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Record W4389246546 · doi:10.1182/blood-2023-188258

Reclassification of Ascertain (ASTX727-02) Myelodysplastic Syndrome (MDS) Patients: Outcomes Including Clinical Response, Overall Survival (OS), and Leukemia Free Survival (LFS) Based on IPSS-R and IPSS-M Scoring Systems

2023· article· en· W4389246546 on OpenAlexaff
Guillermo Garcia‐Manero, James McCloskey, Elizabeth A. Griffiths, Amer M. Zeidan, Karen Yee, Aref Al‐Kali, H. Joachim Deeg, Prapti A. Patel, Mitchell Sabloff, Mary‐Margaret Keating, Nancy Zhu, Nashat Gabrail, Salman Fazal, Joseph Maly, Olatoyosi Odenike, Hagop M. Kantarjian, Amy E. DeZern, Casey L. O’Connell, Gail J. Roboz, Lambert Busque, Rena Buckstein, Harsh Amin, Jasleen K. Randhawa, Brian Leber, Shannon Lee, Winny Chan, Sônia Buongermino de Souza, Yuri Sano, Harold Keer, Michael R. Savona

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsMcMaster UniversityHealth Sciences CentreUniversity of TorontoJuravinski Cancer CentreSunnybrook Health Science CentreHôpital Maisonneuve-RosemontUniversity of AlbertaQueen Elizabeth II Health Sciences CentrePrincess Margaret Cancer CentreOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsDecitabineInternational Prognostic Scoring SystemMedicineInternal medicineDiscontinuationChronic myelomonocytic leukemiaMyelodysplastic syndromesOncologyRegimenBone marrowDNA methylation

Abstract

fetched live from OpenAlex

Background: Oral decitabine/cedazuridine (ASTX727) is a fixed dose combination of decitabine (35 mg) and the cytidine deaminase inhibitor cedazuridine (100 mg) given once daily X 5 days on a 28-day cycle producing pharmacokinetic (PK) exposure equivalent to the standard intravenous (IV) decitabine regimen of 20 mg/m 2 daily X 5 days on a 28-day cycle. This was demonstrated in the pivotal ASCERTAIN study (Garcia-Manero, et al, ASH 2019) as it provided a PK bridge to existing decitabine data and demonstrated median OS was 31.7 mo. (Savona,et al. Intl. MDSF International Congress on MDS, 2021). Subjects were initially classified by the International Prognosis Scoring System (IPSS) for historical reasons, however IPSS-R and IPSS-M are enhancements to IPSS that provide dynamic risk assessment for predicting clinical outcomes in MDS. Here, the objective was to re-classify the MDS subjects enrolled on the ASCERTAIN study by IPSS-R and IPSS-M and measure the impact of informing patient outcomes based on re-calculated risk assessment. Methods: One hundred thirty-three subjects with MDS/CMML (chronic myelomonocytic leukemia) were enrolled in ASCERTAIN and were randomly assigned either IV decitabine for cycle 1 and oral decitabine/cedazuridine for cycle 2 or the opposite treatment sequence. All subjects continuing beyond cycle 2 received oral decitabine/cedazuridine for all subsequent cycles until treatment discontinuation for disease progression, toxicity, patient's decision, or HSCT. Whole blood collected prior to treatment was used for DNA isolation and molecular abnormalities identified using next-generation sequencing (NGS) hematologic malignancy panel of 179 genes including all genes commonly mutated in MDS. In the initial analysis of clinical outcomes, subjects were classified by IPSS with response assessment by IWG 2006 IPSS low and Int-1 risk levels were categorized as lower-risk MDS (LR-MDS), whereas IPSS Int-2 and high-risk categories were categorized as higher-risk MDS (HR-MDS). Subjects with sufficient data based on (e.g., available heme parameters, cytogenetics, NGS etc.) were reclassified by IPSS-R and IPSS-M. Subjects with IPSS-R score of ≤3.5 or IPSS-M of either very low, low, or moderate low were categorized as LR-MDS. Similarly subjects with IPSS-R score of >3.5 or an IPSS-M categorization of either moderate high, high, or very high were categorized as HR-MDS. Reclassified subjects were reassessed for CR (Complete Response), OS, and LFS and Harrell's concordance index (c-index) was used to describe the level of agreement between each scale and outcomes. Results: Based on the available data, the number of MDS subjects in the different risk classifications were the following: IPSS: 117, IPSS-R: 104, and IPSS-M: 105. Thirteen and 12 subjects on the IPSS could not be reclassified in the IPSS-R and IPSS-M, respectively, including 5 Int-1 and 2 low-risk MDS cases. CMML subjects were excluded from these analyses. Re-classification generally resulted in the upgrade of the subjects from LR-MDS to HR-MDS (Fig. 1). Thirty-one (26.5%) subjects from IPSS were reallocated in the IPSS-R to different risk categories; 3 (9.7%) were downgraded and 28 (90.3%) were upgraded. Similarly on reclassification with IPSS-M 34 (32.4%) of the patients reclassified; 5 (14.8%) were downgraded and 29 (85.3%) were upgraded. For IPSS LR-MDS, 23.2% of patients achieved CR, and 22.9% in the IPSS HR-MDS. Similarly, 21.6% patients achieved CR in the IPSS-R LR-MDS, and 23.9% in the IPSS-R HR-MDS; 26.3% patients achieved CR in the IPSS-M LR-MDS, and 20.9% in the IPSS-M HR-MDS. The c-index for the IPSS was .64 (OS) and .67 (LFS), IPSS-R was .70 (OS) and .71 (LFS), and .75 (OS) and .78 (LFS) for the IPSS-M (Table1). Conclusion: Reclassification from IPSS to IPSS-R or IPSS-M upgraded multiple subjects from a LR to a HR category, describing the ASCERTAIN patient population as a majority higher risk population with worse prognosis than previously assumed based on the IPSS. The efficacy as measured by the CR rates did not change when the LR and HR categories were defined by the different risk stratification systems. In contrast, the c-index improves with migration from IPSS to IPSS-R to IPSS-M, indicating an increased discriminatory ability of IPSS-M score in comparison to IPSS and IPSS-R, to predict patient outcomes.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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

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 head0.069
GPT teacher head0.343
Teacher spread0.274 · 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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