Prediction of acute myeloid leukaemia risk in healthy individuals

Abstract

The incidence of acute myeloid leukaemia (AML) increases with age and mortality exceeds 90% when diagnosed after age 65. Most cases arise without any detectable early symptoms and patients usually present with the acute complications of bone marrow failure. The onset of such de novo AML cases is typically preceded by the accumulation of somatic mutations in preleukaemic haematopoietic stem and progenitor cells (HSPCs) that undergo clonal expansion. However, recurrent AML mutations also accumulate in HSPCs during ageing of healthy individuals who do not develop AML, a phenomenon referred to as age-related clonal haematopoiesis (ARCH). Here we use deep sequencing to analyse genes that are recurrently mutated in AML to distinguish between individuals who have a high risk of developing AML and those with benign ARCH. We analysed peripheral blood cells from 95 individuals that were obtained on average 6.3 years before AML diagnosis (pre-AML group), together with 414 unselected age- and gender-matched individuals (control group). Pre-AML cases were distinct from controls and had more mutations per sample, higher variant allele frequencies, indicating greater clonal expansion, and showed enrichment of mutations in specific genes. Genetic parameters were used to derive a model that accurately predicted AML-free survival; this model was validated in an independent cohort of 29 pre-AML cases and 262 controls. Because AML is rare, we also developed an AML predictive model using a large electronic health record database that identified individuals at greater risk. Collectively our findings provide proof-of-concept that it is possible to discriminate ARCH from pre-AML many years before malignant transformation. This could in future enable earlier detection and monitoring, and may help to inform intervention.

Clonal hematopoiesis and preleukemia—Genetics, biology, and clinical implications

“…Two recent case-control studies showed that preleukemic CH with putative leukemia driver mutations can be found in approximately 70% of blood samples obtained 6 to 9 years before the diagnosis of AML ( Figure 2D). 69,84 Compared to persons with CH who did not develop AML (controls), persons who subsequently progressed to AML (patients) on average had a higher number of CH mutations and, importantly, these variants were present at a higher VAF. While the presence of a single CH-associated mutation in any gene, at a VAF of >1%, was associated with an approximately 5-fold increased risk of AML, co-mutations in two different genes signified a 9-fold risk increase.…”

“…Interestingly, the authors of one case-control study also found that an increased red blood cell distribution width (a measure of erythrocyte size heterogeneity) distinguished individuals with CH who later developed AML from persons without CH, and, importantly, from those with CH who did not progress. 84 Based on this finding and data routinely available in electronic medical records, the authors used machine learning to develop an AML prediction model that was able to predict AML 6 to 12 months before diagnosis with a sensitivity of around 25% and an overall specificity of >98%. These data indicate that, while there currently is no role for genetic screening for early detection of AML, at least some persons who develop AML have subtle but detectable and quite specific laboratory abnormalities that may allow early detection of the disease before its full-blown clinical manifestation.…”

The concept of leukaemic stem cells in acute myeloid leukaemia 25 years on: hitting a moving target

“…The clonal landscapes of AML bulk populations has been elucidated thanks to the expanding knowledge of driver and passenger mutations (Cancer Genome Atlas Research Network, ; Ho et al , ) including the more recent realisation that such aberrations precede overt disease by years and can be present in normal individuals (Abelson et al , ; Desai et al , ). Thus, during the last 5 years it has become increasingly clear that any attempt to target the AML LSCs has to take into account that: (i) a population of malignant AML cells in a given patient is much more heterogeneous than hitherto believed, and (ii) this heterogeneity is in a flux as the disease progresses and that cytoreduction is going to affect this.…”

Prognostic impact of red blood cell distribution width in myelodysplastic syndromes

“…Red blood cell (RBC) distribution width (RDW) describes the size heterogeneity of RBCs and is useful in the differential diagnosis of anaemia. A recent study in healthy individuals reported TP53 and U2AF1 mutations and a high RDW correlated with risk of progression to acute myeloid leukaemia (AML) (Abelson et al , ). Some older persons progress from age‐related clonal haematopoiesis (ARCH) to clonal cytopenia of undetermined significance (CCUS) and on to low‐ and high‐risk MDS before developing AML, and then, we hypothesized that RDW might play a role in MDS.…”