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Adding ex vivo chemosensitivity assay to mutational profiling improves the ability to predict the outcome of patients with AML

Mar 13, 2020

Refractory and relapsed acute myeloid leukemia (AML) remain big clinical challenges contributing to patient mortality. Therefore, early identification of patients at high risk of relapse is important. The presence of cytogenetic and molecular aberrations at diagnosis and poor response to therapy are known to be associated with increased risk of AML relapse. They are particularly useful when deciding whether chemotherapy or hematopoietic stem cell transplantation (HSCT) should be used. 1Based on the prognostic relevance of many recurrent somatic mutations, mutational profiling became incorporated as a prognostic factor in AML European LeukaemiaNet (ELN) guidelines. 2

Tailoring novel drugs targeting genetic mutations to patients with a particular sensitivity could improve clinical responses. The limited number of biomarkers associated with drug sensitivity makes predicting patient response before treatment difficult. 3Individual in vitroand ex vivosensitivity assays have been used in the past but are mainly restricted to clinical trials. 4,5

Previously reported data demonstrate that mutations in FLT3, IDH1, KIT, and RAShave an impact on drug sensitivity. 3However, it remains unclear whether combining molecular profiling with ex vivochemosensitivity profiling could improve the potential to predict patients that will respond. The clinical relevance of a such combined strategy was explored in patients with AML in a study by Esther Onechaand colleagues, recently published in the  British Journal of Haematology. 6

Study design

Patients with AML (non-M3 type) from a multicentre, non-interventional cohort study of the Programa espaňol de tratamientos hematológicos (PETHEMA) group were profiled using next-gene sequencing (NGS) and ex vivochemosensitivity flow cytometry assay. In total, 57 (77%) patients were treated with a 3 + 7 schedule and 17 (23%) with fludarabine plus low-dose cytarabine (FLUGA scheme).

Mutational profiling

Patients’ DNA samples (n = 190) from bone marrow or peripheral blood were used in the analysis. All samples were genetically characterized at the time of diagnosis by G-band karyotyping and fluorescence in situhybridization. Additionally, samples were examined for 32 most commonly mutated genes in myeloid diseases, NMP1mutations, and internal duplications.

Chemosensitivity profiling

Assessment of live pathological cells was performed on cells extracted from bone marrow samples after incubation with varied concentrations of therapeutic agents:

  • Cytarabine (n = 74 patients)
  • Idarubicin (n = 74)
  • Fludarabine (n = 73)
  • Clofarabine (n = 54)
  • Daunorubicin (n = 48)
  • Mitoxantrone (n = 49)
  • Etoposide (n = 42)
  • Amsacrine (n = 29)
  • 6-thioguanine (n = 24)
  • Decitabine (n = 23)

Main findings

  • In total, samples from 190 patients were analyzed by NGS and 74 by ex vivo  assay. Patients characteristics are presented in Table 1

Table 1. Selected patient characteristics 6

AML, acute myeloid leukemia; CR, complete remission; ELN, European leukaemiaNet (2010); FLUGA, fludarabine + cytarabine; HSCT, hematopoietic stem cell transplantation; MDS, myelodysplastic syndromes; NGS, next generation sequencing; PR, partial remission; tAML, treatment-related AML

 

NGS assay

(n = 190)

Ex vivoassay

(n = 74)

Gender, %

Male

Female

 

53

47

 

60

40

Median age at diagnosis (range), years

57 (18–91)

58 (19–91)

Median blasts at diagnosis (range), %

63 (4–99)

67 (20–99)

AML origin

De novo

AML-MDS

tAML

 

80

11

9

 

84

11

5

Cytogenetics Risk Group ELN 2010

Low

Intermediate

High

 

7

69

24

 

15

63

22

HSCT, %

Autologous

Allogenic

None

 

24

17

59

 

20

20

60

Induction treatment, %

(3 + 7) scheme

Azacitidine

Decitabine

FLUGA scheme

Support

 

81

1

0.5

14

3.5

 

77

23

Response to induction, %

CR

PR

Resistance

Death

 

58

16

10

16

 

57

26

17

Median follow-up (range), months

26 (1–150)

20 (0.5–70)

  • Overall, 264 non-recurrent somatic variants were detected in 86% of patients, with single nucleotide variants (SNVs) contributing 82.2% and insertion/deletions 17.8%
    • 19.7% of variants were predicted by algorithms to be pathogenic
    • 57.2% were known to be associated with cancer
  • Most frequently mutated genes included
    • NPM1(in 28.6% of patients)
    • DNMT3A(26.5%)
    • TET2(21.1%)
    • NRAS(19.5%)
    • FLT3(25%)
  • There was a significant association between the presence of specific mutations and OS (overall survival; based on data for n = 185), with an increased risk of death (HR 3.29; 95% CI, 1.78–6.08; p < 0.0001) in patients harboring at least one mutation in any of the following genes
    • EZH2(HR 2.44; 95% CI, 1.27–4.86; p = 0.011)
    • KMT2A(HR 2.21; 95% CI, 1.20–4.05; p = 0.011)
    • U2AF1(HR 3.19; 95% CI, 1.47–6.88; p = 0.003)
    • TP53(HR 2.92, 95% CI, 1.78–4.79; p < 0.001)
  • The cytogenetic status (by ELN 2010 criteria) did not show an impact on OS in the complete or ex vivostudy cohorts (p = 0.08 and p = 0.88, respectively)
  • There was a significant correlation between the ex vivoclassification and clinical response to induction therapy
    • Classification as resistant by the assay agreed with lack of clinical response in 63% of patients
    • Classification as sensitive by the assay aligned with clinical response in 74% of patients
    • Overall correlation was correct in 69% of patients (p = 0.001)
  • Presence of certain mutations was associated with ex vivodrug sensitivity while others were associated with resistance. The summary is presented in Table 2

Table 2. Mutations associated with increased and decreased drug sensitivity 6

Mutated gene

Drug

p value (n)

Mutations associated with increased sensitivity

KMT2A

Darubicin

Fludarabine

0.001 (74)

0.044 (73)

FLT3

FLT3-ITD

FLT3-SNV

 

Daunorubicin

6-thioguanine

 

0.007 (48)

0.044 (24)

NPM1

Mitoxantrone

Amsacrine

0.029 (49)

0.031 (29)

Mutations associated with resistance

TP53

Fludarabine

Mitoxantrone

0.044 (73)

0.045 (49)

U2AF1

Amsacrine

6-thioguanine

0.032 (29)

0.047 (24)

IDH2

Cytarabine

0.049 (74)

EPOR

Cytarabine

0.043 (74)

  • In total, 23 patients were identified as multi-drug resistant, with increased risk of death (HR 2.09; 95% CI, 1.14–3.82; p = 0.017)
  • None of the analyzed genes, AML types, prognoses, or cytogenetic risks were found to associate with the multi-drug resistance
  • Mutated KMT2Awas unique to a multi-drug sensitive group
  • Multivariable analysis revealed a higher risk of mortality with
    • the multi-drug resistant profile (HR 2.09; 95% CI, 1.14–3.82; p = 0.017)
    • KMT2Amutations (HR 3.49; 95% CI, 1.16–10.45; p = 0.026)
    • TP53mutations (HR 3.23; 95% CI, 1.36–7.70; p = 0.008)
  • A prognostic index was derived that is able to predict an overall risk custom score based on the integrated information from mutational and chemosensitivity profiles
    • Patients with an overall adverse profile had a greater risk of death than patients with a favorable profile (HR 3.40; 95% CI, 1.69–6.84; p < 0.001)
      • Patients with the adverse mutational and multi-drug resistant profiles (HR 4.82; 95% CI, 1.76–13.25; p = 0.002)
      • Patients with the adverse mutational profile only compared to patients with a favorable mutational profile (HR 4.19; 95% CI, 1.89–9.27; p < 0.001)
      • Patients with the multi-drug resistance profile only compared to patients without the multi-drug resistance profile (HR 2.58; 95% CI, 1.24–5.34; p = 0.011)

Conclusion

The authors developed a new score, combining information about an innate resistance to chemotherapy with mutational analysis. As the impact of somatic mutation present in malignant cells, on drug resistance is still unknown, the ex vivochemosensitivity assay could help to address that. If validated in a prospective study, the findings could improve the risk stratification of patients with AML and aid in more accurately predicting outcomes of chemotherapy at an early stage in clinical care.

  1. Onecha E. et al.A novel deep targeted sequencing method for minimal residual disease monitoring in acute myeloid leukemia. Haematologica. 2019 Feb; 104(2):288–296. DOI: 10.3324/haematol.2018.194712
  2. Papaemmanuil E.,  Gerstung M. et al.Genomic Classification and Prognosis in Acute Myeloid Leukemia. N Engl J Med. 2016 Jun 9; 374(23):2209–2221. DOI: 10.1056/NEJMoa1516192
  3. Tyner J.W. et al. Functional genomic landscape of acute myeloid leukaemia. Nature. 2018 Oct 17; 562(7728):526–531. DOI: 10.1038/s41586-018-0623-z
  4. Swords R.T. et al.Ex-vivo sensitivity profiling to guide clinical decision making in acute myeloid leukemia: A pilot study. Leuk Res. 2018 Jan; 64:34–41. DOI: 10.1016/j.leukres.2017.11.008
  5. Martínez-Cuadrón D. et al.A precision medicine test predicts clinical response after idarubicin and cytarabine induction therapy in AML patients. Leuk Res. 2019 Jan; 76:1–10. DOI: 10.1016/j.leukres.2018.11.006
  6. Onecha E. et al.Improving the prediction of acute myeloid leukaemia outcomes by complementing mutational profiling with ex vivo chemosensitivity. Br J Haematol. 2020 Feb 18. DOI: 10.1111/bjh.16432