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In vivo efficacy and mutation data were included if they were sampled within 2 years from the time of the SP-IPTi study and within a 50km radius.
To gain a global view of genomic alterations occurring in glioblastoma, we restricted our analysis to the 91 TCGA GBM cases, for which both copy number and sequence mutation data were available.
Cancer gene mutation data were downloaded from the Catalogue of Somatic Mutations in Cancer (December 2008, COSMIC, http://www.sanger.ac.uk/genetics/CGP/cosmic/). The mutation frequency of a gene was calculated by dividing the number of total cancer samples that sequenced this gene with that of cancer samples with at least one mutation of this gene, as described by Cui et al. [1].
Of 278 tumors for which chromosomal copy number and/or mutation data were available, amplification and/or somatic non-synonymous mutation of EGFR was found in 40% (n = 111) and of PDGFRA in 7% (n = 19), while chromosomal loss and/or mutation of NF1 was found in 16% (n = 45).
The TP53 mutation data were from the IARC database [ 27].
Mutation data were downloaded from the COSMIC database [ 22].
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Mutation data was again taken as a binary variable, but as opposed to the partial correlation analysis, mutations were stratified into their reported types (e.g., missense mutations are all grouped together, etc.).
Human LATS1 and LATS2 mutation data was collected from Catalogue of Somatic Mutations in Cancer (COSMIC) (top portions) and cBioPortal (bottom portions) databases.
The importance of these somatic mutation data is mechanistic in that these serve to trace metastasis and to evaluate entire pathways since proteins with translated mutations interact with the other proteins that may or may not be mutated.
Given this complexity, more novel methods for the analyses of mutation data are needed for a better understanding of cancer.
The ex vivo mutation data are consistent with the reduced activity observed in the K216A mutant in vitro.
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