Using next-generation sequencing for high resolution multiplex analysis of copy number variation from nanogram quantities of DNA from formalin-fixed paraffin-embedded specimens

Abstract

The use of next-generation sequencing technologies to produce genomic copy number data has recently been described. Most approaches, however, reply on optimal starting DNA, and are therefore unsuitable for the analysis of formalin-fixed paraffin-embedded (FFPE) samples, which largely precludes the analysis of many tumour series. We have sought to challenge the limits of this technique with regards to quality and quantity of starting material and the depth of sequencing required. We confirm that the technique can be used to interrogate DNA from cell lines, fresh frozen material and FFPE samples to assess copy number variation. We show that as little as 5 ng of DNA is needed to generate a copy number karyogram, and follow this up with data from a series of FFPE biopsies and surgical samples. We have used various levels of sample multiplexing to demonstrate the adjustable resolution of the methodology, depending on the number of samples and available resources. We also demonstrate reproducibility by use of replicate samples and comparison with microarray-based comparative genomic hybridization (aCGH) and digital PCR. This technique can be valuable in both the analysis of routine diagnostic samples and in examining large repositories of fixed archival material.

Figure 1.

Examples of a comparison of copy number analysis by sequencing and aCGH. (a) Chromosome 3 analysed by NG sequencing; (b) chromosome 5 analysed by NG sequencing; (c) chromosome 3 analysed by aGCH; (d) chromosome 5 analysed by aCGH.

Figure 2.

Comparison of copy number analysis by (a) sequencing and (b) a digital PCR method (MCC). The 88 MCC markers between 165 and 182 Mb of chromosome 3 of the HONE-1 cell line were analysed and compared to the sequence-based copy number data from the same region.

Figure 3.

Copy number karyograms of DNA extracted from snap frozen and FFPE tissue. Representative individual chromosome plots for two lung squamous cell carcinoma patients (LS041 and LS043) are shown. (a) LS041 snap frozen chromosome 1; (b) LS041 FFPE chromosome 1; (c) LS043 snap-frozen chromosome 9; (d) LS043 FFPE chromosome 9.

Figure 4.

NG sequencing-based copy number karyograms using decremental amounts of starting DNA, i.e. (a) 1 µg, (b) 50 ng, (c) 10 ng and (d) 5 ng. The copy number profiles for chromosome 3 using genomic DNA from the FFPE squamous cell carcinoma LS043 are shown here. Similar results were obtained for the other chromosomes and also using the fresh frozen sample LS010 (not shown).

Figure 5.

Generation of copy number karyograms from small clinical samples. Chromosomes 9 and 10 copy number profiles of DNA from four sequential FFPE specimens obtained from different areas of the oral cavity of the same patient (PG019) over a two year period. The samples shown are (a) PG019-1, a biopsy from May 2007; (b) PG019-4, a biopsy from June 2008; (c) PG019-5, wide local excision from July 2008; (d) specimen from surgical resection from August 2009. All samples show a similar gain of the short arm of chromosome 10. PG019-8 shows a discreet amplification in 9p: this amplification was detected in three additional samples from the August 2009 surgical resection (data not shown).

Figure 6.

The effect of increased multiplexing. Sequencing was performed using 5 and 10 samples per lane of an Illumina GAII. Shown here is chromosome 1 from sample LS010 at the level of (a) five samples per lane generating 2 330 765 sequence reads for this sample or 0.035× genomic coverage; (b) 10 samples per lane generating 1 091 212 reads or 0.016× coverage and (c) an in silico simulation of 80 samples per lane generating 136 405 reads or 0.002× coverage.

Figure 7.

Simulated data showing the effect of increased multiplexing. Twenty different numbers of LUDLU-1 reads between 127 421 and 2 551 569 were compared to five different numbers of AGLCL reads between 2 441 867 and 12 218 030. Shown here are: (a) the mean CNV size; (b) the number of CNVs detected; (c) the smallest detectable CNV.