According to the FDA a prognostic in vitro diagnostic

According to the FDA, a prognostic in vitro diagnostic biomarker would need a 510K or de novo approval (class II device), whereas a predictive biomarker would need ldt pre-market approval (PMA, class III device). The main separating factor is that a prognostic biomarker provides you with an estimate for progression, whereas a predictive biomarker would be used to decide the exact treatment regimen for individual patients, and would therefore have a significant impact on the patient\'s life. A predictive biomarker will often become a companion diagnostic. In addition, the recent “drug development tool (DDT) box” guidelines are also allowing for regulatory assessment of tools to assist in clinical drug development, such as the fibrinogen enrichment of patients in COPD clinical studies with a more severe outcome (fast progressors), which is now classified as a DDT. No biomarkers have yet been qualified as biomarkers for OA, however several biomarkers have been developed targeting cartilage degradation and formation (e.g. CTX-II, ARGS, PIIANP), joint neurotensin receptor (e.g. C3M, Col2-NO2), bone remodelling (e.g. alpha CTXI, osteocalcin) as well as inflammation and metabolic factors (Bay-Jensen et al., 2016a,b). The scientists and clinicians working in the biomarker field cannot expect a “one size fits all” solution for OA. Consequently it is important to test and validate a biomarker to a specific hypothesis. This can be done under the laboratory-developed test (LDT) (Sarata and Johnson, 2014), which is a type of in vitro diagnostic test that is designed, manufactured and used within a single laboratory.
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Conflict of Interest Statement
Competing Interests
Funding Sources A-C B-J and MK are employees of Nordic Bioscience. The authors are members of the D-BOARD Consortium funded by European Commission Framework 7 programme (EU FP7; HEALTH.2012.2.4.5-2, project number 305815, Novel Diagnostics and Biomarkers for Early Identification of Chronic Inflammatory Joint Diseases). AM is co-ordinator of the D-BOARD Consortium and member of the Arthritis Research UK Centre for Sport, Exercise, and Osteoarthritis, funded by Arthritis Research UK (Grant Reference: 20194). AM has received funding from the Deanship of Scientific Research (DSR), King Abdulaziz University (grant no. 1-141/1434 HiCi). The authors are also members of the Applied Public-Private Research enabling OsteoArthritis Clinical Headway (APPROACH) Consortium, a 5-year project funded by the European Commission\'s Innovative Medicines Initiative (IMI). APPROACH is a public-private partnership directed towards osteoarthritis biomarker development through the establishment of a heavily phenotyped and comprehensively analyzed longitudinal cohort. The research leading to these results has received partial support from the Innovative Medicines Initiative (IMI) Joint Undertaking under grant agreement no. 115770, resources of which are composed of financial contribution from the European Union\'s Seventh Framework programme (FP7/2007-2013) and EFPIA companies\' in kind contribution. YH is the Founder, Chairman of the Board, and President at Artialis SA (http://www.artialis.com). He is also the founder and the Chairman of the Board of the spin-off company of the University of Liège Synolyne Pharma SA (http://synolyne-pharma.com), a company developing medical device for the joint viscosupplementation and tissue repair.
For decades, diversification of cancers, in terms of origin of organs or cell types, allows precise understanding of genetic and epigenetic aberrations. Indeed, this approach has vastly improved cancer management for numerous cancer types. For instance, patients of breast cancer are benefitted by different management schemes according to distinct breast cancer subtypes (i.e. luminal A, luminal B, HER2 and basal-like). It is widely accepted that the feasibility of personalized medicine battling cancer is laid by exploiting the complete malignant/driver events within a particular cancer subtype. However, the direction of cancer research is tilted recently by a suggestion that cancer management can be improved by bringing out a bigger picture of the cancer population. Here, the idea of pan-cancer analysis is raised by The Cancer Genome Atlas (TCGA) program in which different cancer types are gathered together for analysis, as it is believed that diverging driver events among various cancer types most often generate converging genetic signatures and biological pathways during cancer development. The pan-cancer analysis project builds a joint data set from separate TCGA disease projects of multiple cancer types, which increases the statistical power to detect functional genomic determinants of disease. Subsequently, it allows the identification of both tissue-specific aspects of cancer and intrinsic molecular commonalities across tumor types (). With this approach, several groups have already identified critical oncogenic signatures (), mutation landscape () and microRNA-target interactions () across diverse cancer types. In this issue of EBioMedicine, Ching et al. utilize the pan-cancer analysis to explore the diagnostic and prognostic potentials of long non-coding RNAs (lncRNAs), and subsequently reveal a panel of six lncRNAs as biomarkers for multiple cancer types ().