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Overview of Therapeutic Biomarkers in Cancer
Published in Sherry X. Yang, Janet E. Dancey, Handbook of Therapeutic Biomarkers in Cancer, 2021
Sherry X. Yang, Janet E. Dancey Treatment
The MammaPrint, a 70-gene signature derived from the gene expression profiling approach, has been validated as a prognostic tool for patients with HR-positive and lymph node-positive or -negative early-stage breast cancer patients who received adjuvant endocrine therapy with or without chemotherapy (Chapter 13). The MINDACT (Microarray in Node-Negative Disease May Avoid Chemotherapy) study revealed that patients at high clinical risk and low genomic risk without chemotherapy had an only 1.5% lower 5-year survival rate without distant metastasis than those who received chemotherapy [97]. As such, the data could translate into about 46% of patients with breast cancer who are at high clinical risk and might not need chemotherapy.
Risk Reduction and Screening for Women’s Cancers
Published in James M. Rippe, Lifestyle Medicine, 2019
Ama McKinney, Jo Marie Tran Janco
A study published in JAMA Oncology in June of 2017 found that a 70-gene expression test called “MammaPrint” could be used to determine risk from breast cancer death. Among postmenopausal women with node-negative breast cancer, those who were deemed “ultra-low risk” but took tamoxifen had a 97% survival rate at 20 years vs. 94% for those who did not take tamoxifen. Utilizing a test like this could identify those at ultra-low risk of recurrence and death, eliminating the need for treatment beyond lumpectomy.19
Radiogenomics
Published in Jun Deng, Lei Xing, Big Data in Radiation Oncology, 2019
Barry S. Rosenstein, Gaurav Pandey, Corey W. Speers, Jung Hun Oh, Catharine M.L. West, Charles S. Mayo
Computational methods are particularly capable of discovering the latter types of biomarkers from diverse types of biomedical data, such as genetic–protein interaction networks, SNP panels, gene expression profiles, and proteomics and metabolomics data (McDermott et al. 2013). Machine learning methods have played a productive role in this area due to their ability to sift through noisy high-dimensional biomedical data sets to find useful and actionable biomarkers. A machine learning subarea that is particularly relevant to biomarker discovery is feature selection (Saeys et al. 2007), which includes techniques for selecting a subset of all the features in a data set that can help accomplish the target data analysis task better than all the features taken together. This is typically accomplished by eliminating or reducing the effect of redundant or irrelevant features based on some objective criteria. In this sense, the identification of differentially expressed genes or SNPs significantly associated with a phenotype can be thought of as traditional univariate feature selection tasks. However, if the end goal is the prediction of a clinically relevant phenotype, a far more powerful approach is to embed the feature selection operation within the predictive modeling process (Ma and Huang 2008). This enables the simultaneous selection and evaluation of feature sets that can collectively be used to eventually learn an accurate predictive model for the target phenotype. Indeed, the MammaPrint gene expression panel for breast cancer risk prediction was derived using a similar approach (van’t Veer et al. 2002).
An integrative view on breast cancer signature panels
Published in Expert Review of Molecular Diagnostics, 2019
Zhen Wang, Xuanhao Zhang, Shuo Zhang, Xiaofeng Dai
MammaPrint™ assay is the first panel approved by the FDA’s new in vitro diagnostic multivariate index assay classification and is the first fully commercialized microarray-based multigene assay for breast cancer diagnosis. MammaPrint™ uses fresh/frozen tissue or FFPE. It was originally developed at the Netherlands Cancer Institute in Amsterdam, and later commercialized in the Agilent microarray platform. It is a 70-gene signature primarily comprised of genes involved in proliferation, and some associated with stromal integrity, angiogenesis and metastasis [38]. MammaPrint™ classifies tumors into groups with good or poor prognosis based on the risk of distant recurrence at 10 years [6]. MammaPrint™ assay helps to refine clinical risk estimations, works at its best when identifying high-risk cases at the extremes of the spectrum of disease outcome and aids in the treatment of early stage breast cancers [39]. NCCN recommends the use of MammaPrint™ in the prognosis of the recurrence risk in node-negative and node-positive breast cancer patients (https://www.nccn.org/).
Assessment of PD-L1 expression across breast cancer molecular subtypes, in relation to mutation rate, BRCA1-like status, tumor-infiltrating immune cells and survival
Published in OncoImmunology, 2018
Marcelo Sobral-Leite, Koen Van de Vijver, Magali Michaut, Rianne van der Linden, Gerrit K.J. Hooijer, Hugo M. Horlings, Tesa M. Severson, Anna Marie Mulligan, Nayana Weerasooriya, Joyce Sanders, Annuska M Glas, Diederik Wehkamp, Lorenza Mittempergher, Kelly Kersten, Ashley Cimino-Mathews, Dennis Peters, Erik Hooijberg, Annegien Broeks, Marc J. van de Vijver, Rene Bernards, Irene L. Andrulis, Marleen Kok, Karin E. de Visser, Marjanka K. Schmidt
To further explore the association of PD-L1 expression and prognosis, we analyzed the RNA expression levels of genes encoding PD-L1 and PD-1 according to the MammaPrint risk classification. This prognostic tool is commonly used in the clinic to support clinical decisions especially in ER-positive breast cancer.35 In total, we analyzed 547 breast tumors from patients included in trials in which data on the MammaPrint gene signature was collected (supplementary material and methods). The expression levels of CD274 (PD-L1) and PDCD1 (PD-1) were compared among high and low risk of recurrence, assessed by the MammaPrint gene expression assay,36 and stratified by breast cancer molecular subtype, using BluePrint gene profile.37 Among the luminal tumors (n = 490), no difference on CD274 expression was found between high or low risk patients (supplementary figure 7A). However, PDCD1 expression was slightly higher in patients with MammaPrint high risk scores, compared with MammaPrint low risk patients (p-value< 0.0001; supplementary figure 7B). This adds evidence that PD-L1 expression has no prognostic value in ER-positive breast cancer.
The budget impact of utilizing the Oncotype DX Breast Recurrence Score test from a US healthcare payer perspective
Published in Journal of Medical Economics, 2023
Vladislav Berdunov, Ewan Laws, Gebra Cuyun Carter, Roger Luo, Christy Russell, Sara Campbell, Jeremy Force, Yara Abdou
Previous budget impact analyses have shown the Oncotype DX test to be cost-saving in the German market12, but no such analysis has been conducted from a US healthcare perspective. A budget impact analysis for MammaPrint informed by data from the MINDACT study reported a net cost saving from a US healthcare payer perspective, suggesting that the use of MGAs can free up substantial healthcare resources13. There is a need to estimate the budget impact of the Oncotype DX test from a US healthcare perspective, considering the new evidence published from the TAILORx and RxPONDER trials, which used an updated RS classification for high-risk patients (RS > 25 instead of RS > 30)10,11.