http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-104119 Assessment of Whole Genome Amplification for Sequence Capture and Massively Parallel Sequencing

Exome sequence capture and massively parallel sequencing can be combined to achieve inexpensive and rapid global analyses of the functional sections of the genome. The difficulties of working with relatively small quantities of genetic material, as may be necessary when sharing tumor biopsies between collaborators for instance, can be overcome using whole genome amplification. However, the potential drawbacks of using a whole genome amplification technology based on random primers in combination with sequence capture followed by massively parallel sequencing have not yet been examined in detail, especially in the context of mutation discovery in tumor material. In this work, we compare mutations detected in sequence data for unamplified DNA, whole genome amplified DNA, and RNA originating from the same tumo

Assessment of Whole Genome Amplification for Sequence Capture and Massively Parallel Sequencing

Exome sequence capture and massively parallel sequencing can be combined to achieve inexpensive and rapid global analyses of the functional sections of the genome. The difficulties of working with relatively small quantities of genetic material, as may be necessary when sharing tumor biopsies between collaborators for instance, can be overcome using whole genome amplification. However, the potential drawbacks of using a whole genome amplification technology based on random primers in combination with sequence capture followed by massively parallel sequencing have not yet been examined in detail, especially in the context of mutation discovery in tumor material. In this work, we compare mutations detected in sequence data for unamplified DNA, whole genome amplified DNA, and RNA originating from the same tumor tissue samples from 16 patients diagnosed with non-small cell lung cancer. The results obtained provide a comprehensive overview of the merits of these techniques for mutation analysis. We evaluated the identified genetic variants, and found that most (74%) of them were observed in both the amplified and the unamplified sequence data. Eighty-nine percent of the variations found by WGA were shared with unamplified DNA. We demonstrate a strategy for avoiding allelic bias by including RNA-sequencing information

Validation of whole genome amplification for analysis of the p53 tumor suppressor gene in limited amounts of tumor samples.

Personalized cancer treatment requires molecular characterization of individual tumor biopsies. These samples are frequently only available in limited quantities hampering genomic analysis. Several whole genome amplification (WGA) protocols have been developed with reported varying representation of genomic regions post amplification. In this study we investigate region dropout using a φ29 polymerase based WGA approach. DNA from 123 lung cancers specimens and corresponding normal tissue were used and evaluated by Sanger sequencing of the p53 exons 5-8. To enable comparative analysis of this scarce material, WGA samples were compared with unamplified material using a pooling strategy of the 123 samples. In addition, a more detailed analysis of exon 7 amplicons were performed followed by extensive cloning and Sanger sequencing. Interestingly, by comparing data from the pooled samples to the individually sequenced exon 7, we demonstrate that mutations are more easily recovered from WGA pools and this was also supported by simulations of different sequencing coverage. Overall this data indicate a limited random loss of genomic regions supporting the use of whole genome amplification for genomic analysis

Genetic variants unique or shared between analysis.

<p>A) Venn diagram illustrating the distribution of SNVs for patient 140. Unique variants found by WGA are represented in light blue, and unique variants found without amplification are shown in light green. Shared variants identified by both methods are shown in green. B) Boxplot of the coverage of genetic variants found uniquely by WGA, without amplification, and with both methods for patient 140. The two leftmost boxes represent shared variant calls with coverage in those positions for WGA and without amplification, respectively. The two rightmost boxes represent the coverage over unique positions for each method. C) Venn diagram illustrating the distribution of SNVs for patient 295. Unique variants found by WGA are represented in light blue, and unique variants found without amplification are shown in light green. Shared variants identified by both methods are shown in green. D) Boxplot of the coverage of genetic variants found uniquely by WGA, without amplification, and with both methods for patient 295. The two leftmost boxes represent shared variant calls with coverage in those positions for WGA and without amplification, respectively. The two rightmost boxes represent the coverage over unique positions for each method.</p

Summary of variant calling following DNA sequencing.

<p>Summary of variant calling following DNA sequencing.</p

Example of coverage representation in unamplified and whole genome amplified DNA in patient 295 across the SPINK1 gene.

<p>Example of coverage representation in unamplified and whole genome amplified DNA in patient 295 across the SPINK1 gene.</p

Summary data for SNVs in positions identified in both WGA and unamplified sequence data, and using only one of the two methods.

<p>Summary data for SNVs in positions identified in both WGA and unamplified sequence data, and using only one of the two methods.</p

Comparison of unique and tumor specific variants in unamplified DNA, WGA DNA and RNA.

<p>Comparison of unique and tumor specific variants in unamplified DNA, WGA DNA and RNA.</p

Patient characteristics.

<p><b>AC = </b>Adenocarcinoma.</p><p><b>SCC = </b>Squamous cell carcinoma.</p><p><b>pT = </b>Postsurgical histopathological classification of primary tumour.</p><p><b>pN = </b>Postsurgical histopathological classification of regional node.</p><p><b>pM = </b>Postsurgical histopathological classification of distant metastasis.</p

The relationship between variant and total reads per position depending on analysis.

<p>A) The relationship between the numbers of variant reads divided by total reads for SNVs identified by sequencing WGA and unamplified DNA per position in patient 140. B) The relationship between the numbers of variant reads divided by total reads for SNVs identified by sequencing WGA and unamplified DNA per position in patient 295.</p