Construction Of Cancer Genome Feature Database And Feature Matching System For Precision Medical Care

How can we treat cancer more effectively?Traditionally,tumors from the same anatomical site are treated as one tumor entity.This concept has been challenged by recent breakthroughs in cancer genomics and translational research that have enabled molecular tumor profiling.The identification and validation of cancer drivers that are shared between different tumor types,spurred the new paradigm to target driver pathways across anatomical sites by off-label drug use,or within so-called basket or umbrella trials which are designed to test whether molecular alterations in one tumors entity can be extrapolated to all others.However,recent clinical and preclinical studies suggest that there are tissue-and cell type-specific differences in tumorigenesis and the organization of oncogenic signaling pathways.In this Opinion article,we focus on the tissue-specific of cancer genome features(CGFs),tissue-specific cancer signaling outputs.Moreover,we investigated the tissue-specific CGFs in cancer patients,and performed tissue-specific analysis for CGFs in patients.The Investigation,recognition and in-depth biological understanding of these differences will be vital for the implementation of molecularly guided cancer therapies in the future.There have been many resources curated for information of common CGFs.However,the tissue origin and tissues characteristics information has not been well connected to these CGFs.In this work,we present CancerINFO,a resource that connect CGFs to their tissue origins for analysis of tissue-specific tumorigenesis and precision medicine practices.CancerINFO uses a hierarchical system to classify the tissue origins of CGFs.CGFs related to diagnosis,prognosis,predictive therapy response,cancer susceptibility were collected from the FDA(Food and Drug Administration)labels,the NCCN(National Comprehensive Cancer Network)guidelines,and other public resources.The current version of CancerINFO includes five types of cancer features,i.e.,simple nucleic variation,copy number variation,gene expression variation,genomic structure variation,DNA methylation variation,which are connected to 30 tissue types and 269 cancer types of five grades.All CGFs spanning 2271 genes were connected to their tissue origins with?14400 associations,which covered 60%of the genes in KEGG cancer related pathways.CancerINFO webserver provides key interactive functions including tissue-specific CGFs browsing and searching,tissue-specific analysis of CGFs in patient sample,patient sample searching of CGF and other CGFs-related function profiling.CancerINFO promotes the tissue-specific CGFs to connect individual cancer progression,thus helping unleash the value of the current data resources.CancerINFO is accessible through its web interface and API.Its URL is http://public.svnergylab.cn/CancerINFO/.Rapid advancement of oncology in its genomic features and affordable clinical sequencing has created an opportunity for novel interventions to target CGFs,which is increasingly recognized as a mainstay cancer care strategy.Although the clinical high-throughput sequencing technology provides comprehensive genomic information for cancer patients efficiently,the genomic information is difficult to be used and interpreted effectively.Even if clinicians can interpret this vast information effectively,.its timeliness and accuracy are hardly guaranteed.In order to effectively match the effective CGFs associated with these data,we developed a tool named OncoAnn,which provides cancer precise medication CGFs matching and accurate medication reports.Our tools are divided into two parts:the precision treatment repository based on CGFs(named PMKB)and the knowledge base matching method.In the process of building PMKB,we collected 2074 pieces of evidence from several authoritative and accurate medical knowledge bases,including FDA,NCCN,My Cancer Genome and GDSC.Finally,through a case of lung cancer,we showed the matching results of our tools,which further explained the efficiency and accuracy of our tools.In summary,the interpretation and report of clinical genomic information still have a huge bottleneck,which hinders the development of precision oncology.Here,our work promotes the clinical application of precision oncology to a certain extent.