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Record W4389231113 · doi:10.1182/blood-2023-190978

Characterization of Clonal Hematopoietic of Indeterminate Potential (CHIP) Mutations in an Imid-Naïve Multiple Myeloma (MM) Autologous Stem Cell Transplant (ASCT) Population: First Results from a Pre-Transplant Time Point in a Prospective, Longitudinal Study

2023· article· en· W4389231113 on OpenAlexaff
Sahar Khan, Salman Basrai, Donna Reece, Sita Bhella, Vishal Kukreti, Anca Prica, A. Keith Stewart, Suzanne Trudel, Harjot Vohra, Chloe Yang, Sagi Abelson, Christine Chen

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity Health NetworkOntario Institute for Cancer ResearchPrincess Margaret Cancer Centre
Fundersnot available
KeywordsPopulationMedicineOncologyInternal medicineMelphalanTransplantationHematopoietic stem cell transplantationMultiple myeloma

Abstract

fetched live from OpenAlex

Background: Clonal Hematopoiesis of Indeterminate Potential (CHIP) is defined by the acquisition of ≥ 1 somatic mutations in the blood of healthy adults, but can also be detected at higher prevalence in patients with malignancies, particularly after DNA damaging therapy. The presence of CHIP mutations in MM confers a reduced overall survival (Mouhieddine, JCO 2020) but high quality, prospective evaluation of CHIP mutation evolution over the transplant sequence, including the impact of high-dose melphalan and immunomodulatory (IMiD)-based therapy, is largely under-studied. Serial assessment of CHIP over the transplant course has been reported from the coMMpass dataset, demonstrating a 4-fold increase in mutation prevalence from 5.8% at diagnosis to 25% after 3.1 years (Mouhieddine, Blood, 2021). In this data set, most patients were exposed to prior IMiD-containing at some point during therapy. Whether the survival benefit reported with IMiD-based maintenance therapy post-transplant is associated with CHIP mutation modulation is unclear. We are therefore investigating the longitudinal evolution of CHIP in a cohort of transplant-eligible, largely IMiD-naïve patients (pts) over the pre- and post-ASCT course in an ongoing study. Here, we present preliminary findings from the pre-transplant time-point after non-IMiD-containing induction but before high-dose melphalan and ASCT. Methods: The ongoing Princess Margaret Cancer Center ARCH 001 trial is a prospective, longitudinal study evaluating evolution of CHIP in a transplant-eligible MM population. Mutation testing is performed using the highly sensitive single-molecule molecular inversion probe (SmMIP) next-generation sequencing (NGS) technique (Abelson, Bioinformatics, 2022). After sequencing with a minimum sequencing depth of 4000x, the list of mutation calls produced by smMIP-tools is subject to various filters to reduce the likelihood of false positives. Synonymous mutations, as well as any variants falling within introns or splice regions are removed. The minor allele frequency of the variant (if available) is required to be above 0.1% so as to exclude mutations that are common SNPs. VAF frequency threshold of 1-30 % is set for calling somatic mutations, with a view to minimize false positives and germ-line mutations. Multiple time points for testing pertinent to therapy include: within 1 month pre-transplant (after induction and stem cell collection), 3 months post-transplant, and two subsequent samples 12 and 24 months post-sample 2. Here we report preliminary results of CHIP mutation testing in the first 66 pts at the pre-transplant time point. Results: Patient, disease and treatment characteristics for all pts and their stratification by the presence of CHIP mutations are outlined in Table 1. The total cohort is typical for a transplant-eligible population with median age 65 years, male predominance (M 59%/F 41%), 60% IgG subtype, 23% high-risk FISH cytogenetics. Most (90%) received a non-IMiD containing induction regimen CyBorD; only 6% received an IMiD pre-ASCT. CHIP mutations were identified in 28/66 pts, for a mutation prevalence rate of 43%; 6 patients (9%) carried ≥ 1 mutation. The median age at transplant as well as other baseline demographics did not differ between those with and without mutations (Table 1). Mutation profiles are shown in Figure 1, and are consistent with those described in the literature with the most commonly mutated genes being DNMT3a, TET2, ASXL1 and PPM1D, and the mutation prevalence rate highest for DNMT3a at 21%. 6 pts (9%) had ≥ 1 mutation (range 2-3) identified, with 3 of 6 involving the DNMT3a gene. Other genes involved in patients with multiple mutations were TET2 (n=3), ASXL1 (n=2), PPM1D (n=2), SRSF2 (n=1), SF3B1 (n=1). Mean allele frequency was 4%, ranging from 1-19. Conclusion: Our results using deep SmMIP sequencing show a mutation prevalence rate of 43% in a cohort of transplant-eligible, largely IMiD-naive MM pts studied after induction. These rates are higher than those generally described in the literature, which may relate to the high sensitivity of the assay used, variation in filtering between institutions, and the deliberate use of a relatively low VAF threshold with the intent to capture clinically significant variants for longitudinal tracking during the course of this ongoing study. Further studies during lenalidomide maintenance will be forthcoming.

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 score0.006

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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.271
Teacher spread0.252 · 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
Published2023
Admission routes1
Has abstractyes

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