Plasma proteomics of biomarkers for inflammation or cancer cannot predict relapse in chronic myeloid leukaemia patients stopping tyrosine kinase inhibitor therapy

Switching from imatinib to nilotinib plus pegylated interferon-α2b in chronic phase CML failing to achieve deep molecular response: clinical and immunological effects

In order to improve molecular response for a discontinuation attempt in chronic myeloid leukemia (CML) patients in chronic phase, who had not achieved at least a molecular response <0.01% BCR-ABL1^IS (MR^4.0) after at least 2 years of imatinib therapy, we prospectively evaluated whether they could attain MR^4.0 after a switch to a combination of nilotinib and 9 months of pegylated interferon-α2b (PegIFN). The primary endpoint of confirmed MR^4.0 at month 12 (a BCR-ABL1^IS level ≤ 0.01% both at 12 and 15 months) was reached by 44% (7/16 patients, 95% confidence interval (CI): 23- 67%) of patients, with 81% (13/16 patients, 95% CI: 57-93%) of patients achieving an unconfirmed MR^4.0. The scheduled combination was completed by 56% of the patients, with premature discontinuations, mainly due to mood disturbances after the introduction of PegIFN, questioning the feasibility of the combination of nilotinib and PegIFN for this patient population and treatment goal. A comprehensive clinical substudy program was implemented to characterize the impact of the treatment changes on the immunological profile. This trial was registered at www.clinicaltrials.gov as #NCT01866553.

Multiplex proteomics using proximity extension assay for the identification of protein biomarkers predictive of acute graft-vs.-host disease in allogeneic hematopoietic cell transplantation

OBJECTIVES

Allogeneic hematopoietic cell transplantation (HCT) is associated with acute graft-vs.-host disease (aGVHD). The presented study applied a novel multiplex antibody-based proximity extension assay (PEA) proteomic platform that can detect thousands of serum proteins simultaneously for the identification of potential biomarkers of aGVHD.

METHODS

Serum samples from 28 patients who underwent allogeneic HCT for acute myeloid leukemia (AML) were analyzed; 17 were diagnosed with grade II-IV aGVHD while 11 patients were not. Samples collected on day -6, day 0, +14, +30, +60 and +90 post-HCT were analyzed for the relative concentrations of 552 proteins. The concentration of each protein from baseline to the closest time point before onset of aGVHD, or to the latest time point in control patients, was documented.

RESULTS

Individualized analysis identified 26 proteins demonstrating ≥3-fold increase at aGVHD onset compared to baseline, eliminating proteins with a similar increase in controls. Another approach used paired t-testing and logistic regression that identified a four-marker panel, including SLAMF7, IL-1ra, BTN3A2 and DAB2, where individual log-likelihood ratios ranged from 3.99 to 8.15 (logistic regression, p=0.004-0.046). When combined, the four-marker panel demonstrated an area under the curve (AUC) of 0.90 (95% CI: 0.78-1.00; p=0.0006) with high negative predictive value of 81.8% and positive predictive value of 86.7%. All four markers play a physiological role in immune regulation. Among these, three were also present in the individualized analysis (SLAMF7, IL-1ra and BTN3A2).

CONCLUSIONS

We conclude that serum proteins identified using multiplex proteomics, particularly SLAMF7, IL-1ra, BTN3A2 and DAB2, may potentially predict aGVHD.

Cross-Domain Text Mining to Predict Adverse Events from Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia

Tyrosine kinase inhibitors (TKIs) are prescribed for chronic myeloid leukemia (CML) and some other cancers. The objective was to predict and rank TKI-related adverse events (AEs), including under-reported or preclinical AEs, using novel text mining. First, k-means clustering of 2575 clinical CML TKI abstracts separated TKIs by significant (p < 0.05) AE type: gastrointestinal (bosutinib); edema (imatinib); pulmonary (dasatinib); diabetes (nilotinib); cardiovascular (ponatinib). Next, we propose a novel cross-domain text mining method utilizing a knowledge graph, link prediction, and hub node network analysis to predict new relationships. Cross-domain text mining of 30+ million articles via SemNet predicted and ranked known and novel TKI AEs. Three physiology-based tiers were formed using unsupervised rank aggregation feature importance. Tier 1 ranked in the top 1%: hematology (anemia, neutropenia, thrombocytopenia, hypocellular marrow); glucose (diabetes, insulin resistance, metabolic syndrome); iron (deficiency, overload, metabolism), cardiovascular (hypertension, heart failure, vascular dilation); thyroid (hypothyroidism, hyperthyroidism, parathyroid). Tier 2 ranked in the top 5%: inflammation (chronic inflammatory disorder, autoimmune, periodontitis); kidney (glomerulonephritis, glomerulopathy, toxic nephropathy). Tier 3 ranked in the top 10%: gastrointestinal (bowel regulation, hepatitis, pancreatitis); neuromuscular (autonomia, neuropathy, muscle pain); others (secondary cancers, vitamin deficiency, edema). Results suggest proactive TKI patient AE surveillance levels: regular surveillance for tier 1, infrequent surveillance for tier 2, and symptom-based surveillance for tier 3.

Emerging concepts for assessing and predicting treatment-free remission in chronic myeloid leukemia patients

INTRODUCTION

In chronic myeloid leukemia (CML) patients who have reached a deep and sustained reduction of residual disease can attempt a discontinuation. The 'treatment-free remission' (TFR) has become a real long-term endpoint for 30-40% of chronic phase patients.

AREAS COVERED

In this review, we focus our attention on possible prognostic features who can predict the success of tyrosine kinase inhibitors discontinuation and how we can assess the minimal residual disease (MRD) during the TFR phase. Broad research was made on Medline, Embase and archives from EHA and ASH congresses.

EXPERT OPINION

Median duration of TKI therapy and of deep molecular response are the main prognostic factors identified in most trials and real-life experiences on discontinuation. Immunological pathways have been proposed as possible control on successful TFR as also early molecular response dynamics. Appropriate molecular monitoring by RQ-PCR in the TFR phase has been proposed by several international recommendations and digital droplet PCR (ddPCR) seems to have a possible role in the future for a better identification of candidate to this possible therapeutic strategy.

Making Treatment-Free Remission (TFR) Easier in Chronic Myeloid Leukemia: Fact-Checking and Practical Management Tools

In chronic-phase chronic myeloid leukemia (CML), tyrosine kinase inhibitors (TKIs) are the standard of care, and treatment-free remission (TFR) following the achievement of a stable deep molecular response (DMR) has become, alongside survival, a primary goal for virtually all patients. The GIMEMA CML working party recently suggested that the possibility of achieving TFR cannot be denied to any patient, and proposed specific treatment policies according to the patient's age and risk. However, other international recommendations (including 2020 ELN recommendations) are more focused on survival and provide less detailed suggestions on how to choose first and subsequent lines of treatment. Consequently, some grey areas remain. After literature review, a panel of Italian experts discussed the following controversial issues: (1) early prediction of DMR and TFR: female sex, non-high disease risk score, e14a2 transcript and early MR achievement have been associated with stable DMR, but the lack of these criteria is not sufficient to exclude any patient from TFR; (2) criteria for first and subsequent line therapy choice: a number of patient and drug characteristics have been proposed to make a personalized decision; (3) monitoring of residual disease after discontinuation: after the first 6 months, the frequency of molecular tests can be reduced based on MR4.5 persistence and short turnaround time; (4) prognosis of TFR: therapy and DMR duration are important to predict TFR; although immunological control of CML plays a role, no immunological predictive phenotype is currently available. This guidance is intended as a practical tool to support physicians in decision making.

Identification of Immunological Parameters as Predictive Biomarkers of Relapse in Patients with Chronic Myeloid Leukemia on Treatment-Free Remission

BCR-ABL is an aberrant tyrosine kinase responsible for chronic myeloid leukemia (CML). Tyrosine kinase inhibitors (TKIs) induce a potent antileukemic response mostly based on the inhibition of BCR-ABL, but they also increase the activity of Natural Killer (NK) and CD8+ T cells. After several years, patients may interrupt treatment due to sustained, deep molecular response. By unknown reasons, half of the patients relapse during treatment interruption, whereas others maintain a potent control of the residual leukemic cells for several years. In this study, several immunological parameters related to sustained antileukemic control were analyzed. According to our results, the features more related to poor antileukemic control were as follows: low levels of cytotoxic cells such as NK, (Natural Killer T) NKT and CD8±TCRγβ+ T cells; low expression of activating receptors on the surface of NK and NKT cells; impaired synthesis of proinflammatory cytokines or proteases from NK cells; and HLA-E*0103 homozygosis and KIR haplotype BX. A Random Forest algorithm predicted 90% of the accuracy for the classification of CML patients in groups of relapse or non-relapse according to these parameters. Consequently, these features may be useful as biomarkers predictive of CML relapse in patients that are candidates to initiate treatment discontinuation.