Proposed new clinicopathological surrogate definitions of luminal A and luminal B (HER2-negative) intrinsic breast cancer subtypes

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.

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

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.1041 ◽

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.

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

Breast Cancer Research ◽

10.1186/s13058-021-01461-4 ◽

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.

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Abstract PD2-11: Ki-67 (30-9) scoring and differentiation in Luminal A and Luminal B breast cancer subtypes

10.1158/1538-7445.sabcs18-pd2-11 ◽

2019 ◽

Author(s):

G Viale ◽

AE Hanlon Newell ◽

E Walker ◽

I Bai ◽

L Russo ◽

...

Keyword(s):

Breast Cancer ◽

Breast Cancer Subtypes ◽

Luminal A ◽

Ki 67 ◽

Cancer Subtypes ◽

Luminal B

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225P Prognostic factors of luminal A and luminal B intrinsic breast cancer subtypes of Croatian patients: A 5-year experience from Osijek

Annals of Oncology ◽

10.1016/j.annonc.2021.08.507 ◽

2021 ◽

Vol 32 ◽

pp. S455

Author(s):

R. Gudelj ◽

J. Rajc ◽

M. Bakula ◽

R. Alaghehbandan

Keyword(s):

Breast Cancer ◽

Prognostic Factors ◽

Breast Cancer Subtypes ◽

Luminal A ◽

Cancer Subtypes ◽

Luminal B

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Quantitative Values from Synthetic MRI Correlate with Breast Cancer Subtypes

10.21203/rs.3.rs-879518/v1 ◽

2021 ◽

Author(s):

Toshiki Kazama ◽

Taro Takahara ◽

Thomas C. Kwee ◽

Noriko Nakamura ◽

Nobue Kumaki ◽

...

Keyword(s):

Breast Cancer ◽

Breast Cancer Subtypes ◽

Strong Positive Correlation ◽

Characteristic Analysis ◽

Luminal A ◽

Ki 67 ◽

Cancer Subtypes ◽

Luminal B ◽

Er Positive ◽

Synthetic Mri

Abstract Purpose To correlate quantitative T1, T2, and proton density (PD) values from synthetic MRI with breast cancer subtypes. Methods Twenty-eight breast cancer patients underwent MRI of the breast including synthetic MRI. T1, T2, and PD values were correlated with Ki-67. T1, T2, and PD values were compared between estrogen receptor (ER) positive and ER negative cancers, and between Luminal A and Luminal B cancers. The effectiveness of T1, T2, and PD in differentiating the ER-negative from the ER-positive group and Luminal A from Luminal B cancers was evaluated using receiver operating characteristic analysis.Results Mean T2 relaxation of ER-negative cancers was significantly higher than that of ER-positive cancers (p < .05). The T1, T2, and PD values exhibited a strong positive correlation with Ki-67 (Pearson's r = 0.75, 0.69, and 0.60 respectively; p < .001). Among ER-positive cancers (n=23), T1, T2, and PD values of Luminal A cancers were significantly lower than those of Luminal B cancers (p < .05). The areas under the curve (AUCs) of T1, T2, and PD for discriminating ER-negative from ER-positive cancers were 0.74 (95% confidence interval (CI): 0.54-0.88), 0.87 (95% CI: 0.69-0.97), and 0.62 (95% CI: 0.42-0.79), respectively. The AUCs of T1, T2, and PD values for discriminating Luminal A from Luminal B cancers were 0.83 (95% CI: 0.61-0.95), 0.75 (95% CI: 0.52-0.90), and 0.75 (95% CI: 0.53-0.91), respectively.Conclusion Quantitative values from synthetic MRI significantly correlate with subtypes of invasive breast cancers and may classify subtypes with reasonably good accuracy.

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Metastatic Behavior of Breast Cancer Subtypes

Journal of Clinical Oncology ◽

10.1200/jco.2009.25.9820 ◽

2010 ◽

Vol 28 (20) ◽

pp. 3271-3277 ◽

Cited By ~ 984

Author(s):

Hagen Kennecke ◽

Rinat Yerushalmi ◽

Ryan Woods ◽

Maggie Chon U. Cheang ◽

David Voduc ◽

...

Keyword(s):

Breast Cancer ◽

Lung Metastases ◽

Early Stage ◽

Growth Factor Receptor ◽

Breast Cancer Subtypes ◽

Early Stage Breast Cancer ◽

Luminal A ◽

Cancer Subtypes ◽

Luminal B ◽

Metastatic Sites

Purpose Prognostic and predictive factors are well established in early-stage breast cancer, but less is known about which metastatic sites will be affected. Methods Patients with early-stage breast cancer diagnosed between 1986 and 1992 with archival tissue were included. Subtypes were defined as luminal A, luminal B, luminal/human epidermal growth factor receptor 2 (HER2), HER2 enriched, basal-like, and triple negative (TN) nonbasal. Distant sites were classified as brain, liver, lung, bone, distant nodal, pleural/peritoneal, and other. Cumulative incidence curves were estimated for each site according to competing risks methods. Association between the site of relapse and subtype was assessed in multivariate models using logistic regression. Results Median follow-up time among 3,726 eligible patients was 14.8 years. Median durations of survival with distant metastasis were 2.2 (luminal A), 1.6 (luminal B), 1.3 (luminal/HER2), 0.7 (HER2 enriched), and 0.5 years (basal-like; P < .001). Bone was the most common metastatic site in all subtypes except basal-like tumors. In multivariate analysis, compared with luminal A tumors, luminal/HER2 and HER2-enriched tumors were associated with a significantly higher rate of brain, liver, and lung metastases. Basal-like tumors had a higher rate of brain, lung, and distant nodal metastases but a significantly lower rate of liver and bone metastases. TN nonbasal tumors demonstrated a similar pattern but were not associated with fewer liver metastases. Conclusion Breast cancer subtypes are associated with distinct patterns of metastatic spread with notable differences in survival after relapse.

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Can Oncotype DX recurrence score (RS) be used in luminal A and luminal B breast cancer patients (pts) to predict the likely benefit of chemotherapy? A retrospective study in the Spanish population.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.e11006 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. e11006-e11006 ◽

Cited By ~ 1

Author(s):

Laura García-Estevez ◽

Ana C Contreras ◽

Isabel Calvo ◽

Maria Fernandez Abad ◽

Juan J de la Cruz ◽

...

Keyword(s):

Breast Cancer ◽

Adjuvant Chemotherapy ◽

Oncotype Dx ◽

Breast Cancer Subtypes ◽

Treatment Recommendation ◽

Nuclear Staining ◽

Luminal A ◽

Ki 67 ◽

Cancer Subtypes ◽

Luminal B

e11006 Background: The 2011 St Gallen guidelines recommend the use of Ki67 as a surrogate marker for luminal A and Luminal B breast cancer subtypes. Pts with luminal B subtypes should be considered for adjuvant chemotherapy. The guidelines also recognize the Oncotype DX RS as a predictor of chemotherapy benefit. The aim of this study is to assess the distribution of the RS in luminal A and luminal B breast cancer subtypes as defined by Ki67. Methods: Data from 91 pts with invasive breast cancer for which Oncotype DX results and pathology data were available. Pathological assessment was evaluated by the same pathologist. Estrogen (ER) and progesterone (PR) receptor was assessed by immunostaining (cut-off 10% nuclear staining). Ki67 was assessed by IHC [high (≥14%) and low (< 14%)]. Grading was performed by using Nottingham grading system. Results: Median age: 50 years (range: 35-78); premenopausal status: 49 pts (53.8%). Median tumor size: 1.5 cm (0, 3-6); Median of Ki 67 index: 15. 5 (range: 3-63); Median ER: 93 (35-100) and PR: 85(0-100). Sixty nine pts (75.8%) had negative-lymph nodes. RS was low in 56 (61. 5%) cases, intermediate in 24 (26. 4%), and high in 11 (12. 1%). The median RS was 16 (range 3-55). Forty six (51%) tumors were classified as luminal B (according to high Ki 67) and 38 (41.7%) as Luminal A. In the Luminal B group RS was low in 28 (61%) pts, intermediate in 10 (22%) and high in 8 (17%) while in the Luminal A group 23 (61%) had low RS; 13 (34%) intermediate and 2 (5%) high RS. RS changed the first treatment recommendation in 23 (50%) cases of Luminal B subtype vs. 10 (26%) cases in Luminal A subtype (p=0.013). Conclusions: Although based in a small case series, the results show that a substantial number (61%) pts with Luminal B breast cancer subtype had a low RS, therefore preserving them from adjuvant chemotherapy treatment. In addition, 5% of patients with Luminal A breast cancer had a high RS and thereby likely to benefit from chemotherapy. The wide range of RS in both Luminal B and Luminal A breast cancer subtypes confirm the important role of Oncotype DX in treatment decision- making.

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Identification of long non-coding RNAs and RNA binding proteins in breast cancer subtypes

Scientific Reports ◽

10.1038/s41598-021-04664-z ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Claudia Cava ◽

Alexandros Armaos ◽

Benjamin Lang ◽

Gian G. Tartaglia ◽

Isabella Castiglioni

Keyword(s):

Breast Cancer ◽

Binding Proteins ◽

Rna Binding ◽

Molecular Subtypes ◽

Rna Binding Proteins ◽

Breast Cancer Subtypes ◽

Luminal A ◽

Cancer Subtypes ◽

Luminal B ◽

Non Coding Rnas

AbstractBreast cancer is a heterogeneous disease classified into four main subtypes with different clinical outcomes, such as patient survival, prognosis, and relapse. Current genetic tests for the differential diagnosis of BC subtypes showed a poor reproducibility. Therefore, an early and correct diagnosis of molecular subtypes is one of the challenges in the clinic. In the present study, we identified differentially expressed genes, long non-coding RNAs and RNA binding proteins for each BC subtype from a public dataset applying bioinformatics algorithms. In addition, we investigated their interactions and we proposed interacting biomarkers as potential signature specific for each BC subtype. We found a network of only 2 RBPs (RBM20 and PCDH20) and 2 genes (HOXB3 and RASSF7) for luminal A, a network of 21 RBPs and 53 genes for luminal B, a HER2-specific network of 14 RBPs and 30 genes, and a network of 54 RBPs and 302 genes for basal BC. We validated the signature considering their expression levels on an independent dataset evaluating their ability to classify the different molecular subtypes with a machine learning approach. Overall, we achieved good performances of classification with an accuracy >0.80. In addition, we found some interesting novel prognostic biomarkers such as RASSF7 for luminal A, DCTPP1 for luminal B, DHRS11, KLC3, NAGS, and TMEM98 for HER2, and ABHD14A and ADSSL1 for basal. The findings could provide preliminary evidence to identify putative new prognostic biomarkers and therapeutic targets for individual breast cancer subtypes.

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