Despite widespread use of Pegylated forms of Inteferon in the management of Myeloproliferative Neoplasms (MPN), most clinicians have experience predominantly with peginterferon alfa-2a (Pegasys). ...Third generation pegylated IFNα, ropeginterferon alfa-2b (ropegIFN; Besremi), was recommended by the European Medicine Authority (EMA) for treatment of Polycythaemia Vera (PV) following a Phase III trial (PROUD-PV / CONTINUATION-PV). FDA approval for PV, regardless of treatment history, was subsequently granted in November 2021. We hereby demonstrate the safety and tolerability of ropegIFN in a series of MPN patients at variable doses. It corroborates reports of efficacy of ropegIFN in patients with PV and use in pregnancy.
Summary
The search for novel targets in chronic myeloid leukaemia (CML) is ongoing, to improve treatment efficacy in refractory disease and increase eligibility for tyrosine kinase inhibitor (TKI) ...discontinuation. Increased frequency of Tregs and effector Tregs was evident at diagnosis, together with increased expression of T‐cell exhaustion markers, including in regulatory T cells at diagnosis and in patients with refractory disease. Plasma analysis revealed significantly increased levels of cytokines including tumour necrosis factor (TNF)‐a and interleukin (IL)‐6 at diagnosis, in keeping with a pro‐inflammatory state prior to treatment. We hence demonstrate T‐cell exhaustion and a pro‐inflammatory state at diagnosis in CML, likely secondary to leukaemia‐associated antigenic overload associated with increased disease burden.
Introduction:
The search for potential new targets to improve outcomes in patients with CML is ongoing, with a view to improve treatment efficacy for those with refractory disease and increase the ...proportion of those eligible to attempt TKI discontinuation. CML is recognised as a particularly immune sensitive tumour and as such immune checkpoint inhibitors, which can enhance inherent immune surveillance mechanisms are an attractive proposition.
Methods:
We performed flow cytometric analysis of peripheral blood mononuclear cells for expression of PD1, CTLA-4, TIM-3 and LAG-3 on T effectors and regulatory T cells. T effectors included CD4+ and CD8+ subsets and a gating strategy of CD4+/CD25+/CD127 lo/FOXP3+ cells for Tregs was employed, whilst FOXP3 hi/CD45RA-ve cells denoted effector Tregs. FMO controls were used to determine positive populations for each immune checkpoint molecule under investigation.
Results:
Samples from 22 patients were analysed, including samples from two different time points in 4 patients. This included patients at diagnosis (n=8), those with refractory disease defined as <CCyR (n=3) and those with a response of MMR (n=6) and MR4 or greater (n=9). Patients with a response of MMR or greater were considered to have low disease burden.
Patients at diagnosis had higher Tregs as proportion of total CD4+ cells compared to those with low disease burden with a mean of 6.3 vs 4.6 (p=0.048). Similarly, effector Tregs, the most functionally suppressive Treg subset, were also higher at diagnosis at 10.3 vs 5.4 (p=0.05). No differences were observed in frequency of Tregs between refractory and low disease burden groups.
PD1 expression was higher at diagnosis in CD4+, CD8+ cells and Tregs when compared to those with low disease burden (4.75 vs 2.75, 5.85 vs 2.43 and 5.2 vs 2.78, p=0.034/p=0.003/p=0.002). Similarly, TIM3 expression was significantly higher at diagnosis compared to at low disease burden; in CD4+ at 6 vs 1.9, CD8+ at 15.71 vs 4.41 and Tregs at 5.15 vs 1.88 (p=0.027/p=<0.001/p=0.002). LAG3 expression was higher at diagnosis in CD8+ 6.67 vs 1.4 and Tregs 2.13 vs 0.68 (p=0.031/p=0.025). No significant differences were observed in CTLA4 expression between these groups although a trend towards significance was observed in Tregs with 2.36 vs 0.67 (p=0.055).
Despite low sample size, PD1 expression was also significantly higher in refractory patients compared to those with low disease burden, in CD4+ cells at 5.34, and Tregs at 5.6 (p=0.031/p=0.026). TIM3 expression was higher in CD8+ subset only at 11.74 (p=0.028). LAG3 showed higher expression in CD4+ cells at 1.56 vs 0.45 and in CD8+ cells at 8.81 (p=0.004/P=<0.001). Finally, CTLA-4 also showed higher expression in CD4+ cells at 1.59 vs 0.47 and Tregs at 1.99 vs 0.67 (p=0.026/p=0.032).
One patient was analysed at diagnosis and then again after achieving MMR following dasatinib treatment for 11 months. A significant trend of downregulation of immune checkpoint expression with successful TKI treatment was observed. For example, Treg expression of PD1 decreased from 7.03 to 4.86, TIM3 decreased from 2.57 to 0.83 and LAG3 decreased from 2.23 to 0.16 (Figure 1a), with CTLA4 remaining at a similar level. Similarly, another patient was analysed whilst in MMR and then again following haematologic relapse, one week after regaining CHR with ponatinib. In contrast, this patient showed evidence of upregulation of checkpoint molecules, particularly in TIM3 expression increasing from 9.2 to 22.2 in CD4+ cells and from 12.6 to 20.1 in CD8+ cells (Figure 1b).
Discussion:
We have shown through an extended analysis of immune checkpoint expression on lymphocytes from blood samples of patients with CML, that expression correlates strongly with leukaemia disease burden. We provide the first report, to our knowledge, to describe the increased expression of TIM3 and LAG3 in peripheral blood lymphocytes, which is of significance given the recent development of inhibitors of these molecules.
These data support the potential future use of immune checkpoint inhibitors in certain patients with high-risk disease at diagnosis as well as addition in those with inadequate response, alongside conventional TKI therapy. Moreover, these data provide a potential basis for the use of combination immune checkpoint blockade which has proven highly efficacious in other settings. Further laboratory and clinical studies evaluating these agents in CML are warranted.
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Harrington: Bristol Myers Squibb: Research Funding; Incyte: Honoraria. Dillon: Shattuck Labs: Membership on an entity's Board of Directors or advisory committees; Menarini: Membership on an entity's Board of Directors or advisory committees; Astellas: Consultancy, Other: Educational Events , Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Other: Session chair (paid to institution), Speakers Bureau; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: educational events; Jazz: Other: Education events; Amgen: Other: Research support (paid to institution); Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Research Support, Educational Events. Radia: Cogent Biosciences Incorporated: Other: Study Steering Committee; Blueprint Medicines Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Study steering group member, Research Funding; EXPLORER and PATHFINDER studies: Other: Member of the Response Adjudication Committee; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Education events. Kordasti: Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Alexion: Honoraria; Beckman Coulter: Honoraria. Harrison: Shire: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Keros: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead Sciences: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Galacteo: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Geron: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: 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; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Constellation Pharmaceuticals: Research Funding; Incyte Corporation: Speakers Bureau; Sierra Oncology: Honoraria; AOP Orphan Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Promedior: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. de Lavallade: Incyte: Honoraria, Research Funding; Novartis: Speakers Bureau; Bristol Myers Squibb: Research Funding.
Introduction: Fundamental to management of patients with essential thrombocythaemia (ET) is assessment, and reduction of thrombotic risk. We present a machine learning approach to summarise patient ...electronic health records (EHR) to determine prevalence of cardiovascular comorbidities and risk factors. We then review use of the QRISK-3 score to assess cardiovascular risk. Methods: We used a natural language processing (NLP) pipeline to identify mentions of hypertension (HTN), hypercholesterolaemia (HC), diabetes mellitus (DM), smoking (SM), unspecified thrombosis (VTE), deep vein thrombosis (DVT), pulmonary embolism (PE), portal vein thrombosis (PVT), myocardial infarction (MI) and stroke (CVA) in EHR. CogStack is an information retrieval and extraction architecture incorporating structured and unstructured EHR components. Data extracted from CogStack was processed by a medical concept annotation toolkit (MedCAT). MedCAT was used to disambiguate and capture synonyms and acronyms for Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) concepts. Using deep learning MedCAT determined linguistic and grammatical context such as negation, subject, and temporality. The base MedCAT model was trained in an unsupervised manner on >18 million EHR documents and this was further fine-tuned through 500 clinician annotated haematology documents. MedCAT mapped mentions of relevant concepts to respective SNOMED-CT codes and total counts were aggregated and grouped by individual patient. Manual validations were performed and an optimizer was applied to convert counts to a binary state by applying a threshold, above which a patient's condition was inferred to be present (Fig. i). QRISK-3 is an advanced validated score incorporating age, ethnicity, body mass index and other cardiovascular risk factors to determine 10-year cardiovascular risk in people aged 25-84. Results: 12905 documents from 560 ET patients were reviewed (median 20 per patient, IQR 8-34). In the manual validation dataset (n=120), MedCAT achieved excellent real-world F1 scores (model accuracy) for most concepts (HTN 0.91, HC 0.81, DM 1.0, VTE 0.73, CVA 0.87 and MI 0.67). Using a threshold of >2 mentions to define a positive population; HTN was identified in 21.3% (119) of patients, DM in 4.6% (26), MI in 3.6% (20), CVA in 7.7% (43), VTE in 8% (45), DVT in 1.4% (8), PE in 1.8% (10), PVT in 1.3% (7) and positive smoking status in 6.6% (37). HC was identified in 9.6% (54) using a threshold >1. 52% (28) of patients with HC and 69.2% (18) of those with DM also had HTN. Obesity was not identified in any patients using this approach. Patients with a diagnosis of HTN were more likely to have CVA than those without (15:104 vs 28:413, p=0.03). Patients with HTN were also more likely to have VTE (13:106 vs 19:422, p=0.01). Of patients with CVA/MI; 58.1% (25) /55% (11) had this event pre or at diagnosis and 30.2% (13)/ 10% (2) while receiving cytoreductive therapy. QRISK-3 analysis was performed in 32 patients with prior thrombosis and baseline criteria to evaluate predictive value; then 137 patients classified as low or intermediate (LIM) risk and not receiving cytoreductive therapy. Mean QRISK-3 was 8 in the thrombosis group, validating its relevance, and 2.5 (p<0.0001, Fig. ii) in the LIM cohort. Using the recognised QRISK-3 score threshold of >7.5 to define a high-risk population, 5.1% (7) patients from the LIM group were reclassified as high-risk due to additional comorbidities relevant to QRISK-3 including HTN in 8% (11), migraine 7.3% (10), DM 2.2% (3), severe mental illness 2.9% (4) and antipsychotic medication 0.7% (1). Discussion: We describe a novel approach to cardiovascular risk assessment in patients with ET, incorporating machine learning, allowing large volume data analysis, and detailed risk assessment using QRISK-3 scoring. We provide a rare ‘real-world’ report on the prevalence of comorbidities in this group, confirming increased CVA and VTE in patients with HTN. A previous report of 891 patients with ET showed prevalence of 5% for CVA, 2% for MI and 4% for VTE, suggesting that detection rate using our approach is within acceptable limits (Carobbio et al., Blood, 2011). Finally, as a novel finding, we show that QRISK-3 scoring is predictive of increased thrombotic risk and identifies a small group of patients who should be considered high-risk and may benefit from cytoreductive therapy, that are not detected using standard approaches.
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