• Splicing factor gene mutations are enriched in older patients with AML and in those with secondary AML.

  • The use of venetoclax with LI or intensive therapies abrogates any negative prognostic impact of these mutations on survival.

Mutations in splicing factor (SF) genes SRSF2, U2AF1, SF3B1, and ZRSR2 are now considered adverse risk in the European LeukemiaNet 2022 acute myeloid leukemia (AML) risk stratification. The prognostic impact of SF mutations in AML has been predominantly derived from younger patients treated with intensive (INT) therapy. We evaluated 994 patients with newly diagnosed AML, including 266 (27%) with a SFmut. Median age was 67 years overall, with patients with SFmut being older at 72 years. SRSF2 (n = 140, 53%) was the most common SFmut. In patients treated with INT, median relapse-free survival (RFS) (9.6 vs 21.4 months, P = .04) and overall survival (OS) (15.9 vs 26.7 months, P = .06) were shorter for patients with SFmut than without SFwt, however this significance abrogated when evaluating patients who received venetoclax with INT therapy (RFS 15.4 vs 20.3 months, P = .36; OS 19.6 vs 30.7 months, P = .98). In patients treated with LI, median RFS (9.3 vs 7.7 months, P = .35) and OS (12.3 vs 8.5 months, P = .14) were similar for patients with and without SFmut , and outcomes improved in all groups with venetoclax. On multivariate analysis, SFmut did not affect hazards of relapse and death for INT arm but reduced both these hazards in LI arm. In a large AML data set with >60% of patients receiving venetoclax with LI/INT therapy, SFmut had no independent negative prognostic impact. Newer prognostic models that consider LI therapy and use of venetoclax among other factors are warranted.

Pathogenic mutations in splicing factor (SF) genes leads to abnormal functioning of the spliceosome complex and promotes oncogenesis through exon skipping and intron retention secondary to alternate splicing.1 In The Cancer Genome Atlas project, acute myeloid leukemia (AML) was associated with the largest burden of alternate spliced events.2 The 4 most commonly studied SF genes in myeloid malignancies are SRSF2, U2AF1, SF3B1, and ZRSR2. In myelodysplastic syndrome (MDS), SF3B1 mutation is associated with a characteristic increase in ringed sideroblasts and improved response to specific therapies (ie, luspatercept),3 whereas the other SF mutations (SFmut) are associated with inferior outcomes.4,5 

In AML, SFmut are enriched in patients with secondary AML (s-AML) or de novo myelodysplasia-related AML.1,6,7 Recent data have shown that these mutations are associated with an inferior outcome in patients with AML.7 In a cohort of 500 patients with de novo AML in which 363 (73%) received intensive (INT) therapy, Hou et al demonstrated that SFmut had an independent negative prognostic impact.8 In another study, van der Werf et al showed that SFmut tend to group with RUNX1 mutations and led to outcomes comparable with patients with historically adverse risk (AR) AML.9 Based on results from these and other studies, the European LeukemiaNet (ELN) 2022 recommendations on diagnosis and management of AML included SFmut in the AR category, unless co-occurring with other favorable-risk factors (ie, NPM1 or bZip CEBPA mutations), which entails important therapeutic interventions, including the consideration of allogeneic stem cell transplantation (SCT) in first remission.10 However, a majority of the patients with SFmut AML in the above studies were younger and received INT chemotherapy–based treatment, raising a question on whether these adverse outcomes are seen in all patients and with all treatment regimens. The ELN prognostic risk scoring is largely derived from data on the outcomes of younger patients treated with INT therapy and might not always be applicable to older patients treated with low-intensity (LI) therapy.

Venetoclax in combination with LI therapy is now approved as frontline therapy for patients aged ≥75 years or young unfit patients with newly diagnosed (ND) AML, based on results from the VIALE-A and VIALE-C trials.11,12 In a previous analysis from our group, Lachowiez et al showed that in patients with ND AML treated with a hypomethylating agent (HMA) and venetoclax, outcomes in patients who had an SFmut (n = 39) were similar to those with wild type (wt) SF (SFwt) (n = 80).13 The majority of patients with ND AML treated with LI therapy now receive it in combination with venetoclax, and venetoclax based combinations are also being increasingly studied in combination with INT chemotherapy.14 In view of these ongoing advances in treatment options, and to better understand the impact of SFmut in reference to their association with AML cytogenetics (CTG) and other concurrent myeloid mutations, we evaluated the impact of SFmut in patients with ND AML and compared their outcomes with patients without these mutations. We factored in the patient age, baseline AML genomics, history of previous myeloid hematologic disorder, intensity of frontline treatment regimens, and the use of venetoclax among other prognostic factors to evaluate the independent prognostic significance of SFmut.

Patients and treatment details

This is a retrospective study including adult patients (≥18 years) with ND AML, treated at The University of Texas MD Anderson Cancer Center, Houston, from January 2017 to April 2022. Clinical data were captured from our methodically curated computerized patient database. Patients were stratified into young (≥18-59 years) and old (≥60 years) age at AML diagnosis. Baseline performance status based on the Eastern Cooperative Oncology Group was captured for all patients. Patients with previous nonmyeloid disorders who had received chemotherapy or radiotherapy were considered as therapy-related AML (t-AML). Patients with previous myeloid disorders were considered as s-AML, and they were further stratified as untreated secondary, if exposed to only erythropoiesis stimulating agents or lenalidomide, and treated secondary, if exposed to HMA or other therapy.

All frontline treatment data were captured and stratified into INT and LI therapy. Patients who received HMA based doublets or triplets and low-dose cytarabine–based doublets or triplets were considered in the LI therapy arm, and all therapies with higher intensity (ie, cytarabine at a dose of ≥1000 mg/m2) were included in the INT arm. Further details of the treatment regimens received are in supplemental Tables 1 and 2, available on the Blood website. The use of venetoclax as part of the frontline therapy was additionally documented.

AML genomic evaluation

All patients had an 81-gene myeloid next generation sequencing (NGS) mutation assessment through high throughput sequencing performed within our in-house Clinical Laboratory Improvement Amendments–certified laboratory. Details of the methodology are in the supplemental data. CTG was performed via conventional G-banded karyotyping, and AML directed fluorescent in situ hybridization was conducted as appropriate.

The SFmut included in our analysis were SRSF2, SF3B1, U2AF1, and ZRSR2 (supplemental Table 3). A cutoff of ≥2% variant allele fraction on NGS was considered as positive for mutation, and germ line variants and variants of undetermined significance were discounted. Given the inclusion of SFmut in ELN 2022 AML risk stratification, we used the ELN 2017 recommendations for risk stratification.15 

Response and outcomes

Treatment response to the induction regimen was tabulated as per the ELN 2017 guidelines.15 Response was documented as overall response rate (ORR) and included a combination of complete remission, complete remission with incomplete count recovery, and morphological leukemia free state; this was the best response anytime on frontline therapy and before proceeding to SCT or any maintenance therapy. Relapse-free survival (RFS) was tabulated from the time of best response to relapse or death and overall survival (OS) from the time of therapy initiation to death; RFS was not censored for SCT. SCT conducted only in first remission was included.

Statistical analysis

Baseline demographics and disease parameters were documented and compared between patients with and without SFmut. Continuous variables were compared using a Mann-Whitney U test and categorical variables using a 2-sided Fisher t test with a significance of <.05.

Kaplan-Meier estimates were used for RFS and OS, and a Mantel-Cox log-rank test was used for comparison. Logistic regression analysis was performed to identify factors associated with response. For analysis of factors affecting survival, a Cox proportional hazard model was used for univariate analysis (UVA) and multivariate analysis (MVA). Analysis was done using GraphPad Prism version 9.3.1 (GraphPad Software, Boston, MA) and the Lumivero (New York, NY) (2023) XLSTAT statistical solution.

From January 2017 to April 2022, 994 patients with ND AML were treated at our center, of whom 456 (46%) were females, with a median age of 67 years (range, 19-95 years). Baseline characteristics are detailed in Table 1. A total of 66 patients (9%) had core-binding factor (CBF) AML, of whom none had a SFmut. Patients with CBF-AML were excluded from further comparative analysis for response and survival outcomes. In total, 266 patients (27%), with a median age of 72 years (range, 27-95) had an SFmut, of whom 76 (29%) were females. The median age of the patients without SFmut was 65 years (range, 19-64); Patients with SFmut were older (225/266 [80%]) than patients with SFwt (463/728 [64%]; P < .0001). There was no difference in the frequency of the patients in different Eastern Co-operative Oncology Group performance status strata based on the status of SFmut and whether they received therapy with venetoclax (supplemental Table 4).

Table 1.

Baseline characteristics of study patients

ParametersN/median (%) [range]P value
SFmut (N = 266)SFwt (N = 728)
Median age (y) 72 [27-95] 65 [19-64] <.0001 
Older age    
Age ≥60 y 225 (85) 463 (64) <.0001 
s/t-AML 135 (51) 301 (41) .009 
CTG    
CK 54/258 (21) 254/710 (36) <.0001 
AR (CK + others) 88/258 (34) 309/710 (43) .009 
CBF-AML 0 (0) 66 (9) <.0001 
Splicing mutations    
SRSF2 140 (53) 0 (0)  
SF3B1 49 (18) 0 (0)  
U2AF1 74 (28) 0 (0)  
ZRSR2 11 (4) 0 (0)  
>1 mutation 8 (3) 0 (0)  
ParametersN/median (%) [range]P value
SFmut (N = 266)SFwt (N = 728)
Median age (y) 72 [27-95] 65 [19-64] <.0001 
Older age    
Age ≥60 y 225 (85) 463 (64) <.0001 
s/t-AML 135 (51) 301 (41) .009 
CTG    
CK 54/258 (21) 254/710 (36) <.0001 
AR (CK + others) 88/258 (34) 309/710 (43) .009 
CBF-AML 0 (0) 66 (9) <.0001 
Splicing mutations    
SRSF2 140 (53) 0 (0)  
SF3B1 49 (18) 0 (0)  
U2AF1 74 (28) 0 (0)  
ZRSR2 11 (4) 0 (0)  
>1 mutation 8 (3) 0 (0)  
ParametersSFmut(n = 266)Non-CBF SFwt(n = 662)P value
Mutations    
TP53 38 (14) 203 (31) <.0001 
ASXL1 85 (32) 66 (10) <.0001 
RUNX1 76 (29) 60 (9) <.0001 
NPM1 31 (12) 146 (22) .002 
FLT3 ITD 23 (9) 100 (15) .01 
IDH1 31 (12) 48 (7) .02 
IDH2 43 (16) 73 (11) .01 
N-RAS/K-RAS 77 (29) 139 (21) .002 
ELN 2017 risk stratification    
Adverse 186 (70) 386 (58) .001 
ParametersSFmut(n = 266)Non-CBF SFwt(n = 662)P value
Mutations    
TP53 38 (14) 203 (31) <.0001 
ASXL1 85 (32) 66 (10) <.0001 
RUNX1 76 (29) 60 (9) <.0001 
NPM1 31 (12) 146 (22) .002 
FLT3 ITD 23 (9) 100 (15) .01 
IDH1 31 (12) 48 (7) .02 
IDH2 43 (16) 73 (11) .01 
N-RAS/K-RAS 77 (29) 139 (21) .002 
ELN 2017 risk stratification    
Adverse 186 (70) 386 (58) .001 

CK, complex karyotype; N, number.

Comparative AML genomics and disease status

The most common SFmut was SRSF2, present in 140 patients with SFmut (53%), followed by U2AF1 in 74 patients (28%), SF3B1 in 49 patients (18%), and ZRSR2 in 11 patients (4%). Only 8 patients (3%) had >1 SFmut consistent with previous reports.8,9 Overall, 88 of 258 (34%) patients with available CTG data had ELN 2017 AR CTG (54/258 patients [21%] with a complex karyotype). In the SFwt group (n = 662), CTG data were available for 644 patients, of whom 309 patients (48%) had AR CTG (254/644 [39%] with a complex karyotype). Using a combination of the available CTG data and myeloid mutation data, all patients could be stratified by ELN 2017 risk stratification. Significantly more patients in the SFmut group had AR disease (n = 186 [70%]) than the SFwt group (n = 386 [58%]; P = .001). This was driven by a higher frequency of ASXL1 (32% vs 10%, P < .0001) and RUNX1 mutations (29% vs 9%, P < .0001) in the SFmut group. There were 4 (0.6%) patients with biallelic CEBPA in the SFwt group compared with 1 (0.4%) in the SFmut group; 2 patients with SFmut (0.8%) and 16 patients with SFwt (2.4%) had a bZip in frame mutation in CEBPA in the absence of concurrent AR CTG or TP53 mutation. Twenty-six (9.8%) SFmut and 123 SFwt (18.6%) patients had an NPM1 mutation in the absence of AR CTG, TP53 mutation or FLT3-ITD high allelic ratio (NPM1 favorable risk), P = .0007.

Supplemental Figure 1 highlights the association of SFmut with other relevant myeloid mutations, and Figure 1A-B shows the heatmap of the relevant myeloid mutations for the SFmut and SFwt groups, respectively. The overall frequency of TP53 mutations was lower in patients with SFmut than those with SFwt (14% vs 31%, P < .0001). Among the patients with SFmut specifically, TP53 mutation(s) were highest in the SF3B1 mutated group (12/49 [24%] vs 26/217[12%]) in the remaining patients with SFmut (P = .039) (Table 2; supplemental Figure 2). Patients with SRSF2 mutations had the lowest frequency of AR CTG (24%), compared with 43% with U2AF1 (P = .009), 47% with SF3B1 (P = .005), and 62% in the ZRSR2 (P = .03) groups. In the SF3B1 group, 12 of 48 (25%) patients with available CTG data had a chromosome 3 aberration, of whom 9 had inv(3)(q21;q26.2) or t(3;21)(q26.2;q11.2). The overall frequency of ELN AR disease was not significantly different among the different SFmut groups; representing 67%, 70%, 79%, and 88% patients with SRSF2, U2AF1, SF3B1, or ZRSR2 mutations, respectively.

Figure 1.

Categorical heatmap showing the patient demographics, status of SF gene mutation, relevant myeloid mutations, treatment intensity, exposure to venetoclax, ORR, and frequency of SCT. (A) SFmut group. (B) SFwt group. ELN AR, ELN 2017 AR; SCT, allogeneic SCT; VEN, venetoclax.

Figure 1.

Categorical heatmap showing the patient demographics, status of SF gene mutation, relevant myeloid mutations, treatment intensity, exposure to venetoclax, ORR, and frequency of SCT. (A) SFmut group. (B) SFwt group. ELN AR, ELN 2017 AR; SCT, allogeneic SCT; VEN, venetoclax.

Close modal
Table 2.

Baseline characteristics of the patients based on the specific SF gene mutation

ParametersSRSF2 (n = 136)U2AF1 (n = 67)SF3B1 (n = 47)ZRSR2 (n = 8)
Age ≥ 60 y 116 (85) 56 (84) 38 (81) 7 (88) 
M/C mutation P95H (n = 54) S34F (n = 36) K700E (n = 23) R290Q (n = 2) 
s/t-AML 60 (44) 36 (54) 28 (60) 7 (88) 
CTG n = 129 n = 67 N = 47 N = 8 
AR 31 (24) 29 (43) 22 (47) 5 (62) 
Complex 20 (15) 13 (19) 17 (36) 3 (37) 
TP53 mutation 13 (9) 11 (16) 12 (25) 2 (25) 
ELN 2017 AR 91 (67) 47 (70) 37 (79) 7 (88) 
INT therapy 25 (18) 14 (21) 12 (25) 1 (12) 
LI therapy 111 (82) 53 (79) 37 (79) 7 (88) 
With venetoclax 74 (67) 35 (66) 24 (65) 5 (71) 
ParametersSRSF2 (n = 136)U2AF1 (n = 67)SF3B1 (n = 47)ZRSR2 (n = 8)
Age ≥ 60 y 116 (85) 56 (84) 38 (81) 7 (88) 
M/C mutation P95H (n = 54) S34F (n = 36) K700E (n = 23) R290Q (n = 2) 
s/t-AML 60 (44) 36 (54) 28 (60) 7 (88) 
CTG n = 129 n = 67 N = 47 N = 8 
AR 31 (24) 29 (43) 22 (47) 5 (62) 
Complex 20 (15) 13 (19) 17 (36) 3 (37) 
TP53 mutation 13 (9) 11 (16) 12 (25) 2 (25) 
ELN 2017 AR 91 (67) 47 (70) 37 (79) 7 (88) 
INT therapy 25 (18) 14 (21) 12 (25) 1 (12) 
LI therapy 111 (82) 53 (79) 37 (79) 7 (88) 
With venetoclax 74 (67) 35 (66) 24 (65) 5 (71) 

The table does not include 8 patients with >1 SF gene mutation.

M/C; most common; n, number; s/t-AML: secondary or therapy-related AML; CTG, cytogenetics; AR, adverse risk; ELN, European LeukemiaNet; INT, intensive; LI, low intensity.

A significantly higher number of patients with SFmut had s/t- AML than those in the SFwt group; 114 (43%) with s-AML (74 [28%] treated secondary) and 21 (8%) with t-AML in the SFmut group, compared with 195 (29%) s-AML (131 [20%] treated secondary) (P = .002) and 98 (15%) t-AML (P = .05) in the SFwt group. In both de novo and s/t-AML groups, more patients in the SFmut group were older (79% vs 58% in the de novo group and 90% vs 75% in the s/t-AML group for SFmut and SFwt respectively) (supplemental Table 5; supplemental Figure 3).

Previous myeloid disorder in the SFmut AML group

Of the 266 patients in the SFmut group, 135 (51%) had s/t-AML, including 114 with s-AML. MDS was the most common myeloid disorder, seen in 72 patients (63%), followed by myeloproliferative neoplasm in 22 patients (20%), chronic myelomonocytic leukemia in 17 patients (15%), and chronic myeloid leukemia and blastic plasmacytic dendritic cell neoplasm in 1 patient each. Overall, 62 of 114 patients (54%) had exposure to HMA any time before their diagnosis of AML; 12 (11%) had exposure to venetoclax, and 40 patients (35%) had received no therapy at all (n = 30) or had exposure to only luspatercept, lenalidomide, ruxolitinib, imatinib, or tagraxofusp (n = 9), before therapy for AML. Only 3 patients (2.6%) with s-AML had a previous SCT.

Comparative response and survival outcomes

A significantly higher percentage of patients in the SFmut group received LI therapy (80% vs 61%, P < .0001) than those in the SFwt group, possibly attributed to the higher number of older patients in the former (supplemental Table 6). Equal proportion of patients with SFmut and those with SFwt received venetoclax as part of their frontline AML therapy, with either LI or INT regimens. There was no difference in the ORR after LI or INT therapy in the 2 groups (LI = 68% vs 67% and INT = 79% vs 81% for SFmut and SFwt groups, respectively).

The median follow-up of the entire cohort of patients was 26 months (range, 2-59). The median RFS was 9.6 vs 10.6 months (P = .04) and the OS was 13.1 vs 11.9 months (P = .27) for patients with SFmut and those with SFwt, respectively. Although RFS and OS were expectedly superior in younger patients compared with older patients, there was no significant difference in RFS and OS in individual age groups based on SFmut (supplemental Figure 4). Patients with SFmut with ELN AR disease had similar RFS (8.3 vs 6.2 months, P = .14) but superior OS (10.1 vs 7.8 months, P = .02) compared with those with SFwt (supplemental Figure 5). There was no difference in survival outcomes in patients with the NPM1–mutated favorable-risk AML based on SFmut (supplemental Figure 6).

Given the significant heterogeneity in patient age and intensity of therapy received, we further stratified survival outcomes and compared patients based on the presence or absence of SFmut and the above characteristics.

LI therapy

More patients with SFmut than those with SFwt received LI therapy as mentioned before. We compared the RFS and OS of the patients in these 2 arms by selecting patients aged ≥60 years who received LI therapy. Among the 200 older patients in the SFmut arm who received LI therapy, 137 (69%) had an ORR with a median RFS of 9.3 months compared with an RFS of 7.7 months in the SFwt arm, in which 253 of 366 older patients (69%) attained an ORR (P = .35); the median OS was 12.3 and 8.5 months, respectively, in the 2 groups (P = .14) (Figure 2A and B). Comparatively, in the above-mentioned treatment groups, the frequency of ELN AR AML, AR CTG, and TP53 mutation was 140 (70%), 65 of 194 (34%), and 28 (14%) in the patients with SFmut and 239 (65%), 196 of 358 (55%) and 144 (39%) in the patients with SFwt, respectively.

Figure 2.

Kaplan-Meier estimates of survival outcomes in older patients treated with low-intensity therapy. (A) RFS. (B) OS. (C) Impact of venetoclax added to low-intensity therapy on RFS. (D) Impact of venetoclax added to low-intensity therapy on OS. (E) RFS in secondary/therapy related AML. (F) OS in secondary/therapy related AM. (G) RFS in de novo AML. (H) OS in de novo AML. mOS, median OS; mRFS, median RFS; NAR, number at risk; Ven, venetoclax.

Figure 2.

Kaplan-Meier estimates of survival outcomes in older patients treated with low-intensity therapy. (A) RFS. (B) OS. (C) Impact of venetoclax added to low-intensity therapy on RFS. (D) Impact of venetoclax added to low-intensity therapy on OS. (E) RFS in secondary/therapy related AML. (F) OS in secondary/therapy related AM. (G) RFS in de novo AML. (H) OS in de novo AML. mOS, median OS; mRFS, median RFS; NAR, number at risk; Ven, venetoclax.

Close modal

In the SFmut group, 129 of 200 older patients (65%) received venetoclax along with LI therapy, whereas 254 of 366 patients (69%) received venetoclax along with LI therapy in the SFwt group. When stratified by exposure to venetoclax, the median RFS in the venetoclax + LI treated patients with SFmut was 9.8 months and OS was 14.1 months, compared with a RFS of 9.1 months (P = .5) and OS of 9.6 months (P = .11) in patients with SFwt (Figure 2C-D). The median RFS and OS were similarly inferior in both mutational groups not treated with venetoclax (RFS 5.3 vs 5.1 months and OS 6.8 vs 7.1 months for patients with SFmut and SFwt, respectively).

Patients with s/t-AML have inferior outcomes compared with patients with de novo AML. Among the older patients treated with LI therapy, 104 of 200 (52%) patients with SFmut and 195 of 366 patients with SFwt (53%) had s/t- AML; the RFS was 6 vs 5 months (P = .64), whereas the OS was 9.1 vs 6.6 months (P = .08) for the 2 groups, respectively (Figure 2E-F). In the de novo AML subset, the median RFS was 13.7 vs 9.8 months (P = .28) and OS was 19.1 vs 13.6 months (P = .44) for patients with SFmut and SFwt, respectively (Figure 2G-H). To better understand the impact of venetoclax addition to LI therapy, we stratified the survival of patients who received venetoclax in the s/t-AML and de novo groups. Cumulatively, in the s/t-AML arm, 60 of 104 patients (58%) with SFmut and 128 of 195 patients (66%) with SFwt had received venetoclax; the RFS and OS in these patients were 6.1 vs 5 months (P = .99) and 9.8 vs 7 months (P = .28) for patients with SFmut and wt, respectively. In the de novo group, 70 of 96 patients (73%) with SFmut and 126 of 171 patients (74%) with SFwt had received venetoclax with LI therapy; RFS and OS was 19.1 vs 12 months (P = .19) and 24.8 vs 14.9 months (P = .19) in the 2 patient groups, respectively.

A minority of younger patients received LI therapy (SFmut = 13/266 and SFwt = 38/662); the median RFS (18 vs 6.5 months) and OS (13.8 vs 7.9 months) were numerically higher in patients with SFmut than in those with SFwt, however, this was not statistically significant.

INT therapy

In the SFmut group, a smaller percentage of patients (53/266 [20%]) received INT therapy than those in the SFwt group (258/662 [39%], P < .0001). Among them, a higher proportion of patients in the SFmut group were ELN AR (66% vs 44%, P = .006) and had s/t-AML (45% vs 28%, P = .02) than those with SFwt (supplemental Table 7). In the INT therapy cohort, the median RFS and OS in the SFmut group (9.6 and 15.9 months) were significantly shorter than the median RFS of 21.4 months (P = .04) and OS of 26.7 months (P = .06) in the SFwt group (Figure 3A-B), consistent with the perceived AR biology of SFmut AML treated with standard INT chemotherapy. Because venetoclax is now administered to a significant percentage of patients in combination with INT therapy at our center, we compared the outcomes of patients treated in the INT arm who received venetoclax as part of their frontline AML therapy. A total of 29 of 53 patients (55%) with SFmut and 131 of 258 patients (51%) with SFwt received venetoclax with their INT therapy, and ORR was attained in 25 (86%) and 108 (82%) patients, respectively. The median RFS was 15.4 vs 20.3 months (P = .36) and median OS 19.6 vs 30.7 months (P = .98) in the SFmut and SFwt groups (Figure 3C-D).

Figure 3.

Kaplan-Meier estimates of survival outcomes in younger patients treated with intensive therapy. (A) RFS. (B) OS. (C) Impact of venetoclax added to intensive therapy on RFS. (D) Impact of venetoclax added to intensive therapy on OS. mOS, median OS; mRFS, median RFS; NAR, number at risk; Ven, venetoclax.

Figure 3.

Kaplan-Meier estimates of survival outcomes in younger patients treated with intensive therapy. (A) RFS. (B) OS. (C) Impact of venetoclax added to intensive therapy on RFS. (D) Impact of venetoclax added to intensive therapy on OS. mOS, median OS; mRFS, median RFS; NAR, number at risk; Ven, venetoclax.

Close modal

We subsequently selected only younger patients (aged <60 years) who received INT therapy (SFmut = 27 and SFwt = 191) with or without venetoclax; the median RFS was 13.5 vs 25.4 months (P = .20) and median OS was 39.5 vs 58.7 months (P = .99) in the SFmut and SFwt groups, respectively. Considering the use of venetoclax in this younger population treated with INT therapy (SFmut = 18 and SFwt = 107), the median RFS and OS were 18.3 vs 20.3 months (P = .82) and the median OS was not-reached vs 30.7 months (P = .22) in the SFmut and SFwt groups, respectively.

A slightly higher proportion of patients with SFwt proceeded to SCT after frontline LI/INT therapy (44% vs 32%, P = .13), possibly because the SFmut group was enriched with older patients (47% vs 26%). On analyzing the impact of SCT in younger patients treated with INT therapy, SCT equivocally improved RFS and OS in both patients with SFmut and those with SFwt (supplemental Figure 7).

Survival in patients with SFmut

We analyzed the survival outcomes of patients with SFmut based on the individual SFmut. Given the small number of patients in the ZRSR2 mutated group, we discounted them from the comparative survival outcomes, along with 8 patients who had ≥1 SF gene mutation. The median RFS was 9.6, 9, and 6 months for patients with SRSF2, U2AF1, or SF3B1 mutations, respectively, and the median OS was 15.2, 10.1, and 8 months, respectively (P = .01) (supplemental Figure 8). Given the frequent association of patients with SFmut with ASXL1 and RUNX1 mutations, we stratified the survival outcomes of patients with SFmut based on those who had AR CTG ± TP53 mutation and those who had ASXL1 ±, RUNX1 mutation; the median RFS was 5.6 vs 12 months (P = .07) and the OS was 6.9 vs 15.7 months (P = .003) between the former and the latter groups, respectively. Importantly the RFS and OS of the ASXL1 ± RUNX1 mutated group was similar to that of patients with SFmut without ELN AR AML (RFS of 12.9 months and OS of 20.6 months).

Assessment of factors affecting response and survival

Understanding the significant heterogeneity in the patient population, we next evaluated additional factors that could affect response and survival. On UVA, SFmut did not affect the attainment of ORR; de novo AML and use of venetoclax was associated with higher odds of ORR, whereas age ≥60 years, ELN AR, and use of LI therapy was associated with lower odds of ORR (Table 3). On MVA, SF and age were not significant, whereas all the other variables remained independently significant.

Table 3.

Regression analysis of factors affecting odds of response and hazards of relapse/death

Logistic regression analysis of factor affecting ORR
UVAMVA
VariableOR95% CIP valueVariableOR95% CIP value
SFmut 0.9 0.66-1.24 .51 SFmut 0.91 0.53-1.32 .63 
Age ≥ 60 years 0.63 0.44-0.87 .006 Age ≥ 60 1.03 0.65-1.63 .89 
De novo AML 2.95 2.19-3.98 <.0001 De novo AML 1.89 1.36-2.64 .0002 
ELN AR 0.26 0.18-0.36 <.0001 ELN Adverse 0.6 0.36-1.01 .05 
LI therapy 0.51 0.36-0.69 <.0001 LI therapy 0.58 0.37-0.92 .02 
Venetoclax 2.14 1.60-2.87 <.0001 Venetoclax 2.45 1.77-3.41 <.0001 
Logistic regression analysis of factor affecting ORR
UVAMVA
VariableOR95% CIP valueVariableOR95% CIP value
SFmut 0.9 0.66-1.24 .51 SFmut 0.91 0.53-1.32 .63 
Age ≥ 60 years 0.63 0.44-0.87 .006 Age ≥ 60 1.03 0.65-1.63 .89 
De novo AML 2.95 2.19-3.98 <.0001 De novo AML 1.89 1.36-2.64 .0002 
ELN AR 0.26 0.18-0.36 <.0001 ELN Adverse 0.6 0.36-1.01 .05 
LI therapy 0.51 0.36-0.69 <.0001 LI therapy 0.58 0.37-0.92 .02 
Venetoclax 2.14 1.60-2.87 <.0001 Venetoclax 2.45 1.77-3.41 <.0001 
Cox regression analysis for hazards of relapse in death in older patients treated with LI therapy
RFS
UVAMVA
VariableHR95% CIP valueVariableHR95% CIP value
SFmut 0.88 0.71-1.14 .35 SFmut 0.75 0.59-0.96 .022 
De novo AML 0.66 0.53-0.83 <.0001 De novo AML 0.69 0.54-0.87 .002 
ELN AR 1.72 1.36-2.19 <.0001 ELN AR 1.65 1.28-2.12 <.0001 
Venetoclax 0.59 0.46-0.77 <.0001 Venetoclax 0.73 0.57-0.95 .02 
SCT 0.32 0.22-0.45 <.0001 SCT 0.31 0.22-0.44 <.001 
Cox regression analysis for hazards of relapse in death in older patients treated with LI therapy
RFS
UVAMVA
VariableHR95% CIP valueVariableHR95% CIP value
SFmut 0.88 0.71-1.14 .35 SFmut 0.75 0.59-0.96 .022 
De novo AML 0.66 0.53-0.83 <.0001 De novo AML 0.69 0.54-0.87 .002 
ELN AR 1.72 1.36-2.19 <.0001 ELN AR 1.65 1.28-2.12 <.0001 
Venetoclax 0.59 0.46-0.77 <.0001 Venetoclax 0.73 0.57-0.95 .02 
SCT 0.32 0.22-0.45 <.0001 SCT 0.31 0.22-0.44 <.001 
OS
SFmut 0.85 0.69-1.05 .14 SFmut 0.74 0.59-0.91 .004 
De novo AML 0.55 0.45-0.68 <.0001 De novo AML 0.60 0.49-0.75 <.0001 
ELN AR 2.01 1.59-2.54 <.0001 ELN AR 1.69 1.33-2.15 .0001 
Venetoclax 0.59 0.48-0.73 <.0001 Venetoclax 0.76 0.62-0.94 .011 
SCT 0.25 0.17-0.38 <.0001 SCT 0.27 0.18-0.41 <.0001 
OS
SFmut 0.85 0.69-1.05 .14 SFmut 0.74 0.59-0.91 .004 
De novo AML 0.55 0.45-0.68 <.0001 De novo AML 0.60 0.49-0.75 <.0001 
ELN AR 2.01 1.59-2.54 <.0001 ELN AR 1.69 1.33-2.15 .0001 
Venetoclax 0.59 0.48-0.73 <.0001 Venetoclax 0.76 0.62-0.94 .011 
SCT 0.25 0.17-0.38 <.0001 SCT 0.27 0.18-0.41 <.0001 
Cox regression analysis for hazards of relapse in death in younger patients treated with INT therapy
RFS
UVAMVA
SFmut 1.45 0.82-2.57 .2 SFmut 0.95 0.53-1.69 .85 
De novo 0.95 0.58-1.54 .83     
ELN AR 1.6 1.06-2.43 .03 ELN AR 2.52 1.61-3.92 <.0001 
Venetoclax 1.07 0.70-1.65 .75     
SCT 0.183 0.12-0.29 <.0001 SCT 0.15 0.09-0.24 <.0001 
Cox regression analysis for hazards of relapse in death in younger patients treated with INT therapy
RFS
UVAMVA
SFmut 1.45 0.82-2.57 .2 SFmut 0.95 0.53-1.69 .85 
De novo 0.95 0.58-1.54 .83     
ELN AR 1.6 1.06-2.43 .03 ELN AR 2.52 1.61-3.92 <.0001 
Venetoclax 1.07 0.70-1.65 .75     
SCT 0.183 0.12-0.29 <.0001 SCT 0.15 0.09-0.24 <.0001 
OS
SFmut 0.94 0.49-1.83 .86 SFmut 0.63 0.32-1.24 .44 
De novo AML 0.55 0.35-0.86 .009 De novo AML 0.54 0.33-0.87 .18 
ELN AR 2.92 1.86-4.58 <.0001 ELN AR 2.94 1.82-4.74 <.0001 
Venetoclax 1.03 0.67-1.60 .89     
SCT 0.26 0.16-0.42 <.0001 SCT 0.21 0.13-0.35 <.0001 
OS
SFmut 0.94 0.49-1.83 .86 SFmut 0.63 0.32-1.24 .44 
De novo AML 0.55 0.35-0.86 .009 De novo AML 0.54 0.33-0.87 .18 
ELN AR 2.92 1.86-4.58 <.0001 ELN AR 2.94 1.82-4.74 <.0001 
Venetoclax 1.03 0.67-1.60 .89     
SCT 0.26 0.16-0.42 <.0001 SCT 0.21 0.13-0.35 <.0001 

Only factors significant on UVA at P < .05 or relevant to the analysis (ie, SF mutations) were used in MVA.

ELN AR, ELN 2017 AR; OR, odds ratio; SCT, allogeneic SCT.

We used Cox regression analysis to understand factors predicting survival. To reduce the wide patient age and treatment heterogeneity we ran the model in prestratified groups. In the first analysis, we looked at factors significant in older patients treated with LI regimen. We included SFmut status, ELN 2017 risk (adverse or not), de novo vs s/t-AML, and use of venetoclax as variables. On UVA, SFmut did not affect the hazard of relapse, whereas ELN AR increased the hazards, and conversely, de novo AML and use of venetoclax were associated with lower hazards of relapse. On MVA, the same attributes were maintained except for SFmut which favorably reduced the hazards of relapse. SCT reduced the hazard of relapse on both UVA and MVA. For assessing hazards of death (OS), the same variables were evaluated; on UVA, SFmut did not have any effect, whereas ELN AR disease increased the hazards of death, and de novo AML, use of venetoclax, and SCT was associated with lower hazards of death. In addition, on MVA for OS, SFmut appeared to favorably reduce hazards of death whereas the other variables remained consistent (Table 3).

Next, we analyzed factors predicting RFS and OS in younger patients who received INT therapy and included the same variables as the previous analysis. On UVA for RFS, SFmut, venetoclax, and de novo AML were not significant and ELN AR increased the hazards, whereas SCT was associated with lower hazards for relapse; on MVA, ELN AR remained a significant (negative) prognostic factor for RFS and SCT was favorable. For OS, on UVA, de novo AML and SCT were protective, whereas ELN AR increased hazards of death, and on MVA, only ELN AR and SCT remained significant; SFmut had no impact on hazards of relapse or death (Table 3).

We analyzed the impact of the SFmut using another Cox regression model replacing the ELN2017 AR variable with the individual myeloid mutational data, including NPM1, ASXL1, RUNX1, TP53, and AR CTG, while including the other variables mentioned in the previous model (supplemental Table 8). The frequency of TP53 mutation was lower in the SFmut group (14%) than in the SFwt group (31%) (Table 1). In both older patients treated with LI therapy and younger patients treated with INT therapy, SFmut did not have any independent prognostic impact, whereas TP53 mutation was independently associated with increased hazards of relapse (hazard ratio [HR], 1.36; 95% confidence interval [CI], 1.00-1.86 for older patients treated with LI and HR, 5.37; 95% CI, 2.47-11.67 for younger patients treated with INT) and death (HR, 1.38; 95% CI, 1.07-1.79 for older patients treated with LI and HR, 3.64; 95% CI, 1.86-7.11 for younger patients treated with INT). ASXL1 and RUNX1 mutations did not show any independent prognostic impact on the hazards of relapse and death in both treatment groups.

Among the patients with SFmut, we ran Cox regression to understand the impact of specific SFmut on relapse and death. Although no individual SFmut had any significant impact on RFS, SRSF2 mutation reduced the hazards of death on UVA but not on MVA. The use of venetoclax remained protective for relapse and death on UVA and MVA (supplemental Table 9).

We present here a large retrospective analysis of patients with ND AML and describe the impact of SFmut on responses and survival in these patients. In this cohort, SFmut did not impact the rates of response to frontline therapy or survival outcomes, irrespective of the patients age and AML risk stratification. Overall, 266 (29%) of patients with non–CBF-AML (n = 928) in our cohort had an SFmut, and in concordance with other published data, only 8 patients (3%) had >1 SFmut, and SFmut occurred more commonly in male patients (71% of patients with SFmut).9 

SFmut have significant relevance in myeloid neoplasms, and have been associated with distinct phenotype and outcomes in patients with MDS.1 In AML, the recent inclusion of SFmut as an adverse prognostic factor in the ELN 2022 risk stratification has added increased clinical relevance.10 Our analysis suggests the negative prognostic impact of SFmut emanates primarily from studies involving younger patients receiving INT therapy that did not include venetoclax.8,9 This negative prognostic impact of SFmut in younger patients receiving standard INT therapy was also seen in our analysis, with a shorter RFS and OS in the unsupervised SFmut group. However, when we selected among them those patients who had received venetoclax along with the INT therapy, there was no difference in survival outcomes; on UVA and MVA, SFmut did not affect the hazard of relapse or death in young patients treated with INT therapy. In the group of patients treated with LI therapy, SFmut led to lower hazards of relapse and death independently.

SFmut are more common in patients with s/t-AML and tend to associate more often with ELN 2017 AR strata.17 Patients with s/t-AML have inferior outcomes compared with patients with de novo AML, especially if they have received previous therapy.18,19 In our study, 51% of patients with SFmut had s/t-AML (28% treated secondary). Furthermore, more patients with SFmut had ELN 2017 AR disease, fueled by a higher frequency of RUNX1 and ASXL1 mutations in these patients, although the frequency of AR CTG and TP53mut was lower in patients with SFmut than in patients with SFwt. In our study, on MVA, when adjusting for baseline patient and disease factors including other AR mutations, SFmut did not negatively affect response rates and survival outcomes.

The retrospective nature of our study along with the heterogeneity in patient age and treatment regimens are relevant limitations. In our analysis, across both age groups and treatment intensity, we show that SCT was associated with lower hazards of relapse/death. However, this benefit could be confounded by the depth of response to frontline therapy, pre-SCT performance status, and comorbidities, and possibly cannot be attributed to SCT alone. Within these limitations, to our knowledge, we showed for the first time in a large cohort of patients, that SFmut do not have independent negative prognostic significance, especially when patients are treated with venetoclax containing regimens. Stratified MVA shows that even in the SFmut group, the use of venetoclax was associated with lower hazards of relapse and death. Newer biological evidence demonstrates that altered splicing may lead to increased susceptibility to venetoclax and warrants focused clinical studies and trials to evaluate the responses and outcomes with the use of venetoclax in patients with SFmut.20,21 

In our study, we used an age cutoff of 60 years at AML diagnosis to stratify the study population. We acknowledge this is an arbitrary cutoff, and the intention to proceed to INT or LI therapy is often determined by the AML genomics, institutional guidelines, patient performance status and comorbidities, and physician preference. Venetoclax is approved for treatment of older and unfit patients with ND AML along with HMA or low-dose cytarabine, and clinical trials are evaluating venetoclax in combination with INT therapy.14,16 Secondly, SFmut are enriched in older patients with AML, who are more often treated with LI therapy regimens containing venetoclax. Given these important considerations, the independent prognostic significance of SFmut in ND AML should be revisited based on treatment approach and intensity. Efforts to develop risk stratification models considering venetoclax as part of the treatment regimen are much needed and ongoing22; for example, a recent analysis from the VIALE-A study has shown that the ELN 2017 risk stratification does not optimally prognosticate patients treated with HMA + venetoclax, and SFmut do not confer a negative prognostic impact.23 Further validation of the ELN 2022 prognostic risk stratification in large patient cohorts treated with venetoclax based combination regimens is warranted.

In conclusion, SFmut are associated with older patients with AML, enriched in patients with s/t-AML, and occur most commonly in the ELN 2017 AR strata. In our cohort of patients, a majority received venetoclax along with LI or INT therapy, and in this treatment milieu, SFmut failed to show an independent prognostic impact. Newer prognostic tools for AML that incorporates history of prior myeloid disorders and therapy exposure, use of venetoclax for AML therapy, along with the AML genomic landscape are increasingly important for precise risk stratification of these patients.

The authors are grateful to the patients who were treated at The University of Texas MD Anderson Cancer Center, Houston, and were included in this study.

The visual abstract was created with BioRender.com.

Contribution: H.M.K. and C.D.D. conceptualized the study; J.S. and C.D.D. designed and wrote the manuscript; J.S. analyzed the data and made the figures; J.S. and S.L. risk stratified the patients; S.L. and K.P. analyzed the laboratory data; N.J.S., G.C.I., A.M., H.A.A., N.G.D., N.P., K.S.C., K.S., T.M.K., D.E.H., G.B., F.R., H.M.K., G.G.-M., and C.D.D. treated patients; S.P. curated the data; C.D.D. supervised the study; J.S. and C.D.D. reviewed the data; and all authors reviewed the manuscript and approved the final version.

Conflict-of-interest disclosure: N.G.D. has received research funding from Daiichi Sankyo, Bristol Myers Squibb, Pfizer, Gilead, Sevier, Genentech, Astellas, Daiichi Sankyo, AbbVie, Hanmi, Trovagene, FATE Therapeutics, Amgen, Novimmune, Glycomimetics, Trillium and ImmunoGen; and has served in a consulting or advisory role for Daiichi Sankyo, Bristol Myers Squibb, Arog, Pfizer, Novartis, Jazz, Celgene, AbbVie, Astellas, Genentech, ImmunoGen, Servier, Syndax, Trillium, Gilead, Amgen, Shattuck Labs, and Agios. N.P. serves on the board of directors for Dan’s House of Hope; is a consultant for AbbVie, Aptitude Health, Astellas Pharma US, Inc, Blueprint Medicines, Bristol Myers Squibb, Celgene Corp, Cimeio Therapeutics AG, ClearView Healthcare Partners, CTI BioPharma, Dava Oncology, Immunogen, Incyte, Intellisphere, LLC, Novartis AG, Novartis Pharmaceuticals Corp, OncLive (owned by Intellisphere, LLC), Patient Power, PharmaEssentia, Protagonist Therapeutics, Sanofi-Aventis, Stemline Therapeutics, Inc and Total CME; has financial relationships (eg, stock, royalty, gift, employment or business ownership) with Karger Publishers; is a scientific/advisory committee member for Cancer.Net, CareDx, CTI BioPharma, EUSA Pharma, Inc, Novartis Pharmaceuticals Corp, Pacylex, and PharmaEssentia; and receives a speaker/preceptorship from AbbVie, Aplastic Anemia & MDS International Foundation, Curio Science LLC, Dava Oncology, Imedex, Magdalen Medical Publishing, Medscape, Neopharm, PeerView Institute for Medical Education, Physician Education Resource, Postgraduate Institute for Medicine, and Stemline Therapeutics, Inc. T.M.K. discloses consulting or advisory roles for Novartis, Jazz Pharmaceuticals, Pfizer, AbbVie/Genentech, Agios, Daiichi Sankyo/UCB Japan, Liberum, Sanofi, Servier, and Pinot Bio and research funding from Bristol Myers Squibb, Celgene, Amgen, BiolineRx, Incyte, Genentech/AbbVie, Pfizer, Jazz Pharmaceuticals, AstraZeneca, Astellas Pharma, Ascentage Pharma, Genfleet Therapeutics, Cyclacel, and Pulmotech. H.M.K. discloses honoraria, advisory board participation, and/or consultancy from AbbVie, Amgen, Amphista, Ascentage, Astellas, Biologix, Curis, Ipsen Biopharmaceuticals, KAHR Medical, Novartis, Pfizer, Precision Biosciences, Shenzhen Target Rx, and Takeda and research grants from AbbVie, Amgen, Ascentage, BMS, Daiichi-Sankyo, ImmunoGen, Jazz, Novartis. Cellenkos, Glycomimetics, Astex Pharmaceuticals, Iterion Therapeutics, and Delta-Fly Pharma. C.D.D. receives research funding or honoraria or consultancy fee from Cleave, Astex, LOXO, ImmuneOnc, Takeda, Gilead, Genmab, GSK, Kura, Foghorn, Astellas, Forma, bluebird bio, Servier, Novartis, Bristol Myers Squibb, AbbVie, and Jazz and previously held a membership on an entity’s board of directors or advisory committees or holds stock options in a privately-held company for Notable Labs. The remaining authors declare no competing financial interests.

Correspondence: Courtney D. DiNardo, Department of Leukemia, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 428, Houston, TX 77030; e-mail: [email protected].

1.
Saez
B
,
Walter
MJ
,
Graubert
TA
.
Splicing factor gene mutations in hematologic malignancies
.
Blood
.
2017
;
129
(
10
):
1260
-
1269
.
2.
Lee
H
,
Palm
J
,
Grimes
SM
,
Ji
HP
.
The Cancer Genome Atlas Clinical Explorer: a web and mobile interface for identifying clinical–genomic driver associations
.
Genome Med
.
2015
;
7
(
1
):
112
.
3.
Papaemmanuil
E
,
Cazzola
M
,
Boultwood
J
, et al;
Chronic Myeloid Disorders Working Group of the International Cancer Genome Consortium
.
Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts
.
N Engl J Med
.
2011
;
365
(
15
):
1384
-
1395
.
4.
Pellagatti
A
,
Armstrong
RN
,
Steeples
V
, et al
.
Impact of spliceosome mutations on RNA splicing in myelodysplasia: dysregulated genes/pathways and clinical associations
.
Blood
.
2018
;
132
(
12
):
1225
-
1240
.
5.
Urrutia
S
,
Li
Z
,
Almanza
E
, et al
.
Characteristics of patients with myelodysplastic neoplasm and spliceosome mutations
.
Leukemia
.
2023
;
37
(
6
):
1397
-
1400
.
6.
Papaemmanuil
E
,
Gerstung
M
,
Bullinger
L
, et al
.
Genomic classification and prognosis in acute myeloid leukemia
.
N Engl J Med
.
2016
;
374
(
23
):
2209
-
2221
.
7.
Tazi
Y
,
Arango-Ossa
JE
,
Zhou
Y
, et al
.
Unified classification and risk-stratification in acute myeloid leukemia
.
Nat Commun
.
2022
;
13
(
1
):
4622
.
8.
Hou
HA
,
Liu
CY
,
Kuo
YY
, et al
.
Splicing factor mutations predict poor prognosis in patients with de novo acute myeloid leukemia
.
Oncotarget
.
2016
;
7
(
8
):
9084
-
9101
.
9.
van der Werf
I
,
Wojtuszkiewicz
A
,
Meggendorfer
M
, et al
.
Splicing factor gene mutations in acute myeloid leukemia offer additive value if incorporated in current risk classification
.
Blood Adv
.
2021
;
5
(
17
):
3254
-
3265
.
10.
Döhner
H
,
Wei
AH
,
Appelbaum
FR
, et al
.
Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN
.
Blood
.
2022
;
140
(
12
):
1345
-
1377
.
11.
DiNardo
CD
,
Jonas
BA
,
Pullarkat
V
, et al
.
Azacitidine and venetoclax in previously untreated acute myeloid leukemia
.
N Engl J Med
.
2020
;
383
(
7
):
617
-
629
.
12.
Wei
AH
,
Montesinos
P
,
Ivanov
V
, et al
.
Venetoclax plus LDAC for newly diagnosed AML ineligible for intensive chemotherapy: a phase 3 randomized placebo-controlled trial
.
Blood
.
2020
;
135
(
24
):
2137
-
2145
.
13.
Lachowiez
CA
,
Loghavi
S
,
Furudate
K
, et al
.
Impact of splicing mutations in acute myeloid leukemia treated with hypomethylating agents combined with venetoclax
.
Blood Adv
.
2021
;
5
(
8
):
2173
-
2183
.
14.
Kadia
TM
,
Reville
PK
,
Borthakur
G
, et al
.
Venetoclax plus intensive chemotherapy with cladribine, idarubicin, and cytarabine in patients with newly diagnosed acute myeloid leukaemia or high-risk myelodysplastic syndrome: a cohort from a single-centre, single-arm, phase 2 trial
.
Lancet Haematol
.
2021
;
8
(
8
):
e552
-
e561
.
15.
Döhner
H
,
Estey
E
,
Grimwade
D
, et al
.
Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel
.
Blood
.
2017
;
129
(
4
):
424
-
447
.
16.
Lachowiez
CA
,
Reville
PK
,
Kantarjian
H
, et al
.
Venetoclax combined with induction chemotherapy in patients with newly diagnosed acute myeloid leukaemia: a post-hoc, propensity score-matched, cohort study
.
Lancet Haematol
.
2022
;
9
(
5
):
e350
-
e360
.
17.
Lindsley
RC
,
Mar
BG
,
Mazzola
E
, et al
.
Acute myeloid leukemia ontogeny is defined by distinct somatic mutations
.
Blood
.
2015
;
125
(
9
):
1367
-
1376
.
18.
Patel
AA
,
Yoon
JJ
,
Johnston
H
, et al
.
Outcomes of patients with accelerated/blast-phase myeloproliferative neoplasms in the current era of myeloid therapies
.
Blood
.
2022
;
140
(
suppl 1
):
6860
-
6862
.
19.
Senapati
J
,
Verstovsek
S
,
Masarova
L
, et al
.
Impact of SF3B1 mutation in myelofibrosis
.
Leuk Lymphoma
.
2022
;
63
(
11
):
2701
-
2705
.
20.
Wang
E
,
Pineda
JMB
,
Kim
WJ
, et al
.
Modulation of RNA splicing enhances response to BCL2 inhibition in leukemia
.
Cancer Cell
.
2023
;
41
(
1
):
164
-
180.e8
.
21.
Ganan-Gomez
I
,
Yang
H
,
Ma
F
, et al
.
Stem cell architecture drives myelodysplastic syndrome progression and predicts response to venetoclax-based therapy
.
Nat Med
.
2022
;
28
(
3
):
557
-
567
.
22.
Short
NJ
,
Tallman
MS
,
Pollyea
DA
,
Ravandi
F
,
Kantarjian
H
.
Optimizing risk stratification in acute myeloid leukemia: dynamic models for a dynamic therapeutic landscape
.
J Clin Oncol
.
2021
;
39
(
23
):
2535
-
2538
.
23.
Döhner
H
,
Pratz
KW
,
DiNardo
CD
, et al
.
ELN risk stratification is not predictive of outcomes for treatment-naïve patients with acute myeloid leukemia treated with venetoclax and azacitidine
.
Blood
.
2022
;
140
(
suppl 1
):
1441
-
1444
.

Author notes

Presented in part at the 64th annual meeting of the American Society of Hematology, New Orleans, LA, 11 December 2022.

Qualified researchers may request access to individual patient-level data reported in this article after print publication of the current article. No identifying data will be provided. All requests for data must include a description of the research proposal and be submitted to the corresponding author, Courtney D. DiNardo ([email protected]).

The online version of this article contains a data supplement.

There is a Blood Commentary on this article in this issue.

The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

Sign in via your Institution