Development of a High-Resolution Melting Analysis for the Detection of the SF3B1 Mutations

Abstract

SF3B1, located on chromosome 2q33.1, encodes a core component of RNA-splicing machinery, and its mutation has been described in myelodysplastic syndromes (MDS) characterized with ring sideroblasts (RS). To explore the reliability and sensitivity of the high-resolution melting analysis (HRMA) technique for the identification of the SF3B1 mutations, mutations in 92 patients with MDS were detected in this study. The sensitivity could reach 5%, obviously higher than the 25% of direct DNA sequencing. A low frequency (5.4%) of SF3B1 mutations were identified in patients with MDS, including three cases of K700E, one case of H662Q, and one case of K666M. Further, SF3B1 mutations were more frequently recurrent in the 33% of patients with MDS characterized with RS, whereas in other subtypes of MDS, only 2.3% of patients were detected with SF3B1 mutations (p=0.006). In conclusion, a rapid, reproducible, sensitive, and high-throughput HRMA assay has been established for the scanning of SF3B1 mutations.

Introduction

Myelodysplastic syndromes (MDS) are a group of clonal diseases characterized by ineffective hematopoiesis and a high risk of transformation into acute myeloid leukemia (AML) (Corey et al., 2007). The pathogenesis of MDS has not been well established. Genetic mutation, haploinsufficiency, and epigenetic changes have been identified to contribute to the disease phenotype (Bejar et al., 2011). Molecular markers are useful in the diagnosis and prognosis of disease, more importantly, providing information for clinical risk classification and treatment decisions. In recent years, several new acquired gene mutations have been identified in MDS or AML by high-throughput whole-genome sequencing or next-generation sequencing, including TET2, EZH2, IDH1/2, DNMT3A, and genes involved in mRNA splicing (Langemeijer et al., 2009; Mardis et al., 2009; Ernst et al., 2010; Ley et al., 2010; Yoshida et al., 2011).

The RNA-splicing system plays an important role in the maintenance of the protein diversity through alternative splicing in the face of a limited number of genes (Pajares et al., 2007; David and Manley, 2010). Aberrant RNA splicing has been identified in a wide variety of cancers (Pajares et al., 2007; David and Manley, 2010). The disruption of interactions of several factors involved in the splicing process can cause various types of mutations in an ever-increasing number of genes (Baralle and Baralle, 2005). SF3B1, one subunit of the splicing factor 3b protein complex, can anchor the U2 small nuclear ribonucleoproteins complex (U2 snRNP) to the pre-mRNA in the process of RNA splicing (Gozani et al., 1998). Recently, SF3B1 mutations have been identified in MDS with an intriguingly high frequency in the subtypes with ring sideroblasts (RS) (Malcovati et al., 2011; Papaemmanuil et al., 2011; Yoshida et al., 2011; Damm et al., 2012b; Patnaik et al., 2012; Visconte et al., 2012). Although the pathological role of SF3B1 mutations in MDS remains unknown, the detection of the SF3B1 mutation is helpful to the molecular diagnosis of MDS. In the present study, we established a sensitive, reliable, and rapid scanning method for detecting SF3B1 mutations using a high-resolution melting analysis (HRMA) technique.

Materials and Methods

Patient samples and DNA extraction

This study was approved by the Ethics Committee Board of the Affiliated People's Hospital of Jiangsu University. Bone marrow samples were collected from 92 patients with primary MDS after informed written consent. These patients underwent diagnosis according to the 2008 World Health Organization classification (Vardiman et al., 2009). The mononuclear cells were separated by density-gradient centrifugation using Ficoll. Genomic DNA was extracted using a Genomic DNA Purification Kit (Gentra). The peripheral mononuclear cells from 100 healthy individuals were used as controls.

Primer design and PCR conditions

Three pairs of primers were designed to amplify the five common mutations (K700, K666, H662, R625, and E622) in the SF3B1 gene with LightScanner primer design software v1.0 (Idaho Technology). The sequences of these primers are shown as follows: 5′-GTCTGGCTACTATGATCTCTACC-3′ (forward, E622/R625), 5′-AAAAGCTCTAGCTGTTGTGT-3′ (reverse, E622/R625); 5′-GAAGTCCTGGCAAGCGA-3′ (forward, H662/K666), 5′-ACTTACCATGTTC AATGATTTCAACTA-3′ (reverse, H662/K666); 5′-TGTAGGTCTTGTGGATGA GC-3′ (forward, K700), 5′-GCCAAAGCACTGATGGT-3′ (reverse, K700). The sizes of PCR products were 83, 119, and 51 bp, respectively. PCR was carried out in a volume of 25 μL containing 50 ng genomic DNA, 1× PCR buffer (Invitrogen), 0.25 mM of each dNTP, 2.5 mM of MgCl₂, 0.8 μM of both forward and reverse primers, 0.8 μM of internal oligonucleotide calibrators (Qian et al., 2010), 1× LCgreen Plus (Idaho Technology), and 1 U Taq polymerase (MBI Fermentas). A 7300 Thermo cycler (Applied Biosystems), consisting of 95°C for 5 min, followed by 40 cycles, including 94°C for 30 s, 60°C for 30 s, and 72°C for 30 s, was used for PCRs.

High-resolution melting analysis

After PCR, SF3B1 mutations were detected by the HRMA using the LightScanner^™ platform (Idaho Technology). Plates were heated from 55°C up to 95°C with a ramp rate of 0.10°C/s. The fluorescence was collected, and the melting curve analysis was performed using the LightScanner software package with CALL-IT^® software (Idaho Technology). Melting profiles were calibrated by internal oligonucleotide controls, and then normalized, grouped, and displayed as fluorescence-versus-temperature plots or subtractive difference plots (−df/dt vs. T).

DNA sequencing

To confirm the results of the HRMA, a separate PCR was carried out to generate a larger product (867 bp) using the primers 5′-TTTAGGCTGCTGGTCT-3′ (forward) and 5′-AAGGTAATTGGTGGATT-3′ (reverse). PCR conditions were similar with that for the HRMA. PCR products were directly sequenced on both strands using an ABI 3730 automatic sequencer.

Statistical analyses

The SPSS 17.0 software package was used for the analysis of clinical data. The difference of continuous variables between groups was compared using Mann-Whitney's U-test. The difference of categorical variables between patient groups was compared using the chi-square analysis or Fisher's exact test. A statistical significance was indicated by two-tailed p-values<0.05.

Results

Purified plasmid DNA cloned with an SF3B1 K700E mutant and wild type was generated from one patient with MDS identified with a heterozygous K700E mutation by sequencing. The sensitivity of the HRMA was evaluated by analyzing plasmid DNA with different concentrations of the K700E mutant diluted by wild type (0%, 1%, 2%, 5%, 10%, 25%, 50%, and 100% mutant). The sensitivity test was carried out in quadruplicate to ensure the reproducibility of the HRMA. The K700E mutation could be easily distinguished with the maximal sensitivity of 5% in a background of wild-type DNA (Fig. 1), higher than that of the direct DNA sequencing (25%) (Fig. 2).

FIG. 1.

Sensitivity of the high-resolution melting analysis (HRMA) for detecting the SF3B1 K700E mutation. 1: 0%, 1% and 2% mutant; 2: 5% mutant; 3: 10% mutant; 4: 25% mutant; 5: 50% mutant; 6: 100% mutant. The K700E mutation was identified at the maximal sensitivity of 5%.

FIG. 2.

Sensitivity of DNA sequencing for detecting the SF3B1 K700E mutation. The maximal sensitivity of 25% was obtained. Straight line shows the mutated codon; arrow shows the mutation site.

Out of 92 patients with MDS, 5 (5.4%) cases were identified with heterozygous SF3B1 mutations using the HRMA, for the presence of obviously complex, gradual melting curve shapes. The identical judgment was interpreted by two blinded investigators. Subsequently, direct DNA sequencing confirmed the five mutations as three K700E (c.2098A>G, AAA→GAA), one H662Q (c.1986C>G, CAC→GAC), and one K666M (c.1997A>T, AAG→ATG), respectively. The representative melting curves and sequence chromatograms of SF3B1 mutations are shown in Figures 3 and 4. The SF3B1 mutation was not detected in all 100 controls (p=0.024).

FIG. 3.

Representative results of the HRMA for detecting SF3B1 mutations in myelodysplastic syndromes. (A) Normalized melting curves of the K666M/H662Q mutation; (B) normalized melting peaks of the K666M/H662Q mutation; (C) normalized melting curves of the K700E mutation; (D) normalized melting peaks of the K700E mutation.

FIG. 4.

Results of DNA sequencing of SF3B1 mutations. (A) K666M mutation; (B) H662Q mutation; (C) K700E mutation; straight line shows the mutated codon; arrow shows the mutation site.

The clinical features of 92 patients with MDS are listed in Table 1. SF3B1 mutations were more frequently found in the subtypes of refractory anemia with ring sideroblasts (RARS) and refractory cytopenia with multilineage dysplasia and ring sideroblasts (RCMD-RS) than the other categories of MDS (33% vs. 2%, p=0.006). No significant differences in age, gender, blood parameters, and overall survival were observed between patients with and without mutations (p>0.05). According to the karyotype classification, all SF3B1 mutations were identified in patients with a favorable and intermediate-risk karyotype. Further, all SF3B1 mutations were classified into low- and intermediate-risk groups according to the IPSS classification. The clinical and hematopoietic parameters of five SF3B1 mutated cases are shown in Table 2.

Table 1.

Distribution of SF3B1 Mutations in Patients with Myelodysplastic Syndromes

	SF3B1 mutation (n=5)	Wild type (n=87)	P
Gender, male/female	2/3	52/35	0.385
Median age at diagnosis, years (range)	53 (50-74)	58 (20-85)	0.737
Median WBC at diagnosis, ×10⁹/L (range)	3.1 (2.0-9.9)	2.7 (0.9-82.4)	0.331
Median hemoglobin at diagnosis, g/L (range)	46 (43-76)	65 (26-128)	0.069
Median platelets at diagnosis, ×19⁹/L (range)	99 (43-1176)	60.5 (1-754)	0.151
WHO, no.			0.001
RA	0	9
RARS	1	0
RCMD	1	33
RCMD-RS	2	6
RAEB-1	1	16
RAEB-2	0	19
Del (5q)	0	3
MDS-U	0	1
Karyotype classification			0.869
Favorable	4	61
Intermediate	1	15
Poor	0	8
No data	0	3
IPSS			0.674
Low	1	8
Int-1	4	53
Int-2	0	17
High	0	6
No data	0	3

WBC, white blood cell count; WHO, World Health Organization; RA, refractory anemia; RARS, refractory anemia with ringed sideroblasts; RCMD, refractory cytopenia with multilineage dysplasia; RCMD-RS, refractory cytopenia with multilineage dysplasia with ringed sideroblasts; RAEB, refractory anemia with excess of blasts; del(5q), MDS with isolated del(5q); MDS-U, myelodysplastic syndrome unclassifiable; IPSS, International Prognostic Scoring System; Int-1: Intermediate-1; Int-2: Intermediate-2.

Table 2.

The Clinical and Hematopoietic Parameters of Five Patients with SF3B1 Mutation

ID	Gender/age (years)	Diagnosis	WBC (×10⁹)	Hemoglobin (g/L)	Platelet (×10⁹)	Karyotype	SF3B1 mutation
1	M/74	RARS	9.9	50	1176	5q	K666M
2	M/51	RCMD-RS	2.0	46	150	N	K700E
3	F/73	RCMD-RS	2.8	76	99	N	K700E
4	F/50	RAEB-1	6.0	45	70	N	H662Q
5	F/53	RCMD	3.1	43	43	+8	K700E

M, male; F, female; N, normal.

Discussion

RNA splicing is a multistep process by a spliceosomal complex assembly that requires one critical component called U2 small nuclear ribonucleoprotein (snRNP), formed by SF3A, SF3B, and the 12S RNA unit (Gozani et al., 1998). SF3B1 is involved in the early stages of spliceosomal assembly, recruiting U2 small nuclear RNA to the branch-point sequence of introns, directly interacting with U2AF, and helping to specify the site of splicing (Maciejewski and Padgett, 2012). Recently, somatic SF3B1 mutations have been found in 20% patients with MDS, particularly in those characterized by RS (57%-68%) (Papaemmanuil et al., 2011; Yoshida et al., 2011), but at a very low frequency in other myeloid hematopoietic malignancies (Papaemmanuil et al., 2011), which was confirmed by several studies subsequently (Malcovati et al., 2011; Damm et al., 2012a; Makishima et al., 2012; Patnaik et al., 2012). Moreover, SF3B1 mutations were related to better overall survival and a lower risk of evolution into AML (Malcovati et al., 2011; Papaemmanuil et al., 2011; Damm et al., 2012a; Makishima et al., 2012). Thus, the detection of SF3B1 mutations may contribute to molecular diagnosis and prognostic prediction.

Currently, direct DNA sequencing is used to detect SF3B1 mutations (Malcovati et al., 2011; Papaemmanuil et al., 2011; Yoshida et al., 2011; Damm et al., 2012a; Makishima et al., 2012). In this study, we established a rapid high-throughput HRMA protocol to detect SF3B1 mutations, which relied on the saturating doubling-stranded DNA-binding dye, LCGreen, included before PCR amplification, and enabled identification of the PCR products based on their different dissociation behaviors. Heterozygotes were identified by a change of the melting curve shape, and different homozygotes were distinguished by a change of the melting temperature. The performance and interpretation time for the HRMA starting from extracted genomic DNA were 2.5 h, significantly shorter than that for the sequencing method (4 days). Further, the cost could be significantly reduced, as only positive amplicons identified by the HRMA need DNA sequencing. Moreover, the sensitivity of the HRMA in scanning the SF3B1 mutation was 5%, significantly higher than that of DNA sequencing (25%). Additionally, quadruplicate analyses showed a good reproducibility. We further scanned SF3B1 mutations in 92 patients with MDS using the HRMA and identified SF3B1 mutations in 5 (5.4%) cases, which were confirmed by DNA sequencing. Although the frequency of SF3B1 mutations in our cohort was lower than those reported previously (Malcovati et al., 2011; Papaemmanuil et al., 2011; Yoshida et al., 2011; Damm et al., 2012a; Makishima et al., 2012), the discrepancy may caused by the small size of MDS with RS, but not by the methodology. It is also possible that the mutations in other sites were not be detected in this study. A consistent finding was that SF3B1 mutations were more prevalent in RARS and RCMD-RS.

In summary, we established a rapid, reproducible, sensitive, and a high-throughput HRMA assay for the scanning of SF3B1 mutations. This HRMA assay should be more useful in the identification of SF3B1 mutations in clinical diagnostic laboratories.

Footnotes

Acknowledgments

This study was supported by the Science and Technology Special Project in Clinical Medicine of Jiangsu Province (BL2012056), the National Natural Science foundation of China (81270630), the 333 Project of Jiangsu Province (BRA2011085), the Natural Science foundation of Jiangsu Province (BK2009206), and the Social Development Foundation of Zhenjiang (SH2010015 and SH2011056).

Author Disclosure Statement

No competing financial interests exist.

References

Baralle

, Baralle

. 2005. Splicing in action: assessing disease causing sequence changes. J Med Genet, 42:737-748.

Bejar

, Levine

, Ebert

. 2011. Unraveling the molecular pathophysiology of myelodysplastic syndromes. J Clin Oncol, 29:504-515.

Corey

, Minden

, Barber

et al. 2007. Myelodysplastic syndromes: the complexity of stem-cell diseases. Nat Rev Cancer, 7:118-129.

Damm

, Kosmider

, Gelsi-Boyer

et al. 2012a. Mutations affecting mRNA splicing define distinct clinical phenotypes and correlate with patient outcome in myelodysplastic syndromes. Blood, 119:3211-3218.

Damm

, Thol

, Kosmider

et al. 2012b. SF3B1 mutations in myelodysplastic syndromes: clinical associations and prognostic implications. Leukemia, 26:1137-1140.

David

, Manley

. 2010. Alternative pre-mRNA splicing regulation in cancer: pathways and programs unhinged. Genes Dev, 24:2343-2364.

Ernst

, Chase

, Score

et al. 2010. Inactivating mutations of the histone methyltransferase gene EZH2 in myeloid disorders. Nat Genet, 42:722-726.

Gozani

, Potashkin

, Reed

. 1998. A potential role for U2AF-SAP 155 interactions in recruiting U2 snRNP to the branch site. Mol Cell Biol, 18:4752-4760.

Langemeijer

, Kuiper

, Berends

et al. 2009. Acquired mutations in TET2 are common in myelodysplastic syndromes. Nat Genet, 41:838-842.

10.

Ley

, Ding

, Walter

et al. 2010. DNMT3A mutations in acute myeloid leukemia. N Engl J Med, 363:2424-2433.

11.

Maciejewski

, Padgett

. 2012. Defects in spliceosomal machinery: a new pathway of leukaemogenesis. Br J Haematol, 158:165-173.

12.

Makishima

, Visconte

, Sakaguchi

et al. 2012. Mutations in the spliceosome machinery, a novel and ubiquitous pathway in leukemogenesis. Blood, 119:3203-3210.

13.

Malcovati

, Papaemmanuil

, Bowen

et al. 2011. Clinical significance of SF3B1 mutations in myelodysplastic syndromes and myelodysplastic/myeloproliferative neoplasms. Blood, 118:6239-6246.

14.

Mardis

, Ding

, Dooling

et al. 2009. Recurring mutations found by sequencing an acute myeloid leukemia genome. N Engl J Med, 361:1058-1066.

15.

Pajares

, Ezponda

, Catena

et al. 2007. Alternative splicing: an emerging topic in molecular and clinical oncology. Lancet Oncol, 8:349-357.

16.

Papaemmanuil

, Cazzola

, Boultwood

et al. 2011. Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. N Engl J Med, 365:1384-1395.

17.

Patnaik

, Lasho

, Hodnefield

et al. 2012. SF3B1 mutations are prevalent in myelodysplastic syndromes with ring sideroblasts but do not hold independent prognostic value. Blood, 119:569-572.

18.

Qian

, Lin

, Yao

et al. 2010. Rapid detection of JAK2 V617F mutation using high-resolution melting analysis with LightScanner platform. Clin Chim Acta, 410:2097-2100.

19.

Vardiman

, Thiele

, Arber

et al. 2009. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood, 114:937-951.

20.

Visconte

, Makishima

, Jankowska

et al. 2012. SF3B1, a splicing factor is frequently mutated in refractory anemia with ring sideroblasts. Leukemia, 26:542-545.

21.

Yoshida

, Sanada

, Shiraishi

et al. 2011. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature, 478:64-69.