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Record W3096329976 · doi:10.1182/blood-2020-137125

Distinct Genetic Pathways Define Leukemia Predisposition Versus Adaptive Clonal Hematopoiesis in Shwachman-Diamond Syndrome

2020· article· en· W3096329976 on OpenAlexaff
Alyssa L. Kennedy, Kasiani C. Myers, James R. Bowman, Christopher J. Gibson, Gwen M. Muscato, Robert H. Klein, Kaitlyn Ballotti, Nicholas D. Camarda, Elissa Furutani, Chad E. Harris, Shanshan Liu, Ashley Galvin, Maggie Malsch, David C. Dale, John M. Gansner, Taizo A. Nakano, Alison A. Bertuch, Adrianna Vlachos, Jeff H. Lipton, Paul Castillo, James A. Connelly, John Edwards, Nobuko Hijiya, Richard Ho, Inga Hofmann, James N. Huang, Sioḃán Keel, Adam J. Lamble, Bonnie Lau, Kelly Walkovich, Maxim Norkin, Wendy Stock, Steffen Boettcher, Christian Brendel, Elliot Stieglitz, Mark D. Fleming, Stella M. Davies, Edie Weller, Chris Bahl, Scott L. Carter, Akiko Shimamura, R. Coleman Lindsley

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBlood disorders and treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsGermline mutationBiologyMutationGeneticsExome sequencingMyeloidGermlineBone marrow failureCancer researchGeneHaematopoiesisStem cell

Abstract

fetched live from OpenAlex

Background: Shwachman-Diamond Syndrome (SDS) is a bone marrow failure disorder caused by impaired removal of EIF6 from the nascent 60S ribosome subunit, resulting in defective ribosome assembly. SDS patients have a high risk of myeloid neoplasms (MN) and the prognosis of those that develop MN is poor. Knowledge of the kinetics and functional consequences of somatic mutation acquisition in SDS may offer insight into mechanism of transformation and the potential for therapuetic intervention. Methods: We performed whole exome sequencing of 45 samples from 30 patients, and validated recurrent somatically mutated genes using targeted sequencing with error suppression in prospectively collected samples from 110 patients in the North American SDS Registry. We correlated mutation status with clinical outcome and performed functional studies to understand the consequence of somatic mutations in SDS. Results: We detected somatic mutations in 74 of 98 (76%) patients with germline biallelic SBDS mutations (median 2 mutations/patient, range 0-21). We found no mutations in patients with SDS-like disease; those who have clinical features of SDS without disease defining mutations. Of the 83 patients with SDS without a MN diagnosis, 60 (72%) had detectable clonal hematopoiesis (CH), 40 of whom had more than one mutation (median 3, range 1-21). The most frequently mutated genes were EIF6 (60/98, 61%),TP53 (44/98, 45%), PRPF8 (12/98, 12%), and CSNK1A1 (6/98, 6%). Among SDS patients with TP53 mutated CH, 90.9% (30 of 33) had concurrent EIF6 mutations. To determine whether EIF6 and TP53 mutations occur in the same or different clones, we performed single cell DNA sequencing. Among the 47 clones identified with either EIF6 or TP53 mutations, 24 had a sole EIF6 mutation, and 21 had a sole TP53 mutation, showing that these mutations arise in separate clones. To study the functional consequences of EIF6 missense mutations, we cloned 7 patient-derived mutations and generated cell lines expressing wild-type or mutant EIF6 cDNA. We found six mutants (I13N, R67W, G69S, P73R, A194T, G196R) reduced levels of EIF6 protein compared with wild type EIF6, despite comparable abundance of mRNA. The most common recurrent mutation, N106S, was found in 20% of patients and, by contrast to others listed above, did not change protein expression. This mutation is located at the EIF6/60S protein interface and disrupted the interaction of N106S-EIF6 with the 60S subunit as measured by polysome profiling followed by western blotting. To compare the effects of EIF6 versus TP53 somatic mutations in context of SDS deficient translation, we measured ribosome maturation and translation in SDS cells containing shRNAs targeting EIF6 or TP53. EIF6 knockdown ameliorated the SDS defect, reflected by improved ribosome joining (normalization of the 80:60s ratio) and enhanced protein translation (increased O-propargyl-puromycin incorporation), whereas TP53 knockdown had no effect. Knockdown of EIF6 in SDS deficient cells decreased p53 pathway activation as demonstrated by decreased CDKN1A expression. TP53 mutations were significantly associated with MN diagnosis (p=0.023), but were also common in SDS CH and typically stable over time. To identify the characteristics associated with transformation, we analyzed exomes from 7 patients with TP53 mutated myeloid malignancy for allelic imbalances at the TP53 locus and found that all 7 had biallelic alteration of TP53. Using single cell DNA sequencing from serial samples, we observed that TP53 LOH can precede transformation by several years and can distinguish pre-leukemic clones from indolent clones with monoallelic TP53 alterations. Somatic EIF6 mutations were not found in the leukemic clones. These results suggest early detection of TP53 LOH may distinguish clones with leukemic potential. Conclusions: In SDS, impairment of ribosome maturation drives selection of clones with somatic EIF6 or TP53 mutations. EIF6 mutations promote competitive fitness by rescuing the SDS ribosome defect and decreasing p53 pathway activation, and do not contribute to malignant transformation. TP53 mutations decrease checkpoint activation without affecting ribosome assembly. These results provide genetic evidence that germline SBDS deficiency causes a global, disease-specific HSC fitness constraint that drives parallel development of somatic CH and provides a mechanistic rationale for clinical surveillance. Disclosures Dale: Emendo BioTherapeutics: Consultancy; X4 Pharmaceuticals: Research Funding; X4 Pharmaceuticals: Honoraria. Gansner:Alnylam Pharmaceuticals: Current Employment, Current equity holder in private company. Edwards:Jazz Pharmaceuticals: Consultancy, Honoraria. Fleming:DISC Medicine: Consultancy, Membership on an entity's Board of Directors or advisory committees. Lindsley:MedImmune: Research Funding; Takeda Pharmaceuticals: Consultancy; Bluebird Bio: Consultancy; Jazz Pharmaceuticals: Consultancy, 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 score0.002
Threshold uncertainty score0.006

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.208
Teacher spread0.193 · 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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Citations1
Published2020
Admission routes1
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