Biomarkers for determining the prognosis in chronic myelogenous leukemia

Bayesian Learning of Personalized Longitudinal Biomarker Trajectory

This work concerns the effective personalized prediction of longitudinal biomarker trajectory, motivated by a study of cancer targeted therapy for patients with chronic myeloid leukemia (CML). Continuous monitoring with a confirmed biomarker of residual disease is a key component of CML management for early prediction of disease relapse. However, the longitudinal biomarker measurements have highly heterogeneous trajectories between subjects (patients) with various shapes and patterns. It is believed that the trajectory is clinically related to the development of treatment resistance, but there was limited knowledge about the underlying mechanism. To address the challenge, we propose a novel Bayesian approach to modeling the distribution of subject-specific longitudinal trajectories. It exploits flexible Bayesian learning to accommodate complex changing patterns over time and non-linear covariate effects, and allows for real-time prediction of both in-sample and out-of-sample subjects. The generated information can help make clinical decisions, and consequently enhance the personalized treatment management of precision medicine.

Targeting FLT3-TAZ signaling to suppress drug resistance in blast phase chronic myeloid leukemia

BACKGROUND

Although the development of BCR::ABL1 tyrosine kinase inhibitors (TKIs) rendered chronic myeloid leukemia (CML) a manageable condition, acquisition of drug resistance during blast phase (BP) progression remains a critical challenge. Here, we reposition FLT3, one of the most frequently mutated drivers of acute myeloid leukemia (AML), as a prognostic marker and therapeutic target of BP-CML.

METHODS

We generated FLT3 expressing BCR::ABL1 TKI-resistant CML cells and enrolled phase-specific CML patient cohort to obtain unpaired and paired serial specimens and verify the role of FLT3 signaling in BP-CML patients. We performed multi-omics approaches in animal and patient studies to demonstrate the clinical feasibility of FLT3 as a viable target of BP-CML by establishing the (1) molecular mechanisms of FLT3-driven drug resistance, (2) diagnostic methods of FLT3 protein expression and localization, (3) association between FLT3 signaling and CML prognosis, and (4) therapeutic strategies to tackle FLT3+ CML patients.

RESULTS

We reposition the significance of FLT3 in the acquisition of drug resistance in BP-CML, thereby, newly classify a FLT3+ BP-CML subgroup. Mechanistically, FLT3 expression in CML cells activated the FLT3-JAK-STAT3-TAZ-TEAD-CD36 signaling pathway, which conferred resistance to a wide range of BCR::ABL1 TKIs that was independent of recurrent BCR::ABL1 mutations. Notably, FLT3+ BP-CML patients had significantly less favorable prognosis than FLT3- patients. Remarkably, we demonstrate that repurposing FLT3 inhibitors combined with BCR::ABL1 targeted therapies or the single treatment with ponatinib alone can overcome drug resistance and promote BP-CML cell death in patient-derived FLT3+ BCR::ABL1 cells and mouse xenograft models.

CONCLUSION

Here, we reposition FLT3 as a critical determinant of CML progression via FLT3-JAK-STAT3-TAZ-TEAD-CD36 signaling pathway that promotes TKI resistance and predicts worse prognosis in BP-CML patients. Our findings open novel therapeutic opportunities that exploit the undescribed link between distinct types of malignancies.

Applications of Machine Learning in Chronic Myeloid Leukemia

Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm characterized by dysregulated growth and the proliferation of myeloid cells in the bone marrow caused by the BCR-ABL1 fusion gene. Clinically, CML demonstrates an increased production of mature and maturing granulocytes, mainly neutrophils. When a patient is suspected to have CML, peripheral blood smears and bone marrow biopsies may be manually examined by a hematologist. However, confirmatory testing for the BCR-ABL1 gene is still needed to confirm the diagnosis. Despite tyrosine kinase inhibitors (TKIs) being the mainstay of treatment for patients with CML, different agents should be used in different patients given their stage of disease and comorbidities. Moreover, some patients do not respond well to certain agents and some need more aggressive courses of therapy. Given the innovations and development that machine learning (ML) and artificial intelligence (AI) have undergone over the years, multiple models and algorithms have been put forward to help in the assessment and treatment of CML. In this review, we summarize the recent studies utilizing ML algorithms in patients with CML. The search was conducted on the PubMed/Medline and Embase databases and yielded 66 full-text articles and abstracts, out of which 11 studies were included after screening against the inclusion criteria. The studies included show potential for the clinical implementation of ML models in the diagnosis, risk assessment, and treatment processes of patients with CML.

Clinical Application of Biomarkers for Hematologic Malignancies

Over the last decade, significant advancements have been made in the molecular mechanisms, diagnostic methods, prognostication, and treatment options in hematologic malignancies. As the treatment landscape continues to expand, personalized treatment is much more important.

With the development of new technologies, more sensitive evaluation of residual disease using flow cytometry and next generation sequencing is possible nowadays. Although some conventional biomarkers preserve their significance, novel potential biomarkers accurately detect the mutational landscape of different cancers, and also, serve as prognostic and predictive biomarkers, which can be used in evaluating therapy responses and relapses. It is likely that we will be able to offer a more targeted and risk-adapted therapeutic approach to patients with hematologic malignancies guided by these potential biomarkers. This chapter summarizes the biomarkers used (or proposed to be used) in the diagnosis and/or monitoring of hematologic neoplasms.;

Bioactive Lipids as Chronic Myeloid Leukemia’s Potential Biomarkers for Disease Progression and Response to Tyrosine Kinase Inhibitors

Chronic myelogenous leukemia (CML) is a myeloproliferative neoplasm that expresses the Philadelphia chromosome and constitutively activated Bcr-Abl tyrosine kinase in hematopoietic progenitor cells. Bcr-Abl tyrosine-kinase inhibitors (TKI) do not definitively cure all CML patients. The efficacy of TKI is reduced in CML patients in the blastic phase—the most severe phase of the disease—and resistance to this drug has emerged. There is limited knowledge on the underlying mechanisms of disease progression and resistance to TKI beyond BCR-ABL1, as well as on the impact of TKI treatment and disease progression on the metabolome of CML patients. The present study reports the metabolomic profiles of CML patients at different phases of the disease treated with TKI. The plasma metabolites from CML patients were analyzed using liquid chromatography, mass spectrometry, and bioinformatics. Distinct metabolic patterns were identified for CML patients at different phases of the disease and for those who were resistant to TKI. The lipid metabolism in CML patients at advanced phases and TKI-resistant patients is reprogrammed, as detected by analysis of metabolomic data. CML patients who were responsive and resistant to TKI therapy exhibited distinct enriched pathways. In addition, ceramide levels were higher and sphingomyelin levels were lower in resistant patients compared with control and CML groups. Taken together, the results here reported established metabolic profiles of CML patients who progressed to advanced phases of the disease and failed to respond to TKI therapy as well as patients in remission. In the future, an expanded study on CML metabolomics may provide new potential prognostic markers for disease progression and response to therapy.

In silico tools and databases for designing cancer immunotherapy

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.

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.

Chimeric RNAs in cancer

Chimeric RNAs are hybrid transcripts containing exons from two separate genes. Chimeric RNAs are traditionally considered to be transcribed from fusion genes caused by chromosomal rearrangement. These canonical chimeric RNAs are well characterized to be expressed in a cancer-unique pattern and/or act as oncogene products. However, benefited by the development of advanced deep sequencing technologies, novel types of non-canonical chimeric RNAs have been discovered to be generated from intergenic splicing without genomic aberrations. They can be formed through trans-splicing or cis-splicing between adjacent genes (cis-SAGe) mechanisms. Non-canonical chimeric RNAs are widely detected in normal physiology, although several have been shown to have a cancer-specific expression pattern. Further studies have indicated that some of them play fundamental roles in controlling cell growth and motility, and may have functions independent of the parental genes. These discoveries are unveiling a new layer of the functional transcriptome and are also raising the possibility of utilizing non-canonical chimeric RNAs as cancer diagnostic markers and therapeutic targets. In this chapter, we will overview different categories of chimeric RNAs and their expression in various types of cancerous and normal samples. Acknowledging that chimeric RNAs are not unique to cancer, we will discuss both bioinformatic and biological methods to identify credible cancer-specific chimeric RNAs. Furthermore, we will describe downstream methods to explore their molecular processing mechanisms and potential functions. A better understanding of the biogenesis mechanisms and functional products of cancer-specific chimeric RNAs will pave ways for the development of novel cancer biomarkers and therapeutic targets.

(-)-Epigallocatechin-3-gallate induces cell apoptosis in chronic myeloid leukaemia by regulating Bcr/Abl-mediated p38-MAPK/JNK and JAK2/STAT3/AKT signalling pathways

Epigallocatechin-3-gallate (EGCG), a major polyphenolic constituent of green tea, possesses remarkable chemopreventive and therapeutic potential against various types of cancer, including leukaemia. However, the molecular mechanism involved in chronic myeloid leukaemia (CML), especially imatinib-resistant CML cells, is not completely understood. In the present study, we investigated the effect of EGCG on the growth of Bcr/Abl+ CML cell lines, including imatinib-resistant cell lines and primary CML cells. The results revealed that EGCG could inhibit cell growth and induce apoptosis in CML cells. The mechanisms involved inhibition of the Bcr/Abl oncoprotein and regulation of its downstream p38-MAPK/JNK and JAK2/STAT3/AKT pathways. In conclusion, we documented the anti-CML effects of EGCG in imatinib-sensitive and imatinib-resistant Bcr/Abl+ cells, especially T315I-mutated cells.

CDA gene silencing regulated the proliferation and apoptosis of chronic myeloid leukemia K562 cells

Background

As a disease of hematopoietic stem cell, chronic myeloid leukemia (CML) possesses unique biological and clinical features. However, the biologic mechanism underlying its development remains poorly understood. Thus, the objective of the present study is to discuss the effect of cytidine deaminase (CDA) gene silencing on the apoptosis and proliferation of CML K562 cells.

Methods

CDA mRNA expression was detected by reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and enzymatic activity of CDA was measured by a nuclide liquid scintillation method. RT-qPCR and Western blot analysis were used to detect CDA mRNA and protein expression. Cell proliferation, apoptosis and cell cycle were measured by CCK-8 assay and flow cytometry. The expression of proteins relevant to cell proliferation, apoptosis and cell cycle was measured by Western blot analysis. Tumor xenografts were implanted in nude mice to verify the effect of CDA silencing on tumor growth in vivo.

Results

CML and AL patients showed increased mRNA expression and enzymatic activity of CDA. Compared with the blank group, the mRNA and protein expression of CDA in the shRNA-1 and shRNA-2 groups decreased significantly. As a result, the proliferation of K562 cells was inhibited after CDA silencing and the cells were mainly arrested in S and G2 phases, while the apoptosis rate of these cells was increased. In addition, CDA gene silencing in K562 cells led to down-regulated p-ERK1/2, t-AKT, p-AKT and BCL-2 expression and up-regulated expression of P21, Bax, cleaved caspase-3/total caspase-3 and cleaved PARP/total PARP. Finally, CDA gene silencing inhibited tumor growth.

Conclusion

Our study demonstrated that CDA gene silencing could inhibit CML cell proliferation and induce cell apoptosis. Therefore, CDA gene silencing may become an effective target for the treatment of leukemia.

Dielectrophoretic analysis of treated cancer cells for rapid assessment of treatment efficacy

Whilst personalized medicine (where interventions are precisely tailored to a patient's genotype and phenotype, as well as the nature and state of the disease) is regarded as an optimal form of treatment, the time and cost associated with it means it remains inaccessible to the greater public. A simpler alternative, stratified medicine, identifies groups of patients who are likely to respond to a given treatment. This allows appropriate treatments to be selected at the start of therapy, avoiding the common "trial and error" approach of replacing a therapy only once it is demonstrated to be ineffective in the patient. For stratification to be effective, tests are required that rapidly predict treatment effectiveness. Most tests use genetic analysis to identify drug targets, but these can be expensive and may not detect changes in the phenotype that affect drug sensitivity. An alternative method is to assess the whole-cell phenotype by evaluating drug response using cells from a biopsy. We assessed dielectrophoresis to assess drug efficacy on short timescales and at low cost. To explore the principle of assessing drug efficacy we examined two cell lines (one expressing EGFR, one not) with the drug Iressa. We then further explored the sensitive cells using combinations of chemotherapeutic and radiotherapeutic therapies. Our results compare with known effects of these cell/treatment combination, and offer the additional benefit over methods such as TUNEL of detecting drug effects such as cell cycle arrest, which do not cause cell death.

Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.

An economic analysis of high-dose imatinib, dasatinib, and nilotinib for imatinib-resistant chronic phase chronic myeloid leukemia in China: A CHEERS-compliant article

BACKGROUND

The aim of the study was to test the cost-effectiveness of dasatinib compared to high-dose imatinib and nilotinib in Chinese patients who were diagnosed with imatinib-resistant chronic myeloid leukemia in the chronic phase (CML-CP).

METHODS

A Markov model combined with clinical effectiveness, utility, and cost data was used. The sensitivity analyses were conducted to determine the robustness of the model outcomes. The impact of patient assistance programs (PAPs) was assessed.

RESULTS

Treatment with dasatinib is expected to produce 3.65, 0.59, and 0.15 more quality-adjusted life years (QALYs) in comparison with high-dose imatinib (600 and 800 mg) and nilotinib, respectively. When a PAP was available, dasatinib yielded an incremental cost of $16,417 per QALY compared to imatinib (600 mg) and was cost-saving compared to imatinib (800 mg) and nilotinib.

CONCLUSION

When PAP is available in the Chinese setting, dasatinib is likely to be a cost-effective strategy for patients with CML-CP standard-dose imatinib resistance. The results should be carefully explained due to the assumptions and limitations used in the study.

Modelling Predictors of Molecular Response to Frontline Imatinib for Patients with Chronic Myeloid Leukaemia

BACKGROUND

Treatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction 'rules' for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia.

PRINCIPLE FINDINGS

The common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models.

Role of tyrosine-kinase inhibitors in myeloproliferative neoplasms: comparative lessons learned

An important pathogenetic distinction in the classification of myeloproliferative neoplasms (MPNs) is the presence or absence of the BCR–ABL fusion gene, which encodes a unique oncogenic tyrosine kinase. The BCR–ABL fusion, caused by the formation of the Philadelphia chromosome (Ph) through translocation, constitutes the disease-initiating event in chronic myeloid leukemia. The development of successive BCR–ABL-targeted tyrosine-kinase inhibitors has led to greatly improved outcomes in patients with chronic myeloid leukemia, including high rates of complete hematologic, cytogenetic, and molecular responses. Such levels of treatment success have long been elusive for patients with Ph-negative MPNs, because of the difficulties in identifying specific driver proteins suitable as drug targets. However, in recent years an improved understanding of the complex pathobiology of classic Ph-negative MPNs, characterized by variable, overlapping multimutation profiles, has prompted the development of better and more broadly targeted (to pathway rather than protein) treatment options, particularly JAK inhibitors. In classic Ph-negative MPNs, overactivation of JAK-dependent signaling pathways is a central pathogenic mechanism, and mutually exclusive mutations in JAK2, MPL, and CALR linked to aberrant JAK activation are now recognized as key drivers of disease progression in myelofibrosis (MF). In clinical trials, the JAK1/JAK2 inhibitor ruxolitinib – the first therapy approved for MF worldwide – improved disease-related splenomegaly and symptoms independent of JAK2^V617F mutational status, and prolonged survival compared with placebo or standard therapy in patients with advanced MF. In separate trials, ruxolitinib also provided comprehensive hematologic control in patients with another Ph-negative MPN – polycythemia vera. However, complete cytogenetic or molecular responses with JAK inhibitors alone are normally not observed, underscoring the need for novel combination therapies of JAK inhibitors and complementary agents that better address the complexity of the pathobiology of classic Ph-negative MPNs. Here, we discuss the role of tyrosine-kinase inhibitors in the current MPN-treatment landscape.

Cytogenetic and Molecular Analyses of Philadelphia Chromosome Variants in CML (chronic myeloid leukemia) Patients from Sindh using Karyotyping and RT-PCR

Objective:

To determine the frequency of Philadelphia chromosome (Ph) and its variants in chronic myeloid leukemia (CML) cases at a tertiary care hospital of Sindh.

Methods:

The study was conducted at the Department of Pathology, Liaquat University of Medical and Health Sciences, Jamshoro and Isra University Hospital, Hyderabad during May-to-September 2014. Bone marrow and peripheral blood samples from a total of 145 diagnosed cases of CML were collected. Cytogenetic analyses were performed using karyotyping as per the International System for Human Cytogenetic Nomenclature guidelines. All karyotypic images were analyzed using the Cytovision software. In order to identify BCR-ABL transcripts, RT-PCR was performed. Statistical analysis of the data was done using SPSS-version-21.0.

Results:

Of the 145 samples, a total of 133 (91.7%) were positive for the Ph (Ph+) while 12 (8.3%) were negative for the Ph (Ph-). Of the 133 Ph+ samples, standard karyotypes were noted in 121 (91%), simple variants in 9 (6.7%) and complex variants in 3 (2.3%) of the samples. All the Ph+ samples (n=133) showed BCR-ABL positivity. Of the 12 Ph- samples, a total of 7 (58.3%) were BCR-ABL-positive and 5 (41.6%) were BCR-ABL-negative.

Conclusion:

Frequency of the Ph was found to be of 90.9% in CML patients using a highly sensitive technique, the RT-PCR. Cytogenetic abnormalities were at a lower frequency. Cytogenetic and molecular studies must be conducted for better management of CML cases. These findings could be very useful in guiding the appropriate therapeutic options for CML patients.

Establishment and genetic characterization of a novel mixed-phenotype acute leukemia cell line with EP300-ZNF384 fusion

Herein, we describe the establishment and characterization of the first mixed-phenotype acute leukemia cell line (JIH-5). The JIH-5 cell line was established from leukemia cells with B lymphoid/myeloid phenotype from a female mixed-phenotype acute leukemia patient. JIH-5 cells exhibit an immunophenotype comprised of myeloid and B lymphoid antigens. Whole-exome sequencing revealed somatic mutations in nine genes in JIH-5 cells. Transcriptional sequencing of JIH-5 cells identified EP300-ZNF384 fusion transcript, which is a recurrent alteration in B cell acute lymphoblastic leukemia. Our results suggest that the JIH-5 cell line may serve as a tool for the study of mixed-phenotype acute leukemia or EP300-ZNF384.

Anti-leukemic activity of axitinib against cells harboring the BCR-ABL T315I point mutation

The BCR-ABL; breakpoint cluster region-Abelson point mutation T315I is resistant to ABL tyrosine kinase inhibitors. However, axitinib, a vascular endothelial growth factor receptor inhibitor, is effective against this mutation. In this study, we investigated axitinib activity against ponatinib-resistant cells and found that axitinib inhibited cellular growth and apoptosis in Ba/F3 T315I-mutant cells and T315I-mutant primary samples, but not in ponatinib-resistant Ba/F3 cells and primary samples. Thus, an alternative strategy may be required to improve the prognosis of Philadelphia-chromosome-positive leukemia patients harboring BCR-ABL point mutations.

Phase 1 dose escalation trial of ilorasertib, a dual Aurora/VEGF receptor kinase inhibitor, in patients with hematologic malignancies

BACKGROUND

Ilorasertib (ABT-348) is a novel inhibitor of Aurora kinase, vascular endothelial growth factor (VEGF) and platelet-derived growth factor receptors, and the Src families of tyrosine kinases. Ilorasertib alone or in combination with azacitidine demonstrated activity in preclinical models in various hematological malignancies, indicating that pan-Aurora kinase and multiple kinase inhibition may have preferential antileukemic activity. This phase 1 trial determined the safety, pharmacokinetics, and preliminary antitumor activity of ilorasertib alone or combined with azacitidine in advanced hematologic malignancies.

PATIENTS AND METHODS

Fifty-two patients (median age, 67 years; 35 % with >4 prior regimens) with acute myelogenous leukaemia (AML; n = 38), myelodysplastic syndrome (n = 12), or chronic myelomonocytic leukaemia (n = 2) received 3 or 6 doses of ilorasertib per 28-day cycle and were assigned to arm A (once-weekly oral), B (twice-weekly oral), C (once-weekly oral plus azacitidine), or D (once-weekly intravenous) treatment.

RESULTS

Maximum tolerated doses were not determined; the recommended phase 2 oral monotherapy doses were 540 mg once weekly and 480 mg twice weekly. The most common grade 3/4 adverse events were hypertension (28.8 %), hypokalemia (15.4 %), anemia (13.5 %), and hypophosphatemia (11.5 %). Oral ilorasertib pharmacokinetics appeared dose proportional, with a 15-hour half-life and no interaction with azacitidine. Ilorasertib inhibited biomarkers for Aurora kinase and VEGF receptors, and demonstrated clinical responses in 3 AML patients.

CONCLUSIONS

Ilorasertib exhibited acceptable safety and pharmacokinetics at or below the recommended phase 2 dose, displayed evidence of dual Aurora kinase and VEGF receptor kinase inhibition, and activity in AML.

Alteration of gene expression profile following PPP2R5C knockdown may be associated with proliferation suppression and increased apoptosis of K562 cells

We reported that knockdown of PPP2R5C by siRNA led to proliferation inhibition and apoptosis induction in K562 cells. In this study, we further characterized the gene expression profiles after PPP2R5C suppression by microarray analysis. Genes which participate in the MAPK, PI3K/AKT, and JAK/STAT pathways, were mainly altered in the K562 cells. We propose that the mechanism for proliferation inhibition and increased apoptosis of K562 cells following PPP2R5C suppression may be related to the alteration of expression profiles of BRAF, AKT2, AKT3, NFKB2 and STAT3 genes.

Diversity of breakpoints of variant Philadelphia chromosomes in chronic myeloid leukemia in Brazilian patients

BACKGROUND

Chronic myeloid leukemia is a myeloproliferative disorder characterized by the Philadelphia chromosome or t(9;22)(q34.1;q11.2), resulting in the break-point cluster region-Abelson tyrosine kinase fusion gene, which encodes a constitutively active tyrosine kinase protein. The Philadelphia chromosome is detected by karyotyping in around 90% of chronic myeloid leukemia patients, but 5-10% may have variant types. Variant Philadelphia chromosomes are characterized by the involvement of another chromosome in addition to chromosome 9 or 22. It can be a simple type of variant when one other chromosome is involved, or complex, in which two or more chromosomes take part in the translocation. Few studies have reported the incidence of variant Philadelphia chromosomes or the breakpoints involved among Brazilian chronic myeloid leukemia patients.

OBJECTIVE

The aim of this report is to describe the diversity of the variant Philadelphia chromosomes found and highlight some interesting breakpoint candidates for further studies.

METHODS

the Cytogenetics Section Database was searched for all cases with diagnoses of chronic myeloid leukemia during a 12-year period and all the variant Philadelphia chromosomes were listed.

RESULTS

Fifty (5.17%) cases out of 1071 Philadelphia-positive chronic myeloid leukemia were variants. The most frequently involved chromosome was 17, followed by chromosomes: 1, 20, 6, 11, 2, 10, 12 and 15.

CONCLUSION

Among all the breakpoints seen in this survey, six had previously been described: 11p15, 14q32, 15q11.2, 16p13.1, 17p13 and 17q21. The fact that some regions get more frequently involved in such rare rearrangements calls attention to possible predisposition that should be further studied. Nevertheless, the pathological implication of these variants remains unclear.

Personalized medicine in hematology - A landmark from bench to bed

Personalized medicine is the cornerstone of medical practice. It tailors treatments for specific conditions of an affected individual. The borders of personalized medicine are defined by limitations in technology and our understanding of biology, physiology and pathology of various conditions. Current advances in technology have provided physicians with the tools to investigate the molecular makeup of the disease. Translating these molecular make-ups to actionable targets has led to the development of small molecular inhibitors. Also, detailed understanding of genetic makeup has allowed us to develop prognostic markers, better known as companion diagnostics. Current attempts in the development of drug delivery systems offer the opportunity of delivering specific inhibitors to affected cells in an attempt to reduce the unwanted side effects of drugs.