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Impact of Genomic Alterations on Outcomes in Myelofibrosis Patients Undergoing JAK1/2 Inhibitor Therapy

2016· article· en· W4237747876 on OpenAlexaffabout
Jay Y. Spiegel, Caroline McNamara, Andrea Arruda, Tony Panzarella, Theodore A. Kennedy, Tracy Stockley, Mahadeo A. Sukhai, Mariam Thomas, Justyna Bartoszko, Jenny Ho, Nancy Siddiq, Aaron D. Schimmer, Andre C. Schuh, Hassan Sibai, Karen Yee, Jaime O. Claudio, Rebecca Devlin, Mark D. Minden, Suzanne Kamel‐Reid, Vikas Gupta

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer CentreOntario Institute for Cancer ResearchUniversity of Toronto
Fundersnot available
KeywordsMyelofibrosisRuxolitinibMedicineInternational Prognostic Scoring SystemOncologyInternal medicinePopulationMyeloidBone marrowMyelodysplastic syndromes

Abstract

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Abstract Introduction: The advent of next generation sequencing (NGS) has brought intense interest to the complex genetic landscape of myeloproliferative neoplasms (MPN). However, data regarding clinical outcomes in the context of novel MPN therapies such as JAK inhibitors are scarce. Limited data indicate that high molecular risk signature (HMR, presence of at least one mutation in ASXL1, EZH2, IDH1/2, SRSF2) or multiple mutations may be associated with decreased spleen response and a shorter time to discontinuation of Ruxolitinib in myelofibrosis (Patel et al, Blood 2015). Methods: All myelofibrosis patients seen in the MPN program at Princess Margaret Hospital between November 2009 and May 2016 and treated with JAK1/2 inhibitor therapy were identified. NGS molecular profiling of 54 genes (39 hotspot region; 15 complete coding region coverage) was performed on peripheral blood or bone marrow samples using the TruSight Myeloid Sequencing Panel. Reporting was restricted to well-covered, exonic nonsynonymous, intronic splice site, and known pathogenic synonymous variants. Variants with global mean allele frequency >1% were identified using multiple population databases (1000 genomes, ESP, ExAC) and excluded. The primary endpoint was time from start of JAK1/2 inhibitor therapy to treatment failure (TTF) defined as treatment discontinuation, progression to accelerated phase or leukemic transformation, spleen progression or death. Secondary endpoints included best spleen, anemia and IWG response achieved by 48 weeks of treatment and overall survival. Response was assessed according to the 2013 revised IWG-MRT criteria. Transfusion dependency was assessed as any transfusion in 12 weeks prior to treatment or being identified as transfusion dependent in medical history. Results: Of 159 patients treated with JAK1/2 inhibitors at our institution, 102 met the inclusion criteria (see Table 1). Patients were excluded if; no sample was available for analysis (19), short use of JAK inhibitor prior to transplant (9), active clinical trial (5), in accelerated phase/acute leukemia (4) and others (20). First JAK inhibitor used was ruxolitinib in 77 patients and momelotinib in 25. At least one mutation was identified in every patient. Twenty (20%) patients had one mutation, 32 (31%) had 2 mutations and 50 (49%) patients had ≥ 3 mutations. Eighty (82%) patients had the JAK2V617F mutation, 15 (15%) had mutations in CALR, 4 (4%) had MPL mutations and one patient was triple negative. One patient had mutations in both CALR and JAK2 while another had mutations in MPL and CALR. Forty-eight (47%) patients had mutations consistent with HMR profile. Mutation profile is summarized in Table 2. With median follow-up of 2.5 years, 51 (50%) patients experienced treatment failure. On univariate analysis, TTF was associated with DIPSS, pre-treatment transfusion status and Hb <100 prior to initiating JAK inhibitor therapy. However, TTF was not associated with specific driver mutations, the number of mutations or HMR profile. Exploratory analysis of genes mutated in ≥5% of the population showed EZH2 (p=0.004) and CBL (p=0.005) mutated patients had shorter TTF. Multi-variable analysis employing anemia <100 and DIPSS with either of the number of mutations or HMR profile did not show any association with TTF. There was a trend towards TTF in patients with Hb<100 (HR 2.21, 95%CI: 0.99-4.95, p=0.05). Of 102 patients, 91 were evaluable for spleen response with 43 (47%) achieving a spleen response by 48 weeks of treatment. Twelve (23%) of the 53 patients evaluated met criteria for anemia response. Neither spleen nor anemia response was associated with the use of a particular JAK inhibitor. Of the 95 patients with at least 48-week follow-up, best response while on treatment was clinical improvement and spleen response in 29 (31%) and 19 (20%) patients respectively. Conclusions: In this study of myelofibrosis patients treated with JAK inhibitors, EZH2 and CBL mutated patients had shorter TTF. We did not find any association between TTF and number of mutations or other high risk mutations such as ASXL1/SRSF2. Anemia was the only significant independent predictor of shorter TTF. Our findings highlight the need for multicenter collaborative studies on a large number of patients and cautious use of mutation profiling results in routine clinical decision making with current treatment approaches. Spiegel and McNamara are co-primary authors. Disclosures Panzarella: Cellgene: Consultancy. Schimmer:Novartis: Honoraria. Schuh:Amgen: Membership on an entity's Board of Directors or advisory committees. Yee:Novartis Canada: Membership on an entity's Board of Directors or advisory committees, Research Funding. Kamel-Reid:BMS: Research Funding. Gupta:Novartis: Consultancy, Honoraria, Research Funding; Incyte Corporation: Consultancy, Research Funding.

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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
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.286
Teacher spread0.268 · 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
Published2016
Admission routes2
Has abstractyes

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