Copy number analysis by low coverage whole genome sequencing using ultra low-input DNA from formalin-fixed paraffin embedded tumor tissue
- Tanjina Kader†1, 2,
- David L. Goode†2, 3,
- Stephen Q. Wong4,
- Jacquie Connaughton1,
- Simone M. Rowley1,
- Lisa Devereux5,
- David Byrne6,
- Stephen B. Fox6,
- Gisela Mir Arnau7,
- Richard W. Tothill2, 8, 9,
- Ian G. Campbell†1, 2, 9 and
- Kylie L. Gorringe†1, 2, 9Email author
© The Author(s). 2016
Received: 13 September 2016
Accepted: 26 October 2016
Published: 15 November 2016
Unlocking clinically translatable genomic information, including copy number alterations (CNA), from formalin-fixed paraffin-embedded (FFPE) tissue is challenging due to low yields and degraded DNA. We describe a robust, cost-effective low-coverage whole genome sequencing (LC WGS) method for CNA detection using 5 ng of FFPE-derived DNA. CN profiles using 100 ng or 5 ng input DNA were highly concordant and comparable with molecular inversion probe (MIP) array profiles. LC WGS improved CN profiles of samples that performed poorly using MIP arrays. Our technique enables identification of driver and prognostic CNAs in archival patient samples previously deemed unsuitable for genomic analysis due to DNA limitations.
Identifying the somatic genetic alterations underlying cancer is critical to our understanding of the disease drivers and can inform diagnosis, prognosis, and response to therapy. One of the major genetic alterations in cancer is copy number alteration (CNA), with aneuploidy and structural alterations present in most malignancies, as well as being common in precursor lesions . CNAs reflect the underlying biology of a tumor  and, given suitable detection methods, could be used in research and potentially in clinical settings to predict patients’ response to treatment and prognosis. However, the challenge of obtaining sufficient quantity and quality of DNA from formalin-fixed paraffin-embedded (FFPE) tissue has severely limited adoption of this approach.
Since the development of comparative genomic hybridization (CGH) , different methods have been attempted, with varying success, to identify CNAs in FFPE-derived DNA, including array-CGH and SNP arrays. One of the most reliable approaches available to date is molecular inversion probe (MIP) technology, which can obtain high-quality CNA and genotype data from FFPE samples with less than 100 ng of input DNA . Additionally, because the MIP assay detects SNPs, it can also detect allelic imbalance and loss of heterozygosity (LOH) . Whole genome sequencing (WGS) at 30× coverage can also be used for CNA and LOH detection, but requires at least 100 ng of high quality input DNA and has challenges associated with cost, bioinformatics processing time, and storage of large datasets. CNA from FFPE-derived DNA using low coverage WGS (LC WGS) (0.1–2× coverage) has been reported from 100 ng to 1 μg of input DNA [6–9] although point mutations and LOH were not assayed. Each method has both common and unique issues related to the required starting DNA amount, specificity, sensitivity, genome coverage, and accuracy as well as cost.
Although the high success rate of CNA by MIP assays in FFPE-derived DNA makes it arguably the best currently used method, its application remains limited since in many research and clinical settings, obtaining 100 ng of input DNA is often unachievable, particularly for small biopsy samples and pre-cancerous lesions. This limitation is particularly relevant in an era where neoadjuvant therapies may be administered before surgery, such that obtaining sufficient pre-treatment tissue for current CNA techniques from small biopsies is not feasible, once conventional diagnostic assays have been performed. A method often used to overcome the challenge of a limiting amount of DNA is whole genome amplification (WGA) to increase the amount of starting template DNA . However, this method carries the risk of introducing unintended positive and/or negative CNA during the amplification process, potentially causing misinterpretation of the CN profile . Therefore, there is an urgent need to develop a technique to detect CNA with high accuracy from very limited input of FFPE-derived DNA.
The primary goal of this study was to assess the performance of LC WGS to detect CNA using an ultra-low input of FFPE-derived DNA. We investigated methods of reducing DNA input and improving performance, including WGA, pre-treatment with a DNA repair procedure, and a low-input WGS library preparation method. The optimal method was then compared to MIP arrays.
Tumor samples and DNA extraction
Archived FFPE pathology blocks of Merkel cell carcinoma (MCC) samples (n = 2) were obtained as previously described . MCC cells from these previously analyzed samples were newly micro-dissected by the Roche Automated Tissue Dissection System (Roche) from 2–3 5-μm hematoxylin and eosin (H&E) stained sections, followed by shearing with sonication with the Covaris LE220 system. DNA was extracted using a MagAttract® HMW DNA mini Kit (Qiagen).
FFPE breast tumor samples (n = 4) were obtained for this study from the LifePool cohort (www.lifepool.org). LifePool prospectively recruits Australian female participants through the population-based mammographic screening program. Participants consent to use of their diagnostic tissue blocks for research. Ten-micron sections were H&E stained and DNA was extracted from manually needle micro-dissected cells using the Qiagen DNeasy FFPE Kit (Qiagen) as previously described  from both FFPE breast tumor samples and two FFPE pre-cancerous breast lesions (papilloma). The quality of DNA was assessed by a multiplex PCR assay  modified to include additional primer sets that produce up to 700 bp fragments from non-overlapping target sites in the GAPDH gene.
This study was approved by the Human Research Ethics Committee at the Peter MacCallum Cancer Centre. This study was carried out in accordance with all relevant regulations and guidelines.
Whole genome amplification
Extracted DNA from FFPE MCC samples were amplified using GenomePlex® Complete Whole Genome Amplification (WGA) kit (Sigma-Aldrich), following the manufacturer’s instruction with several minor modifications. In brief, 50 ng of DNA was prepared in a total volume of 10 μL for the fragmentation, followed by library preparation and 14 cycles of amplification as described in the protocol. The final product was purified using QIAquick® PCR purification kit (Qiagen), followed by quantification to determine the final concentration; the yield was 2–4 μg. The average fragmentation size of WGA products was 200–300 bp. A standard human genomic DNA was used as a positive control provided with the Genome Plex WGA kit (Sigma) and a no template control was used as a negative control.
NEB next FFPE repair
The NEB Next FFPE Repair kit (NEB M6630, New England® Biolabs Inc) was used for repairing 150 ng of total DNA, according to the manufacturer’s protocol with a minor change of eluting DNA in 30 μL instead of 40 μL. A total of 10 μL of eluted DNA (total 50 ng of repaired DNA) was used for WGA using the Sigma WGA kit as described above. The remaining 100 ng of repaired DNA was used for the library prep directly using the KAPA Hyper Prep Kit.
KAPA Hyper library preparation
Library preparation was performed as described in the KAPA Hyper Prep Kit Illumina® platforms (KR0961-v1.14, KAPA Bio systems). Slight modifications of the manufacturer’s protocol were incorporated. Briefly, 100 ng of non-WGA or unamplified DNA (both NEB Next treated repaired DNA and untreated DNA) was sheared with sonication (Covaris S2 system) for 3 × 60 s, with the following parameters: duty cycles of 10, intensity of 5, and 200 cycles/burst.
Subsequently, libraries of the both the fragmented unamplified DNA (200–400 bp) and WGA products were created by end repair and A-tailing, adaptor ligation with a stock concentration of 15 μM adaptor, followed by library amplification of six PCR cycles and eluted in 30 μL after post-amplification clean up. The library distribution was analyzed by TapeStation 2200 (Agilent Technologies) and quantified by Qubit (Life Technologies).
NEBNext® Ultra ™ II DNA Library Prep
Library preparation was performed from MCC samples (n = 2) with 5 ng and 20 ng input, breast tumor samples (n = 4), and pre-cancerous breast lesions (papilloma) (n = 2) with 5 ng DNA input as described in the NEBNext® Ultra ™ II DNA Library Prep Kit (NEB E7645S/L, New England BioLabs ® Inc.) with several minor modifications. In brief, DNA fragmented using the Covaris S2 in 50 μL was used for NEBNext End Prep, followed by an immediate adaptor ligation step with a 1.5 μM diluted adaptor. Clean-up of adaptor-ligated DNA without size selection was carried out followed by PCR amplification with eight cycles and ten cycles for 20 ng and 5 ng input, respectively. After adding resuspended AMPure XP Beads to the PCR products, the mixture was incubated at room temperature for at least 20 min instead of 5 min. Subsequently, after adding 33 μL elution buffer (0.1 × TE) into the beads after washing with ethanol, it was incubated for 10 min instead of 2 min. A total of 2 μL of the final 30 μL library was analyzed with the TapeStation for the size distribution.
Low coverage whole genome sequencing
The libraries prepared by both KAPA Hyper and NEBNext kits were used for LC WGS. An Illumina Nextseq platform (NextSeq 500) (paired-end 75 bp, on a mid-output flow cell) was used to run the pooled, normalized indexed libraries according to the standard protocol. The final concentration was 2 nM pooled and diluted to 1.8 pM as the standard Illumina protocol. Sequencing of those samples led to genome coverage of 1.6–1.8 × per sample.
Molecular inversion probe SNP arrays
The Affymetrix Molecular Inversion Probe (MIP) 330 K OncoScan array was used to analyze four breast cancer samples (version 3) and two papilloma samples (version 2) and was performed according to the manufacturer’s instructions by the Ramaciotti Centre for Genomics (version 3, NSW, Australia) or Affymetrix Inc (version 2, Santa Clara, CA, USA). DNA input was 40–100 ng for this assay.
Reads were aligned with bwa mem (v0.7.12-r1039)  to hg19 (GRCh37) after removal of sequencing primers by cutadapt (v1.7.1) . ControlFREEC (version 6.7)  was used to estimate copy number from the low-coverage WGS data in 50 kb windows across hg19, with default parameters, no matched normal sample and baseline ploidy set to 2. Down-sampling of bam files was performed with samtools .
MIP data were pre-processed by the Ramaciotti Centre for Genomics or Affymterix Inc., with tumor samples batch normalized against Affymetrix controls .
All sample data were imported into Nexus (BioDiscovery Inc., Hawthorne, CA, USA) and segmented using SNP-FASST, a circular binary segmentation algorithm. Copy number gains were called if the log2 ratio of the segment was >0.15 and losses called if < –0.15. To reduce spurious calls, the genome was masked using a list of published problematic regions, including highly repetitive centromeric regions, where DNA copy number cannot be accurately measured .
Total CN profile overlap analysis was performed using Partek Genome Suite (Partek Inc., St. Louis, MO, USA). CNA segments for each matched pair were imported and the “finding regions in multiple samples” tool run, matching for event type (amplification/deletion). This tool reports each CNA region shared at base-pair resolution as well as each CNA region unique to a sample. Shared CN neutral regions were calculated by subtracting the length of all shared CNAs as well as sample only CNA events from the total base pairs covered.
Median Absolute Pair-wise Difference (MAPD) score was calculated as follows: if xi: is the log2 ratio for marker i: then MAPD = median(|x i+1 − x i |,i ordered by genomic position). This metric provides a measure of the noise of the sample that is less dependent on true biological copy number variation than, for example, standard deviation.
FREEC normalized read counts in 50 kb bins were extracted from regions called as a gain or loss by FREEC in at least one of the 5 ng, 20 ng, and 100 ng DNA inputs or the WGA libraries for MCT-4 and MCT-6 LC WGS data. Gains or losses in regions lacking MIP array probes and regions in the blacklist of Scheinin et al.  were filtered out. The Pearson correlation of bin counts in these CNA regions was calculated and used to cluster by Euclidean distance using the hclust() function of R 3.2.1. Correlation between samples was visualized using the pheatmap package.
Comparing copy number alteration calls using low-input DNA
Sequencing performance for all samples
Total reads (millions)
Mapped reads (millions)
Reads mapped (%)
Reads duplicates (%)
Total reads minus duplicates (millions)
Target bases (%) > = onefold coverage
Target bases (%) > = tenfold coverage
Median fragment length
Tissue age (years)
Comparison of CN profiles from low-input LC WGS and MIP arrays
Concordance in CN profiles between samples, expressed as percentage of sites in the genome called diploid, gain, or loss in both the LC WGS and MIP arrays for each breast tumor sample, and the percentage of sites called as gain or loss in the LC WGS or MIP results only, respectively. Concordance in only CNV regions in both LC WGS and MIP arrays, expressed as percentage, for each breast tumor sample
Shared sites (%)
WGS only sites (%)
CNA WGS only (Mbp)
MIP only sites (%)
CNA MIP only (Mbp)
Shared CNV (%)
It is noteworthy that from the overlap analysis (Fig. 3), on average 15 % of the total CN profiles from LC WGS and MIP differed; these differences fell into two categories. First, in some cases, LC WGS provided higher sensitivity to detect small CN changes by providing more even coverage across the genome than SNP arrays, whereas, in other regions where SNP density was high, the MIP arrays were able to detect CNA with length <50 kb, below the detection limit imposed by the chosen window size for LC WGS analysis. Second, many of the large-scale differences were caused by segmentation and thresholding differences, rather than true CN changes (Additional file 1: Figure S1 and S2). For example, in LP S1, MIP arrays called chromosome 4 as a loss whereas no CNA was called from the LC WGS data (Additional file 1: Figure S2). However, that particular loss could be explained by some segments sitting just below the threshold in the MIP data whereas in LC WGS bins they did not, due to normalization subtly shifting read counts upward across the genome. For three samples, we had orthogonal CN data from a targeted sequencing assay. Good concordance was observed for CN variable regions between LC WGS and this assay (LPS1 84 %, LPS2 94 %, and LPS4 61 % of CNA bp concordant). The concordance between MIP arrays and the targeted assay was similar for CNA regions (87 %, 95 %, and 61 %, respectively).
The FFPE repair treatment made little discernible difference to the appearance of the CN profiles (data not shown) or MAPD scores (Fig. 3), although the sequencing metrics were marginally improved compared with untreated samples (Table 1).
Comparison of CN profiles between unamplified and WGA samples with or without NEB FFPE repair treatment
Overall, 77–87 % of the total CN profiles from matched unamplified or WGA samples were concordant (Fig. 3). Unsupervised clustering of MCT-4 and MCT-6 CNA showed high intra-sample concordance with different input amounts and methods, although the WGA data had longer branch lengths (Additional file 1: Figure S5). Variance in read distribution as calculated by MAPD was much higher in WGA samples (Fig. 3), consistent with the higher level of noise observed in CN profiles of WGA samples as compared with unamplified. Down-sampling reads from the untreated unamplified samples to coverage equivalent to the WGA samples (0.2×) revealed the reduction in consistent CNA calls from the latter could not be attributed to differences in read depth alone (Fig. 3).
Identifying CNAs by CGH or MIP arrays has been used successfully for DNA derived from FFPE-fixed tissue, although most often with a DNA input of >100 ng [4, 18, 19]. More recently, LC WGS has also been successfully used to assess CNAs but again mostly using at least 100 ng of input DNA with coverage of 0.1–3× [6, 8, 9]. A very recent study used a complex cell-sorting and single tube Ion Torrent amplicon-based library preparation method to obtain LC WGS CN profiles from 126–300 cells without extracting DNA [8, 9, 20]. To open CN analysis to samples with very limited DNA extracted by standard methods and compatible with the common Illumina sequencing platforms, we evaluated methods to reduce the required DNA input for LC WGS.
A recently released library preparation method (NEB Ultra II) adapted for very low input DNA was successfully tested. The novel combination of this commercially available kit and our modifications for low input DNA enabled us to obtain high-quality CN data, which was not previously possible. When we compared the CN profile and MAPD results of 5 ng, 20 ng, and 100 ng input, we observed almost negligible difference between these three inputs with 95 % overlap of CN profiles. Even down-sampling of low input samples showed almost 92 % overlap of CN calls with the matched UA-UT 100 ng input, which had lower mean coverage, without significantly changing MAPD. This result confirmed that 5 ng of input DNA produces total CN profiles that are highly concordant with those obtained from 100 ng of input DNA at the same depth of sequencing coverage. Interestingly, samples we tested with even less than 5 ng input showed on average 90 % overlap of CN calls (data not shown), however, at 2.5 ng and 1 ng inputs there were assay failures for some samples, suggesting that a novel methodology needs to be developed in future to robustly utilize DNA input lower than 5 ng. We have subsequently tested an additional 12 cases (21 DNA samples) and obtained good quality CN data from all cases using 5 ng DNA (unpublished data). Our tested cases are in the age range of 1–12 years and we found a weak trend between the age of the block and the QC score calculated by Nexus (p = 0.06, Kruskal–Wallis test for samples grouped in 5-year intervals, Additional file 1: Figure S6). The difference in QC score was subtle and did not affect detection of CNA.
We observed a high degree of similarity in CNAs detected by low input LC WGS and MIP arrays, despite the more than tenfold higher input DNA used for the array-based method, with a much-improved CN profile for samples that had performed poorly by MIP. Additionally, the 15 % of total CN profile dissimilarity seen on average between LC WGS and MIP arrays could be explained by more uniform coverage of LC WGS or segmentation and thresholding differences, rather than true CN changes. A limitation of the study is the lack of fresh-frozen tissue to extract high-quality DNA for comparison; however, high concordance of MIP array data between FFPE and fresh-frozen derived DNA has been demonstrated previously .
A limitation of the LC WGS approach is the inability to determine allelic imbalance at high resolution due to the low mean base coverage. However, in a high-quality sample, increasing the average read depth to approximately tenfold would lead to >30 % of the genome having sufficient coverage to call a genotype . Even in a low-quality FFPE-derived sample with reduced call rates, this level would still be more than sufficient for detection of allelic imbalance and provide resolution similar to MIP arrays. An FFPE WGS study of two breast cancer samples identified ~2 million high-confidence SNP calls from ~20-fold coverage .
One common method for increasing available input DNA is WGA, which has been coupled with various array-based systems for CNA studies [9, 23]. However, in our study, WGA resulted in poor sequencing performance (Table 1) and poor overlap (77–87 %) of CNA data derived from unamplified DNA. While high concordance rates and reproducibility has been reported using WGA along with SNP genotyping , some published studies have suggested that false-positive CNA could be introduced randomly during the amplification process and also that use of WGA could obscure true CN changes [10, 23]. The high MAPD values from WGA input and the noise seen in the CN profiles concurs with previous array-based studies .
While amplification bias during WGA may account for some of our observations, the poor performance of the WGA samples in our case also stems from the incorporation of universal adaptors prior to Illumina library preparation, limiting nucleotide diversity during the first bases of sequencing and resulting in a dramatic decrease in the number and quality of sequenced fragments (Additional file 1: Figure S4). Similar results were reported previously . Some of the technical challenges presented by WGA may be overcome by optimization; however, given that low input library preparation methods give reliable and accurate results, the imperative to use WGA is removed.
Interestingly, the FFPE DNA repairing method used did not show any improvement in CN profile for either amplified or unamplified samples, although the sequencing metrics were slightly better. One possible reason for observing no significant differences in CN analysis using the repair method is that while this method repairs single-strand nicks, DNA fragmentation and DNA-protein cross-links remain, likely leading to sub-optimal library preparation. This result suggested that such repair methods might not be necessary for CN analysis; however, the possibility remains that they could improve identification of other genomic alterations such as somatic point mutations.
The major goal of this study was to investigate methods for achieving accurate CN detection with as little input DNA as possible. CNA are often associated with prognosis for a variety of tumor types including pre-cancerous lesions [1, 2, 25]. In situations where more than 10 ng DNA is unavailable, either in research or clinical settings, LC WGS using the low input method described here would be a highly suitable method in terms of accuracy, sensitivity, specificity, speed, and cost to detect CN changes in FFPE samples. This technique opens up the possibility of obtaining high-quality genome-wide copy number from vast archives of FFPE tissue without depleting the tissue resource, thereby enabling highly powered retrospective studies of associations of CN events with clinical features. Small, previously intractable lesions can now be investigated fully and, in addition, this technique could be developed into a clinically feasible assay that, for the same price as FISH, can interrogate the entire genome.
We thank Richard Lupat, Ismael Vergara, and Jan Schroeder (WEHI) for advice on and assistance with LC WGS analysis, and Sreeja Gadipally for advice on and assistance with FFPE-derived DNA library preparation.
This study was funded by the Australian National Health and Medical Research Council (NHMRC APP1063092), the National Breast Cancer Foundation, and Peter MacCallum Cancer Foundation (1448). TK was supported by Melbourne International Research Scholarship and Melbourne International Fee Remission Scholarship and DLG was supported by a NHMRC Early Career Fellowship (APP1052904).
Availability of data and materials
Sequence data are available through NCBI Sequence Read Archive. MIP data are available from Gene Expression Omnibus (GEO), Accession #GSE85035. We have registered a “BioProject” with the Sequence Read Archive (SRA) at NCBI (ID PRJNA327470) where we will put the data once the BioProject is approved.
Undertook experiments: TK, JC, SMR, DB. Analyzed data: TK, DLG. Provided samples: SQW, LD, DB, SBF, RWT. Involved in study design: TK, DLG, SQW, GMA, RWT, IGC, KLG. Drafted the manuscript: TK, DLG, SQW, GMA, RWT, IGC, KLG. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
This study was approved by the Human Research Ethics Committee at the Peter MacCallum Cancer Centre and Royal Melbourne Hospital. Informed consent was obtained from Merkel cell carcinoma and breast cancer cases; waiver of consent was applied for papilloma cases. This study was carried out in accordance with all relevant regulations and guidelines and conformed to the Helsinki Declaration.
The authors declare they have nothing to disclose.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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