Microarray-Based Class Discovery for Molecular Classification of Breast Cancer: Analysis of Interobserver Agreement

Introduction: Breast cancer has heterogeneous tumor biology. Therefore, breast cancer is divided into molecular subtypes. However, molecular subtypes also show heterogeneity within themselves. In our study, we compared whether there were radiological and pathological differences between breast cancer molecular subtypes. Methods: The data of 569 patients operated on for breast cancer in our General Surgery clinic during an 8-year period were analyzed retrospectively. After dividing the patients into groups consisting of molecular subtypes, their radiological and pathological findings were compared. Results: According to the molecular classification of breast cancer, 47.6% of the patients were in the luminal A group, 23.9% in luminal B, 7.7% in HER2-enriched, and 12% in TN, and 8.8% in normal-like groups. DCIS and LCIS were significantly less common in the TN group than in the others (p=0.015). Tumor diameter and grade were considerably higher in the normal-like, HER2-enriched, and TN groups than in the other groups (p<0.001, p<0.001). LVI was significantly lower in luminal A and significantly higher in luminal B (p<0.001). Mortality was significantly higher in the luminal B and TN groups than in the other groups (p = 0.029). Conclusion: The addition of the basal-like group when classifying breast cancer into molecular subtypes showed histopathological differences. More studies are needed to investigate its effects on treatment and prognosis. Keywords: Breast cancer, Molecular subtype, Genotype, Immunohistochemistry, Survival

Most invasive breast cancers are classified as invasive ductal carcinoma not otherwise specified (IDC NOS), whereas about 25% are defined as histological 'special types'. These special-type breast cancers are categorized into at least 17 discrete pathological entities; however, whether these also constitute discrete molecular entities remains to be determined. Current therapy decision-making is increasingly governed by the molecular classification of breast cancer (luminal, basal-like, HER2+). The molecular classification is derived from mainly IDC NOS and it is unknown whether this classification applies to all histological subtypes. We aimed to refine the breast cancer classification systems by analysing a series of 11 histological special types [invasive lobular carcinoma (ILC), tubular, mucinous A, mucinous B, neuroendocrine, apocrine, IDC with osteoclastic giant cells, micropapillary, adenoid cystic, metaplastic, and medullary carcinoma] using immunohistochemistry and genome-wide gene expression profiling. Hierarchical clustering analysis confirmed that some histological special types constitute discrete entities, such as micropapillary carcinoma, but also revealed that others, including tubular and lobular carcinoma, are very similar at the transcriptome level. When classified by expression profiling, IDC NOS and ILC contain all molecular breast cancer types (ie luminal, basal-like, HER2+), whereas histological special-type cancers, apart from apocrine carcinoma, are homogeneous and only belong to one molecular subtype. Our analysis also revealed that some special types associated with a good prognosis, such as medullary and adenoid cystic carcinomas, display a poor prognosis basal-like transcriptome, providing strong circumstantial evidence that basal-like cancers constitute a heterogeneous group. Taken together, our results imply that the correct classification of breast cancers of special histological type will allow a more accurate prognostication of breast cancer patients and facilitate the identification of optimal therapeutic strategies.

Background: Breast cancer is a heterogeneous disease with different biological and histological properties due to genetics and epigenetic changes with varying clinical features and treatment responses. This study was planned and carried out with the objective to classify breast cancers molecularly using surrogate markers, ER, PgR, Her2/neu and Ki67 and correlate the various types with conventional prognostic markers. Materials and Methods: 70 cases of invasive breast carcinomas were subjected to routine staining and immunohistochemistry with estrogen receptors(ER), progesterone receptors (PgR), Her2/neu and Ki67and classified into molecular subtypes as defined in various studies. These were correlated with other conventional prognostic parameters and analyzed statistically. Result: 70 cases of invasive breast cancer were classified into 18(22%) cases of luminal B, 16(25%) cases of Her2/neu and triple negative each and 14(22%) cases of luminal A subtypes. There was an even distribution of molecular subtypes varying from 22 to 28%. Luminal B and Her subtype were commoner in the Indian setting as compare to other studies. Luminal A and Luminal B subtypes were commoner in patients older than 50 years. Her2/neu and TNBC cases were more commonly of higher histological grade and pathological stage, while Luminal A and B subtypes showed lower grade and stages. Luminal B subtype and Her2/neu subtypes showed DCIS more often and Luminal B and TNBC subtypes, more frequent lymph node metastasis. Conclusion: The molecular classification of breast cancer by IHC in this study population showed an almost equal distribution of the 4 subtypes. The association of tumor grade and LVI with the molecular subtypes showed a significant correlation. Keywords: ER, PR, Her2/neu, Ki67.

We aimed to compare the distribution of different molecular subtypes of invasive breast cancer (BC) between patients whose samples were obtained by core needle biopsy (CB) and surgical specimens (SS) and to assess the reliability of CB as a diagnostic method in this context. All patients (222) diagnosed with invasive BC were examined. Immunohistochemistry was performed on 40 samples obtained by CB and on 148 SS, while in 34 patients, the analysis was performed on both CB and SS. Molecular classification of BC was performed based on estrogen receptor (ER), progesterone receptor (PgR), Human epidermal growth factor receptor 2 (HER2), and Ki67 proliferative index status. The most common molecular subtypes were Luminal A (43.2%) and Luminal B HER2- (29.7%). When comparing the frequencies of determined molecular subtypes, no difference was observed between samples obtained by CB and SS (p>0.05). Concordance analysis of molecular subtypes determined by immunohistochemistry on CB and SS was performed in 34 patients whose samples were obtained using both methods. No significant difference was observed in the designation of molecular subtype in relation to the sampling method (p>0.05). Results of immunohistochemistry analysis on CB and SS demonstrated good statistical agreement (Concordance rate=85.29%, Kappa=0.771, p<0.001). CB might be a reliable method for the determination of the molecular subtype of invasive BC.

Breast cancers have traditionally been classified based on histology. However, with the development in imaging modalities and available treatment options, it is imperative to also focus on the molecular classes of breast cancer. Molecular profile and gene expression are important predictors of treatment response and overall prognosis. Radiologists should have a good understanding of molecular categories of breast cancer along with their imaging hallmarks. Highlights: • To familiarize the readers with the principal biomarkers and molecular classes of breast cancer. • To highlight the characteristic imaging features of each molecular class of breast cancer. • To focus on the pivotal role of molecular classification in the dynamic model of breast cancer treatment.

The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Breast cancer is a heterogeneous disease characterized by a wide range of biomarker expressions, resulting in varied progression, behavior, and prognosis. While traditional biopsy-based molecular classification is the gold standard, it is invasive and limited in capturing tumor heterogeneity, especially in deep or metastatic lesions. Molecular imaging, particularly positron emission tomography (PET) imaging, offering a non-invasive alternative, potentially plays a crucial role in the classification and management of breast cancer by providing detailed information about tumor location, heterogeneity, and progression. This narrative review, which focuses on both clinical patients and preclinical studies, explores the latest advancements in PET imaging for breast cancer, emphasizing the development of new tracers targeting hormone receptors such as the estrogen alpha receptor, progesterone receptor, androgen receptor, estrogen beta receptor, as well as the ErbB family of receptors, VEGF/VEGFR, PARP1, PD-L1, and markers for indirectly assessing Ki-67. These innovative radiopharmaceuticals have the potential to guide personalized treatment approaches based on the unique tumor profiles of individual patients. Additionally, they may improve the assessment of treatment efficacy, ultimately leading to better outcomes for those diagnosed with breast cancer.

Breast cancer is a heterogeneous disease and differs greatly among different patients (intertumor heterogeneity) and even within each individual tumor (intratumor heterogeneity). Clinical and morphologic intertumor heterogeneity is reflected by staging systems and histopathologic classification of breast cancer. Heterogeneity in the expression of established prognostic and predictive biomarkers, hormone receptors, and human epidermal growth factor receptor 2 oncoprotein is the basis for targeted treatment. Molecular classifications are indicators of genetic tumor heterogeneity, which is probed with multigene assays and can lead to improved stratification into low- and high-risk groups for personalized therapy. Intratumor heterogeneity occurs at the morphologic, genomic, transcriptomic, and proteomic levels, creating diagnostic and therapeutic challenges. Understanding the molecular and cellular mechanisms of tumor heterogeneity that are relevant to the development of treatment resistance is a major area of research. Despite the improved knowledge of the complex genetic and phenotypic features underpinning tumor heterogeneity, there has been only limited advancement in diagnostic, prognostic, or predictive strategies for breast cancer. The current guidelines for reporting of biomarkers aim to maximize patient eligibility for targeted therapy, but do not take into account intratumor heterogeneity. The molecular classification of breast cancer is not implemented in routine clinical practice. Additional studies and in-depth analysis are required to understand the clinical significance of rapidly accumulating data. This review highlights inter- and intratumor heterogeneity of breast carcinoma with special emphasis on pathologic findings, and provides insights into the clinical significance of molecular and cellular mechanisms of heterogeneity.

Breast cancer classification by proteomic technologies: Current state of knowledge

Identification of molecular signatures that allow detection of the transition from normal breast epithelial cells to malignant invasive cells is a critical component in the development of diagnostic, therapeutic, and preventative strategies for human breast cancer. Substantial efforts have been devoted to deciphering breast cancer etiology at the genome level, but only a limited number of studies have appeared at the proteome level. In this work, we compared individual in situ proteome profiles of nonpatient matched nine noncancerous, normal breast epithelial (NBE) samples with nine estrogen receptor (ER)-positive (luminal subtype), invasive malignant breast epithelial (MBE) samples by combining laser capture microdissection (LCM) and quantitative shotgun proteomics. A total of 12,970 unique peptides were identified from the 18 samples, and 1623 proteins were selected for quantitative analysis using spectral index (SpI) as a measure of protein abundance. A total of 298 proteins were differentially expressed between NBE and MBE at 95% confidence level, and this differential expression correlated well with immunohistochemistry (IHC) results reported in the Human Protein Atlas (HPA) database. To assess pathway level patterns in the observed expression changes, we developed protein set enrichment analysis (PSEA), a modification of a well-known approach in gene expression analysis, Gene Set Enrichment Analysis (GSEA). Unlike single gene-based functional term enrichment analyses that only examines pathway overrepresentation of proteins above a given significance threshold, PSEA applies a weighted running sum statistic to the entire expression data to discover significantly enriched protein groups. Application of PSEA to the expression data in this study revealed not only well-known ER-dependent and cellular morphology-dependent protein abundance changes, but also significant alterations of downstream targets for multiple transcription factors (TFs), suggesting a role for specific gene regulatory pathways in breast tumorigenesis. A parallel GOMiner analysis revealed both confirmatory and complementary data to PSEA. The combination of the two annotation approaches yielded extensive biological feature mapping for in depth analysis of the quantitative proteomic data.

Breast cancer affects more than one million patients annually in the world and is a leading cause of mortality. Histological type, grade, tumor size, lymph node involvement, and estrogen receptor and HER-2 receptor status, all influence prognosis and the probability of response to systemic therapies. P53 gene is mutated in about 30% of breast cancers/.The possible links between alterations of p53 and clinical or pathological features of breast tumors have been widely investigated. The first study to examine gene-expression patterns of breast cancer suggested that at least four major molecular classes of breast cancer exist: luminal-like, basal-like, normal like, and HER-2 positive. Basal-like breast cancer account for 15% of breast cancers and are often described as triple negative breast cancers (TNBCs). In fact, TNBCs, defined by lack of expression of estrogen receptor, progesterone receptor, and HER2, probably include both basal-like breast cancers and some poorly differentiated luminal breast cancers. They are also associated with a younger age and a poor prognosis. TNBCs also have an increased frequency of TP53 mutations. Recently, it was shown that mutant p53 status was a strongly unfavorable prognostic factor for relapse-free survival and overall survival only in a triple negative group in patients treated with adjuvant anthracycline-containing chemotherapy. Inflammatory breast cancer (IBC) is a clinical diagnosis known as the T4d category in the TNM classification. It is a distinct clinical subtype of locally advanced breast cancer (LABC), with a particularly aggressive behavior and poor prognosis. TP53 mutations are more frequent in inflammatory breast cancer (50%) than in non-inflammatory breast cancer (20–30%). The results from these studies served as the justification for attempts to vaccinate patients using p53-derived peptides, and a number of clinical trials are in progress. The most advanced work used a long synthetic peptide mixture derived from p53 (p53-SLP; Netherlands).The vaccine is delivered in the adjuvant setting and induces T helper type I response in the majority of patients. However, the response may not be potent enough to result in clinical benefit as a mono-therapy because most patients had T-helper cells that failed to produce key cytokines, indicating that this treatment should also be associated with another type of conventional or immunotherapy therapy.

Breast cancer is a highly heterogeneous disease with different subtypes presenting distinct clinical characteristics. During the last decade has been shown that molecular classification of breast cancer holds the promise for the development of novel prognostic tools and the identification of novel treatment targets. However, proteins are the main mediators of biological processes and the molecular targets of the majority of drugs. Moreover, the proteome integrates the cellular genetic information and environmental influences. Hence coupling proteomic and transcriptomic studies may augment the development of targeted therapies and the identification of disease-relevant proteins networks. Here, we report an extensive mass spectrometry-based (MS) secretome analysis of eight breast cancer cell lines representing, the three main breast cancer subtypes (luminal, basal and HER2-neu amplified) in search of subtype-specific proteomic signatures. Following a bottom-up proteomic approach coupled to tandem mass spectrometry the conditioned media of 8 breast cancer cell lines were analyzed. More than 5,200 non-redundant proteins were identified in the conditioned media of the 8 cell lines with a false positive rate less than 1%. The mass spectrometry-based identification of 5 proteins was successfully verified by ELISA. For the generation of subtype-specific proteomic signatures, proteins common to all cell lines of the same subtype but not present in the other two subtypes were identified. Twenty-three, four and four proteins were found uniquely in basal, HER2-neu amplified and luminal breast cancer cells. Notably, ERBB2 was one of the proteins uniquely identified in the HER2-neu amplified subtype verifying the validity of our approach. Given that most of these proteins have no quantitative methods available at present, we opted to preliminarily examine the relationship of these candidates with breast cancer subtypes by using an in silico approach, based on transcriptomic data. We performed an in silico mRNA expression analysis using publicly available data from four independent experiments containing a total of 1039 patients with primary breast cancer. The expression profiles of 15 of 31 proteins investigated showed significant correlation with estrogen receptor expression and power to distinguish subtypes. The development of multiplex quantitative mass spectrometry-based assays for the quantification of the proteomic signatures in relevant biological specimens along with immunohistochemical studies for two of the 31 candidates are currently ongoing. The significance of these subtype-specific proteins in breast cancer prognosis and disease progression warrants further investigation. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-05-11.

Cumulative evidence has demonstrated the presence of cancer stem cells (CSC) in breast cancer and its putative role in cancer progression. Nonetheless, the clinical significance of CSC in breast cancer remains elusive. The underlying reasons could be due to the heterogeneity of breast cancer subtypes as well as different markers used to define breast CSC. In this study, three widely used markers (aldehyde dehydrogenase (ALDH)1+ and CD24-CD44+) were used to define two populations of CSC in a large cohort of breast cancers. The expressions of these markers were correlated with different clinicopathological features and the molecular subtypes. ALDH1+ breast cancers were associated with basal-like and HER2-overexpressing subtypes and the characteristics histologic features were related to these two subtypes. On the other hand, CD24-CD44+ breast cancers were associated positively with the presence of extensive in situ component, the absence of lymph node involvement, and basal markers, but negatively with HER2. CD24-CD44+ breast cancers were also positively associated with luminal B cancers. As the expression of CSC markers varied among different molecular subtypes and different clinicopathological features, it appeared that each CSC population could have distinct clinical values in different subgroups of breast cancers. For improved prognostication with CSC, combining the analysis of CSC markers would be required. Within the luminal cancers, CSC appeared to identify cancers with poor outcome. The presence of CSC populations was associated with ER-PR+ cancers and tumors expressing basal markers. Basal marker expression can complement with CSC for improved indicator for poor prognosis in luminal breast cancers. For the first time, the possible contribution of CSC to these aggressive luminal cancers was demonstrated. The association of basal features and CSC in luminal cancers also raised the possibility that luminal cancer cells may acquire basal phenotype and CSC properties together during their progression.

Breast cancer (BC) is a clinically heterogeneous disease affecting around 14% female population worldwide with unique histopathological and biological characteristics. Invasive carcinoma with no special type (NST) is common among histological type of cancer and about 25% breast cancer has been classified as histological special type. Nowadays, molecular classification of breast cancer is used for treatment. But molecular classification is also derived from NST and it has to be explored yet whether molecular classification is applicable to all histological subtypes. Therefore, in present chapter, we have reported the breast cancer classification by the detailed collection of data from different sources, i.e., PubMed, Web of Science, Science Direct, Scopus databases on a series of histological special type, i.e., invasive lobular carcinoma (ILC) and invasive ductal carcinoma (IDC) along with its subtypes: tubular, mucinous, neuroendocrine, apocrine (androgen-receptor positive and estrogen-receptor negative), micropapillary, adenoid, cystic, metaplastic, and medullary carcinoma. Understanding of present study focused on invasive carcinoma with histological types and subtypes with detail origin of heterogeneity may be helpful for the targeting novel therapeutic tools for breast cancer diagnosis and treatment.

Simple SummaryTriple-negative breast cancer (TNBC) is one of the most challenging tumors with aggressive behavior, low recovery rate, poor prognosis, high metastatic potential, and rapid relapse compared to other breast cancer subtypes. Conventional therapies currently have minimal effect on TNBC; thus, using combination therapies is a valid strategy to enhance drug activity and minimize the overall adverse effect. Therefore, combining drugs with a different mechanism of actions such as apoptosis inducers and JAK/STAT3 inhibitors improved TNBC cell lines killing activity in vitro and in vivo. To further improve the hydrophobic drug activity, CD44 targeted polymeric nanoparticles (CD44-T-PNPs) were utilized by encapsulating hydrophobic drug (CFM-4.16) in CD44-T-PNPs to enhance the drug solubility, tumor accumulation, and most importantly, enhance drug potency. Tagging our PNPs with Hyaluronic acid (HA) enhanced tumor accumulation, reduced off-target distribution, and improved therapeutic efficacy.Identified as the second leading cause of cancer-related deaths among American women after lung cancer, breast cancer of all types has been the focus of numerous research studies. Even though triple-negative breast cancer (TNBC) represents 15–20% of the number of breast cancer cases worldwide, its existing therapeutic options are fairly limited. Due to the pivotal role of the presence/absence of specific receptors to luminal A, luminal B, HER-2+, and TNBC in the molecular classification of breast cancer, the lack of these receptors has accounted for the aforementioned limitation. Thereupon, in an attempt to participate in the ongoing research endeavors to overcome such a limitation, the conducted study adopts a combination strategy as a therapeutic paradigm for TNBC, which has proven notable results with respect to both: improving patient outcomes and survivability rates. The study hinges upon an investigation of a promising NPs platform for CD44 mediated theranostic that can be combined with JAK/STAT inhibitors for the treatment of TNBC. The ability of momelotinib (MMB), which is a JAK/STAT inhibitor, to sensitize the TNBC to apoptosis inducer (CFM-4.16) has been evaluated in MDA-MB-231 and MDA-MB-468. MMB + CFM-4.16 combination with a combination index (CI) ≤0.5, has been selected for in vitro and in vivo studies. MMB has been combined with CD44 directed polymeric nanoparticles (PNPs) loaded with CFM-4.16, namely CD44-T-PNPs, which selectively delivered the payload to CD44 overexpressing TNBC with a significant decrease in cell viability associated with a high dose reduction index (DRI). The mechanism underlying their synergism is based on the simultaneous downregulation of P-STAT3 and the up-regulation of CARP-1, which has induced ROS-dependent apoptosis leading to caspase 3/7 elevation, cell shrinkage, DNA damage, and suppressed migration. CD44-T-PNPs showed a remarkable cellular internalization, demonstrated by uptake of a Rhodamine B dye in vitro and S0456 (NIR dye) in vivo. S0456 was conjugated to PNPs to form CD44-T-PNPs/S0456 that simultaneously delivered CFM-4.16 and S0456 parenterally with selective tumor targeting, prolonged circulation, minimized off-target distribution.