Literature DB >> 27992844

(Very) Early technology assessment and translation of predictive biomarkers in breast cancer.

Anna Miquel-Cases1, Philip C Schouten2, Lotte M G Steuten3, Valesca P Retèl4, Sabine C Linn5, Wim H van Harten6.   

Abstract

Predictive biomarkers can guide treatment decisions in breast cancer. Many studies are undertaken to discover and translate these biomarkers, yet few biomarkers make it to practice. Before use in clinical decision making, predictive biomarkers need to demonstrate analytical validity, clinical validity and clinical utility. While attaining analytical and clinical validity is relatively straightforward, by following methodological recommendations, the achievement of clinical utility is extremely challenging. It requires demonstrating three associations: the biomarker with the outcome (prognostic association), the effect of treatment independent of the biomarker, and the differential treatment effect between the prognostic and the predictive biomarker (predictive association). In addition, economical, ethical, regulatory, organizational and patient/doctor-related aspects are hampering the translational process. Traditionally, these aspects do not receive much attention until formal approval or reimbursement of a biomarker test (informed by Health Technology Assessment (HTA)) is at stake, at which point the clinical utility and sometimes price of the test can hardly be influenced anymore. When HTA analyses are performed earlier, during biomarker research and development, they may prevent further development of those biomarkers unlikely to ever provide sufficient added value to society, and rather facilitate translation of the promising ones. Early HTA is particularly relevant for the predictive biomarker field, as expensive medicines are under pressure and the need for biomarkers to guide their appropriate use is huge. Closer interaction between clinical researchers and HTA experts throughout the translational research process will ensure that available data and methodologies will be used most efficiently to facilitate biomarker translation.
Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Breast cancer; Health Technology Assessment; Predictive biomarkers

Mesh:

Substances:

Year:  2016        PMID: 27992844     DOI: 10.1016/j.ctrv.2016.11.008

Source DB:  PubMed          Journal:  Cancer Treat Rev        ISSN: 0305-7372            Impact factor:   12.111


  6 in total

1.  BRCAness digitalMLPA profiling predicts benefit of intensified platinum-based chemotherapy in triple-negative and luminal-type breast cancer.

Authors:  Esther H Lips; Anne Benard-Slagter; Mark Opdam; Caroline E Scheerman; Jelle Wesseling; Frans B L Hogervorst; Sabine C Linn; Suvi Savola; Petra M Nederlof
Journal:  Breast Cancer Res       Date:  2020-07-25       Impact factor: 6.466

2.  Perspectives to mitigate payer uncertainty in health technology assessment of novel oncology drugs.

Authors:  Oriol Solà-Morales; Timm Volmer; Lorenzo Mantovani
Journal:  J Mark Access Health Policy       Date:  2019-01-22

Review 3.  Emerging Biomarkers for Diagnosis, Prevention and Treatment of Brain Metastases-From Biology to Clinical Utility.

Authors:  Priyakshi Kalita-de Croft; Vaibhavi Joshi; Jodi M Saunus; Sunil R Lakhani
Journal:  Diseases       Date:  2022-02-03

Review 4.  Emerging Use of Early Health Technology Assessment in Medical Product Development: A Scoping Review of the Literature.

Authors:  Maarten J IJzerman; Hendrik Koffijberg; Elisabeth Fenwick; Murray Krahn
Journal:  Pharmacoeconomics       Date:  2017-07       Impact factor: 4.981

5.  Treatment With Tumor-infiltrating Lymphocytes in Advanced Melanoma: Evaluation of Early Clinical Implementation of an Advanced Therapy Medicinal Product.

Authors:  Melanie A Lindenberg; Valesca P Retèl; Joost H van den Berg; Marnix H Geukes Foppen; John B Haanen; Wim H van Harten
Journal:  J Immunother       Date:  2018 Nov/Dec       Impact factor: 4.456

Review 6.  Graphene-Based Biosensors for Detection of Biomarkers.

Authors:  Yunlong Bai; Tailin Xu; Xueji Zhang
Journal:  Micromachines (Basel)       Date:  2020-01-03       Impact factor: 2.891

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.