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Unlocking multidimensional cancer therapeutics using geometric data science
Deepak Parashar1,2,3
1Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK. D.Parashar@warwick.ac.uk.
Abstract:
Personalised approaches to cancer therapeutics primarily involve identification of patient sub-populations most likely to benefit from targeted drugs. Such a stratification has led to plethora of designs of clinical trials that are often too complex due to the need for incorporating biomarkers and tissue types. Many statistical methods have been developed to address these issues; however, by the time such methodology is available research in cancer has moved on to new challenges and therefore in order to avoid playing catch-up it is necessary to develop new analytic tools alongside. One of the challenges facing cancer therapy is to effectively and appropriately target multiple therapies for sensitive patient population based on a panel of biomarkers across multiple cancer types, and matched future trial designs. We present novel geometric methods (mathematical theory of hypersurfaces) to visualise complex cancer therapeutics data as multidimensional, as well as geometric representation of oncology trial design space in higher dimensions. The hypersurfaces are used to describe master protocols, with application to a specific example of a basket trial design for melanoma, and thus setup a framework for further incorporating multi-omics data as multidimensional therapeutics.
Insights
Novel geometric methods visualize complex cancer therapeutics and clinical trial designs. This approach aids in targeting multiple therapies for sensitive patient populations across diverse cancer types and biomarkers.
Area of Science:
- Oncology
- Computational Biology
- Biostatistics
Background:
- Personalized cancer therapeutics rely on identifying patient subgroups likely to respond to targeted drugs.
- Stratification for targeted therapies complicates clinical trial designs with biomarkers and tissue types.
- Existing statistical methods lag behind rapid advancements in cancer research.
Purpose of the Study:
- To develop novel analytic tools for complex cancer therapeutics and clinical trial designs.
- To effectively target multiple therapies for sensitive patient populations using biomarker panels across multiple cancer types.
- To establish a framework for incorporating multi-omics data into multidimensional therapeutics.
Main Methods:
- Application of novel geometric methods, specifically the mathematical theory of hypersurfaces.
- Visualization of complex cancer therapeutics data in multidimensional space.
- Geometric representation of oncology trial design space in higher dimensions.
Main Results:
- Demonstrated visualization of complex cancer therapeutics data using hypersurfaces.
- Developed a geometric representation for higher-dimensional oncology trial design space.
- Applied hypersurfaces to describe master protocols, exemplified by a melanoma basket trial design.
Conclusions:
- Geometric methods offer a novel framework for analyzing complex cancer therapeutics and trial designs.
- This approach facilitates the integration of multi-omics data for personalized cancer treatment strategies.
- The developed framework supports the design of adaptive and efficient clinical trials for targeted therapies.
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