Shallow whole genome sequencing for robust copy number profiling of formalin-fixed paraffin-embedded breast cancers

Abstract

Pathology archives with linked clinical data are an invaluable resource for translational research, with the limitation that most cancer samples are formalin-fixed paraffin-embedded (FFPE) tissues. Therefore, FFPE tissues are an important resource for genomic profiling studies but are under-utilised due to the low amount and quality of extracted nucleic acids. We profiled the copy number landscape of 356 breast cancer patients using DNA extracted FFPE tissues by shallow whole genome sequencing. We generated a total of 491 sequencing libraries from 2 kits and obtained data from 98.4% of libraries with 86.4% being of good quality. We generated libraries from as low as 3.8 ng of input DNA and found that the success was independent of input DNA amount and quality, processing site and age of the fixed tissues. Since copy number alterations (CNA) play a major role in breast cancer, it is imperative that we are able to use FFPE archives and we have shown in this study that sWGS is a robust method to do such profiling.

Keywords: Copy number (CN) and breast cancer; Formalin-fixed paraffin-embedded (FFPE); Shallow whole genome sequencing (sWGS).

Fig. 1

Overall Design: Schematic showing the workflow to ensure successful shallow whole genome sequencing (sWGS) libraries.

Fig. 2

Categorisation of copy number profiles. A. Examples of QDNASEQ copy number plots scored as Very Good, Good, Intermediate and Poor. Failed libraries had very few reads and are not shown. Green dots represent regions of gains/amplifications and red dots represent regions of loss/deletion. B. Boxplots showing increasing measured standard deviations with decreasing libraries'qualities. Dots represent individual samples within each category. VG = very good, G = good, I = intermediate, P = poor, F = fail.

Fig. 3

Features of input DNA and libraries generated from blocks less and more than five years. Dot plots represent the range (minimum-maximum) of observed values for each of the following categories and the red dot represents the median. A. The quality of input DNA inferred by ΔCt. B. Fragment sizes of the libraries in base pair. C. The library yield in nanomoles.

Fig. 4

Measured standard deviations from the QDNASEQ copy number plots and associations with the quality of sequencing libraries. A. Bar charts showing proportion of samples with different input DNA quality (based on ΔCt) in each sequencing quality group. B. Bar charts showing proportion of samples from FFPE blocks of different fragment sizes in each sequencing quality group. C. Bar charts showing proportion of samples with different amount of input DNA in each sequencing quality group. VG = very good, G = good, I = intermediate, P = poor, F = fail.

Fig. 5

Features of the sequencing libraries. Boxplots showing different features of input DNA and library yield relative to the different library qualities. A. Input DNA. B. Quality of input DNA inferred from ΔCt. C. Fragment size of libraries. D. Library yield. VG = very good, G = good, I = intermediate, P = poor, F = fail.

Fig. 6

Effect of input DNA quality. Scatterplots showing the association between quality of input DNA with different features of the sequencing libraries. A. Fragment size of libraries. B Library yield. C. Unmapped Reads. D. Unique aligned reads.