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Spliceosome Mutations Are Associated with Frailty in Older Patients with Myeloid Malignancies

2017· article· en· W2968763813 on OpenAlexaboutno aff
Marlise R. Luskin, Angel M. Cronin, Kevin Copson, WooRam Jung, Martha Wadleigh, Daniel J. DeAngelo, David P. Steensma, Jane A. Driver, Andrew A. Lane, R. Coleman Lindsley, Gregory A. Abel

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)MedicineMyeloid leukemiaGrip strengthWeight lossInternal medicineGerontologyOncologyObesityPhysical therapyBiology

Abstract

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Abstract Background: Frailty is a state of depleted physiologic reserve that is common in older patients with myeloid malignancies. While it is associated with chemotherapy-related toxicity, poor response to therapy, and higher mortality (Sherman, 2013; Fega, 2015), little is known about its causes. Moreover, there are increasing data demonstrating that clonal somatic mutations may be related to atherosclerotic disease (Jaiswal, 2017), which is an important driver of frailty. In this context, we sought to determine whether there is an association between hematopoietic DNA mutations and frailty in patients with overt myeloid neoplasms, hypothesizing that both RAS/MAPK and spliceosome mutations would be associated with the frailty phenotype. Methods: Starting in February of 2015,all patients aged 75 and older who presented for initial consultation to the DFCI Adult Leukemia Clinic were approached for evaluation by a trained research assistant. In a 15-minute interview, the assistant used two methods to characterize the patient as frail, pre-frail, or robust. The first employs a cumulative deficit approach (Rockwood, 2007) including 26 questions adapted from the Yale Precipitating Events Project (Searle, 2008), as well as the delayed recall section of the Montreal Cognitive Assessment (Nasreddine, 2005), a clock-in-the-box test (Chester, 2011), a grip strength test (Gill, 2006), and a gait speed test (Studenski, 2011). The second is a phenotype approach (Fried, 2001), which gives equal weight to performance on the gait speed and grip strength tests, as well as three questions about weight loss, energy expenditure and self-reported exhaustion. Patients were assigned frailty designations using the worse of the two scores. 85% of patients approached agreed to participate. Routine assessment of recurrent genetic alterations was performed using the Rapid Heme Panel (RHP), a gene panel sequencing test that evaluates 95 genes recurrently mutated in hematologic malignancies (Kluk, 2016). Associations between mutations and frailty were assessed via tests for trend across frailty status categories as well as a Fisher's exact test comparing the proportion of patients with robust status (versus frail/pre-frail combined). Results: As of July 2017, 93 patients with MDS (n=39), MPN (n=18), MDS/MPN (n=18), or AML (n=18) had completed frailty assessment and disease genetic characterization. Patients were majority male (n=70, 75%) with median age of 78 years (range, 75-89). Hypertension (65%) and hyperlipidemia (42%) were common. 59% (n=55) of patients in the cohort were pre-frail, 23% were frail (n=21), and 18% (n=17) were robust. There were no significant differences in frailty status between AML patients and those with chronic myeloid disorders (MDS, MPN, MDS/MPN). Gene mutations present in at least 5% of the cohort included SRSF2 (25%), SF3B1 (12%) , U2AF1 (8%), TET2 (44%), ASXL1 (26%), DNMT3A (8%), JAK2 (24%), RUNX1 (19%), TP53 (13%), STAG2 (9%), IDH2 (8%), EZH2 (8%) , and CBL (6%). 47% of patients had one of 4 non-co-occurring splicing mutations (SRSF2, SF3B1, U2AF1, ZRSR2). 18% of patients had one or more mutations that cause aberrant activation of RAS/MAPK signaling (NRAS, KRAS, CBL, PTPN11, RIT1, FLT3, KIT) and are associated with leukemic transformation of MDS (Lindsley, 2015). Patient with any splicing mutations were less likely to be robust (5% robust, 73% pre-frail, 23% frail) compared to those without (31% robust, 47% pre-frail, 22% frail; P for trend = 0.05; P -exact for robust versus pre-frail/frail = 0.001). Patients with an SF3B1 mutation specifically were also less likely to be robust then patients without an SF3B1 mutation (P for trend=0.02, Figure). The association between presence of a splicing mutation and frailty persisted within subgroups of patients with chronic myeloid malignancies and patients with AML, although the latter did not reach statistical significance (p=0.10) in that small dataset. No other single mutation, number of mutations present, or presence of a mutation in the RAS/MAPK pathway was significantly associated with frailty. Conclusion: In our cohort, patients with splicing mutations were less likely to be robust than patients without splicing mutations. These findings suggest that molecular characterization of myeloid disease may identify patients at higher risk for frailty and complications associated with treatment. Download : Download high-res image (185KB) Download : Download full-size image Disclosures DeAngelo: Glycomimetics: Research Funding; Amgen: Consultancy, Research Funding; BMS: Consultancy; Blueprint Medicines: Honoraria, Research Funding; Immunogen: Honoraria, Research Funding; Shire: Honoraria; Takeda Pharmaceuticals U.S.A., Inc.: Honoraria; Celgene: Research Funding; Incyte: Consultancy, Honoraria; Pfizer Inc.: Consultancy, Honoraria, Research Funding; ARIAD: Consultancy, Research Funding; Novartis Pharmaceuticals Corporation: Consultancy, Honoraria, Research Funding. Steensma: Celgene: Consultancy; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy; Janssen: Consultancy, Research Funding; H3 Biosciences: Consultancy; Incyte: Equity Ownership; Onconova: Consultancy; Takeda: Consultancy; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees. Lane: Stemline Therapeutics: Research Funding; N-of-one: Consultancy. Lindsley: MedImmune: Research Funding; Jazz Pharmaceuticals: Consultancy; Takeda Pharmaceuticals: Consultancy.

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)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.272
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".

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Citations1
Published2017
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