Characterization and classification of ductal carcinoma tissue using four channel based stokes-mueller polarimetry and machine learning.

Interaction of polarized light with healthy and abnormal regions of tissue reveals structural information associated with its pathological condition. Even a slight variation in structural alignment can induce a change in polarization property, which can play a crucial role in the early detection of abnormal tissue morphology. We propose a transmission-based Stokes-Mueller microscope for quantitative analysis of the microstructural properties of the tissue specimen. The Stokes-Mueller based polarization microscopy provides significant structural information of tissue through various polarization parameters such as degree of polarization (DOP), degree of linear polarization (DOLP), and degree of circular polarization (DOCP), anisotropy (r) and Mueller decomposition parameters such as diattenuation, retardance and depolarization. Further, by applying a suitable image processing technique such as Machine learning (ML) output images were analysed effectively. The support vector machine image classification model achieved 95.78% validation accuracy and 94.81% testing accuracy with polarization parameter dataset. The study's findings demonstrate the potential of Stokes-Mueller polarimetry in tissue characterization and diagnosis, providing a valuable tool for biomedical applications.

Comparative analysis of clinicopathologic characteristics and molecular subtypes of invasive papillary carcinoma of the breast and invasive ductal carcinoma: results from SEER database.

PURPOSE: To investigate the difference of clinicopathologic characteristics and prognosis between invasive papillary carcinoma (IPC) and invasive ductal carcinoma (IDC) in breast cancer patients, and to further confirm the influence of molecular subtype on prognosis of IPC. METHODS: A total of 158,132 eligible patients from 2010 to 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database, of which 348 patients were IPC and 157,784 patients were IDC. We assessed the clinicopathologic characteristics, molecular subtypes and prognostic value of IPC and compared them with those of IDC. RESULTS: IPC was more frequently presented with older age at diagnosis, less proportion of married and white race, lower grade, smaller tumor size, higher rates of negative nodal status, more AJCC stage I disease and HR+/Her2- breast cancer, and was less likely to be treated with mastectomy, chemotherapy, and radiation therapy than IDC (p<0.05). IPC had a better 5-year breast cancer-specific survival (BCSS) and overall survival (OS) rates than IDC. After adjusting confounding and matching the confounding factors, IPC patients were still associated with better BCSS. Regarding patients with specific subtypes, patients with IPC had more HR+/Her2- subtypes. In addition, HR+/Her2--IPC patients had a better BCSS than HR+/Her2--IDC patients, but OS was similar between the two groups. However, BCSS and OS did not differ in the two groups after matching the confounding factors. Subgroup analysis indicated that molecular subtype may be the main confounding factor in IPC prognosis. CONCLUSIONS: IPC showed more favorable behavior than IDC, but prognosis was not as favorable as people once thought. The determination of the appropriate therapeutic regimen for IPC still needs to be made according to risk factors such as histological grade, pathological stage and molecular subtype.

Phylogenetic analysis of combined lobular and ductal carcinoma of the breast.

Breast cancer manifests in diverse forms, with particular reference to various cell types harboring different mutations and gene expression profiles. To elucidate the clonal relationship between cancer cells in tumors composed of both ductal and lobular phenotypes, two combined lobular and ductal carcinoma (CLDC) cases were analyzed, including one mixed ductal­lobular carcinoma (MDL) lesion, by direct sequencing of the mitochondrial DNA D­loop, digital PCR targeting of chromosomes 1q and 16q, as well as next­generation sequencing. DNA was extracted from formalin­fixed paraffin­embedded tissue sections of different histological types, including invasive ductal carcinoma, invasive lobular carcinoma, ductal carcinoma in situ, lobular carcinoma in situ, flat epithelial atypia, non­neoplastic mammary gland and extramammary organs, using laser­assisted microdissection. Mutations detected by the comprehensive cancer panel were validated by SYBR green allele­specific quantitative PCR (RRM1, AKT1, PIK3CA, RALGDS, EGFR, TP53, IL21R, DPYD, SGK1, CDH1, TIMP3 and KMT2C). CLDC, which shared the basic genetic alterations of 1q gain or 16q loss, progresses to invasive lobular or ductual carcinoma with the accumulation of further mutations. Cancer cells contained in an MDL lesion shared closely related genetic alterations, suggesting that these cells have the same origin, despite different histological features, namely 'lobular' or 'ductal'. By contrast, multiple lesions located away from the main tumor, diagnosed as CLDC (excluding an MDL lesion) were not always identical with different genetic alterations, despite being diagnosed as ductal carcinoma in situ. Thus, MDL should be defined as a distinct category separate from CLDC, whose components of 'lobular' and 'ductal' may have the same cellular origin.

"Switch of E-Cadherin to N-Cadherin expression in different molecular subtypes of breast invasive duct carcinomas and its correlation with clinicopathological features".

BACKGROUND: In breast cancer, metastasis and recurrence is the main culprit in treatment failure. This study aimed to explore the role of E-cadherin/N-cadherin Switch in progression, spread and metastasis in breast invasive duct carcinoma. MATERIALS AND METHODS: A cross-sectional study on 118 formalinfixed paraffinembedded mastectomy specimens of invasive breast duct carcinoma. Primary antibodies for E-cadherin (monoclonal, clone HECD-1; Zymed Laboratories; dilution 1:600) and N-cadherin (monoclonal, clone 3B9; Zymed Laboratories, Inc., Montrouge, France; dilution 1:200) were applied for all cases. The study revealed that E-cadherin high expression was significantly associated with advanced TNM clinical stage (P = 0.021), and nodal metastasis (P < 0.001). High expression of N-cadherin was significantly positively correlated with tumor sizes (P < 0.00), advanced clinical stage (P < 0.00), and nodal metastasis (P < 0.008). Mean OS was 39.99 months in cases with negative expression versus 41.8 months in cases with positive expression. Mean DFS in cases with positive E. cadh expression was 41.89 months was higher than mean DFS in cases with negative E. cadh expression which was 40.52 months, but it showed no statistical significance (P = 0.57). CONCLUSIONS/SIGNIFICANCE: This study demonstrated that loss of E-cadherin and gain of N-cadherin promotes invasion, migration, and metastasis in invasive ductal carcinoma cells. Importantly, these findings may exploit new cancer therapies using N-cadherin antagonists.

Visualizing "featureless" regions on mammograms classified as invasive ductal carcinomas by a deep learning algorithm: the promise of AI support in radiology.

PURPOSE: To demonstrate how artificial intelligence (AI) can expand radiologists' capacity, we visualized the features of invasive ductal carcinomas (IDCs) that our algorithm, developed and validated for basic pathological classification on mammograms, had focused on. MATERIALS AND METHODS: IDC datasets were built using mammograms from patients diagnosed with IDCs from January 2006 to December 2017. The developing dataset was used to train and validate a VGG-16 deep learning (DL) network. The true positives (TPs) and accuracy of the algorithm were externally evaluated using the test dataset. A visualization technique was applied to the algorithm to determine which malignant findings on mammograms were revealed. RESULTS: The datasets were split into a developing dataset (988 images) and a test dataset (131 images). The proposed algorithm diagnosed 62 TPs with an accuracy of 0.61-0.70. The visualization of features on the mammograms revealed that the tubule forming, solid, and scirrhous types of IDCs exhibited visible features on the surroundings, corners of the masses, and architectural distortions, respectively. CONCLUSION: We successfully showed that features isolated by a DL-based algorithm trained to classify IDCs were indeed those known to be associated with each pathology. Thus, using AI can expand the capacity of radiologists through the discovery of previously unknown findings.

Subtype-specific characterization of breast cancer invasion using a microfluidic tumor platform.

Understanding progression of breast cancers to invasive ductal carcinoma (IDC) can significantly improve breast cancer treatments. However, it is still difficult to identify genetic signatures and the role of tumor microenvironment to distinguish pathological stages of pre-invasive lesion and IDC. Presence of multiple subtypes of breast cancers makes the assessment more challenging. In this study, an in-vitro microfluidic assay was developed to quantitatively assess the subtype-specific invasion potential of breast cancers. The developed assay is a microfluidic platform in which a ductal structure of epithelial cancer cells is surrounded with a three-dimensional (3D) collagen matrix. In the developed platform, two triple negative cancer subtypes (MDA-MB-231 and SUM-159PT) invaded into the surrounding matrix but the luminal A subtype, MCF-7, did not. Among invasive subtypes, SUM-159PT cells showed significantly higher invasion and degradation of the surrounding matrix than MDA-MB-231. Interestingly, the cells cultured on the platform expressed higher levels of CD24 than in their conventional 2D cultures. This microfluidic platform may be a useful tool to characterize and predict invasive potential of breast cancer subtypes or patient-derived cells.

Expression changes of BIK in breast cancer tissues of different histological subtypes.

OBJECTIVE: The objective of the study was to determine the expression levels of BIK in breast cancer (BC) tissues of different histological subtype and to delve into the participation of BIK in this type of cancer. MATERIALS AND METHODS: BIK and p-BIK (the phosphorylated form) protein expressions were tested by immunohistochemistry in BC tissue microarrays (Tumoral [n = 90] and adjacent [n = 40] tissues). RESULTS: The data revealed an overexpression of BIK in invasive ductal (Grades I, IIA, and IIB) and in lobular (Grades IIA and IIB) carcinomas compared to their respective adjacent tissues. By contrast, canalicular carcinoma (Grades I and IIB) and phyllodes tumors had very low expression levels of BIK. Only levels of p-BIK were shown to be increased in invasive ductal carcinoma (Grades I, IIA, and IIB). Meanwhile, quantitative polymerase chain reaction analysis showed lower BIK levels in MCF-10A and MCF-7 cells than in MDA-MB-231 and human mammary epithelial cells. In agreement with this, BIK protein was shown to be overexpressed in MDA-MB 231 relative to MCF-7 cells. CONCLUSIONS: Our results showed an association between BIK expression and the BC tumor subtype under study, which could be related to different BIK functions in the BC subtypes.

OBJETIVO: Determinar el grado de expresión de BIK en tejidos de cáncer de mama de diferente subtipo histológico para ahondar en la participación de BIK en este tipo de cancer. MÉTODO: Por medio de inmunohistoquímica se determinó la expresión de BIK y de su forma fosforilada (p-BIK) en microarreglos de tejidos (tumores [n = 90] y tejidos adyacentes [n = 40]) y líneas celulares. RESULTADOS: Los datos mostraron una sobreexpresión de BIK en los carcinomas de tipo ductal invasivo (grados I, IIA y IIB) y lobular (grados IIA y IIB) con respecto a sus tejidos adyacentes respectivos. En contraste, el carcinoma canalicular (grados I y IIB) y los tumores filoides mostraron una baja expresión de BIK en relación con sus tejidos adyacentes respectivos. El análisis de la qPCR mostró una menor expresión de BIK en las células MCF-10A y MCF-7 en comparación con las células MDA-MB-231 y HMEC. En concordancia con esto, la expresión proteica de BIK fue mayor en las células MDA-MB 231 que en las células MCF-7. CONCLUSIÓN: Nuestros resultados mostraron una asociación entre la expresión de BIK y el subtipo tumoral en estudio, lo cual sugiere una función diferencial de BIK en el cáncer de mama.

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning.

Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients. However, despite considerable efforts and progress, there is a need to identify the specific genomic factors responsible for, or accompanying Invasive Ductal Carcinoma (IDC) progression stages, which can aid the determination of the correct cancer stages. We have developed two-class machine-learning classification models to differentiate the early and late stages of IDC. The prediction models are trained with RNA-seq gene expression profiles representing different IDC stages of 610 patients, obtained from The Cancer Genome Atlas (TCGA). Different supervised learning algorithms were trained and evaluated with an enriched model learning, facilitated by different feature selection methods. We also developed a machine-learning classifier trained on the same datasets with training sets reduced data corresponding to IDC driver genes. Based on these two classifiers, we have developed a web-server Duct-BRCA-CSP to predict early stage from late stages of IDC based on input RNA-seq gene expression profiles. The analysis conducted by us also enables deeper insights into the stage-dependent molecular events accompanying IDC progression. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP.

The Contribution of Germline Predisposition Gene Mutations to Clinical Subtypes of Invasive Breast Cancer From a Clinical Genetic Testing Cohort.

BACKGROUND: The germline cancer predisposition genes associated with increased risk of each clinical subtype of breast cancer, defined by estrogen receptor (ER), progesterone receptor (PR), and HER2, are not well defined. METHODS: A total of 54 555 invasive breast cancer patients with 56 480 breast tumors were subjected to clinical hereditary cancer multigene panel testing. Heterogeneity for predisposition genes across clinical breast cancer subtypes was assessed by comparing mutation frequencies by gene among tumor subtypes and by association studies between each tumor subtype and reference controls. RESULTS: Mutations in 15 cancer predisposition genes were detected in 8.6% of patients with ER+/HER2-; 8.9% with ER+/HER2+; 7.7% with ER-/HER2+; and 14.4% of ER-/PR-/HER2- tumors. BRCA1, BRCA2, BARD1, and PALB2 mutations were enriched in ER- and HER2- tumors; RAD51C and RAD51D mutations were enriched in ER- tumors only; TP53 mutations were enriched in HER2+ tumors, and ATM and CHEK2 mutations were enriched in both ER+ and/or HER2+ tumors. All genes were associated with moderate (odds ratio > 2.00) or strong (odds ratio > 5.00) risks of at least one subtype of breast cancer in case-control analyses. Mutations in ATM, BARD1, BRCA1, BRCA2, CHEK2, PALB2, RAD51C, RAD51D, and TP53 had predicted lifetime absolute risks of at least 20.0% for breast cancer. CONCLUSIONS: Germline mutations in hereditary cancer panel genes confer subtype-specific risks of breast cancer. Combined tumor subtype, age at breast cancer diagnosis, and family history of breast and/or ovarian cancer information provides refined categorical estimates of mutation prevalence for women considering genetic testing.

In Situ DESI-MSI Lipidomic Profiles of Breast Cancer Molecular Subtypes and Precursor Lesions.

Clinically meaningful molecular subtypes for classification of breast cancers have been established, however, initiation and progression of these subtypes remain poorly understood. The recent development of desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) facilitates the convergence of analytical chemistry and traditional pathology, allowing chemical profiling with minimal tissue pretreatment in frozen samples. Here, we characterized the chemical composition of molecular subtypes of breast cancer with DESI-MSI. Regions of interest were identified, including invasive breast cancer (IBC), ductal carcinoma in situ (DCIS), and adjacent benign tissue (ABT), and metabolomic profiles at 200 µm elaborated using Biomap software and the Lasso method. Top ions identified in IBC regions included polyunsaturated fatty acids, deprotonated glycerophospholipids, and sphingolipids. Highly saturated lipids, as well as antioxidant molecules [taurine (m/z 124.0068), uric acid (m/z 167.0210), ascorbic acid (m/z 175.0241), and glutathione (m/z 306.0765)], were able to distinguish IBC from ABT. Moreover, luminal B and triple-negative subtypes showed more complex lipid profiles compared with luminal A and HER2 subtypes. DCIS and IBC were distinguished on the basis of cell signaling and apoptosis-related ions [fatty acids (341.2100 and 382.3736 m/z) and glycerophospholipids (PE (P-16:0/22:6, m/z 746.5099, and PS (38:3), m/z 812.5440)]. In summary, DESI-MSI identified distinct lipid composition between DCIS and IBC and across molecular subtypes of breast cancer, with potential implications for breast cancer pathogenesis. SIGNIFICANCE: These findings present the first in situ metabolomic findings of the four molecular subtypes of breast cancer, DCIS, and normal tissue, and add to the understanding of their pathogenesis.

Diverse histomorphology of HER2-positive breast carcinomas based on differential ER expression.

AIMS: HER2-positive (HER2+) breast carcinoma (BC) cases are often treated similarly; however, they can be classified as either luminal B (LH) or non-luminal type (NLH) BC, which have different prognoses. In this study, we investigated the clinicohistomorphological features of each HER2+ BC subgroup. METHODS AND RESULTS: We classified 166 patients with HER2+ invasive BC into LH (n = 110, 66.3%) and NLH groups (n = 56, 33.7%). We further subclassified LH into patients with carcinomas expressing high levels of hormone receptors [LH-high; Allred score, oestrogen receptor (ER) and/or progesterone receptor (PgR) 4-8, n = 89, 53.6%] or low levels (LH-low; Allred score, ER and/or PgR 2 or 3, n = 21, 12.7%) for clinicohistomorphological characterisation. Morphological review showed that NLH included a percentage of patients with comedo necrosis, while LH patients had significantly more central scarring. In terms of immune responsiveness, NLH showed significantly higher rates of tumour-infiltrating lymphocytes and healing. The LH-high and NLH groups showed distinct characteristics (by both models, P < 0.05) and the LH-low group appeared to demonstrate intermediate characteristics according to multinomial analyses using covariates reflecting tumour morphology and immune response outcomes. CONCLUSIONS: These results support the classification of HER2+ BC into two major subgroups, LH-high and NLH, based on tumour morphology and immune response; LH-high proliferates via scirrhous and/or spiculated growth with a central scar, while the primary proliferation pattern of NLH is based on in-situ carcinomas containing comedo necrosis with noticeable TILs and healing.

Luminal B is the Most Common Intrinsic Molecular Subtypes of Invasive Ductal Breast Carcinoma Patients in East Kalimantan, Indonesia.

Objective: Breast carcinoma has no longer been considered as a single and standalone disease. Its subtypes have been known to vary in terms of risk factors, natural histories, and responses to therapies. In particular, intrinsic molecular subtypes based on St. Gallen International Expert Consensus 2013 have classified breast carcinoma into luminal A, luminal B, HER2+, and triple-negative, depending on the expression of ER, PgR, HER2, and Ki-67. Research on intrinsic molecular subtypes of breast carcinoma in Indonesia, however, are rarely conducted, which then triggers the intention to conduct this study. Methods: In this work, a retrospective study was conducted on 92 formalin-fixed paraffin-embedded samples of invasive ductal breast carcinoma patients. These samples were from patients at Abdul Wahab Sjahranie County General Hospital Samarinda, East Kalimantan, Indonesia, in 2016. Next, immunohistochemical staining using anti-ER, PgR, HER2, and Ki-67 antibodies was applied to classify intrinsic molecular subtypes. Then, an association between clinical and immunohistochemical factors with intrinsic molecular subtypes of breast carcinoma were analyzed using Chi-square test. Results: Looking at results of the retrospective study, luminal B was discovered as the most common intrinsic molecular subtypes of breast carcinoma (42.39%) in East Kalimantan, Indonesia. The next ranks of breast carcinoma subtypes in the region included HER2+ (39.13%), triple-negative (10.87%), and luminal A (7.61%). In fact, there was a significant association between age (p = 0.019) with intrinsic molecular subtypes of breast carcinoma. Conclusion: The study found luminal B as the most common intrinsic molecular subtypes of Indonesian breast carcinoma in the region under investigation. In the future, the higher positivity rate of luminal B in breast carcinoma patients compared to prior studies would require further investigations.

Clinicopathological Features of Triple-Negative Breast Cancer Epigenetic Subtypes.

BACKGROUND/OBJECTIVE: Triple-negative breast cancer (TNBC) is a heterogeneous collection of breast tumors with numerous differences including morphological characteristics, genetic makeup, immune-cell infiltration, and response to systemic therapy. DNA methylation profiling is a robust tool to accurately identify disease-specific subtypes. We aimed to generate an epigenetic subclassification of TNBC tumors (epitypes) with utility for clinical decision-making. METHODS: Genome-wide DNA methylation profiles from TNBC patients generated in the Cancer Genome Atlas project were used to build machine learning-based epigenetic classifiers. Clinical and demographic variables, as well as gene expression and gene mutation data from the same cohort, were integrated to further refine the TNBC epitypes. RESULTS: This analysis indicated the existence of four TNBC epitypes, named as Epi-CL-A, Epi-CL-B, Epi-CL-C, and Epi-CL-D. Patients with Epi-CL-B tumors showed significantly shorter disease-free survival and overall survival [log rank; P = 0.01; hazard ratio (HR) 3.89, 95% confidence interval (CI) 1.3-11.63 and P = 0.003; HR 5.29, 95% CI 1.55-18.18, respectively]. Significant gene expression and mutation differences among the TNBC epitypes suggested alternative pathway activation that could be used as ancillary therapeutic targets. These epigenetic subtypes showed complementarity with the recently described TNBC transcriptomic subtypes. CONCLUSIONS: TNBC epigenetic subtypes exhibit significant clinical and molecular differences. The links between genetic make-up, gene expression programs, and epigenetic subtypes open new avenues in the development of laboratory tests to more efficiently stratify TNBC patients, helping optimize tailored treatment approaches.

Invasive ductal carcinoma with coexisting ductal carcinoma in situ (IDC/DCIS) versus pure invasive ductal carcinoma (IDC): a comparison of clinicopathological characteristics, molecular subtypes, and clinical outcomes.

PURPOSE: Ductal carcinoma in situ (DCIS) is widely recognized as the precursor of invasive ductal carcinoma (IDC). We aimed to analyze the clinicopathological characteristics and clinical outcomes of coexisting DCIS component in IDC and its clinical significance according to molecular subtypes. METHODS: Data from 3001 patients with IDC (79.4%) and IDC/DCIS (20.6%) who underwent surgery from January 2009 to June 2016 were retrospectively assessed. The clinical outcomes of IDC with coexistent DCIS in different molecular subtypes were evaluated. RESULTS: IDC/DCIS patients were more likely to be younger (P < 0.001), had low tumor grade (P = 0.001), had less lymph node involvement (P = 0.038) and received more mastectomy (P = 0.002) than IDC patients. In the comparison of molecular subtype prevalence, IDC/DCIS patients were more frequently presented with luminal B/HER2 positive (12.5% vs 11.0%, P < 0.001) and HER2 positive subtypes (20.9% vs 9.8%, P < 0.001). The 5-year disease-free survival (DFS, 90.9% vs 87.5%, P = 0.021) and 5-year overall survival (OS 96.1% vs 94.0%, P = 0.018) were significantly improved in IDC/DCIS patients compared to IDC patients. In multivariate analysis, the presence of coexisting DCIS (P = 0.048), tumor size (P < 0.001), lymph node status (P < 0.001), lymphovascular invasion (P = 0.007) and molecular subtypes (P < 0.001) were independent prognostic factors for DFS. Furthermore, coexistence of DCIS component in IDC significantly improved DFS in HER2 positive (94.8% vs 78.5%, P = 0.003), but had no association in luminal and triple negative subtypes. CONCLUSIONS: IDC with coexisting DCIS was associated with improved prognosis. Patients with IDC/DCIS presented with more HER2 positive expression and might improve DFS in HER2 positive breast cancer.

Increased RNA Expression of von Willebrand Factor Gene Is Associated With Infiltrating Lobular Breast Cancer and Normal PAM50 Subtype.

BACKGROUND: Infiltrating lobular carcinoma (ILC) is the second most common histologicaI subtype of breast cancer, accounting for 10% of all cases. ILC has a characteristic genomic profile. ILC shows a high frequency of cadherin 1 (CDH1) mutations, along with loss of phosphatase and tensin homolog (PTEN), activation of alpha serine/threonine kinase (AKT), and mutations in T-box transcription factor (TBX3) and forkhead box protein A1 (FOXA1). We suspected that another gene, von Willebrand factor (VWF), might also be part of the profile, since coagulation tests reveal significant differences in patients with breast cancer. MATERIALS AND METHODS: For newly-diagnosed breast cancer, the association between VWF and histology in the GDC Breast Cancer dataset in The Cancer Genome Atlas (TCGA) was evaluated. The following were used to access and analyze the data: Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov/); Xena browser (https://xenabrowser.net); cBioportal (http://cbioportal.org); Oncomine (https://oncomine.org); and Prediction Analysis of Microarray 50 (PAM50). RESULTS: Patients with ILC had higher VWF RNA expression than patients with infiltrating ductal carcinoma and other histology. The difference of expression was present to the same degree in both pre-menopausal and post-menopausal patients. Nine alterations in VWF and PTEN were significantly co-occurrent. Considering all histologies in 843 samples, Tukey's honest significant difference post hoc test showed that VWF RNA expression of the normal subtype was significantly greater than that of the other subtypes (p<0.001). CONCLUSION: Our finding of significantly higher VWF RNA expression in the PAM50 normal (non-basal-like) breast cancer subtype suggests that VWF protein measurement might complement or corroborate PAM50 results. VWF and PAM50 results both suggesting a low risk of recurrence might make the decision whether to give chemotherapy easier, especially if VWF protein were an independent predictor.

Breast Cancer Subtype Classification Using 4-Plex Droplet Digital PCR.

BACKGROUND: Infiltrating ductal carcinoma (IDCA) is the most common form of invasive breast cancer. Immunohistochemistry (IHC) is widely used to analyze estrogen receptor 1 (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) that can help classify the tumor to guide the medical treatment. IHC examinations require experienced pathologists to provide interpretations that are subjective, thereby lowering the reproducibility of IHC-based diagnosis. In this study, we developed a 4-plex droplet digital PCR (ddPCR) for the simultaneous and quantitative analyses of estrogen receptor 1 (ESR1), progesterone receptor (PGR), erb-b2 receptor tyrosine kinase 2 (ERBB2), and pumilio RNA binding family member 1 (PUM1) expression levels in formalin-fixed paraffin-embedded (FFPE) samples. METHODS: We evaluated the sensitivity, reproducibility, and linear dynamic range of 4-plex ddPCR. We applied this method to analyze 95 FFPE samples from patients with breast IDCA and assessed the agreement rates between ddPCR and IHC to evaluate its potential in classifying breast cancer subtypes. RESULTS: The limits of quantification (LOQ) were 25, 50, 50, and 50 copies per reaction for ERBB2, ESR1, PGR, and PUM1, respectively. The dynamic ranges of ESR1, PGR, and PUM1 extended over 50-1600 copies per reaction and those of ERBB2 from 25 to 1600 copies per reaction. The concordance correlation coefficients between 4-plex ddPCR and IHC were 96.8%, 91.5%, and 85.1% for ERBB2, ESR1, and PGR, respectively. Receiver operating characteristic curve area under the curve values of 0.991, 0.977, and 0.920 were generated for ERBB2, ESR1, and PGR, respectively. CONCLUSIONS: Evaluation of breast cancer biomarker status by 4-plex ddPCR was highly concordant with IHC in this study.

A high-risk luminal A dominant breast cancer subtype with increased mobility.

PURPOSE: Breast cancer is a heterogeneous disease, and although advances in molecular subtyping have been achieved in recent years, most subtyping strategies target individual genes independent of one another and primarily concentrate on proliferative markers. The contributions of biological processes and immune patterns have been neglected in breast cancer subtype stratification. METHODS: We performed a gene set variation analysis to simplify the information on biological processes using hallmark terms and to decompose immune cell data using the immune cell gene terms on 985 breast invasive ductal/lobular carcinoma RNAseq samples in the TCGA database. RESULTS: The samples were gathered into three clusters following implementation of the t-SNE and DBSCAN algorithms and were categorized as 'hallmark-tsne' subtypes. Here, we identified a high-risk luminal A dominant breast cancer subtype (C3) that displayed increased motility, cancer stem cell-like features, a higher expression of hormone/luminal-related genes, a lower expression of proliferation-related genes and immune dysfunction. With regard to immune dysfunction, we observed that the motility-increased C3 subtype exhibited high granulocyte colony stimulating factor (G-CSF) expression accompanied by neutrophil aggregation. Cancer cells that produce high levels of G-CSF can stimulate neutrophils to form neutrophil extracellular traps, which promote cancer cell migration. This finding sheds light on one potential explanation for why the C3 subtype correlates with poor prognosis. CONCLUSIONS: The hallmark-tsne subtypes confirmed again that even the luminal A subtype is heterogeneous and can be further subdivided. The biological processes and immune heterogeneity of breast cancer must be understood to facilitate the improvement of clinical treatments.

Identification of a subtype of poorly differentiated invasive ductal carcinoma of the breast based on vimentin and e-cadherin expression / Identificação de um subtipo pouco diferenciado de carcinoma ductal invasivo de mama, baseado na expressão da vimentina e E-caderina

Abstract Objective The use of molecular markers can identify a subgroup of tumors with distinct recurrence patterns. The present study aimed to characterize the immunohistochemical expression of vimentin (VIM), of E-cadherin (CDH1), and of cytokeratin 5 (CK5) in patients with invasive ductal carcinomas (IDCs). Methods We have constructed a tissuemicroarray (TMA) from87 patients with IDC of the breast. Immunohistochemistry (IHC) was performed to study the expression of estrogen and progesterone receptors (ER and PgR), human epidermal growth factor receptor 2 (HER2), VIM, CDH1, CK5, and Ki67. The tumors were classified as luminal A and B (n = 39), HER2 enriched (n = 25), and triple-negative (TNBC) (n = 23), based on the IHC expression. Results We have observed that luminal A and B tumors lack the VIM+/CDH1-/low phenotype. This phenotype was observed in 16.5% of the HER2+ tumors and in 60% of the TNBC tumors (p = 0.0001). Out of a total of 20 TNBC tumors, the CK5 (basal-like marker) was positive in 11 of them. The VIM+/CDH1-/low phenotype was observed in 5 CK5+ TNBC tumors (45%) and in 7 out of 9 CK5- TNBC tumors (78%) (p = 0.02). The median Ki67 index in the VIM+/CDH1-/low tumors was 13.6 (range: 17.8-45.4) compared with 9.8 (range: 4.1-38.1) in other tumors (p = 0.0007). The presence of lymph nodemetastasis was less frequent in patients with VIM+/CDH1-/low tumors (23% versus 61%; X2 test; p = 0.01). Conclusion Our findings suggest that the expression of VIM and CDH1 can identify a subset of IDCs of the breast with a mesenchymal phenotype associated with poor prognosis, high-grade lesion, and high mitotic index.

Resumo Objetivo O uso de marcadores moleculares pode identificar subtipos tumorais com diferentes taxas de recidiva. O objetivo do presente estudo é caracterizar a expressão imunohistoquímica da vimentina (VIM), da E-caderina (CDH1) e de CK5 em pacientes com carcinoma ductal invasivo (CDI) da mama. Métodos Utilizamos uma matriz de amostras teciduais (TMA, na sigla em inglês) de 87 pacientes com CDI da mama. Para avaliar a expressão dos receptores de estrogênio (RE) e receptores de progesterona (RP), HER2, VIM, CDH1, CK5 e Ki67, utilizamos imunohistoquímica. Os tumores foram classificados como luminal A e B (n = 39), HER2+ (n = 25) e triplo negativo (TNBC) (n = 23). Resultados Foi observado que tumores luminais A e B não expressaram o fenótipo VIM+/CDH1-/low. Este fenótipo foi observado em 16,5% dos tumores HER2+ e em 60% dos tumores TNBC (p = 0,0001). Dos 20 tumores TNBC, a CK5 (marcador de tumor basalóide) foi super expressa em 11 amostras. O fenótipo VIM+/CDH1-/low foi observado em 5 tumores CK5+ TNBC (45%) e em 7 dos 9 tumores CK5- TNBC (78%) (p = 0,02). A expressão média de Ki67 nos tumores VIM+/CDH1-/low foi 13.6 (amplitude de 17,8 a 45,4) comparado com 9,8 (amplitude de 4,1 a 38,1) nos outros tumores (p = 0,0007). A presença demetástase linfonodal foimenor em tumores com fenótipo VIM+/CDH1-/low (23% contra 61%; teste X2; p = 0,01). Conclusão Nossos achados sugerem que a expressão de VIM e CDH1 pode identificar um subtipo de CDI da mama com fenótipo mesenquimal associado a pior prognóstico, lesões de alto grau e alto índice mitótico.

Cancer stem cell markers ALDH1 and CD44+/CD24- phenotype and their prognosis impact in invasive ductal carcinoma.

Breast cancer is a very heterogeneous disease. The intrinsic molecular subtypes can explain the intertumoral heterogeneity and the cancer stem cell (CSC) hypothesis can explain the intratumoral heterogeneity of this kind of tumor. CD44+/CD24- phenotype and ALDH1 expression are the major CSC markers described in invasive breast cancer. In the present study, 144 samples of invasive breast carcinoma, no special type were distributed in 15 tissue microarrays (TMA) and then evaluated for expression of the CD44+/CD24- phenotype and ALDH1 to understand the importance of these CSC markers and the clinical aspects of breast cancer. The samples were classified into four molecular subtypes according to clinicopathological criteria: Luminal A, Luminal B, HER2, and Basal-like. A statistical association was found between the molecular subtypes and the CSC markers, with HER2 the most frequent subtype for both markers. ALDH1 was also associated with other poor prognostic variables, such as a high histological grade and larger tumors, but it was not associated with the patients' prognosis in this sample and nor was the CD44+/CD24- phenotype in a multivariate analysis. There are still many controversies about the role of these markers in breast cancer molecular subtypes. The identification of these populations of cells, through immunohistochemical markers, can help to better understand the CSC theory in clinical practice and, in the near future, contribute to developing new target therapies.

Fast unsupervised nuclear segmentation and classification scheme for automatic allred cancer scoring in immunohistochemical breast tissue images.

BACKGROUND AND OBJECTIVE: This paper presents an improved scheme able to perform accurate segmentation and classification of cancer nuclei in immunohistochemical (IHC) breast tissue images in order to provide quantitative evaluation of estrogen or progesterone (ER/PR) receptor status that will assist pathologists in cancer diagnostic process. METHODS: The proposed segmentation method is based on adaptive local thresholding and an enhanced morphological procedure, which are applied to extract all stained nuclei regions and to split overlapping nuclei. In fact, a new segmentation approach is presented here for cell nuclei detection from the IHC image using a modified Laplacian filter and an improved watershed algorithm. Stromal cells are then removed from the segmented image using an adaptive criterion in order to get fast tumor nuclei recognition. Finally, unsupervised classification of cancer nuclei is obtained by the combination of four common color separation techniques for a subsequent Allred cancer scoring. RESULTS: Experimental results on various IHC tissue images of different cancer affected patients, demonstrate the effectiveness of the proposed scheme when compared to the manual scoring of pathological experts. A statistical analysis is performed on the whole image database between immuno-score of manual and automatic method, and compared with the scores that have reached using other state-of-art segmentation and classification strategies. According to the performance evaluation, we recorded more than 98% for both accuracy of detected nuclei and image cancer scoring over the truths provided by experienced pathologists which shows the best correlation with the expert's score (Pearson's correlation coefficient = 0.993, p-value < 0.005) and the lowest computational total time of 72.3 s/image (±1.9) compared to recent studied methods. CONCLUSIONS: The proposed scheme can be easily applied for any histopathological diagnostic process that needs stained nuclear quantification and cancer grading. Moreover, the reduced processing time and manual interactions of our procedure can facilitate its implementation in a real-time device to construct a fully online evaluation system of IHC tissue images.