Abstract
BackgroundThe decision environment for cancer care is becoming increasingly complex due to the discovery and development of novel genomic tests that offer information regarding therapy response, prognosis and monitoring, in addition to traditional histopathology. There is, therefore, a need for translational clinical tools based on molecular bioinformatics, particularly in current cancer care, that can acquire, analyze the data, and interpret and present information from multiple diagnostic modalities to help the clinician make effective decisions.ResultsWe present a platform for molecular signature discovery and clinical decision support that relies on genomic and epigenomic measurement modalities as well as clinical parameters such as histopathological results and survival information. Our Physician Accessible Preclinical Analytics Application (PAPAyA) integrates a powerful set of statistical and machine learning tools that leverage the connections among the different modalities. It is easily extendable and reconfigurable to support integration of existing research methods and tools into powerful data analysis and interpretation pipelines. A current configuration of PAPAyA with examples of its performance on breast cancer molecular profiles is used to present the platform in action.ConclusionPAPAyA enables analysis of data from (pre)clinical studies, formulation of new clinical hypotheses, and facilitates clinical decision support by abstracting molecular profiles for clinicians.
Highlights
The decision environment for cancer care is becoming increasingly complex due to the discovery and development of novel genomic tests that offer information regarding therapy response, prognosis and monitoring, in addition to traditional histopathology
There is unique clinical value to be added by providing the clinician with an integrated view of the patient molecular profile and where the patient is compared to patients with similar clinical parameters and history
In this paper we introduce a Physician Accessible Preclinical Analytics Application – PAPAyA, a platform for clinical decision making that relies on multiple information modalities: gene expression and differential DNA methylation as well as clinical parameters such as histopathological results and survival information
Summary
The decision environment for cancer care is becoming increasingly complex due to the discovery and development of novel genomic tests that offer information regarding therapy response, prognosis and monitoring, in addition to traditional histopathology. There is, a need for translational clinical tools based on molecular bioinformatics, in current cancer care, that can acquire, analyze the data, and interpret and present information from multiple diagnostic modalities to help the clinician make effective decisions. Clinicians acknowledge that there is a need to accelerate the translation of knowledge discovery from genome scale studies to effective treatment and tailored cancer management. Available tools such as GeneSpring or open source tools can process and visualize genomics data for preclinical applications. For the latest genomic tests that have entered the clinical guidelines, there is need for clinician driven analysis with patient-centric data and informatics-assisted discovery in an configurable environment that could be quickly tuned to new clinical questions
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