Targetable genetic abnormalities in patients with acute myeloblastic leukemia across age groups
Authors: Alex Bataller, Courtney D. DiNardo, Alexandre Bazinet, Naval G. Daver, Abhishek Maiti, Gautam Borthakur, Nicholas Short, Koji Sasaki, Elias J. Jabbour, Ghayas C. Issa, Naveen Pemmaraju, Musa Yilmaz, Guillermo Montalban‐Bravo, Sanam Loghavi, Guillermo Garcia‐Manero, Farhad Ravandi, Hagop M. Kantarjian, Tapan M. Kadia
Journal: American Journal of Hematology (2024)

Incidence of potential targetable genetic abnormalities by age in AML. Acute myeloblastic leukemia (AML) is an aggressive neoplasm that arises from a hematopoietic progenitor, leading to an uncontrolled proliferation of blast cells and impaired hematopoiesis. It occurs primarily in older people, with a median age at diagnosis of 65 years old. AML usually presents with genetic abnormalities (such as chromosomal abnormalities or gene mutations), some of them with important prognostic implications.1, 2 Standard treatment consists of intensive cytarabine and anthracycline-based chemotherapy, and in eligible patients, allogeneic stem cell transplantation is often recommended to achieve long-term remissions.3 In older adults or patients not eligible for intensive treatments, the standard of care includes low intensity therapy combining hypomethylating agents or low dose cytarabine with the Bcl-2 inhibitor venetoclax.3 Some mutations and cytogenetic abnormalities common in AML are targetable with approved drugs (such as FLT3 or IDH1/2 inhibitors) as well as novel treatments that may be approved in the coming years (e.g., menin inhibitors for patients with KMT2A rearrangements or NPM1 mutations). These targeted therapies are generally non-cytotoxic oral compounds with an acceptable tolerance, which make them attractive in combination with both intensive and low intensity treatments. However, the true incidence of these potentially targetable genetic abnormalities remains unclear, especially in older patients, since most studies analyzing large cohorts focus on young patients receiving intensive treatment.4 Herein, we report the genetic analysis of patients older than 60 years in comparison to a younger contemporaneous cohort, focusing on the incidence of genetic abnormalities that are potentially targetable. This is a single-center retrospective study that included patients of age 18 or more who had been diagnosed with AML at The University of Texas MD Anderson Cancer Center from April 2017 to October 2022. Cytogenetic analysis was performed using conventional karyotype banding and fluorescence in situ hybridization. Mutational analysis was performed using an 81-gene next-generation sequencing (NGS) panel (Data S1). NGS sensitivity was established as 1%–2% mutant reads in a background of wild-type reads. In addition, FLT3 internal tandem duplications (FLT3-ITD) were detected using a polymerase chain reaction-based DNA analysis. Genetic abnormalities considered potentially targetable were FLT3-ITD and FLT3 point mutations (including mutations in the tyrosine kinase domain), IDH1 and IDH2 mutations, NPM1 mutations, and KMT2A rearrangements. Correlation was evaluated using the Phi coefficient and statistical significance was evaluated with Fisher's exact test applying the Holm–Bonferroni correction for multiple comparisons. A total of 875 patients diagnosed with AML were included in this analysis. A total of 295 (34%) patients were <60 years old and 580 (66%) patients were ≥60 years old. The baseline characteristics are described in Figure 1A. Overall, older patients were more likely to have complex cytogenetics (defined by ≥3 unrelated chromosome abnormalities without other class-defining genetic abnormalities), higher risk European LeukemiaNet 2022 (ELN 2022) classification, and therapy-related disease. A normal karyotype was present in 291 (36%) patients (n = 100 [36%] and n = 191 [35%] for patients <60 and ≥60 years old, respectively, p = .94). A complex karyotype was found in 208 patients (25%), (n = 45 [16%] and n = 163 [30%] for patients <60 and ≥60 years old, respectively, p < .001). According to the ELN 2022 risk classification, 175 (21%) were classified as favorable risk (n = 68 [24%] and n = 107 [19%] for patients <60 and ≥60 years old, respectively, p = .172), 199 (24%) were classified as intermediate risk (n = 99 [34%] and n = 100 [18%] for patients <60 and ≥60 years old, respectively, p < .001), and 470 (56%) were classified as adverse risk (n = 122 [42%] and n = 348 [63%] for patients <60 and ≥60 years old, respectively, p < .001). A total of 168 (19%) patients were classified as therapy-related myeloid neoplasms (n = 31 [11%] and n = 137 [24%] patients <60 and ≥60 years old, respectively, p < .001). Overall, the most frequent mutations were DNMT3A (n = 196, [23%]), TP53 (n = 183 [22%]), and NPM1 (n = 175 [21%]). FLT3-ITD (n = 68 [24%]) and NRAS (n = 64 [23%]) were the most frequent mutations in patients <60 years old, and TP53 (n = 148 [27%]) and DNMT3A (n = 143 [26%]) were the most frequent in patients ≥60 years old (Figure 1B and Data S4). The median number of mutations per patient was two (range, 0–9) and three (range, 0–9) for patients <60 and ≥60 years old, respectively (p < .001, Data S3). There was a significant difference in the incidence of mutations in ASXL1, DNMT3A, FLT3, KIT, RUNX1, SETBP1, SRSF2, STAG2, TET2, TP53, and U2AF1, between patients <60 and ≥60 years old (Figure 1B,C). A total of 412 (49%) patients had a potentially targetable genetic abnormality, including 172 (60%) and 240 (44%) in patients <60 and ≥60 years old, respectively (p < .001). FLT3 mutations were enriched in younger patients; detected in 106 (37%) patients <60 years old (n = 68 [24%] with FLT3-ITD and n = 52 [18%] with FLT3 point mutations), and in 107 (19%) patients ≥60 years old (n = 63 [11%] with FLT3-ITD [p < .001] and n = 53 [10%] with FLT3 point mutations [p < .001]). KMT2A rearrangements were also significantly more common in younger patients, detected in 34 (12%) and 14 (3%) patients <60 and ≥60 years old, respectively (p < .001). NPM1 mutations were detected in 62 (22%) and 113 (20%) of patients <60 and ≥60 years old, respectively (p = .7). IDH1/2 mutations were detected in 52 (18%) patients <60 years old (n = 19 [7%] with IDH1 mutations and n = 36 [12%] with IDH2 mutations) and in 114 (21%) patients ≥60 years old (n = 43 [8%] with IDH1 mutations [p = .6] and n = 74 [13%] with IDH2 mutations [p = .8]). Among all patients ≥60 years old with potentially targetable genetic abnormalities, 93 patients (39% of all patients with potentially targetable abnormalities) had an additional potentially targetable genetic abnormality. The different overlaps between potentially targetable abnormalities in patients ≥60 years old are shown in Figure 1D. A significant positive correlation was observed in FLT3-ITD and NPM1 (n = 33), FLT3 point mutations and NPM1 (n = 28), FLT3 point mutations and DNMT3A (n = 26), FLT3 point mutations and NRAS (n = 19), FLT3 point mutations and PTPN11 (n = 12), IDH1 and NPM1 (n = 19), IDH2 and STAG2 (n = 12), and NPM1 and DNMT3A (n = 53) (p < .05). TP53 mutation showed a significant negative correlation with NPM1 (n = 5), FLT3-ITD (n = 4), FLT3 point mutations (n = 5), and IDH2 (n = 6) (p < .05). Correlations between the most frequent mutations are detailed in Figure 1E,F. In this report we dissected the genetic landscape of AML in a large cohort, with special emphasis in the incidence of cytogenetic and gene abnormalities by presenting age. Older patients presented with a higher incidence of adverse cytogenetic risk features, such as complex karyotype or chromosome 5 and 7 abnormalities. Moreover, these patients have increased frequency of adverse risk mutations, such as ASXL1, RUNX1, or TP53. This suggests that older individuals with AML experience poorer outcomes not only due to the general risks associated with aging, such as higher presence of comorbidities or concomitant diseases, but also because AML within this patient subgroup is enriched in unfavorable genomics that are currently untargetable, highlighting a critical unmet need. Nonetheless, 44% of older patients with AML had a potentially targetable genetic abnormality. IDH1/2 mutations were the most frequent targetable mutations in older patients (21%), which can be successfully targeted with IDH1 inhibitors (such as ivosidenib and olutasidenib) and IDH2 inhibitors (such as enasidenib).5, 6 These inhibitors are currently approved for patients with relapsed or refractory disease, and ivosidenib is also approved for newly diagnosed IDH1-mutated AML patients alone or in combination with azacitidine. FLT3 mutations can be targeted with different approved FLT3 inhibitors (such as midostaurin, gilteritinib, or quizartinib), which have demonstrated to improve the outcomes of these patients.7 In addition to FLT3 and IDH1/2 inhibitors, novel targeted therapies are being investigated for AML, and notably menin inhibitors are being explored in patients with KMT2A rearrangements or NPM1 mutation. This is because menin is a necessary cofactor for the KMT2A-dependent expression of HOX and MEIS1 genes, which are the hallmark of leukemias with KMT2r or NPM1 mutation. Early-stage clinical trials have shown promising efficacy, and ongoing clinical trials are evaluating these inhibitors in combination with other treatments in both newly diagnosed and relapsed/refractory populations.8 We also identified that potentially targetable mutations often co-occur with other potentially targetable abnormalities. This is a previously underappreciated and crucial finding, as rational combinations or sequencing of effective therapies with nonoverlapping mechanisms of action (and toxicity) are more likely to lead to durable responses. For this reason, it is likely that future clinical trials would evaluate the efficacy and safety of multiple inhibitor combinations. Performing genetic testing in all AML patients not only helps identify potentially targetable mutations but also provides crucial additional information. Some genetic abnormalities, such as TP53 predict a high treatment resistance, which can lead to important therapeutic decisions like prioritizing treatments within a clinical trial. Moreover, there is a possibility that specific mutations may be sensitive to future drugs or combinations. Lastly, the detection of certain mutations, like DDX41 in older adults, plays a crucial role in identifying patients with a germline predisposition myeloid neoplasm. To conclude, the landscape of genetic abnormalities in older adults with AML differs from that of younger patients. However, in both populations, a significant proportion of patients will present with a potentially targetable mutation. For this reason, in all patients diagnosed with AML and at each new treatment timepoint, it is crucial to perform genetic testing to improve available treatment options and hopefully in the future lead to design-adapted combination treatments to improve the outcome of AML patients of all ages. Alex Bataller: Study conception, data curation, formal data analysis and interpretation, patient care, writing of the manuscript, and review and editing of the manuscript. Courtney D. DiNardo: Study conception, patient care, data interpretation and review and editing of the manuscript. Alexandre Bazinet: Data curation, patient care, and comprehensive review and intensive editing of the manuscript. Abishek Maiti: Patient care and review and editing of the manuscript. Gautam Borthakur: Patient care and review and editing of the manuscript. Naval Daver: Patient care and review and editing of the manuscript. Nicholas Short: Patient care and review and editing of the manuscript. Koji Sasaki: Patient care and review and editing of the manuscript. Elias Jabbour: Patient care and review and editing of the manuscript. Ghayas C. Issa: Patient care and review and editing of the manuscript. Naveen Pemmaraju: Patient care and review and editing of the manuscript. Musa Yilmaz: Patient care and review and editing of the manuscript. Guillermo Montalban-Bravo: Patient care and review and editing of the manuscript. Sanam Loghavi: Diagnostic analysis and molecular interpretation, and review and editing of the manuscript. Guillermo Garcia-Manero: Patient care and review and editing of the manuscript. Farhad Ravandi: Patient care and review and editing of the manuscript. Hagop M. Kantarjian: Study conception, patient care, data interpretation, writing of the manuscript, and review and editing of the manuscript. Tapan M. Kadia: Study conception, patient care, data interpretation, writing of the manuscript, and review and editing of the manuscript. Alex Bataller has no conflict of interest to disclose. Courtney D. DiNardo has been a board of directors or advisory committee member for Genmab, GSK, Kura Oncology, and Notable Labs; has received honoraria from Kura, Astellas Pharma, Bluebird Bio, Bristol Myers Squibb, Foghorn Therapeutics, Immune-Onc Therapeutics, Novartis, Takeda Oncology, Gilead Sciences, and Jazz Pharmaceuticals; is a current holder of stock options for Notable Labs; has been a consultant for AbbVie and Servier; and has received research funding from Servier, Bristol Myers Squibb, Foghorn, Immune-Onc Therapeutics, Loxo Oncology, Astex Pharmaceuticals, Cleave, and Forma. Alexandre Bazinet has no conflict of interest to disclose. Naval G. Daver has received research funding from Astellas Pharma, AbbVie, Genentech, Daiichi Sankyo, Gilead Sciences, ImmunoGen, Pfizer, Bristol Myers Squibb, Trovagene, Servier, Novimmune, Incyte, Hanmi Pharm, Fate Therapeutics, Amgen, Kite Pharma, Novartis, Astex Pharmaceuticals, KAHR, Shattuck, Sobi, GlycoMimetics, and Trillium; has been an advisor for Astellas Pharma, AbbVie, Genentech, Daiichi Sankyo, Novartis, Jazz Pharmaceuticals, Amgen, Servier, Karyopharm Therapeutics, Trovagene, Trillium, Syndax, Gilead Sciences, Pfizer, Bristol Myers Squibb, Kite Pharma, Actinium Pharmaceuticals, Arog Pharmaceuticals, ImmunoGen, Arcellx, and Shattuck; has been a data monitoring committee member for Kartos Therapeutics and Jazz Pharmaceuticals; has been a consultant or board of directors or advisory committee member for Agios, Celgene, Sobi, and STAR Therapeutics; and has received research funding from Karyopham Therapeutics and Newave Pharmaceutical. Abhishek Maiti reports support from BioSight, Sanofi, and Astex Pharmaceuticals. Gautam Borthakur has received research funding from Astex Pharmaceuticals, Ryvu Therapeutics, and PTC Therapeutics; has been a board of directors or advisory committee member for Pacyclex Pharmaceuticals, Novartis, CytomX, and Bio Ascend; and has been a consultant for Catamaran Bio, AbbVie, PPD Development, Protagonist Therapeutics, and Janssen. Nicholas Short has been a consultant for Takeda Oncology, AstraZeneca, Amgen, Novartis, and Pfizer and received research funding from Takeda Oncology, Astellas, and Stemline Therapeutics as well as honoraria from Amgen. Koji Sasaki has no conflict of interest to disclose. Elias Jabbour declares research funding from Amgen, Pfizer, Abbvie, Adaptive Biotechnologies, Astex, Ascentage, and consultancy from Amgen, Pfizer, Abbvie, Takeda, Adaptive Biotechnologies, Astex, Ascentage, Genentech, Novartis, BMS, Jazz Pharmaceuticals, Hikma Pharmaceuticals, and Incyte. Ghayas Issa has been a consultant for Novartis, Kura Oncology, and NuProbe and received research funding from Celgene, Kura Oncology, Syndax, Merck, Cullinan, and Novartis. Naveen Pemmaraju has been a consultant for AbbVie, Astellas Pharma, Cimeio Therapeutics, CTI BioPharma, ImmunoGen, Intellisphere, Patient Power, PharmaEssentia, Protagonist Therapeutics, and TotalCME; has been a scientific/advisory committee member for Blueprint Medicines, Cancer.Net, CareDx, CTI BioPharma, Incyte, Menarini Group, and Pacylex Pharmaceuticals; has been a speaker or held a preceptorship for AbbVie, the Aplastic Anemia & MDS International Foundation, Curio Bioscience, DAVA Oncology, Intellisphere, Magdalen Medical Publishing, Medscape, Novartis, the Physician's Education Resource, Protagonist Therapeutics, and Targeted Oncology; and has received research support from the U.S. Department of Defense. Musa Yilmaz has received research funding from Daiichi-Sankyo and Pfizer. Guillermo Montalban-Bravo has no conflict of interest to disclose. Sanam Loghavi has no conflict of interest to disclose. Guillermo Garcia-Manero has received research funding from Astex Pharmaceuticals, Novartis, AbbVie, BMS, Genentech, Aprea Therapeutics, Curis, and Gilead Sciences; has been a consultant for Astex Pharmaceuticals, Acceleron Pharma, and BMS; and has received honoraria from Astex Pharmaceuticals, Acceleron Pharma, AbbVie, Novartis, Gilead Sciences, Curis, Genentech, and BMS. Farhad Ravandi has received research funding from Amgen, Astex Pharmaceuticals/Taiho Oncology, BMS/Celgene, Syos, AbbVie, Prelude, Xencor, Astellas Pharma, and Biomea Fusion as well as honoraria from Amgen, BMS/Celgene, Syos, AbbVie, and Astellas Pharma; has been a board of directors or advisory committee member for Astex Pharmaceuticals/Taiho Oncology; and has been a consultant for BMS/Celgene, Syos, Novartis, AbbVie, AstraZeneca, and Astellas Pharma. Hagop M. Kantarjian has received research funding from AbbVie, Amgen, Ascentage Pharma, BMS, Daiichi Sankyo, ImmunoGen, Jazz Pharmaceuticals, and Novartis as well as honoraria from AbbVie, Amgen, Amphista Therapeutics, Ascentage Pharma, Astellas Pharma, Biologix, Curis, Ipsen, KAHR, Novartis, Pfizer, Precision Biosciences, Shenzhen TargetRx, and Takeda Oncology. Tapan M. Kadia has been a consultant for AbbVie, Agios, BMS, Genentech, Jazz Pharmaceuticals, Novartis, Servier, and PinotBio; has received research funding from AbbVie, BMS, Genentech, Jazz Pharmaceuticals, Pfizer, Cellenkos, Ascentage Pharma, GenFleet Therapeutics, Astellas Pharma, AstraZeneca, Amgen, Cyclacel Pharmaceuticals, Delta-Fly Pharma, Iterion Therapeutics, GlycoMimetics, and Regeneron Pharmaceuticals; and has received honoraria from Astex Pharmaceuticals. The study data is not publicly available in order to respect patient confidentiality. Requests for sharing of deidentified data should be directed to the corresponding author. Data S1. Supporting Information. 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.