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Record W4310107696 · doi:10.1182/blood-2022-157949

The Combination of Navitoclax and Ruxolitinib in JAK Inhibitor-Naïve Patients with Myelofibrosis Mediates Responses Suggestive of Disease Modification

2022· article· en· W4310107696 on OpenAlexaff
Francesco Passamonti, James M. Foran, Anand Tandra, Valerio De Stefano, María Laura Fox, Ahmad Mattour, Mary Frances McMullin, Andrew C. Perkins, Gabriela Rodríguez‐Macías, Hassan Sibai, Qin Qin, Yan Sun, Jalaja Potluri, Jason G. Harb, Jonathan How

Bibliographic record

VenueBlood · 2022
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreMcGill UniversityUniversity Health Network
Fundersnot available
KeywordsRuxolitinibMyelofibrosisMedicineDiseasePolycythemia veraInternal medicineCancer researchBone marrow

Abstract

fetched live from OpenAlex

Background: Myelofibrosis (MF) is a myeloproliferative neoplasm associated with splenomegaly, high symptom burden, bone marrow fibrosis (BMF), and poor prognosis. The Janus kinase inhibitor (JAKi) ruxolitinib is the current standard of care for patients with MF. It improves splenomegaly and disease symptoms with limited impact on disease biology, highlighting an unmet medical need in MF. Driver mutations of JAK2, CALR, and MPL are key to the pathogenesis of MF. Reduction in variant allele frequency (VAF) of driver mutations and reversal of BMF are suggestive of disease modification (Pemmaraju, Cancer, 2022). Additionally, mutations in ASXL1, EZH2, SRSF2, IDH1/2, and U2AF1 p.Q157 are classified as high molecular risk (HMR) mutations associated with a worse prognosis (Arber et al., Blood, 2016). We previously demonstrated evidence of disease modification independent of HMR mutations by adding navitoclax to ruxolitinib in patients with suboptimal responses to ruxolitinib monotherapy (Pemmaraju, Lancet Oncology, 2022). Herein, we investigated whether the type of MF and risk (e.g., age, Dynamic International Prognostic Scoring System [DIPSS], and HMR) correlated with clinical outcomes and responses suggestive of disease modification (e.g., VAF reduction and improvement in BMF) among JAKi treatment-naïve patients with MF treated with the combination of navitoclax and ruxolitinib. Methods: Cohort-3 of the phase-2 multicenter REFINE study (NCT03222609) enrolled JAKi treatment-naïve patients with MF. Enrolled patients had primary or secondary MF with splenomegaly (DIPSS ≥ Intermediate-1) and did not receive prior JAK-2 therapy or bromodomain and extra terminal motif (BET) inhibitors. Patients initiated navitoclax at 100 mg QD or 200 mg QD if baseline platelet count was ≤ 150 × 109/L or > 150 × 109/L, respectively. Ruxolitinib was given BID with starting dose based on baseline platelet count per local label. The primary endpoint was spleen volume reduction of ≥ 35% (SVR35) from baseline at week 24, assessed by MRI conducted by central review. Key secondary and exploratory endpoints evaluated in this analysis were a reduction in BMF obtained from BM biopsies by local evaluation and a reduction in VAF for the driver gene mutations (JAK2V617, CALR, or MPL), respectively. Driver gene VAF and HMR mutations were determined in whole blood with the 50-gene Focus Myeloid next-generation sequencing panel (Interspace Pharma Solutions, Morrisville, NC, USA). Results: As of Feb 07, 2022, all enrolled patients (N = 32) treated with navitoclax and ruxolitinib were evaluable for biomarker analysis. The median duration of follow-up was 6.1 months (range, 1.9 – 18.6); median age was 69 years (range, 44 – 83), and median spleen volume was 1889.08 cm3 (range, 645.6 – 7339.6). At baseline, 22 (69%) patients had JAK2, 6 (19%) had CALR, and 3 (9%) had MPL mutations. One (3%) patient was triple negative. Nineteen (59%) patients had HMR mutations. SVR35 at week 24 was observed in all subgroups known to confer poor prognosis, including age (≥75 years, 50% [n = 4/8]), high DIPSS score (Intermediate-2, 63% [n = 12/19]; high, 33% [n = 1/3]), and HMR mutations (47% [n = 9/19]; Figure). BMF grade improvement was evaluable in 26/32 (81%) patients, of which 9/26 (35%) achieved ≥ 1-grade improvement at any time during treatment with a median time-to-improvement of 12.3 weeks (range, 12.1 – 24.1). Complete resolution of BMF was observed in 2/9 (22%) patients. The baseline BMF grades of these patients were 2 and 3. BMF ≥ 1-grade improvement was achieved by 7/13 (54%) patients with HMR mutations and 2/13 (15%) without HMR mutations. Reduction in driver gene JAK2V617 mutation VAF > 20% from baseline at week 12 or 24 was observed in 14/28 (50%) patients, and 5/14 (36%) patients achieved > 50% VAF reduction from baseline. There were no differences in > 20% VAF reductions from baseline to week 12 or 24 between those with or without HMR mutations (7/15 [47%] versus 7/13 [54%], respectively). Conclusions: Among JAKi treatment-naïve patients with MF, the combination of navitoclax and ruxolitinib reduced splenomegaly in several high-risk groups known to confer poor prognosis. Reductions in BMF and VAF were independent of HMR mutations. The reduction in BMF and VAF for the driver mutation JAK2V617 is encouraging and suggestive of evidence of disease modification with the combination of navitoclax and ruxolitinib. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

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: Non-randomized trial · Consensus signal: none
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)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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 designNon-randomized trial
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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Citations14
Published2022
Admission routes1
Has abstractyes

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