been explored. This study aimed to investigate the prognostic value of PS in Chinese patients with luminal B-like human epidermal growth factor receptor 2 (HER2)-negative breast cancer.

Methods

A total of 407 eligible cases were included in the study. All of the cases were restaged using the 8th edition AJCC Anatomic Staging System (AS) and PS. The Kaplan–Meier method was used to calculate estimated survival and the Log rank test was used to compare the survival differences between groups.

Results

The 5-year disease-specific survival (DSS) and overall survival (OS) rates were 90.3% and 93.5%, respectively, and there were statistically significant differences in the 5-year DSS and 5-year OS rates among the different anatomic and prognostic stage groups. The application of the PS resulted in the assignment of 215 (52.8%) patients to a different group. Different prognostic stage groups restaged from anatomic Stage III had significant differences in both DSS (χ² = 4.366, p = 0.037) and OS (χ² = 7.549, p = 0.006); additionally, different prognostic stage groups from the anatomic Stage II group had significant differences in DSS (χ² = 7.724, p = 0.021) but no significant differences in OS (χ² = 5.182, p = 0.075). However, different prognostic stage groups from anatomic Stage I had no significant differences in either DSS (χ²= 0.159, p = 0.690) or OS (χ² = 0.099, p = 0.753).

Conclusion

The 8th edition AJCC PS refined the anatomic stage grouping in luminal B-like HER2-negative breast cancer and could lead to a more personalized approach to breast cancer treatment.

O3C Glass-Class: A Machine-Learning Framework for Prognostic Prediction of Ovarian Clear-Cell Carcinoma

Ovarian clear cell carcinoma (OCCC), one of the histopathological types of ovarian cancer, has a poor prognosis when it recurs; however, it is difficult to precisely predict the risk of recurrence. Here, we analyzed pathological images of OCCC to elucidate the relationship between pathological findings and recurrence, and using machine learning, we established a classifier to predict the recurrence and several other prognosis indicators of this disease. In total, 110 patients with OCCC treated with primary surgery at a single institution were enrolled in this study. We used the deep-learning neural networks to process the whole slide images of OCCC obtained by digitally scanning the original hematoxylin and eosin-stained glass slides. The images were preprocessed and used as input to the machine learning pipeline. We fine-tuned its parameters to predict the recurrence, progression-free survival, and the overall survival days of all patients. We predicted the recurrence of OCCC with an overall accuracy of 93%, area under the receiver operating characteristic curve of 0.98, and sensitivity/specificity above 0.92 using Resnet 34. Furthermore, we predicted progression-free survival/overall survival of the patients with ~90% accuracy. In conclusion, our study demonstrates the feasibility of using a machine learning system to predict different features of OCCC samples using histopathological images as input. This novel application provides accurate prognosis information and aids in the development of personalized treatment strategies.

TLX, an Orphan Nuclear Receptor With Emerging Roles in Physiology and Disease

TLX (NR2E1), an orphan member of the nuclear receptor superfamily, is a transcription factor that has been described to be generally repressive in nature. It has been implicated in several aspects of physiology and disease. TLX is best known for its ability to regulate the proliferation of neural stem cells and retinal progenitor cells. Dysregulation, overexpression, or loss of TLX expression has been characterized in numerous studies focused on a diverse range of pathological conditions, including abnormal brain development, psychiatric disorders, retinopathies, metabolic disease, and malignant neoplasm. Despite the lack of an identified endogenous ligand, several studies have described putative synthetic and natural TLX ligands, suggesting that this receptor may serve as a therapeutic target. Therefore, this article aims to briefly review what is known about TLX structure and function in normal physiology, and provide an overview of TLX in regard to pathological conditions. Particular emphasis is placed on TLX and cancer, and the potential utility of this receptor as a therapeutic target.

Expression profile of tumour suppressor protein p53 and its regulator MDM2 in a cohort of breast cancer patients in a Tertiary Hospital in Ghana

BACKGROUND

Inactivation or mutation of the tumour suppressor gene p53 or its regulator mouse double minute 2 (MDM2) is the commonest event in breast cancer. These altered genes usually express abnormally high levels of their proteins in many carcinomas. The phenotypic expression of p53 and MDM2 in breast cancer cases in our setting is not known. This study investigated the expression of the tumour suppressor protein p53 and its regulator MDM2, using immunohistochemistry in a Ghana breast cancer cohort.

METHOD

A 9-year retrospective cross-sectional study on archived tissue blocks-formalin fixed paraffin embedded tissue (FFPE) was carried out. Demographic data were abstracted. Based on complete clinical data and availability of FFPE archived blocks 203 cases were selected for tissue micro array (TMA) construction. The TMA sections were subjected to immunohistochemistry (IHC) (ER, PR, HER2, p53, and MDM2). Expression of p53 and MDM2 were related to grade and molecular subtypes.

RESULTS

The age ranged from 17 to 92 years (mean = 49.34 ± 13.74). Most of the cases were high grade; grade II (34.9%) and grade III (55.7%). Fifty-four percent of the cases were triple negative. Invasive ductal carcinoma no special type was the commonest histotype (87.1%). Thirty-six percent (36%) of the cases expressed p53. Significant associations were found between p53 overexpression and histological grade (p = 0.034), triple negative (p = 0.0333) and luminal B (p<0.01) tumors. Most cases (93.1%) were negative for MDM2 expression. Significant association was found between MDM2 and HER2 over-expression as well as Ki-67. There was no significant positive correlation between MDM2 and p53 co-expression (p>0.05).

CONCLUSION

The elevated level of p53 expression in the aggressive breast cancer phenotypes (high histological grade and triple negative) in our cohort suggest that P53 elevation may be a poor prognostic marker in our setting. High expression of MDM2 in our cohort with high Ki67; also in cases with Her2/neu overexpression known with predictable poor prognosis in the absence of target therapy suggest MDM2 may be associated with aggressive biological behaviour in our breast cancer cases. The non-significant association of p53 and MDM2 expression in the same cases as also documented by previous studies suggest independent genetic pathway in tumourigenesis.

How to deal with non-detectable and outlying values in biomarker research: Best practices and recommendations for univariate imputation approaches

Non-detectable (ND) and outlying concentration values (OV) are a common challenge of biomarker investigations. However, best practices on how to aptly deal with the affected cases are still missing. The high methodological heterogeneity in biomarker-oriented research, as for example, in the field of psychoneuroendocrinology, and the statistical bias in some of the applied methods may compromise the robustness, comparability, and generalizability of research findings. In this paper, we describe the occurrence of ND and OV in terms of a model that considers them as censored data, for instance due to measurement error cutoffs. We then present common univariate approaches in handling ND and OV by highlighting their respective strengths and drawbacks. In a simulation study with lognormal distributed data, we compare the performance of six selected methods, ranging from simple and commonly used to more sophisticated imputation procedures, in four scenarios with varying patterns of censored values as well as for a broad range of cutoffs. Especially deletion, but also fixed-value imputations bear a high risk of biased and pseudo-precise parameter estimates. We also introduce censored regressions as a more sophisticated option for a direct modeling of the censored data. Our analyses demonstrate the impact of ND and OV handling methods on the results of biomarker-oriented research, supporting the need for transparent reporting and the implementation of best practices. In our simulations, the use of imputed data from the censored intervals of a fitted lognormal distribution shows preferable properties regarding our established criteria. We provide the algorithm for this favored routine for a direct application in R on the Open Science Framework (https://osf.io/spgtv). Further research is needed to evaluate the performance of the algorithm in various contexts, for example when the underlying assumptions do not hold. We conclude with recommendations and potential further improvements for the field.

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ND and OV are considered as censored data, e.g. due to measurement error cutoffs.

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Several common univariate approaches in handling ND and OV are presented.

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In a simulation study, their performances are compared.

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A novel algorithm shows preferable properties.

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General recommendations on how to deal with ND and OV are presented.

Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer

We investigated the data-driven relationship between immune cell composition in the tumor microenvironment (TME) and the ≥5-year survival rates of breast cancer patients using explainable artificial intelligence (XAI) models. We acquired TCGA breast invasive carcinoma data from the cbioPortal and retrieved immune cell composition estimates from bulk RNA sequencing data from TIMER2.0 based on EPIC, CIBERSORT, TIMER, and xCell computational methods. Novel insights derived from our XAI model showed that B cells, CD8+ T cells, M0 macrophages, and NK T cells are the most critical TME features for enhanced prognosis of breast cancer patients. Our XAI model also revealed the inflection points of these critical TME features, above or below which ≥5-year survival rates improve. Subsequently, we ascertained the conditional probabilities of ≥5-year survival under specific conditions inferred from the inflection points. In particular, the XAI models revealed that the B cell fraction (relative to all cells in a sample) exceeding 0.025, M0 macrophage fraction (relative to the total immune cell content) below 0.05, and NK T cell and CD8+ T cell fractions (based on cancer type-specific arbitrary units) above 0.075 and 0.25, respectively, in the TME could enhance the ≥5-year survival in breast cancer patients. The findings could lead to accurate clinical predictions and enhanced immunotherapies, and to the design of innovative strategies to reprogram the breast TME.

A two-miRNA signature of upregulated miR-185-5p and miR-362-5p as a blood biomarker for breast cancer

BACKGROUND

Differentially expressed microRNAs (miRNAs) in the blood of breast cancer patients may serve as diagnostic biomarkers.

METHODS

In this study, miRNA microarray of the blood of breast cancer patients and healthy controls was performed. Candidate differentially expressed miRNAs were further verified by real-time polymerase chain reaction in 68 breast cancer patients and 13 healthy controls.

RESULTS

Six upregulated blood miRNAs (miR-26b-5p, miR-106b-5p, miR-142-3p, miR-142-5p, miR-185-5p, and miR-362-5p) were identified in breast cancer patients. These six miRNAs could discriminate breast cancer patients from healthy controls, with areas under the receiver operating characteristic curve (AUCs) of 0.8891, 0.8158, 0.8529, 0.8507, 0.9050, and 0.9333, respectively. Bioinformatic analysis showed that the six miRNAs were potentially involved in numerous cancer-related pathways, including the mitogen-activated protein kinase signaling pathway, nuclear factor-kappa B signaling pathway, and the transforming growth factor-beta signaling pathway. Importantly, two miRNAs (miR-185-5p and miR-362-5p) were used to construct a two-miRNA panel by logistic regression. The two-miRNA panel displayed a better diagnostic performance than each of the miRNAs alone, with a higher AUC (0.957), sensitivity (92.65 %), and specificity (92.31 %). Additionally, the high expression of the six miRNAs or the two-miRNA panel was associated with poor prognosis of breast cancer.

CONCLUSIONS

We identified six upregulated miRNAs and a two-miRNA panel in the blood as potential biomarkers for the diagnosis and prognosis of breast cancer.

ER-/PR+ breast cancer: A distinct entity, which is morphologically and molecularly close to triple-negative breast cancer

Determining the status of steroid hormone receptors [oestrogen (ER) and progesterone receptors (PR)] is a crucial part of the breast cancer workup. Thereby, breast cancers can be classified into four subtypes. However, the existence of ER-/PR+ tumours, often reported to be ill-classified due to technical errors, remains controversial. In order to address this controversy, we reviewed the hormone receptor status of 49 breast tumours previously classified as ER-/PR+ by immunohistochemistry, and compared clinical, pathological and molecular characteristics of confirmed ER-/PR+ tumours with those of ER+ and triple-negative tumours. We unequivocally confirmed the ER-/PR+ status in 27 of 49 tumours (0.3% of all breast cancers diagnosed in our institution between 2000 and 2014). We found that ER-/PR+ were morphologically and histologically similar to triple-negative tumours, but very distinct from ER+ tumours, with more aggressive phenotypes and more frequent basal marker expression than the latter. On the molecular level, RNA sequencing revealed different gene expression profiles between the three groups. Of particular interest, several genes controlled by the suppressor of zest 12 (SUZ12) were upregulated in ER-/PR+ tumours. Overall, our results confirm that ER-/PR+ breast cancers are an extremely rare but 'real' tumour subtype that requires careful diagnosis and has distinct features warranting different responsiveness to therapies and different clinical outcomes. Studies on larger cohorts are needed to further characterise these tumours. The likely involvement of SUZ12 in their biology is an interesting finding which may - in a long run - give rise to the development of new therapeutic alternatives.

Advanced Approaches to Breast Cancer Classification and Diagnosis

The International Agency for Research on Cancer (IARC) has recently reported a 66% increase in the global number of cancer deaths since 1960. In the US alone, about one in eight women is expected to develop invasive breast cancer(s) (breast cancer) at some point in their lifetime. Traditionally, a BC diagnosis includes mammography, ultrasound, and some high-end molecular bioimaging. Unfortunately, these techniques detect BC at a later stage. So early and advanced molecular diagnostic tools are still in demand. In the past decade, various histological and immuno-molecular studies have demonstrated that BC is highly heterogeneous in nature. Its growth pattern, cytological features, and expression of key biomarkers in BC cells including hormonal receptor markers can be utilized to develop advanced diagnostic and therapeutic tools. A cancer cell's progression to malignancy exhibits various vital biomarkers, many of which are still underrepresented in BC diagnosis and treatment. Advances in genetics have also enabled the development of multigene assays to detect genetic heterogeneity in BC. However, thus far, the FDA has approved only four such biomarkers-cancer antigens (CA); CA 15-3, CA 27-29, Human epidermal growth factor receptor 2 (HER2), and circulating tumor cells (CTC) in assessing BC in body fluids. An adequately structured portable-biosensor with its non-invasive and inexpensive point-of-care analysis can quickly detect such biomarkers without significantly compromising its specificity and selectivity. Such advanced techniques are likely to discriminate between BC and a healthy patient by accurately measuring the cell shape, structure, depth, intracellular and extracellular environment, and lipid membrane compositions. Presently, BC treatments include surgery and systemic chemo- and targeted radiation therapy. A biopsied sample is then subjected to various multigene assays to predict the heterogeneity and recurrence score, thus guiding a specific treatment by providing complete information on the BC subtype involved. Thus far, we have seven prognostic multigene signature tests for BC providing a risk profile that can avoid unnecessary treatments in low-risk patients. Many comparative studies on multigene analysis projected the importance of integrating clinicopathological information with genomic-imprint analysis. Current cohort studies such as MINDACT, TAILORx, Trans-aTTOM, and many more, are likely to provide positive impact on long-term patient outcome. This review offers consolidated information on currently available BC diagnosis and treatment options. It further describes advanced biomarkers for the development of state-of-the-art early screening and diagnostic technologies.

Targeting the purinergic pathway in breast cancer and its therapeutic applications

Breast cancer (BC) is the most frequent cause of death among women, representing a global public health problem. Here, we aimed to discuss the correlation between the purinergic system and BC, recognizing therapeutic targets. For this, we analyzed the interaction of extracellular nucleotides and nucleosides with the purinergic receptors P1 and P2, as well as the influence of ectonucleotidase enzymes (CD39 and CD73) on tumor progression. A comprehensive bibliographic search was carried out. The relevant articles for this review were found in the PubMed, Scielo, Lilacs, and ScienceDirect databases. It was observed that among the P1 receptors, the A1, A2A, and A2B receptors are involved in the proliferation and invasion of BC, while the A3 receptor is related to the inhibition of tumor growth. Among the P2 receptors, the P2X7 has a dual function. When activated for a short time, it promotes metastasis, but when activated for long periods, it is related to BC cell death. P2Y2 and P2Y6 receptors are related to BC proliferation and invasiveness. Also, the high expression of CD39 and CD73 in BC is strongly related to a worse prognosis. The receptors and ectonucleotidases involved with BC become possible therapeutic targets. Several purinergic pathways have been found to be involved in BC cell survival and progression. In this review, in addition to analyzing the pathways involved, we reviewed the therapeutic interventions already studied for BC related to the purinergic system, as well as to other possible therapeutic targets.

Ki67 Immunohistochemistry Quantification in Breast Carcinoma: A Comparison of Visual Estimation, Counting, and ImmunoRatio

BACKGROUND

Molecular analysis has shown that breast carcinomas can be classified into several intrinsic subtypes, with implications for management and prognosis. In the majority of pathology laboratories molecular analysis of each case is not possible and immunohistochemistry is used for subtyping. This includes analysis of hormone receptors as well as HER2-neu and Ki67. The methodology for the interpretation of the proliferation index using Ki67 remains an area of uncertainty. We investigated the degree of agreement between different methods of Ki67 interpretation.

MATERIALS AND METHODS

We analyzed 204 breast core biopsies diagnostic of breast carcinoma using visual estimation/eyeballing (EB), ImmunoRatio, and counting by 2 pathologists (CP1 and CP2). The correlation between the different methods and the interobserver agreement between the 2 pathologists was assessed. Specific analysis was also done with respect to classification of cases into low Ki67 groups (using Ki67 values<14% and <20%) since this is critical in classifying tumors into luminal A and luminal B subtypes.

RESULTS

Correlation between the different methods was best achieved comparing ImmunoRatio and CP1, and worst comparing CP1 and EB. Correlation was better when considering interobserver variability (CP1 vs. CP2). Comparing the number of cases classified as low Ki67 (<14% and <20%) the Cohen κ statistic varied from κ=0.267 to 0.814 with different methods. When limiting the analysis to cases with a Ki67 of 10% to 25% according to any method, there was greater disagreement.

CONCLUSIONS

At the higher and lower Ki67 levels, the correlation between the methods of assessment was acceptable, however, at levels close to the cut-off values for lumial A versus luminal B, several patients would be differently classified by the different methods and therefore potentially receive suboptimal management.

Breast cancers, mammary stem cells, and cancer stem cells, characteristics, and hypotheses

The cellular heterogeneity of breast cancers still represents a major therapeutic challenge. The latest genomic studies have classified breast cancers in distinct clusters to inform the therapeutic approaches and predict clinical outcomes. The mammary epithelium is composed of luminal and basal cells, and this seemingly hierarchical organization is dependent on various stem cells and progenitors populating the mammary gland. Some cancer cells are conceptually similar to the stem cells as they can self-renew and generate bulk populations of nontumorigenic cells. Two models have been proposed to explain the cell of origin of breast cancer and involve either the reprogramming of differentiated mammary cells or the dysregulation of mammary stem cells or progenitors. Both hypotheses are not exclusive and imply the accumulation of independent mutational events. Cancer stem cells have been isolated from breast tumors and implicated in the development, metastasis, and recurrence of breast cancers. Recent advances in single-cell sequencing help deciphering the clonal evolution within each breast tumor. Still, few clinical trials have been focused on these specific cancer cell populations.

Estrogen Actions in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) lacks estrogen receptor (ER) α, but the expression of estrogen receptors ERβ and G protein-coupled estrogen receptor 1 (GPER-1) is able to trigger estrogen-responsivity in TNBC. Estrogen signaling in TNBC can also be activated and modulated by the constitutively active estrogen-related receptors (ERRs). In this review article, we discuss the role of ERβ and GPER-1 as mediators of E2 action in TNBC as well as the function of ERRs as activators and modulators of estrogen signaling in this cancer entity. For this purpose, original research articles on estrogen actions in TNBC were considered, which are listed in the PubMed database. Additionally, we performed meta-analyses of publicly accessible integrated gene expression and survival data to elucidate the association of ERβ, GPER-1, and ERR expression levels in TNBC with survival. Finally, options for endocrine therapy strategies for TNBC were discussed.

Combination of Hematology Indicators and Oncological Characteristics as a New Promising Prognostic Factor in Localized Clear Cell Renal Cell Carcinoma

Purpose

This study aimed to construct a predictive model for recurrence and metastasis in patients with localized clear cell renal cell carcinoma (ccRCC) based on multiple preoperative blood indexes and oncological characteristics.

Patients and Methods

Overall, 442 patients with localized ccRCC between 2013 and 2015 were included. Using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, the top three risk factors from the peripheral blood indicators were screened to construct a risk score, and a prognostic model was established. Harrell's concordance index (C-index) was applied to evaluate the predictive accuracy of the model for predicting disease-free survival (DFS) in ccRCC.

Results

Out of 38 blood indexes, the top three predictors were fibrinogen (FIB), C-reactive protein (CRP) and neutrophil-lymphocyte ratio (NLR). The FIB-CRP-NLR (FCN) score (hazard ratio [HR]: 1.86, 95% confidence interval [CI]: 1.21-2.9, P = 0.005) was an independent prognostic factor in multivariate analysis. Furthermore, the FIB-CRP-NLR-T-Grade (FCNTG) risk model combining FCN score, T stage and Furhman grade achieved a higher prognostic accuracy (mean C-index, 0.728) than both the FCN score alone (mean C-index, 0.675) and the stage, size, grade, and necrosis (SSIGN) score (mean C-index, 0.686) in the validation cohort.

Conclusion

The FCN score combining peripheral blood indicators of inflammation and coagulation is an independent prognostic marker of ccRCC. The FCNTG model, which systemically incorporates preoperative blood indexes to oncological characteristics, shows its advantages of convenience and high prediction efficiency.

Negative estrogen receptors and positive progesterone receptors breast cancers

CONTEXT

Hormone receptors (estrogen receptor ER and progesterone receptor PR) are prognostic and predictive factors of outcome for invasive breast cancer. Some tumors only express one of these hormone receptors (ER or PR). ER negative/PR positive breast cancer is a rare subtype (1-4 %) and its existence still controversial. The aim of this study was to evaluate characteristics of this group of tumors.

METHODS

We collected data of all consecutive patients managed in our institution for invasive breast cancer between the 1st January 2007 and 31 December 2013. The aim of the study was to compare data of patients with ER-/PR+tumors with the three other subgroups.

RESULTS

Of the 2071 patients included during the study period, 1.2 % were ER-/PR+. These patients were younger than those with the two ER+groups (p<0.0001). The ER-/PR+tumors differed from the ER+groups for several histological prognostic factors: greater histological size (p=0.0004), higher histological grade, more HER2 overexpression/amplification, more association with ductal carcinoma in situ, more lymphovascular invasion, more nodal metastasis (p<0.0001). Chemotherapy was more often used as an adjuvant treatment in addition of endocrine therapy. Survival was equivalent for patients with ER-/PR+tumors and ER+tumors and significantly higher than patients with ER-/PR- tumors (p<0.0001).

CONCLUSION

Women with ER-/PR+breast cancer have worse prognostic factors than women with ER+cancers but have better overall survival than women with ER-/PR- tumors. We may think that the more frequent association of chemotherapy and endocrine therapy is responsible for this better outcome.

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.

Optimizing Radiation Therapy to Boost Systemic Immune Responses in Breast Cancer: A Critical Review for Breast Radiation Oncologists

Immunotherapy using immune checkpoint blockade has revolutionized the treatment of many types of cancer. Radiation therapy (RT)-particularly when delivered at high doses using newer techniques-may be capable of generating systemic antitumor effects when combined with immunotherapy in breast cancer. These systemic effects might be due to the local immune-priming effects of RT resulting in the expansion and circulation of effector immune cells to distant sites. Although this concept merits further exploration, several challenges need to be overcome. One is an understanding of how the heterogeneity of breast cancers may relate to tumor immunogenicity. Another concerns the need to develop knowledge and expertise in delivery, sequencing, and timing of RT with immunotherapy. Clinical trials addressing these issues are under way. We here review and discuss the particular opportunities and issues regarding this topic, including the design of informative clinical and translational studies.

Experimental Models as Refined Translational Tools for Breast Cancer Research

Breast cancer is one of the most common cancers worldwide, which makes it a very impactful malignancy in the society. Breast cancers can be classified through different systems based on the main tumor features and gene, protein, and cell receptors expression, which will determine the most advisable therapeutic course and expected outcomes. Multiple therapeutic options have already been proposed and implemented for breast cancer treatment. Nonetheless, their use and efficacy still greatly depend on the tumor classification, and treatments are commonly associated with invasiveness, pain, discomfort, severe side effects, and poor specificity. This has demanded an investment in the research of the mechanisms behind the disease progression, evolution, and associated risk factors, and on novel diagnostic and therapeutic techniques. However, advances in the understanding and assessment of breast cancer are dependent on the ability to mimic the properties and microenvironment of tumors in vivo, which can be achieved through experimentation on animal models. This review covers an overview of the main animal models used in breast cancer research, namely in vitro models, in vivo models, in silico models, and other models. For each model, the main characteristics, advantages, and challenges associated to their use are highlighted.

Insights into the Role of Estrogen Receptor β in Triple-Negative Breast Cancer

Estrogen receptors (ERα and ERβ) are ligand-activated transcription factors that play different roles in gene regulation and show both overlapping and specific tissue distribution patterns. ERβ, contrary to the oncogenic ERα, has been shown to act as an oncosuppressor in several instances. However, while the tumor-promoting actions of ERα are well-known, the exact role of ERβ in carcinogenesis and tumor progression is not yet fully understood. Indeed, to date, highly variable and even opposite effects have been ascribed to ERβ in cancer, including for example both proliferative and growth-inhibitory actions. Recently ERβ has been proposed as a potential target for cancer therapy, since it is expressed in a variety of breast cancers (BCs), including triple-negative ones (TNBCs). Because of the dependence of TNBCs on active cellular signaling, numerous studies have attempted to unravel the mechanism(s) behind ERβ-regulated gene expression programs but the scenario has not been fully revealed. We comprehensively reviewed the current state of knowledge concerning ERβ role in TNBC biology, focusing on the different signaling pathways and cellular processes regulated by this transcription factor, as they could be useful in identifying new diagnostic and therapeutic approaches for TNBC.

Naturally-Occurring Canine Mammary Tumors as a Translational Model for Human Breast Cancer

Despite extensive research over many decades, human breast cancer remains a major worldwide health concern. Advances in pre-clinical and clinical research has led to significant improvements in recent years in how we manage breast cancer patients. Although survival rates of patients suffering from localized disease has improved significantly, the prognosis for patients diagnosed with metastatic disease remains poor with 5-year survival rates at only 25%. In vitro studies using immortalized cell lines and in vivo mouse models, typically using xenografted cell lines or patient derived material, are commonly used to study breast cancer. Although these techniques have undoubtedly increased our molecular understanding of breast cancer, these research models have significant limitations and have contributed to the high attrition rates seen in cancer drug discovery. It is estimated that only 3-6% of drugs that show promise in these pre-clinical models will reach clinical use. Models that can reproduce human breast cancer more accurately are needed if significant advances are to be achieved in improving cancer drug research, treatment outcomes, and prognosis. Canine mammary tumors are a naturally-occurring heterogenous group of cancers that have several features in common with human breast cancer. These similarities include etiology, signaling pathway activation and histological classification. In this review article we discuss the use of naturally-occurring canine mammary tumors as a translational animal model for human breast cancer research.

Clinical Features and Survival of Single Hormone Receptor-Positive Breast Cancer: A Population-Based Study of 531,605 Patients

PURPOSE

To investigate the prognosis of single hormone receptor-positive (HR⁺) breast cancer (estrogen receptor [ER] positive and progesterone receptor [PR] negative, and ER^-PR⁺) compared to double HR⁺ (ER⁺PR⁺) and double HR^- (ER^-PR^-) tumors.

METHODS

We included 531,605 cases of invasive breast cancer between 1990 and 2012 from the US Surveillance, Epidemiology, and End Results (SEER) database for study and classified cases into 4 phenotypes according to expression of ER and PR: ER⁺PR⁺, ER⁺PR^-, ER^-PR⁺, and ER^-PR^-.

RESULTS

Overall, 66,091 ER⁺PR^- tumors and 9320 ER^-PR⁺ tumors were identified. The clinical characteristics of the ER⁺PR^- group were similar to those of the double HR⁺ group, while those of the ER^-PR⁺ and double HR^- groups were similar. Overall survival of patients with single HR⁺ tumors was intermediate between that of double HR⁺ and double HR^- tumors. However, we observed no differences in disease-specific survival between ER^-PR⁺ and ER^-PR^- patients. In multivariate analysis, outcomes were similar. Relative to the double HR⁺ patient group, risk of death in the ER⁺PR^- group was higher (hazard ratio, 1.422, 95% confidence interval, 1.394-1.452). However, risk of death was comparable between ER^-PR⁺ and ER^-PR^- patients (hazard ratio, 1.03; 95% confidence interval, 0.98-1.08). Multivariate Cox proportional analysis showed that survival times of patients in the younger age bracket (< 60 years), those positive for human epidermal growth factor receptor 2 (HER2), and patients with tumor stage I-III were longer in the ER^-PR⁺ group.

CONCLUSION

Disease-specific survival of single HR⁺ tumor cases was longer than that of double HR^- tumors but poorer than double HR⁺ tumors. However, differences in disease-specific survival were not significant between the ER^-PR⁺ and ER^-PR^- groups.

lncRNA TCL6 correlates with immune cell infiltration and indicates worse survival in breast cancer

BACKGROUND

Long non-coding RNA (lncRNA) T-cell leukemia/lymphoma 6 (TCL6) has been reported as a potential tumor suppressor. However, its expression and function in breast cancer remain unknown. This study was performed to investigate the expression of lncRNA TCL6 in breast cancer and its clinical significance.

METHODS

The survival and clinical molecular roles of TCL6 in breast cancer were analyzed. The underlying mechanism modulated by TCL6 and its correlation with immune-infiltrating cells were investigated. Gene Expression Omnibus (GEO) datasets were further used to confirm the prognostic role of TCL6.

RESULTS

TCL6 low expression was not correlated with age, clinical stage, T stage, lymph node metastasis, distant metastasis, human epidermal growth factor 2 status, but was associated with estrogen receptor and progesterone receptor (PR) status and was an independent factor for worse survival (HR 1.876, P = 0.016). Specifically, low TCL6 expression correlated with worse prognosis in PR-negative patients. TCL6 could predict worse survival in luminal B breast cancer based on intrinsic subtypes. Immune-related pathways such as Janus kinase-signal transducer of activators of transcription were regulated by TCL6. Further finding revealed that TCL6 correlated with immune infiltrating cells such as B cells (r = 0.25, P < 0.001), CD8+ T cells (r = 0.23, P < 0.001), CD4+ T cells (r = 0.25, P < 0.001), neutrophils (r = 0.21, P < 0.001), and dendritic cells (r = 0.27, P < 0.001). TCL6 was also positively correlated with tumor-infiltrating lymphocytes infiltration and PD-1, PD-L1, PD-L2, and CTLA-4 immune checkpoint molecules (P < 0.001).

CONCLUSION

Our findings suggest that lncRNA TCL6 correlates with immune infiltration and may act as a useful prognostic molecular marker in breast cancer.

Midkine (MDK) growth factor: a key player in cancer progression and a promising therapeutic target

Midkine is a heparin-binding growth factor, originally reported as the product of a retinoic acid-responsive gene during embryogenesis, but currently viewed as a multifaceted factor contributing to both normal tissue homeostasis and disease development. Midkine is abnormally expressed at high levels in various human malignancies and acts as a mediator for the acquisition of critical hallmarks of cancer, including cell growth, survival, metastasis, migration, and angiogenesis. Several studies have investigated the role of midkine as a cancer biomarker for the detection, prognosis, and management of cancer, as well as for monitoring the response to cancer treatment. Moreover, several efforts are also being made to elucidate its underlying mechanisms in therapeutic resistance and immunomodulation within the tumor microenvironment. We hereby summarize the current knowledge on midkine expression and function in cancer development and progression, and highlight its promising potential as a cancer biomarker and as a future therapeutic target in personalized cancer medicine.

Clinical Characteristics and Outcomes of Single Versus Double Hormone Receptor-Positive Breast Cancer in 2 Large Databases

PURPOSE

To identify biologic and outcome differences between double hormone receptor (HR)-positive (dHR⁺, estrogen receptor (ER)⁺/progesterone receptor [PgR⁺]) and single HR-positive (sHR⁺, either ER⁺/PgR^- or ER^-/PgR⁺) breast cancer; and to explore whether hormone therapy (HT) response in HER2-negative breast cancer correlates with HR status.

PATIENTS AND METHODS

This retrospective study was conducted by using 2 large breast cancer databases: the Surveillance, Epidemiology, and End Results (SEER) database and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) clinical data set. Cox regression analysis was used to estimate overall survival (OS) and breast cancer-specific survival (BCSS) among sHR⁺ and dHR⁺ patients.

RESULTS

In the SEER database, dHR⁺ patients had significantly longer OS and BCSS than ER⁺/PgR^- patients in short-term follow-up (OS: hazard ratio = 0.620; 95% confidence interval [CI], 0.590, 0.652; P < .001; BCSS: hazard ratio = 0.493; 95% CI, 0.462, 0.526; P < .001). Meanwhile, ER^-/PgR⁺ patients had younger age, larger tumor size, and higher disease grade than dHR⁺ and ER⁺/PgR^- patients. In patients who received HT, dHR⁺ patients had a more favorable OS than ER⁺/PgR^- patients (hazard ratio = 0.789; 95% CI, 0.635, 0.982; P = .034), and ER^-/PgR⁺ patients had a worse OS than ER⁺/PgR^- patients at 10 years' follow-up (hazard ratio = 7.991; 95% CI, 1.053, 60.644; P = .044). However, these groups had similar outcomes over longer periods.

CONCLUSION

In HER2-negative breast cancer, sHR⁺ patients are associated with relatively worse characteristics and worse short-term outcomes than dHR⁺ patients. Additionally, the outcome of patients receiving HT may differ according to the HR status. However, further studies are needed to confirm these conclusions.

Genomics applied to the treatment of breast cancer

Breast cancer remains a major health issue in the world with 1.7 million new cases in 2012 worldwide. It is the second cause of death from cancer in western countries. Genomics have started to modify the treatment of breast cancer, and the developments should become more and more significant, especially in the present era of treatment personalization and with the implementation of new technologies. With molecular signatures, genomics enabled a de-escalation of chemotherapy and personalized treatments of localized forms of estrogen-dependent breast cancers. Genomics can also make a real contribution to constitutional genetics, so as to identify mutations in a panel of candidate genes. In this review, we will discuss the contributions of genomics applied to the treatment of breast cancer, whether already validated contributions or possible future applications linked to research data.

Impact of biomarkers and genetic profiling on breast cancer prognostication: A comparative analysis of the 8th edition of breast cancer staging system

The 8th edition of the American Joint Committee on Cancer (AJCC) staging guidelines combine traditional TNM system with biomarkers to reflect our current understanding of tumor biology and targeted therapy. In this study, we investigated the impact of the TNM + Biomarkers staging system and the additive value of Oncotype Dx™ genomic profile recurrence score (RS) (TNM + Biomarkers+RS <11) for the staging of breast cancer (BC) using data from two tertiary referral cancer centers. Compared to TNM alone, the TNM + Biomarkers system changed the stage group in 32.7% of BCs (27% downstage, 5.7% upstage). Most (98.3%) of the downstaged BCs were estrogen receptor (ER)+/progesterone receptor (PR)+, whereas 78% of the upstaged BCs were ER-/PR-/human epidermal growth factor receptor 2 (HER2)-. Compared to TNM + Biomarkers staging, the addition of genetic profile data (TNM + Biomarker+RS <11) downstaged only <1% BCs. Our analysis suggests that for T1-T2N0 ER+/HER2- BCs, Oncotype Dx™ RS <11 provides added value as a staging parameter only in a very small group of cases compared to TNM + Biomarkers alone.