Molecular landscape and validation of new genomic classification in 2668 adult AML patients: real life data from the PETHEMA registry

Citation: Sargas, C.; Ayala, R.; Larráyoz, M.J.; Chillón, M.C.; Carrillo-Cruz, E.; Bilbao-Sieyro, C.; Prados de la Torre, E.; Martínez-Cuadrón, D.; Rodríguez-Veiga, R.; Boluda, B.; et al. Molecular Landscape and Validation of New Genomic Classification in 2668 Adult AML Patients: Real Life Data from the PETHEMA Registry. Cancers 2023,15, 438. https:// doi.org/10.3390/cancers15020438 Academic Editor: Sibaji Sarkar Received: 22 November 2022 Revised: 30 December 2022 Accepted: 7 January 2023 Published: 10 January 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). cancers Article Molecular Landscape and Validation of New Genomic Classification in 2668 Adult AML Patients: Real Life Data from the PETHEMA Registry Claudia Sargas 1, Rosa Ayala 2, María JoséLarráyoz 3, María Carmen Chillón4, Estrella Carrillo-Cruz 5, Cristina Bilbao-Sieyro 6, Esther Prados de la Torre 7, David Martínez-Cuadrón8,9, Rebeca Rodríguez-Veiga 8, Blanca Boluda 8, Cristina Gil 10, Teresa Bernal 11, Juan Miguel Bergua 12, Lorenzo Algarra 13, Mar Tormo 14 , Pilar Martínez-Sánchez 2, Elena Soria 5, Josefina Serrano 7, Juan Manuel Alonso-Domínguez 15 , Raimundo García-Boyero 16, María Luz Amigo 17, Pilar Herrera-Puente 18, María JoséSayas 19, Esperanza Lavilla-Rubira 20 , Joaquín Martínez-López 2, María JoséCalasanz 3, Ramón García-Sanz 4, JoséAntonio Pérez-Simón5, María Teresa Gómez-Casares 6, Joaquín Sánchez-García7, Eva Barragán10,21,*,†, Pau Montesinos 8,10,† and on behalf of PETHEMA group ‡ 1Grupo Acreditado de Investigación en Hematología, Instituto de Investigación Sanitaria La Fe (IIS La Fe), 46026 Valencia, Spain 2Hospital Universitario 12 de Octubre, National Cancer Research Center, Complutense University, 28041 Madrid, Spain 3CIMA LAB Diagnostics, Departamento de Bioquímica y Genética, Universidad de Navarra, 31008 Pamplona, Spain 4Servicio de Hematología, Hospital Universitario de Salamanca (HUS/IBSAL), CIBERONC, Centro de Investigación del Cáncer–IBMCC (USAL–CSIC), 37007 Salamanca, Spain 5Hospital Universitario Virgen del Rocío, Instituto de Biomedicina (IBIS/CSIC/CIBERONC), Universidad de Sevilla, 41013 Sevilla, Spain 6Hospital Universitario de Gran Canaria Dr. Negrín, 35010 Las Palmas de Gran Canaria, Spain 7Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Universitario Reina Sofía, Universidad de Córdoba (UCO), 14004 Córdoba, Spain 8Servicio de Hematología, Grupo Acreditado de Investigación en Hematología, Hospital Universitario y Politécnico La Fe, Instituto de Investigación Sanitaria La Fe (IIS La Fe), 46026 Valencia, Spain 9Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Instituto de Investigación Sanitaria La Fe (IIS La Fe), 46026 Valencia, Spain 10 Hospital General Universitario de Alicante, 03010 Alicante, Spain 11 Hospital Universitario Central de Asturias, Instituto Universitario (IUOPA), Instituto de Investigación del Principado de Asturias (ISPA), 33011 Oviedo, Spain 12 Hospital Universitario San Pedro de Alcántara, 10003 Cáceres, Spain 13 Hospital Universitario General de Albacete, 02006 Albacete, Spain 14 Hospital Clínico Universitario–INCLIVA, 46010 Valencia, Spain 15 Hospital Universitario Fundación Jiménez Díaz, 28040 Madrid, Spain 16 Hospital Universitario General de Castellón, 12004 Castellón de la Plana, Spain 17 Hospital Universitario Morales Messeguer, 30008 Murcia, Spain 18 Hospital Universitario Ramón y Cajal, 28034 Madrid, Spain 19 Hospital Universitari Dr. Peset, 46017 Valencia, Spain 20 Complexo Hospitalario Lucus Augusti, 27003 Lugo, Spain 21 Servicio Análisis Clínicos, Grupo Acreditado de Investigación en Hematología, Hospital Universitario y Politécnico La Fe, Instituto de Investigación Sanitaria La Fe (IIS La Fe), 46026 Valencia, Spain *Correspondence: [email protected]; Tel.: +34-961-244589 † These authors contributed equally to this work. ‡ The consortium members are listed in Appendix A. Simple Summary: Next–Generation Sequencing (NGS) has provided a deeper genetic understanding of acute myeloid leukemia (AML) that has been recently incorporated into AML classification and risk–stratification guidelines. Single molecular analysis has become inefficient and molecular testing based on NGS is emerging as an irreplaceable diagnostic tool in clinical settings. The Cancers 2023,15, 438. https://doi.org/10.3390/cancers15020438 https://www.mdpi.com/journal/cancers Cancers 2023,15, 438 2 of 21 PETHEMA cooperative group has constituted a nationwide NGS network with centralized analysis in seven high–skilled laboratories. The study of molecular profiles in the “real–life” PETHEMA cohort supports the increasing role of NGS on the clinical management of AML patients. Abstract: Next–Generation Sequencing (NGS) implementation to perform accurate diagnosis in acute myeloid leukemia (AML) represents a major challenge for molecular laboratories in terms of specialization, standardization, costs and logistical support. In this context, the PETHEMA cooperative group has established the first nationwide diagnostic network of seven reference laboratories to provide standardized NGS studies for AML patients. Cross–validation (CV) rounds are regularly performed to ensure the quality of NGS studies and to keep updated clinically relevant genes recommended for NGS study. The molecular characterization of 2856 samples (1631 derived from the NGS–AML project; NCT03311815) with standardized NGS of consensus genes (ABL1,ASXL1,BRAF, CALR,CBL,CEBPA,CSF3R,DNMT3A,ETV6,EZH2,FLT3,GATA2,HRAS,IDH1,IDH2,JAK2,KIT, KRAS,MPL,NPM1,NRAS,PTPN11,RUNX1,SETBP1,SF3B1,SRSF2,TET2,TP53,U2AF1 and WT1) showed 97% of patients having at least one mutation. The mutational profile was highly variable according to moment of disease, age and sex, and several co–occurring and exclusion relations were detected. Molecular testing based on NGS allowed accurate diagnosis and reliable prognosis stratification of 954 AML patients according to new genomic classification proposed by Tazi et al. Novel molecular subgroups, such as mutated WT1 and mutations in at least two myelodysplasia–related genes, have been associated with an adverse prognosis in our cohort. In this way, the PETHEMA cooperative group efficiently provides an extensive molecular characterization for AML diagnosis and risk stratification, ensuring technical quality and equity in access to NGS studies. Keywords: acute myeloid leukemia; Next–Generation Sequencing; cross–validations; mutational profile; genomic classification; clinical validation 1. Introduction Introduction of Next–Generation Sequencing (NGS) into routine molecular diagnosis has provided deep molecular knowledge of acute myeloid leukemia (AML). These findings have allowed for the refinement of classification and risk stratification systems based on recurrent genetic abnormalities. In 2016, Papaemmanuil et al. proposed the first genomic classification of AML that identifies 11 molecular classes, each with distinct diagnostic features and clinical outcomes [ 1 ]. This classification has been recently revised and updated in Tazi et al., 2022, proposing 16 molecular classes based on cytogenetics and the mutational status of 32 genes [ 2 ]. The importance of genomic characterization has also been reflected in the recently revised World Health Organization (WHO) Classification [ 3 ], new International Consensus Classification (ICC) [ 4 ] and European LeukemiaNet (ELN) risk stratification [ 5 ], which prioritize genetic abnormalities to establish diagnosis and prognosis to evaluate measurable residual disease (MRD) and to select treatment. In this situation, molecular analysis by single–gene techniques has become inefficient in order to provide a complete characterization of AML. In contrast, NGS represents a more sensitive tool to capture all the relevant molecular markers in one assay and is widely recommended to study the molecular landscape of this disease [6]. NGS implementation to perform accurate diagnosis in AML is currently demanded by physicians and patients. However, introduction of NGS into clinical routine faces novel challenges [ 7 ]. NGS requires large batches of samples in order to be cost–effective, workflows are time–consuming, and interpretation needs highly qualified specialists. Moreover, the diversity of NGS panels, platforms and quality control criteria might prevent the success of the approach [ 8 ]. Hence, to efficiently introduce NGS into routine Cancers 2023,15, 438 3 of 21 molecular diagnostics, it is necessary to establish quality requirements and to standardize gene panels and variant reporting. In order to provide comprehensive NGS studies to AML patients and to guarantee equity of access, the PETHEMA cooperative group (Programa Español de Tratamientos en Hematología) has established a nationwide network of central laboratories aimed to harmonize NGS results under consensual criteria in newly diagnosed and relapsed/refractory AML patients [9]. This study summarized the NGS–AML project (NCT03311815), reporting quality control assays and the molecular profile of 2668 AML patients reported in the PETHEMA AML registry. We show co–occurring and mutual exclusion relationships among genes and distinct molecular profiles according to disease stage, age and sex and genomic classification in the “real–life PETHEMA cohort”. 2. Materials and Methods 2.1. Development of the Diagnostic Platform Implementing NGS studies in the routine molecular diagnosis of AML patients requires specialization, budgetary stability and logistical support. The PETHEMA cooperative group established a nationwide network of NGS studies for fast and standardized molecular diagnosis of AML. This strategy aims to provide coverage of NGS studies to 38.5 million inhabitants distributed in geographical areas ranging from 2.2 to 8.9 million habitants. For this purpose, seven centers with logistical and technical capacity for the management of a high number of samples were designed as reference laboratories for the centralization of samples submitted by PETHEMA institutions in each area. In this way, a large territory and population was covered. The platform was supported by PETHEMA in logistical management, as well as, closer and well–established relationships between the sample referral institution and the assigned central laboratory. The designated reference centers for NGS analysis concentrate a large number of AML samples, allowing for the rapid completion of the sequencing runs and their management by highly specialized staff. 2.2. Study Design and Reference Laboratories We show a prospective, multi–center, non–interventional and translational biomedical research, performed in seven Spanish PETHEMA reference laboratories: Hospital Universitario La Fe (HULF, Valencia, Spain), Hospital Universitario de Salamanca (HUS, Salamanca, Spain), Hospital Universitario 12 de Octubre (H12O, Madrid, Spain), Hospital Universitario Virgen del Rocío (HUVR, Sevilla, Spain), Hospital Universitario Reina Sofía (HURS, Córdoba, Spain), Hospital Universitario de Gran Canaria Dr. Negrín (HUDN, Las Palmas de Gran Canaria, Spain), CIMA LAB Diagnostics (UNAV, Pamplona, Spain). 2.3. Consensus Genes Establishment The development of the diagnostic platform required several meetings to coordinate criteria on which genes should be analyzed based on their clinical relevance in AML. After extensive bibliographic revision of current molecular basis of AML, all reference laboratories should assess by NGS the mutational status of genes that define the diagnosis and prognosis as well as guide treatment options (ASXL1,BCOR,CEBPA,EZH2,FLT3, IDH1,IDH2,NPM1,RUNX1,SF3B1,SRSF2,STAG2,U2AF1,ZRSR2 and TP53). Moreover, there was a recommendation for the study of other genes with proven evidence on their relevance in AML pathogenesis (ABL1, BRAF, CALR, CBL, CSF3R, DNMT3A, ETV6, GATA2, HRAS, JAK2, KIT, KRAS, MPL, NRAS, PTPN11, SETBP1, TET2 and WT1). The establishment of consensus genes has enabled laboratories to work with the NGS platforms and panels according to their individual requirements, which have largely enabled the development and maintenance of the diagnostic platform. NGS panels and platforms used by each center are described in Supplementary Materials Tables S1 and S2. Cancers 2023,15, 438 4 of 21 2.4. NGS Standardization Procedures and Cross–Validation Rounds The diagnostic platform established a quality control assay by exchanging control samples among reference laboratories every 9–12 months. To date, three cross validation (CV) rounds have been performed. As previously reported, in the first and second CV rounds, minimum quality parameters [uniformity (>85%) and mean read depth of 1000X] and consensus recommendations for variant report [All centers should report: (1) mutations in relevant genes for clinical guidelines, targeted therapy and risk stratification and (2) all pathogenic variants detected with VAF >5% excepting those described at hotspot regions which will be reported up to 1% VAF] were stablished. For both CV rounds, 10 samples harboring 54 variants were distributed. In first CV round, VAF for all variants was >5% while in the second CV round, 5 from 30 total variants had low VAF ranging from 1.8–4.9%. In the third CV round, to explore the accuracy and reliability of low VAF variants (<5%) detection, 4 samples harboring 32 variants (11 VAF < 5%) were analyzed, according to previously established criteria [9]. 2.5. Patients and Inclusion Criteria All adult patients ( ≥ 18 years) with newly diagnosed or relapsed/refractory AML (excluding acute promyelocytic leukemia) according to the World Health Organization criteria (2008 and 2016), regardless of the treatment received, were eligible for mutational profile study by NGS. The Institutional Ethics Committee for Clinical Research of each institution approved this study. Written informed consent in accordance with the recommendations of the Declaration of Human Rights, the Conference of Helsinki, and institutional regulations were obtained from all patients. 2.6. Clinical Validation Clinical validation was performed based on the new genomic classification which proposes unified framework for disease classification and risk–stratification in AML based on cytogenetic analysis and an NGS–panel of 32 genes [ 2 ]. Molecular class defining genes were: NPM1 , TP53 , WT1 , CEBPA , DNMT3A , IDH1 , IDH2 ,ZRSR2, U2AF1 , SRSF2 , SF3B1 , ASXL1 ,STAG2,BCOR, RUNX1 , EZH2 ,MLL,PHF6,SF1,NF1,CUX1, SETBP1 , FLT3 and TET2. * Bold genes represent PETHEMA consensus genes. This new classification categorizes AML in 16 molecular classes with different prognostic values and encompass the established WHO entities: “WHO2016 set 1” [inv(16), t(8;21) and NPM1] and “WHO2016 set 2” [t(11;x), t(6;9), inv(3) and CEBPAbi]; and also novel categories: “TP53 and complex karyotype (CK)”, “sAML1” (Mutated SRSF2,SF3B1, U2AF1,ASXL1,EZH2,RUNX1 or SETBP1), “sAML2” (More than one mutations in sAML1 genes including DNMT3A and TET2), “WT1”, “Trisomies”, “DNMT3A +IDH1/2”, “Not class defining mutations (mNOS)” and “No events” category. Since our study excludes acute promyelocytic leukemia, category “t(15;17)” is not applicable. This classification also proposes an integrated risk score based on the 16 molecular classes: “NPM1”, “inv(16)”, “t(8;21)”, “CEBPAbi” and “No events” define the favorable risk group; “sAML1”, “t(6;9)”, “WT1”, “mNOS”, “t(11;X)”, “DNMT3A–IDH1/2” and “trisomies” the intermediate and “TP53–CK”, “sAML2” and “inv(3)” the adverse risk group. Genomic groups and sub–classifications are summarized in Supplementary Table S3. Internal tandem duplications (ITD) in FLT3 are the only genetic alterations with independent prognosis value from class membership. These mutations were not considered as “class defining” alterations as they are represented in all classes but modulate risk groups classification as follows: In the favorable risk group, patients with mutated NPM1 who also harbored a FLT3–ITD mutation were reclassified to the intermediate risk group. Similarly, a FLT3–ITD mutation reclassifies intermediate risk patients to an adverse risk group. Moreover, in order to assess the prognostic impact of TP53 configurations, we classified patients according TP53 mono–allelic or multi–hit as described by Tazi et al.: Mono–allelic: One TP53 mutation with VAF ≤ 65%; and multi–hit: Two TP53 mutations or one TP53 mutation with VAF > 65% or one TP53 mutation + del(17). Cancers 2023,15, 438 5 of 21 2.7. Statistics All statistics were performed using SPSS version 22 (IBM, Armonk, NY, USA) and GraphPad Prism 4 (GraphPad, La Jolla, CA, USA) software programs. Chi square test was used to assess associations between categorical variables. Survival analyses were performed using the Kaplan–Meier method and the log–rank test. The Cox proportional–hazards model was used to evaluate the risk of death among groups. p–value (p) < 0.05 was considered as a statistically significant test. 3. Results 3.1. Third Cross Validation Round In the third CV round, the error rate (ER) for variants with VAF > 5% decreased from previous rounds: 1st: 39%, 2nd: 14.4% and 3rd: 4.76%. However, the ER in variants with VAF < 5% increased from 28.6% (five variants with mean VAF 3.3%) to 59.6% ( 11 variants with mean VAF 1.2%). ER, mean VAF and standard deviation (SD) for the last CV round are summarized in Supplementary Table S4 and Figure S1. Therefore, the diagnostic platform maintained: (1) the cut–off of VAF > 5% to report clinically relevant variants and (2) the criteria to report only low VAF variants (<5%) in hotspot regions with strong clinical evidence, suggesting variant confirmation in an additional sample. 3.2. Baseline Demographics and Molecular Profile in NGS–AML Protocol Cohort From October 2017 to October 2019, NGS analyses were performed in 1631 samples from 1471 AML patients enrolled in the NGS–AML protocol (NCT03311815), with available clinical date (i.e., treatment approach). Disease status at samples collection was: 1268 diagnosis (DX), 204 relapse (REL) and 159 refractoriness (RES) (Table 1). Table 1. Diagnosis cohort (N = 1268). Demographic and baseline characteristics. Characteristic Mean Median Range N (%) Age, years 64.9 67.7 18–98 1268 100 <65 540 42.6 ≥65 728 57.4 Sex 1268 100 Male 712 56.2 Female 556 43.8 ECOG 1075 100 0 420 39.1 1 452 42.0 2 135 12.6 3 53 4.9 4 15 1.4 Not available 193 WBC (×109/L) 32.8 8.8 0.24–407 1118 BM blast cells, % 53.4 52.0 0–100 1026 Creatinine, mg/dL 1.1 0.90 0.28–10.3 1071 MRC cytogenetic profile 1011 100 Favorable 57 5.6 Intermediate 178 17.6 Unfavorable 269 26.6 Normal karyotype 507 50.1 Not available 257 AML FAB subtype 715 100 M0 88 12.3 M1 144 20.1 M2 126 17.6 M4 173 24.2 M5 144 20.1 M6 31 4.3 M7 9 1.3 Not available 553 Therapeutic approach 1268 100 Intensive 695 54.8 Non–intensive 513 40.5 Supportive care only 60 4.7 Type of AML 1268 100 De novo 920 72.6 Secondary 348 27.4 Cancers 2023,15, 438 6 of 21 An additional cohort of 1225 samples analyzed by NGS between November 2019–May 2021 has been included only for molecular characterization of AML: 1166 (DX), 47 (REL) and 12 (RES). Only those samples that met NGS quality requirements established by the diagnostic platform were included in the study. Overall, 2856 samples were analyzed (2434 DX, 251 REL, and 171 RES). 3.3. Summary Mutation Profile In the global cohort (N = 2856 samples), 7768 variants were detected. A total of 96.5% of samples showed at least 1 mutation, mean 2.7 mutations/sample (range 0–9). Most patients had three variants (21.1%), followed by patients with two (20.9%) and four (17.8%). FLT3 (24.6%), DNMT3A (24.3%), NPM1 (22.4%) and TET2 (21.6%) were the most frequently mutated genes (Supplementary Figure S2). According to ELN–2022 [ 5 ], 85.3% of patients at diagnosis showed at least one mutation in clinically relevant genes to establish the diagnosis, prognosis or to select treatment. 3.4. Co–Mutations and Exclusivity Patterns NPM1,FLT3 and DNMT3A were significantly co–mutated for all combinations (p< 0.001) .PTPN11 and NPM1 showed a strong association (p< 0.001) as well as PTPN11– DNMT3A (p= 0.0017) and PTPN11–FLT3 (p= 0.024). Mutations in NPM1 were highly associated with mutated IDH1 (p< 0.001) but were exclusive with R172–IDH2 (p< 0.001). In contrast, DNMT3A mutations were highly associated with both mutated IDH1 and IDH2 (p< 0.001). CEBPA was frequently co–mutated with GATA2 (p< 0.001); and ASXL1, RUNX1 and SRSF2 were strongly associated with each other (p< 0.001). On the other hand, mutations in TP53 were the most exclusive with all the analyzed genes (p< 0.05). Mutations in IDH1 and IDH2 were also mutually exclusive of each other (p< 0.001). Mutated NPM1 was highly exclusive with mutations in: RUNX1 (p< 0.001), SRSF2 (p< 0.001) and ASXL1 (p< 0.001). The main association and exclusivity patterns for all genes are shown in Figure 1. Cancers 2023, 15, x FOR PEER REVIEW 7 of 22 Figure 1. Co–occurrence and mutual exclusivity patterns among genes. Red: exclusive relationship; Green: co–occurring relationship. Higher color intensity indicates stronger association: * p < 0.05, ** p < 0.01, *** p < 0.001. We also identified co–occurring and exclusivity patterns according to functional categories of AML [10]. Genes were grouped into nine categories based on their biological function (Supplementary Table S5). We identified commonly co–occurring events between “transcription–factor (TF) fusions” and “activating signaling genes” (p < 0.001). Associations were also found between “NPM1” with “DNA–methylation genes” (p < 0.001) and “activating signaling genes” (p < 0.001)” as well as co–occurring events between “Spliceosome–genes” with “myeloid TFs” (p < 0.001), “chromatin modifiers” (p < 0.01) and “DNA–methylation genes” (p < 0.001). On the other hand, several mutually exclusive relationships were observed: “NPM1” mutations were highly exclusive with “myeloid TFs” (p < 0.001), “Spliceosome–genes” (p < 0.001), and “chromatin–modifying” (p < 0.001); mutations in “Spliceosome–genes” were highly exclusive with “TF fusions” (p < 0.001) and remarkably, the “Tumor suppressor–genes” category was highly exclusive with all other functional categories (p < 0.001) (Figure 2). Figure 2. Heatmap of association and exclusivity patterns among functional categories. Red: exclusivity relationship; Green: co–occurring relationship. Higher color intensity indicates stronger association. * p < 0.05, ** p < 0.01, *** p < 0.001. Figure 1. Co–occurrence and mutual exclusivity patterns among genes. Red: exclusive relationship; Green: co–occurring relationship. Higher color intensity indicates stronger association: * p< 0.05, ** p< 0.01, *** p< 0.001. We also identified co–occurring and exclusivity patterns according to functional categories of AML [ 10 ]. Genes were grouped into nine categories based on their biological function (Supplementary Table S5). We identified commonly co–occurring events between “transcription–factor (TF) fusions” and “activating signaling genes” (p< 0.001). Associations were also found between “NPM1” with “DNA–methylation genes” (p< 0.001) and “activating signaling genes” (p< 0.001)” as well as co–occurring events between “Spliceosome–genes” with “myeloid TFs” (p< 0.001), “chromatin modifiers” (p< 0.01) and “DNA–methylation genes” (p< 0.001). On the other hand, several mutually exclusive Cancers 2023,15, 438 7 of 21 relationships were observed: “NPM1” mutations were highly exclusive with “myeloid TFs” (p< 0.001), “Spliceosome–genes” (p< 0.001), and “chromatin–modifying” (p< 0.001); mutations in “Spliceosome–genes” were highly exclusive with “TF fusions” (p< 0.001) and remarkably, the “Tumor suppressor–genes” category was highly exclusive with all other functional categories (p< 0.001) (Figure 2). Cancers 2023, 15, x FOR PEER REVIEW 7 of 22 Figure 1. Co–occurrence and mutual exclusivity patterns among genes. Red: exclusive relationship; Green: co–occurring relationship. Higher color intensity indicates stronger association: * p < 0.05, ** p < 0.01, *** p < 0.001. We also identified co–occurring and exclusivity patterns according to functional categories of AML [10]. Genes were grouped into nine categories based on their biological function (Supplementary Table S5). We identified commonly co–occurring events between “transcription–factor (TF) fusions” and “activating signaling genes” (p < 0.001). Associations were also found between “NPM1” with “DNA–methylation genes” (p < 0.001) and “activating signaling genes” (p < 0.001)” as well as co–occurring events between “Spliceosome–genes” with “myeloid TFs” (p < 0.001), “chromatin modifiers” (p < 0.01) and “DNA–methylation genes” (p < 0.001). On the other hand, several mutually exclusive relationships were observed: “NPM1” mutations were highly exclusive with “myeloid TFs” (p < 0.001), “Spliceosome–genes” (p < 0.001), and “chromatin–modifying” (p < 0.001); mutations in “Spliceosome–genes” were highly exclusive with “TF fusions” (p < 0.001) and remarkably, the “Tumor suppressor–genes” category was highly exclusive with all other functional categories (p < 0.001) (Figure 2). Figure 2. Heatmap of association and exclusivity patterns among functional categories. Red: exclusivity relationship; Green: co–occurring relationship. Higher color intensity indicates stronger association. * p < 0.05, ** p < 0.01, *** p < 0.001. Figure 2. Heatmap of association and exclusivity patterns among functional categories. Red: exclusivity relationship; Green: co–occurring relationship. Higher color intensity indicates stronger association. * p< 0.05, ** p< 0.01, *** p< 0.001. 3.5. Disease Stage Mutational Profile Subgroup analyses based on disease stage (DX or REL or RES) showed statistically significant results: NPM1 (p< 0.001) and signaling pathway genes such as KRAS (p= 0.007) and NRAS (p< 0.001) were more frequently mutated at diagnosis. In refractory AML, WT1 (p< 0.001) was more frequent, meanwhile relapse AML exhibited more mutations in RUNX1 (p= 0.037), DNMT3A (p= 0.018), IDH1 (p= 0.017) and WT1 (p< 0.001). Mutational frequency and p–values for each correlation are described in Supplementary Table S6A, Figure 3. Cancers 2023, 15, x FOR PEER REVIEW 8 of 22 3.5. Disease Stage Mutational Profile Subgroup analyses based on disease stage (DX or REL or RES) showed statistically significant results: NPM1 (p < 0.001) and signaling pathway genes such as KRAS (p = 0.007) and NRAS (p < 0.001) were more frequently mutated at diagnosis. In refractory AML, WT1 (p < 0.001) was more frequent, meanwhile relapse AML exhibited more mutations in RUNX1 (p = 0.037), DNMT3A (p = 0.018), IDH1 (p = 0.017) and WT1 (p < 0.001). Mutational frequency and p–values for each correlation are described in Supplementary Table S6A, Figure 3. Figure 3. Mutational frequency according to disease stage. Blue bars: Diagnosis, green bars: Relapse and red bars: refractoriness. * p < 0.05, ** p < 0.01, *** p < 0.001. ITD: internal tandem duplication; TKD: tyrosine kinase domain. 3.6. Age–Related Mutational Profile NGS studies revealed distinct mutational profile in young (<65 years–old) and elderly (≥65 years–old) AML patients. The mean number of gene mutations at diagnosis was higher in older patients than younger (2.9 ± 0.04 vs. 2.5 ± 0.04; p < 0.001). Older patients also had a higher frequency of TET2 (p < 0.001), RUNX1 (p < 0.001), TP53 (p < 0.001), IDH2 (p < 0.01), ASXL1 (p < 0.001), SRSF2 (p < 0.001), U2AF1 (p < 0.01), SF3B1 (p = 0.028), JAK2 (p < 0.001) and EZH2 (p < 0.001). In contrast FLT3 (p < 0.001), NPM1 (p < 0.001), DNMT3A (p = 0.032), NRAS (p < 0.01), PTNP11 (p < 0.001) and WT1 (p < 0.001), were frequently mutated in young AML. Mutational frequencies and p–values are shown in Supplementary Table S6B, Figure 4. Figure 3. Mutational frequency according to disease stage. Blue bars: Diagnosis, green bars: Relapse and red bars: refractoriness. * p< 0.05, ** p< 0.01, *** p< 0.001. ITD: internal tandem duplication; TKD: tyrosine kinase domain. 3.6. Age–Related Mutational Profile NGS studies revealed distinct mutational profile in young (<65 years–old) and elderly ( ≥ 65 years–old) AML patients. The mean number of gene mutations at diagnosis was higher in older patients than younger (2.9 ± 0.04 vs. 2.5 ± 0.04; p< 0.001). Older patients also had a higher frequency of TET2 (p< 0.001), RUNX1 (p< 0.001), TP53 (p< 0.001), IDH2 (p< 0.01), ASXL1 (p< 0.001), SRSF2 (p< 0.001), U2AF1 (p< 0.01), SF3B1 (p= 0.028), JAK2 (p< 0.001) and Cancers 2023,15, 438 8 of 21 EZH2 (p< 0.001). In contrast FLT3 (p< 0.001), NPM1 (p< 0.001), DNMT3A (p= 0.032), NRAS (p< 0.01), PTNP11 (p< 0.001) and WT1 (p< 0.001), were frequently mutated in young AML. Mutational frequencies and p–values are shown in Supplementary Table S6B, Figure 4. Cancers 2023, 15, x FOR PEER REVIEW 9 of 22 Figure 4. Age–related mutational profile. Bar chart representing mutational frequencies according to age at diagnosis. Green; <65 years old, red; ≥65 years old. * p < 0.05, ** p < 0.01, *** p < 0.001. ITD: internal tandem duplication; TKD: tyrosine kinase domain. 3.7. Sex–Related Mutational Profile Our cohort showed a well–balanced distribution between male (56.2%) and female (43.8%) patients, similar to that described in previous AML cohorts [1,10]. Sex–specific mutational profiles were observed. Females harbored lower number of mutations than male patients (2.8 ± 0.04 vs. 2.6 ± 0.04; p < 0.01). Mutations in DNMT3A (p < 0.001), FLT3 (p < 0.01) and NPM1 (p < 0.001) were overrepresented in females and TET2 (p = 0.014), RUNX1 (p < 0.001), ASXL1 (p < 0.001), SRSF2 (p < 0.001), EZH2 (p < 0.001), U2AF1 (p < 0.001), JAK2 (p = 0.01) and CBL (p = 0.025) mutations in male patients (Supplementary Table S6C, Figure 5). Figure 4. Age–related mutational profile. Bar chart representing mutational frequencies according to age at diagnosis. Green; <65 years old, red; ≥ 65 years old. * p< 0.05, ** p< 0.01, *** p< 0.001. ITD: internal tandem duplication; TKD: tyrosine kinase domain. 3.7. Sex–Related Mutational Profile Our cohort showed a well–balanced distribution between male (56.2%) and female (43.8%) patients, similar to that described in previous AML cohorts [ 1 , 10 ]. Sex–specific mutational profiles were observed. Females harbored lower number of mutations than male patients (2.8 ± 0.04 vs. 2.6 ± 0.04; p< 0.01). Mutations in DNMT3A (p< 0.001), FLT3 (p< 0.01) and NPM1 (p< 0.001) were overrepresented in females and TET2 ( p= 0.014 ), RUNX1 (p< 0.001), ASXL1 (p< 0.001), SRSF2 (p< 0.001), EZH2 (p< 0.001), U2AF1 ( p< 0.001) ,JAK2 (p= 0.01) and CBL (p= 0.025) mutations in male patients (Supplementary Table S6C, Figure 5). Cancers 2023,15, 438 9 of 21 Cancers 2023, 15, x FOR PEER REVIEW 10 of 22 Figure 5. Sex–related mutational profile. Bar chart representing mutational frequencies. Orange; female, blue; male. * p < 0.05, ** p < 0.01, *** p < 0.001. ITD: internal tandem duplication; TKD: tyrosine kinase domain. 3.8. Paired Samples and Mutation Stability Molecular profiling of paired samples was conducted in 97 patients at diagnosis– relapse (DX–REL) and 59 patients at diagnosis–refractoriness (DX–RES). In DX–REL comparison, loss events were more frequently observed than gain events (43.7% vs. 26.4%) in relapse samples. Interestingly, 17.2% of patients showed simultaneous mutation loss and gain events, while 24.1% maintain the diagnosis’ mutational profile. The most stable mutated genes were TP53, WT1 and NPM1, with stability rates of 81.3%, 80% and 77.8%, respectively. In contrast, signaling activating genes were found to be highly unstable: KIT, FLT3–ITD and FLT3–TKD mutations, NRAS, KRAS and PTPN11 showed stability rates below 50%. Moreover, while mutations in KIT, FLT3–TKD, KRAS and PTPN11 were almost equally lost and acquired, mutations in NRAS and FLT3–ITD were predominantly lost (Supplementary Figure S3). In refractory AML, 49.2% of patients retained the mutational status found at diagnosis. NPM1 mutations were also the most stable gene at refractory AML (83.3%). Signaling activating genes were highly unstable: NRAS, KRAS, PTPN11 and FLT3–TKD showed stability rates below 45%, being more frequent the loss of these mutations (Supplementary Figure S4). 3.9. New Genomic Classification Applied to PETHEMA–NGS Cohort Figure 5. Sex–related mutational profile. Bar chart representing mutational frequencies. Orange; female, blue; male. * p< 0.05, ** p< 0.01, *** p< 0.001. ITD: internal tandem duplication; TKD: tyrosine kinase domain. 3.8. Paired Samples and Mutation Stability Molecular profiling of paired samples was conducted in 97 patients at diagnosis–relapse (DX–REL) and 59 patients at diagnosis–refractoriness (DX–RES). In DX–REL comparison, loss events were more frequently observed than gain events (43.7% vs. 26.4%) in relapse samples. Interestingly, 17.2% of patients showed simultaneous mutation loss and gain events, while 24.1% maintain the diagnosis’ mutational profile. The most stable mutated genes were TP53, WT1 and NPM1, with stability rates of 81.3%, 80% and 77.8%, respectively. In contrast, signaling activating genes were found to be highly unstable: KIT,FLT3–ITD and FLT3–TKD mutations, NRAS,KRAS and PTPN11 showed stability rates below 50%. Moreover, while mutations in KIT,FLT3–TKD, KRAS and PTPN11 were almost equally lost and acquired, mutations in NRAS and FLT3–ITD were predominantly lost (Supplementary Figure S3). Cancers 2023,15, 438 16 of 21 Acknowledgments: The authors would like to thank María D. García, Carlos Pastorini, Rafael Vianney, Yolanda Mendizabal and Alvaro Fernández for data collection and management. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Appendix A Institutions and clinicians participating in the PETHEMA epidemiologic registry of acute myeloid leukemia and acute promyelocytic leukemia. Argentina (Grupo Argentino para el Tratamiento de la Leucemia Aguda—GATLA)— Hospital de Clínicas, Buenos Aires: F. Rojas; H. Longoni; Fundaleu, Buenos Aires: G. Milone, I. Fernández, Clínica Conciencia, Neuquén: R. Ramirez; Hospital Rossi, La Plata: C. Canepa, S. Saba, G. Balladares, Hospital General San Martin, Parana: G. Milone, C. Ventiurini, R. Mariano, P. Negri; Hospital Italiano de La Plata, La Plata: M. V. Prates, J. Milone; Hospital General San Martín, La Plata:P. Fazio, M. Gelemur; Hospital Clemente Alvarez, Rosario: G. Milone, S. Ciarlo, F. Bezares; Hospital de Córdoba, Córdoba: L. López, Hospital Privado de Córdoba, Córdoba: J. J.García; Instituto Privado Hematología, Paraná: P. Negri, M. Giunta, G. Milone; Hospital Teodoro Alvarez, Buenos Aires: M. Kruss; Hospital Tornú, Buenos Aires: D. Lafalse, G. Milone; Hospital Gobernador Centeno, La Pampa: E. Marquesoni, M. F. Casale; Hospital Italiano de Buenos Aires, Buenos Aires: A. Gimenez, E. B. Brulc, M. A. Perusini; Complejo Médico Policía Federal, La Plata: G. Milone, L. Palmer; Colombia (Asociación Colombian de Hematología y Oncología—ACHO)—Clínica La Estancia, Popayán: M. E. Correa; Fundación Valle del Lili, Cauca: F.J. Jaramillo, J. Rosales; Instituto FOSCAL, Bucaramanga: C. Sossa, J. C. Herrera; Hospital Pablo Tobón Uribe, Antioquia: M. Arango; Poland (Polish Adult Leukemia Group—PALG)—City Hospital Legnica, Baja Silesia: J. Holojda; IHIT Hematology and transfusiology institute, Warszawa: A. Golos, A. Ejduk; Wojewódzki Szpital Specjalistyczny w Olsztynie, Olsztyn: B. Ochrem; WIM (Military Institute of Medicine in Warsaw), Warszawa: G. Małgorzata; Poland Medical University of Warsaw Banacha, Warszawa:A. Waszczuk-Gajda, J. Drozd-Sokolowska, M. Czemerska, M. Paluszewska; Medical University School Gdansk, Gdansk: E. Zarzycka; Wojewódzki Szpital Specjalistyczny im. ´ Sw.. Jadwigi ´ Sl ˛askiej, Opole: A. Masternak; Hospital Brzozow, Brzozow: Dr. Hawrylecka; Medical University Lublin, Lublin: M. Podhoreka, K. Giannopoulos, T. Gromek; Medical University Bialystok, Bialystok: J. Oleksiuk; Silesian Medical University Katowice, Katowice:bA. Armatys, G. Helbig; Universitary Hospital Wroclaw, Wroclaw: M. Sobas; Poznan University of Medical Sciences, Pozna: A. Szczepaniak; Rydigier City Hospital Krakow, Krakow: E. Rzenno, M. Rodzaj; Collegium Medicum Jagiellonian University Krakow, Krakow: B. Piatkowska-Jakubas; City Hospital Rzeszów, Rzeszów: A. Skret; Medical University Lodz, Lodz: A. Pluta, M. Czemerska; Center of Oncology Kielce, Kielce: E. Bara´nska; Medical University of Warsaw, Warsaw: M. Paluszewska; Portugal—Hospital de Santa Maria-Lisboa, Lisboa:G. Vasconcelos, J. Brioso; IPOFG Lisboa, Lisboa: A. Nunes, I. Bogalho; Centro Hospitalar e Universitário de Coimbra, Coimbra: A. Espadana, M. Coucelo, S. Marini, J. Azevedo, A. I. Crisostomo, L. Ribeiro, V. Pereira; Centro Hospitalar de Lisboa Central E. P. E, Lisboa: A. Botelho; Instituto Português Oncologia do Porto Francisco Gentil, Porto: J. M. Mariz; Centro Hospitalar São João, Porto: J. E. Guimaraes, E. Aguiar; Centro Hospitalar do Porto E.P.E. Porto: J. Coutinho; Spain (Programa Español de Tratamiento de las Hemopatías Malignas, PETHEMA)—Complejo Hospitalario Universitario A Coruña, A Coruña: V. Noriega, L. García, C. Varela, G. Debén, M. R. González; Hospital Clínico Universitario de Santiago, A Coruña: M. Encinas, A. Bendaña, S. González, J.L. Bello, M. Albors; Hospital General de Albacete, Albacete: L. Algarra, J.R.Romero, J.S.Bermon, M.J. Varo; Hospital Vinalopó, Alicante: V. López, E. López; Hospital Virgen de los Lirios, Alcoy: C. Mora, C. Amorós; Hospital General Elche, Alicante: E. López, A. Romero; Hospital Torrevieja Salud, Alicante: A. Jaramillo, N. Valdez, I. Molina, A. Fernández, B. Cancers 2023,15, 438 17 of 21 Sánchez; Hospital de la Marina Baixa Villajoyosa, Alicante: A. García; Hospital General de Elda, Alicante: V. Castaño, T. López, J. Bernabeu; Hospital de Denia-Marina Salud, Alicante: M.J. Sánchez; Hospital de la Vega Baja de Orihuela, Alicante: C. Fernández; Hospital General de Alicante, Alicante: C. Gil, C. Botella, P. Fernández, M. Pacheco, F. Tarín; J.J. Verdú; Complejo Hospitalario Torrecardenas, Almeria: M.J. García, A. Mellado, M.C. García, J. González; Hospital Central de Asturias, Asturias: T. Castillo, E. Colado, S. Alonso; Complejo Asistencial Ávila, Ávila: I. Recio, M. Cabezudo, J. Davila, M. J. Rodríguez, A. Barez, B. Díaz; Hospital Don Benito-Villanueva, Badajoz: J. Prieto; Institut Catala d’Oncologia L’Hospitalet, Barcelona: M. Arnan, C. Marín, M. Mansilla; Hospital de Cruces, Bizkaia: A. Balaberdi, M. E. Amutio, R. A. del Orbe, I. Ancin, J. C. Ruíz; Hospital Galdakao-Usansolo, Bizkaia: M. Olivalres, C. Gómez, I. gonzález, M. Celis, K. Atutxa, T. Carrascosa, T. Artola, M. Lizuain; Basurtuko Ospitalea, Bizkaia: J.I. Rodriguez, O. Arce, J. A. Márquez, J. Atuch, F. Marco de Lucas, Z. Díez, B. Dávila; Hospital Santos Reyes, Burgos: R. Cantalejo, M. Díaz; Hospital Universitario de Burgos, Burgos: J. Labrador, F. Serra, G. Hermida, F. J. Díaz, P. de Vicente, R. Álvarez: Hospital Santiago Apóstol, Burgos: C. Alonso, Hospital San Pedro de Alcántara, Cáceres: J. M. Bergua; Hospital Campo Arañuelo, Cáceres: N. Ugalde; Hospital Virgen del Puerto, Cáceres: E. Pardal; Hospital General Jerez de la Frontera, Cádiz: R. Saldaña, F. Rodríguez, E. Martín, L. Hermosín; Hospital Universitario Puerta del Mar, Cádiz: M. P. Garrastazul, I. Marchante, J. A. Raposo, F. J. Capote; Hospital U. Marqués de Valdecilla, Cantabria: M. Colorado, A. Batlle, L. Yañez, S. García, P. González, E. M. Ocio, M. Briz, A. Bermúdez, S. García; Consorcio Hospitalario Provincial de Castellón, Castellón: C. Jiménez, S. Beltrán; Hospital de Vinaroz: M. Montagud; Hospital Universitario de La Plana, Castellón: I. Castillo; Hospital General de Castellón, Castellon: R. García, A. Gascón, J. Clavel, A. Lancharro, L. Lnares; Hospital Santa Bárbara, Ciudad Real: M. M. Herráez, A. Milena; Hospital Virgen de Altagracia, Ciudad Real: M. J. Romero, Hospital General de Ciudad Real, Ciudad Real: B. Hernández, C. Calle, R. Benegas; Hospital Gutierrez Ortega de Valdepeñas, Ciudad Real: Dr. Bolívar; Hospital General La Mancha Centro, Ciudad Real: M. A. Pozas; Hospital Reina Sofia, Córdoba: J. Serrano, F. J. Dorado, J. Sánchez, M. C. Martínez; Hospital Virgen de la Luz, Cuenca: C. J. Cerveró, M. J. Busto; Hospitales HUVN-HC San Cecilio de Ganada, Granada: M. Bernal, E. López, L. Moratalla, Z. Mesa, M. Jurado, A. Romero, P. González; Complejo Hospitalario Universitario Granada, Granada: L. Moratalla, A. Romero, L. López; Hospital Universitario de Guadalajara, Guadalajara: M. Díaz, D. De Miguel, A. B. Santos, J. Arbeteta; Hospital Donostia, Donosti: E. Pérez, N. Caminos, N. Uresandi, N. Argoitiaituart, T. Artola, J. Swen, A. Uranga, I. Olazaba, M. Lizuain, E. Gainza, P. Romero; Hospital Juan Ramón Jimenez Huelva, Huelva: E. Gil, A. J. Palma, K. G. Gómez, M. Solé, J. N. Rodríguez; Hospital San Jorge, Huesca: I. M. Murillo, J. Marco, J. Serena, V. Marco; Hospital de Barbastro, Huesca: M. Perella, L. Costilla; Hospital General Ciudad de Jaen, Jaén: J. A. López, A. Baena, P. Almagro; Hospital San Pedro de Logroño, La Rioja: M. Hermosilla, A. Esteban, B. A. Campeny, M. J. Nájera, P. Herrra; Hospital Insular de Las Palmas, Las Palmas: R. Fernández, J. D. González, L. Torres; Hospital Dr. Negrín, Las Palmas: S. Jiménez; M. T. Gómez, C. Bilbao, C. Rodríguez; Hospital Doctor JoséMolina Orosa, Las Palmas: A. Hong, Y. Ramos de Laón, V. Afonso; Hospital Universitario de León, León: F. Ramos, M. Fuertes; Hospital Comarcal del Bierzo, León: E. de Cabo, C. Aguilera, M. Megido; Hospital Universitari Arnau de Vilanova de Lleida, Leida: T. García; Hospital Lucus Augusti, Lugo: E. Lavilla, M. Varela, S. Ferrero, M. J. Sánchez, L. López, J. Arias, L. Vizcaya; Hospital Infanta Sofía, Madrid: A. Roldán, A. Vilches, M. J. Penalva, J. Vázquez; Hospital Central de la Defensa Gómez Ulla, Madrid: M. T. Calderón, A. Matilla, C. Serí, M. J. Otero, N. García, E. Sandoval; Hospital de Fuenlabrada, Madrid: C. Franco, R. Flores, P. Bravo, A. López; Hospital Fundación Jiménez Díaz, Madrid: J. L. López, C. Blas, A. Díez, J. M. Alonso, C. Soto, A. Arenas; Hospital U. Príncipe de Asturias, Madrid: J. García, Y. Martín, P. S. Villafuerte, E. Magro; Hospital Puerta de Hierro, Madrid: G. Bautista; A. De Laiglesia; Hospital Gregorio Marañón, Madrid: G. Cancers 2023,15, 438 18 of 21 Rodríguez, L. Solán, M. Chicano, P. Balsalobre, S. Monsalvo, P. Font, D. Carbonell, C. Martínez; Hospital U. La Paz, Madrid: K. Humala, A. E. Kerguelen, D. Hernández, M. Gasior, P. Gómez, I. Sánchez; Hospital Madrid Norte Sanchinarro, Madrid: S. Redondo, L. Llorente, M. Bengochea, J. Pérez; Hospital Sanitas Torrejón, Madrid: A. Sebrango, M. santero, A. Morales; Hospital La Princesa, Madrid: A. Figuera, P. Villafuerte, A. Alegre, E. Fernández; Hospital Ruber Internacional, Madrid: A. Alonso; Hospital 12 de Octubre, Madrid: M. P. Martínez, J. Martínez, M. T. Cedena, L. Moreno; MD Anderson Cancer Center, Madrid: A. De la Fuente; Hospital Sanitas La Zarzuela, Madrid: D. García; Hospital Universitario Quiron, Madrid: C. Chamorro, V. Pradillo, E. Martí, J. M. Sánchez, I. Delgado, A. Alonso; Hospital Rey Juan Carlos, Madrid: B. Rosado, A. Velasco, C. Miranda, G. Salvatierra, J. M. Alonso„ J. L. López; Hospital Infanta Leonor, Madrid: M. Foncillas, J. A. Hernández; Hospital Universitario de Getafe, Madrid: C. Escolano, L. García, I. Delgado; Hospital Clínico San Carlos, Madrid: C. Benabente, R. Martínez, M. Polo, E. Anguita; Hospital Universitario Severo Ochoa, Madrid: R. Riaza, G. Amores, M. J. Requena; Hospital Universitario Fundación Alcorcón, Madrid: F. Javier, L. Villaloón; Hospital Universitario Moncloa, Madrid: C. Aláez, V. Pradillo, S. Nistal, B. Navas; Hospital Universitario de Móstoles, Madrid: J. Sánchez, M. A. Andreu; Hospital Ramon y Cajal, Madrid: P. Herrera, J. López; Hospital U. Virgen de la Victoria, Málaga: M. García, M. J. Moreno, A. Fernández, M. P. Queipo; Hospital Quirónsalud Málaga, Málaga: A. Hernández; Hospital Regional de Málaga, Málaga: M. Barrios, A. Heiniger, A. Jiménez, A. Contento, F. López, M. Alcalá; Hospital Vithas Xanit Internacional, Málaga: S. Lorente, M. González, E. M. Morales, J. Gutierrez; Hospital Virgen del Castillo, Murcia: M. J. Serna, V. Beltrán; Hospital Santa Lucía de Cartagena, Murcia: M. Romera, M. Berenguer, A. Martínez, A. Tejedor; Hospital Morales Meseguer, Murcia: M. L. Amigo, F. Ortuño, L. García, A. Jerez, O. López; Hospital U. Virgen de la Arrixaca, Murcia: J. M. Moraleda, P. Rosique, J. Gómez, M. C. Garay; Hospital Los Arcos Mar Menor, Murcia: P. Cerezuela, C. Martínez, A. B. MArtínez, A. González; Hospital ST ª M ª del Rosell, Murcia: J. Ibáñez; Clínica San Miguel, Navarra: M. J. Alfaro; Complejo Hospitalario de Navarra, Navarra: M. Mateos, M. A. Goñi, M. A. Araiz, A. Gorosquieta, M. Zudaire, M. Viguria, A. Zabala, M. Alvarellos, I. Quispe, M. P. Sánchez, G. Hurtado, M. Pérez, Y. Burguete, N. Areizaga, T. Galicia; Clínica Universitaria de Navarra, Navarra: J. Rifón, A. Alfonso, F. Prósper, M. Marcos, L. E. Tamariz, V. Riego. A. Manubens, M. J. Larrayoz, M. J. Calasanz, A. Mañú, B. Paiva, I. Vázquez, L. Burgos; Complejo Hospitalario de Ourense (CHOU), Ourense: M. Pereiro, M. Rodríguez, M. C. Pastoriza, J. A. Mendez, J. L. Sastre, M. Iglesias, C. Ulibarrena, F. Campoy; Hospital Valdeorras, Ourense: D. Jaimes; Hospital Rio Carrión, Palencia: J. M. Alonso, B. Albarrán, J. Solano, A. Silvestre; Complexo Hospitalario Universitario de Vigo, Vigo: C. Albo, S. Suarez, C. Loureiro, I. Figueroa, M. Rodríguez, M. A. Fernández, A. Martínez, C. Poderós, J. Vazquez, L. Iglesias, A. Nieto, T. Torrado, A. M. Martínez; Hospital Provincial de Pontevedra, Pontevedra: M.L. Amador, P. Oubiña, E. Feijó, A. Dios, I. Loyola, R. Roreno; Hospital POVISA, Pontevedra: A. Simiele, L. Álvarez, V. Turcu; Hospital U. Salamanca, Salamanca: B. Vidriales, M. González, R. García, A. Avendaño, C. Chillón, E. Pérez, V. González; Hospital General La Palma, Santa Cruz de Tenerife: J. V. Govantes, S. Rubio, M. Tapia; Hospital General de Segovia, Segovia: C. Olivier, J. A. Queizán; Hospital U. Virgen Macarena, Sevilla: O. Pérez, J. A. Vera, C. Muñoz, A. rodriguez, N. González; Hospital U. Virgen del Rocio, Sevilla: J. A. Pérez, E. Soria, I.Espigado, J. Falantes, I. Montero, P. García, E. Rodríguez, E. Carrillo, T. Caballero, C. García; Hospital Virgen de Valme, Sevilla: C. Couto, I. Simón, M. Gómez; Hospital Virgen del Mirón de Soria, Soria: C. Aguilar; Hospital Universitario Canarias, Tenerife: B. J. González, S. Lakhwani, A. Bienert, B. González; Hospital Universitario Nuestra Señora de Candelaria, Tenrife: A. Cabello, A. Y. Oliva, H. González; Hospital Obispo Polanco, Teruel: N. González, Hospital de Alcañiz, Teruel: L. Sancho, M. Paricio, L. Perdiguer; Hospital General Nuestra Señora del Prado, Toledo: F. Solano, A. Lerma, M. D. Martínez; Hospital Virgen de la Salud de Toledo, Toledo: M. I. Gómez, A. Yeguas; Hospital U. La Fe, Valencia: P. Cancers 2023,15, 438 19 of 21 Montesinos, E. Barragán, C. Sargas, R. Amigo, D. Martinez, B. Boluda, R. Rodríguez, E. Acuña, I. Cano; Hospital de Requena, Valencia: A. Escrivá, M. Pedreño; Hospital de Lluis Alcanyis de Xativa, Valencia: R. Renart; IVO (Instituto Valenciano de Oncología), Valencia: A. Navalón; Hospital de Sagunto, Valencia: I. Castillo, M. Orts; Hospital Dr. Peset, Valencia: M. J. Sayas, M. J. Fernández, M. L. Juan, E. Gómez, M. Gimeno, E. Donato, M. Cejalvo, J. Marco; Hospital Clínico Universitario, Valencia: M. Tormo, M. Calabuig, B. Navarro, I. Martin, E. Villamont, A. Miralles; Hospital de La Ribera, Valencia: R. Lluch; Hospital Casa de la Salud, Valencia: J. García; Hospital de Gandía, Valencia: M. Moragues, M. A. Ruiz; Hospital Arnau de Vilanova, Valencia: A. López, C. Benet, M. Valero; Hospital General de Valencia, Valencia: M. Linares, R. Collado, M. Orero, P. Ibañez, M. J. Lis, P. L. Pérez, M. Roig, M. López, A. V. Mena; Hospital Manises, Valencia: I. Picón, V. Cánovas, A. Palacios, E. Martí; Hospital Clínico de Valladolid, Valladolid: R. Cuello, J. Borrego, M. burgois; Hospital Rio Hortega, Valladolid: A. Cantalapiedra, O. Norberto, E. Angomas, B. Cidoncha; Hospital Universitario Araba, Victoria: L. Cuevas, D. Robles, A. Mendiazabal, I. Oiartzabal, J. M. Guinea de Castro; Hospital Virgen de la Concha, Zamora: C. Montes, M. Pérez, L. García; Hospital Royo Villanova, Zaragoza: V. Carrasco, A. Pérez, L. López, J. J. Moneva; Hospital Clínico U. Lozano Blesa, Zaragoza: M. Olave, E. Bonafonte, L. Mayor, G. Azaceta, L. Palomera; Hospital Ernest Lluch Martin, Zaragoza: M. Malo, M. J. Escobar; Hospital Quiron Salud Zaragoza, Zaragoza: J. M. Grasa; Hospital Miguel Servet, Zaragoza: B. De Rueda, A. Aulés, C. Salvador, V. Ansó, A. Iborra, P. Delagado, A. Rubio; Uruguay—Hospital de Clínicas, Montevideo: M. Stevenazzi, I. Alpire, V. Irigoin, L. Díaz, C. Guillermo, R. Guadagna, S. Grille, C. Oliver, M. Boada, V. Vales; Hospital Maciel, Montevideo: A. I. Prado; COMERO (Cooperativa Medica de Rocha), Rocha: A. P. De los Santos. References 1. Papaemmanuil, E.; Gerstung, M.; Bullinger, L.; Gaidzik, V.I.; Paschka, P.; Roberts, N.D.; Potter, N.E.; Heuser, M.; Thol, F.; Bolli, N.; et al. Genomic Classification and Prognosis in Acute Myeloid Leukemia. N. Engl. J. Med. 2016 ,374, 2209–2221. [CrossRef] [PubMed] 2. Tazi, Y.; Arango-Ossa, J.E.; Zhou, Y.; Bernard, E.; Thomas, I.; Gilkes, A.; Freeman, S.; Pradat, Y.; Johnson, S.J.; Hills, R.; et al. Unified Classification and Risk-Stratification in Acute Myeloid Leukemia. medRxiv 2022, 2022.03.09.22271087. [CrossRef] [PubMed] 3. Khoury, J.D.; Solary, E.; Abla, O.; Akkari, Y.; Alaggio, R.; Apperley, J.F.; Bejar, R.; Berti, E.; Busque, L.; Chan, J.K.C.; et al. The 5th Edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic Neoplasms. Leukemia 2022,36, 1703–1719. [CrossRef] [PubMed] 4. Arber, D.A.; Orazi, A.; Hasserjian, R.P.; Borowitz, M.J.; Calvo, K.R.; Kvasnicka, H.M.; Wang, S.A.; Bagg, A.; Barbui, T.; Branford, S.; et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemia: Integrating Morphological, Clinical, and Genomic Data. Blood 2022,140, 1200–1228. [CrossRef] [PubMed] 5. Döhner, H.; Wei, A.H.; Appelbaum, F.R.; Craddock, C.; DiNardo, C.D.; Dombret, H.; Ebert, B.L.; Fenaux, P.; Godley, L.A.; Hasserjian, R.P.; et al. Diagnosis and Management of AML in Adults: 2022 ELN Recommendations from an International Expert Panel. Blood 2022,129, 424–447. [CrossRef] 6. Levine, R.L.; Valk, P.J.M. Next-Generation Sequencing in the Diagnosis and Minimal Residual Disease Assessment of Acute Myeloid Leukemia. Haematologica 2019,104, 868. [CrossRef] [PubMed] 7. Bacher, U.; Shumilov, E.; Flach, J.; Porret, N.; Joncourt, R.; Wiedemann, G.; Fiedler, M.; Novak, U.; Amstutz, U.; Pabst, T. Challenges in the Introduction of Next-Generation Sequencing (NGS) for Diagnostics of Myeloid Malignancies into Clinical Routine Use. Blood Cancer J. 2018,8, 113. [CrossRef] 8. Haferlach, T. Advancing Leukemia Diagnostics: Role of Next Generation Sequencing (NGS) in Acute Myeloid Leukemia. Hematol. Rep. 2020,12, 8957. [CrossRef] 9. Sargas, C.; Ayala, R.; Chillón, M.C.; Larráyoz, M.J.; Carrillo-Cruz, E.; Bilbao, C.; Yébenes-Ramírez, M.; Llop, M.; Rapado, I.; García-Sanz, R.; et al. Networking for Advanced Molecular Diagnosis in Acute Myeloid Leukemia Patients Is Possible: The PETHEMA NGS-AML Project. Haematologica 2021,106, 3079–3089. [CrossRef] 10. Ley, T.J.; Miller, C.; Ding, L.; Raphael, B.J.; Mungall, A.J.; Robertson, G.; Hoadley, K.; Triche, T.J.; Laird, P.W.; Baty, J.D.; et al. Genomic and Epigenomic Landscapes of Adult de Novo Acute Myeloid Leukemia. N. Engl. J. Med. 2013 ,368, 2059–2074. [CrossRef] 11. Leisch, M.; Jansko, B.; Zaborsky, N.; Greil, R.; Pleyer, L. Next Generation Sequencing in AML-On the Way to Becoming a New Standard for Treatment Initiation and/or Modulation? Cancers 2019,11, 252. [CrossRef] [PubMed] Cancers 2023,15, 438 20 of 21 12. Luthra, R.; Patel, K.P.; Routbort, M.J.; Broaddus, R.R.; Yau, J.; Simien, C.; Chen, W.; Hatfield, D.Z.; Medeiros, L.J.; Singh, R.R. A Targeted High-Throughput Next-Generation Sequencing Panel for Clinical Screening of Mutations, Gene Amplifications, and Fusions in Solid Tumors. J. Mol. Diagn. 2017,19, 255–264. [CrossRef] [PubMed] 13. Petrackova, A.; Vasinek, M.; Sedlarikova, L.; Dyskova, T.; Schneiderova, P.; Novosad, T.; Papajik, T.; Kriegova, E. Standardization of Sequencing Coverage Depth in NGS: Recommendation for Detection of Clonal and Subclonal Mutations in Cancer Diagnostics. Front. Oncol. 2019,9, 851. [CrossRef] [PubMed] 14. Endrullat, C.; Glökler, J.; Franke, P.; Frohme, M. Standardization and Quality Management in Next-Generation Sequencing. Appl. Transl. Genom. 2016,10, 2–9. [CrossRef] 15. Pandzic, T.; Ladenvall, C.; Engvall, M.; Mattsson, M.; Hermanson, M.; Cavelier, L.; Ljungström, V.; Baliakas, P. Five Percent Variant Allele Frequency Is a Reliable Reporting Threshold for TP53 Variants Detected by Next Generation Sequencing in Chronic Lymphocytic Leukemia in the Clinical Setting. Hemasphere 2022,6, e761. [CrossRef] 16. Jennings, L.J.; Arcila, M.E.; Corless, C.; Kamel-Reid, S.; Lubin, I.M.; Pfeifer, J.; Temple-Smolkin, R.L.; Voelkerding, K.V.; Nikiforova, M.N. Guidelines for Validation of Next-Generation Sequencing-Based Oncology Panels: A Joint Consensus Recommendation of the Association for Molecular Pathology and College of American Pathologists. J. Mol. Diagn. 2017,19, 341–365. [CrossRef] 17. Guillermin, Y.; Lopez, J.; Chabane, K.; Hayette, S.; Bardel, C.; Salles, G.; Sujobert, P.; Huet, S. What Does This Mutation Mean? The Tools and Pitfalls of Variant Interpretation in Lymphoid Malignancies. Int. J. Mol. Sci. 2018,19, 1251. [CrossRef] 18. Strom, S.P. Current Practices and Guidelines for Clinical Next-Generation Sequencing Oncology Testing. Cancer Biol. Med. 2016 , 13, 3–11. [CrossRef] 19. Qin, G.; Ilya, S.; Kim, T.-K.; Tercan, B.; Martins, T.J.; Dai, J.; Chien, S.; Carson, A.; Patay, B.; Estey, E.H.; et al. Co-Occurring Mutation Clusters Predict Drug Sensitivity in Acute Myeloid Leukemia. Blood 2020,136, 12–13. [CrossRef] 20. Lachowiez, C.A.; Reville, P.K.; Kantarjian, H.; Jabbour, E.; Borthakur, G.; Daver, N.; Issa, G.; Furudate, K.; Tanaka, T.; Pierce, S.; et al. Contemporary Outcomes in IDH-Mutated Acute Myeloid Leukemia: The Impact of Co-Occurring NPM1 Mutations and Venetoclax-Based Treatment. Am. J. Hematol. 2022,97, 1443–1452. [CrossRef] 21. Bullinger, L.; Döhner, K.; Dohner, H. Genomics of Acute Myeloid Leukemia Diagnosis and Pathways. J. Clin. Oncol. 2017 ,35, 934–946. [CrossRef] [PubMed] 22. Ding, L.; Ley, T.J.; Larson, D.E.; Miller, C.A.; Koboldt, D.C.; Welch, J.S.; Ritchey, J.K.; Young, M.A.; Lamprecht, T.; McLellan, M.D.; et al. Clonal Evolution in Relapsed Acute Myeloid Leukaemia Revealed by Whole-Genome Sequencing. Nature 2012 ,481, 506–510. [CrossRef] [PubMed] 23. Niu, J.; Peng, D.; Liu, L. Drug Resistance Mechanisms of Acute Myeloid Leukemia Stem Cells. Front. Oncol. 2022 ,12, 896426. [CrossRef] [PubMed] 24. van Gils, N.; Denkers, F.; Smit, L. Escape From Treatment; the Different Faces of Leukemic Stem Cells and Therapy Resistance in Acute Myeloid Leukemia. Front. Oncol. 2021,11, 659253. [CrossRef] [PubMed] 25. Zhang, J.; Gu, Y.; Chen, B. Mechanisms of Drug Resistance in Acute Myeloid Leukemia. Onco. Targets Ther. 2019 ,12, 1937–1945. [CrossRef] 26. Benard, B.A.; Leak, L.B.; Azizi, A.; Thomas, D.; Gentles, A.J.; Majeti, R. Clonal Architecture Predicts Clinical Outcomes and Drug Sensitivity in Acute Myeloid Leukemia. Nat. Commun. 2021,12, 7244. [CrossRef] 27. Thol, F.; Heuser, M. Treatment for Relapsed/Refractory Acute Myeloid Leukemia. Hemasphere 2021,5, E572. [CrossRef] 28. Daver, N.; Schlenk, R.F.; Russell, N.H.; Levis, M.J. Targeting FLT3 Mutations in AML: Review of Current Knowledge and Evidence. Leukemia 2019,33, 299–312. [CrossRef] 29. Tarlock, K.; Zhong, S.; He, Y.; Ries, R.; Severson, E.; Bailey, M.; Morley, S.; Balasubramanian, S.; Erlich, R.; Lipson, D.; et al. Distinct Age-Associated Molecular Profiles in Acute Myeloid Leukemia Defined by Comprehensive Clinical Genomic Profiling. Oncotarget 2018,9, 26417–26430. [CrossRef] 30. Grimwade, D.; Ivey, A.; Huntly, B.J.P. Molecular Landscape of Acute Myeloid Leukemia in Younger Adults and Its Clinical Relevance. Blood 2016,127, 29. [CrossRef] 31. di Nardo, C.D.; Cortes, J.E. Mutations in AML: Prognostic and Therapeutic Implications. Hematol. Am. Soc. Hematol. Educ. Program 2016,2016, 348. [CrossRef] 32. Yu, J.; Jiang, P.Y.Z.; Sun, H.; Zhang, X.; Jiang, Z.; Li, Y.; Song, Y. Advances in Targeted Therapy for Acute Myeloid Leukemia. Biomark. Res. 2020,8, 17. [CrossRef] 33. Ranieri, R.; Pianigiani, G.; Sciabolacci, S.; Perriello, V.M.; Marra, A.; Cardinali, V.; Pierangeli, S.; Milano, F.; Gionfriddo, I.; Brunetti, L.; et al. Current Status and Future Perspectives in Targeted Therapy of NPM1-Mutated AML. Leukemia 2022 ,36, 2351–2367. [CrossRef] [PubMed] 34. Ayala, R.; Rapado, I.; Onecha, E.; Martínez-Cuadrón, D.; Carreño-Tarragona, G.; Bergua, J.M.; Vives, S.; Algarra, J.L.; Tormo, M.; Martinez, P.; et al. The Mutational Landscape of Acute Myeloid Leukaemia Predicts Responses and Outcomes in Elderly Patients from the PETHEMA-FLUGAZA Phase 3 Clinical Trial. Cancers 2021,13, 2458. [CrossRef] [PubMed] 35. Prassek, V.V.; Rothenberg-Thurley, M.; Sauerland, M.C.; Herold, T.; Janke, H.; Ksienzyk, B.; Konstandin, N.P.; Goerlich, D.; Krug, U.; Faldum, A.; et al. Genetics of Acute Myeloid Leukemia in the Elderly: Mutation Spectrum and Clinical Impact in Intensively Treated Patients Aged 75 Years or Older. Haematologica 2018,103, 1853. [CrossRef] [PubMed] Cancers 2023,15, 438 21 of 21 36. Ni, J.; Hong, J.; Long, Z.; Li, Q.; Xia, R.; Zeng, Q. Mutation Profile and Prognostic Relevance in Elderly Patients with de Novo Acute Myeloid Leukemia Treated with Decitabine-Based Chemotherapy. Int. J. Lab. Hematol. 2020 ,42, 849–857. [CrossRef] [PubMed] 37. Hellesøy, M.; Engen, C.; Grob, T.; Löwenberg, B.; Valk, P.J.M.; Gjertsen, B.T. Sex Disparity in Acute Myeloid Leukaemia with FLT3 Internal Tandem Duplication Mutations: Implications for Prognosis. Mol. Oncol. 2021,15, 2285–2299. [CrossRef] 38. De-Morgan, A.; Meggendorfer, M.; Haferlach, C.; Shlush, L. Male Predominance in AML Is Associated with Specific Preleukemic Mutations. Leukemia 2021,35, 867–870. [CrossRef] 39. Bullinger, L. CEBPA Mutations in AML: Site Matters. Blood 2022,139, 6–7. [CrossRef] 40. Wakita, S.; Sakaguchi, M.; Oh, I.; Kako, S.; Toya, T.; Najima, Y.; Doki, N.; Kanda, J.; Kuroda, J.; Mori, S.; et al. Prognostic Impact of CEBPA BZIP Domain Mutation in Acute Myeloid Leukemia. Blood Adv. 2022,6, 238–247. [CrossRef] 41. Mannelli, F.; Ponziani, V.; Bencini, S.; Bonetti, M.I.; Benelli, M.; Cutini, I.; Gianfaldoni, G.; Scappini, B.; Pancani, F.; Piccini, M.; et al. CEBPA–Double-Mutated Acute Myeloid Leukemia Displays a Unique Phenotypic Profile: A Reliable Screening Method and Insight into Biological Features. Haematologica 2017,102, 529–540. [CrossRef] [PubMed] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.