Mutational Landscape and Factors Affecting Prognosis in Acute Myeloid Leukaemia (AML) with RUNX1::RUNX1T1
Authors: Kusurkar Saurabh , Achrekar Aarti Ramesh , Ugale Priyanka Rajesh , Joshi Swapnali , Terse Vishram , Shetty Dhanlaxmi Lalit , Jindal Nishant , Tembhare Prashant , Mirgh Sumeet , Shetty Alok , Gokarn Anant , Punatar Sachin , Nayak Lingaraj , Jain Hasmukh , Sengar Manju , Khattry Navin , Bagal Bhausaheb , Rajpal Sweta , Chatterjee Gaurav , Subramanian Papagudi Ganesan , Gujral Sumeet , Patkar Nikhil 
Published: December 2024

Introduction: AML withRUNX1::RUNX1T1 is one of the commonest AMLs seen in young patients. It exhibits variable clinical outcomes due to significant heterogeneity driven by underlying disease biology. This AML has been classified as a favourable risk disease by European LeukaemiaNet (ELN22). This scheme does not take other gene mutations or additional cytogenetic abnormalities into account. We aimed to evaluate the genomic landscape of AML with RUNX1::RUNX1T1 and analyzed additional variables such as variant allele fractions of commonly mutated genes in this category. Furthermore, we validated a previously published machine learning score I- CBFit (Utsun et al. Cancer Med 2018) for prognostication of this entity.

Methods: A total of 506 patients of adult AML (&gt;18 years) were accrued over 10 years (2012 to 2023) from a single center, of which 119 patients were AML with RUNX1::RUNX1T1 fusion. Patients were uniformly treated with “3 + 7” induction chemotherapy. Diagnostic samples were sequenced using a 50-gene myeloid panel (till 2020) based on single molecule molecular inversion probes and subsequently using a 135-gene hybrid capture-based panel. We incorporated mutations that have been reported to influence outcome in AML with RUNX1::RUNX1T1, post induction measurable residual disease as measured by multiparameter flow cytometry (PI MFC-MRD) and other cytogenetic abnormalities as parameters. A previously published algorithm, I-CBFit machine learning score for risk stratification (Utsun et al. Cancer Med 2018) which utilises age, white blood cell counts, KIT D816V mutation status and ploidy as parameters was also evaluated. Overall survival (OS) and relapse free survival (RFS) were computed using the Kaplan - Meier method and compared using log-rank test and multivariate analysis was done using the cox proportional hazards regression model.

Results: Median age of the cohort was 30.5 years (M:F,1.7:1) with median follow-up of 44.6 months. The median OS was 71.9 months (95% CI 29.7-71.9) and median RFS was 70.8 months (95% CI 28.1-87.8). A total of 73.94% cases achieved morphological remission at the end of induction. The mutational landscape revealed that signalling pathway mutations [(KIT (28.3%) NRAS (24%) FLT3 (10%)] were commonest (73.1%) followed by ASXL2 (19%) mutations. At least 1 mutation was identified in 89.91% cases with a mean of 2.4 mutations per patient. Cases with ≥2 mutations were associated with an inferior OS [(HR:3.64; 95% CI 1.8-7.1; p = 0.003)]. PI MRD positivity was identified in 42.8% and predicted an inferior OS [(HR: 1.84; 95%CI 0.9-3.5; p = 0.05)]. Patients with KIT exon 17 gene mutations, specifically KIT D816V had an inferior OS [(HR: 2.37, 95%CI 0.7-8.0; p = 0.04)]. Other KIT gene mutations had no significant impact on OS and RFS. JAK2 gene (p.V617F) mutations were seen in 6% of cases and were associated with an exceedingly inferior OS compared to wild-type cases [(HR: 5.2, 95% CI 0.9-29; p &lt; 0.0001)]. FLT3 gene internal tandem duplications (FLT3-ITD) were identified in 5.9% of cases and were associated with an inferior RFS [(HR: 2.60; 95% CI 0.64-10.4; p = 0.03)]. MR gene mutations (14.28%), variant t[v;8;21] (12.64%), aneuploidy or additional chromosomal abnormalities had no bearing on outcome. Patients with favourable I-CBFit risk score (score ≤0) had superior OS [(HR 2.3; 95% CI 0.7-7.3; p = 0.03)] and RFS (HR 2.3; 95% CI 0.7-7.3; p = 0.05) compared to those with poor (score &gt;0) risk score. On multivariate analysis PI MRD status (p= 0.04) KITD816V (p = 0.02) and JAK2V617F (p &lt;0.0001) gene mutations had impact on OS while I-CBFit poor risk (p =0.05) FLT3-ITD (p = 0.03) and JAK2 (p=0.001) gene mutations had negative impact on RFS.

Conclusion: High mutational burden, post induction MRD status, signalling pathway mutations like KIT D816V, FLT3-ITD and JAK2 V617F predict inferior outcomes in AML with RUNX1::RUNX1T1. Multiparametric machine learning score I-CBFit additionally helps in risk stratification.