Discover the first predictive therapy decision support tool
PredictionStar is an innovative aid for Breast Cancer physicians and patients, deserving optimal therapy.
Tumor typing has never facilitated optimal treatment with predictable outcomes – until now.
Empowering optimal therapies with
predictable outcomes – every patient!
All of this, highly cost-effectively and neutral for health care budgets.
Our Story
OncoGenomX offers the first therapy decision support solution reflective of biology, clinical behavior, and pharmacological vulnerabilities of individual cancers to guide decisions on the composition of therapy regimen, combining the most effective drugs with the highest chance of conveying maximum clinical and health economic benefit.
We envision a future of AI and deep learning-enhanced therapy decision support, predicting which treatments are likely to work on the specific tumor of each individual patient – accurate, confident, tailor-made, and clinically actionable. Only therapy that hits a tumor’s biological weak points eliminates cancer effectively as a basis for prolonged life expectancy, if not curing it.
The current risk-adopted Breast Cancer therapy approach – intensive therapy for high-risk, moderate therapy for low-risk patients – is all but optimal and has a variety of undesirable consequences, including but not limited to frequently unprecise drug – tumor pairings, and treatment results that fall short of the pharmaceutically possible and of clinical expectations.
It has long been known that half of the women in Europe and the US newly diagnosed with Breast Cancer (750.000 every year) run the risk of overtreatment. Now study resultsÂą suggests that 50% of patients with advanced breast cancer are undertreated.
The same studyÂą illustrates that accounting for the biological properties of individual cancers provides the opportunity of making a difference to patients.
Our Approach
The technology works by using patented algorithms for analysis of tumor-specific genomic, biological, pathological, and patient-specific clinical information, complemented by intelligence-augmented drug – tumor matching, calibration with real-world outcome data to guide evidence-based therapy decisions.
Our Approach
Utilization of our service requires no additional invasive procedures and no additional diagnostic tests.
Patient data, personal or population-level, never leaves the hospital IT environment. Technology components fulfill governing quality requirements and Medical Device, Digital, and Cyber Laws.
A specifically trained system operator will ensure smooth processing and real-time delivery of analysis reports.
The ubiquitious risk of suboptimal treatment choice makes PredictionStar a must-have therapy decision support tool.
40% of patients with advanced breast cancer (aBC) are eligible for costly molecularly targeted combination treatment¹. In more than 60% of patients, the disease progresses within 2 years². Type 3 aBC has a higher than 80% chance of progressing within 1 year³. Study OGX-1019-HE0033 that evaluated guideline conform first- and second line treatment of advanced stage patients revealed alarming insights of high clinical relevance:
- The annual spendings of a cancer hospital for anticancer therapies required for 200 aBC patients amounts to 20M USD.
- 9M USD thereof are spent for anticancer treatments unlikely to work for tumor-specific biological reasons.
Selecting cancer therapy
When selecting cancer therapy regimens, oncologists worldwide lean towards the current treatment guidelines. However, the recommended treatment combinations are not equally suitable for each tumor and patient.
PredictionStar provides the granular information required to compose maximally efficacious treatment regimens as the rule, thus avoiding the costly consequences of suboptimal treatment prescriptionsÂł.
1, https://seer.cancer.gov/statfacts/html/breast.html. 2, Mc Andrew & Finn, JCO Oncology Practice 2021, No. 18, 5, 319. 3, Study Report OGX-1019-HE003, OncogenomX, data on file.
Support
The PredictionStar approach goes beyond cancer profiling and easy-to-read therapy guidance through a routine-fit application on a mobile device.
To maximize the clinical utility, PredictionStar offers an effortless and intuitive feedback routine powered by advanced machine learning for continuous improvement of therapy decision support and increasing numbers of patients benefitting from PredictionStar use.
Our team
We have assembled a team of 7 accomplished industry pioneers with complementary expertise, including company-building, diagnostics platform-building, clinical oncology, and molecular cancer diagnostics.Â
We are united by the passion of making PredictionStar a diagnostic routine for all Breast Cancer patients, as the steppingstone to biology- and clinical evidence-guided cancer therapy.
Wolfgang Hackl, Founder
Lead Physician
20+ years experience in developing and registering molecular cancer treatments
David Demanse, Founder
Lead Data Scientists
15+ years experience in clinical statistics and prediction modelling
Karl Andersson, Partner
Lead Engineer
20+ years experience in test development and diagnostic instrument engineering
Stephanie Harloff, LLM
Legal Advisor
20+ years experience in Pharma Law, Partnering & Digital Health
Annika Remaeus, Partner
Projekt Management, Project Coodination
20+ years experience in IVD platform development and implementation
Seppo Maekinen, Partner
Financing & Partnerships
Investor, VC partner, serial founder
25+ years experience in the investment and IVD industry
Marco Witteveen, Partner
Commercialization
20+ years experience in strategic marketing & sales at global IVD companies
Annika Remaeus, Partner
Projekt Management, Project Coodination
20+ years experience in IVD platform development and implementation
Seppo Maekinen, Partner
Financing & Partnerships
Investor, VC partner, serial founder
25+ years experience in the investment and IVD industry
Marco Witteveen, Partner
Commercialization
20+ years experience in strategic marketing & sales at global IVD companies
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