UNASSIGNED: 57 Background: It has been suggested that the sequential analysis of the mutational spectra at loci involved in the pathogenesis of @entity5 (mutational load distribution analysis [MLDA]) may reflect the biology of the @entity5 and/or the risk of @entity5 emerges. Metrics derived from MLDA could assist quantifying risk, in early diagnosis, and in monitoring response to therapy. The aim of this study was to assess the efficacy of the NGS platform to obtain clinically relevant metrics derived from MLDA. METHODS: Two sets of DNA samples were assessed: (i) 6 paired @entity5 /normal @entity14 and 8 @entity2513 ; and (ii) 21 @entity3123 and 6 non-matched normal pancreas. Thirty amplicons from 6 genes ( @entity984 , @entity817 , @entity470 , @entity986 , @entity876 , and PI3KCA) were sequenced using the Roche 454 GS FLX-TI platform. Data analysis was carried out by Variant Identification Pipeline (VIP) software and R (computing environment) commands. @entity9420 ( @entity9420 ) was calculated as the sum of all variants frequency for each sample. RESULTS: In all, 18,626 variants were identified. Optimal filters applied to discard false positives were: (1) variants with an absolute frequency < 10x, (2) variants in homopolymer (HP) >=  6 @entity2036 , (3) insertions and deletions (INDELs) in HP >=  5 @entity2036 , and (4) INDELs in only one strand. Filtering yielded 870 variants from all samples remained as valid, 92 being unique. After discarding polymorphisms, variants classified as neutral by the Condel software and variants preferentially detected in normal tissues, 46 variants were used to compute @entity9420 . @entity694 and @entity652 was higher than normal mucosa (0.83 vs. 0.607 vs. 0.128, p = 0.01). In @entity660 , @entity9420 for @entity3123 was higher than normal pancreas (0.716 vs. 0.135, p = 0.01). @entity984 and @entity876 analysis provide most of the information in both @entity5 , while PI3KCA adds information for @entity14 exclusively and CDKNA2 is informative in pancreatic samples. CONCLUSIONS: NGS provides a robust and quantitative measure of genetic alterations in @entity5 samples. MLDA is an effective tool for the classification of @entity5 .

Mutational load distribution analysis of XXXX by next generation sequencing platform.

@entity652 :: ('MESH:D000236', 'Disease') :: ['adenomas'] @entity3123 :: ('MESH:C562463', 'Disease') :: ['pancreatic carcinoma'] @entity986 :: ('1029', 'Gene') :: ['CDKN2A'] @entity5 :: ('MESH:D009369', 'Disease') :: ['tumor', 'tumors', 'cancer before the tumor', 'GI tumors'] @entity984 :: ('3845', 'Gene') :: ['KRAS'] @entity470 :: ('1956', 'Gene') :: ['EGFR'] @entity9420 :: ('MESH:C536761', 'Disease') :: ['TML', 'Total mutational load'] @entity2036 :: ('MESH:D009711', 'Chemical') :: ['nucleotides'] @entity660 :: ('MESH:D010182', 'Disease') :: ['pancreatic disease'] @entity876 :: ('7157', 'Gene') :: ['TP53'] @entity817 :: ('673', 'Gene') :: ['BRAF'] @entity14 :: ('MESH:D015179', 'Disease') :: ['colorectal carcinomas'] @entity2513 :: ('MESH:C563365', 'Disease') :: ['colorectal adenomas'] @entity694 :: ('MESH:D003110', 'Disease') :: ['TML in colonic carcinomas']
@entity14 :: (MESH:D015179,Disease) :: ['colorectal and pancreatic carcinoma']
