Gene-expression signatures to inform neoadjuvant treatment decision in HR+/HER2− breast cancer: Available evidence and clinical implications

The addition of adjuvant chemotherapy to hormonal therapy is often considered questionable in patients with estrogen receptor-positive early breast cancer. Low risk of disease relapse after endocrine treatment alone and/or a low sensitivity to chemotherapy are reasons behind not all patients benefit from chemotherapy. Most of the patients could be exposed to unnecessary treatment- related adverse events and health care costs when treatment decision-making is based only on classical clinical histological features. Gene expression profile has been developed to refine physician’s decision-making process and to tailor personalized treatment to patients. In particular, these tests are designed to spare patients the side effects of unnecessary treatment, and ensure that adjuvant chemotherapy is correctly recommended to patients with early breast cancer. In this review, we will discuss the main diagnostic tests and their potential clinical applications (Oncotype DX, MammaPrint, PAM50/Prosigna, EndoPredict, MapQuant Dx, IHC4, and Theros-Breast Cancer Gene Expression Ratio Assay).

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Immune microenvironment and intrinsic subtyping in hormone receptor-positive/HER2-negative breast cancer

npj Breast Cancer ◽

10.1038/s41523-021-00223-x ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Gaia Griguolo ◽

Maria Vittoria Dieci ◽

Laia Paré ◽

Federica Miglietta ◽

Daniele Giulio Generali ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Gene Expression Data ◽

Hormone Receptor ◽

Tumor Biology ◽

Expression Data ◽

Immune Microenvironment ◽

Immune Infiltrate ◽

Hormone Receptor Positive ◽

Her2 Breast Cancer

AbstractLittle is known regarding the interaction between immune microenvironment and tumor biology in hormone receptor (HR)+/HER2− breast cancer (BC). We here assess pretreatment gene-expression data from 66 HR+/HER2− early BCs from the LETLOB trial and show that non-luminal tumors (HER2-enriched, Basal-like) present higher tumor-infiltrating lymphocyte levels than luminal tumors. Moreover, significant differences in immune infiltrate composition, assessed by CIBERSORT, were observed: non-luminal tumors showed a more proinflammatory antitumor immune infiltrate composition than luminal ones.

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Key Genes and Prognostic Analysis in HER2+ Breast Cancer

Technology in Cancer Research & Treatment ◽

10.1177/1533033820983298 ◽

2021 ◽

Vol 20 ◽

pp. 153303382098329

Author(s):

Yujie Weng ◽

Wei Liang ◽

Yucheng Ji ◽

Zhongxian Li ◽

Rong Jia ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Differentially Expressed Genes ◽

Protein Interaction ◽

Differentially Expressed ◽

Protein Kinase Activity ◽

Protein Protein Interaction ◽

Risk Of Death ◽

Her2 Breast Cancer ◽

Key Genes

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes ( CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

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Development and validation for research assessment of Oncotype DX® Breast Recurrence Score, EndoPredict® and Prosigna®

npj Breast Cancer ◽

10.1038/s41523-021-00216-w ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Richard Buus ◽

Zsolt Szijgyarto ◽

Eugene F. Schuster ◽

Hui Xiao ◽

Ben P. Haynes ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Early Stage ◽

Recurrence Score ◽

Distant Recurrence ◽

Research Assessment ◽

Expression Data ◽

Her2 Breast Cancer ◽

Validation Set ◽

Development And Validation

AbstractMulti-gene prognostic signatures including the Oncotype® DX Recurrence Score (RS), EndoPredict® (EP) and Prosigna® (Risk Of Recurrence, ROR) are widely used to predict the likelihood of distant recurrence in patients with oestrogen-receptor-positive (ER+), HER2-negative breast cancer. Here, we describe the development and validation of methods to recapitulate RS, EP and ROR scores from NanoString expression data. RNA was available from 107 tumours from postmenopausal women with early-stage, ER+, HER2− breast cancer from the translational Arimidex, Tamoxifen, Alone or in Combination study (TransATAC) where previously these signatures had been assessed with commercial methodology. Gene expression was measured using NanoString nCounter. For RS and EP, conversion factors to adjust for cross-platform variation were estimated using linear regression. For ROR, the steps to perform subgroup-specific normalisation of the gene expression data and calibration factors to calculate the 46-gene ROR score were assessed and verified. Training with bootstrapping (n = 59) was followed by validation (n = 48) using adjusted, research use only (RUO) NanoString-based algorithms. In the validation set, there was excellent concordance between the RUO scores and their commercial counterparts (rc(RS) = 0.96, 95% CI 0.93–0.97 with level of agreement (LoA) of −7.69 to 8.12; rc(EP) = 0.97, 95% CI 0.96–0.98 with LoA of −0.64 to 1.26 and rc(ROR) = 0.97 (95% CI 0.94–0.98) with LoA of −8.65 to 10.54). There was also a strong agreement in risk stratification: (RS: κ = 0.86, p < 0.0001; EP: κ = 0.87, p < 0.0001; ROR: κ = 0.92, p < 0.001). In conclusion, the calibrated algorithms recapitulate the commercial RS and EP scores on individual biopsies and ROR scores on samples based on subgroup-centreing method using NanoString expression data.

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