Validation of a yeast functional assay for p53 mutations using clonal sequencing

Abstract

We have previously tested biopsies from 1469 breast tumours with a p53 functional assay in the context of a prospective clinical trial (EORTC 10994/BIG 1-00). The goal of the trial was to determine whether p53 status could be used to select patients who would benefit from inclusion of taxanes in anthracycline-based chemotherapy. The results of the trial were negative. To test whether this was because the functional assay misclassified the tumours, we have reanalysed two groups of biopsies by Sanger sequencing and Roche 454 next generation sequencing (NGS). Comparison of yeast data with pooled cDNA sequencing data in an initial cohort of 69 biopsies showed that conventional sequencing is insensitive when the mutant p53 content is low. A second cohort of 48 biopsies was used to compare directly the yeast assay with Sanger and NGS technology. The mutant sequence was difficult to detect in sequence chromatograms of pooled cDNA, whereas NGS unequivocally identified mutations in every case classified as mutant by the functional assay. The NGS data showed that small deletions, probably caused by PCR splicing, account for most of the unexplained background in the yeast assay. We conclude that mutation detection techniques that test multiple clones, such as the p53 functional assay and NGS, are more reliable than Sanger sequencing of pooled DNA; that the high p53 mutation rate (44%) seen with the yeast assay in the EORTC 10994/BIG 1-00 trial reflects this high sensitivity; and that NGS with Roche 454 technology could be used to identify the p53 mutations in the remaining tumours previously tested in yeast in the EORTC10994/BIG 1-00 trial.

SomaticTP53Mutations in the Era of Genome Sequencing

“…However, significant somatic variations occur in TP53 introns at sites other than those implicated in splice junctions, which are not covered in many conventional or exome-sequencing programs and thus are not reported in mutation databases. In BC, mutations in intron 9 colocalizing with alternative exons 9a and 9b have been detected using yeast functional assays (Iggo et al 2013). These mutations alter the balance between fully spliced and alternatively spliced p53 transcripts, leading to the preferential synthesis of a protein that lacks part of the oligomerization domain.…”

Recommended Guidelines for Validation, Quality Control, and Reporting of TP53 Variants in Clinical Practice

“…The intron 9 splice donor site contains three single-nucleotide substitutions and one insertion in three different tumors. The NC_000017.10:g.7576525A>C substitutions modify TP53 splicing by leading to an unbalanced ratio of the various TP53 mRNAs and a greater abundance of b isoforms (86). Similarly, the three-nucleotide insertion detected in a lymphoma (NC_000017.10:g.7576522_7576523insCTT) probably has a deleterious effect on splicing.…”

MICADo – Looking for Mutations in Targeted PacBio Cancer Data: An Alignment-Free Method

“…We previously reported (Iggo et al, 2013) by independent 454 sequencing that for samples of the difficult group and positive control, we expect one or two significant alterations (mutations) per sample, while we expect no mutations for the negative control group. The only sample that carries two mutations is 276_1, belonging to the difficult group, where MICADo correctly call one of them (the other one is a false negative).…”

“…The PacBio sequencing data is available from the NCBI SRA database under the accession number SRP064161 BioProject PRJNA290142. The main interest of this dataset for testing the accuracy of SNV detection is the existence of SNV calling results obtained from 454 Roche sequencing data generated for the 48 samples (available from NCBI SRA database under the accession number SRP020456, BioProject PRJNA193388; see Iggo et al, 2013 for details). Moreover, for these 48 samples there exists a classification into three categories (based on the percentage of red colonies in the yeast assay, (see Bonnefoi et al, 2011; Iggo et al, 2013).…”

“…The main interest of this dataset for testing the accuracy of SNV detection is the existence of SNV calling results obtained from 454 Roche sequencing data generated for the 48 samples (available from NCBI SRA database under the accession number SRP020456, BioProject PRJNA193388; see Iggo et al, 2013 for details). Moreover, for these 48 samples there exists a classification into three categories (based on the percentage of red colonies in the yeast assay, (see Bonnefoi et al, 2011; Iggo et al, 2013). These categories are:

negative control : 12 samples considered to be wild type;

positive control : 18 samples that are mutated with high rate of altered reads;

difficult group : 18 samples that are mutated with low rate or complex mutations.

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