PAPAyA: a platform for breast cancer biomarker signature discovery, evaluation and assessment.
BACKGROUND
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, 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.


RESULTS
We 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.


CONCLUSIONS
PAPAyA enables analysis of data from (pre)clinical studies, formulation of new clinical hypotheses, and facilitates clinical decision support by abstracting molecular profiles for clinicians.
Janevski
Angel
A
Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY10510, USA. angel.janevski@philips.com
Kamalakaran
Sitharthan
S
Banerjee
Nilanjana
N
Varadan
Vinay
V
Dimitrova
Nevenka
N
D015415:Biomarkers / Q000378:metabolism*; D001943:Breast Neoplasms / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D003661:Decision Support Techniques; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D012984:Software
D015415:Biomarkers