Baseline Serum Ferritin Differentially Predicts W24 Transfusion Independence Response for Momelotinib and Ruxolitinib in Patients with Myelofibrosis
Bibliographic record
Abstract
Abstract Introduction Myelofibrosis (MF) typically presents with constitutional symptoms, splenomegaly, and anemia with the degree of anemia and transfusion dependency being among the most important predictors of poor overall survival (OS). Momelotinib (MMB) is a differentiated JAK1, JAK2 inhibitor with potent activity against ACVR1/ALK2, a critical regulator of hepcidin production and iron metabolism. MMB has demonstrated clinical activity against all 3 of these hallmark features of MF in the SIMPLIFY Ph 3 studies including SIMPLIFY-1 (S1) in the JAK inhibitor (JAKi) naïve setting compared directly to ruxolitinib (RUX) and in the previously JAKi experienced patients (SIMPLIFY-2 [S2]) in comparison to best available therapy (BAT) which was predominately RUX. MMB has also demonstrated robust OS in the JAKi naïve population (S1) and JAKi experienced population (S2) (Verstovsek et al. ASH 2020). Critically, patients randomized to MMB who maintain or achieve transfusion independence (TI) by Week 24 (W24) had better OS, further suggesting that MMB's anemia benefits positively impact long term outcomes in patients with MF (Mesa et al. EHA 2021). Increased hepcidin and ferritin are associated with dysregulated iron metabolism and inflammation, both of which have previously been shown to be strong negative prognostic factors for OS in patients with MF at the time of first referral (Pardanani et al. 2013). A Ph 2 translational biology study previously demonstrated MMB acutely and chronically suppresses elevated levels of hepcidin and restores iron homeostasis in transfusion dependent patients who achieve a W24 TI-response (TI-R; Oh et al. 2020). Interestingly, several baseline factors, such as hepcidin, ferritin and CRP from this study suggested a potential threshold of W24 TI-R for patients treated with MMB, warranting further exploration. Methods Based on the findings from the Ph 2 translational biology study we retrospectively analyzed the relationship between serum ferritin, hepcidin and CRP and the W24 TI-R rates for patients randomized to MMB and RUX in S1 using generalized linear regression models where the degree of predictiveness measured by the interaction between treatment and the biomarkers as continuous or categorical variables using various cutoffs were examined. These findings were then independently confirmed in the previously RUX treated patients from S2. Results These analyses identified pre-treatment serum ferritin level as the most predictive biomarker for the treatment effect of MMB vs RUX on W24 TI-R rate in S1. The TI-R treatment effect of MMB vs RUX was significantly greater (p=0.0051) in the ≥90ng/mL cohort (62% vs 35% respectively, with response ratio [RR]=1.8) than in the <90ng/mL cohort (79% and 73%, RR=1.1). This trend was also observed in the S2 study, where TI-R treatment effect of MMB vs BAT/RUX was greater in the ≥90ng/mL cohort (41% vs 11%, RR=3.8) than in the <90ng/mL cohort (57% and 50%, RR=1.1) although the interaction was marginally significant (p=0.0707) potentially due to the smaller sample size. Data also demonstrated a significant increase in serum ferritin for RUX vs MMB at W24 vs baseline (RUX mean ferritin change +226.1ng/mL vs MMB +13.8ng/mL, p=0.0003), irrespective of baseline ferritin. Conclusion Ferritin is a well-established and easily measured clinical biomarker that is associated with both iron metabolism and uncontrolled inflammation. Prior analyses demonstrate patients randomized to MMB who achieve a W24 TI-R have increased OS compared to non-TI responders. These new analyses expand on these findings, demonstrating that the MMB vs RUX treatment effect is greater in baseline serum ferritin ≥90ng/mL vs <90ng/mL in JAKi naïve and in RUX-experienced patients. Further, data presented here show JAKi naïve patients in S1 randomized to RUX have significantly elevated ferritin levels by W24 when compared to MMB. These data suggest the possibility that, in contrast to RUX, treatment with MMB may have the capacity to attenuate ferritin elevation in a manner that corresponds to achievement of TI-R. The mechanistic explanation for these observations requires further investigation. These data additionally suggest serum ferritin may become an important biomarker to help inform therapy selection in the front line as well as potentially guide the transition to MMB in the post-RUX setting. This association should be examined prospectively in future MMB trials. Figure 1 Figure 1. Disclosures Oh: Abbvie: Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Membership on an entity's Board of Directors or advisory committees; Celgene Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Constellation: Membership on an entity's Board of Directors or advisory committees; CTI Biopharma: Membership on an entity's Board of Directors or advisory committees; Disc Medicine: Membership on an entity's Board of Directors or advisory committees; Geron: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees; Kartos Therapeutics: Membership on an entity's Board of Directors or advisory committees; PharamaEssentia: Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Membership on an entity's Board of Directors or advisory committees. Gerds: PharmaEssentia Corporation: Consultancy; CTI BioPharma: Research Funding; Sierra Oncology: Consultancy; AbbVie: Consultancy; Celgene/Bristol Myers Squibb: Consultancy; Constellation: Consultancy; Novartis: Consultancy. Mesa: CTI: Research Funding; Pharma: Consultancy; Incyte Corporation: Consultancy, Research Funding; Abbvie: Research Funding; AOP: Consultancy; Constellation Pharmaceuticals: Consultancy, Research Funding; Samus: Research Funding; Celgene: Research Funding; Gilead: Research Funding; Promedior: Research Funding; La Jolla Pharma: Consultancy; Sierra Oncology: Consultancy, Research Funding; CTI: Research Funding; Novartis: Consultancy; Genentech: Research Funding. Gupta: Pfizer: Consultancy; Roche: Consultancy; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Constellation Pharma: Consultancy, Honoraria; BMS-Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Honoraria; Sierra Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Research Funding. Huang: Sierra Oncology: Current Employment; BioMarin: Ended employment in the past 24 months. Ro: Sierra Oncology: Current Employment; BeiGene: Ended employment in the past 24 months. Strouse: Sierra Oncology: Current Employment. Klencke: Sierra Oncology: Current Employment, Current equity holder in publicly-traded company. Verstovsek: Blueprint Medicines Corp: Research Funding; Roche: Research Funding; PharmaEssentia: Research Funding; NS Pharma: Research Funding; Ital Pharma: Research Funding; Protagonist Therapeutics: Research Funding; Incyte Corporation: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; CTI BioPharma: Research Funding; Gilead: Research Funding; Genentech: Research Funding; Sierra Oncology: Consultancy, Research Funding; Promedior: Research Funding; AstraZeneca: Research Funding; Novartis: Consultancy, Research Funding; Constellation: Consultancy; Pragmatist: Consultancy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".