Literature DB >> 34847232

Molecular, clinical, and prognostic implications of PTPN11 mutations in acute myeloid leukemia.

Sydney Fobare1, Jessica Kohlschmidt2,3, Hatice Gulcin Ozer4, Krzysztof Mrózek2, Deedra Nicolet2,3, Alice S Mims1, Ramiro Garzon1, James S Blachly1, Shelley Orwick1, Andrew J Carroll5, Richard M Stone6, Eunice S Wang7, Jonathan E Kolitz8, Bayard L Powell9, Christopher C Oakes1, Ann-Kathrin Eisfeld1,2, Erin Hertlein1,10, John C Byrd1,10.   

Abstract

Prognostic factors associated with chemotherapy outcomes in patients with acute myeloid leukemia (AML) are extensively reported, and one gene whose mutation is recognized as conferring resistance to several newer targeted therapies is protein tyrosine phosphatase non-receptor type 11 (PTPN11). The broader clinical implications of PTPN11 mutations in AML are still not well understood. The objective of this study was to determine which cytogenetic abnormalities and gene mutations co-occur with PTPN11 mutations and how PTPN11 mutations affect outcomes of patients treated with intensive chemotherapy. We studied 1725 patients newly diagnosed with AML (excluding acute promyelocytic leukemia) enrolled onto the Cancer and Leukemia Group B/Alliance for Clinical Trials in Oncology trials. In 140 PTPN11-mutated patient samples, PTPN11 most commonly co-occurred with mutations in NPM1, DNMT3A, and TET2. PTPN11 mutations were relatively common in patients with an inv(3)(q21q26)/t(3;3)(q21;q26) and a normal karyotype but were very rare in patients with typical complex karyotype and core-binding factor AML. Mutations in the N-terminal SH2 domain of PTPN11 were associated with a higher early death rate than those in the phosphatase domain. PTPN11 mutations did not affect outcomes of NPM1-mutated patients, but these patients were less likely to have co-occurring kinase mutations (ie, FLT3-ITD), suggesting activation of overlapping signaling pathways. However, in AML patients with wild-type NPM1, PTPN11 mutations were associated with adverse patient outcomes, providing a rationale to study the biology and treatment approaches in this molecular group. This trial was registered at www.clinicaltrials.gov as #NCT00048958 (CALGB 8461), #NCT00899223 (CALGB 9665), and #NCT00900224 (CALGB 20202).
© 2022 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.

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Year:  2022        PMID: 34847232      PMCID: PMC8905707          DOI: 10.1182/bloodadvances.2021006242

Source DB:  PubMed          Journal:  Blood Adv        ISSN: 2473-9529


Introduction

Acute myeloid leukemia (AML) is the most commonly diagnosed acute leukemia in adults and is best characterized by the aberrant proliferation of clonal myeloid stem or progenitor cells with a differentiation block.[1] Although AML has a common myeloid origin, the pathogenesis is believed to be due to one or more genetic driver events such as chromosome translocations and/or gene mutations followed by the acquisition of mutations that promote the full phenotype of the disease. The complexity of the disease is further amplified by specific, age-associated disease characteristics. Recognition that AML is not one disease, but likely many, may explain why the cure rate remains very low, with similar chemotherapy given to all patients with this disease. Indeed, induction chemotherapy with an anthracycline plus cytarabine regimen followed by intensive consolidation without allogeneic stem cell transplant cures 35% to 40% of patients aged <60 years and 5% to 15% of patients aged ≥60 years.[2] Despite a relatively frequent occurrence, one gene mutation in AML only recently characterized is mutation in the protein tyrosine phosphatase non-receptor type 11 (PTPN11) gene. The PTPN11 gene encodes the protein Src homology region 2 (SH2)-containing protein tyrosine phosphatase 2 (SHP2). SHP2 is ubiquitously expressed and required for the normal development and function of hematopoietic cells.[3,4] SHP2 is composed of two SH2 domains at the N-terminal (sequentially labeled N- and C-terminal), a phosphatase (PTP) domain, and a C-terminal tail. The N-terminal SH2 (N-SH2) domain self-inhibits the PTP domain.[5,6] Upstream signaling recruits the N-SH2 domain and releases this self-inhibition to induce downstream signaling.[6,7] Oncogenic PTPN11 mutations induce prolonged SHP2 activation through the removal of self-inhibition.[8] PTPN11 mutations have been found in various hematologic malignancies, including AML.[9-11] A PTPN11 mutation is found in ∼7% of patients with de novo AML and ∼12% of patients with therapy-related AML.[12-15] Given the recent emergence of primary resistance to targeted therapy such as ivosidenib, enasidenib, venetoclax, and entospletinib,[16-20] a reassessment of the associations of PTPN11 mutations with cytogenetic findings, mutations of other genes, clinical characteristics, and outcome features in AML patients treated with standard 7 + 3 chemotherapy is warranted. These analyses are necessary considering that many patients with AML still receive frontline chemotherapy, especially fit, younger patients. There is little information regarding how PTPN11 mutations affect prognosis of adult patients with AML in response to standard therapy or about associations with co-existing mutations and/or cytogenetic abnormalities. To our knowledge, ours is the largest study of PTPN11-mutated patients, in which we examine in detail the exact mutation sites and variant allele frequencies (VAFs) of PTPN11 mutations, chromosome abnormalities, co-occurring mutations in other genes, clinical features, and outcomes of adult patients with AML treated on clinical studies performed by the Cancer and Leukemia Group B (CALGB)/Alliance for Clinical Trials in Oncology (Alliance).

Methods

Patients and treatment

We analyzed the 1725 adults (≥17 years of age; age range, 17-92 years) with newly diagnosed, de novo AML (excluding acute promyelocytic leukemia) whose pretreatment bone marrow (BM) or blood samples underwent next-generation sequencing analysis.[21] There were 1131 younger patients, defined as those aged <60 years and 594 older patients, defined as those ≥60 years of age. The patients were treated on CALGB trials with standard chemotherapy treatment as described in the supplemental Methods. CALGB is now part of the Alliance. Ninety-five percent of patients received intensive treatment, whereas 5% of patients received nonintensive treatment as described in the supplemental Methods. All patients were considered for outcome analyses, including those who experienced an early death, defined as death within 30 days of starting therapy irrespective of cause. Patients provided written informed consent to participate in treatment studies and companion protocols. CALGB 8461 (cytogenetic studies), CALGB 9665 (leukemia tissue bank), and/or CALGB 20202 (molecular studies) involved collection of pretreatment BM and blood samples. Treatment protocols were in accordance with the Declaration of Helsinki and approved by the Institutional Review Boards at each center.

Cytogenetic and molecular analyses

Cytogenetic analyses of pretreatment BM and/or blood samples were performed by institutional laboratories approved by CALGB/Alliance using unstimulated short-term (24- or 48-hour) cultures. Normal karyotype was determined in patients for whom at least 20 BM metaphase cells from a short-term culture were analyzed, and no clonal abnormality was found. Cytogenetic results were confirmed by central karyotype review.[22] Viable cryopreserved BM or blood cells were stored for future analyses before starting treatment. Mononuclear cells from BM or blood were enriched by Ficoll-Hypaque gradient and cryopreserved in liquid nitrogen until thawed at 37°C for analysis. DNA extractions were performed by using the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany). The mutational status of 80 protein-coding genes was determined centrally at The Ohio State University by targeted amplicon sequencing using the MiSeq platform (Illumina, San Diego, CA), as previously described[21] and outlined in the supplemental Methods. Testing for the presence or absence of FLT3-ITD was performed as previously described.[23] In addition to the 80 genes analyzed by using the targeted amplicon sequencing panel, testing for CEBPA mutations was performed with Sanger sequencing as previously described,[24] thus resulting in a total of 81 genes whose mutational status was assessed in the current study. In accordance with the revision of the World Health Organization classification of myeloid neoplasms and acute leukemia and the European LeukemiaNet guidelines for AML,[25] only patients with biallelic CEBPA mutations were considered in the CEBPA-mutant category.

Statistical analysis

Definitions of clinical end points are provided in the supplemental Methods. Demographic and clinical features of any 2 patient groups were compared by using the Fisher’s exact test for categorical variables and Wilcoxon rank sum tests for continuous variables. The Kaplan-Meier method was used for estimating probabilities of overall survival (OS), disease-free survival (DFS), and event-free survival (EFS) and differences between survival distributions were tested by using the log-rank test.[26] We used logistic regression for modeling complete remission (CR) attainment, Cox proportional hazards regression for modeling DFS and OS for univariable and multivariable outcome analyses, and P values adjusted to control for per-family error rate. For the multivariable analysis, a limited backward selection technique was used to build the final model. Variables considered in the multivariable model were significant at the likelihood ratio test–adjusted P value < .20 from the univariable models. All statistical analyses were performed by the Alliance Statistics and Data Center, and SAS 9.4 (SAS Institute, Inc., Cary, NC) was used. The database was locked on June 9, 2020.

Results

Baseline characteristics of patients with PTPN11 mutations

Of the 1725 patients with AML examined, the presence of a PTPN11 mutation was detected in 140 (8.1%) patients, which is comparable to the reported mutation frequency in other studies.[12,27] There were 98 younger patients and 42 older patients. The median follow-up of patients still alive was 9.0 years. There was a wide range of VAFs for PTPN11 mutations among patients, ranging from 0.05 to 0.54, with 59 (42%) patients having a VAF above 0.30 (Figure 1).[28,29] The majority of the mutations (61%) were localized in the N-SH2 domain, a known PTPN11 mutation hotspot location that is associated with increased SHP2 activity, whereas a minority of mutations were in other portions of the gene, such as the PTP domain (Figure 2).[27-30] With regards to pretreatment clinical characteristics, patients with mutated PTPN11 (PTPN11mut) presented more often with higher platelet counts (median, 72 vs 54 × 109/L; P < .001) and were more likely to have extramedullary involvement (33% vs 24%; P = .03) compared with PTPN11 wild-type (PTPN11wt) patients (Table 1). All other clinical features of patients with PTPN11mut were similar to those of patients with PTPN11wt.
Figure 1.

Oncoprint of mutations co-occurring with Each column represents an individual patient. The top row represents PTPN11 VAFs, ranging from 0.05 (blue) to 0.54 (yellow). Each subsequent row represents a gene. Green squares indicate the presence of a mutation, insertion, or deletion; gray squares represent no alteration detected; and white squares represent unavailable gene alteration status.

Figure 2.

Lollipop plot depicting the location of aa, amino acid.

Table 1.

Clinical characteristics of AML patients with and without PTPN11 mutations

Characteristic PTPN11 mut PTPN11 wt P *
(n = 140)(n = 1585)
Age, y .74
 Median5353
 Range18-8417-92
Sex, n (%) .11
 Male70 (50)907 (57)
 Female70 (50)678 (43)
Race, n (%) .59
 White124 (89)1356 (87)
 Non-white15 (11)197 (13)
Hemoglobin, g/dL .93
 Median9.19.2
 Range5.7-15.02.3-25.1
Platelet count, ×109/L <.001
 Median7254
 Range10-6484-989
WBC count, ×109/L .22
 Median29.323.3
 Range1.4-355.00.4-560.0
% Blood blasts .90
 Median4853
 Range0-970-99
% BM blasts .30
 Median6367
 Range12-990-99
Extramedullary involvement, n (%)45 (33)363 (24).03

BM, bone marrow; WBC, white blood cell.

P values are from Fisher’s exact test for discrete variables and from the Wilcoxon rank sum test for continuous variables.

Oncoprint of mutations co-occurring with Each column represents an individual patient. The top row represents PTPN11 VAFs, ranging from 0.05 (blue) to 0.54 (yellow). Each subsequent row represents a gene. Green squares indicate the presence of a mutation, insertion, or deletion; gray squares represent no alteration detected; and white squares represent unavailable gene alteration status. Lollipop plot depicting the location of aa, amino acid. Clinical characteristics of AML patients with and without PTPN11 mutations BM, bone marrow; WBC, white blood cell. P values are from Fisher’s exact test for discrete variables and from the Wilcoxon rank sum test for continuous variables. Cytogenetic findings at diagnosis are important factors affecting the outcome for patients with AML.[31,32] In our study, patients with PTPN11mut more commonly had a normal karyotype (61% vs 45%; P < .001) or inv(3)(q21q26)/t(3;3)(q21;q26) (5% vs 1%; P = .004) than patients with PTPN11wt. The latter finding was especially striking because as many as 26% (7 of 27) of patients with inv(3)/t(3;3) harbored a PTPN11 mutation, as previously reported.[33] Moreover, all 7 of these patients also had abnormalities in chromosome 7, including ‒7 in six and a deletion of the short arm of chromosome 7 [del(7)(p13p15)] in one patient. In contrast, PTPN11 mutations were less commonly observed in patients with a typical complex karyotype (3% vs 8%; P = .04)[34] and in those with core-binding factor AML. There were no PTPN11mut patients with t(8;21)(q22;q22) (0% vs 100%; P = .005), and only 2% of patients with PTPN11mut harbored inv(16)(p13;q22)/t(16;16)(p13;q22) compared with 7% of patients with PTPN11wt (P = .03) (supplemental Table 1). For other cytogenetic abnormalities, there were no significant associations with PTPN11 mutations. In addition to cytogenetic findings at diagnosis, recurrent gene mutations have emerged as important factors affecting the outcome of patients with AML.[25] Previous studies focusing on PTPN11-mutant AML mainly included a limited number of recurrently mutated genes, whereas 2 very recently published papers and our own study examined a broader mutation panel relevant to AML.[27,35] We noted that patients with PTPN11mut have a higher mutation rate (median number of mutations, 4 vs 3; P < .001) than PTPN11wt patients, albeit this finding is based on a targeted sequencing panel. An oncoprint of the 140 patients with PTPN11 mutations shows the co-occurring gene mutations (Figure 1).[28,29] Notably, patients with PTPN11mut more frequently harbored NPM1 (61% vs 31%; P < .001), DNMT3A (39% vs 22%; P < .001), and STAG2 (6% vs 3%; P = .04) mutations than those with PTPN11wt. In a similar fashion, patients with PTPN11mut less frequently had double-mutated CEBPA (1% vs 8%; P = .003), KIT (1% vs 5%; P = .04), ZRSR2 (1% vs 5%; P = .04), and TP53 (4% vs 8%; P = .05) mutations (supplemental Table 2). Because PTPN11 mutations tend to cluster in the N-SH2 and PTP domains, which are both involved in SHP2 self-inhibition, we questioned whether mutations in different domains of the PTPN11 gene resulted in comparable pretreatment clinical characteristics. Eighty-six patients had N-SH2 domain mutations, and 45 patients had PTP domain mutations. We found that the only difference at baseline was that patients with N-SH2 mutations had a higher percentage of blasts in the BM (median, 65% vs 52%; P = .03) (Table 2). There were no significant differences in distribution of cytogenetic findings between N-SH2 and PTP PTPN11-mutated patients (supplemental Table 3). Patients with N-SH2 mutations were less likely to have GATA2 (0% vs 7%; P = .04) and PLCG2 (0% vs 7%; P = .04) mutations than patients with PTP mutations (supplemental Table 4).
Table 2.

Pretreatment characteristics of PTPN11mut patients according to the location of the mutation within the gene

CharacteristicPTPN11mut N-SH2 (n = 86)PTPN11mut phosphatase (n = 45) P *
Age, y .46
 Median5451
 Range18-8223-79
Sex, n (%) .46
 Male41 (48)25 (56)
 Female45 (52)20 (44)
Race, n (%) 1.00
 White76 (88)39 (89)
 Non-white10 (12)5 (11)
Hemoglobin, g/dL .98
 Median9.19.2
 Range5.7-13.86.0-15.0
Platelet count, ×109/L .22
 Median7282
 Range13-64817-415
WBC count, ×109/L .89
 Median31.331.6
 Range1.5-355.01.4-135.0
% Blood blasts .22
 Median5242
 Range0-970-88
% BM blasts .03
 Median6552
 Range12-9915-90
Extramedullary involvement, n (%)27 (33)15 (34)1.00

BM, bone marrow; WBC, white blood cell.

P values are from Fisher’s exact test for discrete variables and from the Wilcoxon rank sum test for continuous variables.

Pretreatment characteristics of PTPN11mut patients according to the location of the mutation within the gene BM, bone marrow; WBC, white blood cell. P values are from Fisher’s exact test for discrete variables and from the Wilcoxon rank sum test for continuous variables.

Outcomes of AML patients with PTPN11 mutations

We compared clinical outcomes of patients with and without PTPN11 mutations both in the entire patient cohort and, separately, in younger and older patients. There were no significant differences in CR, early death rates, or DFS, OS, and EFS between patients with PTPN11mut and PTPN11wt in the entire cohort (supplemental Table 5). We then stratified patients into two age groups, those aged <60 years and those aged ≥60 years, because these patients were treated differently on CALGB/Alliance protocols. Although the presence of PTPN11 mutations did not associate with significant differences in early death rates, CR rates, OS, or EFS in either older or younger patients, older patients harboring a PTPN11mut had a shorter DFS (3-year rates, 5% vs 15%; P = .05) than PTPN11wt patients (supplemental Table 6). We also studied whether mutations in different domains of the PTPN11 gene affected patients’ outcomes. The only difference we detected was that 20% of patients with the PTPN11 mutations located in the N-SH2 domain died early as opposed to no early deaths among patients with PTPN11 mutations in the PTP domain (P < .001) (supplemental Table 7). There were no significant differences in CR rates or DFS, OS, and EFS between the 2 groups (Figure 3). In addition, higher early death rates (P = .02), but no other significant outcome differences, were found in younger patients with N-SH2 domain PTPN11 mutations compared with those with a mutation in the PTP domain. In the older age group, there were no significant differences in outcome (supplemental Table 8).
Figure 3.

Outcomes of patients with mutations in the N-SH2 domain of the (A) DFS based on the presence of an N-SH2 domain (blue line) or PTP domain (red line) PTPN11 mutation. (B) OS for patients with an N-SH2 domain (blue line) or PTP domain (red line) PTPN11 mutation. (C) EFS based on the presence of an N-SH2 domain (blue line) or PTP domain (red line) PTPN11 mutation.

Outcomes of patients with mutations in the N-SH2 domain of the (A) DFS based on the presence of an N-SH2 domain (blue line) or PTP domain (red line) PTPN11 mutation. (B) OS for patients with an N-SH2 domain (blue line) or PTP domain (red line) PTPN11 mutation. (C) EFS based on the presence of an N-SH2 domain (blue line) or PTP domain (red line) PTPN11 mutation.

PTPN11 mutations result in a different mutational phenotype but do not affect outcomes in NPM1mut patients

Given that 85 (61%) of the 140 patients with PTPN11mut also harbored an NPM1 mutation, we next sought to determine if the clinical and molecular features differed between NPM1-mutated patients with or without PTPN11 mutations. With regard to pretreatment characteristics, patients with NPM1mut/PTPN11mut had a higher baseline platelet counts (median, 78 vs 59 × 109/L; P = .008) (supplemental Table 9). Distribution of cytogenetic aberrations was similar between the 2 groups (supplemental Table 10). Notably, patients with NPM1mut/PTPN11mut had a higher frequency of DNMT3A mutations (56% vs 43%; P = .03), whereas FLT3-ITD (19% vs 44%; P < .001) was less frequent in this genomic group compared with patients with NPM1mut/PTPN11wt (supplemental Table 11). This suggests NPM1mut/PTPN11mut clones are less dependent on additional signaling mutations such as FLT3-ITD. Despite these differences in baseline biology, there were no significant differences in any of the outcome end points between NPM1-mutated patients with and those without PTPN11 mutations regardless of age (Figure 4A; supplemental Tables 12 and 13).
Figure 4.

OS of younger (age <60 years) patients. (A) OS in younger patients with NPM1mut/PTPN11wt (blue line) and NPM1mut/PTPN11mut (red line). (B) OS for younger patients based on the presence of NPM1wt/PTPN11wt (blue line) and NPM1wt/PTPN11mut (red line).

OS of younger (age <60 years) patients. (A) OS in younger patients with NPM1mut/PTPN11wt (blue line) and NPM1mut/PTPN11mut (red line). (B) OS for younger patients based on the presence of NPM1wt/PTPN11wt (blue line) and NPM1wt/PTPN11mut (red line).

PTPN11 mutations negatively influence outcome of patients with NPM1wt

We were also interested if PTPN11 mutations can influence outcomes of patients with NPM1wt. A comparison of pretreatment characteristics between patients with PTPN11wt and PTPN11mut revealed no significant differences (supplemental Table 14). Cytogenetically, patients with NPM1wt/PTPN11mut were more likely to harbor prognostically unfavorable inv(3)(q21q26)/t(3;3)(q21;q26) (13% vs 2%; P < .001), other balanced rearrangements involving 3q26 (4% vs 0.2%; P = .01), and t(11;19)(q23;p13.3)/KMT2A-MLLT1 (4% vs 0.4%; P = .03) (supplemental Table 15) compared with patients with NPM1wt/PTPN11wt. Moreover, patients with NPM1wt/PTPN11mut had a higher median number of mutations (3 vs 2; P < .001) than those with NPM1wt/PTPN11wt and were more likely to have KMT2A (7% vs 1%; P = .006) and NF1 (21% vs 6%; P = .01) mutations (supplemental Table 16). Among combined younger and older patients with NPM1wt, those with PTPN11mut had a lower CR rate (36% vs 61%; P < .001) and shorter EFS (3-year rates, 9% vs 19%; P = .003) than patients with PTPN11wt (supplemental Table 17). Likewise, younger patients with NPM1wt/PTPN11mut had a lower CR rate (45% vs 71%; P = .002), OS (3-year rates, 30% vs 41%; P = .04) (Figure 4B), and EFS (3-year rates, 13% vs 27%; P = .008), but not DFS, than those with NPM1wt/PTPN11wt. Older patients with NPM1wt/PTPN11mut also had a lower CR rate (18% vs 43%; P = .04), DFS (3-year rates, 0% vs 10%; P = .02), and EFS (3-year rates, 0% vs 4%; P = .02), but not OS, compared with patients with NPM1wt/PTPN11wt (Table 3).
Table 3.

Outcomes of AML patients with wild-type NPM1 based on the presence of a PTPN11 mutation

End point PTPN11 mut PTPN11 wt P *
(n = 38)(n = 703)
Younger patients (age <60 y)
 Early death, n (%)3 (8)33 (5).42
 CR, n (%)17 (45)498 (71).002
 DFS.96
  Median, y2.21.2
  % Disease free at 1 y (95% CI)65 (38-82)55 (51-60)
  % Disease free at 3 y (95% CI)29 (11-51)38 (33-42)
 OS.04
  Median, y0.81.8
  % Alive at 1 y (95% CI)46 (30-61)67 (63-70)
  % Alive at 3 y (95% CI)30 (16-45)41 (38-45)
 EFS.008
  Median, y0.20.8
  % Event-free at 1 y (95% CI)29 (16-44)41 (37-45)
  % Event-free at 3 y (95% CI)13 (5-26)27 (24-30)

CI, confidence interval.

P values are from Fisher’s exact test for early death and CR and from the log-rank test for DFS, OS, and EFS.

Outcomes of AML patients with wild-type NPM1 based on the presence of a PTPN11 mutation CI, confidence interval. P values are from Fisher’s exact test for early death and CR and from the log-rank test for DFS, OS, and EFS. Multivariable analyses were performed to determine what other factors, including gene mutations, associated with inferior outcomes of AML patients with NPM1wt. We could not perform separate multivariable analyses in younger and older patients because there would have been too few patients to obtain meaningful results. In the multivariable modeling for CR attainment, mutations in PTPN11, TP53, and FLT3-ITD and age remained in the final model (Table 4), indicating that PTPN11 mutations still affect the probability of CR achievement even when accounting for other variables (P < .001). However, in the multivariable analyses of OS and EFS, PTPN11 mutations did not remain significant in the final models.
Table 4.

Multivariable analysis for CR attainment, OS, and EFS in AML patients with wild-type NPM1 (younger and older patients combined)

CR
Variable P * Odds ratio (95% CI)
PTPN11, mutated vs wild-type<.0010.30 (0.16-0.56)
TP53, mutated vs wild-type<.0010.37 (0.24-0.56)
FLT3-ITD, positive vs negative<.0010.44 (0.30-0.63)
Age, continuous<.0010.70 (0.64-0.76)

CI, confidence interval; WBC, white blood cell.

P values for logistic and proportional hazard regression are from the likelihood ratio test. An odds ratio <1 (>1) means higher (lower) CR rate for higher values of continuous variables and the first level listed of a dichotomous variable. A hazard ratio >1 (<1) corresponds to a higher (lower) risk for higher values of continuous variables and the first level listed of a dichotomous variable.

Multivariable analysis for CR attainment, OS, and EFS in AML patients with wild-type NPM1 (younger and older patients combined) CI, confidence interval; WBC, white blood cell. P values for logistic and proportional hazard regression are from the likelihood ratio test. An odds ratio <1 (>1) means higher (lower) CR rate for higher values of continuous variables and the first level listed of a dichotomous variable. A hazard ratio >1 (<1) corresponds to a higher (lower) risk for higher values of continuous variables and the first level listed of a dichotomous variable.

Discussion

Herein, we showed that PTPN11 mutations may affect clinical outcomes dependent on age group and mutation subset analyses in a retrospective study of patients with AML receiving intensive therapy in clinical trials performed by the CALGB/Alliance. Although the presence of a PTPN11 mutation in addition to an NPM1 mutation did not associate with poorer outcomes (with the exception of older patients with PTPN11mut having a marginally reduced DFS compared with patients with wild-type PTPN11), PTPN11 mutations did associate with inferior outcomes in AML patients with NPM1wt regardless of age. We also found that patients with PTPN11 mutations in the N-SH2 domain had higher BM blast counts and early death rate than those with PTP domain mutations. These results suggest that an N-SH2 mutation might generate a different phenotype. We hypothesize this phenotype could be immunosuppressive, explaining the higher early death rate but no difference in response to chemotherapy induction, DFS, OS, or EFS. Collectively, our study outlines the complex effects of a PTPN11 mutation in AML and provides evidence that its prognostic impact should be considered in the context of NPM1 mutation status. Similar to others, we also found an association between PTPN11 mutations and inv(3)(q21q26)/t(3;3)(q21;q26), the later aberration being a marker of poor prognosis in AML.[25,33] We also confirmed that PTPN11 mutations are less likely to occur in patients with typical complex karyotype and those with core-binding factor AML[27] but are most often found together with NPM1 mutations.[27,36] We also found an association between PTPN11 mutations and mutations in DNMT3A or STAG2. Furthermore, we observed that there were few patients with PTPN11mut who also had co-mutations in CEBPA, KIT, TP53, and ZRSR2. Our analysis of co-occurring mutations in patients with NPM1mut/PTPN11mut revealed that these patients had a lower frequency of co-occurring FLT3-ITD mutations, suggesting that PTPN11 and FLT3-ITD mutations result in activation of overlapping signaling pathways. There are few published data regarding how PTPN11 mutations affect clinical outcomes. Hou et al[37] and Swoboda et al[35] have shown that patients with NPM1wt/PTPN11mut had reduced OS compared with patients with NPM1wt/PTPN11wt, and Alfayez et al[27] reported that PTPN11 mutations are associated with poor outcomes for both de novo and relapsed/refractory AML. Our current study validates these findings but also goes further by analyzing a larger cohort of patients, which allowed us to stratify the patients according to age. Furthermore, our study analyzed associated mutations and questioned how the location of the mutation within the PTPN11 gene affected outcome. A limitation of our study is the time span over which these patients were treated and the fact that, on these clinical trials, patients received only intensive induction followed by consolidation chemotherapy. Supportive care for AML has clearly improved over time with the addition of more effective proton pump inhibitors, antifungal agents, and transfusion support. In addition, patients with FLT3 mutations on this study typically did not receive midostaurin. Among AML patients with NPM1wt, 15% of patients with PTPN11mut also harbored FLT3-ITD, raising a possibility that inferior outcomes in PTPN11-mutated patients could be associated with FLT3-ITD. However, both PTPN11 mutations and FLT3-ITD stayed in the multivariable model, suggesting that they negatively affect outcomes independently from each other. Hence, we believe our findings are relevant to the current era of AML therapy, and moving forward, it will be important to study how PTPN11 mutations affect responses to the newly approved targeted therapies, as early evidence suggests that these patients might be resistant.[16-20,38] More clinical studies and basic science research are needed to understand how SHP2 and NPM1 proteins are interacting and why PTPN11 mutations are associated with worse outcome in AML patients with NPM1wt.

Supplementary Material

The full-text version of this article contains a data supplement. Click here for additional data file.
  36 in total

1.  Prognostic significance of, and gene and microRNA expression signatures associated with, CEBPA mutations in cytogenetically normal acute myeloid leukemia with high-risk molecular features: a Cancer and Leukemia Group B Study.

Authors:  Guido Marcucci; Kati Maharry; Michael D Radmacher; Krzysztof Mrózek; Tamara Vukosavljevic; Peter Paschka; Susan P Whitman; Christian Langer; Claudia D Baldus; Chang-Gong Liu; Amy S Ruppert; Bayard L Powell; Andrew J Carroll; Michael A Caligiuri; Jonathan E Kolitz; Richard A Larson; Clara D Bloomfield
Journal:  J Clin Oncol       Date:  2008-09-22       Impact factor: 44.544

2.  Genetic alterations and their clinical implications in older patients with acute myeloid leukemia.

Authors:  C-H Tsai; H-A Hou; J-L Tang; C-Y Liu; C-C Lin; W-C Chou; M-H Tseng; Y-C Chiang; Y-Y Kuo; M-C Liu; C-W Liu; L-I Lin; W Tsay; M Yao; C-C Li; S-Y Huang; B-S Ko; S-C Hsu; C-Y Chen; C-T Lin; S-J Wu; H-F Tien
Journal:  Leukemia       Date:  2016-03-17       Impact factor: 11.528

3.  The Clinical impact of PTPN11 mutations in adults with acute myeloid leukemia.

Authors:  Mansour Alfayez; Ghayas C Issa; Keyur P Patel; Feng Wang; Xuemei Wang; Nicholas J Short; Jorge E Cortes; Tapan Kadia; Farhad Ravandi; Sherry Pierce; Rita Assi; Guillermo Garcia-Manero; Courtney D DiNardo; Naval Daver; Naveen Pemmaraju; Hagop Kantarjian; Gautam Borthakur
Journal:  Leukemia       Date:  2020-06-19       Impact factor: 11.528

4.  Absence of the wild-type allele predicts poor prognosis in adult de novo acute myeloid leukemia with normal cytogenetics and the internal tandem duplication of FLT3: a cancer and leukemia group B study.

Authors:  S P Whitman; K J Archer; L Feng; C Baldus; B Becknell; B D Carlson; A J Carroll; K Mrózek; J W Vardiman; S L George; J E Kolitz; R A Larson; C D Bloomfield; M A Caligiuri
Journal:  Cancer Res       Date:  2001-10-01       Impact factor: 12.701

5.  Noonan syndrome-associated SHP2/PTPN11 mutants cause EGF-dependent prolonged GAB1 binding and sustained ERK2/MAPK1 activation.

Authors:  Alessandra Fragale; Marco Tartaglia; Jie Wu; Bruce D Gelb
Journal:  Hum Mutat       Date:  2004-03       Impact factor: 4.878

Review 6.  Diagnosis and management of acute myeloid leukemia in adults: recommendations from an international expert panel, on behalf of the European LeukemiaNet.

Authors:  Hartmut Döhner; Elihu H Estey; Sergio Amadori; Frederick R Appelbaum; Thomas Büchner; Alan K Burnett; Hervé Dombret; Pierre Fenaux; David Grimwade; Richard A Larson; Francesco Lo-Coco; Tomoki Naoe; Dietger Niederwieser; Gert J Ossenkoppele; Miguel A Sanz; Jorge Sierra; Martin S Tallman; Bob Löwenberg; Clara D Bloomfield
Journal:  Blood       Date:  2009-10-30       Impact factor: 22.113

7.  Central review of cytogenetics is necessary for cooperative group correlative and clinical studies of adult acute leukemia: the Cancer and Leukemia Group B experience.

Authors:  Krzysztof Mrózek; Andrew J Carroll; Kati Maharry; Kathleen W Rao; Shivanand R Patil; Mark J Pettenati; Michael S Watson; Diane C Arthur; Ramana Tantravahi; Nyla A Heerema; Prasad R K Koduru; Annemarie W Block; Mazin B Qumsiyeh; Colin G Edwards; Lisa J Sterling; Kelsi B Holland; Clara D Bloomfield
Journal:  Int J Oncol       Date:  2008-08       Impact factor: 5.650

8.  Mutated Ptpn11 alters leukemic stem cell frequency and reduces the sensitivity of acute myeloid leukemia cells to Mcl1 inhibition.

Authors:  L Chen; W Chen; M Mysliwski; J Serio; J Ropa; F A Abulwerdi; R J Chan; J P Patel; M S Tallman; E Paietta; A Melnick; R L Levine; O Abdel-Wahab; Z Nikolovska-Coleska; A G Muntean
Journal:  Leukemia       Date:  2015-02-04       Impact factor: 11.528

9.  The mutational oncoprint of recurrent cytogenetic abnormalities in adult patients with de novo acute myeloid leukemia.

Authors:  A-K Eisfeld; K Mrózek; J Kohlschmidt; D Nicolet; S Orwick; C J Walker; K W Kroll; J S Blachly; A J Carroll; J E Kolitz; B L Powell; E S Wang; R M Stone; A de la Chapelle; J C Byrd; C D Bloomfield
Journal:  Leukemia       Date:  2017-03-24       Impact factor: 11.528

10.  Integrated analysis of patient samples identifies biomarkers for venetoclax efficacy and combination strategies in acute myeloid leukemia.

Authors:  Haijiao Zhang; Yusuke Nakauchi; Thomas Köhnke; Melissa Stafford; Daniel Bottomly; Rozario Thomas; Beth Wilmot; Shannon K McWeeney; Ravindra Majeti; Jeffrey W Tyner
Journal:  Nat Cancer       Date:  2020-08-18
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  2 in total

1.  Impact of PTPN11 mutations on clinical outcome analyzed in 1529 patients with acute myeloid leukemia.

Authors:  Sebastian Stasik; Jan-Niklas Eckardt; Michael Kramer; Christoph Röllig; Alwin Krämer; Sebastian Scholl; Andreas Hochhaus; Martina Crysandt; Tim H Brümmendorf; Ralph Naumann; Björn Steffen; Volker Kunzmann; Hermann Einsele; Markus Schaich; Andreas Burchert; Andreas Neubauer; Kerstin Schäfer-Eckart; Christoph Schliemann; Stefan Krause; Regina Herbst; Mathias Hänel; Norbert Frickhofen; Richard Noppeney; Ulrich Kaiser; Claudia D Baldus; Martin Kaufmann; Zdenek Rácil; Uwe Platzbecker; Wolfgang E Berdel; Jiri Mayer; Hubert Serve; Carsten Müller-Tidow; Gerhard Ehninger; Martin Bornhäuser; Johannes Schetelig; Jan M Middeke; Christian Thiede
Journal:  Blood Adv       Date:  2021-09-14

2.  Molecular profiling and clinical implications of patients with acute myeloid leukemia and extramedullary manifestations.

Authors:  Jan-Niklas Eckardt; Friedrich Stölzel; Desiree Kunadt; Christoph Röllig; Sebastian Stasik; Lisa Wagenführ; Korinna Jöhrens; Friederike Kuithan; Alwin Krämer; Sebastian Scholl; Andreas Hochhaus; Martina Crysandt; Tim H Brümmendorf; Ralph Naumann; Björn Steffen; Volker Kunzmann; Hermann Einsele; Markus Schaich; Andreas Burchert; Andreas Neubauer; Kerstin Schäfer-Eckart; Christoph Schliemann; Stefan W Krause; Regina Herbst; Mathias Hänel; Maher Hanoun; Ulrich Kaiser; Martin Kaufmann; Zdenek Rácil; Jiri Mayer; Frank Kroschinsky; Wolfgang E Berdel; Gerhard Ehninger; Hubert Serve; Carsten Müller-Tidow; Uwe Platzbecker; Claudia D Baldus; Johannes Schetelig; Martin Bornhäuser; Christian Thiede; Jan Moritz Middeke
Journal:  J Hematol Oncol       Date:  2022-05-13       Impact factor: 23.168

  2 in total

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