Large Breast Cancer Cohort Research Articles

Deciphering the Role of ASPM in Breast Cancer: A Comprehensive Multicohort Study

Background: Assembly factor for spindle microtubules (ASPM) has gained significant attention in cancer research due to its association with tumor growth and progression. Through the analysis of large-scale genomic datasets, ASPM has been identified as the top upregulated gene in breast cancer (BC), characterized by high proliferation. This multicohort study aimed to investigate the clinicopathological and prognostic significance of ASPM mRNA and protein expression in BC. Methods: ASPM mRNA expression was assessed using the Cancer Genome Atlas (TCGA) BC cohort and has been further validated in the Molecular Taxonomy of BC International Consortium (METABRIC) (n = 1980), The Uppsala cohort (n = 249), in addition to the combined multicentric cohort (n = 7252). ASPM protein expression was evaluated in a large BC cohort (n = 1300) using immunohistochemistry. The correlations between ASPM expression, clinicopathological parameters, molecular subtypes and outcome were assessed. The response to taxane treatment was compared to the clinical prognosis of ASPM using the ROC plotter. Results: High ASPM mRNA and protein expression were significantly associated with aggressive BC features and poor survival across all cohorts. The association with poor outcomes was maintained in the adjuvant chemotherapy and radio-therapy-treated patients. Responders to taxane treatment showed significantly elevated ASPM levels compared to non-responders. Conclusions: High ASPM expression predicts poor prognosis in BC. It may play a role in treatment resistance within a specific subgroup of patients. Further clinical trials are warranted to explore the potential of ASPM as a target for therapeutic interventions in cancer.

Breast cancer survival prediction using an automated mitosis detection pipeline.

Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter- and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)-supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm2. We employed this method on a breast cancer cohort with long-term follow-up from the University Medical Centre Utrecht (N = 912) and compared predictive values for overall survival of AI-based MC and light-microscopic MC, previously assessed during routine diagnostics. The MIDOG21 model was prognostically comparable to the original MC from the pathology report in uni- and multivariate survival analysis. In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light-microscopic MC.

Evaluation of breast-specific marker expression in metastatic breast cancers: Correlation with subtype switch.

This study evaluates the utility of breast specific markers in identifying breast cancer subtypes within metastatic settings. The subtype alteration in metastatic disease and its consequent impact on breast-specific marker expression is also examined. GATA-binding protein 3 (GATA3), mammaglobin (MMG), transcriptional repressor GATA binding 1 (TRSP1) and SRY-box transcription factor 10 (SOX10) expression were assessed in a large cohort of metastatic breast cancer (MBC) cases and correlated with the characteristics of both MBC and primary breast cancer (PBC). GATA3 was the most sensitive in MBC (83.1%), followed by TRPS1 (77.0%), MMG (58.5%) and SOX10 (7.1%). This trend was consistent in hormonal receptor (HR)+ and HR- MBC. Combining GATA3/TRPS1 yielded the highest detection rates in the overall cohort (90.1%) and HR+ MBC (97.1%), while TRSP1/MMG was most effective in HR- (76.2%) and TN (71.1%) MBC. Marker expression did not correlate with metastatic site, except SOX10 in lung metastases (P = 0.031). Subtype discordance between MBC and PBC occurred in 43 cases (24.4%), with GATA3 expression in HR- MBC significantly linked to subtype discordance (P = 0.005). Conversely, SOX10 expression was significantly associated with subtype concordance in HR- and TNBC (P ≤ 0.003). Despite a higher expression of GATA3 in all HR- cases, TRSP1 outperformed GATA3 in detecting concordant HR- cases (64.0% versus 38.5%). TRPS1 and SOX10 were expressed in more than 50% of concordant TNBC cases. The expression of breast-specific markers is mainly determined by the PBC subtype. GATA3 retains high sensitivity in HR+ cancers, even after HR loss during metastasis. TRPS1 and SOX10 are identified as valuable markers in TNBC metastasis.

Real world clinical outcomes from targeted intraoperative radiotherapy (TARGIT-IORT) during lumpectomy for breast cancer: data from a large cohort at a national cancer institute.

Randomised evidence supports the use of partial breast irradiation (PBI) with targeted intraoperative radiotherapy (TARGIT-IORT) for early stage breast cancer, but prospective data from real-world adoption of this technique is also important. The aim of this study was to determine if the outcome reported in TARGIT-A trial could be replicated in large cohort of early stage breast cancer treated with TARGIT-IORT. This prospective observational study analysed all patients treated with TARGIT-IORT between 2004 and 2021 in a single national cancer institute. TARGIT-IORT during lumpectomy was performed according to the risk-adapted TARGIT-A protocol using the Intrabeam® device. We analysed the completeness of follow up, 5-year in-breast-tumour-recurrence (IBTR), long term local recurrence free survival, distant disease-free survival, overall survival and breast-cancer-related survival, using the Kaplan-Meier method. A covariate analysis was performed to investigate risk factors for IBTR. We also analysed high grade toxicity events. The study included 814 patients and the a median follow up was 72 months. The majority of patients (60.3%) received TARGIT-IORT as PBI modality ("exclusive IORT" group); 39.7% received additional EBRT. There was no significant difference between the 5 years IBTR for the whole study population and the "exclusive IORT" cohort (1.6% (95%CI=1.1-2.1%) and 2.5% (95%CI=1.7%-3.3%) respectively). 5 years overall survival and tumour related survival were >95%. In 21% of patients with recurrence, breast was preserved. Radiotherapy toxicity (CTCAE Grade>2) was very rare (0.9%). This large single institute study found that breast cancer control and survival outcomes with TARGIT-IORT were consistent with TARGIT-A trial results. This "real world" experience confirmed that the randomised evidence showing the value of TARGIT-IORT as partial breast irradiation modality that can be replicated in routine clinical practice.

Insights into the performance of PREDICT tool in a large Mainland Chinese breast cancer cohort: a comparative analysis of versions 3.0 and 2.2.

PREDICT is a web-based tool for forecasting breast cancer outcomes. PREDICT version 3.0 was recently released. This study aimed to validate this tool for a large population in mainland China and compare v3.0 with v2.2. Women who underwent surgery for nonmetastatic primary invasive breast cancer between 2010 and 2020 from the First Affiliated Hospital of Wenzhou Medical University were selected. Predicted and observed 5-year overall survival (OS) for both v3.0 and v2.2 were compared. Discrimination was compared using receiver-operator curves and DeLong test. Calibration was evaluated using calibration plots and chi-squared test. A difference greater than 5% was deemed clinically relevant. A total of 5424 patients were included, with median follow-up time of 58 months (IQR 38-89 months). Compared to v2.2, v3.0 did not show improved discriminatory accuracy for 5-year OS (AUC: 0.756 vs 0.771), same as ER-positive and ER-negative patients. However, calibration was significantly improved in v3.0, with predicted 5-year OS deviated from observed by -2.0% for the entire cohort, -2.9% for ER-positive and -0.0% for ER-negative patients, compared to -7.3%, -4.7% and -13.7% in v2.2. In v3.0, 5-year OS was underestimated by 9.0% for patients older than 75 years, and 5.8% for patients with micrometastases. Patients with distant metastases postdiagnosis was overestimated by 10.6%. PREDICT v3.0 reliably predicts 5-year OS for the majority of Chinese patients with breast cancer. PREDICT v3.0 significantly improved the predictive accuracy for ER-negative groups. Furthermore, caution is advised when interpreting 5-year OS for patients aged over 70, those with micrometastases or metastases postdiagnosis.

Abstract PO4-15-03: Quantitative Measurement of TROP2 via Chromogenic Immunohistochemistry in Breast Cancer

Abstract Sacituzumab govitecan (SG) is an antibody-drug conjugate that targets human trophoblast cell-surface antigen 2 (TROP2), which is expressed in over 90% of breast cancer cases (Zaman et al, 2019). By delivering the cytotoxic SN-38 (topoisomerase I inhibitor) to TROP2 expressing breast cancer cells, SG showed promising antitumor activities in clinical trials and is now an approved treatment for triple-negative breast cancer (TNBC) and hormone receptor (HR)-positive HER2-negative breast cancer in the metastatic setting. Despite the prevalent expression of TROP2 in breast cancer, the objective response rates reported from clinical trials were about 30%, indicating the need for a diagnostic test that can identify which patients are likely to benefit from therapy. Although the drug is approved without a companion diagnostic assay, recent data from the TROPiCS-02 trial has shown that TROP2-low (H-score < 100) showed a non-significant hazard ratio for benefit from SG. In the same study, patients treated with SG with H-score >100 showed a significant hazard ratio for benefit compared to physician’s choice (Tolaney et al, ASCO 2023). This suggests that TROP2 expression level is associated with SG response and that a threshold for TROP2 expression level may aid patient selection. Although over 90% of breast cancer patients were considered TROP2-positive by IHC, the expression level of TROP2 was never quantitatively assessed in large breast cancer cohorts. Here we describe a quantitative chromogenic immunohistochemistry (IHC) assay with a cell line standard. Mass spectrometry was used to measure TROP2 peptide concentrations in the six standard cell lines, and the correlation between the mass spectrometry and IHC data for each cell line was then used to convert chromogenic signals to concentrations of TROP2 protein (fmol/mm2). The antibody used in this assay was rigorously validated, and the antibody concentration was optimized for the best signal-to-noise ratio. The optical density (OD) of chromogenic staining was measured using QuPath 0.4.3 (Qymia extension) by identifying the tumor area via object classifiers and manual editing and then calculating the area-normalized sum of OD. Collectively, this assay can measure up to 29.1 fmol/mm2 of TROP2. By applying this assay to two serial retrospective primary breast cancer cohorts from Yale University, we quantitatively measured TROP2 expression levels in 332 clinical cases. Not surprisingly, over 90% of cases showed some chromogenic signal, with a median TROP2 concentration of 2.1 fmol/mm2 and a maximum of 10.5 fmol/mm2. TROP2 expression levels showed no significant association with clinicopathologic characteristics including race, stage, BRCA mutation status, molecular subtype, HER2 IHC levels, and outcome. Further work is underway to optimize the assay toward the goal of determination of whether there is a quantitatively definable threshold for TROP2 expression below which patients are unlikely to benefit from SG or other TROP2-targeted therapies. Citation Format: Mengni He, Matthew Liu, Thazin Aung, Sneha Burela, Katherine Bates, Charles Robbins, David Rimm. Quantitative Measurement of TROP2 via Chromogenic Immunohistochemistry in Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-15-03.

Abstract PO4-17-11: Normalized Breast Cancer Survival Outcomes in U.S. Tumor Registries

Abstract Background The largest U.S. tumor registries, NCDB (National Cancer Database) and SEER (Surveillance, Epidemiology, and End Results Program), are vital sources of epidemiological data and are frequently utilized in research. Although the NCDB includes the majority of patients diagnosed with breast cancer, it is a hospital-based registry. In contrast, SEER is a population-based registry, but includes less than half of all breast cancer patients diagnosed each year. While the US Cancer Statistics Public Use Database (USCS) captures nearly all breast cancers, treatment and survival data are not included. As such, we sought to normalize the NCDB and SEER populations to mirror the USCS population and examine survival outcomes after normalization. Methods All patients diagnosed with stage I-IV breast cancer (2010-2018) were selected from the NCDB and SEER. Frequencies of patients by select characteristics were exported from the USCS. Rates from the USCS were then used to normalize the NCDB and SEER cohorts, using patient weighted frequencies for age, sex, race/ethnicity, tumor biomarkers [estrogen receptor (ER), HER2], and extent of disease (local, regional, distant; anatomic staging data not available in USCS). Of note, the USCS does not have individual patient-level data (only summary data for select variables), and thus, weighted frequencies were used for normalization. The weighted frequencies were calculated based on the total number of patients in the USCS database divided by the total number of patients in the NCDB or SEER separately for each variable (age, sex, race/ethnicity, etc). Weighted and unweighted data were summarized with N (%). Unweighted patient and disease characteristics (crude data) were compared using Chi-square tests. Overall survival (OS) was estimated using the Kaplan-Meier method before and after normalization. Results The USCS cohort included 2,473,739 patients; the NCDB included 1,441,556 and SEER 504,938. The median followup was 54.9 months for NCDB and 57 months for SEER. There were minimal differences between the cohorts based on age (age < 50y: USCS 18.1%, NCDB 19.3%, SEER 18.9%) or sex (female: USCS 99.1%, NCDB 99.1%, SEER 99.3%). However, there were notable differences in the racial/ethnic composition; non-Hispanic White: USCS 75%, NCDB 78.2%, SEER 68.2%; non-Hispanic Black: USCS 11.4%, NCDB 11.4%, SEER 9.9%; Hispanic: USCS 8.3%, NCDB 5.9%, SEER 11.7%; p< 0.001 for USCS vs NCDB and USCS vs SEER). There were minimal differences in tumor biomarkers (ER+: USCS 82.9%, NCDB 83%, SEER 84.9%; HER2+: USCS 14.5%, NCDB 14.1%, SEER 13.8%), but significant differences in extent of disease (local: USCS 66.1%, NCDB 80.2%, SEER 68.4%; distant: USCS 6%, NCDB 3.9%, SEER 3.9%; p< 0.001 for USCS vs NCDB and USCS vs SEER). For the variables that were similar without weighting (age, sex, tumor biomarkers), OS was also similar after weighting (Table). After normalizing the NCDB based on race, 8-year OS remained comparable (crude 77.2% vs weighted 77.3%); similar findings were noted after normalizing SEER (crude 75.8% vs weighted 75.3%). After normalizing the NCDB based on extent of disease, 8-year OS was notably worse (crude 77.2% vs weighted 74.2%); similar findings were noted for SEER (crude 75.8% vs weighted 74.4%). Conclusions While national tumor registries afford researchers the opportunity to study large breast cancer cohorts, they do not always fully represent the entire breast cancer population. This limitation should be considered when working with these data sets. Table. Citation Format: Tori Chanenchuk, Kerri-Anne Crowell, Samantha Thomas, Rachel Greenup, Jennifer Plichta. Normalized Breast Cancer Survival Outcomes in U.S. Tumor Registries [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-17-11.

Stromal Type I Collagen in Breast Cancer: Correlation to Prognostic Biomarkers and Prediction of Chemotherapy Response

IntroductionFibrillar collagens accumulate in the breast cancer stroma and appear as poorly defined spiculated masses in mammography imaging. The prognostic value of tissue type I collagen remains elusive in treatment-naïve and chemotherapy-treated breast cancer patients. Here, type I collagen mRNA and protein expression were analysed in 2 large independent breast cancer cohorts. Levels were related to clinicopathological parameters, prognostic biomarkers, and outcome. MethodCOL1A1 mRNA expression was analysed in 2509 patients with breast cancer obtained from the cBioPortal database. Type I collagen protein expression was studied by immunohistochemistry in 1395 women diagnosed with early invasive breast cancer. ResultsLow COL1A1 mRNA and protein levels correlated with poor prognosis features, such as hormone receptor negativity, high histological grade, triple-negative subtype, node positivity, and tumour size. In unadjusted analysis, high stromal type I collagen protein expression was associated with improved overall survival (OS) (HR = 0.78, 95% CI = 0.61-0.99, p = .043) and trended towards improved breast cancer–specific survival (BCSS) (HR = 0.65, 95% CI = 0.42-1.01, P = 0.053), although these findings were lost after adjustment for other clinical variables. In unadjusted analysis, high expression of type I collagen was associated with better OS (HR = 0.70, 95% CI = 0.55-0.90, P = .006) and BCSS (HR = 0.55, 95% CI = 0.34-0.88, P = .014) among patients not receiving chemotherapy. Strikingly, the opposite was observed among patients receiving chemotherapy. There, high expression of type I collagen was instead associated with worse OS (HR = 1.83, 95% CI = 0.65-5.14, P = .25) and BCSS (HR = 1.72, 95% CI = 0.54-5.50, P = .357). ConclusionLow stromal type I collagen mRNA and protein expression are associated with unfavourable tumour characteristics in breast cancer. Stromal type I collagen might predict chemotherapy response.

CD301 and LSECtin glycan-binding receptors of innate immune cells serve as prognostic markers and potential predictors of immune response in breast cancer subtypes.

Glycosylation is a prominent posttranslational modification, and alterations in glycosylation are a hallmark of cancer. Glycan-binding receptors, primarily expressed on immune cells, play a central role in glycan recognition and immune response. Here, we used the recombinant C-type glycan-binding receptors CD301, Langerin, SRCL, LSECtin, and DC-SIGNR to recognize their ligands on tissue microarrays (TMA) of a large cohort (n=1859) of invasive breast cancer of different histopathological types to systematically determine the relevance of altered glycosylation in breast cancer. Staining frequencies of cancer cells were quantified in an unbiased manner by a computer-based algorithm. CD301 showed the highest overall staining frequency (40%), followed by LSECtin (16%), Langerin (4%) and DC-SIGNR (0.5%). By Kaplan-Meier analyses, we identified LSECtin and CD301 as prognostic markers in different breast cancer subtypes. Positivity for LSECtin was associated with inferior disease-free survival in all cases, particularly in estrogen receptor positive (ER+) breast cancer of higher histological grade. In triple negative breast cancer, positivity for CD301 correlated with a worse prognosis. Based on public RNA single-cell sequencing data of human breast cancer infiltrating immune cells, we found CLEC10A (CD301) and CLEC4G (LSECtin) exclusively expressed in distinct subpopulations, particularly in dendritic cells and macrophages, indicating that specific changes in glycosylation may play a significant role in breast cancer immune response and progression.

TRPS1 is a promising marker for all subtypes of breast cancer.

Trichorhinophalangeal syndrome-1 (TRPS1) has been proposed as a novel breast marker with equally high expression in breast cancer (BC) subtypes, making it a useful diagnostic tool. Here, its expression was evaluated alongside other commonly used markers [GATA3, GCDFP15, mammaglobin (MGB) and SOX10] in a large cohort of BCs (n = 1852) and their corresponding nodal metastases. Its usefulness as a diagnostic tool and its correlation with clinicopathological features were assessed. TRPS1 was expressed at 75.8% overall in the BC cohort, with at least 58% expression among BC subtypes. It was less sensitive than GATA3 for luminal and HER2-overexpressing (HER2-OE) cancers (luminal A: 82 versus 97%; luminal B: 80 versus 95%; HER2-OE: 62 versus 76%), but it was the most sensitive for TNBC (60 versus ≤ 41%). It showed a stable expression in nodal metastases (primary tumour 76 versus nodal metastasis 78%), unlike a reduced nodal expression for GATA3 (86 versus 77%). TRPS1 outperformed GATA3 in detecting non-luminal cancers whenpaired with other breast markers. TRPS1 and GCDFP15 was the most sensitive combination in TNBC detection, with a 76% detection rate. For TRPS1-negative and GCDFP15-negative TNBCs, SOX10 was more sensitive than GATA3 (29 versus 24%). TRPS1 is a highly sensitive marker for all breast cancer subtypes, outperforming GATA3 in non-luminal cancers and displaying the highest sensitivity for TNBC detection when combined with GCDFP15. It is a valuable addition to the breast marker panel for accurate identification of BC.

Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application

In recent years, artificial intelligence (AI) has demonstrated exceptional performance in mitosis identification and quantification. However, the implementation of AI in clinical practice needs to be evaluated against the existing methods. This study is aimed at assessing the optimal method of using AI-based mitotic figure scoring in breast cancer (BC). We utilized whole slide images from a large cohort of BC with extended follow-up comprising a discovery (n = 1715) and a validation (n = 859) set (Nottingham cohort). The Cancer Genome Atlas of breast invasive carcinoma (TCGA-BRCA) cohort (n = 757) was used as an external test set. Employing automated mitosis detection, the mitotic count was assessed using 3 different methods, the mitotic count per tumor area (MCT; calculated by dividing the number of mitotic figures by the total tumor area), the mitotic index (MI; defined as the average number of mitotic figures per 1000 malignant cells), and the mitotic activity index (MAI; defined as the number of mitotic figures in 3 mm2 area within the mitotic hotspot). These automated metrics were evaluated and compared based on their correlation with the well-established visual scoring method of the Nottingham grading system and Ki67 score, clinicopathologic parameters, and patient outcomes. AI-based mitotic scores derived from the 3 methods (MCT, MI, and MAI) were significantly correlated with the clinicopathologic characteristics and patient survival (P < .001). However, the mitotic counts and the derived cutoffs varied significantly between the 3 methods. Only MAI and MCT were positively correlated with the gold standard visual scoring method used in Nottingham grading system (r = 0.8 and r = 0.7, respectively) and Ki67 scores (r = 0.69 and r = 0.55, respectively), and MAI was the only independent predictor of survival (P < .05) in multivariate Cox regression analysis. For clinical applications, the optimum method of scoring mitosis using AI needs to be considered. MAI can provide reliable and reproducible results and can accurately quantify mitotic figures in BC.

Comprehensive genomic profiling of breast cancers characterizes germline-somatic mutation interactions mediating therapeutic vulnerabilities

Germline-somatic mutation interactions are universal and associated with tumorigenesis, but their role in breast cancer, especially in non-Caucasians, remains poorly characterized. We performed large-scale prospective targeted sequencing of matched tumor-blood samples from 4079 Chinese females, coupled with detailed clinical annotation, to map interactions between germline and somatic alterations. We discovered 368 pathogenic germline variants and identified 5 breast cancer DNA repair-associated genes (BCDGs; BRCA1/BRCA2/CHEK2/PALB2/TP53). BCDG mutation carriers, especially those with two-hit inactivation, demonstrated younger onset, higher tumor mutation burden, and greater clinical benefits from platinum drugs, PARP inhibitors, and immune checkpoint inhibitors. Furthermore, we leveraged a multiomics cohort to reveal that clinical benefits derived from two-hit events are associated with increased genome instability and an immune-activated tumor microenvironment. We also established an ethnicity-specific tool to predict BCDG mutation and two-hit status for genetic evaluation and therapeutic decisions. Overall, this study leveraged the large sequencing cohort of Chinese breast cancers, optimizing genomics-guided selection of DNA damaging-targeted therapy and immunotherapy within a broader population.

Quantitative expression of oestrogen receptor in breast cancer: Clinical and molecular significance

BackgroundOestrogen receptor (ER) positive breast cancer (BC) patients are eligible for endocrine therapy (ET), regardless of ER immunohistochemical expression level. There is a wide spectrum of ER expression and the response to ET is not uniform. This study aimed to assess the clinical and molecular consequences of ER heterogeneity with respect to ET-response. MethodsER expression, categorised by percentage and staining intensity in a large BC cohort (n = 7559) was correlated with clinicopathological parameters and patient ET response. The Cancer Genome Atlas Data BC cohort (n = 1047) was stratified by ER expression and transcriptomic analysis completed to better understand the molecular basis of ER heterogeneity. ResultsThe quantitative proportional increase in ER expression was positively associated with favourable prognostic parameters. Tumours with 1–9% ER expression were characteristically similar to ER-negative (<1%) tumours. Maximum ET-response was observed in tumours with 100% ER expression, with responses significantly different to tumours exhibiting ER at < 100% and significantly decreased survival rates were observed in tumours with 50% and 10% of ER expression. The Histochemical-score (H-score), which considers both staining intensity and percentage, added significant prognostic value over ER percentage alone with significant outcome differences observed at H-scores of 30, 100 and 200. There was a positive correlation between ER expression and ESR1 mRNA expression and expression of ER-regulated genes. Pathway analysis identified differential expression in key cancer-related pathways in different ER-positive groups. ConclusionET-response is statistically proportionally related to ER expression with significant differences observed at 10%, 50% and 100%. The H-score adds prognostic and predictive information.

The Clinical and Biological Significance of Estrogen Receptor-Low Positive Breast Cancer

Estrogen receptor (ER) status in breast cancer (BC) is determined using immunohistochemistry (IHC) with nuclear expression in ≥1% of cells defined as ER-positive. BC with 1%-9% expression (ER-low-positive), is a clinically and biologically unique subgroup. In this study, we hypothesized that ER-low-positive BC represents a heterogeneous group with a mixture of ER-positive and ER-negative tumor, which may explain their divergent clinical behavior. A large BC cohort (n = 8171) was investigated and categorized into 3 groups: ER-low-positive (1%-9%), ER-positive (≥10%), and ER-negative (<1%) where clinicopathological and outcome characteristics were compared. A subset of ER-low-positive cases was further evaluated using IHC, RNAscope, and RT-qPCR. PAM50 subtyping and ESR1 mRNA expression levels were assessed in ER-low-positive cases within The Cancer Genome Atlas data set. The reliability of image analysis software in assessment of ER expression in the ER-low-positive category was also assessed. ER-low-positive tumors constituted <2% of BC cases examined and showed significant clinicopathological similarity to ER-negative tumors. Most of these tumors were nonluminal types showing low ESR1 mRNA expression. Further validation of ER status revealed that 45% of these tumors were ER-negative with repeated IHC staining and confirmed by RNAscope and RT-qPCR. ER-low-positive tumors diagnosed on needle core biopsy were enriched with false-positive ER staining. BCs with 10% ER behaved similar to ER-positive, rather than ER-negative or low-positive BCs. Moderate concordance was found in assessment of ER-low-positive tumors, and this was not improved by image analysis. Routinely diagnosed ER-low-positive BC includes a proportion of ER-negative cases. We recommend repeat testing of BC showing 1%-9% ER expression and using a cutoff ≥10% expression to define ER positivity to help better inform treatment decisions.

Immunohistochemical Characterization of a Large Cohort of Triple Negative Breast Cancer.

Introduction. Triple negative breast carcinomas are characterized by a lack of hormone receptor and HER2 expression and inconsistent expression of breast-specific immunohistochemical markers. The expression of many site-specific markers in these tumors is largely unknown. The objective of the study was to examine the expression of widely used immunohistochemical markers on a large cohort of triple negative breast cancer. Methods. Sections from tissue microarrays were stained with 47 markers using routine protocols. Most markers were scored using a modified Allred method. ATRX, BAP1, SMAD4, e-cadherin, and beta-catenin were scored as retained or lost. Mammaglobin was considered positive if there was at least moderate intensity staining in any tumor cells. P16 was scored as overexpressed or not overexpressed; p53 was scored as wildtype, overexpressed, null, or cytoplasmic. Results. The cohort consisted of 639 tumors including 601 primary and 32 metastases. Overall, 96% expressed GATA3, mammaglobin, and/or SOX10 while 97% of no special type tumors expressed this panel. Carcinoma of apocrine differentiation demonstrated an AR positive, SOX10 negative, K5 negative/focal immunophenotype. PAX8 (SP348), WT1, Napsin A, and TTF1 (8G7G3/1) were never or rarely expressed while CA9, CDX2, NKX3.1, SATB2 (SATBA410), synaptophysin, and vimentin were variably expressed. Conclusions. Almost all TNBC express at least 1 of the 3 IHC markers: GATA3, mammaglobin, and/or SOX10. Carcinoma of apocrine differentiation is characterized by an AR positive, SOX10 negative, K5 negative or focal immunophenotype. Cautious interpretation of so-called site-specific markers, with knowledge of antibody clones, is required in excluding the diagnosis of triple negative breast cancer.

High Inner Centromere Protein Expression Correlates with Aggressive Features and Predicts Poor Prognosis in Patients with Invasive Breast Cancer

Introduction: Inner centromere protein (INCENP) is a member of the chromosomal passenger complex and plays a key role in mitosis and cell proliferation. This study aimed to evaluate the clinical and prognostic significance of INCENP in invasive breast cancer (BC). Methods: INCENP expression was evaluated on a tissue microarray of a large BC cohort (n = 1,295) using immunohistochemistry. At the mRNA level, INCENP expression was assessed using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (n = 1,980) and The Cancer Genome Atlas (TCGA) BC cohorts (n = 854). The correlations between INCENP expression, clinicopathological parameters, and patient outcome were investigated. Results: INCENP expression was detected in the nucleus and cytoplasm of the tumour cells. Its expression was significantly associated with features characteristic of aggressive BC behaviour including high tumour grade, larger tumour size, and high Nottingham prognostic index scores. High INCENP nuclear expression was a predictor of shorter BC-specific survival in the whole cohort, as well as in the luminal subtype (p < 0.001). High INCENP nuclear expression was predictive of poor prognosis in BC patients who received hormone treatment or chemotherapy. Conclusion: High INCENP expression is a poor prognostic biomarker in BC with potential therapeutic benefits.

Evaluation oncotype DX® 21-gene recurrence score andclinicopathological parameters: a single institutional experience.

Oncotype DX recurrence score (RS) is a clinically validated assay, which predicts the likelihood of disease recurrence in oestrogen receptor-positive/HER2-negative (ER+/HER2-) breast cancer (BC). In this study we aimed to compare the performance of Oncotype DX against the conventional clinicopathological parameters using a large BC cohort diagnosed in a single institution. A cohort (n= 430) of ER+/HER2- BC patients who were diagnosed at the Nottingham University Hospitals NHS Trust and had Oncotype DX testing was included. Correlation with the clinicopathological and other biomarkers, including the proliferation index, was analysed. The median Oncotype DX RS was 17.5 (range=0-69). There was a significant association between high RS and grade 3 tumours. No grade 1BC or grade 2 tumours with mitosis score 1 showed high RS. Low RS was significantly associated with special tumour types where none of the patients with classical lobular or tubular carcinomas had a high RS. There was an inverse association between RS and levels of ER and progesterone receptor (PR) expression and a positive linear correlation with Ki67 labelling index. Notably, six patients who developed recurrence had an intermediate RS; however, four of these six cases (67%) were identified as high-risk disease when the conventional clinical and molecular parameters were considered. Oncotype DX RS is correlated strongly with the conventional clinicopathological parameters in BC. Some tumour features such as tumour grade, type, PR status and Ki67 index can be used as surrogate markers in certain scenarios.

Prognostic relevance of mixed histological subtypes in invasive breast carcinoma: a retrospective analysis

PurposeThe prognostic and therapeutic power of special histological subtypes in breast cancer in pure form or in combination with other histological subtypes is still not established, and diagnostic guidelines are cautious regarding prognostic power based on the histological subtype alone. Therapy decisions are guided in most cases independently of the histological subtype and are directed by biomarkers and tumor stage. In this study, we analyzed a comprehensive large retrospective breast cancer cohort with a special focus on histological subtype (other than ductal non-special type or lobular carcinoma) and correlated pure or mixed histological forms with pathological tumor stage and overall disease-free survival.Materials and methodsA total of 827 breast cancer cases with pure or mixed special histological types were retrospectively analyzed. Survival information was available in 645 of 827 cases.ResultsA total of 293 cases had pure forms, and 534 cases had mixed histological subtypes. The most common pure special types were mucinous (23.9%), micropapillary (21.2%), high-grade metaplastic (13%), male breast cancer (8.2%), cribriform (6.8%), metastases (6.1%), apocrine and papillary (each 5.46%), NST with medullary and clear cell pattern (up to 3.4%) and high-grade neuroendocrine carcinomas (2.7%). Mixed forms were most frequently encountered in NST carcinomas with micropapillary components (41.8%), followed by mucinous (9.93%) and cribriform (6.74%) mixed patterns. In univariate analysis, no pure form had prognostic relevance compared with any mixed form with the basic pure element. Pooling pure histological subtypes with tumor stage and age in a linear random-effects model, the cribriform subtype had the most favorable prognosis, while male breast cancer showed the poorest outcome (p < 0.001). All other frequent pure forms had intermediate prognostic power (p < 0.001).ConclusionOur results show that the analyzed special histological breast cancer subtypes (other than ductal and lobular carcinomas) do not carry prognostic information alone, either in pure form or in any combination with other subtypes. Prognostic groups including special subtypes, however, can strongly stratify breast cancer if tumor stage, age and biomarkers are included in the prognostic measurements.

Comparison of dyslipidemia incidence in Chinese early-stage breast cancer patients following different endocrine therapies: A population-based cohort study.

BackgroundThere is lack of large-scale real-world research evidence showing the impact of endocrine therapy on blood lipids in Chinese breast cancer patients, especially those with premenopausal breast cancer. Based on a large breast cancer cohort at West China Hospital, we aimed to compare the risk of dyslipidemia between premenopausal and postmenopausal women based on the endocrine therapy used.MethodsA total of 1,883 early-stage breast cancer (EBC) patients who received endocrine monotherapy [selective estrogen receptor modulator (SERM) and aromatase inhibitor (AI), with or without ovarian function suppression] with normal blood lipid levels at baseline were retrospectively included between October 2008 and April 2017. Dyslipidemia was defined as an abnormality in cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein, and total cholesterol (TC) levels. The risk accumulation function was used to calculate the incidence of dyslipidemia in order to assess the absolute risk, while the multivariate Cox regression model was used to calculate the relative risk of dyslipidemia between the groups.ResultsPatients with EBC were followed up for 60 months to monitor their blood lipid levels. The accumulated 5-year incidence of dyslipidemia in postmenopausal patients was higher than that in premenopausal patients (adjusted HR [95% confidence interval], 1.25 [1.01–1.56], 41.7% vs. 31.2%, p = 0.045). In premenopausal patients, the risk of abnormal TC was significantly higher in the OFS+AI group compared with that in the SERM group (adjusted HR [95% CI], 6.24 [3.19–12.20], p < 0.001, 5-year abnormal rates: 21.5% vs. 2.4%), and that of abnormal LDL-C level also increased (adjusted HR [95% CI], 10.54 [3.86–28.77], p < 0.001, 5-year abnormal rates: 11.1% vs. 0.9%). In postmenopausal patients, the risk of abnormal TC or LDL-C levels showed a similar trend in the AI and SERM groups.ConclusionsIn addition to postmenopausal patients, dyslipidemia is also common in premenopausal Chinese patients with EBC who received endocrine therapy. Irrespective of menopausal status, AI treatment increases the risk of TC/LDL-C dyslipidemia than SERM treatment.

Mammography radiomics features at diagnosis and progression-free survival among patients with breast cancer

BackgroundThe associations between mammographic radiomics and breast cancer clinical endpoints are unclear. We aimed to identify mammographic radiomics features associated with breast cancer prognosis.MethodsNested from a large breast cancer cohort in our institution, we conducted an extreme case-control study consisting of 207 cases with any invasive disease-free survival (iDFS) endpoint <5 years and 207 molecular subtype-matched controls with >5-year iDFS. A total of 632 radiomics features in craniocaudal (CC) and mediolateral oblique (MLO) views were extracted from pre-treatment mammography. Logistic regression was used to identify iDFS-associated features with multiple testing corrections (Benjamini–Hochberg method). In a subsample with RNA-seq data (n = 96), gene set enrichment analysis was employed to identify pathways associated with lead features.ResultsWe identified 15 iDFS-associated features from CC-view yet none from MLO-view. S(1,−1)SumAverg and WavEnLL_s-6 were the lead ones and associated with favourable (OR 0.64, 95% CI 0.42–0.87, P = 0.01) and poor iDFS (OR 1.53, 95% CI 1.31–1.76, P = 0.01), respectively. Both features were associated with eight pathways (primarily involving cell cycle regulation) in tumour but not adjacent normal tissues.ConclusionOur findings suggest mammographic radiomics features are associated with breast cancer iDFS, potentially through pathways involving cell cycle regulation.