SkylineDx discovers, optimizes and develops biomarkers based on gene expression signatures. In our R&D centered partnerships with clinicians, patient organizations, academia and the industry, entrepreneurial drive interacts with scientific expertise to take on the challenge of unmet needs in personalized healthcare.
In our biomarker discovery program, this extensive collaboration is named Strix Services. Strix is best the best-known genus of the Owl-family and stands for proverbial wisdom and perception. While leading a somewhat hidden nocturnal existence, this bird keeps the ecosystem well-balanced. With his sharp attention to details and incredible line of sight, Strix Services is uniquely equipped to make visible what otherwise remains hidden.
Under the wings of Strix Services, a series of specific projects have been initiated aimed at biomarker discovery, with novel, peer-reviewed and in-house developed biomarker tools. This service is now open for additional projects. Independent from the type of data, if you want to identify a sub-population through biomarkers, we will find it.
Biomarkers used to detect or confirm presence of a disease or condition of interest or to identify individuals with a subtype of the disease. SkylineDx signed a collaboration agreement with Imperial College London and the University of California, San Diego School of Medicine, for the joint development of a 13-gene classifier that facilitates early diagnosis of Kawasaki Disease.
Biomarkers that indicate an increased (or decreased) likelihood of a future clinical event, disease recurrence or progression in an identified population. SkylineDx developed a 92-gene classifier for the prediction of prognosis in multiple myeloma and an 8-gene signature combined with clinicopathologic variables to predict the prognosis in melanoma.
Biomarkers used to identify individuals who are more likely to respond to exposure to a particular medical treatment (companion diagnostic). The response could be a symptomatic benefit, improved survival or an adverse event. SkylineDx signed an agreement with public company BioInvent International AB (OMXS:BINV) to research and develop predictive immunological signatures to help identify patients with Non-Hodgkin lymphoma and solid cancers who are likely to show clinical response if treated with BI-1206. With academic institute UMC Utrecht, SkylineDx is collaborating on data algorithms that predict critical care events and with the Mayo Clinic they discovered and developed an 8-gene classifier with clinicopathologic variables to predict sentinel lymph node metastasis in melanoma.
Biomarkers are indicators that allow to identify a specific sup-population. While the promise has been big, and there is a strong focus on the discovery, very few biomarkers progress beyond initial publication to multi-center clinical validation. Challenges beyond the fundamental science and bioinformatics are, cut-point optimization and a solid performance transitioning from discovery to validation studies.
Implementing biomarkers in clinical practice, is a cumbersome process with long lead times and high development costs. However, with a balanced multi-disciplinary team including scientists, laboratory, QA/RA and market access, your biomarker can be developed into an actual diagnostic test, used in clinical practice to solve an unmet medical need and support your strategy direction.
GESTURE is the first in-house developed, automated algorithm for biomarker discovery. It identifies two genetically similar patients with a larger than expected survival difference between them that received different treatments. On the basis of the genomic similarities, it builds gene expression profiles of prototype patients to test for treatment prediction. The end product is a classifier, used to stratify patients for an optimal treatment strategy. GESTURE works in any disease with datasets that have genomic (technology agnostic) and clinical data. Peer-reviewed publication in Nature (open access).
SkylineDx aims to publish its in-house scientific and clinical (validation) research as open access peer reviewed manuscripts, available for every stakeholder in the biomarker discovery field.
Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects.
Ubels et al. 2018. Nature Communications. Open Access.
Identification of stage I/IIA melanoma patients at high risk for disease relapse using a clinicopathologic and gene expression model
Eggermont et al. 2020. European Journal of Cancer. Open Access.