Gene expression profiling predicts clinical outcome of breast cancer

  title={Gene expression profiling predicts clinical outcome of breast cancer},
  author={Laura J. van't Veer and Hongyue Dai and Marc J. van de Vijver and Yudong D. He and Augustinus A. M. Hart and Mao Mao and Hans L. Peterse and Karin van der Kooy and Matthew John Marton and Anke T. Witteveen and George J. Schreiber and Ron M. Kerkhoven and Chris J. Roberts and Peter S. Linsley and René Bernards and Stephen H. Friend},
Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. [] Key Method Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases (‘poor prognosis’ signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative).
Gene Expression Profiling of Node-Negative Breast Tumors Reveals Metastatic Recurrence Risk
The aim is to characterize the genetic markers that are differentially expressed in good versus bad outcome node-negative breast cancers, help dichotomize node- negative patients into high and low-risk categories so that adjuvant treatment could be more effectively utilized, and delineate the genetic pathways associated with metastatic phenotype.
Classification of Human Breast Cancer Using Gene Expression Profiling as a Component of the Survival Predictor Algorithm
Purpose: Selection of treatment options with the highest likelihood of successful outcome for individual breast cancer patients is based to a large degree on accurate classification into subgroups
Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer.
Preliminary evidence that incorporation of gene expression signatures into clinical risk stratification can refine prognosis is provided, to demonstrate the value in integrating genomic information with clinical and pathological risk factors and to improve therapeutic strategies for early stage breast cancer.
Indications for Prognostic Gene Expression Profiling in Early Breast Cancer
The 21-gene recurrence score (Oncotype Dx®), estimates 10-year risk of breast cancer recurrence in patients with estrogen receptor (ER)-positive, HER2-negative, node-negative EBC and is likely predictive of chemotherapy benefit is indicated in this patient population to help inform decisions regarding administration of adjuvant chemotherapy.
Sentinel node biopsy versus conventional axillary dissection in clinically node-negative breast cancer patients
The identified 76-gene signature provides a powerful tool for identification of patients at high or low risk for distant recurrence or death due to breast cancer, allowing clinicians to adapt choices of adjuvant systemic therapy.
The 70-gene prognosis-signature predicts disease outcome in breast cancer patients with 1–3 positive lymph nodes in an independent validation study
The 70-gene prognosis-signature outperforms traditional prognostic factors in predicting disease outcome in patients with 1–3 positive nodes and may be safely spared adjuvant chemotherapy in node-positive breast cancer.
Prognostic Value of Gene Signatures and Proliferation in Lymph-Node-Negative Breast Cancer
In the subgroup of patients with high proliferation, Notch signalling pathway genes appear to be expressed at higher levels in patients who develop distant metastasis, indicating that proliferation has greater prognostic value than the expressions of either MammaPrint- or Oncotype-DX-related genes.
Clinical utility of gene-expression signatures in early stage breast cancer
The available data on the clinical validity of the most widely available assays in patients with early stage breast cancer are described, with a focus on the development, validation, and clinical application of these assays, in addition to the anticipated outcomes of ongoing prospective trials.
Genomic Applications in Breast Carcinoma
Sequencing-based assays will soon be incorporated into the diagnostic armamentarium of pathologists for the optimal selection of systemic therapies for breast cancer patients, with the advent of massively parallel sequencing and the ability to characterize cancer genomes at base pair resolution, driver genes and actionable mutations have been identified.
Gene Expression Profiling of Carcinoma Breast and its, Prognostic Signature: A Review
  • S. Agrawal
  • Medicine, Biology
    Academia Journal of Surgery
  • 2019
An overview of the development and application of molecular assays as applied to breast cancer continues to be a work in progress and this approach is evolving quickly due to strong scientific pieces of evidence to become a standard of practice in the near future.


Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications
Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.
Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning
Genes implicated in DLBCL outcome included some that regulate responses to B-cell–receptor signaling, critical serine/threonine phosphorylation pathways and apoptosis, and identify rational targets for intervention.
Randomized adjuvant chemotherapy trial in high-risk, lymph node-negative breast cancer patients identified by urokinase-type plasminogen activator and plasminogen activator inhibitor type 1.
Using uPA and PAI-1, the Chemo N(0) prospective randomized multicenter therapy trial is able to classify about half of the patients with lymph node-negative breast cancer as low risk, for whom adjUvant chemotherapy may be avoided, and half as high risk, who appear to benefit from adjuvant chemotherapy.
Molecular profiling of breast cancer: portraits but not physiognomy
Microarray profiling of 38 invasive breast cancers now confirms striking molecular differences between ductal carcinoma specimens and suggests a new classification for oestrogen-receptor negative breast cancer.
Molecular portraits of human breast tumours
Variation in gene expression patterns in a set of 65 surgical specimens of human breast tumours from 42 different individuals were characterized using complementary DNA microarrays representing 8,102 human genes, providing a distinctive molecular portrait of each tumour.
Cyclin D1 expression in invasive breast cancer. Correlations and prognostic value.
Cyclin D1 is mainly overexpressed in the well differentiated and lobular types of invasive breast cancer and is strongly associated with estrogen receptor positivity and it is negatively correlated with the proliferation marker mitoses count and with the differentiation markers nuclear area and nuclear volume.
Identification of gene expression profiles that predict the aggressive behavior of breast cancer cells.
The results demonstrated that the GEP of a cell line is predictive of its invasive and migratory behavior, as manifest by the morphology of its colonies when cultured on a matrix of basement membrane constituents (i.e., Matrigel).
Gene-expression profiles in hereditary breast cancer.
Significantly different groups of genes are expressed by breast cancers with BRCA1 mutations and breast cancersWith BRCa2 mutations, the results suggest that a heritable mutation influences the gene-expression profile of the cancer.
Multifactorial analysis of differences between sporadic breast cancers and cancers involving BRCA1 and BRCA2 mutations.
Key features of the histologic phenotypes of breast cancers in carriers of mutant BRCA1 and BRCa2 genes are identified and this information may improve the classification of breast cancer in individuals with a family history of the disease and may ultimately aid in the clinical management of patients.
Breast cancer prognostic factors: evaluation guidelines.
  • W. L. McGuire
  • Medicine
    Journal of the National Cancer Institute
  • 1991
In the present issue of this journal, Thor and colleagues attempt to evaluate heat shock protein 27 (also known as the 27 000-dalton stress response protein or srp-27) measured in tumors from breast cancer patients as a prognostic factor, but state that a multivariate analysis failed to recognize srP-27 expression as a significant independent predictive factor.