uPA/PAI-1, Oncotype DX™, MammaPrint® Valeurs pronostique et prédictive pour une utilité clinique dans la prise en charge du cancer du sein

EVIDENCE REQUIRED BY HEALTH TECHNOLGY ASSESSMENT AND REIMBURSEMENT BODIES EVALUATING DIAGNOSTIC OR PROGNOSTIC ALGORITHMS THAT INCLUDE OMICS DATA

OBJECTIVES

Multi-analyte assays with algorithmic analyses (MAAAs) use combinations of circulating and clinical markers including omics-based sources for diagnostic and/or prognostic purposes. Assessing MAAAs is challenging under existing health technology assessment (HTA) methods or practices. We undertook a scoping review to explore the HTA methods used for MAAAs to identify the criteria used for clinical research and reimbursement purposes.

METHODS

This review included only non-companion (stand-alone) tests that are actionable and that have been evaluated by leading HTA or insurer/reimbursement bodies up to September 2017.

RESULTS

Twenty-five reports and articles evaluating seventeen MAAAs were examined, most of which have been developed in oncology. The two main models used were the EUnetHTA Core model and the Evaluation of Genomic Applications in Practice and Prevention ACCE framework. Clinical validity and utility criteria were used, as were economic, ethical, legal, and social aspects. Economic evidence on MAAAs was scarce, and there is no consensus on whether the perspectives used are sufficiently broad to include all relevant stakeholders.

CONCLUSIONS

Clinical utility and efficiency were the most used criteria, with stronger evidence needed linking the use of the algorithm with the clinical outcomes in real-life practice. HTA bodies must as well consider questions related to the analytical validity of MAAAs or with organizational aspects. The two main models, the EUnetHTA Core model and the ACCE framework, could be adapted to the assessment of MAAAs.

Plasminogen Activator System and Breast Cancer: Potential Role in Therapy Decision Making and Precision Medicine

Shifting from the historical TNM paradigm to the determination of molecular and genetic subtypes of tumors has been a major improvement to better picture cancerous diseases. The sharper the picture is, the better will be the possibility to develop subsequent strategies, thus achieving higher efficacy and prolonged survival eventually. Recent studies suggest that urokinase-type plasminogen activator (uPA), uPA Receptor (uPAR), and plasmino-gen activator inhibitor-1 (PAI-1) may play a critical role in cancer invasion and metastasis. Consistent with their role in cancer dissemination, high levels of uPA, PAI-1, and uPAR in multiple cancer types correlate with dismal prognosis. In this respect, upfront determination of uPA and PAI-1 as invasion markers has further opened up the possibilities for individualized therapy of breast cancer. Indeed, uPA and PAI-1 could help to classify patients on their risk for metastatic spreading and subsequent relapse, thus helping clinicians in their decision-making process to propose, or not propose, adjuvant therapy. This review covers the implications for cancer diagnosis, prognosis, and therapy of uPA and PAI-1, and therefore how they could be major actors in the development of a precision medicine in breast cancer.