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Record W4224264729 · doi:10.3324/haematol.2021.280203

The mutational landscape in chronic myelomonocytic leukemia and its impact on allogeneic hematopoietic cell transplantation outcomes: a Center for Blood and Marrow Transplantation Research (CIBMTR) analysis

2022· article· en· W4224264729 on OpenAlexaff
Matthew Mei, Raju Pillai, Soyoung Kim, Noel Estrada‐Merly, Michelle Afkhami, Lixin Yang, Zhuo Meng, Muhammad Bilal Abid, Mahmoud Aljurf, Ulrike Bacher, Amer Beitinjaneh, Christopher Bredeson, Jean‐Yves Cahn, Jan Černý, Edward A. Copelan, Corey Cutler, Zachariah DeFilipp, Miguel Ángel Díaz, Nosha Farhadfar, César O. Freytes, Shahinaz M. Gadalla, Siddhartha Ganguly, Robert Peter Gale, Usama Gergis, Michael R. Grunwald, Betty K. Hamilton, Shahrukh K. Hashmi, Gerhard Hildebrandt, Hillard M. Lazarus, Mark R. Litzow, Reinhold Munker, Hemant S. Murthy, Sunita Nathan, Taiga Nishihori, Sagar S. Patel, David A. Rizzieri, Sachiko Seo, Mithun Vinod Shah, Melhem Solh, Leo F. Verdonck, Ravi Vij, Ronald Sobecks, Betül Oran, Bart L. Scott, Wael Saber, Ryotaro Nakamura

Bibliographic record

VenueHaematologica · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsOttawa Hospital
FundersNational Cancer Institute
KeywordsMedicineInternal medicineChronic myelomonocytic leukemiaTransplantationKRASHematopoietic stem cell transplantationNeuroblastoma RAS viral oncogene homologInternational Prognostic Scoring SystemOncologyHematopoietic cellMultivariate analysisGastroenterologyBone marrowHaematopoiesisMyelodysplastic syndromesCancerStem cellBiologyGenetics

Abstract

fetched live from OpenAlex

Somatic mutations are recognized as an important prognostic factor in chronic myelomonocytic leukemia (CMML). However, limited data are available regarding their impact on outcomes after allogeneic hematopoietic cell transplantation (HCT). In this registry analysis conducted in collaboration with the Center for International Blood and Marrow Transplantation Registry database/sample repository, we identified 313 adult patients with CMML (median age: 64 years, range, 28- 77) who underwent allogeneic HCT during 2001-2017 and had an available biospecimen in the form of a peripheral blood sample obtained prior to the start of conditioning. In multivariate analysis, a CMML-specific prognostic scoring system (CPSS) score of intermediate-2 (HR=1.46, P=0.049) or high (HR=3.22, P=0.0004) correlated significantly with overall survival. When the molecularly informed CPSS-Mol prognostic model was applied, a high CPSS-Mol score (HR=2 P=0.0079) correlated significantly with overall survival. The most common somatic mutations were in ASXL1 (62%), TET2 (35%), KRAS/NRAS (33% combined), and SRSF2 (31%). DNMT3A and TP53 mutations were associated with decreased overall survival (HR=1.70 [95% CI: 1.11-2.60], P=0.0147 and HR=2.72 [95% CI: 1.37-5.39], P=0.0042, respectively) while DNMT3A, JAK2, and TP53 mutations were associated with decreased disease-free survival (HR=1.66 [95% CI: 1.11-2.49], P=0.0138, HR=1.79 [95% CI: 1.06-3.03], P=0.0293, and HR=2.94 [95% CI: 1.50-5.79], P=0.0018, respectively). The only mutation associated with increased relapse was TP53 (HR=2.94, P=0.0201). Nonetheless, the impact of TP53 mutations specifically should be interpreted cautiously given their rarity in CMML. We calculated the goodness of fit measured by Harrell's C-index for both the CPSS and CPSS-Mol, which were very similar. In summary, via registry data we have determined the mutational landscape in patients with CMML who underwent allogeneic HCT, and demonstrated an association between CPSS-Mol and transplant outcomes although without major improvement in the risk prediction beyond that provided by the CPSS.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.025
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.358
Teacher spread0.321 · 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 teacher head, 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

Citations26
Published2022
Admission routes1
Has abstractyes

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