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Expanded Phenotypic and Genetic Heterogeneity in the Clinical Spectrum of FPD-AML: Lymphoid Malignancies and Skin Disorders Are Common Features in Carriers of Germline RUNX1 Mutations

2016· article· en· W2646599743 on OpenAlexaff
Anna Brown, Christopher N Hahn, Catherine Carmichael, Ella Wilkins, Milena Babic, Chan‐Eng Chong, Xiaochun Li, Joëlle Michaud, Ping Cannon, Nicola Poplawski, Meryl Altree, Kerry Phillips, Louise Jaensch, Miriam Fine, Andreas Schreiber, Jinghua Feng, Lesley Rawlings, Cassandra Vakulin, Carolyn M Butcher, Richard J. D’Andrea, Ian D. Lewis, Nigel Patton, Cecily Forsyth, Sally Mapp, Helen Mar Fan, Rachel Susman, Sue Morgan, Julian Cooney, Mark S. Currie, Uday Popat, Kenneth F. Bradstock, April Sorrell, Carolyn Owen, Marshall S. Horwitz, Devendra Hiwase, Alwin Krämer, Stefan Fröhling, Lucy A. Godley, Jane E. Churpek, Hamish S. Scott

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPlatelet disorderRUNX1GermlineGermline mutationGeneticsLeukemiaBiologyCancer researchMutationCEBPAMedicineImmunologyGeneHaematopoiesisStem cellPlatelet

Abstract

fetched live from OpenAlex

Abstract Background: This year, germline predisposition to haematological malignancy (HM) debuts in the World Health Organization classification of myeloid neoplasms and acute leukemia (Blood, 2016;127:2391). It has been 17 years since germline mutations in RUNX1 were found to lead to familial platelet disorder (FPD) with predisposition to myelodysplastic syndrome and acute myeloid leukaemia (MDS/AML) (Nat Genet. 1999;23:166). Now, nearly 80 families have been reported with damaging germline mutations or deletions affecting RUNX1 function, associated with FPD, making it an increasingly significant clinical presence. Although thrombocytopenia and platelet dysfunction are present in almost all RUNX1 mutant carriers, we and others have observed that the predisposition to HM varies between family members, with respect to age at diagnosis and the type of malignancy, and in some cases RUNX1 mutation carriers have no apparent HM development over their lifespan. The reasons for this heterogeneity are currently unknown. Aims: We are conducting an international collaborative study examining RUNX1 mutated families. The aim of the research project is to classify the range of phenotypes correlated with RUNX1 mutations comprehensively (including non-malignant phenotypes such as skin disorders) and to determine if the type of RUNX1 mutation and the presence of other germline and acquired mutations in relevant HM genes correlate with the likelihood of HM development, or the type of HM that develops. Across all of our data we aim to analyse clinically relevant information that will be used to inform prognosis and clinical management in germline RUNX1 mutation carriers. Results:From a review of the literature for previously characterised RUNX1 mutant families most mutations are predicted to be loss-of-function, with the combination of frameshift, stopgain, splicing and deletion accounting for the majority of alterations (57, 70%) compared to missense mutations (22, Figure 1). The most common sites of mutation are R201 and R204, affected by both missense and stopgain (10 total), which lie within the nuclear localisation signal at the end of the RUNT domain (Figure 1). We also surveyed in detail 12 RUNX1 pedigrees with both novel and previously described missense, frameshift, stopgain and deletion mutations and found that, while all families developed myeloid malignancies, 6 families also had individuals who developed lymphoid malignancy (most often Acute lymphoblastic leukemia (ALL)) which was heritable in sub-families, and subject to anticipation (e.g see IV-5 and V-5 in Figure 2). Consistent with population genome wide association studies identifying RUNX1 as a susceptibility locus for psoriasis (J Autoimmun. 2015;64:66), we find that skin conditions (psoriasis, eczema) are common, and present in germline RUNX1 carriers in 50% of our families; most commonly observed in families with stopgain and frameshift mutations. Genomic analysis of selected samples confirms that mutation of the other RUNX1 allele is the most commonly acquired mutation in germline RUNX1 mutation carriers developing HM. Alterations of chromosomes 21 and 7 are also common. DNMT3A and PHF6 acquired mutations were the next most frequently observed in tumors and mutations in U2AF1 and ASXL1 in the blood of RUNX1 carriers without HM were observed, suggestive of pre-HM clonal expansion. Finally, in a family with a novel R169I RUNX1 mutation, a rare germline ASXL1 variant (E1102D, 1.0% in ExAC) was found in two RUNX1 carriers who developed early onset AML. This variant is also significantly enriched in an MDS cohort unselected for family history compared to the general population (HR 1.3, p=0.02), as well as ASXL1 N986S (0.1% in ExAC, HR 3.3, p=0.0002) suggesting they operate as germline HM risk modifiers. Interestingly RUNX1 and ASXL1 acquired mutations often co-occur in sporadic MDS/AML and our data suggests this collaboration may also occur at the germline level. Conclusions:Annotation of skin phenotypes co-existent with a family history of haematological malignancy may assist in identifying RUNX1 mutant families. Both acquired and germline mutations in known HM genes may modify germline RUNX1 driven HM penetrance and phenotype. Our data suggest that screening of RUNX1 germline mutation carriers for germline and acquired variants in other HM genes could provide an important tool for defining risk and requires further investigation. Disclosures Owen: Pharmacyclics: Research Funding; Janssen: Honoraria; Roche: Honoraria, Research Funding; Novartis: Honoraria; Gilead: Honoraria, Research Funding; Lundbeck: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Abbvie: Honoraria. Godley:UpToDate: Honoraria; Onconova, Inc.: Research Funding.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.018
GPT teacher head0.311
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations4
Published2016
Admission routes1
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