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The use of whole-genome sequencing as an alternative diagnostic tool for cytogenetic analysis in myeloid cancers

Jun 18, 2021
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The identification of genomic mutations by molecular analysis is vital to determine diagnosis, prognosis, and treatment in patients with acute myeloid leukemia (AML) or myelodysplastic syndromes (MDS).1 Detection of clinically significant genomic mutations, such as chromosomal rearrangements, gene amplifications and deletions, and single-nucleotide changes, form the basis for the AML genomic classification system of the World Health Organization. Next-generation sequencing (NGS) has been integrated into routine clinical practice; however, some reoccurring AML mutations, including CEBPACALR, and FLT3, have been difficult to sequence with NGS.2 Whole-genome sequencing (WGS) has allowed for the extrapolation of genetic information and subsequent risk stratification from limited amounts of DNA.1

Duncavage and colleagues recently published a comparative study in The New England Journal of Medicine that analyzed the efficiency of WGS in providing rapid and accurate genomic profiling for diagnostic risk stratification, compared to conventional cytogenetic analysis, in patients with AML or MDS.1 This review summarizes the key findings.

Study design

  • A total of 263 samples were collected from patients with a known or suspected diagnosis of AML or MDS who were seen at the Washington University School of Medicine, St Louis, US (see Table 1 for patient characteristics) 
  • Retrospective samples: 146
    • Samples collected from patients with AML: 107
      • Successful cytogenic analysis: 87
      • Unsuccessful cytogenic analysis: 20
      • Underwent targeted sequencing: 15
    • All 39 samples collected from patients with MDS had successful cytogenetic analysis
  • Prospective samples: 117
    • Samples collected from patients with AML: 68
      • Successful cytogenic analysis: 64
      • Unsuccessful cytogenic analysis: 4
      • Underwent targeted sequencing: 62
    • A total of 38 out of 42 samples collected from patients with MDS had successful cytogenetic analysis, and 35 underwent targeted sequencing
  • Patients were categorized into European LeukemiaNet (ELN) or International Prognostic Scoring System–Revised (IPSS-R) risk groups according to cytogenetic, molecular, and whole-genome sequencing results  
  • Confirmatory analysis using FISH (fluorescence in situ hybridization), PCR (polymerase chain reaction), chromosomal microarray analyses, and RNA-sequencing was conducted to compare the findings from WGS cytogenetic analysis

Table 1. Patient characteristics* 

Characteristics

Retrospective cohort

Prospective cohort

All study patients

Patients, N

146

117

Number of patients with successful cytogenetic analysis

126

109

Patients with AML

Patients, n

107

68

Mean age, years

53.7

60.6

Female sex, %

44

44

ELN genetic risk group, n

            APL with t(15;17)(q22;q21)/PML–RARA

5

5

            Favorable risk

28

19

                        t(8;21)(q22;q22.1)/RUNX1RUNX1T1

6

1

                        inv(16)(p13.1q22) or t(16;16)(p13.1;q22)/CBFBMYH11

11

2

                        NPM1c without FLT3-ITD or with FLT3-ITDlow

8

15

                        Biallelic CEBPA

3

1

            Intermediate risk

22

10

                        t(9;11)(p21;q23)/KMT2AMLLT3

1

1

                        wt NPM1 without FLT3-ITD or with FLT3-ITDlow

11

6

                        NPM1c with FLT3-ITD or FLT3-ITDhigh

7

3

            Adverse

20

27

                        Complex karyotype or mutated TP53

13

13

                        t(v;11q23.3)/KMT2A rearranged

3

0

                        inv(3)(q21.3q26.2) or t(3;3)(q21.3;26.2)/GATA2MECOM

0

2

                        Chromosome 5 deletion, del(5q), or chromosome 7 deletion

2

3

                        wt NPM1 with FLT3-ITD or FLT3-ITDhigh

2

3

                        Mutated RUNX1 or ASXL1

0

6

            Undetermined

32

7

Patients with MDS

Patients, n

39

42

Mean age, years

59.8

68.9

Female sex, %

44

29

IPSS-R risk category, n

            Very good

1

2

            Good

11

17

            Intermediate

10

3

            Poor

4

5

            Very poor

13

6

            Undetermined

0

9

AML, acute myeloid leukemia; APL, acute promyelocytic leukemia; ELN, European LeukemiaNet; MDS, myelodysplastic syndromes; IPSS-R, International Prognostic Scoring System–Revised; wt, wild type.
*Data from Duncavage et al.1

Results

  • WGS had a sensitivity of 100% compared to conventional cytogenetic analysis and targeted gene sequencing for recurrent translocations
  • WGS was able to identify structural variants that were previously cytogenetically difficult in 13 patients, which were all confirmed with orthogonal methods:
    • Chromosomal translocations involving the inv(16)(p13.1q22) fusion gene CBFB–MYH11 (n = 2)
    • t(7;21)(p22;q22) fusion gene USP42–RUNX1 (n =1)
    • Rearrangements involving KMT2A (n = 10)
  • WGS was able to detect 100% of the clonal copy-number alterations, using data from conclusive karyotyping-confirmed patients (n =143)
  • WGS was able to identify 21 new copy-number aberrations in 14 patients, 12 of which were confirmed by other testing
  • WGS identified copy-number alterations in 13 more patients that were previously classified as inconclusive by cytogenetic testing. In total, an additional 17% of patients were identified as having new structural variants that had not been detected by cytogenetic testing
  • In the prospective cohort, WGS analysis revealed new genetic alterations that were absent in the karyotype testing or reported by FISH in 25% of patients
    • These alterations included complex chromosomal rearrangements (n = 5)
    • New copy-number abnormalities with complex karyotype (n = 4)
    • An additional four patients were identified with cytogenetic abnormalities using WGS analysis vs cytogenetic analysis
  • A total of 15% of patients were reclassified to another risk group, different from the one that was based on conventional analysis, using data from WGS and a PCR assay for FLT3-ITD
  • Similar results were seen in the 42 prospective patients with MDS, as 29% of these patients had been categorized as inconclusive on cytogenetic analysis and were reclassified using WGS; 21% were reassigned to a new IPSS-R risk group
  • From the prospective cohort (n = 117), 24.8% of new genetic information was identified using WGS, and 16.2% of patients were reassigned to a different risk category when testing using WGS vs conventional testing
  • WGS was better at identifying patients with adverse risk compared to conventional testing; WGS had a hazard ratio (HR) for mortality of 0.32 (95% confidence interval [CI], 0.11–0.92) vs a HR of 0.66 (95% CI, 0.17–1.05) for conventional risk-group testing
  • Survival analysis demonstrated that risk predictions based on WGS correlated with patient outcomes; the overall survival was significantly longer in 21 patients classified as intermediate or favorable risk (median survival, 20.5 months; 95% CI, 5.6–38.8) compared with the six patients that were classified with adverse risk (median survival, 3.3 months; 95% CI, 1.7–18.9; p = 0.03) (HR, 0.29; 95% CI, 0.09–0.94)
  • WGS analysis was able to identify all 40 recurrent translocations and 91 copy- number aberrations that had been detected by cytogenetic testing

Conclusion

This study demonstrates the potential of WGS to add prognostic value by expanding risk stratification. WGS could be used to quickly examine the entire genome for mutations and structural alterations with minimal DNA samples, thus increasing the diagnostic potential compared with conventional cytogenetic analysis with greater efficiency in risk stratification. However, larger confirmatory studies with more patients are needed to fully establish the clinical performance of WGS.

  1. Duncavage E, Schroeder M, O’Laughlin M, et al. Genome sequencing as an alternative to cytogenetic analysis in myeloid cancers. N Engl J Med. 2021;384(10):924-935. DOI: 1056/NEJMoa2024534.
  2. Rosenthal SH, Gerasimova A, Ma C, et al. Analytical validation and performance characteristics of a 48-gene next-generation sequencing panel for detecting potentially actionable genomic alterations in myeloid neoplasms. PLoS One. 2021;16(4):e0243683. DOI: 1371/journal.pone.0243683.

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