Expansion of <i>GATA1</i>-Mutant Clones in Blood Samples of Patients with Transient Abnormal Myelopoiesis (TAM) during Clinical Follow up Heralds Transformation to Myeloid Leukemia Associated with Down Syndrome (ML-DS)
Bibliographic record
Abstract
Background. Children with Down syndrome have a 150-fold increased incidence of myeloid leukemia. Myeloid leukemia associated with Down syndrome (ML-DS) evolves from a subclone of the neonatal preleukemic disorder transient abnormal myelopoiesis (TAM). TAM is initiated when somatic GATA1 mutations occur in fetal hematopoietic progenitors with trisomy 21. Co-operating mutations in genes coding for cohesin complex genes, epigenetic modifiers and signal transducers propel transformation of TAM to ML-DS in approximately 20 % of patients, typically during the first four years of life. GATA1 mutations are patient-specific and concordant between blasts of TAM and subsequent ML-DS within the same individuals. It is not known whether the co-operating mutations detected at diagnosis of ML-DS are already present at diagnosis of TAM in a minor clone or acquired postnatally when infants and children with TAM are in clinical follow up. We analyzed blood and bone marrow samples of patients with TAM who either went on to develop ML-DS or not to chart the size of the GATA1-mutant cell clone and determine how early co-operating mutations become detectable prior to clinical diagnosis of ML-DS. Material and Methods. We extracted DNA from cryopreserved diagnostic blood and bone marrow samples, formalin-fixed paraffin-embedded (FFPE) bone marrow biopsy samples when available and archival fixed and stained blood or bone marrow smears on glass sides from patients with TAM in clinical follow up at SickKids between 2016 and 2021 (TAM followed by ML-DS, n=4; TAM without ML-DS in subsequent 6 years, n=3). 50-100ng DNA was used to prepare libraries for targeted sequencing with an institutional panel of 905 cancer genes (next generation sequencing, NGS). Variants with a VAF of 0.02 or below were excluded from analysis unless they were found to be present in a different sample from the same patient at a higher VAF. Droplet digital PCR (ddPCR) assays were designed to quantify the size of the mutation-positive cell clones at different time points between diagnosis of TAM and ML-DS. Results. In patient 20035, who developed ML-DS 19 months after diagnosis of TAM, the calculated proportion of GATA1 mutation-positive cells (c.181_182insCC), detected by ddPCR decreased to 5.88% at 2 months after diagnosis of TAM, 0% at 5 months, 2.36% at 7.5 months, 2.81% at 10.5 months, 2.01% at 13 months before rising to 5.48% at 17 months after diagnosis of TAM (equivalent to 2 months prior to clinical diagnosis of ML-DS). In patient 20056, diagnosed with ML-DS 15 months after onset of TAM, the calculated proportion of GATA1 mutation-positive cells (c.C35A) was 0.59% at 2 months, 0.27% at 5 months and 0.15% at 8 months from diagnosis of TAM before rising to 2.04 % at 11 months and 15.9% at 11.75 months from diagnosis of TAM (equivalent to 4.5 and 3.75 months prior to clinical diagnosis of ML-DS, respectively) and 18.2% at diagnosis of ML-DS. Co-operating mutations of STAG2 (c.C1810T) and EZH2(c.T734C) were found by NGS in the diagnostic ML-DS bone marrow sample (bone marrow glass smear and FFPE blocks) but were not detectable by ddPCR at 2, 5 and 8 months after diagnosis of TAM. The EZH2 mutation was first detected at 0.54%4.5 months prior to ML-DS and increased to 7.4% three weeks later and reached 17.47% at diagnosis of ML-DS. The STAG2 mutation was first detected 3.75 months prior to diagnosis of ML-DS (7.9%) and increased to 17.37% at diagnosis of ML-DS. The quantity of DNA extracted from archival blood smears (1.0-20.0ng, n=-9) was in most instances deemed insufficient for targeted sequencing. Development of ddPCR assays was feasible for all mutations of interest (n=4). Conclusions. Longitudinal monitoring of patients in clinical follow up after a diagnosis of TAM based on detection of patient-specific GATA1 mutations by ddPCR is feasible. Increase in size of the cell clone positive for mutant GATA1 precedes overt diagnosis of ML-DS. The quantity of DNA extracted from archival blood smears was frequently insufficient for targeted sequencing using a cancer panel. We anticipate that monitoring the size of the GATA1 mutation-positive cell clones in the peripheral blood by ddPCR or NGS methods using prospectively collected peripheral blood samples from patients with TAM during their clinical follow-up has the potential to become a standard of care test that allows monitoring, prediction of ML-DS onset in a subset and may guide future strategies of leukemia prevention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".