The prognostic impact of non‐driver gene mutations and variant allele frequency in primary myelofibrosis
Authors: Alberto Hernández‐Sánchez, Ángela Villaverde‐Ramiro, Eduardo Arellano‐Rodrigo, Marta Garrote, Iván Martín, Adrián Mosquera‐Orgueira, María‐Teresa Gómez‐Casares, Francisca Ferrer‐Marín, Esperanza Such, Patricia Velez, Rosa Ayala, Anna Angona, Natalia de las Heras, Elena Magro, María‐Isabel Mata‐Vázquez, María‐Laura Fox, Sonia González de Villambrosía, María‐José Ramírez, Ana García, Valentín García‐Gutiérrez, Amparo Cáceres, María‐Antonia Durán, Alicia Senín, José‐María Raya, José Antonio González, Beatriz Cuevas, Blanca Xicoy, Manuel Pérez‐Encinas, Beatriz Bellosillo, Alberto Álvarez‐Larrán, Jesús María Hernández‐Rivas, Juan Carlos Hernández‐Boluda
Journal: American Journal of Hematology (2024)

Prognostic impact of non-MPN driver gene mutations in primary myelofibrosis. MIPSS70: Mutation-Enhanced International Prognostic Score System. Myelofibrosis is a Philadelphia-negative chronic myeloproliferative neoplasm (MPN) characterized by alteration of the JAK–STAT pathway, primarily through activating mutations in MPN driver genes (JAK2, CALR, and MPL). However, patients usually have mutations in other genes related to myeloid neoplasms that may influence the clinical phenotype or treatment response (non-MPN driver genes). Allogeneic hematopoietic stem cell transplantation (allo-HSCT) remains the only curative treatment, but given the significant mortality associated with the procedure, it is essential to identify high-risk patients who can benefit from it. The Mutation-Enhanced International Prognostic Score System (MIPSS70) and its subsequent update (MIPSS70+ v2.0) integrate clinical, molecular (mutations in ASXL1, SRSF2, EZH2, IDH1/2, and U2AF1 genes), and cytogenetic data to identify high-risk patients in whom allo-HSCT should be considered.1, 2 Recently, the adverse prognostic significance of ASXL1 mutations has been questioned, with their impact seemingly limited to primary myelofibrosis (PMF).3, 4 Moreover, several additional gene mutations not originally included in the MIPSS models have been associated with poor prognosis in PMF3. A recent study examined the prognostic value of mutations in NRAS, KRAS, CBL, RUNX1, and TP53 genes in PMF, along with their contribution on the discriminative accuracy of the MIPSS models.5 Considering the low frequency of these high-risk mutations, external validation of their findings is necessary. In the present study, approved by the institutional committee of the Spanish MPN Group (GEMFIN), our aim was to analyze the impact of non-MPN driver somatic mutations in PMF patients from 60 Spanish institutions. Out of a total of 581 patients with myelofibrosis (either PMF or secondary myelofibrosis after polycythemia vera or essential thrombocythemia) according to WHO criteria in use at the time of diagnosis, and with availability of molecular analysis by massive next-generation sequencing (NGS), we selected 312 patients with PMF for this analysis. Targeted NGS sequencing was performed locally, although most cases (>75%) were analyzed at nine referral centers. Despite evaluating up to 56 genes, only 20 were consistently analyzed across NGS panels, with a missing rate <5% (ASXL1, CALR, CBL, DNMT3A, EZH2, IDH1, IDH2, JAK2, KIT, KRAS, MPL, NRAS, RUNX1, SETBP1, SF3B1, SRSF2, TET2, TP53, U2AF1, and ZRSR2). A centralized review of NGS results was performed, and only pathogenic or likely-pathogenic genetic variants with a variant allele frequency (VAF) higher than 1% were considered as mutations. Cytogenetic information was available for less than 40% of the patients and was therefore excluded for this analysis. Patients who received allo-HSCT were censored at the date of transplantation in survival analyses. The main characteristics of the series are elicited in Table S1. The median age of the cohort was 68 years. After a median follow-up of 4 years (5.8 years for surviving patients), 9% of patients had progressed to acute myeloid leukemia (AML) and 47% had died. Overall, this cohort exhibited older age and a higher proportion of high-risk features compared to that reported by Loscocco et al.5 Non-MPN driver mutations were detected in 72% of the patients and 47% presented two or more mutations (range: 0–7). The most frequent mutations were in ASXL1 (34%), TET2 (22%), SRSF2 (17%), U2AF1 Q157 (9%), CBL (8%), EZH2 (7%), TP53 (6%), DNMT3A (6%), and SETBP1 genes (5%). A single MIPSS70 high-risk mutation was detected in 35%, while 17% cases had two or more mutations. Overall, the mere presence of any NGS-detected mutation was associated with worse outcome, with a median overall survival (OS) of 12.4 years for unmutated cases, 8.7 years for patients with a single non-MPN driver mutation, and 4.4 years for cases with ≥2 mutations (p < .001). In the univariate analysis, the following gene mutations were associated with shorter OS: SRSF2 (p < 0.001), CBL (p < 0.001), SETBP1 (p = .012), TP53 (p = .016), U2AF1 Q157 (p = .021), NRAS (p = .032), and EZH2 (p = .034), with a trend for KRAS (p = .053). Of note, we did not observe statistically significant differences in OS when comparing ASXL1-mut patients to the rest of the cohort (p = .102), taking into account the VAF cutoff of our study (>1%). However, when considering ASXL1-mut with a VAF >5%, this mutation was also associated with a shorter OS (p = .018). Subsequently, other cutoff points were explored, with the most significant results observed for VAF > 20% (p = .003), as shown in Table 1. In fact, patients with ASXL1-mut and VAF 1%–20% presented similar OS to cases with ASXL1-wildtype, which was significantly longer than that of ASXL1-mut patients with VAF > 20% (median OS: 9.2, 8.7, and 4.8 years, respectively, p = .007), as shown in Figure S1. Interestingly, patients with ASXL1 G646Wfs*12 mutation had similar OS compared to patients with other ASXL1 mutations, whereas VAF 20% cutoff was prognostically relevant for both ASXL1 G646Wfs*12 and other ASXL1 mutations (Figure S2). The prognostic impact of gene mutations that showed significance in the univariate analysis but were not included in MIPSS70 (CBL, SETBP1, TP53, NRAS, and KRAS) was also analyzed to assess their potential for enhancing the discriminative capacity of the model. In the intermediate risk group of MIPSS70, patients with any of the aforementioned five gene mutations had a significant shorter OS when compared to the remaining MIPSS70 intermediate risk patients (median OS: 4.4 years vs. 8.7 years, p = .001, Figure S3). Additionally, in the high-risk group of MIPSS70, patients with a mutation in any of the five genes also had inferior OS when compared to the remaining MIPSS70 high-risk patients (median OS: 3.5 years vs. 6.2 years, p = .002, Figure S4). Moreover, the incorporation of the abovementioned five gene mutations to MIPSS70 score was able to enhance the discriminative capacity of the model (C-index 0.67 for MIPSS70 alone and 0.71 for the combined model), with independent prognostic value for CBL-mut (p = .008), SETBP1-mut (p < .001), and NRAS-mut (p = .015) and a trend for KRAS-mut (p = .054) and TP53-mut (p = .091), as shown in Figure S5. In the multivariate analysis of OS using Cox proportional hazards model, which incorporated clinical and hematological variables of MIPSS70, the following non-MPN driver gene mutations were identified as independent prognostic factors: SRSF2 (HR 2.4, p < .001), CBL (HR 2.4, p = .003), EZH2 (HR 2.3, p = .013), SETBP1 (HR 2.6, p = .024), and ASXL1 with VAF > 20% (p = .042). In addition, there was a trend close to statistical significance for TP53 gene mutations (HR 2, p = .068), as shown in Table 1. A second multivariate model was performed, including patient age as a variable. In this model, the following gene mutations independently impacted OS: SRSF2 (HR 2, p = .004), CBL (HR 2.7, p < .001), EZH2 (HR 2.6, p = .005), SETBP1 (HR 3.1, p = .01), TP53 (HR 2.4, p = .024), and ASXL1 with VAF > 20% (HR 1.7, p = .01). Interestingly, a multivariate model including only age and gene mutations presented a high discriminative capacity for predicting OS (C-index: 0.78, Figure S6), with independent prognostic value for ASXL1-mut with VAF > 20% (p = .003), EZH2-mut (p = .006), SRSF2-mut (p = .012), TP53-mut (p = .011), CBL-mut (p = .003), and SETBP1-mut (p = .002), highlighting the prognostic value of non-MPN driver gene mutations in PMF. Finally, separate univariate and multivariate OS analyses were performed for patients aged ≤70 years (Table S2) and >70 years (Table S3). Overall, we confirm the adverse prognostic role of genes included in MIPSS70, except for IDH1/2, possibly due to limitations in sample size. To our knowledge, this is the first study to report that the prognostic value of ASXL1 mutations is dependent on VAF, exhibiting an incremental effect in which the higher the VAF the worse OS. This finding may contribute to understand the conflicting results reported previously on this issue. The pioneering multicenter study by Vannucchi et al. demonstrated the detrimental effect of ASXL1 mutations on OS in PMF using Sanger sequencing—a technique with low sensitivity, capable of detecting mutants only with at least 15%–20% VAF.6 Subsequently, Paz et al., using a VAF cutoff of >2%, failed to identify an adverse impact of isolated ASXL1-mut on OS,3 while Loscocco et al, applying a VAF >5%, confirmed the deleterious effect of ASXL1-mut.5 Moreover, Guglielmelli et al. demonstrated that ASXL1 VAF is significantly higher in PMF compared to secondary myelofibrosis, potentially explaining the prognostic impact of ASXL1-mut limited to PMF4. In addition, we confirmed the independent prognostic value of CBL-mut and TP53-mut in our series. The adverse prognostic role of SETBP1-mut in PMF is also noteworthy, a finding that warrants validation in future studies. Finally, it should be noted that a major limitation of this study was the lack of cytogenetic information for most patients, which prevented an adequate assessment of the presence of multi-hit TP53 alterations. In conclusion, the present study confirms the adverse prognostic role of gene mutations included in current prognostic scores for PMF.1, 2 Furthermore, it lends support to the independent prognostic impact of CBL, TP53, and SETBP1 mutations. In line with the findings by Loscocco et al., we advocate for the inclusion of TP53 mutations in the MIPSS models, considering their independent prognostic significance, while acknowledging the need for more data on CBL and SETBP1 mutations. Our data indicate that the prognostic impact of ASXL1 mutations is dependent on VAF, with a VAF >20% emerging as an adverse prognostic factor in PMF. This finding is particularly noteworthy given that ASXL1 mutations are the most prevalent high-risk mutations in PMF, influencing decisions regarding allo-HSCT. Juan Carlos Hernández-Boluda prepared the GEMFIN database; Alberto Hernández-Sánchez and Ángela Villaverde-Ramiro performed the statistical analysis; Alberto Hernández-Sánchez, Jesús María Hernández-Rivas, and Juan Carlos Hernández-Boluda wrote the manuscript; all co-authors critically evaluated the manuscript, made substantial recommendations, and approved the submission of this manuscript. We are indebted to all centres participating in the GEMFIN databases. The Spanish Registry of Myelofibrosis was initially sponsored by a grant from Novartis Pharmaceuticals, Inc. All authors declare no competing financial interests. After the publication of this article, data collected for this analysis and related documents will be made available to others upon reasonably justified request, which needs to be written and addressed to the attention of the corresponding author Dr Juan Carlos Hernández Boluda at the following e-mail address: [email protected]. Juan Carlos Hernández Boluda, on behalf of Spanish MPN Group (GEMFIN) is responsible to evaluate and eventually accept or refuse every request to disclose data and their related documents, in compliance with the ethical approval conditions, in compliance with applicable laws and regulations, and in conformance with the agreements in place with the involved subjects, the participating institutions, and all the other parties directly or indirectly involved in the participation, conduct, development, management, and evaluation of this analysis. Table S1. Main characteristics of the study cohort (GEMFIN) compared to the one reported by Loscocco et al. Table S2. Univariate and multivariate analysis of overall survival in patients aged ≤70 years with primary myelofibrosis from the GEMFIN registry (n = 178). Table S3. Univariate and multivariate analysis of overall survival in patients aged >70 years with primary myelofibrosis from the GEMFIN registry (n = 134). Figure S1. Overall survival by ASXL1 status (wildtype, mutated with VAF 1–20% and mutated with VAF > 20%). VAF: variant allele frequency. Figure S2. Overall survival by ASXL1 mutation type (G646Wfs*12 vs. others) and mutation VAF >20% (A) or VAF <20% (B). Figure S3. Prognostic impact of CBL, SETBP1, TP53, NRAS, and KRAS mutations, limited to patients with MIPSS70 intermediate risk. Figure S4. Prognostic impact of CBL, SETBP1, TP53, NRAS, and KRAS mutations, limited to patients with MIPSS70 high risk. Figure S5. Multivariate analysis of overall survival, with MIPSS70 categories only (A) and the incorporation of CBL, SETBP1, TP53, NRAS, and KRAS mutations (B). Figure S6. Multivariate analysis of overall survival, including age and gene mutations only. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.