UNASSIGNED: 3535 Background: In the era of personalized @entity5 treatment, @entity1 selection to enrich for specific targets is key to improve treatment outcome. Given the genomic instability of @entity5 in time we addressed whether analysis of the @entity5 or the @entity3 is most suitable for @entity1 selection for targeted treatment. METHODS: With next generation (SOLiD 3+ system, Life Technologies) and targeted sequencing (DNA capture-arrays, Agilent) we have analyzed the genetic environment of a @entity5 mini-genome-the exons of 1,265 @entity5 -associated genes- of the @entity5 and its sequential liver @entity3 in 21 @entity14 ( @entity14 ) @entity1 (pts). Using laser microdissection >80% @entity5 cell-enriched DNA was isolated from in total 47 samples, including healthy tissue of 5 pts. Sequence data was aligned and mapped against the @entity1 reference genome (GRCh37/hg19) using BWA. To limit the influence of SNV (Single Nucleotide Variation) caller artifacts we applied 3 different published methods (our PERL script, VarScan and GATK toolkit) and considered the overlapping variants as reliable. To predict the possible impact of identified SNV's on protein function polymorphism phenotyping version2 was used. RESULTS: All 47 samples were successfully sequenced (median coverage=87x, range: 52-217). In total 6,696 known (84%) and 1,305 novel (16%) SNV's were identified. These included 1,460 non-synonymous and 29 gained/lost stop-codons, potentially altering protein function. On average 83 potentially relevant SNV's were gained in the @entity3 and 70 SNVs present in the @entity5 were lost. Interestingly, these genetic variations affected several essential pathways. This demonstrated that @entity5 evolution caused losses and gains of critical genes in time. In this sample set genetic selection pressure concerning the amount of gained/ lost SNV's on the @entity3 due to chemotherapy could not be shown. CONCLUSIONS: These results indicate that @entity3 significantly differs from its primary origin in @entity14 pts. This suggests that for the selection of the appropriate @entity1 population for targeted therapy the @entity3 better reflects the @entity66 status.

Primary XXXX and their @entity3 are genetically not the same: Implications for choice of targeted treatment?

@entity1 :: ('9606', 'Species') :: ['human', 'patients', 'patient'] @entity66 :: ('MESH:D004194', 'Disease') :: ['actual disease'] @entity5 :: ('MESH:D009369', 'Disease') :: ['tumor', 'cancer', 'primary tumor', 'tumors'] @entity14 :: ('MESH:D015179', 'Disease') :: ['CRC', 'colorectal cancer'] @entity3 :: ('MESH:D009362', 'Disease') :: ['metastasis', 'metastatic disease']
@entity14 :: (MESH:D015179,Disease) :: ['colorectal tumors']
