Category Archives: Research

JBI-802 initial Phase I data suggests therapeutic potential in sensitizing immunotherapy resistant tumors and in Myeloproliferative Neoplasms with thrombocytosis

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BEDMINSTER, N.J., Jan. 8, 2024 /PRNewswire/ — Jubilant Therapeutics Inc., a clinical-stage biotechnology company pioneering the development of a first-in-class CoREST (Co-repressor of Repressor Element-1 Silencing Transcription) inhibitor JBI-802 with the dual activity on LSD1 and HDAC6, today announced preliminary safety, pharmacokinetic and initial efficacy results of the Phase I trial in advanced cancer patients. Furthermore, the study results provide a human proof of principle for expanding the development of JBI-802 in Essential Thrombocythemia (ET) and related Myeloproliferative Neoplasms (MPN/MDS) with thrombocytosis.

The data from first 11 patients with advanced cancer revealed a dose-proportional increase in exposure across cohorts and a strong correlation between the exposure and the on-target effects of platelet decrease, indicating that pharmacological relevant level of LSD1 inhibition have been achieved. At the same time, platelet decrease is the only adverse event above grade 1 observed in these patients, which differentiates JBI-802 from LSD1-only inhibitors. Specifically, no AEs (Adverse Events) of anemia has been observed, which is potentially due to the positive benefit of inhibition of HDAC6 in erythrocytes. Also, there are no reports of Dysgeusia, an adverse event that has been observed with LSD1-only inhibitors.

Among the 11 patients, two were NSCLC (Non-small Cell Lung Cancer) patients, both had progressed on doublet immunotherapy, nivolumab+ipilimumab as first line treatment and both showed anti-tumor activity. Both the patients were treated at lower dose level (10mg) where no relevant decrease of platelets is seen, suggesting that in patients with sensitive tumors this dose can be pharmacologically active with a desirable safety profile.

Both NSCLC patients had failed first line treatment with doublet immunotherapy, nivoluman/ipilumab prior to enrolling in the JBI-802 study. The first patient had a STK11 mutation, known to decrease the effectiveness of immunotherapy, present in around 10% of NSCLC patients (higher frequency in lung adenocarcinoma). JBI-802 showed a confirmed partial response in this IO-refractory NSCLC patient with a 39% decrease in the target lung tumor mass. The tumor shrinkage outcome was accompanied by a complete resolution of pancoast syndrome (lung lesion affecting the nerves of brachial plexus). The response appears to be durable after nine cycles and the patient remains on JBI-802 therapy.

The second patient had both lung lesion and liver metastasis, which are known to confer resistance to immunotherapy and lead to poor prognosis. Treatment with JBI-802 resulted in over 50% shrinkage of the patient’s liver metastasis and a complete resolution of related portal hypertension, edema and improvement of quality of life.

Dr. Alexander Starodub, The Christ Hospital – Cincinnati, treating physician for the above patients commented, “The anti-tumor activity seen in these two NSCLC patients is remarkable given the poor prognosis based on their genetic and metastatic pattern. The 10 mg dose of JBI-802 was well-tolerated without any clinically significant adverse effects and the initial clinical data suggest a good therapeutic index for JBI-802.”

Preclinical studies showed a synergistic anti-tumor effect by combining immunotherapy and JBI-802 in xenograft models. In addition, the CoREST inhibition was reported to sensitize immunotherapy resistant tumors, especially those with STK11 mutations. Taken together, the preliminary efficacy data from the JBI-802 Phase I study suggest the opportunity that a combination between immunotherapy and JBI-802 could bring a new therapy option to such patient populations with limited treatment options.

In addition, the on-target dose/exposure-proportional decrease in platelet constitute a proof-of-principle that JBI-802 can be an active compound in hematological malignancies like Essential Thrombocythemia (ET) and other MPN/ MDS characterized by thrombocytosis. A follow up Phase I/II study in MPN/ET and MPN/MDS with thrombocytosis is being planned in the first quarter of this year to investigate JBI-802 as potential novel treatment option.

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AI Tool Accurately Differentiates MPNs Using Bone Marrow Biopsies

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Jordyn Sava

Andrew Srisuwananukorn, MD, assistant professor at The Ohio State University Comprehensive Cancer Center, discussed the use of a novel artificial intelligence model that aids in the differentiating between prefibrotic primary myelofibrosis and essential thrombocythemia.

An artificial intelligence (AI) model is being investigated that will help clinicians distinguish between prefibrotic primary myelofibrosis (pre-PMF) and essential thrombocythemia (ET) using bone marrow biopsy images from 200 patients.

The AI tool had previously been trained with 32,000 pan-cancer biopsy images. It was also familiar with general pathologic features. From this, investigators tested if the AI could differentiate between the 2 types of myeloproliferative neoplasms (MPNs) in patients.

According to findings presented by Andrew Srisuwananukorn, MD, at the American Society of Hematology 2023 Meeting, the tool demonstrated a 92.3% rate of agreement with human experts, and the sensitivity and specificity for pre-PMF diagnosis was 66.6% and 100%, respectively.

Based on these promising findings, experts will continue to update the AI tool and test it in larger data sets.

“I view this type of tool as a companion diagnostic tool, but I do not believe [that] AI tools can replace the judgment of a human physician. I think it is really up to us as pathologists and clinicians to say when an AI algorithm tool is not working appropriately. What I hope is that this can be used for better information for the patient to understand their disease,” said Srisuwananukorn, assistant professor at The Ohio State University Comprehensive Cancer Center, in an interview with Targeted OncologyTM.

In the interview, Srisuwananukorn discussed the use of a novel AI model that aids in the differentiating between prefibrotic primary myelofibrosis and essential thrombocythemia.

Targeted Oncology: What can you tell us about this AI tool?

Srisuwananukorn: Our research is in developing an artificial intelligence tool to differentiate between rare myeloid malignancies, including prefibrotic myelofibrosis and essential thrombocythemia. As a brief overview, these diagnoses are challenging to differentiate because there’s similar criteria, including clinical and laboratory abnormalities, mutational profiling, and assessment of the bone marrow, which can be very subjective, particularly when looking at the megakaryocyte morphologies in the fibrosis. Our hope is to create a more objective or at least consistent tool using artificial intelligence to differentiate between the 2.

What is the motivation behind the development of this tool? What specific challenges or clinical needs there are in distinguishing between different disease types?

Our motivation is that this is a diagnostic dilemma for our patients. Essential thrombocythemia and prefibrotic primary myelofibrosis behave quite differently. The prefibrotic myelofibrosis cases have more symptoms and are at higher risk for progression to acute myeloid leukemia. To me, it behooves the physician to know exactly which disease they have. It’ll help guide their therapies in the future. Our hope is that a tool such as this can help guide that management and potentially to help enroll in clinical trials for more appropriate diagnosis and therapy creations.

In your study, how did AI demonstrate its efficacy when differentiating between the myelofibrosis and ET?

Our model had very high performance with [an] area under the receiver operator curve of 0.9, a sensitivity of 66.6% specificity of 100%, and an accuracy of 92.3% in diagnosing prefibrotic myelofibrosis. In addition, we did a qualitative analysis to try to understand what is being used in these AI algorithms to make those predictions. With this qualitative interpretation of quote unquote, opening the black box of our AI algorithms, we were able to see that preferentially, areas of bone marrow cellularity were chosen for the prediction of 1 vs the other disease. Reassuringly, the algorithm was not using nonsensical portions of the image, such as fat or cortical bone or even background artifacts. We believe this AI algorithm is using biological reasons.

What distinguishes this tool from others and how can it be interpreted in these settings moving forward?

I view this type of tool as a companion diagnostic tool, but I do not believe [that] AI tools can replace the judgment of a human physician. I think it is really up to us as pathologists and clinicians to say when an AI algorithm tool is not working appropriately. What I hope is that this can be used for better information for the patient to understand their disease.

Are there any particular patient subgroups that the AI tools showed notable effectiveness?

For right now, we’re only looking at 2 particular diseases: prefibrotic myelofibrosis and essential thrombocythemia. However, that does not mean that this is our only goal. These algorithms are agnostic of disease and outcome. Potentially, the same types of algorithms or workflows can be used for any type of disease that other clinicians might be able to implement in their clinical practice. Our particular goal is to do better for patients [with MPNs], and we have multiple ideas of how we can do that.

Moving forward, what are some of the next steps for this research?

Our next steps are in 2 domains. One, for this particular algorithm in differentiating pre-PMF and ET, we hope to validate it in further, larger retrospective cohorts at other academic centers and potentially within clinical trials that enroll patients [with ET]. Our thought is potentially, the ET trials did not have great performance because they were accidentally enrolling these pre-PMF cases. That’s our next step into rigorously seeing if this algorithm can be effective. Outside of that, we’re hoping that we can develop similar algorithms in other outcomes of interest, including risk stratification and therapy response.

What should oncologists know about the growing use of AI?

For physicians at large, what I want to reiterate is that it is important that we understand how it’s being used and when to use it. I do not believe that algorithms can supplant physician judgment, but I do think that they will be used in clinical practice and it’s up for us to know when it’s not working. I hope that it’s a support tool and will help guide your decision management but it’s always up to the physician.

REFERENCE
Srisuwananukorn A, Loscocco GG, Kuykendall AT, et al. Interpretable Artificial Intelligence (AI) Differentiates Prefibrotic Primary Myelofibrosis (prePMF) from Essential Thrombocythemia (ET): A Multi-Center Study of a New Clinical Decision Support Tool. Blood. 2023;142(suppl 1): 901.doi:10.1182/blood-2023-173877

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SRSF2 Mutations Lead to Lessened Frequency of Polycythemia in Preclinical MPN Models

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Kyle Doherty

The presence of mutated SRSF2 in knock-in mouse models of JAK2 V617F–driven myeloproliferative neoplasms (MPNs) reduced the rate of polycythemia and hampered hematopoietic progenitor functions, according to findings from a preclinical study published in Blood Cancer Journal.

Findings from the study demonstrated that coexpression of mutant SRSF2 P95H decreased red blood cell (RBC), neutrophil, and platelet counts, as well as attenuated splenomegaly in JAK2 V617F-positive mice. Notably, bone marrow fibrosis was not induced in JAK2 V617F-positive mice. Coexpression of SRSF2 P95H was also found to reduce the competitiveness of JAK2 V617F–mutated hematopoietic stem/progenitor cells.

Additionally, RBC, hemoglobin, and hematocrit levels were significantly reduced in the bone marrow of JAK2 V617F–positive mice that displayed enforced expression of S100A9. Mutated SRSF2 P95H decreased TGF-β levels and increased S100A8 and S100A9 expression in JAK2 V617F–positive mice.

“We demonstrated that SRSF2 P95H mutant reduces polycythemia and impairs competitiveness of JAK2 V617F–mutant hematopoietic stem/progenitor cells but does not promote the development of bone marrow fibrosis inJAK2 V617F-induced MPN,” lead study author Yue Yang, MD, of the Department of Biochemistry and Molecular Genetics at the University of Virginia School of Medicine in Charlottesville, and coinvestigators wrote.

To conduct their study, investigators created JAK2 V617F knock-in, SRSF2 P95H knock-in, and Mx1Cre transgenic mouse models, all on a C57BL/6 background. Intraperitoneal injection of 3 doses of polyinosine-polycytosine 300 μg were given at 4 weeks after birth in order to induce Mx1Cre expression. Wild-type C57BL/6 and UBC-GFP mice were acquired from an outside laboratory.

To create non-competitive bone marrow transplantation assays, 1 x 106 bone marrow cells were taken from the mice in each of the 4 groups (control of wild-type or Mx1Cre; SRSF2 P95H-positive; JAK2 V617F-positive; and SRSF2 P95H/JAK2 V617F-positive) and transplanted into lethally irradiated C57BL/6 mice. Polyinosine-polycytosine was administered to the recipient mice at a dose of 300 μg 3 times at 4 weeks following transplantation.

Competitive transplantation assays were created by mixing bone marrow cells from uninduced JAK2 V617F-positive/GFP-positive or SRSF2 P95H/JAK2 V617F-positive/GFP-positive were mixed with wild-type competitor bone marrow cells at a 1:1 ratio and transplanted into wild-type C57BL/6. Recipient mice received 3 doses of polyinosine-polycytosine 300 μg at 4 weeks post transplantation.

To create colony-forming assays, investigators plated 2 X 104 mouse bone marrow cells in cytokine-containing complete methylcellulose medium. After 1 week, burst forming units-erythroid and granulocyte-macrophage colony-forming units were tallied. Spleen cells at a quantity of 1 x 105 were plated in MethoCult M3234 medium without cytokine to detect epo-independent colony-forming units-erythroid.

Epo-independent colony-forming units-erythroid were stained with benzidine solution and counted 2 days afterwards. Colony-forming units-megakaryocytes were determined by plating 1 x 105 bone marrow cells in collagen-based MegaCult medium with Tpo, IL-3, IL-6, and IL-11. Colony-forming units-megakaryocytes were scored at day 8.

S100A8 or S100A9 overexpression’s effect on granulocyte-macrophage colony-forming units and burst forming units-erythroid formation of JAK2 V617F-positive bone marrow, cells lineage-negative cells were isolated from the bone marrow. Puromycin 2.5 μg/mL administered for 48 hours was used to select infected cells and 2.5 × 103 lineage-negative cells were plated in duplicates in cytokine-supplemented complete methylcellulose medium.

Study authors analyzed the mice models using flow cytometry and real-time quantitative PCR. Additionally, the TGF-β1 ELISA kit was used to determine TGF-β1 serum levels.

Further findings revealed that mice with heterozygous JAK2 V617F displayed polycythemia vera with increased white blood cell, neutrophil, platelet, RBC, hemoglobin, and hematocrit counts in peripheral blood compared with control mice. Those with heterozygous SRSF2 P95H experienced decreased hemoglobin with increased mean corpuscular volume vs the control group. SRSF2 P95H/JAK2 V617F-positive mice had significantly decreased white blood cell, neutrophil, platelet, RBC, hemoglobin, and hematocrit levels vs JAK2 V617F–positive mice. Concurrent expression of JAK2 V617F and SRSF2 P95H mutations resulted in higher mean corpuscular volume values and reduced spleen size and weight vs JAK2 V617F–positive mice, which displayed splenomegaly.

JAK2 V617F–positve mice bone marrow sections had hypercellularity with significant increase in erythroid precursors and megakaryocyte clusters compared with JAK2 V617F/SRSF2 P95H–positive mice, which had normal bone marrow cellularity and a reduction of erythroid precursors and megakaryocyte clusters. At 24 weeks, reticulin staining of bone marrow of SRSF2 P95H/JAK2 V617F–positive mice did not reveal fibrosis; bone marrow fibrosis was also not observed at 1 year following induction.

Together, JAK2 V617F and SRSF2 P95H mutations significantly reduced LSK, short- and long-term hematopoietic stem cell, and multipotent progenitor counts in the bone marrow of mice with both alterations. In comparison, mice with only JAK2 V617F mutations had increased frequencies and totals in terms of LSK, short- and long-term hematopoietic stem cells, and multipotent progenitors. The presence of both mutations also resulted in decreased frequency and total numbers of myeloid progenitors, common myeloid progenitors, granulocyte-macrophage progenitors, and megakaryocyte-erythroid progenitors in the bone marrow compared with mice with JAK2 V617F mutations alone.

Expression of an SRSF2 P95H mutation was also found to reduce the competitiveness of JAK2 V617F hematopoietic stem/progenitor cells. Mice that received of JAK2 V617F–positive bone marrow displayed significantly higher percentages of GFP-positive granulocyte, erythroid, megakaryocyte, B-lymphocyte, and T-lymphocyte cells in the peripheral blood compared with those that received SRSF2 P95H/ JAK2 V617F–positive bone marrow. Reduced percentages of the same hematopoietic stem/progenitor cells were observed in the bone marrow of mice that received SRSF2 P95H/ JAK2 V617F–positive bone marrow vs JAK2 V617F–positive bone marrow.

“Similar observations [to ours] have been made in a recent study by Willekens et al. Additional mutations or genetic abnormalities are required in association with SRSF2 P95H and JAK2 V617F mutations in the development of full-blown myelofibrosis,” the study investigators concluded.

Reference

Yang Y, Abbas S, Sayem MA, et al. SRSF2 mutation reduces polycythemia and impairs hematopoietic progenitor functions in JAK2V617F-driven myeloproliferative neoplasm. Blood Cancer J. 2023;13(1):171. doi:10.1038/s41408-023-00947-y

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AI Model May Help Distinguish Between Two Rare Hematologic Malignancies

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By The ASCO Post Staff

Posted: 12/13/2023

A novel artificial intelligence (AI) model may help physicians distinguish and identify prefibrotic primary myelofibrosis from essential thrombocythemia, according to new findings presented by Srisuwananukorn et al at the 2023 American Society of Hematology (ASH) Annual Meeting and Exposition (Abstract 901).

Background

Myeloproliferative neoplasms are a type of cancer in which the bone marrow overproduces certain types of blood cells. Prefibrotic primary myelofibrosis is rarer and has a much worse prognosis than essential thrombocythemia—with a median survival of 12 years vs 22 years, respectively. As a result, prefibrotic primary myelofibrosis may require more aggressive treatment; however, experts may not always agree on a definitive diagnosis when interpreting laboratory and biopsy results.

Despite being integral to informing treatment approaches and enrolling patients in clinical trials, distinguishing the two hematologic malignancies is often challenging with current diagnostic methods.

Study Methods and Results

In the new study, researchers used a novel AI model—which had previously been trained with 32,000 pan-cancer biopsy images and was familiar with general pathologic features—to analyze images from U.S. and Italian patients in order to differentiate between prefibrotic primary myelofibrosis and essential thrombocythemia.

To aid diagnosis, the researchers trained an AI model to distinguish features indicating the two conditions in bone marrow biopsy images from 200 patients. They then tested the model’s ability to differentiate the two types of myeloproliferative neoplasms in biopsies from 26 additional patients.

The researchers found that the AI model was able to return results in an average of just over 6 seconds for a new patient and performed well, demonstrating a 92.3% rate of agreement with human experts. The sensitivity and specificity for prefibrotic primary myelofibrosis diagnosis was 66.6% and 100%, respectively.

Conclusions

“With the combined accuracy, sensitivity, and specificity we saw, it would allow the physician to be confident in one diagnosis vs another and help rule in or rule out the rarer [prefibrotic primary myelofibrosis] diagnosis, particularly for clinical trials,” emphasized lead study author Andrew Srisuwananukorn, MD, Assistant Professor at The Ohio State University Comprehensive Cancer Center. “[Our] hope is that it would maintain this accuracy when tested in larger cohorts,” he added.

The researchers hope that with further testing, the novel AI model could potentially be used as a companion tool for clinical diagnoses and may help physicians match patients with the most appropriate clinical trials—which could result in more effective treatments. However, the researchers cautioned that the model was intended to complement, not replace, human experts.

“What we’re trying to develop is a clinical decision support tool, with an emphasis on support. Physicians with no computer science backgrounds are increasingly recognizing the value of AI [models] and closer to being able to use them for their clinical practice. [M]ore investigations would be needed for this [model] to be used in clinical practice, including testing in cohorts with different racial backgrounds,” underscored Dr. Srisuwananukorn.

The researchers plan to continue refining the AI model and hope to test it with larger data sets. The researchers concluded that AI models could potentially be utilized in the advancement of basic research on myeloproliferative neoplasms to link biologic processes with particular morphological features visible on biopsy slides and develop strategies to predict prognoses or response to treatment.

Disclosure: For full disclosures of the study authors, visit ash.confex.com.

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.

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Fedratinib Demonstrates Promising Efficacy in MDS/MPN and Chronic Neutrophilic Leukemia

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Janelle Bradley

12/09/2023

Fedratinib demonstrates promising clinical activity in patients with myelodysplastic syndrome (MDS)/myeloproliferative neoplasms (MPN) and chronic neutrophilic leukemia (CNL), according to data presented at the 2023 ASH Annual Meeting.

Fedratinib is a JAK2 inhibitor that is currently approved by the FDA for the treatment of higher-risk myelofibrosis. Given fedratinib potential inhibition of FLT3 and BRD4 and suppression of c-Myc expression, researchers hypothesize that the drug could have biologic relevance in MDS/MPN.

Andrew Kuykendall, MD, Moffitt Cancer Center, Tampa, Florida, presented results from the ongoing phase 2, multinational investigator-initiated clinical trial that evaluated the efficacy of fedratinib in atypical chronic myeloid leukemia (CML), CNL, MDS/MPN-unclassifiable, and MDS/MPN-ring sideroblasts and thrombocytosis. The primary end point of this trial is overall response rate, which is defined as complete or partial response or clinical benefit at 24 weeks.

Bone marrow was collected at baseline and week 24 and stained for c-Myc. C-myc expression was scored by multiplying the percentage of positive cells by intensity.

Eligible patients had splenomegaly ≥5 cm below left costal margin or ≥450 cc and/or an MPN total symptom score ≥10. Patients were excluded if they had a platelet count higher than 35 x109/L or peripheral peripheral/marrow blasts >10%. The planned trial enrollment is 25 patients with an interim analysis completed after 9 patients are eligible for efficacy.

At data cutoff, 10 patients have been enrolled in the trial (1 with atypical CML, 4 with CNL, 4 with MDS/MPN-ring sideroblasts and thrombocytosis, and 1 with MDS/MPN-unclassifiable) with a median follow-up of 5 months. Of whom, 8 patients remain on treatment.

Overall, 5 patients were evaluable for response. Of whom, 3 had a response at week 24, including 3 symptom responses and 1 spleen response. A total of 6 patients completed 12 weeks of treatment with 1 spleen response and 2 symptom responses. Among these 6 patients, spleen volume decreased in 5 by an average of -23%. Among 5 patients with significant baseline symptom burden, 4 experienced an improvement in symptom burden by an average of -43%.

IHC staining was done in a median of 10% of cells to demonstrate c-Myc expression at baseline. The average baseline c-Myc expression was 26.5. Among 4 patients with paired samples, c-Myc expression decreased in all cases by an average of 51% (P = .02).

For safety analysis, 10 patients were evaluable. The most common adverse events (AEs) were anemia, platelet count decrease, diarrhea, nausea, muscle cramp, and constipation. Grade ≥3 AEs included anemia and neutropenia. One patient discontinued treatment due to disease progression after initial response and another due to patient decision unrelated to disease or treatment.

“Fedratinib demonstrates promising clinical efficacy in MDS/MPN and CNL patients with proliferative features. The safety profile is consistent with prior experience,” concluded Dr Kuykendall and colleagues, adding “fedratinib’s unique kinase inhibition profile may provide a mechanism for enhanced effectiveness in this patient population.”

Source:

Kukendall AT, Pettit KM, Singh A, et al. A Phase 2 Study of Fedratinib in Patients with MDS/MPN and Chronic Neutrophilic Leukemia; December 9-12, 2023; San Diego, CA. Abstract 73.

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No Significant Differences in Outcomes Seen Between Hydroxyurea and IFNα in Patients With MPNs

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December 12, 2023

John Schieszer

The final analysis of the DALIAH trial (ClinicalTrials.gov Identifier: NCT01387763), which was presented at the ASH Annual Meeting 2023, showed no significant differences with hydroxyurea (HU) and pegylated interferon-alpha2 (IFNα) in patients with myeloproliferative neoplasms (MPNs) receiving long-term treatment.

This modified intention-to-treat (ITT) analysis detected no significant difference in the molecular response (MR) or clinical hematologic response (CHR) rates between HU and IFNα with long-term treatment among patients with MPNs.

However, a higher treatment discontinuation rate in the IFNα group (65%) was noted, and when using the per-protocol principle the MR or CHR rates were superior in the IFNα group at 36 months and beyond. The study authors also noted that increasing evidence on the efficacy and safety of IFNα is emerging.

The DAHLIA study was a randomized phase III trial of HU versus IFNα in newly diagnosed or untreated patients with MPN. The cohort included a total of 203 patients with essential thrombocythemia (ET), polycythemia vera (PV), prefibrotic myelofibrosis (PreMF), and primary myelofibrosis (PMF).

All participants older than 60 years were randomly assigned (1:1:1) to HU, IFNα-2a, or IFNα-2b. Participants who were 60 years or younger were randomly assigned to receive IFNα-2a or IFNα-2b. The primary outcome was the JAK2V617F MR rate at 18 months, at 36 months, and at 60 months per 2009 European LeukemiaNetwork (ELN; ET, PV, PreMF) or 2005 European Myelofibrosis Network (EUMNET; PMF) criteria.

The 203 patients in the modified ITT cohort were made up of ET, 73 (36%); PV, 89 (44%); PreMF, 16 (8%); and PMF, 25 (12%). The baseline characteristics were well balanced in the different groups. However, the median age varied (HU, 68 years vs IFNα, 59 years; P <.0001).

The MR rate by ITT analysis was similar between HU and IFNα at 18 months (19% vs 21%), at 36 months (19% vs 26%) and at 60 months (23% vs 24%). However, the JAK2V617F allele burden was significantly lower in the IFNα group at month 36 and beyond.

The CHR rate by ITT analysis was higher with HU at 18 months (58% vs 38%, P =.03) but similar at all other time points. Comparable efficacy results were found in a post hoc subgroup analysis comparing HU with IFNα in patients older than 60 years. However, the MR and CHR rates were superior in the IFNα group compared to the HU group at 36 months and beyond among patients remaining on treatment.

The MR rates by per-protocol analysis were 23% HU versus 56% IFNα at 36 months, 27% HU versus 59% INFa at 48 months and 35% HU versus 67% INFα at 60 months. The CHR rates were significantly different at 36 months (33% HU versus 67% INFα) and at 60 months (38% HU versus 62% INFα).

Overall treatment discontinuation at 60 months was 60% (HU, 37%; IFNα, 65%; P =.0019). The most common cause of treatment discontinuation was adverse events (AEs; HU, 16%; IFNα, 43%). More AEs ≥ grade 3 occurred in HU (58%) vs IFNα (45%). In 16 patients, 19 major thrombotic events were reported (4 events in 4 patients with HU; 12 events in 10 patients with IFNα in patients older than 60 years; 3 events in 2 patients with IFNα in patients 60 years or younger).

No participants had their disease morph into secondary acute myeloid leukemia; however, 5 patients died during follow-up (HU, 2; IFNα, 3).

Disclosures: Some study authors declared affiliations with biotech, pharmaceutical, or device companies. Please see the original reference for a full list of disclosures.

Reference

Knudsen TA, Lund Hansen D, Frans Ocias L, et al. Final analysis of the Daliah trial: a randomized phase III trial of interferon-α versus hydroxyurea in patients with MPN. Abstract 746.

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The Potential of Tasquinimod in Treating Advanced Myeloproliferative Neoplasms: A Glimpse into the Future

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By:Anthony Emecheta
12 Dec 2023

Dr. Warren Fiskus, a renowned hematologist, recently presented the results of a promising study at #ASH23. His research focused on the use of tasquinimod, a novel drug, in preclinical models of advanced myeloproliferative neoplasms (MPNs). The findings suggest that tasquinimod warrants further investigation as a potential treatment for these rare but severe conditions.

Understanding Myeloproliferative Neoplasms (MPNs)

Before delving into the specifics of the study, it’s crucial to understand what MPNs are. These are rare forms of blood cancers that occur when the bone marrow, the body’s cell powerhouse, produces an excess of red blood cells, platelets, or certain white blood cells. This overproduction disrupts the balance of cells in the blood, leading to various symptoms and complications. The primary subtypes of MPNs include myelofibrosis, polycythemia vera, and essential thrombocythemia. Each condition is unique and presents its own set of challenges.

Myelofibrosis: A Closer Look

Myelofibrosis (MF), a primary subtype of MPNs, is a particularly severe condition. It causes scarring in the bone marrow, which hinders the normal production of blood cells. In some cases, MF is a secondary development following a diagnosis of polycythemia vera (PV) or essential thrombocythemia (ET). The risk factors for primary MF are not entirely clear, however, a history of PV or ET are known risk factors for the development of secondary MF. The disease is categorized as low, intermediate, or high risk, based on various International Prognostic Scoring System scales. The prognosis depends on individual risk factors, including age, comorbidities, and the response to treatment.

Tasquinimod: A Beacon of Hope

Enter tasquinimod. Dr. Fiskus’ study explored the efficacy of this drug in preclinical models of advanced MPNs. The findings were encouraging, suggesting that tasquinimod may present a viable treatment option for these conditions. While the research is in its early stages, this represents a significant step forward in the search for effective therapies for these severe diseases.

Implications and Next Steps

The positive results from Dr. Fiskus’ study indicate that tasquinimod should be further investigated as a potential treatment for advanced MPNs. More comprehensive studies are required to assess the drug’s safety, tolerability, and efficacy in a broader patient population. Additionally, further research is needed to identify the best ways to integrate tasquinimod into the current treatment landscape. This could involve using the drug as a standalone therapy or in combination with other treatments.

Overall, the findings from Dr. Fiskus’ study at #ASH23 bring a glimmer of hope for patients suffering from advanced MPNs. While there’s still a long road ahead, the potential of tasquinimod offers a new avenue for exploration in the quest to #EndCancer.

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