MétaCan
Menu
Back to cohort
Record W4310125603 · doi:10.1182/blood-2022-168228

The Prognostic Impact of <i>HMGCLL1</i> Gene Variant on Treatment Outcomes in Chronic Myeloid Leukemia (CML) Patients: Adverse Impact on the Response, Failure, and Progression with Imatinib Which Can be Abrogated By the Use of 2nd Generation Tyrosine Kinase Inhibitor (TKI) Upfront Therapy

2022· article· en· W4310125603 on OpenAlexaff
María Agustina Perusini, Taehyung Simon Kim, Daniela Žáčková, Kátia Bórgia Barbosa Pagnano, Jiří Mayer, Carolina Pavlovsky, Ivana Ježíšková, Anežka Kvetková, Tomáš Jurček, Beatriz Moiraghi, Ana Inés Varela, Michele Bianchini, Ana Beatriz Pascoal Lopes, Gislaine Duarte, Young Seok Yoo, Cármino Antônio De Souza, Jessie J.F. Medeiros, Hye Won Lee, Kyoung Ha Kim, Seong Yoon Yi, Myung Hee Chang, Sagi Abelson, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2022
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsPrincess Margaret Cancer CentreOntario Institute for Cancer ResearchUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsImatinibMyeloid leukemiaMedicineAdverse effectInternal medicineOncologyImatinib mesylateImmunologyCancer research

Abstract

fetched live from OpenAlex

BackgroundHMGCLL1 is characterized as a lyase activity enzyme located in the extramitochondrial region. Its biological and physiological role is not fully elucidated. Our previous study (Leukemia 2019) demonstrated that: 1) The HMGCLL1 gene variant, ACGTAATG haplotype (A-haplotype) is associated with increased expression of overall levels and isoform 3 (IS3) of the HMGCLL1 gene and predicts a decreased likelihood of deep molecular response after Imatinib (IM) therapy, 2) blockade of HMGCLL1 IS3 results in G0/G1 cell cycle arrest, leading to increased eradication of CML cell lines and CML leukemic stem cells, 3) blockade of HMGCLL1 isoform 3 significantly suppressed CD34+ fractions in CML patients (pts). It was unclear whether a second-generation tyrosine kinase inhibitor (2G-TKI) can reverse the adverse impact of the HMGCLL1 gene variant in CML pts. The present study assessed the prognostic impact of the HMGCLL1 gene variant in CML pts treated with IM or 2G-TKI, and its impact on treatment-free remission (TFR) after TKI discontinuation. Patients and methods Samples from 387 pts were sequenced. Group 1 included 252 pts from 2 centers whose samples were archived retrospectively. These pts were treated with IM (n=190, 75.3%) or second-generation TKIs (n=62) including Dasatinib (n=15, 6.0%), Nilotinib (n=37, 14, 7%), and Bosutinib (n=10; 4.0%). Group 2 included 135 patient samples from 4 countries that attempted to discontinue TKI for TFR. We applied our internal CML-specific smMIP panel with 332 amplicon probes including 8 HMGCLL1 SNPs (rs10948926, rs10948927, rs9370435, rs4546489, rs4275061, rs9475323, rs9475327, rs9296791). Linkage disequilibrium (LD) was assessed using Haploview software (Bioinformatics 2005). For Group 1, treatment outcomes were evaluated concerning major molecular response (MMR), molecular response (MR) with 2 log/deeper (MR2) or 4 log/deeper (MR4), failure-free survival (FFS), progression, and overall survival (OS). Disease characteristics and treatment outcomes were evaluated and compared according to the HMGCLL1 variants using a dominant model. For Group 2, molecular relapse-free survival (RFS) was calculated from the date of TKI discontinuation to the date of confirmed loss of molecular response (either loss of major MMR or two consecutive episodes of MR loss). Kaplan-Meier survival estimate was used to evaluate RFS and Cox's proportional hazard regression model was applied using the dominant model. Results HMGCLL1genotype and haplotype As shown in Fig A, the LD plot showed no recombination among the 8 SNPs, leading to one block for the 8 SNPs to construct a haplotype. Two haplotypes were constructed as CTCAGGCA (C-) or ACGTAATG (A-haplotype) with MAF of 0.674:0.279 in Group 1, and 0.703:0.216 in Group 2, respectively. Summary of outcomes in table 1. Treatment outcomes following TKI therapy according to the HMGCLL1 haplotype Homozygous A-haplotype (A/A) was associated with worse outcomes, showing a lower MR2 (65.9% vs 78.9% at 12 months, p=0.012), lower MMR (64.4% vs 86.8% at 3 years, p=0.00029), lower MR4 (34.7% vs 62.3% at 5 years, p=0.0001; worse FFS (62.8% vs 77.3% at 3 years, p=0.0014), higher risk of progression (16.2% vs 3.4% at 5 years, p=0.000678), and worse OS (56.1% vs 77.7% at 10 years, p=0.0021). The adverse impact of A/A-haplotype was more prominent in the pts treated with Imatinib: A/A-haplotype pts showed a 30-50% lower chance of MR2, MMR, and MR4 achievement, and 2-3 times higher risk of TFR and progression in comparison to those having C-haplotype when treated with Imatinib. However, the use of 2G-TKIs could abrogate the adverse impact of the A/A-haplotype. Impact of HMGCLL1 variant on treatment-free remission following TKI discontinuation While the 12-month RFS rate was 59.9% in overall pts (n=135), an inferior 12-month RFS rate was noted in A/A haplotype (43.8% vs 62.3%) but with borderline significance (p=0.116). Conclusion: The present study demonstrated that the HMGCLL1 gene variant is confirmed as predictive of CML outcomes. Moreover, our result suggests that the adverse impact of the HMGCLL1 genotype can be abrogated by using 2G-TKIs as upfront therapy. We aim to validate this data in a larger cohort of pts treated with 2G-TKI therapy to reach a clearer conclusion on this result. HMGCLL1 genotype information would be helpful to guide CML therapy given that pts with adverse HMGCLL1 genotypes could benefit from 2G-TKI therapy as front-line therapy. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.274
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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBloodSame topicChronic Myeloid Leukemia TreatmentsFrench-language works237,207