# Lomustine is beneficial to older AML with ELN2017 adverse risk profile and intermediate karyotype: a FILO study

Laetitia Largeaud, Pascale Cornillet-Lefebvre, Jean-François Hamel, Pierre-Yves Dumas, Naïs Prade, Stéphanie Dufrechou, Julien Plenecassagnes, Isabelle Luquet, Odile Blanchet, Anne Banos, et al.

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## ► To cite this version:

Laetitia Largeaud, Pascale Cornillet-Lefebvre, Jean-François Hamel, Pierre-Yves Dumas, Naïs Prade, et al. Lomustine is beneficial to older AML with ELN2017 adverse risk profile and intermediate karyotype: a FILO study. Leukemia, 2021, 35 (5), pp.1291-1300. 10.1038/s41375-020-01031-1. hal-03623649

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# Lomustine is beneficial to older AML with ELN2017 adverse risk profile and intermediate karyotype: a FILO study

Laetitia Largeaud¹, Pascale Cornillet-Lefebvre², Jean-François Hamel³, Pierre-Yves Dumas⁴, Naïs Prade¹,
Stéphanie Dufrechou¹, Julien Plenecassagnes⁵, Isabelle Luquet¹, Odile Blanchet⁶, Anne Banos⁷, Marie C. Béné⁸,
Marc Bernard⁹, Sarah Bertoli¹⁰, Caroline Bonmati¹¹, Luc Matthieu Fornecker¹², Romain Guièze¹³, Lamya Haddaoui¹⁴,
Mathilde Hunault¹⁵, Jean Christophe Ianotto¹⁶, Eric Jourdan¹⁷, Mario Ojeda¹⁸, Pierre Peterlin¹⁹, Norbert Vey²⁰,
Hacene Zerazhi²¹, Hicheri Yosr²⁰,²², Ariane Mineur²³,²⁴, Jean-Yves Cahn²⁵, Norbert Ifrah¹⁵, Christian Récher¹⁰,
Arnaud Pigneux⁴, Eric Delabesse¹, on behalf of the French Innovative Leukemia Organization (FILO)

We previously reported the benefit of lomustine addition to conventional chemotherapy in older acute myeloid leukemias with nonadverse chromosomal aberrations in the LAM-SA 2007 randomized clinical trial (NCT00590837). A molecular analysis of 52 genes performed in 330 patients included in this trial, 163 patients being treated with lomustine in combination with idarubicin and cytarabine and 167 without lomustine, identified 1088 mutations with an average of 3.3 mutations per patient. NPM1, FLT3, and DNMT3A were the most frequently mutated genes. A putative therapeutic target was identified in 178 patients (54%). Among five molecular classifications analyzed, the ELN2017 risk classification has the stronger association with the clinical evolution. Patients not treated with lomustine have an expected survival prognosis in agreement with this classification regarding the overall and event-free survivals. In strong contrast, lomustine erased the ELN2017 classification prognosis. The benefit of lomustine in nonadverse chromosomal aberrations was restricted to patients with RUNX1, ASXL1, TP53, and FLT3-ITDhigh/NPM1WT mutations in contrast to the intermediate and favorable ELN2017 patients. This post-hoc analysis identified a subgroup of fit elderly AML patients with intermediate cytogenetics and molecular markers who may benefit from lomustine addition to intensive chemotherapy.

## Introduction

The incidence of acute myeloid leukemia (AML) increases with age, the majority of patients being diagnosed after 60

years. In these older patients, the prognosis is poor, with a median overall survival (OS) usually shorter than 1 year [1, 2]. In patients deemed fit for intensive chemotherapy, no significant improvement in outcome has been achieved except with CPX-351, a dual-drug liposomal encapsulation of cytarabine and daunorubicin recently approved for secondary AML, whereas the combination of the BCL2 inhibitor venetoclax with azacitidine improved outcome of patients unfit for chemotherapy [3]. AML have been classified initially according to cytogenetics profiles into three risk categories: favorable, intermediate, and poor [4]. Favorable cytogenetics abnormalities (core binding factor leukemias and t(15;17) translocation) have long been established as a key predictor for improved clinical outcome as opposed to patients with complex or monosomal karyotypes. The intermediate cytogenetics risk group, representing ~60% of the patients, forms a heterogeneous group [4]. Molecular analysis, especially in this latter group, is highly complementary to cytogenetics.

These authors contributed equally: Laetitia Largeaud, Pascale Cornillet-Lefebvre, Jean-François Hamel, Christian Récher, Arnaud Pigneux, Eric Delabesse

Members of the French Innovative Leukemia Organization (FILO) are listed below Acknowledgements.

Extended author information available on the last page of the article

<footnote>☑ Eric Delabesse
delabesse.eric@iuct-oncopole.fr</footnote>
Mutations in epigenetic modifiers, such as *ASXL1*, *DNMT3A*, or *TET2*, are early oncogenic events, frequently found in clonal hematopoiesis [5–7]. Moreover, *RUNX1*, *ASXL1*, and *TP53* mutations are poor prognostic markers in patients with intermediate cytogenetics risk and have been recently included in the European LeukemiaNet (ELN) 2017 prognostic classification for AML, which combines cytogenetics and molecular biology [8].

We previously reported the benefit of the addition of lomustine (also known as chlorethyl-cyclohexyl-nitrosourea) to conventional chemotherapy (idarubicin and cytarabine) for older patients with de novo AML and non-adverse cytogenetics in a randomized clinical trial that enrolled 459 patients [9]. Lomustine is an alkylating agent with a significant antileukemic activity linked to DNA damage and/or impairment of cell replication [10–12]. In the LAM-SA 2007 trial, its addition significantly improved the complete response (CR) or CR with incomplete recovery rate (84.7% vs. 74.9%, *p* = 0.01) and reduced the cumulative incidence (CI) of relapse (41.2% vs. 60.9%, *p* = 0.003) resulting in improved 2-year event-free (41% vs. 26%, *p* = 0.01) and OS (56% vs. 48%, *p* = 0.02).

Here, we report the molecular analysis of this prospective, randomized cohort of older AML patients selected for intensive chemotherapy. The specific profile of the mutations was investigated regarding their functional pathways. Finally, the impact of lomustine in this molecular landscape was investigated through five molecular classifications of AML defined mainly from younger patients [8, 13–16]. A strong benefit to the addition of lomustine was identified in a subset of patients in the ELN2017 adverse risk group [8] with intermediate cytogenetics, bearing *RUNX1*, *ASXL1*, *TP53*, and *FLT3-ITD*<sup>high</sup>/*NPM1*<sup>WT</sup> (RATFIN) mutations.

# Materials and methods

## Patients

The LAM-SA 2007 trial was registered at clinicaltrials.gov as NCT00590837. It involved 32 clinical centers of the French Innovative Leukemia Organization (FILO) study group that enrolled 459 patients from February 2008 to December 2011. All patients were older than 60 years and had been diagnosed with de novo AML. Patients had to be considered fit without adverse cytogenetics (defined after the analysis of 20 mitosis at least if no abnormal clone was identified) [17], promyelocytic leukemia nor isolated granulocytic sarcoma. The trial was conducted according to the Declaration of Helsinki and approved by the ethical committee of Bordeaux University Hospital and the Agence Française de Sécurité Sanitaire des Produits de Santé. All patients provided written informed

consent at enrollment. Before initiation of the induction chemotherapy course, patients were registered and randomized to receive or not lomustine during induction and postinduction treatment phases [9]. The clinical analysis was performed with 424 patients as 35 were excluded, including 14 from a single center by decision of the data and safety monitoring board as a result of noncompliance with the chemotherapy regimen. Ten patients were reclassified to adverse cytogenetics, eight had myelodysplasia, two a Sorror score of 3, and one withdrew its informed consent [9]. Molecular analyses were performed centrally on samples stored at the FILOothèque, FILO tumor cell bank (DC 2009-944). DNA material was available for 330 patients (78%) with no difference of prognosis according to the DNA availability (Fig. S1).

## Molecular analysis

The presence of *FLT3-ITD* was tested as described [18]. Electrophoregrams peaks were quantified using GeneMarker 2.2 (SoftGenetics, State College, PA, USA). *CEBPA* screening was performed by classical Sanger sequencing according to Pabst et al. [19]. Six recurrent and frequent mutations (*ASXL1* exon 12, *DNMT3A* exon 23, *FLT3* exon 20, *IDH1* exon 4, *IDH2* exon 4 and *NPM1* exon 12) were sequenced using next generation sequencing and a multiplex PCR amplicon based library with the following primers: qI_Halo_ASXL1_R634_F2 (CCACCACGGAGTCCTCCT), qI_Halo_ASXL1_R634_R2 (GCCTCACCACCATCACCA), qI_DNMT3A_X23_F1 (CTGGCCAGCACTCACCCT), qI_DNMT3A_X23_R1 (TGTTTAACTTTGTGTCGCTACCT CA), qI_FLT3_X20_F3 (GTTTACCATGATAACGACAC AACAC), qI_FLT3_X20_R3 (GATTGCACTCCAGGA- TAATACACA), qI_IDH1_X4_F1 (GGCTTGTGAGTGG ATGGGTAA), qI_IDH1_X4_R2 (GCATTTCTCAATTT CATACCTTGCTTA), qI_IDH2_X4_F1 (GAAAGATGGC GGCTGCAGT), qI_IDH2_X4_R2 (CACCCTGGCCTACC TGGTC), qI_NPM1_X12_F1 (GAAGTGTTGTGGTTCCT TAAC) and qI_NPM1_X12_R1 (TGGACAACACATTC TTGGCA). The library was sequenced using a MiSeq sequencer (Illumina, San Diego, CA, USA) and Miseq Reagent kit V2 (paired-end sequencing 2×150 cycles). Alignment was performed using BWA aligner and variant calling was performed using FreeBayes and Mutect2 variant callers.

An extended DNA resequencing was performed using a Illumina NextSeq500 and Haloplex HS (Agilent, Santa Clara, CA, USA) targeted on the complete coding regions of 52 genes: *ASXL1*, *ASXL2*, *ATM*, *BCOR*, *BCORL1*, *CBL*, *CCND2*, *CEBPA*, *CSF3R*, *CUX1*, *DDX41*, *DHX15*, *DNMT3A*, *EP300*, *ETV6*, *EZH2*, *FLT3*, *GATA1*, *GATA2*, *IDH1*, *IDH2*, *JAK2*, *KDM5A*, *KDM6A*, *KIT*, *KMT2D*, *KRAS*, *MGA*, *MPL*, *MYC*, *NF1*, *NPM1*, *NRAS*, *PHF6*, *PIGA*, *PPM1D*, *PRPF8*, *PTPN11*, *RAD21*, *RUNX1*, *SETBP1*,
SF3B1, SMC1A, SMC3, SRSF2, STAG2, TET2, TP53, U2AF1, WT1, ZBTB7A, and ZRSR2. Data were processed through two algorithms from GATK (https://software.broa.dinstitute.org/gatk), HaplotypeCaller (scaling accurate genetic variant discovery to tens of thousands of samples, Poplin et al. [20]) and Mutect2 [21]. The mean depth was 2,190. Identified variants were curated manually and named according to the rules of the Human Genome Variation Society (hgvs.org). Molecular data have been stored in the European Nucleotide Archive (https://www.ebi.ac.uk/ena/).

## Statistics

The clinical database was frozen in June 2015 and follow-up was updated in May 2018 [9]. Statistical analyses were conducted according to intention to treat. Categorical data were presented as percentages and compared using Fisher exact tests. Continuous data were presented as mean and standard deviation and compared using Mann and Whitney tests.

The endpoints considered were OS, event-free survival (EFS), CR, and relapse. For mutation impact, OS and EFS were studied using log rank tests for equality of survivor functions and graphically represented using Kaplan-Meier curves. For model evaluation, OS and EFS were studied using Cox models, whereas CR and relapse were studied using Fine and Gray models considering death as a competing event. The prognostic value of each score was assessed through their inclusion as categorical covariates in these models.

The impact of disease severity on lomustine benefit was assessed by considering the interaction between lomustine treatment and each studied score with the allogeneic stem-cell transplantation being introduced as a time-dependent covariate. The global effects of covariates introduced in the model were assessed through likelihood ratio tests and the effects of each modality of covariates were assessed through Wald tests. The proportional hazard assumption was checked through the use of the Schoenfeld residuals analysis. The differential impact of lomustine depending on disease severity was graphically represented using Kaplan-Meier graphs.

All tests were considered as two-sided considering a type I error set to 0.05. All analyses were performed using Stata 13.1 software (StataCorp, College Station, TX, USA).

## Results

### Molecular landscape in older AML patients with nonadverse cytogenetics

A molecular analysis of 52 genes was performed in 330 patients included in the LAM-SA 2007 clinical trial (78% of

<figure><img src="image_3.png" /><figcaption>Fig. 1 LAM-SA 2007 molecular workflow. Patients included in the LAM-SA 2007 clinical trial and analyzed in the present study (K: karyotype).</figcaption></figure>

the cohort). Cytogenetic risk was intermediate for 281 patients (203 normal and 78 other intermediate karyotypes) and favorable for 22 [15 inv(16)/t(16;16) and 7 t(8;21)] (Fig. 1). Mutations were identified in 319 patients with an average of 3.3 mutations per patient (0–12 mutations per patient; Figs. 2 and 3 and Table S1). Eleven patients have no mutation detected, including 3 with inv(16) and 1 with a KMT2A rearrangement.

NPM1, FLT3, and DNMT3A were the most frequently mutated genes (Figs. 2 and 3 and Table S1). NPM1 was mutated in 113 patients (34%, 86 type A, 7 B, and 9 D), FLT3 in 98 (30%, 76 with ITD [1–4 per patient] and 27 with other mutations including 20 of the tyrosine kinase domain), and DNMT3A in 88 (27%, 43 located at the Arg882 hot spot amino acid). Mutations of IDH2 were present in 60 patients (18%, Arg140 codon in 48 patients and Arg172 codon in 12 patients) and of IDH1 in 36 (11%). Additional therapeutic targets were found in 15 patients (TP53 in 8, 2%; JAK2 in 4, 1%; KIT in 3, 1%). Overall, a putative therapeutic target for tyrosine kinase inhibitors, IDH inhibitors or TP53 activators was identified in 178 patients (54%).

### Mutation associations in older AML patients are not random

The mutations detected were analyzed using functional categories (Table S2) [16]. Mutations of DNA methylation genes were the most frequent, occurring in 178 patients (54%; Figs. 3 and 4a). DNMT3A mutations were strongly associated to those of NPM1, IDH1, and IDH2 and strictly separate from EZH2 mutations (Figs. 3 and 4a). IDH1 and IDH2 mutations were strictly separate from TET2 mutations. One patient presented both IDH1 and IDH2 mutations (#308), the former being subclonal to the latter (variant allelic frequencies of 1% and 32%, respectively). In addition, IDH2 and TET2 mutations were strictly separate from DDX41 mutations. Finally for this category, univariate analysis for patients in both study
<figure><img src="image_4.png" /><figcaption>**Fig. 2** LAM-SA 2007 mutation pattern. Number of mutations (gray bars) and patients with mutations (black bars) per gene.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 3** Mutations associations classified as gene categories as defined by Bullinger et al. [16], NPM1, signalization, DNA methylation, splicing, chromatin, transcription factors, cohesin, and antioncogenes. "Other genes" regroups genes not classified in the</figcaption></figure>

eight previous groups (ATM, BCOR, BCORL1, CCND2, CUX1, DDX41, DHX15, MGA, PPM1D, PRPF8, ZBTB7A). NK normal karyotype, IR_Other intermediate karyotype other than normal.

arms showed that *IDH1* mutations were associated with a significantly worse prognosis for OS (*p* = 0.021) and EFS (*p* = 0.019; Fig. S2).

Mutations of genes encoding tyrosine kinases and proteins of the RAS signaling pathway were identified in 51% of the patients, more frequently FLT3 in 98 patients (30%)

and RAS genes in 75 (23%). RAS mutations were secondary events as demonstrated by their very low VAF (Fig. S3). FLT3 mutations were significantly associated with NPM1, WT1, and SMC3 mutations (Figs. 3 and 4b) and leukocytosis (*p* < 0.0001, Fig. S4). FLT3 mutations were strictly separate from DDX41 mutations, NRAS strongly
<figure><img src="image_6.png" /><figcaption>**Fig. 4** Circos plots of gene categories showing the profiles of co-mutations. a DNA methylation genes (DNMT3A, IDH1, IDH2, and TET2). b Signaling genes (CBL, CSF3R, FLT3, JAK2, KIT, KRAS, NRAS, NF1, PTPN11). c NPM1. d Transcription factors genes (CBFB-MYH11 and RUNX1-RUNX1T1 fusion genes; CEBPA, EP300, ETV6, GATA1, GATA2, MYC, RUNX1, SETBP1). e Splicing</figcaption></figure>

genes (SF3B1, SRSF2, U2AF1, ZRSR2). f Chromatin genes (KMT2A fusion gene; ASXL1, ASXL2, EZH2, KDM5A, KDM6A, KMT2D). g Cohesin genes (RAD21, SMC1A, SMC3, STAG2). h Antioncogenes (PHF6, TP53, WT1). i Other genes (ATM, BCOR, BCORL1, CCND2, CUX1, DDX41, DHX15, MGA, PPM1D, PRPF8, ZBTB7A).

separate from STAG2 mutations and KRAS from U2AF1 mutations (Figs. 3 and 4b). FLT3-ITD mutations were not linked to prognosis (OS, *p* = 0.49; EFS, *p* = 0.82; Fig. S5).

NPM1 mutations, present in 34% of the patients, were associated with a significantly better prognosis for OS (*p* = 0.027) and EFS (*p* = 0.020; Fig. S6). These mutations were significantly associated with FLT3, DNMT3A, TET2, IDH2,

and PTPN11 mutations (Figs. 3 and 4c) and leukocytosis (*p* < 0.0001, Fig. S4) but strictly separate from DDX41 and RUNX1 mutations and CBFB-MYH11 fusion gene.

Transcription factors abnormalities were identified in 104 patients (32%), including 82 patients with intermediate cytogenetics (Figs. 3 and 4d). CBFB-MYH11 fusions were strongly associated with RAS and CSF3R mutations. RUNX1
mutations were significantly associated with *EZH2*, *SF3B1*, and *BCORL1* mutations (in contrast to be significantly separate from *BCOR* mutations). *CEBPA* mutations were significantly associated with *TET2* and *STAG2* mutations and separate from *WT1* mutations, although these differences were not significant when the number of CEBPA mutations per patient (19 mono-allelic vs. 10 bi-allelic) was specifically analyzed due to a lack of power (Fig. S7).

Mutations of genes involved in splicing were identified in 82 patients (25%, Figs. 3 and 4e), especially *SRSF2* mutations (15%) that were significantly associated with *ASXL1*, *TET2*, *STAG2*, and *CBL* and separate from *FLT3* mutations.

Chromatin regulators were mutated in 77 patients (23%, Figs. 3 and 4f), especially *ASXL1* (15%). *ASXL1* mutations were strongly associated with *STAG2*, *SRSF2*, *EZH2*, and *ZRSR2* mutations and separate from *FLT3*, *NPM1*, and *DNMT3A* mutations. In univariate analysis, *ASXL1* mutations were associated with a very poor prognosis in our series, especially for EFS ($p = 0.0002$; OS: $p = 0.0069$; Fig. S8).

Cohesin mutations were identified in 61 patients (18%, Figs. 3 and 4g), particularly *STAG2* (12%). These mutations were significantly associated with leukopenia at diagnosis ($p < 0.0001$, Fig. S4) and mutations of *ASXL1*, *SRSF2*, *CEBPA*, *EZH2*, *ETV6*, and *GATA2* and separate from *DNMT3A* and *NRAS* mutations.

Mutations of tumor-suppressor genes were more infrequent, identified in 28 patients (8%, Figs. 3 and 4h), the most frequent being *WT1*. Its mutations were strongly associated with those of *FLT3* and separate from *RAD21* and *CEBPA* mutations. Few patients had *TP53* mutations (2%; Fig. 3), coherent with the exclusion of patient with adverse cytogenetics in this clinical trial.

The remaining genes, not classified in one of the eight categories detailed above, were found mutated in 67 patients (20%, Figs. 3 and 4i), the two most frequently mutated being *BCOR* (8%) and *DDX41* (7%). *BCOR* mutations were strongly associated to *BCORL1* mutations and separate from *NPM1*, *U2AF1*, and *RUNX1* mutations. Patients with *DDX41* mutations had a unique mutation pattern, without mutations of *FLT3*, *NPM1*, *IDH2*, nor *TET2* and were strongly associated with leukopenia at diagnosis ($p < 0.0001$, Fig. S2). They might have had a better prognosis but the small number of cases led to a lack of statistical power (Fig. S9). This group of patients deserves a specific investigation.

## Prognostic significance of molecular classifications in older AML patients

Different molecular classifications for AML have been proposed, either prognostic (Patel et al. [13], Papaemmanuil

et al. [15], Bullinger et al. [16], and ELN2017 [8]) or ontogenic (Lindsley et al. [14]). Their impact in the LAM-SA 2007 clinical trial was evaluated (Fig. S10). The 3-month CI of CR was 85% (95% CI: 81–89%). Papaemmanuil et al.'s and Lindsley et al.'s classifications were significantly associated with variations of CR CI (respectively, $p < 0.0001$ and $p = 0.0257$). The 3- and 5-year CI of relapse (CIR) were respectively 73% (95% CI: 68–77%) and 79% (95% CI: 74–83%). The five classifications were significantly associated with the CIR (ELN2017 [$p < 0.0001$], Bullinger et al. [$p < 0.0001$], Papaemmanuil et al. [$p < 0.0001$], Patel et al. [$p = 0.0047$], and Lindsley et al. [$p = 0.0152$]). The 3- and 5-year EFS were 23% (95% CI: 19–27%) and 17% (95% CI: 13–21%), respectively. The five classifications were also significantly associated with EFS (ELN2017 [$p = 0.0001$], Patel et al. [$p = 0.0006$], Lindsley et al. [$p = 0.0046$], Bullinger et al. [$p = 0.0096$], and Papaemmanuil et al. [$p = 0.0139$]). The 3- and 5-year OS were 39% (95% CI: 35–44%) and 24% (95% CI: 20–29%), respectively. The ELN2017 classification was strongly associated to OS ($p = 0.001$) as well as to, to a lower extent, those of Patel et al. ($p = 0.014$) and Lindsley et al. ($p = 0.050$). Overall, ELN2017 was the best molecular classification to summarize the clinical evolution of the patients included in the LAM-SA 2017 clinical trial.

## Impact of lomustine in the genomic landscape of AML

We evaluated the impact of these five classifications according to the treatment, 163 patients being assigned to arm A (with lomustine) and 167 to arm B (without lomustine). Regarding CR, a significant interaction between the Papaemmanuil classification and lomustine treatment was highlighted ($p < 0.001$), lomustine being significantly associated with a lower CR rate in the CBFB-MYH11 subgroup ($p = 0.002$) and a better CR rate in the IDH2 R172K subgroup ($p < 0.001$). Regarding CIR, a significant interaction between lomustine treatment and the ELN2017 classification was also highlighted ($p = 0.027$). Lomustine is significantly associated with a lower relapse rate in the subset of the ELN2017 adverse group with a nonadverse karyotype, i.e., with RATFIN mutations ($p = 0.001$) but not in the ELN2017 favorable nor intermediate groups ($p = 0.879$ and $0.861$, respectively). A significant interaction with lomustine and the risk of relapse was also highlighted using the Papaemmanuil classification ($p = 0.003$) with a lower relapse rate in the subgroup of AML with mutated chromatin and/or RNA-splicing genes ($p = 0.039$) and a higher relapse rate in patients with *RUNX1-RUNX1T1* fusion gene ($p < 0.001$). A significant interaction with lomustine was also highlighted using the Bullinger classification ($p = 0.005$) with a lower relapse rate
<figure><img src="image_7.png" /><figcaption>**Fig. 5** Clinical responses (OS and EFS) to lomustine (lomustine) according to the ELN2017 risk classification. a ELN2017 risk classification of patients not treated with lomustine. b ELN2017 risk classification of patients treated with lomustine. c RATFIN mutated patients treated or not with lomustine. d ELN2017 intermediate risk classification of patients treated or not with lomustine. e ELN2017 favorable risk classification of patients treated or not with lomustine.</figcaption></figure>

in the chromatin-spliceosome group and *TP53* mutations (*p* = 0.030 and *p* < 0.001, respectively). No interaction was observed between lomustine treatment and the

classifications of Patel et al., Lindsley et al., Papaemmanuil et al., or Bullinger et al. for EFS (*p* = 0.867, 0.370, 0.232, and 0.127, respectively) nor OS (*p* = 0.896, 0.758, 0.261,
and 0.127, respectively). However, there was a significant
interaction between the ELN2017 classification and
lomustine (p = 0.036 for EFS; p = 0.048 for OS; Fig. 5a, b),
indicating that lomustine was significantly associated with a
better EFS (p < 0.001) and OS (p = 0.023) in RATFIN
mutations (Fig. 5c) but not intermediate (p = 0.162 for EFS;
p = 0.599 for OS; Fig. 5d) nor favorable (p = 0.763 for
EFS; p = 0.199 for OS, Fig. 5e) subgroups. The advantage
of the lomustine addition was stronger in patients with TP53
and FLT3-ITD<sup>high</sup>/NPM1<sup>WT</sup> mutations (Fig. S11).

Discussion

This study corresponds to a post-hoc analysis of the LAM-SA 2017 phase 3 randomized trial and describes the genomic landscape of older AML patients with nonadverse cytogenetics risk selected for intensive chemotherapy. Whereas we confirmed previous studies regarding the distribution and gene-gene interactions of the most frequent mutations including FLT3-ITD, NPM1, DNMT3A, RUNX1, or ASXL1 [22, 23], we also described a rare subgroup of patients with DDX41 mutations, which is characterized by few co-mutations, leukopenia, and a probable better outcome in agreement with the recent publication of Sebert et al. [24].

The FILO study group has been using lomustine for
decades with consistent favorable results regarding CR
achievement after one course of induction and survival
endpoints [9, 25]. However, adding a third cytotoxic agent
to an anthracycline-cytarabine induction may increase
general toxicity especially hematologic toxicity and infec-
tions. Although not significant, the early death rate was
slightly increased in the lomustine arm in the LAM-SA
2017 trial [9]. Thus, defining patients who benefit most
from lomustine is of considerable importance.

We assessed the prognostic impact of five recent mole-
cular classifications [8, 13–16]. Overall, these classifications
have been relatively effective in predicting EFS and risk of
relapse in older AML patients although only ELN2017,
Patel et al.’s, and Lindsley et al.’s classifications were
associated with OS. Moreover, with regard to the main
clinical endpoints, no consistent pattern of interaction
between the impact of lomustine and most molecular clas-
sifications was observed except for the ELN2017 classifi-
cation [8] suggesting that lomustine could benefit mostly to
patients with RATFIN mutations (ELN2017 adverse risk
with nonadverse risk cytogenetics) [8]. We acknowledge
that this result could correspond to a type I error (i.e., a false
positive result) linked to the multiplication of analyses or a
true interaction between the ELN2017 score and lomustine
treatment; therefore, these results should be confirmed by a

prospective randomized trial in this specific subgroup of
patients with RATFIN mutations.

Lomustine is an alkylating agent of the nitrosourea type
that alkylates and cross-links DNA thereby inhibiting DNA
and RNA synthesis [26]. DNA damage repair is mainly
mediated by the O⁶-methylguanine DNA methyltransferase
the expression of which is very low in AML compared to
other cancers [26]. Moreover, lomustine activity is cell
cycle-phase nonspecific, a property not shared by anthra-
cyclines and cytarabine, that may be important to target
noncycling cells of the AML clones. Of note, lomustine is
lipophilic and crosses the blood–brain barrier. The addition
of lomustine may explain the absence of prognosis of FLT3-
ITD in our series, in contrast to the well-described worse
prognosis of patients with FLT3-ITD [27]. TP53 allows the
repair of interstrand cross-links through the upregulation of
the DNA repair factors XPC and DDB2 [28]. In a model of
glioma, the DNA double strand breaks generated by
chloroethylating nitrosourea were not repaired when TP53
was mutated contrary to cells with normal TP53 [28].
Nevertheless, as RUNX1 and ASXL1 represent the most
frequent high-risk mutations in this RATFIN group, the
impact of lomustine could affect these mutations through
mechanisms that remain to be elucidated.

As lomustine is used during front-line treatment, the
ELN2017 status must be rapidly defined for an optimal use
of this drug. This may be challenging especially for ASXL1
and RUNX1 genes. However, we have previously shown
that waiting a short period of time before induction che-
motherapy is safe in AML patients and allow a molecular
testing before choosing the most appropriate induction
regimen [29]. A recent study has also shown an interaction
between genetic profiles and gemtuzumab ozogamycin
efficacy indicating that molecular stratification is useful for
a rational use of targeted therapies but also of cytotoxic
agents in AML patients [30].

**Acknowledgements** We thank physicians from participating centers as well as central and local data managers for collecting and monitoring patients’ data. We thank Filothèque members for the conservation of patients samples; Catherine Lacombe, Rachid Boulghana, Laure Cabaret, Ségolène Diry, Krishshanthi Sinnadurai, and Shari Dini Mohamed.

**French Innovative Leukemia Organization** J.-P. Marolleau²⁶, A. Aleme²⁶, F. Orsini-Piocelle²⁷, N. Cadoux²⁷, N. Ifrah²⁸, M. Hunault²⁸, C. Marie²⁸, A. Al Jijakli²⁹, G. Lepeu³⁰, H. Zerazhi³⁰, M. Beyne³⁰, A. Banos³¹, S. Labarrere³¹, E. Deconinck³², M. Peria³², A. El Yamani³³, O. Kadiri³³, B. Choufi³⁴, M. Brument³⁴, A. Pigneux³⁵, T. Leguay³⁵, P.-Y. Dumas³⁵, C. Berthou³⁶, G. Guillerm³⁶, G. Drugmanne³⁶, O. Tournilhac³⁷, G. Roy³⁷, B. Audhuy³⁸, S. Camara³⁸, D. Caillot³⁹, M. Grandjean³⁹, J.-Y. Cahn⁴⁰, C.-E. Bulabois⁴⁰, B. Fief⁴⁰, N. Vey⁴¹, C. Ladraa⁴¹, V. Dorvaux⁴², M. Hagopian⁴², N. Fegueux⁴³, C. Fenoll⁴³, V. Sabadash⁴³, M. Ojeda⁴⁴, C. Haby⁴⁴, F. Witz⁴⁵, C. Bonmati⁴⁵, M. Lhuire⁴⁵, J. Delaunay⁴⁶, P. Peterlin⁴⁶, L. Airiau⁴⁶, L. Mannone⁴⁷, I.
Touitou⁴⁷, E. Jourdan⁴⁸, D. Umuhire⁴⁸, M. Alexis⁴⁹, O. Michel⁴⁹, F. Dreyfus⁵⁰, D. Bouscary⁵⁰, A. Cheung⁵⁰, L. Sanhes⁵¹, F. Touhami⁵¹, E. Ribas⁵¹, M. Puyade⁵², M.-P. Gallego-Hernanz⁵², N. Hugon⁵², C. Himberlin⁵³, L. Maggi⁵³, T. Lamy⁵⁴, A. Testu⁵⁴, E. Tavernier⁵⁵, S. Marchand⁵⁵, B. Lioure⁵⁶, C. Kravanja⁵⁶, L. Benboubker⁵⁷, D. Nollet⁵⁷, M. Attal⁵⁸, C. Recher⁵⁸, A. Sarry⁵⁸, A. Lhermitte⁵⁹, G. Yrica⁵⁹, D. Schwartz⁵⁹, N. Le Montagner⁵⁹, C. Fenoll⁵⁹, V. Sabadash⁵⁹, D. Nollet⁵⁹, L. Auvray⁵⁹, R. Delepine⁵⁹, A. Fayault⁵⁹

²⁶Hematology Department, CHU Amiens, Amiens, France; ²⁷Hematology Department, CH Annecy-Genevois, Annecy, France; ²⁸Hematology Department, CHU Angers, Angers, France; ²⁹Hematology Department, CH Argenteuil, Argenteuil, France; ³⁰Hematology Department, CH Avignon, Avignon, France; ³¹Hematology Department, CH Côte Basque, Bayonne, France; ³²Hematology Department, CHU Besancon, Besancon, France; ³³Hematology Department, CH Simone Veil, Blois, France; ³⁴Hematology Department, CH Boulogne sur Mer, Boulogne sur Mer, France; ³⁵Hematology Department, CHU Bordeaux, Bordeaux, France; ³⁶Hematology Department, CHU Brest, Brest, France; ³⁷Hematology Department, CHU Clermont-Ferrand, Clermont-Ferrand, France; ³⁸Hematology Department, Hôpitaux Civils de Colmar, Colmar, France; ³⁹Hematology Department, CHU Dijon, Dijon, France; ⁴⁰Hematology Department, CHU Grenoble-Alpes, Grenoble, France; ⁴¹Hematology Department, Institut Paoli-Calmettes, Marseille, France; ⁴²Hematology Department, CHR Metz-Thionville, Metz, France; ⁴³Hematology Department, CHU Montpellier, Montpellier, France; ⁴⁴Hematology Department, CH Emile-Muller, Mulhouse, France; ⁴⁵Hematology Department, CHRU Nancy, Nancy, France; ⁴⁶Hematology Department, CHU Nantes, Nantes, France; ⁴⁷Hematology Department, CHU Nice, Nice, France; ⁴⁸Hematology Department, CHU Nimes, Nimes, France; ⁴⁹Hematology Department, CHR Orléans, Orleans, France; ⁵⁰Hematology Department, CHU Cochin, Paris, France; ⁵¹Hematology Department, CH Perpignan, Perpignan, France; ⁵²Hematology Department, CHU Poitiers, Poitiers, France; ⁵³Hematology Department, CHU Reims, Reims, France; ⁵⁴Hematology Department, CHU Rennes, Rennes, France; ⁵⁵Hematology Department, CHU Saint-Etienne, Saint Priest En Jarez, France; ⁵⁶Hematology Department, CHU Strasbourg, Strasbourg, France; ⁵⁷Hematology Department, CHU Tours, Tours, France; ⁵⁸Hematology Department, CHU Toulouse, Toulouse, France; ⁵⁹Filo Central Office, Tours, France

## Compliance with ethical standards

**Conflict of interest** The authors declare that they have no conflict of interest.

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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## Affiliations

Laetitia Largeaud¹ · Pascale Cornillet-Lefebvre² · Jean-François Hamel³ · Pierre-Yves Dumas ID⁴ · Naïs Prade¹ ·

Stéphanie Dufrechou¹ · Julien Plenecassagnes⁵ · Isabelle Luquet¹ · Odile Blanchet⁶ · Anne Banos⁷ ·

Marie C. Béné ID⁸ · Marc Bernard⁹ · Sarah Bertoli ID¹⁰ · Caroline Bonmati¹¹ · Luc Matthieu Fornecker¹² ·

Romain Guièze¹³ · Lamya Haddaoui¹⁴ · Mathilde Hunault¹⁵ · Jean Christophe Ianotto¹⁶ · Eric Jourdan¹⁷ ·

Mario Ojeda¹⁸ · Pierre Peterlin¹⁹ · Norbert Vey ID²⁰ · Hacene Zerazhi²¹ · Hicheri Yosr²⁰,²² · Ariane Mineur²³,²⁴ ·

Jean-Yves Cahn²⁵ · Norbert Ifrah¹⁵ · Christian Récher ID¹⁰ · Arnaud Pigneux⁴ · Eric Delabesse ID¹ · on behalf of the French Innovative Leukemia Organization (FILO)

1. Hematology Biology, Centre Hospitalier Universitaire de Toulouse, Institut Universitaire du Cancer de Toulouse Oncopôle, Université Toulouse III Paul Sabatier, Toulouse, France

2. Hematology Biology, Reims University Hospital, Reims, France

3. Department of Statistics, Angers University Hospital, Angers, France

4. Clinical Hematology, Bordeaux University Hospital, Bordeaux University, Inserm 1035, Bordeaux, France

5. Bioinformatic Department, Claudius Regaud Institute, Institut Universitaire du Cancer de Toulouse Oncopôle, Toulouse, France

6. Hematology Biology, Angers University Hospital, Angers University, Inserm, CRCINA, Angers, France

7. Clinical Hematology, Cote Basque General Hospital, Bayonne, France

8. Hematology Biology, Nantes University Hospital, Nantes, France

9. Clinical Hematology, Rennes University Hospital, Rennes, France

10. Clinical Hematology, Centre Hospitalier Universitaire de Toulouse, Institut Universitaire du Cancer de Toulouse Oncopôle, Université Toulouse III Paul Sabatier, Toulouse, France

11. Clinical Hematology, Nancy University Hospital, Nancy, France

12. Clinical Hematology, Strasbourg University Hospital, Strasbourg, France

13. Clinical Hematology, Clermont-Ferrand University Hospital, Clermont-Ferrand, France

14. FILO Tumor Bank, Pitié-Salpêtrière Hospital, Paris, France

15. Clinical Hematology, Angers University Hospital, INSERM U 892/CNRS 6299, Angers, France

16. Clinical Hematology, Institute of Oncology and Hematology, Brest University Hospital, Brest, France

17. Clinical Hematology, Nîmes University Hospital, Nîmes, France

18. Clinical Hematology, GHRMSA, Hôpital E Muller, Mulhouse, France

19. Clinical Hematology, Nantes University Hospital, Nantes, France

20. Clinical Hematology, Aix-Marseille University, Inserm, CNRS, Institut Paoli-Calmettes, CRCM, Marseille, France

21. Clinical Hematology, Avignon General Hospital, Avignon, France

22. Clinical Hematology, Montpellier University Hospital, Montpellier, France

23. Clinical Research Unit, Bordeaux University Hospital, Bordeaux, France

24. FILO, Tours University Hospital, Tours, France

25. Clinical Hematology, Grenoble University Hospital, Grenoble, France