Use of Sequenom Sample ID Plus® SNP Genotyping in Identification of FFPE Tumor Samples

Abstract

Short tandem repeat (STR) analysis, such as the AmpFlSTR® Identifiler® Plus kit, is a standard, PCR-based human genotyping method used in the field of forensics. Misidentification of cell line and tissue DNA can be costly if not detected early; therefore it is necessary to have quality control measures such as STR profiling in place. A major issue in large-scale research studies involving archival formalin-fixed paraffin embedded (FFPE) tissues is that varying levels of DNA degradation can result in failure to correctly identify samples using STR genotyping. PCR amplification of STRs of several hundred base pairs is not always possible when DNA is degraded. The Sample ID Plus® panel from Sequenom allows for human DNA identification and authentication using SNP genotyping. In comparison to lengthy STR amplicons, this multiplexing PCR assay requires amplification of only 76–139 base pairs, and utilizes 47 SNPs to discriminate between individual samples. In this study, we evaluated both STR and SNP genotyping methods of sample identification, with a focus on paired FFPE tumor/normal DNA samples intended for next-generation sequencing (NGS). The ability to successfully validate the identity of FFPE samples can enable cost savings by reducing rework.

About the Authors:

Jessica K. Miller

* E-mail: [email protected]

Affiliation: Department of Genome Technologies, Ontario Institute for Cancer Research, Toronto, Canada

Nicholas Buchner

Affiliation: Department of Genome Technologies, Ontario Institute for Cancer Research, Toronto, Canada

Lee Timms

Affiliation: Department of Genome Technologies, Ontario Institute for Cancer Research, Toronto, Canada

Shirley Tam

Affiliation: Department of Genome Technologies, Ontario Institute for Cancer Research, Toronto, Canada

Xuemei Luo

Affiliation: Department of Genome Technologies, Ontario Institute for Cancer Research, Toronto, Canada

Andrew M. K. Brown

Affiliation: Department of Genome Technologies, Ontario Institute for Cancer Research, Toronto, Canada

Danielle Pasternack

Affiliation: Department of Genome Technologies, Ontario Institute for Cancer Research, Toronto, Canada

Robert G. Bristow

Affiliations Ontario Cancer Institute/Princess Margaret Hospital, Toronto, Canada, Department of Medical Biophysics, University of Toronto, Toronto, Canada

Michael Fraser

Affiliation: Ontario Cancer Institute/Princess Margaret Hospital, Toronto, Canada

Paul C. Boutros

Affiliations Informatics & Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, Canada, Department of Medical Biophysics, University of Toronto, Toronto, Canada, Department of Pharmacology & Toxicology, University of Toronto, Toronto, Canada

John D. McPherson

Affiliations Department of Genome Technologies, Ontario Institute for Cancer Research, Toronto, Canada, Department of Medical Biophysics, University of Toronto, Toronto, Canada

Introduction

STR profiling is a powerful, multiplex PCR-based assay that uses up to 16 tetranucleotide loci repeats [1], [2]. Generally, the certainty of identity with one locus is less than 0.01, and is 1.0×10−15 for 12 or more loci, making STR a very effective method for sample identification [2], [3]. Despite its efficacy, improvements to STR assays have been required for forensic analysis on highly-degraded DNA samples, namely reducing amplicon size to increase amplification efficiency [4], [5]. Previous studies on degraded DNA have demonstrated that STR analysis using amplicons of 280 nt or less generates 48% more genotype profiles than would be possible with longer STRs [5]. STR analysis has been advocated by the American Type Culture Collection Standards Development Organization (ATCC SDO) Workgroup for human cell line authentication in general [6], [7]. Monitoring sample quality at the beginning of a process is especially essential to ensure that cell lines and tissues are authenticated for correct input DNA in order to ensure validity of data output [8]. Failure to authenticate samples may result in unwanted, incorrect, or duplicate data; consequently, these superfluous errors can be costly. Common causes leading to sample misidentification include mislabeling of cell cultures or DNA samples, cross-contamination, co-cultivation, and xenograft propagation. For example, it has been revealed that at least 360 cell lines are known to have some cross-contamination [9]. Furthermore, STR profiling demonstrated the misidentification of multiple ovarian cancer cell lines [10]. Formalin fixed paraffin embedded (FFPE) sample misidentification is likely to occur from some of the same types of errors and circumstances. Next generation sequencing (NGS) technology has rapidly progressed and expanded over the past decade [11], [12]. With such abrupt growth and development, there is an increasing need for accuracy at each stage of a sequencing pipeline. Quality control checkpoints are typically implemented throughout the process, including STR profiling which is now regularly used to verify sample identity after sample reception, or before NGS library preparation. Furthermore, with NGS technology now capable of producing whole genome and exome data from FFPE-derived DNA, it will be beneficial for 100% of tissue authentication to be successful despite inputs of fragmented DNA. In a clinical setting, FFPE tissues are an indispensable DNA resource and the standard for pathological examination of tissues; however, this preservation method is typically prone to analyte degradation [13]. To accommodate such DNA size limitations for NGS sample quality control, a further reduction in amplicon size is necessary. A single nucleotide polymorphism (SNP) genotyping panel has been designed to require amplification of shorter targets, and provide greater than 1×10−18 discriminatory power [14], [15]. This study explores the use of Sample ID Plus® SNP assay technology (Sequenom Bioscience, San Diego, CA) in generating complete genotype profiles from FFPE tissue DNA.

Materials and Methods

Ethics Statement

FFPE samples were taken as part of a larger NGS study of genomic prognostic factors within pre-treatment biopsies derived from prostate cancer patients with Institutional Review Board (IRB) approval and patient written consent (Canadian Prostate Cancer Genome Network (CPC-GENE) project; University Health Network-Research Ethics Board UHN06-0822-CE and UHN11-0024CE [16]. The samples analyzed in this study were taken from a larger deep-sequencing dataset deposited in the European Genome-phenome Archive (dataset ID EGAS00001000549).

Sample Extraction

DNA was extracted from patient blood samples (N1-2, N2-2, N3-2, AND N4-2) using the ArchivePure DNA Blood Kit from 5 Prime (Gaithersburg, MD) following the manufacturer’s recommended protocol. DNA was also extracted from corresponding FFPE prostate tumors (T1-2, T2-2, T3S1-2/T3S2-2, and T4-2). Two different sections of tumor sample 3, T3S1-2 and T3S2-2, were extracted and tested as individual samples, but were both paired with N3-2. The approximate age of the FFPE tissue blocks were as follows: T1-2, 5 years; T2-2 and T4-2, 8 years; T3S1-2 and T3S2-2, 11 years. FFPE prostate tumors were macro-dissected, proteinase K-treated, and phenol-chloroform extracted using a modified protocol based on the xylene-free MagMAX FFPE DNA Isolation Kit (Life Technologies, Carlsbad, CA).

STR Genotyping

For STR profiling with the AmpFlSTR® Identifiler® Plus PCR Amplification Kit (Applied Biosystems, Foster City, CA), 25 ng of FFPE tumor/normal DNA or 5 ng of R1 reference DNA was used. Data were analyzed using GeneMarker HID V1.95. The mean size of the 15 STR markers evaluated in this study range from 101–359 nt; the amelogenin sex determination marker was excluded from analysis for simplicity since gender information was not required. STR marker sizes are displayed as calculated fractions based on comparisons to internal size standards performed by Applied Biosystems. Individual markers have variable, but specific size ranges.

Multiplex PCR FFPE DNA Quality Assessment

For multiplexed PCR, four primer pairs (IDT) were used to amplify 100, 200, 300, and 400 bp non-overlapping regions of the GAPDH gene from 5 ng of each sample [17]. The products were amplified in a 50 µl reaction, the final concentrations of 100 F/R and 300 F/R were 0.133 µM, 200 F/R were 0.200 µM, and 400 F/R were 0.067 µM. Multiplex PCR reaction conditions were as follows: 98°C for 30 sec, followed by 35 cycles each of 98°C for 10 sec, 62°C for 30 sec, and 72°C for 30 sec with a final 10 min extension at 72°C. PCR was carried out using 1 U Phusion HF Taq DNA polymerase and buffer (NEB, Ipswitch, MA), 500 mM KCl, and 10 mM dNTPs (Invitrogen, Carlsbad, CA). For positive controls, the same PCR reaction was performed on corresponding normal samples, as well as on a reference prostate gDNA, R1. A no template control (NTC) consisting of nuclease-free water (Ambion, Carlsbad, CA) was also included. All PCR reactions were purified using the MinElute PCR Cleanup Kit (QIAGEN, Venlo, Netherlands), and quantified with a Qubit 1.0 fluorimeter (Invitrogen). To evaluate GAPDH amplification, gel electrophoresis was performed on PCR reactions using a 2% agarose gel. PCR product sizes and quantity of approximately 100, 200, 300, 400 bp were confirmed with a DNA Bioanalyzer assay (Agilent Technologies, Santa Clara, CA).

SNP Genotyping

For Sequenom Bioscience’s Sample ID Plus® panel, 10 ng of FFPE tumor/normal DNA or 2.5 ng of R1 reference DNA was used to determine 47 SNP calls; however a total of 45 SNPs were evaluated due to the general failure of two SNP calls, rs735155 and rs1029047. These two SNP calls also failed consistently in previous, unrelated assay tests; therefore, they were excluded from our analysis. In addition, other studies have observed similar poor performances for rs735155 and rs1029047 [18]. This method consists of five steps: PCR amplification, shrimp alkaline phosphatase treatment, single base extension, nanodispensing, and matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometry [19]. Two previously confirmed primary non-FFPE tumor/normal pair matches (Pos1-T, Pos1-N and Pos2-T, and Pos2-N) were used as positive controls. Two mouse samples, Neg1 and Neg2, were used as negative controls. NTC wells were also included on the assay plate. All SNP calling from Sequenom Typer v4.0.20 software reports was analyzed using Microsoft Excel 2010 spreadsheets. Sex determination markers were noted but excluded from analysis to retain consistency with STR profiling analysis. Current SNP amplicon sizes ranged from 76–139 bp. Heat map analysis plots were generated by R v2.15.1.

DNA Fragmentation

To simulate limited DNA size as observed in FFPE samples, 3 µg of reference gDNA, R1, was fragmented by sonication using a Covaris S2 instrument. Performance of a shearing time-course determined that 75 sec was an appropriate shearing time for this sample in order to obtain a broad fragment range when standard 400 bp DNA shear conditions were applied as follows: 10% duty cycle, intensity 4, 200 cycles per burst, 6–8°C water bath. The R1 shearing profile was confirmed with a DNA Bioanalyzer assay, followed by electrophoresis with a 2% agarose gel. From this gel, size selection was done manually for targeted R1 fragment pools of approximately 100, 200, 300, and 400 bp. These sizes were selected to coincide with GAPDH target regions used for prior FFPE DNA quality assessment. Gel cuts were purified with Zymoclean Gel DNA Recovery columns (Zymo Research, Irvine, CA). Purified R1 pools were quantified with a Qubit 1.0 fluorimeter (Invitrogen). Using a DNA Bioanalyzer assay, actual size-selected R1 targets were detected as 132, 233, 347, and 450 bp, and fragment pools were correspondingly named R1 132, R1 233, R1 347, and R1 450.

Results and Discussion

To confirm FFPE sample identification, STR profiling was performed. Typically, at least 80% or more of alleles are required to match in order to properly authenticate DNA samples using STR profiling [20]. However, recent examinations indicate that, depending on the sample type, even more stringent thresholds would be beneficial for validating sample identity [21]. For sensitive and costly downstream applications such as NGS, at least 85% concordance was more reliable. Profiles were successfully obtained for all normal samples; however, several STRs consistently failed for each tumor sample (Figure 1). The failed STR markers ranged from 255–359 nt. Except for the success of the 274 nt D16S539 marker, data suggest that failure to generate a complete profile for FFPE samples was likely due to fragmentation and degradation of DNA impeding amplification of the larger amplicons. To predict successful array comparative genomic hybridization (aCGH) with FFPE DNA, van Beers et al. established a reliable multiplexed PCR assay which targeted 100, 200, 300, and 400 bp non-overlapping regions of the GAPDH gene [17]. We applied this assay to our FFPE tumor/normal pairs to explore the effects of DNA fragment size on successful amplification. Although all GAPDH regions amplified for all normal samples, PCR products were not seen for all four of the targeted regions for the corresponding tumor samples (Figure S1). The inability to amplify the larger GAPDH targets in FFPE samples implied that fragmentation of DNA could affect detection of loci greater than 200 bp in length. However, this result is not exhaustive, as formalin fixation can alter the DNA is other ways which could contribute to whether or not detection is possible [22], [23].

[Figure omitted. See PDF.]

Figure 1. Frequency of STR marker detection.

T1-2/N1-2, T2-2/N2-2, T3S1-2/T3S2-2/N3-2, T4-2/N4-2 tumor and normal samples are represented by bars only when an STR marker was successfully amplified. Nucleotide size ranges are listed above the corresponding STR marker.

https://doi.org/10.1371/journal.pone.0088163.g001

To evaluate the capability of SNP genotyping to confirm the identities of the FFPE tumor/normal pairs subjected to STR profiling, samples were processed with Sequenom Bioscience’s Sample ID Plus® panel. The average number of matched SNP calls between unrelated samples in previously-developed identification panels has been described as 40.38%, and ranging from 13.46–78.85% [14]. Additionally, Gilbert et al. observed a 86–100% matched call rate on relatively higher quality FFPE samples when utilizing a similar 44-SNP genotyping method [24]. According to Sequenom Bioscience, samples are called mismatches when less than 30 SNP calls are concordant. However, to be consistent with the concordance rate of 85% we used for STR profiling, at least 38 SNP matches were required in our study. Heat map analysis illustrated that all FFPE tumor/normal pairs were detected (Figure 2). The T1-2/N1-2 pair displayed a 97.78% (44/45) call rate, as the rs104954 locus did not match. All other tumor/normal pairs and positive control pairs obtained 100% call rates. In addition, all other possible sample-sample, control-control, and sample-control combinations showed 0–51.11% (0–23) matched SNP calls, indicating no other relationships existed. Overall, successful SNP genotyping profiles were obtainable for FFPE tumor DNA, despite incomplete STR profiles. Furthermore, STR loci are more likely to have greater genetic instability than biallelic SNPs [25]. This and other factors, such as relatedness, could affect the minimal match threshold of an FFPE tumor/normal pair. However, the high success rate of SNP genotyping in this case renders these factors insignificant when compared to STR profiling results. These data demonstrated the efficacy and sensitivity of the Sequenom Bioscience’s Sample ID Plus® SNP genotyping panel over typical STR profiling methods, especially in the case of FFPE or types of highly-degraded samples.

[Figure omitted. See PDF.]

Figure 2. FFPE tumor/normal pair relatedness assessed by Sequenom SNP genotyping.

Heat map analysis plots generated by R v2.15.1 visualize the number of matched SNP calls between paired samples. Positive (Pos1-T/N and Pos2-T/N) and negative (Neg1 and Neg2) controls are included in each heat map. Match failures are considered to be in the range of 0–38 SNP matches, while matched samples (38–45 SNP matches) are shown in pink.

https://doi.org/10.1371/journal.pone.0088163.g002

To further support that SNP genotyping, compared to STR profiling, is largely unaffected by target DNA size we devised a three-step approach: shear a reference DNA sample and separate into fragment pools, submit pools to STR profiling, and perform a SNP genotyping assay on the same pools. STR genotyping was performed on each sheared R1 fragment pool, and on intact R1 gDNA using the same methods described for the FFPE tumor/normal pairs (Table 1). All STR markers amplified successfully for R1 450, R1, C1, and C2. Six STR markers ranging from 216–359 nt expectedly failed for R1 132. Additionally, the larger TH01, vWA, D21S11, TPOX, and FGA STR markers were profiled successfully for R1 132. Bioanalyzer traces revealed that trace amounts of larger DNA fragments were present in the R1 132 fragment pool, which is likely why these unexpected STR markers amplified. All other STR marker failures were consistent with the R1 fragment pool size cutoffs, with the exception of CSF1PO, which failed for R1 347 despite its average size of 324 nt.. It was important to determine if SNP genotyping could be more effective at generating complete identification profiles than STR profiling using samples of known fragment size. For each sample, Figure 3 displays all SNP amplicons in the assay based on size, and classifies each as failed or successful. One SNP (dbSNP ID rs733164) failed to amplify for R1 and all fragment pools, despite its amplicon length of 128 bp. All other 44 SNP amplicons were successfully called for R1, R1 233, R1 347, and R1 450, providing a 97.78% call rate. In contrast, only 66.67% (30/45) of SNP calls were detected for R1 132. To reach the STR profiling concordance rate of 85%, at least 38 SNP calls are required to match. For all 15 failed SNP calls for R1 132, the amplicon sizes ranged from 97–139 bp, while the sizes varied from 76–138 bp for the successful SNP amplicons. Therefore, there appeared to be no direct relationship between amplicon size and a SNP’s successful detection. However, 97.78% (44/45) of SNP calls were detected when approximately twice the amount (5 ng) of R1 132 DNA input was utilized for genotyping, suggesting that the assay may be more sensitive to sample input quantity when DNA is fragmented due to the lower amounts of intact amplicon target. Johansen et al. described the impact of low DNA input amounts on allelic dropouts as well [18]. Overall, these fragment pool results support that DNA fragment integrity can limit STR profiling success, whereas DNA sample quantity, but not amplicon size, affects SNP genotyping results.

[Figure omitted. See PDF.]

Figure 3. SNP amplicon size does not indicate SNP call failure.

All 45–139 bp. One SNP amplicon of 128 bp (dbSNP ID rs733164) does not amplify in all instances. For R1 132, amplification fails for 14 additional SNPs ranging in size from 97–139 bp.

https://doi.org/10.1371/journal.pone.0088163.g003

[Figure omitted. See PDF.]

Table 1. STR marker amplification for R1 fragment pools and intact R1 DNA.

https://doi.org/10.1371/journal.pone.0088163.t001

SNP genotyping analysis was successful for FFPE tumors and the R1 132 and R1 233 fragment pools, where STR profiling efforts were rendered incomplete. Table 2 summarizes the input and results comparison between each genotyping method. Extraction method and sample age are variables that may have contributed to poor FFPE DNA quality, and subsequent STR failure. A method recently developed to extract DNA from FFPE tissue by xylene-free deparaffinization was directly compared to the QIAamp DNA FFPE Tissue Kit (QIAGEN), and showed that although greater yields were recovered, the DNA integrity suffered [26]. Another study showed that application of an automated, xylene-free deparaffinization approach allowed for 99% of samples to be analyzed by SNP genotyping, which yielded higher than 91% SNP calls [27]. In our study, FFPE tissues were not deparaffinized using xylene; instead the proteinase K incubation time was extended to last overnight at 55°C to maximize DNA recovery. The age of FFPE tissue blocks has also been shown to negatively affect DNA quality and quantity, especially after 5–6 years of storage [28]. For our study, all FFPE tumor DNA was extracted from blocks ranging in age from 5–11 years. Age alone is likely not the single factor here but rather changes in formalin fixation protocols in the intervening years. Comparative DNA extraction method studies employing STR analysis on FFPE tissues of variable age have indicated that increased storage time, extraction method, and reagent quality all play key roles in poor DNA quality [23]. Additionally, the aforementioned study demonstrated that decreasing amplicon size is most appropriate for retrieving improved results overall.

[Figure omitted. See PDF.]

Table 2. Comparison of STR and Sample ID (SID) Plus genotyping methods used for FFPE samples.

https://doi.org/10.1371/journal.pone.0088163.t002

Conclusions

Altogether, the data presented in this report support the efficacy of Sequenom Bioscience’s Sample ID Plus® SNP assay technology in generating genotype profiles from FFPE tissue DNA to achieve accurate sample authentication for NGS application. The high genotyping success rate of this assay provided more definitive tumor/normal pair match results for FFPE samples than the corresponding STR profiles. It has been observed that SNP genotyping with this panel may not be adequate for forensic analysis due to low DNA input requirements and a lack of sensitivity [18]. However, this SNP genotyping platform is a promising and suitable alternative to STR profiling for NGS sample identification, especially for FFPE tissue DNA of lower integrity. In the future, application of the Sample ID Plus® panel as a quality control checkpoint before NGS library preparation will add cost savings by reducing rework due to sample mismatches. Furthermore, the ability to utilize FFPE-derived retrospective DNA samples for genomic analyses will be extremely important in cancers such as prostate cancer where the long natural history of the disease limits prospective assessments.

Supporting Information

[Figure omitted. See PDF.]

Figure S1.

Multiplex PCR amplification of target GAPDH regions. Tumor samples (T1-2, T2-2, T3S1-2, T3S2-2, and T4-2) display failed or lower amplification of larger targets. Corresponding normal samples (N1-2, N2-2, N3-2, N4-2) are shown for comparison. A no template control (NTC) and a positive reference control (R1 (Pos)) lane are shown.

https://doi.org/10.1371/journal.pone.0088163.s001

(TIF)

Author Contributions

Conceived and designed the experiments: JKM NB LT ST JDM. Performed the experiments: JKM. Analyzed the data: JKM NB XL. Contributed reagents/materials/analysis tools: JKM NB ST XL AMKB DP RGB MF PCB. Wrote the paper: JKM. Major manuscript edits: RGB MF PCB JDM.

Citation: Miller JK, Buchner N, Timms L, Tam S, Luo X, Brown AMK, et al. (2014) Use of Sequenom Sample ID Plus® SNP Genotyping in Identification of FFPE Tumor Samples. PLoS ONE 9(2): e88163. https://doi.org/10.1371/journal.pone.0088163

References

1. Butler JM (2004) Short tandem repeat analysis for human identity testing. Curr Protoc Hum Genet 14: 8.1–8.22.

2. Thompson R, Zoppis S, McCord B (2012) An overview of DNA typing methods for human identification: Past, present, and future. In DNA Electrophoresis Protocols for Forensic Genetics. New York: Humana Press. 3–16 p.

3. Tracey M (2001) Short tandem repeat-based identification of individuals and parents. Croat Med J 42: 233–238.

4. Butler JM, Shen Y, McCord BR (2003) The development of reduced size STR amplicons as tools for the analysis of degraded DNA. J Forensic Sci 48: 1054–1064.

5. Opel KL, Chung DT, Drabek J, Tatarek NE, Jantz LM, et al. (2006) The application of miniplex primer sets in the analysis of degraded DNA from human skeletal remains. J Forensic Sci 51: 351–356.

6. Nims RW, Sykes G, Cottrill K, Ikonomi P, Elmore E (2010) Short tandem repeat profiling: part of an overall strategy for reducing the frequency of cell misidentification. In Vitro Cell Dev Biol 46: 811–819.

7. Capes-Davis A, Reid YA, Kline MC, Storts DR, Strauss E, et al. (2012) Match criteria for human cell line authentication: Where do we draw the line? Int J Cancer 000: 000–000.

8. Alston-Roberts C, Barallon R, Butler J, Capes-Davis A, Dirks WG, et al. (2010) Cell line misidentification: The beginning of the end. Nat Rev Cancer 10: 441–448.

9. Capes-Davis A, Theodosopoulos G, Atkin I, Drexler HG, Kohara A, et al. (2010) Check your cultures! A list of cross-contaminated or misidentified cell lines. Int J Cancer 000: 000–000.

10. Korch C, Spillman MA, Jackson TA, Jacobsen BM, Murphy SK, et al. (2012) DNA profiling analysis of endometrial and ovarian cell lines reveals misidentification, redundancy and contamination. Gynecol Oncol 127: 241–248.

11. Metzker ML (2010) Sequencing technologies – the next generation. Nat Rev Genet 11: 31–46.

12. Liu L, Li Y, Li S, Hu N, He Y, et al. (2012) Comparison of next-generation sequencing systems. J Biomed Biotechnol 2012: 1–11.

13. Frankel A (2012) Formalin fixation in the ‘-omics’ era: A primer for the surgeon-scientist. ANZ J Surg 82: 395–402.

14. Sanchez JJ, Phillips C, Borsting C, Balogh K, Bogus M, et al. (2006) A multiplex assay with 52 single nucleotide polymorphisms for human identification. Electrophor 27: 1713–1724.

15. Demichelis F, Greulich H, Macoska JA, Beroukhim R, Sellers WR, et al. (2008) SNP panel identification assay (SPIA): A genetic-based assay for the identification of cell lines. Nuc Acids Res 36 (7): 2446–2456.

16. Locke JA, Zafarana G, Ishkanian AS, Milosevic M, Thoms J, et al. (2012) NKX3.1 haploinsufficiency is prognostic for prostate cancer relapse following surgery or image-guided radiotherapy. Clin Cancer Res 18(1): 308–316.

17. van Beers EH, Joosse SA, Ligtenberg MJ, Fles R, Hogervorst FBL, et al. (2006) A multiplex PCR predictor for aCGH success of FFPE samples. Br J Cancer 94: 333–337.

18. Johansen P, Andersen JD, Borsting C, Morling N (2013) Evaluation of the iPLEX® Sample ID Plus Panel designed for the Sequenom MassARRAY system. A SNP typing assay developed for human identification and sample tracking based on the SNPforID panel. Forensic Sci Int Genet 7: 482–487.

19. Gabriel S, Ziaugra L, Tabbaa D (2009) SNP genotyping using the Sequenom MassARRAY iPLEX platform. Curr Prot Human Genet 60: 2.12.1–2.12.18.

20. Masters JR, Thomson JA, Daly-Burns B, Reid YA, Dirks WG, et al. (2001) Short tandem repeat profiling provides an international reference standard for human cell lines. PNAS 98(14): 8012–8017.

21. Somaschini A, Amboldi N, Nuzzo A, Scacheri E, Ukmar G, et al. (2013) Cell line identity finding by fingerprinting, an optimized resource for short tandem repeat profile authentication. Genet Test Mol Biomarkers 17(3): 254–259.

22. Steinau M, Patel SS, Unger ER (2011) Efficient DNA extraction for HPV genotyping in formalin-fixed, paraffin-embedded tissues. J Mol Diagn 13(4): 377–381.

23. Funabashi KS, Barcelos D, Visona I, e Silva MS, e Sousa ML, et al. (2012) DNA extraction and molecular analysis of non-tumoral liver, spleen, and brain from autopsy samples: The effect of formalin fixation and paraffin embedding. Pathol Res Pract 208: 584–591.

24. Gilbert MTP, Sanchez JJ, Haselkorn T, Jewell LD, Lucas SB, et al. (2007) Multiplex PCR with minisequencing as an effective high-throughput SNP typing method for formalin-fixed tissue. Electrophoresis 28: 2361–2367.

25. Chakraborty R, Stivers DN, Su B, Zhong Y, Budowle B (1999) The utility of short tandem repeat loci beyond human identification: Implications for development of new DNA typing systems. Electrophoresis 20: 1682–1696.

26. Chung J, Yi JM, Xie R, Brown V, Lee O, et al. (2012) A pressure cooking-based DNA extraction from archival formalin-fixed, paraffin-embedded tissue. Anal Biochem 425: 128–134.

27. Hennig G, Gehrmann M, Stropp U, Brauch H, Fritz P, et al. (2010) Automated extraction of DNA and RNA from a single formalin-fixed paraffin-embedded tissue section for analysis of both single-nucleotide polymorphisms and mRNA expression. Clin Chem 56(12): 1845–1853.

28. Van Neste L, Bigley J, Toll A, Otto G, Clark J, et al. (2012) A tissue biopsy-based epigenetic multiplex PCR assay for prostate cancer detection. BMC Urol 12(16): 1–8.