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Record W3213022568 · doi:10.1182/blood-2021-149018

Characteristics and Outcomes of Adolescent and Young Adult (AYA) Patients with Myelodysplastic Syndrome (MDS) and Chronic Myelomonocytic Leukemia (CMML): A Single-Center Retrospective Analysis

2021· article· en· W3213022568 on OpenAlexaboutno aff
Shehab Mohamed, Tareq Abuasba, Kelly S. Chien, Guillermo Montalban‐Bravo, Faezeh Darbaniyan, Sherry Pierce, Kelly A. Soltysiak, Fadi Haddad, David McCall, Branko Cuglievan, Elias Jabbour, Naval Daver, Tapan M. Kadia, Naveen Pemmaraju, Hagop M. Kantarjian, Guillermo Garcia‐Manero

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineChronic myelomonocytic leukemiaMyelodysplastic syndromesInternal medicineRetrospective cohort studyInternational Prognostic Scoring SystemCancerPediatricsBone marrow

Abstract

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Abstract Introduction Myelodysplastic syndrome (MDS) is mainly a disease of the elderly, with a median age of 72 years. There is little information regarding Adolescent and Young Adult (AYA) Patients with Myelodysplastic syndrome (MDS). AYA cancer patients are defined as those patients ages between 15-39 years according to NCCN guidelines. This retrospective study describes the general characteristics, cytogenetics, mutational profiles, treatments, and outcomes of AYA with MDS Diagnosis. Methodology We analyzed the clinical database of a single tertiary care center for patients with MDS ages between 18- 39 years from January 2012 through December 2020. We used 18 years as age cut-off, and not 15, due to the structure of our cancer center. Results In this retrospective study, 65 patients were identified. The median age was 30 (18-39) years, with female sex predominance (n=37) (57%). Baseline laboratory findings: median hemoglobin (HgB) was 9.45gm/dL (6.2-14.8), white blood cells (WBC) (3.4x10e9/L [0.3-136.9], platelets 63,000 (5,000-479,000), bone marrow blast 4% [0-17], IPSS was low in 11 patients (17%), intermediate- 1 in 23 (35%), intermediate- 2 16(25%) and high 8 (12%). 58 patients (89%) had MDS and seven (11%) had CMML. Twenty patients (30.7%) had a previous history of other cancers, with sarcomas being the most frequent with 6 cases (9.2%). Therapy-related MDS (t-MDS) was observed in 18 patients (27.6%). Ten patients (15.3%) had bone marrow failure syndrome, with GATA2 syndrome being the most frequent. Fanconi anemia and Schwachman-Diamond Syndrome was documented in two patients respectively. The most recurrent cytogenetics alterations were diploid in 20 patients (30.7%), followed by complex in 11 (16.9%). The most frequent mutations were RUNX1 (somatic)15%, followed by DNMT3A, TP53, NRAS, GATA2 and TET2, as shown in the Figure 1. Hypomethylating agents (HMAs) were the most frequent first line treatment used in 16 patients (24.6%). Forty-three patients (66%) underwent an allogeneic bone marrow transplant with a median OS (95% CI) of 27 months (9-45). While for the group of patients who didn't receive transplant, it was 21 months (7-69) (p=0.19) vs patients who didn't receive transplant. Allogeneic transplantation in TP53-mutated patients resulted in a Median OS (95% CI) of 21 months (12-65). Patients who progressed into AML had an inferior median OS (95% CI) of 21 months (12-65) for vs 28 months (11-47) for those who did not progressed to AML(p=0.025). In multivariate analysis expression of RUNX1 and NOTCH1, was associated with inferior outcomes (p-value=0.035, 0.004 respectively) (Figure 2,3 and 4) Conclusion In our cohort, MDS occurred as part of marrow failure syndrome or consequence of therapy t-MDS. Somatic RUNX1 was the most frequent mutation in AYA group with MDS. RUNX1, NOTCH1 and Tp53 mutated patients had worse outcome. Most patients underwent bone marrow transplant Figure 1 Figure 1. Disclosures Jabbour: Amgen, AbbVie, Spectrum, BMS, Takeda, Pfizer, Adaptive, Genentech: Research Funding. Daver: Abbvie: Consultancy, Research Funding; Gilead Sciences, Inc.: Consultancy, Research Funding; Astellas: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Bristol Myers Squibb: Consultancy, Research Funding; Glycomimetics: Research Funding; Novimmune: Research Funding; Amgen: Consultancy, Research Funding; FATE Therapeutics: Research Funding; Hanmi: Research Funding; Sevier: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Trovagene: Consultancy, Research Funding; Trillium: Consultancy, Research Funding; ImmunoGen: Consultancy, Research Funding; Novartis: Consultancy; Jazz Pharmaceuticals: Consultancy, Other: Data Monitoring Committee member; Dava Oncology (Arog): Consultancy; Celgene: Consultancy; Syndax: Consultancy; Shattuck Labs: Consultancy; Agios: Consultancy; Kite Pharmaceuticals: Consultancy; SOBI: Consultancy; STAR Therapeutics: Consultancy; Karyopharm: Research Funding; Newave: Research Funding. Kadia: Liberum: Consultancy; Novartis: Consultancy; Pfizer: Consultancy, Other; Jazz: Consultancy; BMS: Other: Grant/research support; Amgen: Other: Grant/research support; Pulmotech: Other; Genentech: Consultancy, Other: Grant/research support; Aglos: Consultancy; Sanofi-Aventis: Consultancy; Genfleet: Other; Astellas: Other; Ascentage: Other; AstraZeneca: Other; AbbVie: Consultancy, Other: Grant/research support; Dalichi Sankyo: Consultancy; Cure: Speakers Bureau; Cellonkos: Other. Pemmaraju: LFB Biotechnologies: Consultancy; Incyte: Consultancy; Protagonist Therapeutics, Inc.: Consultancy; Abbvie Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; CareDx, Inc.: Consultancy; DAVA Oncology: Consultancy; Stemline Therapeutics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Cellectis S.A. ADR: Other, Research Funding; Celgene Corporation: Consultancy; Novartis Pharmaceuticals: Consultancy, Other: Research Support, Research Funding; Roche Diagnostics: Consultancy; Daiichi Sankyo, Inc.: Other, Research Funding; Affymetrix: Consultancy, Research Funding; Plexxicon: Other, Research Funding; ASH Communications Committee: Membership on an entity's Board of Directors or advisory committees; Samus: Other, Research Funding; ASCO Leukemia Advisory Panel: Membership on an entity's Board of Directors or advisory committees; Aptitude Health: Consultancy; Springer Science + Business Media: Other; MustangBio: Consultancy, Other; Sager Strong Foundation: Other; HemOnc Times/Oncology Times: Membership on an entity's Board of Directors or advisory committees; Dan's House of Hope: Membership on an entity's Board of Directors or advisory committees; Clearview Healthcare Partners: Consultancy; Blueprint Medicines: Consultancy; Bristol-Myers Squibb Co.: Consultancy; ImmunoGen, Inc: Consultancy; Pacylex Pharmaceuticals: Consultancy. Kantarjian: NOVA Research: Honoraria; KAHR Medical Ltd: Honoraria; Precision Biosciences: Honoraria; BMS: Research Funding; Amgen: Honoraria, Research Funding; Jazz: Research Funding; Ascentage: Research Funding; Immunogen: Research Funding; Daiichi-Sankyo: Research Funding; Ipsen Pharmaceuticals: Honoraria; Astra Zeneca: Honoraria; Astellas Health: Honoraria; Aptitude Health: Honoraria; Pfizer: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; AbbVie: Honoraria, Research Funding; Taiho Pharmaceutical Canada: Honoraria.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.007
GPT teacher head0.228
Teacher spread0.220 · 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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Citations2
Published2021
Admission routes1
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