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Record W3214440901 · doi:10.1182/blood-2021-150102

Clinical Significance of Clonal Hematopoiesis in the Setting of Autologous Stem Cell Transplantation for Lymphoma

2021· article· en· W3214440901 on OpenAlexaff
Sharon Ben Barouch, Tracy Lackraj, Jessie J.F. Medeiros, Mehran Bakhtiari, Jesse Joynt, Kit I. Tong, Andrea Arruda, Mark D. Minden, M.C Barroso Alvarez, John Kuruvilla, Sita Bhella, Vishal Kukreti, Michael Crump, Anca Prica, Christine I. Chen, Armand Keating, John E. Dick, Sagi Abelson, Robert Kridel

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsOntario Institute for Cancer ResearchPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsTransplantationMinimal residual diseaseBiologyHematopoietic stem cell transplantationLymphomaStem cellMyeloidAutologous stem-cell transplantationOncologyImmunologyLeukemiaCancer researchInternal medicineGeneticsMedicine

Abstract

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Abstract Introduction : Peripheral blood samples of healthy individuals may harbour detectable mutations in genes recurrently mutated in myeloid malignancies, a situation referred to as clonal hematopoiesis (CH). Risk factors for CH include increasing age as well as previous exposure to cytotoxic therapy. CH has been associated with an increased risk of overall mortality, including in the setting of autologous stem cell transplantation (ASCT) for non-Hodgkin lymphoma (Gibson et al, JCO, 2017). The excess mortality is largely driven by cardio-vascular disease, but may also be additionally attributable to an increased risk of myeloid malignancies that arise through the selection of CH subclones. Herein, we aimed to investigate the prognostic implications of CH after ASCT in an independent and diversified, large cohort of lymphoma patients using ultra-deep, highly sensitive error-correction sequencing. Methods : DNA was obtained from 420 residual apheresis products obtained from patients who had undergone autologous stem cell transplantation for lymphoma at the Princess Margaret Cancer Center between 2002 and 2018. Target DNA sequences corresponding to regions recurrently mutated in myeloid neoplasms (affecting n = 36 genes) were captured using single molecule molecular inversion probes (smMIPs) that incorporate molecular tagging. Single nucleotide variants and short insertions and deletions were identified using SmMIP-tools (Medeiros et al, bioRxiv, 2021), which implements a series of steps including probabilistic modeling of allele-specific error rates and generation of consensus sequences to suppress next-generation sequencing-associated errors. Given the high sensitivity and precision of our method, we did not prespecify a variant allele fraction cut-off. Results : All patients had relapsed/refractory lymphoma, except for 98 (23.3%) mantle cell lymphoma patients and one patient with extranodal NK/T-cell lymphoma where ASCT was part of frontline management. The most common conditioning regimens were high-dose melphalan and etoposide (77.5%) and high-dose melphalan and Ara-C (16.4%). We identified 275 high-confidence mutations in 181 out of 420 patients (43.1%), with 64 of these 181 patient samples (35.4%) having more than one mutation. The median age was higher in patients with CH than in patients without (55 years vs. 51, P = 0.002). The most frequently mutated gene were PPM1D (11.9%), followed by TET2 (11.4%), DNMT3A (8.8%), ASXL1 (5.2%) and TP53 (4.5%). The lymphoma subtype with the highest prevalence of CH was T-cell lymphoma (CH found in 72.2% of cases), followed by transformed indolent lymphoma (51.4%), mantle cell lymphoma (47.5%), diffuse large B-cell lymphoma (40.4%) and Hodgkin lymphoma (33.3%). While there was no difference in the number of CD34+ cells infused for patients with and without CH, the median time to neutrophil engraftment and the median time to platelet engraftment were significantly longer in patients with CH (11 days vs. 10 days, P = 0.025; and 14 days vs. 13 days, P < 0.001, respectively). The median follow-up of living patients was 4.2 years. Patients with CH had inferior 5-year OS from the time of first relapse (38.9% vs. 45.5%, P = 0.037) and from the time of ASCT (51.2% vs. 59.1%, P = 0.017, see figure). Five-year OS from ASCT was 47.5% vs. 53.7% in patients with 1 mutation and > 1 mutation, respectively, compared to 59.1% in patients without CH (P = 0.005). The presence of CH did not have an impact on the risk of post-ASCT relapse. In multivariate Cox regression analysis in which CH and age (as a continuous variable) were included, CH remained significantly associated with adverse OS post-ASCT (HR 1.39, 95% 1.02-1.91, P = 0.038). Only seven patients out of 420 (1.7%) developed a therapy-related myeloid neoplasm (TMN). The cumulative incidence of TMN was not significantly increased in patients with CH (10-year cumulative incidence 3.3% vs. 3.0% in those without CH, P = 0.433). Conclusions : Our results show that CH was associated with delayed neutrophil and platelet engraftment. Moreover, CH conferred an increased risk of death after ASCT that was not explained by lymphoma relapse. The risk of TMN was low in our cohort and CH was not a risk factor for TMN, an observation that is distinct from prior observations (e.g. Gibson et al, JCO, 2017 and Husby et al, Leukemia, 2020). Our results raise the possibility that the risk of TMN may be modulated by factors other than CH. Figure 1 Figure 1. Disclosures Minden: Astellas: Consultancy. Kuruvilla: Janssen: Honoraria, Research Funding; Antengene: Honoraria; AstraZeneca: Honoraria, Research Funding; Amgen: Honoraria; Incyte: Honoraria; Novartis: Honoraria; Karyopharm: Honoraria, Other: Data and Safety Monitoring Board; Pfizer: Honoraria; AbbVie: Honoraria; TG Therapeutics: Honoraria; Medison Ventures: Honoraria; Merck: Honoraria; Gilead: Honoraria; BMS: Honoraria; Roche: Honoraria, Research Funding; Seattle Genetics: Honoraria. Crump: Roche: Research Funding; Epizyme: Research Funding; Kyte/Gilead: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees. Prica: Kite Gilead: Honoraria; Astra-Zeneca: Honoraria. Chen: Beigene: Membership on an entity's Board of Directors or advisory committees; Astrazeneca: Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy. Kridel: Gilead Sciences: 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)0.001
metaresearch head score (Gemma)0.002
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.030
GPT teacher head0.327
Teacher spread0.297 · 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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Citations0
Published2021
Admission routes1
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