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Record W2983461972 · doi:10.1182/blood-2019-125449

Patient Characteristics and Outcomes in Adolescents and Young Adults (AYA) with Acute Myeloid Leukemia (AML): Princess Margaret Cancer Centre Experience

2019· article· en· W2983461972 on OpenAlexaffabout
Muhned Alhumaid, Georgina S. Daher-Reyes, Wilson Lam, Arjun Law, Tracy Murphy, Caroline McNamara, Gil Yerushalmi, Aaron D. Schimmer, Andre C. Schuh, Dawn Maze, Eshetu G. Atenafu, Karen Yee, Mark D. Minden, Steven M. Chan, Vikas Gupta, Hassan Sibai

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineMyeloid leukemiaAcute promyelocytic leukemiaInternal medicineYoung adultRetrospective cohort studyCancerLeukemiaLog-rank testSurvival analysisOncologyPediatrics

Abstract

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Introduction: Clinical outcomes of acute myeloid leukemia (AML) in adolescents and young adults (AYA) are rarely reported as an isolated subgroup. Treatments vary little across age groups, and treatment intensity depends upon comorbid conditions and performance status. Optimal treatment strategies focused on disease behavior, biological factors, and the distinct needs of this subset of AML patients remain elusive. The purpose of this retrospective analysis is to determine the characteristics and outcomes of AYA AML patients treated at a specialized adult leukemia cancer center in comparison to older adults with AML (40-60 years). Methods: A retrospective analysis was performed on all patients treated at Princess Margaret Cancer Center from 2008-2018. Patients with acute promyelocytic leukemia were excluded. Clinical characteristics, treatment strategies, and survival outcomes were recorded for all patients. Overall survival (OS) and disease-free survival (DFS) rates were calculated using the Kaplan-Meier product-limit method and the impact of covariates were assessed using the Log-rank test. Finally, we compared the outcomes of AYA patients treated at our centre between 2015-2018 with older patients. Results: A total of 175 patients aged 18-39 were identified. Patient characteristics are shown in (Table 1). Cytogenetic were available in 163 patients. Based on MRC criteria, 27 (16%) were favorable risk, intermediate in 95 (54%), adverse in 39 (22%), and missing/failed in 14(8%). NPM1 status was available in 110 patients of whom 38 (35%) were positive. FLT3-ITD was available in 67 patients with 24 (36%) positive. Both mutations were present in 13 (54%) patients. There were no significant differences in terms of risk stratification based on cytogenetic and molecular markers based on age (18-29 vs.30-39) (P= 0.98). Most patients 172 (98%) received induction, 157 (91%) with 3+7, and 15 (9%) with FLAG-IDA. Complete remission (CR) was achieved in 133 (77%) after first induction [120 (76%) after 3+7 and 11 (73%) after FLAG-IDA]. Induction related mortality was low (2%). Of the 39 who did not achieve CR, thirty-four patients received re-induction (13 FLAG-IDA, 16 NOVE-HiDAC, 5 others) with CR in 21 (62%). Overall, 154 (89.5%) achieved CR1. Sixty-four (42%) proceeded to hematopoietic stem cell transplantation (HSCT) in CR1. 59 (38%) patients relapsed in CR1 with 8 (12%) relapsing post HSCT. Fifty-five (5 post HSCT) patients received reinduction with 30 (51%) (2 after HSCT) achieving CR2. Fifteen patients received HSCT in CR2. OS and DFS at 2 years were 62% (95% CI 0.53-0.69) and 50% (95% CI 0.41-0.57), respectively. Stratified by cytogenetic risk, OS was 81% for favorable risk, 61% for intermediate, and 50% for adverse risk (P=0.0001), respectively. DFS in these groups was 85%, 57%, and 46 % (P=0.0025), respectively. We further compared outcomes in the 18-29y and 30-39y age groups. The OS was 61.9% compared to 62.5% (P=0.91) and DFS of 52.1% compared to 47% (P=0.65) respectively. On univariate analysis for OS and DFS, cytogenetic risk stratification was the only significant variable (P=0.0004 and P=0.0042). We then compared the outcomes 67 sequential patients aged I8-39 treated from 2014-2018, with those of 176 sequential patients aged 40-60 treated during the same period (table 2). OS at 2 years was not statistically higher in the younger group compared to the older group (66.7% vs. 61.2%, P=0.372). While relapse rate was lower in older patients (15.5% vs. 22.6%, P=0.093), NRM was higher in older patients (29.7% vs. 18.8%,P=0.094). Conclusion: AYA pts. occupy a unique niche amongst AML as a whole. While treatment responses have improved in general, there may be potential for further gains in these patients. Increased tolerance for more intense treatment strategies as well as the incorporation of novel agents into standard treatment protocols may provide a means to optimize care in AYA patients. Finally, research is needed to elucidate biological mechanisms and predictors of disease behavior instead of arbitrary, age-stratified treatment schema. Disclosures McNamara: Novartis Pharmaceutical Canada Inc.: Consultancy. Schimmer:Jazz Pharmaceuticals: Consultancy; Medivir Pharmaceuticals: Research Funding; Novartis Pharmaceuticals: Consultancy; Otsuka Pharmaceuticals: Consultancy. Schuh:Astellas: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Teva Canada Innovation: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Agios: Honoraria; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees. Maze:Pfizer Inc: Consultancy; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees. Yee:Astellas: Membership on an entity's Board of Directors or advisory committees; Millennium: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Astex: Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; MedImmune: Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Hoffman La Roche: Research Funding. Minden:Trillium Therapetuics: Other: licensing agreement. Gupta:Incyte: Honoraria, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sierra Oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees.

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 score0.005
Threshold uncertainty score0.010

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.252
Teacher spread0.246 · 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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Citations1
Published2019
Admission routes2
Has abstractyes

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