Invasive Ductal Breast Cancer Patients Research Articles

Can we skip invasive biopsy of sentinel lymph nodes? A preliminary investigation to predict sentinel lymph node status using PET/CT-based radiomics

BackgroundSentinel lymph node (SLN) biopsy (SLNB) is considered the gold standard for detecting SLN metastases in patients with invasive ductal breast cancer (IDC). However, SLNB is invasive and associated with several complications. Thus, this study aimed to evaluate the diagnostic performance of a non-invasive radiomics analysis utilizing 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography/computed tomography (18F-FDG-PET/CT) for assessing SLN metastasis in IDC patients.MethodsThis retrospective study included 132 patients with biopsy-confirmed IDC, who underwent 18F-FDG PET/CT scans prior to mastectomy or breast-conserving surgery with SLNB. Tumor resection or SLNB was conducted within one-week post-scan. Clinical data and metabolic parameters were analyzed to identify independent SLN metastasis predictors. Radiomic features were extracted from each PET volume of interest (VOI) and CT-VOI. Feature selection involved univariate and multivariate logistic regression analysis, and the least absolute shrinkage and selection operator (LASSO) method. Three models were developed to predict SLN status using the random forest (RF), decision tree (DT), and k-Nearest Neighbors (KNN) classifiers. Model performance was assessed using the area under the receiver operating characteristic curve (AUC).ResultsThe study included 91 cases (32 SLN-positive and 59 SLN-negative patients) in the training cohort and 41 cases (29 SLN-positive and 12 SLN-negative patients) in the validation cohort. Multivariate logistic regression analysis identified Ki 67 and TLG as independent predictors of SLN status. Five PET-derived features, three CT-derived features, and two clinical variables were selected for model development. The AUC values of the RF, KNN, and DT models for the training cohort were 0.887, 0.849, and 0.824, respectively, and for the validation cohort were 0.856, 0.830, and 0.819, respectively. The RF model demonstrated the highest accuracy for the preoperative prediction of SLN metastasis in IDC patients.ConclusionThe PET-CT radiomics approach may offer robust and non-invasive predictors for SLN status in IDC patients, potentially aiding in the planning of personalized treatment strategies for IDC patients.

CD105 expression in cancer-associated fibroblasts: a biomarker for bone metastasis in early invasive ductal breast cancer patients.

Introduction: Bone metastasis is one of the causes that mainly decrease survival in patients with advanced breast cancer. Therefore, it is essential to find prognostic markers for the occurrence of this type of metastasis during the early stage of the disease. Currently, cancer-associated fibroblasts, which represent 80% of the fibroblasts present in the tumor microenvironment, are an interesting target for studying new biomarkers and developing alternative therapies. This study evaluated the prognostic significance of the CD105 expression in cancer-associated fibroblasts in early breast cancer patients. Methods: Immunohistochemistry was used to assess CD105 expression in invasive ductal breast carcinomas (n = 342), analyzing its association with clinical and pathological characteristics. Results: High CD105 expression in cancer-associated fibroblasts was associated with an increased risk of metastatic occurrence (p = 0.0003), particularly bone metastasis (p = 0.0005). Furthermore, high CD105 expression was associated with shorter metastasis-free survival, bone metastasis-free survival, and overall survival (p = 0.0002, 0.0006, and 0.0002, respectively). CD105 expression also constituted an independent prognostic factor for metastasis-free survival, bone metastasis-free survival, and overall survival (p = 0.0003, 0.0006, and 0.0001, respectively). Discussion: The high CD105 expression in cancer-associated fibroblasts is an independent prognostic marker for bone metastasis in early breast cancer patients. Therefore, the evaluation of CD105(+) CAFs could be crucial to stratify BCPs based on their individual risk profile for the development of BM, enhancing treatment strategies and outcomes.

Evaluating the Correlation of Hedgehog Pathway with Claudin-1 Expression in Invasive Breast Cancer.

Being a heterogeneous disease, breast cancer illustrates different biological and phenotypic features which make its diagnosis and treatment challenging. This study aimed to evaluate the expression levels of crucial components of the Hedgehog signaling pathway, the correlation between the signal transducer Smo, and clinicopathologic features (lymph node metastasis and metastasis stage) in invasive breast carcinoma. Besides, the inverse correlation was considered between expression levels of Smo and Claudin-1. For this purpose, in a case-control study, we evaluated 72 tumor and adjacent normal tissue specimens obtained from invasive ductal breast cancer patients. The expression levels of Hedgehog signaling components (Smo, Gli1, and Ptch), Claudin-1, E-cadherin, and MMP2 were measured by qRT-PCR. The correlations between Smo expressions with some clinicopathologic parameters were also analyzed. Compared to normal adjacent tissues, the results showed up-regulation of Hedgehog signaling in invasive breast carcinoma samples. Upregulation of the signal transducer Smo correlated with tumor stages and lymph node metastasis of the breast tumors. This correlation was affected by the expression of Her2. A significant correlation existed between expression levels of the signal transducer Smo and Claudin-1, E-cadherin as an epithelial cell marker, and MMP2 as a metastasis-related gene in advanced metastatic tumor samples. Based on the obtained results, a new layer of molecular complexity was found, which should be considered in managing patients with invasive breast carcinoma. The results suggested a key role for Hedgehog signaling in invasive breast carcinoma. In terms of the inverse correlation between expression levels of Claudin-1 and Hedgehog signaling, Claudin-1 could serve as a candidate gene in diagnostic studies. Thus, its clinical significance should be further clarified.

Correlation of Her-2 Expression with Clinicopathological Characteristics in Invasive Ductal Breast Cancer Patients

Breast cancer is the most common cancer in the world and a leading cause of death in women. This cancer is the major cause of mortality and morbidity in Indonesian women. This study aims to investigate the correlation of Her-2 expression with a clinicopathological profile in invasive ductal breast cancer. This study was retrospective in design. Histopathological diagnosis and immunohistochemical ER, PR, and Her2 from 95 patients with invasive ductal breast cancer were included during the period January 2017 to June 2018.

Predictive Value of 18F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer.

Background: To develop and validate a radiomics model based on 18F-FDG PET/CT images to preoperatively predict occult axillary lymph node (ALN) metastases in patients with invasive ductal breast cancer (IDC) with clinically node-negative (cN0); Methods: A total of 180 patients (mean age, 55 years; range, 31–82 years) with pathologically proven IDC and a preoperative 18F-FDG PET/CT scan from January 2013 to January 2021 were included in this retrospective study. According to the intraoperative pathological results of ALN, we divided patients into the true-negative group and ALN occult metastasis group. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, t-tests, and LASSO were used to screen the feature, and the random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD), and k-nearest neighbor (KNN) were used to build the prediction models. The best-performing model was further tested by the permutation test; Results: Among the four models, RF had the best prediction results, the AUC range of RF was 0.661–0.929 (mean AUC, 0.817), and the accuracy range was 65.3–93.9% (mean accuracy, 81.2%). The p-values of the permutation tests for the RF model with maximum and minimum accuracy were less than 0.01; Conclusions: The developed RF model was able to predict occult ALN metastases in IDC patients based on preoperative 18F-FDG PET/CT radiomic features.

Impact of hormone receptor, HER2, and Ki-67 status conversions on survival after neoadjuvant chemotherapy in breast cancer patients: a retrospective study

BackgroundThe discordance of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67 cell nuclear proliferation antigen status in patients with locally advanced breast cancer pre- and post-neoadjuvant chemotherapy (NAC) is quite common. This study aimed to assess the frequency of changes in receptor status after NAC in patients with invasive ductal breast cancer and the prognostic impact of such changes.MethodsThe study comprised 670 patients who were diagnosed with invasive ductal breast carcinoma and treated with both NAC and surgery from 2012–2017. Hormone receptor (HR; including ER and PR), HER2, and Ki-67 status was assessed before NAC and in residual invasive tumor cells of the surgical specimens. The prognostic impact of receptor conversions in breast cancer patients treated with NAC was evaluated in this retrospective study.ResultsThe conversion of ER was related to overall survival (OS; P=0.008) and disease-free survival (DFS; P=0.004). Patients whose ER status was always positive had the best prognosis, and those who were always negative had the worst prognosis. Similar results were also found for PR status, as the conversion of PR status was also related to OS (P<0.001) and DFS (P<0.001). At the same time, the conversion of Ki-67 status was related to OS (P=0.042) and DFS (P=0.037), and patients whose Ki-67 status was ≤20% persistently after NAC had the best survival among the 4 groups, while those whose Ki-67 status changed from ≤20% to >20% after NAC had the worst survival. Nevertheless, there was no statistical significance in the conversion of HER2 status. In multivariate Cox regression analyses, PR conversion and post-neoadjuvant pathological lymph node stage (ypN) were independent prognostic factors for DFS (P=0.008, <0.001) and OS (P=0.002, <0.001).ConclusionsChanges in receptor status between pre-treatment and residual disease after NAC are common. Moreover, these alterations have an impact on the survival outcome of invasive ductal breast cancer patients. Thus, receptor status should be re-evaluated routinely before and after NAC to guide individualized treatment.

The effectiveness of multiflora honey to prevent hepatotoxicity in invasive ductal breast cancer patients with FAC chemotherapy

Background: Breast cancer is an epithelial malignancy of the breast duct, which chemotherapy is one of the treatment modalities. Hepatotoxicity is known as one of the side effects of chemotherapy. Multiflora honey provides hepatoprotective effects through its phenolic acid and flavonoid compounds. This study will evaluate the effect of Honey in preventing hepatotoxicity in ductal invasive breast cancer patients receiving FAC chemotherapy.Methods: An experimental study with double-blind, randomized pre and post-test with control group design. About 36 ductal invasive breast cancer patients were divided into 2 groups, the control group who received FAC chemotherapy and the treatment group who received chemotherapy and 90 ml/day honey consumption for 14 days. The patient's Aspartate Transaminase (AST), Alanine Transaminase (ALT), Alkaline Phosphatase (ALP), and Gamma-Glutamyl Transferase (GGT) enzyme levels before and after 14 days of the study were assessed. Data were analyzed using SPSS version 15 for Windows by Mann-Whitney test and Wilcoxon tests.Results: There was a significant difference in AST (33.72±44.35 U/L vs. 23.22±4.48 U/L; p&lt;0.001) and ALT (29.72±16.96 U/L vs. 26.06±9.19 U/L; p=0.020) levels in the treatment vs. control group. In addition, a significant difference of ALP (107.00±65.89 U/L vs. 103.17±35.30 U/L; p=0.009) and GGT (56.61±58.16 U/L vs. 51.33±27.50 U/L; p=0.035) levels in the treatment vs. control group was also found in this study.Conclusion: Multiflora honey has a hepatoprotective effect for invasive ductal breast cancer patients receiving FAC chemotherapy.

Decreased Survival of Invasive Ductal Breast Cancer Patients With Two Macrometastatic Lymph Nodes Among Few Resected Ones: Should Current Sentinel-Lymph-Node Guidelines Be Revised?

PurposeSentinel-lymph-node (SLN) biopsy (SLB) is an efficient and safe axillary surgical approach with decreased morbidity than total axillary lymph node dissection (ALND) in initial patients (T1–T2). Current guidelines strongly suggest avoiding completion of ALND in patients with one or two positive SLNs that will be submitted to whole-breast radiation therapy, but must be done when three SLNs are affected.MethodsWe performed a SEER-based study with breast invasive ductal carcinoma patients treated between 2010 and 2015. Optimal cutoffs of positive LNs predictive of survival were obtained with ROC curves and survival as a continuous variable. Bias was reduced through propensity score matching. Cox regression was employed to estimate prognosis. Nomograms were constructed to analyze the predictive value of clinicopathological factors for axillary burden.ResultsOf 43,239 initial patients that had one to three analyzed LNs, only 425 had two positive LNs and matched analysis demonstrated no survival difference versus pN2 patients [HR: 0.960 (0.635–1.452), p = 0.846]. The positive-to-analyzed LN proportion demonstrated a strong prognostic factor for a low rate (1 positive to ≤1.5 analyzed) [HR = 1.567 (1.156–2.126), p = 0.004], and analysis derived from the results demonstrated that a “negative LN margin” improves survival. Nomograms shows that tumor size is the main factor of axillary burden.ConclusionMacrometastasis of two LNs is a poor prognostic factor, similar to pN2, in SLNB (-like) patients; more extensive studies including preconized therapies must be done in order to corroborate or refute the resistance of this prognostic difference in patients with two macrometastatic lymph nodes within few resected.

CD1a- and CD83-positive dendritic cells as prognostic markers of metastasis development in early breast cancer patients.

Dendritic cells (DCs) are the most potent antigen-presenting cells that play a major role in initiating the antitumor immune response in different types of cancer. However, the prognostic significance of the accumulation of these cells in human early breast tumors is not totally clear. The aim of this study is to evaluate the prognostic relevance of CD1a( +) and CD83( +) dendritic cells in early breast cancer patients. We conducted immunohistochemical assays to determine the number of stromal CD1a( +) and CD83( +) DCs in primary tumors from early invasive ductal breast cancer patients, and analyzed their association with clinico-pathological characteristics. Patients with high CD1a( +) DC number had lower risk of bone metastatic occurrence, as well as, longer disease-free survival (DFS), bone metastasis-free survival (BMFS) and overall survival (OS). Moreover, CD1a( +) DC number was an independent prognostic factor for BMFS and OS. In contrast, we found that patients with high number of CD83( +) DCs had lower risk of mix (bone and visceral)-metastatic occurrence. Likewise, these patients presented better prognosis with longer DFS, mix-MFS and OS. Furthermore, CD83( +) DC number was an independent prognostic factor for DFS and OS. The quantification of the stromal infiltration of DCs expressing CD1a or CD83 in early invasive breast cancer patients serves to indicate the prognostic risk of developing metastasis in a specific site.

Abstract PS6-09: An AI-digital breast cancer risk discrimination platform (PreciseDx) using a representative H&amp;E image and selected clinical variables accurately categorized patients with oncotype Dx low risk recurrence scores (RS)

Abstract Background: Clinical practice guidelines emphasize the critical importance of grading and stage in breast cancer treatment. Although histologic grade is subjective, non-quantitative, skill-dependent, and oftentimes inaccurate it remains an independent prognostic feature and therefore plays a direct role in patient management including neoadjuvant therapy vs surgery, and interpretation of genomic studies. We developed an AI-based platform which combines digital H&amp;E features with select clinical variables to assess risk of breast cancer recurrence and evaluated ability to predict Oncotype low risk RS categorization. Methods: Retrospective study to identify a subset of Mt. Sinai, NY invasive ductal breast cancer (IDC) patients from 2010-2016 with H&amp;E stained slides, clinical features and OncotypeDx recurrence scores (RS). Recurrence endpoint(s): local- regional, distant-recurrence free and overall survival. Digital images generated with Philips scanning system; reviewed by two pathologist for tumor content and quality prior to image analysis and feature extraction. Support vector machine learning models were used for initial feature performance and final models generated. Positive predictive value (PPV), sensitivity (S) and likelihood ratios (LR) were used for performance. Results: 391 patients: mean age 57 years, 100% Stage I/II, 59% Grade 2, and 6% LN+ve 0-3; 97% IDC, 100% ER+ve, 94% PR+ve, 0% Her2 amplified; median follow-up 61 months; 323 (83%) low risk (&amp;lt;/=25) RS and 68 (17%) high risk RS (&amp;gt;25). There were 23 events (6%) and 13 (56%) were locoregional recurrence. PreciseDx model with age, and PR levels combined with imaging features reflective of mitotic activity and nuclear characteristics (clinical grade not selected) correctly identified LR RS categorization: PPV of 91%, [95CI 0.87-0.94], Se 92% [0.86-0.95] and positive / negative likelihood ratio : 6.8, and 0.5, respectively. Conclusions: Application of an AI-digital breast cancer risk assessment platform using only the H&amp;E image and limited clinical data successfully classified low risk RS patients with high accuracy. Future models will extend outcome to 10 years and evaluate treatment selection and duration Citation Format: Michael Joseph Donovan, Nina Shpalensky, Marcel Prastawa, SM Abishek, Richard Scott, Mary Sawyer, Jack Zeineh, Gerardo Fernandez. An AI-digital breast cancer risk discrimination platform (PreciseDx) using a representative H&amp;E image and selected clinical variables accurately categorized patients with oncotype Dx low risk recurrence scores (RS) [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS6-09.

Association of Axillary Lymph Node Evaluation With Survival in Women Aged 70 Years or Older With Breast Cancer.

BackgroundSurvival in elderly patients undergoing sentinel lymph node biopsy (SLNB) and axillary lymph node dissection (ALND) has not been specifically analyzed. This study aimed to explore the association between different types of axillary lymph node (ALN) evaluations and survival of elderly breast cancer patients.MethodsA retrospective cohort study was conducted of invasive ductal breast cancer patients 70 years and older in the Surveillance, Epidemiology, and End Results database (2004–2016). Analyses were performed to compare the characteristics and survival outcomes of patients who received surgical lymph node dissection and those who did not. Breast cancer specific survival (BCSS) and overall survival were compared by using Cox proportional hazards regression analysis and propensity score matching (PSM) methods to account for selection bias from covariate imbalance.ResultsOf the 75,950 patients analyzed, patients without ALN evaluation had a significantly worse prognosis, while there was no significant difference for BCSS between using a sentinel lymph node biopsy (SLNB) and an axillary lymph node dissection (ALND) after adjustment for known covariates [adjusted hazard ratio (HR) = 0.991, 95% confidence interval (CI) = 0.925–1.062, p = 0.800]. In the stratification analyses after PSM, the ALND did not show a significant BCSS advantage compared with SLNB in any subgroups except for the pN1 stage or above. Furthermore, after PSM of the pN1 stage patients, SLNB was associated with a significantly worse BCSS in hormone receptor negative (HR−) patients (HR = 1.536, 95%CI = 1.213–1.946, p < 0.001), but not in the hormone receptor positive (HR+) group (HR = 1.150, 95%CI = 0.986–1.340, p = 0.075).ConclusionIn our study, ALND does not yield superior survival compared with SLNB for elderly patients with pN1 stage HR+ breast cancer. Although our findings are limited by the bias associated with retrospective study design, we believe that in the absence of results from randomized clinical trials, our findings should be considered when recommending the omission of ALND for elderly breast cancer patients.

A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer.

The aim of this study was to develop and validate machine learning-based radiomics model for predicting axillary lymph-node (ALN) metastasis in invasive ductal breast cancer (IDC) using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). A total of 100 consecutive IDC patients who underwent surgical resection of primary tumor with sentinel lymph-node biopsy and/or ALN dissection without any neoadjuvant treatment were analyzed. Volume of interests (VOIs) were drawn more than 2.5 of standardized uptake value in the primary tumor on the PET scan using 3D slicer. Pyradiomics package was used for the extraction of texture features in python. The radiomics prediction model for ALN metastasis was developed in 75 patients of the training cohort and validated in 25 patients of the test cohort. XGBoost algorithm was utilized to select features and build radiomics model. The sensitivity, specificity, and accuracy of the predictive model were calculated. ALN metastasis was found in 43 patients (43%). The sensitivity, specificity, and accuracy of F-18 FDG PET/CT for the diagnosis of ALN metastasis in the entire patients were 55.8%, 93%, and 77%, respectively. The radiomics model for the prediction of ALN metastasis was successfully developed. The sensitivity, specificity, and accuracy of the radiomics model for the prediction of ALN metastasis in the test cohorts were 90.9%, 71.4%, and 80%, respectively. The machine learning-based radiomics model showed good sensitivity for the prediction of ALN metastasis and could assist the preoperative individualized prediction of ALN status in patients with IDC.

Metabolic and volume-based parameters of (18F)FDG PET/CT for primary mass and axillary lymph node metastasis in patients with invasive ductal carcinoma: a retrospective analysis in relation to molecular subtype, axillary lymph node metastasis and immunohistochemistry and inflammatory markers.

To evaluate metabolic and volume-based parameters of 18-Fluorodeoxyglucose positron emission tomography/computed tomography ((F)FDG PET/CT) for primary mass and axillary lymph node (ALN) metastasis in relation to molecular subtype, and immunohistochemistry and inflammatory markers in patients with invasive ductal carcinoma. A total of 129 patients (mean±SD age: 49.2±13.0 years) with invasive ductal breast cancer who had (F)FDG PET/CT imaging prior to chemotherapy or surgery were included in this single-center retrospective study. Data on patient age, molecular subtype, ALN metastasis status, inflammatory markers, immunohistochemistry markers and (F)FDG PET/CT imaging parameters for primary mass and ALN were recorded. ALN metastasis was evident in 52.7% of patients and associated with significantly higher median diameter (P=0.027), MTV (P<0.001) and TLG (P<0.001). NLR was positively correlated with all primary mass (p ranged from 0.041 to 0.001) and ALN (P ranged from 0.026 to <0.001) PET parameters. PET parameters did not change with respect to molecular subtype or immunohistochemistry markers. Primary mass and ALN metastasis PET parameters showed significant positive correlations for TLG (r=0.274, P=0.001) and SUVmax (r=0.358, P<0.001). In conclusion, our findings in a retrospective cohort of invasive ductal breast cancer patients revealed primary mass PET parameters to significantly differ with respect to ALN metastasis status and NLR levels, but not according to molecular subtype or immunohistochemistry markers. Accordingly, our findings highlight the value of acquisition of preoperative PET/CT imaging and role of both metabolic and volume-based parameters in predicting aggressiveness of the tumor as correlated with presence of ALN metastasis and high NLR.

The prognostic significance of co-existence ductal carcinoma in situ in invasive ductal breast cancer: a large population-based study and a matched case-control analysis.

To evaluate the prognostic significance of co-existence ductal carcinoma in situ (DCIS) in invasive ductal breast cancer (IDC) compared with pure IDC. The Surveillance, Epidemiology, and End Results (SEER) database was searched to identify unilateral IDC cases between 2004 and 2015, which were grouped into pure IDC and IDC with DCIS component (IDC-DCIS). Comparisons of the distribution of clinical-pathological characteristics the two groups were performed using Pearson's chi-square. Breast cancer-specific survival (BCSS) and overall survival (OS) were estimated using the Kaplan-Meier method and compared across RS groups using the log-rank statistic. Cox models were fitted to assess the factors independently associated with survival. A 1:1 matched case-control analysis was conducted with each clinical-pathological characteristic matched completely. A total of 98,097 pure IDC cases (39.6%) and 149,477 IDC-DCIS cases (60.4%) were enrolled. IDC-DCIS patients were presented with less aggressive characteristics such as lower proportion of histologic grade III (34.2% vs. 42.2%, P<0.001), ER negative (16.8% vs. 26.1%, P<0.001) and PR negative (26.5% vs. 35.7%, P<0.001) disease and higher proportion of T1 cases (68.7% vs. 58.2%, P<0.001) compared with pure IDC patients. Co-existence DCIS was an independent prognostic factor for BCSS and OS in the whole cohort. According to the multivariate analysis, it was an independent favorable prognostic factor among ER positive cases, but an independent negative prognostic factor among ER negative cases based on the matched cohort. Co-existence DCIS showed quite different prognostic significance among ER positive and negative disease.

ESR1 mutations in metastatic lobular breast cancer patients

Invasive lobular breast cancer (ILC) represents the second most common histology of breast cancer after invasive ductal breast cancer (IDC), accounts for up to 15% of all invasive cases and generally express the estrogen receptor (ER, coded by the ESR1 gene). ESR1 mutations have been associated with resistance to endocrine therapy, however these have not been specifically evaluated in ILC. We assessed the frequency of ESR1 mutations by droplet digital PCR in a retrospective multi-centric series of matched primary tumor and recurrence samples (n = 279) from 80 metastatic ER-positive ILC patients. We further compared ESR1 mutations between IDC and ILC patients in metastatic samples from MSKCC-IMPACT (n = 595 IDC and 116 ILC) and in ctDNA from the SoFEA and PALOMA-3 trials (n = 416 IDC and 76 ILC). In the retrospective series, the metastases from seven patients (9%) harbored ESR1 mutations, which were absent from the interrogated primary samples. Five patients (6%) had a mutation in the primary tumor or axillary metastasis, which could not be detected in the matched distant metastasis. In the MSKCC-IMPACT cohort, as well as in the SoFEA and PALOMA-3 trials, there were no differences in prevalence and distribution of the mutations between IDC and ILC, with D538G being the most frequent mutation in both histological subtypes. To conclude, no patient had an identical ESR1 mutation in the early and metastatic disease in the retrospective ILC series. In the external series, there was no difference in terms of prevalence and type of ESR1 mutations between ILC and IDC.

Frequent methylation of genes encoding wnt pathway antagonists: Secreted frizzled-related protein 1 and dickkopf 3 in invasive breast cancer

Introduction: Wnt signaling pathway is often dysregulated in the pathogenesis of various malignancies, including breast cancer. This might be related to methylation of the genes encoding antagonists of this signaling pathway. Aim: The aim of the study was to analyze the methylation status of the promoter regions of Wnt antagonists-secreted frizzled-related protein 1 (sFRP1) and Dickkopf 3 (DKK3) and to determine their correlation with clinicopathological parameters and survival outcome in patients with primary invasive ductal breast cancer. Materials and Methods: The methylation status of sFRP1 and DKK3 was analyzed in 160 breast tumor samples using methylation-specific polymerase chain reaction. Statistical analysis was performed using SPSS software. P ≤ 0.05 was considered as statistically significant. Results: The promoter region of sFRP1 and DKK3 genes was found to be methylated in 76% and 64% of total invasive ductal breast cancer patients, respectively. The promoter methylation in sFRP1 and DKK3 genes was significantly associated with larger tumor size, positive lymph nodes, advanced stage, and perinodal extension of breast tumors. Further, sFRP1 methylation was associated with human epidermal growth factor receptor 2-positive tumors while DKK3 methylation was associated with Grade 3 tumors. Survival analysis demonstrated that sFRP1 methylation was correlated with reduced overall survival in breast cancer patients. Conclusion: Promoter methylation of Wnt pathway antagonists is frequent in breast cancer ultimately leading to probable upregulation of the pathway in these tumors. Hence, sFRP1 and DKK3 methylation may be used as a valuable biomarker in clinical breast cancer management.

Added value of mean and entropy of apparent diffusion coefficient values for evaluating histologic phenotypes of invasive ductal breast cancer with MR imaging.

To study the added value of mean and entropy of apparent diffusion coefficient (ADC) values at standard (800 s/mm2) and high (1500 s/mm2) b-values obtained with diffusion-weighted imaging in identifying histologic phenotypes of invasive ductal breast cancer (IDC) with MR imaging. One hundred thirty-four IDC patients underwent diffusion-weighted imaging with b-values of 800 and 1500 s/mm2, and corresponding ADC800 and ADC1500 maps were generated. Mean and entropy of volumetric ADC values were compared with molecular markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67). Associations among morphologic features, ADC metrics, and phenotypes (luminal A, luminal B [HER2 negative], luminal B [HER2 positive], HER2 positive, and triple negative) were evaluated. Mean ADC values were significantly decreased in ER-positive, PR-positive, and HER2-negative tumors (p < 0.01). Ki-67 ≥ 20% tumors demonstrated significantly higher ADC entropy values compared with Ki-67 < 20% tumors (p < 0.001). Luminal A subtype tended to display lower ADC entropy values compared with other subtypes, while HER2-positive subtype tended to display higher mean ADC values. ADC1500 entropy provided superior diagnostic performance over ADC800 entropy (p = 0.04). Independent risk factors were ADC1500 entropy (p = 0.002) associated with luminal A, irregular mass shape (p = 0.018) and ADC1500 entropy (p = 0.022) with luminal B (HER2 positive), mean ADC1500 (p = 0.018)with HER2 positive, and smooth mass margin (p = 0.012) and rim enhancement (p = 0.003) with triple negative. Mean and entropy of ADC values provided complementary information and added value for evaluating IDC histologic phenotypes. High-b-value ADC1500 may facilitate better phenotype discrimination. • ADC metrics are associated with molecular marker status in IDC. • ADC 1500 improves differentiation of histologic phenotypes compared with ADC 800 . • ADC metrics add value to morphologic features in IDC phenotyping.

Predictive value of primary tumor parameters using 18F-FDG PET/CT for occult lymph node metastasis in breast cancer with clinically negative axillary lymph node.

This study aimed to demonstrate the clinical significance of total lesion glycolysis (TLG) of primary breast cancer using 18F-FDG PET/CT to predict axillary lymph node (ALN) metastasis in invasive ductal breast cancer (IDC) with a clinically negative axillary lymph node (cN-ALN). 135 patients, newly diagnosed with IDC with CN-ALN between July 2016 and October 2017, were retrospectively enrolled. We estimated primary tumor PET/CT parameters including the maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), and TLG, as well as clinicopathologic findings. All patients received breast surgery followed by pathologic axillary lymph node examination. Of the 135 patients, 31 (23.0%) were diagnosed with pathologically proven metastatic ALN. In univariate analysis, SUVmax, MTV, and TLG of the primary breast tumor were correlated with metastatic ALN along with tumor size, lymphovascular invasion, CD34, and D2-40. On multivariate analysis, TLG (> 5.74, p = 0.009) had independent significance for predicting ALN metastasis in IDC with cN-ALN. We demonstrated that TLG of primary tumors can be useful in predicting pathologic ALN metastasis in IDC patients with cN-ALN.

Tumor necrosis factor: An inflammatory microenvironment marker in primary breast cancer patients

&lt;strong&gt;Aim: &lt;/strong&gt;The present study was planned to analyze plasma levels of tumor necrosis factor (TNF) in invasive ductal primary breast cancer (BC) patients in a South Indian population. TNF alpha (TNF α) and TNF beta (TNF β) are produced during inflammation as proinflammatory cytokine markers. The plasma levels of TNF α and TNF β (lymphotoxin α) were correlated with clinicopathological features of BC. &lt;strong&gt;Materials and Methods: &lt;/strong&gt;Blood samples were collected from patients before treatment. We analyzed plasma levels of TNF α, and TNF β in 70 female BC cases and 35 age-matched healthy controls using Millipore magnetic bead kits. &lt;strong&gt;Results: &lt;/strong&gt;Plasma TNF α levels in BC cases were significantly elevated (median 10.1 pg/ml) when compared to the control groups. Plasma values of TNF α and TNF β both were significantly elevated in BC patients with hormone receptor negative cases. Plasma TNF α level was elevated in lymph node metastasis and triple negative BC. Plasma values of TNF α inversely correlated with estrogen receptor and progesterone receptor positivity. &lt;strong&gt;Conclusion: &lt;/strong&gt;The plasma levels of TNF α were more significantly overexpressed than TNF β in BC patients. Further, the patients with aggressive cancer had higher levels of inflammation markers. The present study shows that TNF levels were elevated in hormone receptor negative and triple-negative cases.&lt;p&gt; &lt;/p&gt;

Serum Leptin Might Be Causally Correlated to Invasive Ductal Breast Carcinoma

Introduction: Leptin, the hormone secreted from white adipose tissue is suspected of being a causative factor in tumorigenesis in various tissues. Thus it might be a link between obesity and breast cancer. Objectives: In this project we tried to study causative association of obesity and serum leptin with invasive ductal breast cancer (IDC) in a sample of Malaysian population. Methods: We measured various obesity parameters, and estimated serum leptin levels in a group of healthy control and IDC patients. After confirming diagnosis by standard histopathological methods, serum leptin levels were estimated using ELISA. Both the pre-and postoperative leptin levels were measured with the Patient group. Results: The difference between the serum leptin levels of the Control and the Patient group were highly significant with a P value of < 0.001. There was no significant difference in the serum leptin level between the pre and post-operative states in the IDC patients (P=0.414). There was significant positive correlation between the serum leptin level and the Body Mass Index (BMI) with the Control group (rs=0.598, P < 0.01), however, the breast cancer cases showed a very weakly positive correlation (rs = 0.217, P > 0.05). Conclusion: Since high serum leptin in IDC patients persists unabated in the post-operative state, we infer that the source of leptin in these cases cannot be the breast cancer tissue itself and leptin cannot be a marker for breast cancer. Highly significant association between serum leptin and IDC patients suggests that leptin in serum might play a causative role in the tumorigenesis of breast cancer, and it might be an important connection between obesity and breast cancer. Therefore, leptin signal transduction pathway might be a prospective therapeutic target in the treatment and prevention of IDC.