scholarly journals Propensity for Early Metastatic Spread in Breast Cancer: Role of Tumor Vascularization Features and Tumor Immune Infiltrate

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 5917
Author(s):  
Mario Rosario D’Andrea ◽  
Vittore Cereda ◽  
Luigi Coppola ◽  
Guido Giordano ◽  
Andrea Remo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Clinical Decision Making ◽  
Morphological Changes ◽  
Metastatic Spread ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B ◽  
Molecular Features

Breast cancer is a complex and highly heterogeneous disease consisting of various subtypes. It is classified into human epidermal growth receptor 2 (HER-2)-enriched, luminal A, luminal B and basal-like/triple negative (TNBC) breast cancer, based on histological and molecular features. At present, clinical decision-making in breast cancer is focused only on the assessment of tumor cells; nevertheless, it has been recognized that the tumor microenvironment (TME) plays a critical biologic role in breast cancer. This is constituted by a large group of immune and non-immune cells, but also by non-cellular components, such as several cytokines. TME is deeply involved in angiogenesis, immune-evasion strategies, and propensity for early metastatic spread, impacting on prognosis and prediction of response to specific treatments. In this review, we focused our attention on the early morphological changes of tumor microenvironment (tumor vasculature features, presence of immune and non-immune cells infiltrating the stroma, levels of cytokines) during breast cancer development. At the same time, we correlate these characteristics with early metastatic propensity (defined as synchronous metastasis or early recurrence) with particular attention to breast cancer subtypes.

scholarly journals The association of women’s birth size with risk of molecular breast cancer subtypes: a cohort study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Marie S. Sandvei ◽  
Signe Opdahl ◽  
Marit Valla ◽  
Pagona Lagiou ◽  
Ellen Veronika Vesterfjell ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Birth Weight ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Head Circumference ◽  
Birth Length ◽  
Birth Size ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B

Abstract Background Because birth size appears to be positively associated with breast cancer risk, we have studied whether this risk may differ according to molecular breast cancer subtypes. Methods A cohort of 22,931 women born 1920–1966 were followed up for breast cancer occurrence from 1961 to 2012, and 870 were diagnosed during follow-up. Archival diagnostic material from 537 patients was available to determine molecular breast cancer subtype, specified as Luminal A, Luminal B (human epidermal growth factor receptor 2 (HER2)-), Luminal B (HER2+), HER2 type, and Triple negative (TN) breast cancer. Information on the women’s birth weight, birth length and head circumference at birth was used to estimate hazard ratios (HR) with 95% confidence intervals (CI) for each molecular subtype, applying Cox regression, and stratified by maternal height. Results Birth length (per 2 cm increments) was positively associated with Luminal A (HR = 1.2, 95% CI, 1.0–1.3), Luminal B (HER2+) (HR = 1.3, 95% CI, 1.0–1.7), and TN breast cancer (HR = 1.4, 95% CI, 1.0–1.9). No clear association was found for birth weight and head circumference. The positive associations of birth length were restricted to women whose mothers were relatively tall (above population median). Conclusion We found a positive association of birth length with risk of Luminal A, Luminal B (HER2+) and TN breast cancer that appears to be restricted to women whose mothers were relatively tall. This may support the hypothesis that breast cancer risk is influenced by determinants of longitudinal growth and that this finding deserves further scrutiny.

scholarly journals Data Perturbation Independent Diagnosis and Validation of Breast Cancer Subtypes Using Clustering and Patterns

2006 ◽  
Vol 2 ◽  
pp. 117693510600200 ◽  
Author(s):  
G. Alexe ◽  
G.S. Dalgin ◽  
R. Ramaswamy ◽  
C. Delisi ◽  
G. Bhanot
Keyword(s):  
Breast Cancer ◽  
Data Perturbation ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B ◽  
Molecular Stratification ◽  
Core Cluster ◽  
Therapeutic Decisions ◽  
Disease Subtypes

Molecular stratification of disease based on expression levels of sets of genes can help guide therapeutic decisions if such classifications can be shown to be stable against variations in sample source and data perturbation. Classifications inferred from one set of samples in one lab should be able to consistently stratify a different set of samples in another lab. We present a method for assessing such stability and apply it to the breast cancer (BCA) datasets of Sorlie et al. 2003 and Ma et al. 2003. We find that within the now commonly accepted BCA categories identified by Sorlie et al. Luminal A and Basal are robust, but Luminal B and ERBB2+ are not. In particular, 36% of the samples identified as Luminal B and 55% identified as ERBB2+ cannot be assigned an accurate category because the classification is sensitive to data perturbation. We identify a “core cluster” of samples for each category, and from these we determine “patterns” of gene expression that distinguish the core clusters from each other. We find that the best markers for Luminal A and Basal are (ESR1, LIV1, GATA-3) and (CCNE1, LAD1, KRT5), respectively. Pathways enriched in the patterns regulate apoptosis, tissue remodeling and the immune response. We use a different dataset (Ma et al. 2003) to test the accuracy with which samples can be allocated to the four disease subtypes. We find, as expected, that the classification of samples identified as Luminal A and Basal is robust but classification into the other two subtypes is not.

scholarly journals Ki-67 (30-9) scoring and differentiation of Luminal A- and Luminal B-like breast cancer subtypes

2019 ◽  
Vol 178 (2) ◽  
pp. 451-458 ◽  
Author(s):  
Giuseppe Viale ◽  
Amy E. Hanlon Newell ◽  
Espen Walker ◽  
Greg Harlow ◽  
Isaac Bai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Subtypes ◽  
Luminal A ◽  
Ki 67 ◽  
Cancer Subtypes ◽  
Luminal B

scholarly journals Chemokine axes in breast cancer: factors of the tumor microenvironment reshape the CCR7-driven metastatic spread of luminal-A breast tumors

2016 ◽  
Vol 99 (6) ◽  
pp. 1009-1025 ◽  
Author(s):  
Polina Weitzenfeld ◽  
Olga Kossover ◽  
Cindy Körner ◽  
Tsipi Meshel ◽  
Stefan Wiemann ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Microenvironment ◽  
Breast Tumors ◽  
Metastatic Spread ◽  
Luminal A

Response to neoadjuvant chemotherapy and outcome based on breast cancer subtype

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e11516-e11516
Author(s):  
A. Guerrero-Zotano ◽  
J. Gavila ◽  
M. A. Climent ◽  
M. J. Juan ◽  
V. Guillem ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Breast Cancer Subtypes ◽  
Nuclear Staining ◽  
Luminal A ◽  
Long Term Outcome ◽  
Cancer Subtypes ◽  
Luminal B ◽  
Her 2

e11516 Background: Gene expression profiling identifies several breast cancer subtypes with different chemosensitivity and outcome. We used immunohistochemistry surrogate markers to classify tumors according to known breast cancer subtypes and examined the relationship between neoadjuvant chemotherapy (NAC) response and long-term end points, including distant disease-free survival (DDFS) and overall survival (OS). Methods: Review of clinical and pathological data from 271 breast cancer patients treated in our institution with NAC between 1991–2008. Breast cancer subtypes were defined as follows: Luminal A: Estrogen receptor positive (ER+) and/or progesterone peceptor positive (PR+), human epidermal growth factor receptor 2-positive (Her-2+); Luminal B: ER+ and/or PR+,Her-2+; Basal: ER-,PR-,Her-2-;HER2: ER-,PR-,Her-2 +. ER and PR positive scored as positive if tumor cell nuclear staining was at least 2+. Her-2 scored as positive if test DAKO scored 3+ or FISH ratio Her-2/CEP-17>2.2. Results: 121 (45.8%) patients were classifed as Luminal A; 22 (8.1%) as Luminal B; 75 (27.7%) as Basal, and 50 (18.5%) as HER2. Most patients (63%) received NAC based on anthracyclines and taxanes. 36% Her-2+ patients were treated with NAC based on trastuzumab, and 43% received trastuzumab as adjuvant treatment. Response and outcome results are shown below (Table). Independently from subtype, only four patients out of 58 with pCR relapsed. Among patients who didn´t achieved pathologic complete response (pCR), basal and HER2 subtypes have the worst outcome (4 years SG 80% and 72% respectevely) compared with Luminal A (4 years SG: 94.7%), (log-rank p=0.009). Conclusions: Basal and HER2 tumor despite high chemosensitivity have worst long term outcome, particularly if pCR is not achieved after NAC. [Table: see text] No significant financial relationships to disclose.

Epigenetic aspects of triple-negative in patients with breast cancer.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 1041-1041
Author(s):  
Joaquina Martínez-Galan ◽  
Sandra Rios ◽  
Juan Ramon Delgado ◽  
Blanca Torres-Torres ◽  
Jesus Lopez-Peñalver ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Triple Negative ◽  
Breast Cancer Subtype ◽  
Regulation Of Gene Expression ◽  
Breast Cancer Subtypes ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B

1041 Background: Identification of gene expression-based breast cancer subtypes is considered a critical means of prognostication. Genetic mutations along with epigenetic alterations contribute to gene-expression changes occurring in breast cancer. However, the reproducibility of differential DNA methylation discoveries for cancer and the relationship between DNA methylation and aberrant gene expression have not been systematically analysed. The present study was undertaken to dissect the breast cancer methylome and to deliver specific epigenotypes associated with particular breast cancer subtypes. Methods: By using Real Time QMSPCR SYBR green we analyzed DNA methylation in regulatory regions of 107 pts with breast cancer and analyzed association with prognostics factor in triple negative breast cancer and methylation promoter ESR1, APC, E-Cadherin, Rar B and 14-3-3 sigma. Results: We identified novel subtype-specific epigenotypes that clearly demonstrate the differences in the methylation profiles of basal-like and human epidermal growth factor 2 (HER2)-overexpressing tumors. Of the cases, 37pts (40%) were Luminal A (LA), 32pts (33%) Luminal B (LB), 14pts (15%) Triple-negative (TN), and 9pts (10%) HER2+. DNA hypermethylation was highly inversely correlated with the down-regulation of gene expression. Methylation of this panel of promoter was found more frequently in triple negative and HER2 phenotype. ESR1 was preferably associated with TN(80%) and HER2+(60%) subtype. With a median follow up of 6 years, we found worse overall survival (OS) with more frequent ESR1 methylation gene(p>0.05), Luminal A;ESR1 Methylation OS at 5 years 81% vs 93% when was ESR1 Unmethylation. Luminal B;ESR1 Methylation 86% SG at 5 years vs 92% in Unmethylation ESR1. Triple negative;ESR1 Methylation SG at 5 years 75% vs 80% in unmethylation ESR1. HER2;ESR1 Methylation SG at 5 years was 66.7% vs 75% in unmethylation ESR1. Conclusions: Our results provide evidence that well-defined DNA methylation profiles enable breast cancer subtype prediction and support the utilization of this biomarker for prognostication and therapeutic stratification of patients with breast cancer.

First results of the prospective Neoadjuvant Breast Registry Symphony Trial (NBRST).

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e22117-e22117
Author(s):  
Pat W. Whitworth ◽  
Mark Gittleman ◽  
Stephanie Akbari ◽  
Lisette Stork ◽  
Femke De Snoo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Endocrine Therapy ◽  
Complete Response ◽  
Adjuvant Endocrine Therapy ◽  
Excision Biopsy ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B ◽  
Prior Therapy ◽  
Basal Type

e22117 Background: Classification into molecular subtypes may be important for the selection of therapy for patients with early breast cancer. Previous analyses had shown that breast cancer subtypes have distinct clinical outcome (Sorlie, PNAS 2001; Esserman, BCRT 2011). The aim of the prospective NBRST study is to measure chemosensitivity as defined by pathological Complete Response (pCR), or endocrine sensitivity as defined by partial response (PR) and metastasis-free survival in molecular subgroups. Methods: The study includes women aged 18–90 with histologically proven breast cancer, who are scheduled to start neo-adjuvant chemotherapy (CT) or neo-adjuvant endocrine therapy (ET), and who provide written informed consent. Additional inclusion criteria include no excision biopsy or axillary dissection, no confirmed distant metastatic disease, and no prior therapy for breast cancer. Treatment is at the discretion of the physician adhering to NCCN approved regimens. 500 Patients will be enrolled. Results: 128 Patients (median age 52, range 22-79), T1-4 N0-3, had definitive surgery and the overall pCR rate was 22%.14 (11%) patients are classified as Luminal A-type (BluePrint Luminal/MammaPrint Low Risk) of whom 11 received neo-adjuvant CT; none of these patients had a pCR. While 3 patients received neo-adjuvant ET and all 3 had a PR. 47 (37%) Patients are classified as Luminal B-type (BluePrint Luminal/MammaPrint High Risk). All but 1 patient received neo-adjuvant CT and 4 (9%) had a pCR. 20 (16%) Patients are classified as BluePrint HER2-type and received neo-adjuvant CT plus Trastuzumab; 8 (40%) had a pCR. 47 (37%) Patients are classified as BluePrint Basal-type and received neo-adjuvant CT; 15 (32%) had a pCR. Of the patients with IHC/FISH HER2+ cancer 13/39 (33%) had a pCR and 6/21 (29%) of the patients with IHC/FISH triple negative breast cancer. Conclusions: We observed differences in pCR to neo-adjuvant treatment in groups stratified by BluePrint and MammaPrint. Patients with Luminal A-type breast cancer have a high response to neo-adjuvant endocrine therapy (100% PR) and no pCR to neo-adjuvant CT. While patients with BluePrint HER2-type and Basal-type breast cancer have a high pCR rate to neo-adjuvant CT. Clinical trial information: NCT01479101.

Chemosensitivity and endocrine sensitivity prediction by MammaPrint and BluePrint in the Neoadjuvant Breast Registry Symphony Trial (NBRST).

2014 ◽  
Vol 32 (26_suppl) ◽  
pp. 29-29
Author(s):  
Pat W. Whitworth ◽  
Mark Gittleman ◽  
Stephanie Akbari ◽  
Lisette Stork ◽  
Femke De Snoo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative ◽  
Pathologic Complete Response ◽  
Complete Response ◽  
Excision Biopsy ◽  
Luminal A ◽  
Neoadjuvant Endocrine Therapy ◽  
Cancer Subtypes ◽  
Luminal B ◽  
Prior Therapy

29 Background: Classification into molecular subtypes is important for the selection of therapy for patients with breast cancer. Previous analyses demonstrated that breast cancer subtypes have distinct clinical outcome (Gluck, BCRT 2013). The aim of the prospective NBRST study is to measure chemosensitivity as defined by pathologic complete response (pCR), or endocrine sensitivity as defined by partial response (PR) and metastasis-free survival in molecular subgroups. Methods: The study includes women aged 18 to 90 with histologically proven breast cancer, who are scheduled to start neoadjuvant chemotherapy (NCT) or neoadjuvant endocrine therapy (NET), and who provide written informed consent. Additional inclusion criteria include no excision biopsy or axillary dissection, no confirmed distant metastatic disease, and no prior therapy for breast cancer. Treatment is at the discretion of the physician adhering to NCCN approved regimens. Results: Of 336 patients, T1-4 N0-3, had definitive surgery and the overall pCR rate was 24%. 32/167 (19%) IHC/FISH ERPR+/Her2- patients were reclassified by BluePrint (31 Basal). 43/95 (45%) IHC/FISH Her2+ patients were reclassified by BluePrint (25 Luminal and 18 Basal). 3/74 (3%) IHC/FISH triple-negative patients were not Basal by BluePrint. Of 45 (13%) patients classified as Luminal A 32 received NCT; one patient (3%) had a pCR; 13 patients received NET and 9 (70%) had a PR. Of 116 (35%) patients classified as Luminal B, 111 received NCT and seven (6%) had a pCR. The pCR rate (17/149 (11%)) in IHC/FISH ERPR+/HER2- patients was higher. Fifty-five (16%) are BluePrint HER2 and received NCT (51 plus trastuzumab); 27 (49%) had a pCR compared to 35/95 (37%) in IHC/FISH HER2+ patients. One-hundred twenty (36%) are BluePrint Basal and received NCT; 46 (38%) had a pCR, similar to the pCR percentage seen in the 74 patients designated triple-negative by IHC/FISH. Conclusions: Molecular subtyping using MammaPrint and BluePrint leads to a reclassification of 23% (78/336) of tumors. BluePrint reclassification resulted in better grouping of patients into expected response groups compared to local surrogate subtyping with immunostains.

scholarly journals Evaluation of FGFR targeting in breast cancer through interrogation of patient-derived models

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Nicole J. Chew ◽  
Terry C. C. Lim Kam Sian ◽  
Elizabeth V. Nguyen ◽  
Sung-Young Shin ◽  
Jessica Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Therapeutic Target ◽  
Breast Cancer Subtype ◽  
Breast Cancer Subtypes ◽  
Cancer Tissue ◽  
Breast Cancers ◽  
Luminal A ◽  
Tissue Samples ◽  
Cancer Subtypes ◽  
Luminal B

Abstract Background Particular breast cancer subtypes pose a clinical challenge due to limited targeted therapeutic options and/or poor responses to the existing targeted therapies. While cell lines provide useful pre-clinical models, patient-derived xenografts (PDX) and organoids (PDO) provide significant advantages, including maintenance of genetic and phenotypic heterogeneity, 3D architecture and for PDX, tumor–stroma interactions. In this study, we applied an integrated multi-omic approach across panels of breast cancer PDXs and PDOs in order to identify candidate therapeutic targets, with a major focus on specific FGFRs. Methods MS-based phosphoproteomics, RNAseq, WES and Western blotting were used to characterize aberrantly activated protein kinases and effects of specific FGFR inhibitors. PDX and PDO were treated with the selective tyrosine kinase inhibitors AZD4547 (FGFR1-3) and BLU9931 (FGFR4). FGFR4 expression in cancer tissue samples and PDOs was assessed by immunohistochemistry. METABRIC and TCGA datasets were interrogated to identify specific FGFR alterations and their association with breast cancer subtype and patient survival. Results Phosphoproteomic profiling across 18 triple-negative breast cancers (TNBC) and 1 luminal B PDX revealed considerable heterogeneity in kinase activation, but 1/3 of PDX exhibited enhanced phosphorylation of FGFR1, FGFR2 or FGFR4. One TNBC PDX with high FGFR2 activation was exquisitely sensitive to AZD4547. Integrated ‘omic analysis revealed a novel FGFR2-SKI fusion that comprised the majority of FGFR2 joined to the C-terminal region of SKI containing the coiled-coil domains. High FGFR4 phosphorylation characterized a luminal B PDX model and treatment with BLU9931 significantly decreased tumor growth. Phosphoproteomic and transcriptomic analyses confirmed on-target action of the two anti-FGFR drugs and also revealed novel effects on the spliceosome, metabolism and extracellular matrix (AZD4547) and RIG-I-like and NOD-like receptor signaling (BLU9931). Interrogation of public datasets revealed FGFR2 amplification, fusion or mutation in TNBC and other breast cancer subtypes, while FGFR4 overexpression and amplification occurred in all breast cancer subtypes and were associated with poor prognosis. Characterization of a PDO panel identified a luminal A PDO with high FGFR4 expression that was sensitive to BLU9931 treatment, further highlighting FGFR4 as a potential therapeutic target. Conclusions This work highlights how patient-derived models of human breast cancer provide powerful platforms for therapeutic target identification and analysis of drug action, and also the potential of specific FGFRs, including FGFR4, as targets for precision treatment.

Comparison of molecular (BluePrint+MammaPrint) and pathological subtypes for breast cancer among the first 800 patients from the EORTC 10041/BIG 3-04 (MINDACT) trial.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 1022-1022
Author(s):  
Giuseppe Viale ◽  
Leen Slaets ◽  
Femke De Snoo ◽  
Laura J. van 't Veer ◽  
Emiel J. Rutgers ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Treatment Decision ◽  
Luminal A ◽  
Luminal Subtype ◽  
Cancer Subtypes ◽  
Luminal B ◽  
Treatment Decision Making ◽  
Main Driver ◽  
Micro Array ◽  
Treatment Plans

1022 Background: Biology has become the main driver of breast cancer therapy. Intrinsic biological subtypes by gene expression profiling have been identified. Pathology can be used to define surrogates of these subtypes but these are not always concordant, which may lead to different treatment plans. We investigated the concordance between BluePrint (BP) + MammaPrint (MP) (micro array based) breast cancer subtypes and pathological surrogates (based on ER, PR, HER2, and Ki67). Contrary to the Perou gene set (evolved into PAM50), BluePrint was trained using pathological data. Methods: Using available data (centrally assessed pathology and genomic) from the MINDACT pilot phase (Rutgers et al 2011) 621 tumors were analyzed. Two pathology classifications were used: one with 4 categories and one with 5 categories (Goldhirsch et al 2011). Based on BP 3 subtypes are formed: Luminal, HER2 and Basal. The Luminal subtype is further split into Luminal A (MP low risk) and Luminal B (MP high risk). Results: See table. Conclusions: All pathological Basal cases are BP Basal, apart from 1 BP HER2 case. Of the BP Basal cases, 15 are not pathological Basal: 1 is Luminal A, 11 are Luminal B (of which 8 are IHC ER/PR borderline (≥1% and < 10%)) and 3 are HER2. All pathological Luminal (A & B) that are BP HER2 are HER2- by TargetPrint. 25 of the 26 pathological HER2+ that are BP Luminal A are ER+. Most discordant cases are seen within the Luminal subtype, indicating that Ki67 discriminates Luminal A vs. B differently than MammaPrint does. The observed subtype discrepancies reveal potential important impact for treatment-decision making. MINDACT will provide important information. [Table: see text]

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