Genetic Alterations at Diagnosis Predict Outcome of AML Patients Age 60 or Older Undergoing Allogeneic Transplantation in First Remission

Background: Older age is associated with inferior outcomes after allogeneic hematopoietic stem cell transplantation (HSCT) for acute myeloid leukemia (AML). High risk genetic characteristics are common among older patients and linked to poor outcomes in the non-transplant setting. An enhanced understanding of genetic risk may thus provide a basis for improving transplant outcomes in these patients. We evaluated the impact of leukemia genetic characteristics at diagnosis on HSCT outcomes in a multi-center cohort of AML patients age 60 or older receiving HSCT in first complete remission (CR1).

Methods: We performed targeted sequencing of 112 genes on diagnostic leukemia samples from 257 patients with AML age 60 or older who received allogeneic HSCT in CR1 at 5 US transplant centers. Median age at diagnosis and HSCT were 65 (range 59-76) and 66 (range 60-76), respectively. 31% had clinically defined secondary AML, 11% had therapy-related AML, and 23% had adverse cytogenetics by 2017 ELN classification. Most (84%) were treated with anthracycline-based induction chemotherapy, while 16% received non-intensive induction. Conditioning was either reduced-intensity or non-myeloablative in 94% of patients. Median follow-up for survivors was 3.7 years; 3-year overall survival (OS) and leukemia-free survival (LFS) were 48% and 44%, respectively.

Results: All patients had recurrent genetic alterations at the time of diagnosis, including 251 (98%) with gene mutations and 6 with only cytogenetic abnormalities. The most frequent gene mutations were DNMT3A (25%), NPM1 (23%), FLT3-ITD (22%), ASXL1 (21%), TET2 (21%), RUNX1 (20%), and SRSF2 (18%). Secondary-type mutations associated with antecedent MDS occurred in 42%, and 10% had TP53 mutations. As expected, secondary-type and TP53 mutations were associated with clinically-defined secondary AML (p<0.001), need for reinduction (p=0.03), and CR with incomplete count recovery (p= 0.03). Despite the older age at leukemia diagnosis, putative germline pathogenic variants were identified in 22 (8.6%) patients, including 17 (6.6%) with DDX41 mutations (13/17 with somatic mutation of the second allele), and 5 with TERT or TERC variants not found in population databases.

We evaluated the impact of gene mutations on LFS using univariable and multivariable Cox models and developed a hierarchical model of 3 molecular genetic risk groups according to the hazard ratios (Fig 1A): (1) patients with TP53 mutation or JAK2 mutation or FLT3-ITD/NPM1-WT (high risk), (2) patients without high risk mutations who have DNMT3A or GATA2 or DDX41 mutations (low risk) (3) patients without high- or low-risk mutations (intermediate risk), with 3-year LFS of 8%, 65%, and 47% (p<0.001), respectively. Next, we combined molecular genetic and cytogenetic risk to derive a final genetic model comprised of 4 groups with distinct 3-year LFS (69%, 50%, 27%, and 0%) (Fig 1B). Poor LFS in the very high-risk group was due almost entirely to relapse (3-year relapse rate > 90%), but was driven by a combination of relapse and non-relapse mortality in the intermediate and high-risk groups (Fig 2).

Conclusion: Genetic characteristics at diagnosis are highly associated with OS and LFS in AML patients age 60 or older who undergo allogeneic transplantation in CR1. We identify patients with low genetic risk and remarkably good outcomes who may be candidates for strategies aimed at reducing toxicity, and those with very high-risk genetics who have limited benefit from current transplant approaches. Among intermediate and high-risk patients, the impact of disease genetics on LFS is mostly due to relapse, suggesting that a model incorporating measurement of residual disease in CR1 and after transplantation could enable a more dynamic estimation of risk.

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