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Record W2985271579 · doi:10.1182/blood-2019-124739

Baseline Mutational Status of Patients with Myelofibrosis and Anemia in the Realise Trial and Impact on Outcome

2019· article· en· W2985271579 on OpenAlexaff
Haifa Kathrin Al‐Ali, Heinz Gisslinger, Francesco Passamonti, Lynda Foltz, David M. Ross, Nicola Vianelli, Alessandro M. Vannucchi, Norio Komatsu, Pierre Zachée, Ranjan Tiwari, Evren Zor, Shalini Chaturvedi, Geralyn Gilotti, Francisco Cervantes

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMyelofibrosisMedicineRuxolitinibInternal medicineClinical endpointAnemiaEssential thrombocythemiaSurrogate endpointPolycythemia veraAdverse effectGastroenterologyThrombocytosisClinical trialBone marrowPlatelet

Abstract

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Background: The Janus kinase (JAK) inhibitor ruxolitinib (RUX) is approved for the treatment of disease-related splenomegaly and symptoms in patients with myelofibrosis (MF). Treatment with RUX has been shown to significantly reduce splenomegaly and provide marked improvements in MF-related symptoms and quality-of-life. A number of mutations have been identified with known or likely functional significance in patients with MF (Vannucchi AM, et al. Leukemia. 2013), which may therefore have the potential to affect treatment response. This exploratory analysis aimed to investigate the mutational status of patients in the REALISE trial and to assess the relationship between baseline mutational status and outcome. Methods: REALISE was a multicenter, open label, single arm phase 2 study (NCT02966353). Eligible patients (N=51) had primary MF, post-essential thrombocythemia (ET) MF or post-polycythemia vera (PV) MF, with palpable (≥5 cm) spleen and hemoglobin level <10 g/dL. Patients started RUX at 10 mg bid with up titrations to 15 or 20 mg bid allowed after 12 weeks based on efficacy and platelet counts. The primary endpoint was achievement of ≥50% reduction in spleen length at Week 24. Secondary endpoints included transfusion requirements/dependence over time, adverse events, and patient-reported outcomes (PRO) (7-point MF score [MF-7], MF Symptom Assessment Form [MFSAF] version 2.0). Next generation sequencing (NGS) analysis using a 236 gene panel (Navigate BioPharma, Carlsbad, CA, USA) was performed on whole blood samples to identify genetic alterations. Results: NGS analysis data were available for 49/51 patients, median age was 67 years, 67.3% (33/49) had primary MF, 10.2% (5/49) had post-PV MF and 22.4% (11/49) had post-ET MF. DIPSS was available for 45 patients, 16.3% (8/49) were intermediate (Int)-1, 57.1% (28/49) were Int-2 and 18.4% (9/49) were high risk. The most frequent baseline mutations are shown in Figure 1. Classic driver mutations were found in JAK2 (n=33), CALR (n=11) and MPL (n=7), and did not affect response to RUX treatment. Two patients (4.1%) were triple negative for JAK2/CALR/MPL mutations, both responded to RUX treatment. The most commonly found non-driver mutations in patients with ≥50% reduction in spleen length at Week 24 (n=28) were TET2, ASXL1, U2AF1 and SRSF2; in non-responders, the most common non-driver mutations were TP53, FAT1 and ASXL1. The median number of mutations per patient was 2 (range 1-7); 35.7% (10/28) of patients with a response had ≥3 non-driver mutations vs 14.3% (3/21) of non-responders. Overall, no difference was seen in mutational distribution by change in spleen length at Week 24. In general, similar findings were seen for transfusion dependence status at baseline and improvements in symptom score with treatment (Table 1). However, there was a higher incidence of U2AF1 mutation in patients who were transfusion-dependent at baseline vs. non-transfusion dependent patients (4/8 [50%] vs 3/41 [7.3%], respectively). U2AF1 mutation is known to be associated with anemia and/or thrombocytopenia in myelodysplastic syndromes (Li B, et al. Genes Chromosomes Cancer. 2018). Mutations in TP53 were present in 6 patients. One patient showed a response to treatment at Week 24, and 5 were classified as non-responders. None of these 5 patients completed 24 weeks of treatment and 3 died during the study or safety follow-up period. Two progressed to acute myeloid leukemia prior to death. All patients were ≥60 years old, 4 were male and 4 were DIPSS Int-2 risk. Four patients had primary MF, 1 had post-ET MF and 1 had post-PV MF. Conclusions: Though these data should be interpreted with caution due to the small patient numbers, patients in the REALISE study showed variation in the type and number of genetic alterations with known/likely functional significance in MF. Compared with published mutational data on MF patients treated with RUX (Spiegel, et al. Blood Advances. 2017; Pacilli et al. Blood Cancer Journal. 2018) 12.2% of patients had a TP53 mutation compared to 4% and 4.2% respectively. This molecular difference may reflect the anemic study population. Despite the higher TP53 mutational burden, and alternative dosing strategy, 57.1% (28/49) patients had a response to RUX at Week 24. Although there was no strong association between mutation patterns and response, patients with TP53 mutations tended to have a poor outcome overall. Disclosures Al-Ali: Celgene: Research Funding; Novartis: Consultancy, Honoraria, Research Funding; CTI: Honoraria. Gisslinger:Novartis Pharma GmbH: Consultancy, Honoraria, Research Funding; Roche Austria GmbH: Consultancy; Myelopro GmbH: Consultancy; Celgene GmbH: Honoraria; Pharma Essentia: Other: Personal fees; Janssen-Cilag: Honoraria; AOP Orphan Pharmaceuticals: Consultancy, Honoraria, Research Funding. Passamonti:Janssen: Honoraria, Other: Advisory board , Speakers Bureau; Novartis: Honoraria, Other: Advisory board , Speakers Bureau; Celgene: Honoraria, Other: Advisory board , Speakers Bureau. Foltz:Novartis: Consultancy, Honoraria, Research Funding; Celgene: Consultancy; Amgen: Other: Spouse Employment; Incyte: Research Funding; Constellation Pharma: Research Funding. Ross:Celgene: Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees. Vannucchi:Incyte: Membership on an entity's Board of Directors or advisory committees; Italfarmaco: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; CTI BioPharma: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Komatsu:Pharma Essentia: Research Funding, Speakers Bureau; Novartis K.K: Speakers Bureau; Wako Pure Chemical Industries, Ltd.: Research Funding; Takeda Pharmaceutical Company Limited: Research Funding, Speakers Bureau; Fuso Pharmaceutical Industries, Ltd.: Research Funding. Tiwari:Novartis: Employment. Zor:Novartis: Employment. Chaturvedi:Novartis Pharmaceuticals: Employment. Gilotti:Novartis Pharmaceuticals: Employment. Cervantes:Novartis: Honoraria, Speakers Bureau; Celgene: Consultancy, Speakers Bureau.

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)0.004
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.281
Teacher spread0.269 · 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
Published2019
Admission routes1
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