Abstract P4-08-35: Young age at diagnosis is associated with worse prognosis in the luminal A breast cancer subtype. A retrospective institutional cohort study

In the past, treatment decisions regarding adjuvant chemotherapy in early breast cancer (EBC) were made solely based on clinicopathologic factors. However, with increased awareness of the importance of underlying tumor biology, we are now able to use genomic analyses to determine molecular breast cancer subtype and thus identify patients with tumors that are chemotherapy resistant and unlikely to benefit from the addition of chemotherapy. Although genomics has allowed some patients to avoid chemotherapy—specifically those with luminal A–like breast cancer—these assays do not indicate which regimen is most appropriate. For this, consideration must be given to the combination of underlying tumor biology, tumor stage, and patient characteristics, such as age and tolerability of side effects.

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Relationship between functional imaging and immunohistochemical markers and prediction of breast cancer subtype: a PET/MRI study

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-018-4010-7 ◽

2018 ◽

Vol 45 (10) ◽

pp. 1680-1693 ◽

Cited By ~ 10

Author(s):

Mariarosaria Incoronato ◽

Anna Maria Grimaldi ◽

Carlo Cavaliere ◽

Marianna Inglese ◽

Peppino Mirabelli ◽

...

Keyword(s):

Breast Cancer ◽

Functional Imaging ◽

Breast Cancer Subtype ◽

Immunohistochemical Markers ◽

Cancer Subtype ◽

Mri Study ◽

Subtype A

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Prediction of clusters of miRNA binding sites in mRNA candidate genes of breast cancer subtypes

PeerJ ◽

10.7717/peerj.8049 ◽

2019 ◽

Vol 7 ◽

pp. e8049 ◽

Cited By ~ 4

Author(s):

Dana Aisina ◽

Raigul Niyazova ◽

Shara Atambayeva ◽

Anatoliy Ivashchenko

Keyword(s):

Breast Cancer ◽

Candidate Genes ◽

Binding Sites ◽

Target Genes ◽

Breast Cancer Subtype ◽

Luminal A ◽

Cancer Subtypes ◽

Nucleotide Binding Sites ◽

Cancer Subtype ◽

Gene Mrna

The development of breast cancer (BC) subtypes is controlled by distinct sets of candidate genes, and the expression of these genes is regulated by the binding of their mRNAs with miRNAs. Predicting miRNA associations and target genes is thus essential when studying breast cancer. The MirTarget program identifies the initiation of miRNA binding to mRNA, the localization of miRNA binding sites in mRNA regions, and the free energy from the binding of all miRNA nucleotides with mRNA. Candidate gene mRNAs have clusters (miRNA binding sites with overlapping nucleotide sequences). mRNAs of EPOR, MAZ and NISCH candidate genes of the HER2 subtype have clusters, and there are four clusters in mRNAs of MAZ, BRCA2 and CDK6 genes. Candidate genes of the triple-negative subtype are targets for multiple miRNAs. There are 11 sites in CBL mRNA, five sites in MMP2 mRNA, and RAB5A mRNA contains two clusters in each of the three sites. In SFN mRNA, there are two clusters in three sites, and one cluster in 21 sites. Candidate genes of luminal A and B subtypes are targets for miRNAs: there are 21 sites in FOXA1 mRNA and 15 sites in HMGA2 mRNA. There are clusters of five sites in mRNAs of ITGB1 and SOX4 genes. Clusters of eight sites and 10 sites are identified in mRNAs of SMAD3 and TGFB1 genes, respectively. Organizing miRNA binding sites into clusters reduces the proportion of nucleotide binding sites in mRNAs. This overlapping of miRNA binding sites creates a competition among miRNAs for a binding site. From 6,272 miRNAs studied, only 29 miRNAs from miRBase and 88 novel miRNAs had binding sites in clusters of target gene mRNA in breast cancer. We propose using associations of miRNAs and their target genes as markers in breast cancer subtype diagnosis.

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Towards cancer mega-cohorts: A novel homogenization algorithm applied to diverse breast cancer RNA-Seq datasets.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e13507 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e13507-e13507

Author(s):

Talal Ahmed ◽

Mark Carty ◽

Stephane Wenric ◽

Raphael Pelossof

Keyword(s):

Breast Cancer ◽

Molecular Subtype ◽

Breast Cancer Subtype ◽

Model Performance ◽

Test Results ◽

Rna Seq ◽

Luminal A ◽

Test Set ◽

Luminal B ◽

Cancer Subtype

e13507 Background: Recent advances in transcriptomics have resulted in the emergence of several publicly available breast cancer RNA-Seq datasets, such as TCGA, SCAN-B, and METABRIC. However, molecular predictors cannot be applied across datasets without the correction of batch differences. In this study, we demonstrate a homogenization algorithm that allows the transfer of molecular subtype predictors from one RNA-Seq cohort to another. The algorithm only uses cohort-level RNA-Seq summary statistics, and therefore, does not require joint normalization of both datasets nor the transfer of patient information. Using this approach, we transferred a breast cancer subtype (Luminal A, Luminal B, HER2+, Basal) predictor trained on SCAN-B data to accurately predict subtypes from TCGA. Methods: First, we randomly split the TCGA cohort (n = 481 Luminal A, n = 189 Luminal B, n = 73 Her2+, n = 168 Basal) into two sets: TCGA-train and held-out TCGA-test (n = 455 and n = 456, respectively). Second, the SCAN-B cohort (n = 837) was homogenized with the TCGA-train set. Third, a molecular subtype predictor, based on a logistic regression model, was trained on homogenized SCAN-B RNA-Seq samples and used to predict the subtypes of TCGA-test RNA-Seq samples. For baseline comparison, a similar predictor trained on the non-homogenized SCAN-B cohort was tested on the TCGA-test set. The experimental framework was iterated 250 times. Reported P-values reflect a paired one-sided t-test. Results: To quantify model performance, we measured the average F1 score for each tumor subtype prediction from the held-out TCGA test set with and without cohort homogenization. The average F1 scores with vs. without homogenization were: Luminal A, 0.88 vs. 0.85 ( P< 1e-69); Luminal B, 0.74 vs. 0.51 ( P< 1e-183); Her2+, 0.73 vs. 0.53 ( P< 1e-99); Basal, 0.98 vs. 0.97 ( P< 1e-53). Overall, homogenization significantly outperformed no homogenization. Conclusions: We developed a novel homogenization algorithm that accurately transfers subtype predictors across diverse, independent breast cancer cohorts.

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Influence of young age at diagnosis and family history of breast or ovarian cancer on breast cancer outcomes in a population-based cohort study

Breast Cancer Research and Treatment ◽

10.1007/s10549-006-9433-3 ◽

2006 ◽

Vol 105 (1) ◽

pp. 69-80 ◽

Cited By ~ 37

Author(s):

Jane C. Figueiredo ◽

Marguerite Ennis ◽

Julia A. Knight ◽

John R. McLaughlin ◽

Nicky Hood ◽

...

Keyword(s):

Breast Cancer ◽

Ovarian Cancer ◽

Cohort Study ◽

Family History ◽

Population Based ◽

Young Age ◽

Age At Diagnosis ◽

Cancer Outcomes ◽

Breast Cancer Outcomes ◽

History Of

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Lack of Either Estrogen or Progesterone Receptor Expression Is Associated with Poor Survival Outcome among Luminal A Breast Cancer Subtype

Annals of Surgical Oncology ◽

10.1245/s10434-012-2772-x ◽

2012 ◽

Vol 20 (5) ◽

pp. 1505-1513 ◽

Cited By ~ 19

Author(s):

Seho Park ◽

Byeong-Woo Park ◽

Tae Hyun Kim ◽

Chang Wan Jeon ◽

Han-Sung Kang ◽

...

Keyword(s):

Breast Cancer ◽

Progesterone Receptor ◽

Breast Cancer Subtype ◽

Survival Outcome ◽

Receptor Expression ◽

Progesterone Receptor Expression ◽

Poor Survival ◽

Luminal A ◽

Cancer Subtype ◽

Poor Survival Outcome

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Abstract P4-09-14: Invasive Lobular Carcinoma Is a Prototype of Luminal A Breast Cancer Subtype and Rare in Korea

10.1158/0008-5472.sabcs10-p4-09-14 ◽

2010 ◽

Author(s):

S-Y Jung ◽

Y Kwon ◽

E-A Kim ◽

KL Ko ◽

KH Shin ◽

...

Keyword(s):

Breast Cancer ◽

Invasive Lobular Carcinoma ◽

Breast Cancer Subtype ◽

Lobular Carcinoma ◽

Luminal A ◽

Cancer Subtype

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Molecular analysis in breast cancer subtypes and correlation with pathologic complete response (pCR) to neoadjuvant chemotherapy.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.e22119 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. e22119-e22119

Author(s):

Maria Gonzalez Cao ◽

Carlota Costa ◽

Miguel Angel Molina-Vila ◽

Maria Teresa Cusido ◽

Santiago Viteri Ramirez ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Neoadjuvant Chemotherapy ◽

Breast Cancer Subtype ◽

Pathologic Complete Response ◽

Luminal A ◽

Luminal B ◽

Mutational Status ◽

Cancer Subtype ◽

Low Levels

e22119 Background: Although it is know that pCR following neoadjuvant chemotherapy is more frequent in some subtypes of breast cancer such as Triple Negative (TN) or erb2 tumors, the predictive role of gene expression and mutation status is not well defined in this setting. Methods: We analyzed samples from 41 patients (p) prospectively treated with neoadjuvant chemotherapy (sequential AC followed by weekly TXL, or inverse sequence, plus trastuzumab for erb2 positive p). Pathologic response (PR) was classified according to Miller-Payne and RCB criteria. Radiologic evaluation was performed by ultrasonography, dynamic MR and PET-TAC after each chemotherapy sequence. We performed expression analysis of AXL, BRCA1, RAP80, BIM, EZH2, ROR1, FGFR1, PTPN12, YAP, GAS6, beta-TRCP, HIF1 alpha and ZNF217 by RT-PCR, and mutational status of p53 and PI3K genes in pretreatment biopsies. Statistical analysis was performed using Mann-Whitney U and Pearson’s chi-squared tests. Results: pCR was detected in 5 p (3TN, 2 erb2) of 25 p (9 luminal A, 5 luminal B, 6 erb2 and 5 TN) evaluated for PR at time of submitting this abstract. TN tumors had lower levels of RAP80 (p=.0013), PTPN12 (p=.003), beta TRCP (p=.001), ZNF217 (p=.014) and YAP (p=.097). Luminal B tumors had low levels of YAP and the highest levels of FGFR1 (p=.09) and ZNF217 (p=.014). Luminal A tumors had high levels of beta-TRCP (p=.003). We found no differences in BRCA1, AXL, BIM, EZH2, ROR1, GAS6 and HIF1 levels by breast cancer subtype. P with low levels of FGFR1 (p=.087), HIF1alpha (p=.07) or EZH2 (p=.005) had higher probability of pCR. No pCR was observed in p with higher levels of AXL, EZH2, RAP80, GAS6, beta TRCP, HIF alpha. Four p had p53 mutations (1 luminal B, 1 erb2 and 2 TN) and 4 p had PI3K mutations (2 luminal A, 1 erb2, 1 luminal B). There was no correlation between p53 status and PR. P with PI3K mutations did not achieve pCR vs 46% of p with wild type PI3K (p=.23). Conclusions: Gene expression profile varies by breast cancer subtype. Chemosensitivity could be higher in tumors with lower levels of FGFR1, HIF1alpha or EZH2. Further results will be presented.

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