An eight-gene expression signature for the prediction of survival and time to treatment in chronic lymphocytic leukemia

Exploring the prognostic value of T follicular helper cell levels in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) presents with heterogeneous clinical outcomes, suggesting varied underlying pathogenic mechanisms. This study aims to elucidate the impact of T follicular helper (Tfh) cells on CLL progression and prognosis. Gene expression profile data for CLL were collected from GSE22762 and GSE39671 datasets. Patients were divided into high and low groups using Tfh levels using the optimal cutoff value based on overall survival (OS) and time-to-first treatment (TTFT). Differential expression analysis was performed between these groups, followed by co-expression network analysis and single-sample Gene Set Enrichment Analysis (ssGSEA). Marker genes of Tfh cells were used to construct prognostic models. Additionally, 40 CLL patients were recruited and categorized based on median Tfh levels. Marker gene expression was assessed using RT-qPCR and Western Blot, and immune cell levels were determined through flow cytometry. The high group showed better prognosis compared to the low group. Among the 1121 differentially expressed genes identified, five co-expression networks were constructed, with the turquoise module showing the highest correlation with Tfh cells. Genes within this module significantly participate in cytokine-cytokine receptor interaction, PI3K-Akt signaling pathway, and natural killer cell mediated cytotoxicity. Tfh cells were significantly negatively correlated with activated B cells and positively correlated with Tregs. The Random Survival Forest (RSF) model identified 10 marker genes, and further analysis using Lasso regression and nomogram selected CLEC4A, RAE1, CD84, and PRDX1 as prognostic markers. In the high group, levels of CLEC4A and RAE1 were higher than in the low group, whereas CD84 and PRDX1 were lower. Flow cytometry revealed that the level of activated B cells in the high Tfh group was significantly lower than in the low Tfh group, while the level of Tregs is significantly higher in the high Tfh group. This study seeks to contribute to a more detailed understanding of the pathogenesis of CLL, delving into the prognostic significance of Tfh.

A review of disease risk prediction methods and applications in the omics era

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

EFNB1 levels determine distinct drug response patterns guiding precision therapy for B-cell neoplasms

The multi-omics data has greatly improved the molecular diagnosis of B-cell neoplasms, but there is still a lack of predictive biomarkers to guide precision therapy. Here, we analyzed publicly available data and found that B-cell neoplasm cell lines with different levels of EFNB1 had distinctive drug response patterns of inhibitors targeting SRC/PI3K/AKT. Overexpression of EFNB1 promoted phosphorylation of key proteins in drug response, such as SRC and STMN1, conferring sensitivity to SRC inhibitor and cytotoxic drugs. EFNB1 phosphorylation signaling network was significantly associated with the prognosis of GCB-DLBCL patients. Moreover, EFNB1 levels were correlated with cell of origin (COO) scores, suggesting that EFNB1 is a quantitative indicator of cell differentiation. Ultimately, we proposed a model for the stratification of human B-cell malignancies and the implementation of targeted therapies based on EFNB1 levels. Our findings highlight that EFNB1 level is a promising biomarker for predicting drug response, COO and prognosis.

Effects of blocking CD24 and CD47 'don't eat me' signals in combination with rituximab in mantle-cell lymphoma and chronic lymphocytic leukaemia

Mantle-cell lymphoma (MCL) is a B-cell non-Hodgkin Lymphoma (NHL) with a poor prognosis, at high risk of relapse after conventional treatment. MCL-associated tumour microenvironment (TME) is characterized by M2-like tumour-associated macrophages (TAMs), able to interact with cancer cells, providing tumour survival and resistance to immuno-chemotherapy. Likewise, monocyte-derived nurse-like cells (NLCs) present M2-like profile and provide proliferation signals to chronic lymphocytic leukaemia (CLL), a B-cell malignancy sharing with MCL some biological and phenotypic features. Antibodies against TAMs targeted CD47, a 'don't eat me' signal (DEMs) able to quench phagocytosis by TAMs within TME, with clinical effectiveness when combined with Rituximab in pretreated NHL. Recently, CD24 was found as valid DEMs in solid cancer. Since CD24 is expressed during B-cell differentiation, we investigated and identified consistent CD24 in MCL, CLL and primary human samples. Phagocytosis increased when M2-like macrophages were co-cultured with cancer cells, particularly in the case of paired DEMs blockade (i.e. anti-CD24 + anti-CD47) combined with Rituximab. Similarly, unstimulated CLL patients-derived NLCs provided increased phagocytosis when DEMs blockade occurred. Since high levels of CD24 were associated with worse survival in both MCL and CLL, anti-CD24-induced phagocytosis could be considered for future clinical use, particularly in association with other agents such as Rituximab.

Genes selection using deep learning and explainable artificial intelligence for chronic lymphocytic leukemia predicting the need and time to therapy

Analyzing gene expression profiles (GEP) through artificial intelligence provides meaningful insight into cancer disease. This study introduces DeepSHAP Autoencoder Filter for Genes Selection (DSAF-GS), a novel deep learning and explainable artificial intelligence-based approach for feature selection in genomics-scale data. DSAF-GS exploits the autoencoder's reconstruction capabilities without changing the original feature space, enhancing the interpretation of the results. Explainable artificial intelligence is then used to select the informative genes for chronic lymphocytic leukemia prognosis of 217 cases from a GEP database comprising roughly 20,000 genes. The model for prognosis prediction achieved an accuracy of 86.4%, a sensitivity of 85.0%, and a specificity of 87.5%. According to the proposed approach, predictions were strongly influenced by CEACAM19 and PIGP, moderately influenced by MKL1 and GNE, and poorly influenced by other genes. The 10 most influential genes were selected for further analysis. Among them, FADD, FIBP, FIBP, GNE, IGF1R, MKL1, PIGP, and SLC39A6 were identified in the Reactome pathway database as involved in signal transduction, transcription, protein metabolism, immune system, cell cycle, and apoptosis. Moreover, according to the network model of the 3D protein-protein interaction (PPI) explored using the NetworkAnalyst tool, FADD, FIBP, IGF1R, QTRT1, GNE, SLC39A6, and MKL1 appear coupled into a complex network. Finally, all 10 selected genes showed a predictive power on time to first treatment (TTFT) in univariate analyses on a basic prognostic model including IGHV mutational status, del(11q) and del(17p), NOTCH1 mutations, β2-microglobulin, Rai stage, and B-lymphocytosis known to predict TTFT in CLL. However, only IGF1R [hazard ratio (HR) 1.41, 95% CI 1.08-1.84, P=0.013), COL28A1 (HR 0.32, 95% CI 0.10-0.97, P=0.045), and QTRT1 (HR 7.73, 95% CI 2.48-24.04, P<0.001) genes were significantly associated with TTFT in multivariable analyses when combined with the prognostic factors of the basic model, ultimately increasing the Harrell's c-index and the explained variation to 78.6% (versus 76.5% of the basic prognostic model) and 52.6% (versus 42.2% of the basic prognostic model), respectively. Also, the goodness of model fit was enhanced (χ2 = 20.1, P=0.002), indicating its improved performance above the basic prognostic model. In conclusion, DSAF-GS identified a group of significant genes for CLL prognosis, suggesting future directions for bio-molecular research.

Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges

BACKGROUND

In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions.

METHODS

Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 "High-dimensional data" of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD.

RESULTS

The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided.

CONCLUSIONS

This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses.

Glyoxalase 1 inhibitor BBGC suppresses the progression of chronic lymphocytic leukemia and promotes the efficacy of Palbociclib

Chronic lymphocytic leukemia (CLL) is a highly heterogeneous disease. Despite recent tremen-dous progress in managing CLL, the disease remains incurable with clinical therapies, and relapse is inevitable. To overcome this, new diagnostic and prognostic markers need to be investigated. We thus screened through the public database for genes with diagnostic, prognostic, and therapeutic implications in CLL. We further performed RT-qPCR and Western blot analysis to measure the candidate gene and protein expression levels, respectively, in peripheral blood mononuclear cells. Our results indicated that Glyoxalase 1 (GLO1) expression was significantly higher in patients with CLL than in healthy controls. Furthermore, cell proliferation, apoptosis, and cell cycle assay results together indicated that S-p-bromobenzylglutathione cyclopentyl diester (BBGC), an effective inhibitor of GLO1, suppresses the progression of CLL. Bioinformatics analysis revealed that GLO1 expression is closely associated with CDK4 expression in a wide variety of cancer types, and inhibition of CDK4 through silencing of genes or inhibitors can downregulate GLO1 expression. Subsequent validation experiments demonstrated that GLO1 protein levels were downregulated in MEC-1 and Jurkat cell lines after palbociclib exposure, and combination treatment of palbociclib with GLO1 inhibitor BBGC effectively delayed the growth of tumor cell lines.

A survival prediction model and nomogram based on immune-related gene expression in chronic lymphocytic leukemia cells

Introduction

There are many different chronic lymphoblastic leukemia (CLL) survival prediction models and scores. But none provide information on expression of immune-related genes in the CLL cells.

Methods

We interrogated data from the Gene Expression Omnibus database (GEO, GSE22762; Number = 151; training) and International Cancer Genome Consortium database (ICGC, CLLE-ES; Number = 491; validation) to develop an immune risk score (IRS) using Least absolute shrinkage and selection operator (LASSO) Cox regression analyses based on expression of immune-related genes in CLL cells. The accuracy of the predicted nomogram we developed using the IRS, Binet stage, and del(17p) cytogenetic data was subsequently assessed using calibration curves.

Results

A survival model based on expression of 5 immune-related genes was constructed. Areas under the curve (AUC) for 1-year survivals were 0.90 (95% confidence interval, 0.78, 0.99) and 0.75 (0.54, 0.87) in the training and validation datasets, respectively. 5-year survivals of low- and high-risk subjects were 89% (83, 95%) vs. 6% (0, 17%; p < 0.001) and 98% (95, 100%) vs. 92% (88, 96%; p < 0.001) in two datasets. The IRS was an independent survival predictor of both datasets. A calibration curve showed good performance of the nomogram. In vitro, the high expression of CDKN2A and SREBF2 in the bone marrow of patients with CLL was verified by immunohistochemistry analysis (IHC), which were associated with poor prognosis and may play an important role in the complex bone marrow immune environment.

Conclusion

The IRS is an accurate independent survival predictor with a high C-statistic. A combined nomogram had good survival prediction accuracy in calibration curves. These data demonstrate the potential impact of immune related genes on survival in CLL.

Biological relevance of ZNF224 expression in chronic lymphocytic leukemia and its implication IN NF-kB pathway regulation

Chronic lymphocytic leukemia (CLL) is a heterogeneous disease, whose presentation and clinical course are highly variable. Identification of novel prognostic factors may contribute to improving the CLL classification and providing indications for treatment options. The zinc finger protein ZNF224 plays a key role in cell transformation, through the control of apoptotic and survival pathways. In this study, we evaluated the potential application of ZNF224 as a novel marker of CLL progression and therapy responsiveness. To this aim, we analyzed ZNF224 expression levels in B lymphocytes from CLL patients at different stages of the disease and in patients showing different treatment outcomes. The expression of ZNF224 was significantly increased in disease progression and dramatically decreased in patients in complete remission after chemotherapy. Gene expression correlation analysis performed on datasets of CLL patients revealed that ZNF224 expression was well correlated with that of some prognostic and predictive markers. Moreover, bioinformatic analysis coupled ZNF224 to NF-κB pathway, and experimental data demonstrated that RNA interference of ZNF224 reduced the activity of the NF-κB survival pathway in CLL cells. Consistently with a pro-survival role, ZNF224 knockdown raised spontaneous and drug-induced apoptosis and inhibited the proliferation of peripheral blood mononuclear cells from CLL patients. Our findings provide evidence for the involvement of ZNF224 in the survival of CLL cells via NF-κB pathway modulation, and also suggest ZNF224 as a prognostic and predictive molecular marker of CLL disease.

Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels

Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made.

In silico investigations identified Butyl Xanalterate to competently target CK2α (CSNK2A1) for therapy of chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is an incurable malignancy of B-cells. In this study, bioinformatics analyses were conducted to identify possible pathogenic roles of CK2α, which is a protein encoded by CSNK2A1, in the progression and aggressiveness of CLL. Furthermore, various computational tools were used to search for a competent inhibitor of CK2α from fungal metabolites that could be proposed for CLL therapy. In CLL patients, high-expression of CSNK2A1 was associated with early need for therapy (n = 130, p < 0.0001) and short overall survival (OS; n = 107, p = 0.005). Consistently, bioinformatics analyses showed CSNK2A1 to associate with/play roles in CLL proliferation and survival-dependent pathways. Furthermore, PPI network analysis identified interaction partners of CK2α (PPI enrichment p value = 1 × 10^-16) that associated with early need for therapy (n = 130, p < 0.003) and have been known to heavily impact on the progression of CLL. These findings constructed a rational for targeting CK2α for CLL therapy. Consequently, computational analyses reported 35 fungal metabolites out of 5820 (filtered from 19,967 metabolites) to have lower binding energy (ΔG: - 10.9 to - 11.7 kcal/mol) and better binding affinity (Kd: 9.77 × 10⁷ M^-1 to 3.77 × 10⁸ M^-1) compared with the native ligand (ΔG: - 10.8, Kd: 8.3 × 10⁷ M-^-1). Furthermore, molecular dynamics simulation study established that Butyl Xanalterate-CK2α complex continuously remained stable throughout the simulation time (100 ns). Moreover, Butyl Xanalterate interacted with most of the catalytic residues, where complex was stabilized by more than 65% hydrogen bond interactions, and a significant hydrophobic interaction with residue Phe113. Here, high-expression of CSNK2A1 was implicated in the progression and poor prognosis of CLL, making it a potential therapeutic target in the disease. Butyl Xanalterate showed stable and strong interactions with CK2α, thus we propose it as a competitive inhibitor of CK2α for CLL therapy.

Decreased TCF1 and BCL11B expression predicts poor prognosis for patients with chronic lymphocytic leukemia

T cell immune dysfunction is a prominent characteristic of chronic lymphocytic leukemia (CLL) and the main cause of failure for immunotherapy and multi-drug resistance. There remains a lack of specific biomarkers for evaluating T cell immune status with outcome for CLL patients. T cell factor 1 (TCF1, encoded by the TCF7 gene) can be used as a critical determinant of successful anti-tumor immunotherapy and a prognostic indicator in some solid tumors; however, the effects of TCF1 in CLL remain unclear. Here, we first analyzed the biological processes and functions of TCF1 and co-expressing genes using the GEO and STRING databases with the online tools Venny, Circos, and Database for Annotation, Visualization, and Integrated Discovery (DAVID). Then the expression and prognostic values of TCF1 and its partner gene B cell leukemia/lymphoma 11B (BCL11B) were explored for 505 CLL patients from 6 datasets and validated with 50 CLL patients from Henan cancer hospital (HNCH). TCF1 was downregulated in CLL patients, particularly in CD8+ T cells, which was significantly correlated with poor time-to-first treatment (TTFT) and overall survival (OS) as well as short restricted mean survival time (RMST). Function and pathway enrichment analysis revealed that TCF1 was positively correlated with BCL11B, which is involved in regulating the activation and differentiation of T cells in CLL patients. Intriguingly, BCL11B was highly consistent with TCF1 in its decreased expression and prediction of poor prognosis. More importantly, the combination of TCF1 and BCL11B could more accurately assess prognosis than either alone. Additionally, decreased TCF1 and BCL11B expression serves as an independent risk factor for rapid disease progression, coinciding with high-risk indicators, including unmutated IGHV, TP53 alteration, and advanced disease. Altogether, this study demonstrates that decreased TCF1 and BCL11B expression is significantly correlated with poor prognosis, which may be due to decreased TCF1+CD8+ T cells, impairing the effector CD8+ T cell differentiation regulated by TCF1/BCL11B.

Super-Enhancer–Associated nine-gene prognostic score model for prediction of survival in chronic lymphocytic leukemia patients

Chronic lymphocytic leukemia (CLL) is a type of highly heterogeneous mature B-cell malignancy with various disease courses. Although a multitude of prognostic markers in CLL have been reported, insights into the role of super-enhancer (SE)–related risk indicators in the occurrence and development of CLL are still lacking. A super-enhancer (SE) is a cluster of enhancers involved in cell differentiation and tumorigenesis, and is one of the promising therapeutic targets for cancer therapy in recent years. In our study, the CLL-related super-enhancers in the training database were processed by LASSO-penalized Cox regression analysis to screen a nine-gene prognostic model including TCF7, VEGFA, MNT, GMIP, SLAMF1, TNFRSF25, GRWD1, SLC6AC, and LAG3. The SE-related risk score was further constructed and it was found that the predictive performance with overall survival and time-to-treatment (TTT) was satisfactory. Moreover, a high correlation was found between the risk score and already known prognostic markers of CLL. In the meantime, we noticed that the expressions of TCF7, GMIP, SLAMF1, TNFRSF25, and LAG3 in CLL were different from those of healthy donors (p &lt; 0.01). Moreover, the risk score and LAG3 level of matched pairs before and after treatment samples varied significantly. Finally, an interactive nomogram consisting of the nine-gene risk group and four clinical traits was established. The inhibitors of mTOR and cyclin-dependent kinases (CDKs) were considered effective in patients in the high-risk group according to the pRRophetic algorithm. Collectively, the SE-associated nine-gene prognostic model developed here may be used to predict the prognosis and assist in the risk stratification and treatment of CLL patients in the future.

The pyroptosis-related gene signature predicts prognosis and indicates the immune microenvironment status of chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is the most common leukemia in the Western world with great heterogeneity. Pyroptosis has recently been recognized as an inflammatory form of programmed cell death (PCD) and shares a close relationship with apoptosis. Although the role of apoptosis in CLL was comprehensively studied and successfully applied in clinical treatment, the relationship between pyroptosis genes and CLL remained largely unknown. In this study, eight differentially expressed pyroptosis-related genes (PRGs) were identified between CLL and normal B cells. In order to screen out the prognostic value of differentially expressed PRGs, univariate and multivariate Cox regression analyses were conducted and a risk model with three PRG signatures (GSDME, NLRP3, and PLCG1) was constructed. All CLL samples were stratified into high- and low-risk subgroups according to risk scores. The risk model showed high efficacy in predicting both overall survival (OS) and time to first treatment (TTFT). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) showed the dysregulation of immune and inflammatory response in the high-risk group. Single-sample GSEA (ssGSEA) of immune cell infiltration and the activity of immune-related pathways also displayed decreased antitumor immunity in the high-risk group. In conclusion, PRGs are of prognostic value in CLL and may play important roles in tumor immunity, and the underlying relationship between PRGs and CLL needs to be explored further.

Fusion gene detection by RNA-sequencing complements diagnostics of acute myeloid leukemia and identifies recurring NRIP1-MIR99AHG rearrangements

Identification of fusion genes in clinical routine is mostly based on cytogenetics and targeted molecular genetics, such as metaphase karyotyping, fluorescence in situ hybridization and reverse-transcriptase polymerase chain reaction. However, sequencing technologies are becoming more important in clinical routine as processing time and costs per sample decrease. To evaluate the performance of fusion gene detection by RNAsequencing compared to standard diagnostic techniques, we analyzed 806 RNA-sequencing samples from patients with acute myeloid leukemia using two state-of-the-art software tools, namely Arriba and FusionCatcher. RNA-sequencing detected 90% of fusion events that were reported by routine with high evidence, while samples in which RNA-sequencing failed to detect fusion genes had overall lower and inhomogeneous sequence coverage. Based on properties of known and unknown fusion events, we developed a workflow with integrated filtering strategies for the identification of robust fusion gene candidates by RNA-sequencing. Thereby, we detected known recurrent fusion events in 26 cases that were not reported by routine and found discrepancies in evidence for known fusion events between routine and RNA-sequencing in three cases. Moreover, we identified 157 fusion genes as novel robust candidates and comparison to entries from ChimerDB or Mitelman Database showed novel recurrence of fusion genes in 14 cases. Finally, we detected the novel recurrent fusion gene NRIP1- MIR99AHG resulting from inv(21)(q11.2;q21.1) in nine patients (1.1%) and LTN1-MX1 resulting from inv(21)(q21.3;q22.3) in two patients (0.25%). We demonstrated that NRIP1-MIR99AHG results in overexpression of the 3' region of MIR99AHG and the disruption of the tricistronic miRNA cluster miR-99a/let-7c/miR-125b-2. Interestingly, upregulation of MIR99AHG and deregulation of the miRNA cluster, residing in the MIR99AHG locus, are known mechanisms of leukemogenesis in acute megakaryoblastic leukemia. Our findings demonstrate that RNA-sequencing has a strong potential to improve the systematic detection of fusion genes in clinical applications and provides a valuable tool for fusion discovery.

O-GlcNAcylation in Chronic Lymphocytic Leukemia and Other Blood Cancers

In the past decade, aberrant O-GlcNAcylation has emerged as a new hallmark of cancer. O-GlcNAcylation is a post-translational modification that results when the amino-sugar β-D-N-acetylglucosamine (GlcNAc) is made in the hexosamine biosynthesis pathway (HBP) and covalently attached to serine and threonine residues in intracellular proteins by the glycosyltransferase O-GlcNAc transferase (OGT). O-GlcNAc moieties reflect the metabolic state of a cell and are removed by O-GlcNAcase (OGA). O-GlcNAcylation affects signaling pathways and protein expression by cross-talk with kinases and proteasomes and changes gene expression by altering protein interactions, localization, and complex formation. The HBP and O-GlcNAcylation are also recognized to mediate survival of cells in harsh conditions. Consequently, O-GlcNAcylation can affect many of the cellular processes that are relevant for cancer and is generally thought to promote tumor growth, disease progression, and immune escape. However, recent studies suggest a more nuanced view with O-GlcNAcylation acting as a tumor promoter or suppressor depending on the stage of disease or the genetic abnormalities, proliferative status, and state of the p53 axis in the cancer cell. Clinically relevant HBP and OGA inhibitors are already available and OGT inhibitors are in development to modulate O-GlcNAcylation as a potentially novel cancer treatment. Here recent studies that implicate O-GlcNAcylation in oncogenic properties of blood cancers are reviewed, focusing on chronic lymphocytic leukemia and effects on signal transduction and stress resistance in the cancer microenvironment. Therapeutic strategies for targeting the HBP and O-GlcNAcylation are also discussed.

Multi-omics reveals clinically relevant proliferative drive associated with mTOR-MYC-OXPHOS activity in chronic lymphocytic leukemia

Chronic Lymphocytic Leukemia (CLL) has a complex pattern of driver mutations and much of its clinical diversity remains unexplained. We devised a method for simultaneous subgroup discovery across multiple data types and applied it to genomic, transcriptomic, DNA methylation and ex-vivo drug response data from 217 Chronic Lymphocytic Leukemia (CLL) cases. We uncovered a biological axis of heterogeneity strongly associated with clinical behavior and orthogonal to the known biomarkers. We validated its presence and clinical relevance in four independent cohorts (n=547 patients). We find that this axis captures the proliferative drive (PD) of CLL cells, as it associates with lymphocyte doubling rate, global hypomethylation, accumulation of driver aberrations and response to pro-proliferative stimuli. CLL-PD was linked to the activation of mTOR-MYC-oxidative phosphorylation (OXPHOS) through transcriptomic, proteomic and single cell resolution analysis. CLL-PD is a key determinant of disease outcome in CLL. Our multi-table integration approach may be applicable to other tumors whose inter-individual differences are currently unexplained.

Musashi 2 influences chronic lymphocytic leukemia cell survival and growth making it a potential therapeutic target

Progression of chronic lymphocytic leukemia (CLL) results from the expansion of a small fraction of proliferating leukemic B cells. When comparing the global gene expression of recently divided CLL cells with that of previously divided cells, we found higher levels of genes involved in regulating gene expression. One of these was the oncogene Musashi 2 (MSI2), an RNA-binding protein that induces or represses translation. While there is an established role for MSI2 in normal and malignant stem cells, much less is known about its expression and role in CLL. Here we report for the first time ex vivo and in vitro experiments that MSI2 protein levels are higher in dividing and recently divided leukemic cells and that downregulating MSI2 expression or blocking its function eliminates primary human and murine CLL and mature myeloid cells. Notably, mature T cells and hematopoietic stem and progenitor cells are not affected. We also confirm that higher MSI2 levels correlate with poor outcome markers, shorter time-to-first-treatment, and overall survival. Thus, our data highlight an important role for MSI2 in CLL-cell survival and proliferation and associate MSI2 with poor prognosis in CLL patients. Collectively, these findings pinpoint MSI2 as a potentially valuable therapeutic target in CLL.

A hotspot mutation in transcription factor IKZF3 drives B cell neoplasia via transcriptional dysregulation

Hotspot mutation of IKZF3 (IKZF3-L162R) has been identified as a putative driver of chronic lymphocytic leukemia (CLL), but its function remains unknown. Here, we demonstrate its driving role in CLL through a B cell-restricted conditional knockin mouse model. Mutant Ikzf3 alters DNA binding specificity and target selection, leading to hyperactivation of B cell receptor (BCR) signaling, overexpression of nuclear factor κB (NF-κB) target genes, and development of CLL-like disease in elderly mice with a penetrance of ~40%. Human CLL carrying either IKZF3 mutation or high IKZF3 expression was associated with overexpression of BCR/NF-κB pathway members and reduced sensitivity to BCR signaling inhibition by ibrutinib. Our results thus highlight IKZF3 oncogenic function in CLL via transcriptional dysregulation and demonstrate that this pro-survival function can be achieved by either somatic mutation or overexpression of this CLL driver. This emphasizes the need for combinatorial approaches to overcome IKZF3-mediated BCR inhibitor resistance.

Genome-wide association study identifies risk loci for progressive chronic lymphocytic leukemia

Prognostication in patients with chronic lymphocytic leukemia (CLL) is challenging due to heterogeneity in clinical course. We hypothesize that constitutional genetic variation affects disease progression and could aid prognostication. Pooling data from seven studies incorporating 842 cases identifies two genomic locations associated with time from diagnosis to treatment, including 10q26.13 (rs736456, hazard ratio (HR) = 1.78, 95% confidence interval (CI) = 1.47-2.15; P = 2.71 × 10-9) and 6p (rs3778076, HR = 1.99, 95% CI = 1.55-2.55; P = 5.08 × 10-8), which are particularly powerful prognostic markers in patients with early stage CLL otherwise characterized by low-risk features. Expression quantitative trait loci analysis identifies putative functional genes implicated in modulating B-cell receptor or innate immune responses, key pathways in CLL pathogenesis. In this work we identify rs736456 and rs3778076 as prognostic in CLL, demonstrating that disease progression is determined by constitutional genetic variation as well as known somatic drivers.

Prognostic models for newly-diagnosed chronic lymphocytic leukaemia in adults: a systematic review and meta-analysis

BACKGROUND

Chronic lymphocytic leukaemia (CLL) is the most common cancer of the lymphatic system in Western countries. Several clinical and biological factors for CLL have been identified. However, it remains unclear which of the available prognostic models combining those factors can be used in clinical practice to predict long-term outcome in people newly-diagnosed with CLL.

OBJECTIVES

To identify, describe and appraise all prognostic models developed to predict overall survival (OS), progression-free survival (PFS) or treatment-free survival (TFS) in newly-diagnosed (previously untreated) adults with CLL, and meta-analyse their predictive performances.

SEARCH METHODS

We searched MEDLINE (from January 1950 to June 2019 via Ovid), Embase (from 1974 to June 2019) and registries of ongoing trials (to 5 March 2020) for development and validation studies of prognostic models for untreated adults with CLL. In addition, we screened the reference lists and citation indices of included studies.

SELECTION CRITERIA

We included all prognostic models developed for CLL which predict OS, PFS, or TFS, provided they combined prognostic factors known before treatment initiation, and any studies that tested the performance of these models in individuals other than the ones included in model development (i.e. 'external model validation studies'). We included studies of adults with confirmed B-cell CLL who had not received treatment prior to the start of the study. We did not restrict the search based on study design.

DATA COLLECTION AND ANALYSIS

We developed a data extraction form to collect information based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Independent pairs of review authors screened references, extracted data and assessed risk of bias according to the Prediction model Risk Of Bias ASsessment Tool (PROBAST). For models that were externally validated at least three times, we aimed to perform a quantitative meta-analysis of their predictive performance, notably their calibration (proportion of people predicted to experience the outcome who do so) and discrimination (ability to differentiate between people with and without the event) using a random-effects model. When a model categorised individuals into risk categories, we pooled outcome frequencies per risk group (low, intermediate, high and very high). We did not apply GRADE as guidance is not yet available for reviews of prognostic models.

MAIN RESULTS

From 52 eligible studies, we identified 12 externally validated models: six were developed for OS, one for PFS and five for TFS. In general, reporting of the studies was poor, especially predictive performance measures for calibration and discrimination; but also basic information, such as eligibility criteria and the recruitment period of participants was often missing. We rated almost all studies at high or unclear risk of bias according to PROBAST. Overall, the applicability of the models and their validation studies was low or unclear; the most common reasons were inappropriate handling of missing data and serious reporting deficiencies concerning eligibility criteria, recruitment period, observation time and prediction performance measures. We report the results for three models predicting OS, which had available data from more than three external validation studies: CLL International Prognostic Index (CLL-IPI) This score includes five prognostic factors: age, clinical stage, IgHV mutational status, B2-microglobulin and TP53 status. Calibration: for the low-, intermediate- and high-risk groups, the pooled five-year survival per risk group from validation studies corresponded to the frequencies observed in the model development study. In the very high-risk group, predicted survival from CLL-IPI was lower than observed from external validation studies. Discrimination: the pooled c-statistic of seven external validation studies (3307 participants, 917 events) was 0.72 (95% confidence interval (CI) 0.67 to 0.77). The 95% prediction interval (PI) of this model for the c-statistic, which describes the expected interval for the model's discriminative ability in a new external validation study, ranged from 0.59 to 0.83. Barcelona-Brno score Aimed at simplifying the CLL-IPI, this score includes three prognostic factors: IgHV mutational status, del(17p) and del(11q). Calibration: for the low- and intermediate-risk group, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of four external validation studies (1755 participants, 416 events) was 0.64 (95% CI 0.60 to 0.67); 95% PI 0.59 to 0.68. MDACC 2007 index score The authors presented two versions of this model including six prognostic factors to predict OS: age, B2-microglobulin, absolute lymphocyte count, gender, clinical stage and number of nodal groups. Only one validation study was available for the more comprehensive version of the model, a formula with a nomogram, while seven studies (5127 participants, 994 events) validated the simplified version of the model, the index score. Calibration: for the low- and intermediate-risk groups, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of the seven external validation studies for the index score was 0.65 (95% CI 0.60 to 0.70); 95% PI 0.51 to 0.77.

AUTHORS' CONCLUSIONS

Despite the large number of published studies of prognostic models for OS, PFS or TFS for newly-diagnosed, untreated adults with CLL, only a minority of these (N = 12) have been externally validated for their respective primary outcome. Three models have undergone sufficient external validation to enable meta-analysis of the model's ability to predict survival outcomes. Lack of reporting prevented us from summarising calibration as recommended. Of the three models, the CLL-IPI shows the best discrimination, despite overestimation. However, performance of the models may change for individuals with CLL who receive improved treatment options, as the models included in this review were tested mostly on retrospective cohorts receiving a traditional treatment regimen. In conclusion, this review shows a clear need to improve the conducting and reporting of both prognostic model development and external validation studies. For prognostic models to be used as tools in clinical practice, the development of the models (and their subsequent validation studies) should adapt to include the latest therapy options to accurately predict performance. Adaptations should be timely.

A Single Gene Expression Set Derived from Artificial Intelligence Predicted the Prognosis of Several Lymphoma Subtypes; and High Immunohistochemical Expression of TNFAIP8 Associated with Poor Prognosis in Diffuse Large B-Cell Lymphoma

Objective: We have recently identified using multilayer perceptron analysis (artificial intelligence) a set of 25 genes with prognostic relevance in diffuse large B-cell lymphoma (DLBCL), but the importance of this set in other hematological neoplasia remains unknown. Methods and Results: We tested this set of genes (i.e., ALDOB, ARHGAP19, ARMH3, ATF6B, CACNA1B, DIP2A, EMC9, ENO3, GGA3, KIF23, LPXN, MESD, METTL21A, POLR3H, RAB7A, RPS23, SERPINB8, SFTPC, SNN, SPACA9, SWSAP1, SZRD1, TNFAIP8, WDCP and ZSCAN12) in a large series of gene expression comprised of 2029 cases, selected from available databases, that included chronic lymphocytic leukemia (CLL, n = 308), mantle cell lymphoma (MCL, n = 92), follicular lymphoma (FL, n = 180), DLBCL (n = 741), multiple myeloma (MM, n = 559) and acute myeloid leukemia (AML, n = 149). Using a risk-score formula we could predict the overall survival of the patients: the hazard-ratio of high-risk versus low-risk groups for all the cases was 3.2 and per disease subtype were as follows: CLL (4.3), MCL (5.2), FL (3.0), DLBCL not otherwise specified (NOS) (4.5), multiple myeloma (MM) (5.3) and AML (3.7) (all p values < 0.000001). All 25 genes contributed to the risk-score, but their weight and direction of the correlation was variable. Among them, the most relevant were ENO3, TNFAIP8, ATF6B, METTL21A, KIF23 and ARHGAP19. Next, we validated TNFAIP8 (a negative mediator of apoptosis) in an independent series of 97 cases of DLBCL NOS from Tokai University Hospital. The protein expression by immunohistochemistry of TNFAIP8 was quantified using an artificial intelligence-based segmentation method and confirmed with a conventional RGB-based digital quantification. We confirmed that high protein expression of TNFAIP8 by the neoplastic B-lymphocytes associated with a poor overall survival of the patients (hazard-risk 3.5; p = 0.018) as well as with other relevant clinicopathological variables including age >60 years, high serum levels of soluble IL2RA, a non-GCB phenotype (cell-of-origin Hans classifier), moderately higher MYC and Ki67 (proliferation index), and high infiltration of the immune microenvironment by CD163-positive tumor associated macrophages (CD163+TAMs). Conclusion: It is possible to predict the prognosis of several hematological neoplasia using a single gene-set derived from neural network analysis. High expression of TNFAIP8 is associated with poor prognosis of the patients in DLBCL.

Aspartic Aminopeptidase Is a Novel Biomarker of Aggressive Chronic Lymphocytic Leukemia

Treatment of chronic lymphocytic leukemia has advanced substantially as our understanding of the kinase signal transduction pathways driven by the B cell receptor (BcR) has developed. Particularly, understanding the role of Bruton tyrosine kinase and phosphatidyl inositol 3 kinase delta in driving prosurvival signal transduction in chronic lymphocytic leukemia (CLL) cells and their targeting with pharmacological inhibitors (ibrutinib and idelalisib, respectively) has improved patient outcomes significantly. The kinase signaling pathway induced by the BcR is highly complex and has multiple interconnecting branches mediated by tyrosine and serine/threonine kinases activated downstream of the BcR. There is a high level of redundancy in the biological responses, with several BcR-signaling kinases driving nuclear factor kappa B activation or inducing antiapoptotic Bcl-2 genes. Accordingly, common gene targets of BcR-signaling kinases may serve as biomarkers indicating enhanced BCR-signaling and aggressive disease progression. This study used a gene expression correlation analysis of malignant B cell lines and primary CLL cells to identify genes whose expression correlated with BCR-signaling kinases overexpressed and/or overactivated in CLL, namely: AKT1, AKT2, BTK, MAPK1, MAPK3, PI3KCD and ZAP70. The analysis identified a 32-gene signature with a strong prognostic potential and DNPEP, the gene coding for aspartic aminopeptidase, as a predictor of aggressive CLL. DNPEP gene expression correlated with MAPK3, PI3KCD, and ZAP70 expression and, in the primary CLL test dataset, showed a strong prognostic potential. The inhibition of DNPEP with a pharmacological inhibitor enhanced the cytotoxic potential of idelalisib and ibrutinib, indicating a biological functionality of DNPEP in CLL. DNPEP, as an aminopeptidase, contributes to the maintenance of the free amino acid pool in CLL cells found to be an essential process for the survival of many cancer cell types, and thus, these results warrant further research into the exploitation of aminopeptidase inhibitors in the treatment of drug-resistant CLL.

Genomic deregulation of PRMT5 supports growth and stress tolerance in chronic lymphocytic leukemia

Patients suffering from chronic lymphocytic leukemia (CLL) display highly diverse clinical courses ranging from indolent cases to aggressive disease, with genetic and epigenetic features resembling this diversity. Here, we developed a comprehensive approach combining a variety of molecular and clinical data to pinpoint translocation events disrupting long-range chromatin interactions and causing cancer-relevant transcriptional deregulation. Thereby, we discovered a B cell specific cis-regulatory element restricting the expression of genes in the associated locus, including PRMT5 and DAD1, two factors with oncogenic potential. Experimental PRMT5 inhibition identified transcriptional programs similar to those in patients with differences in PRMT5 abundance, especially MYC-driven and stress response pathways. In turn, such inhibition impairs factors involved in DNA repair, sensitizing cells for apoptosis. Moreover, we show that artificial deletion of the regulatory element from its endogenous context resulted in upregulation of corresponding genes, including PRMT5. Furthermore, such disruption renders PRMT5 transcription vulnerable to additional stimuli and subsequently alters the expression of downstream PRMT5 targets. These studies provide a mechanism of PRMT5 deregulation in CLL and the molecular dependencies identified might have therapeutic implementations.

A Three-Gene Expression Signature Identifies a Cluster of Patients with Short Survival in Chronic Lymphocytic Leukemia

Chronic lymphocytic leukemia (CLL) is a lymphoproliferative disorder characterized by its heterogeneous clinical evolution. Despite the discovery of the most frequent cytogenomic drivers of disease during the last decade, new efforts are needed in order to improve prognostication. In this study, we used gene expression data of CLL samples in order to discover novel transcriptomic patterns associated with patient survival. We observed that a 3-gene expression signature composed of SCGB2A1, KLF4, and PPP1R14B differentiate a group of circa 5% of cases with short survival. This effect was independent of the main cytogenetic markers of adverse prognosis. Finally, this finding was reproduced in an independent retrospective cohort. We believe that this small gene expression pattern will be useful for CLL prognostication and its association with CLL response to novel drugs should be explored in the future.

Transcriptomics-based validation of the relatedness of heterogeneous nuclear ribonucleoproteins to chronic lymphocytic leukemia as potential biomarkers of the disease aggressiveness

OBJECTIVES

To use independent transcriptomics data sets of cancer patients with prognostic information from public repositories to validate the relevance of our previously described chronic lymphocytic leukemia (CLL)-related proteins at the level of transcription (mRNA) to the prognosis of CLL.  Methods: This is a validation study that was conducted at Majmaah University, Kingdom of Saudi Arabia between January-2017 and July-2018. Two independent data sets of CLL transcriptomics from Gene Expression Omnibus (GEO) with time-to-first treatment (TTFT) data (GSE39671; 130 patients) and information about overall survival (OS) (GSE22762; 107 patients) were used for the validation analyses. To further investigate the relatedness of a transcript of interest to other neoplasms, 6 independent data sets of cancer transcriptomics with prognostic information (1865 patients) from the cancer genomics atlas (TCGA) were used. Pathway-enrichment analyses were conducted using Reactome; and correlation analyses of gene expression were performed using Pearson score. Results: Nine of the CLL-related proteins exhibited transcript expression that predicted TTFT and 7 of the CLL-related proteins showed mRNA levels that predicted OS in CLL patients (p≤0.05). Of these transcripts, 8 were different types of heterogeneous nuclear ribonucleoproteins (HNRNPs); and 2 (HNRNPUL2 and HIST1C1H) retained prognostic significance in the 2 independent data sets. Furthermore, genes that enriched CLL-related pathways (p≤0.05; false discovery rate [FDR] ≤0.05) were found to correlate with the expression of HNRNPUL2 (Pearson score: ≥0.50; p lessthan 0.00001). Finally, increased expression of HNRNPUL2 was indicative of poor prognosis of various types of cancer other than CLL (p less than 0.05). Conclusion: The cognate transcripts of 14 of our CLL-related proteins significantly predicted CLL prognosis.

Investigating epigenetic effects of activation-induced deaminase in chronic lymphocytic leukemia

Activation induced deaminase (AID) has two distinct and well defined roles, both relying on its deoxycytidine (dC) deaminating function: one as a DNA mutator and another in DNA demethylation. In chronic lymphocytic leukemia (CLL), AID was previously shown to be an independent negative prognostic factor. While there is substantial impact on DNA mutations, effects of AID on gene expression by promoter demethylation of disease related target genes in leukemia has not been addressed. To shed light on this question, we aimed at determining genome wide methylation changes as well as gene expression changes in response to AID expression in CLL. Although we found minor differences in individual methylation variable positions following AID expression, we could not find recurrent methylation changes of specific target sites or changes in global methylation.

Inhibition of maternal embryonic leucine zipper kinase with OTSSP167 displays potent anti-leukemic effects in chronic lymphocytic leukemia

TP53 pathway defects contributed to therapy resistance and adverse clinical outcome in chronic lymphocytic leukemia (CLL), which represents an unmet clinical need with few therapeutic options. Maternal embryonic leucine zipper kinase (MELK) is a novel oncogene, which plays crucial roles in mitotic progression and stem cell maintenance. OTSSP167, an orally administrated inhibitor targeting MELK, is currently in a phase I/II clinical trial in patients with advanced breast cancer and acute myeloid leukemia. Yet, no investigation has been elucidated to date regarding the oncogenic role of MELK and effects of OTSSP167 in chronic lymphocytic leukemia (CLL). Previous studies confirmed MELK inhibition abrogated cancer cell survival via p53 signaling pathway. Thus, we aimed to determine the biological function of MELK and therapeutic potential of OTSSP167 in CLL. Herein, MELK over-expression was observed in CLL cells, and correlated with higher WBC count, advanced stage, elevated LDH, increased β2-MG level, unmutated IGHV, positive ZAP-70, deletion of 17p13 and inferior prognosis of CLL patients. In accordance with functional enrichment analyses in gene expression profiling, CLL cells with depletion or inhibition of MELK exhibited impaired cell proliferation, enhanced fast-onset apoptosis, induced G2/M arrest, attenuated cell chemotaxis and promoted sensitivity to fludarabine and ibrutinib. However, gain-of-function assay showed increased cell proliferation and cell chemotaxis. In addition, OTSSP167 treatment reduced phosphorylation of AKT and ERK1/2. It decreased FoxM1 phosphorylation, expression of FoxM1, cyclin B1 and CDK1, while up-regulating p53 and p21 expression. Taken together, MELK served as a candidate of therapeutic target in CLL. OTSSP167 exhibits potent anti-tumor activities in CLL cells, highlighting a novel molecule-based strategy for leukemic interventions.

Subtype assignment of CLL based on B-cell subset associated gene signatures from normal bone marrow - A proof of concept study

Diagnostic and prognostic evaluation of chronic lymphocytic leukemia (CLL) involves blood cell counts, immunophenotyping, IgVH mutation status, and cytogenetic analyses. We generated B-cell associated gene-signatures (BAGS) based on six naturally occurring B-cell subsets within normal bone marrow. Our hypothesis is that by segregating CLL according to BAGS, we can identify subtypes with prognostic implications in support of pathogenetic value of BAGS. Microarray-based gene-expression samples from eight independent CLL cohorts (1,024 untreated patients) were BAGS-stratified into pre-BI, pre-BII, immature, naïve, memory, or plasma cell subtypes; the majority falling within the memory (24.5-45.8%) or naïve (14.5-32.3%) categories. For a subset of CLL patients (n = 296), time to treatment (TTT) was shorter amongst early differentiation subtypes (pre-BI/pre-BII/immature) compared to late subtypes (memory/plasma cell, HR: 0.53 [0.35-0.78]). Particularly, pre-BII subtype patients had the shortest TTT among all subtypes. Correlates derived for BAGS subtype and IgVH mutation (n = 405) revealed an elevated mutation frequency in late vs. early subtypes (71% vs. 45%, P < .001). Predictions for BAGS subtype resistance towards rituximab and cyclophosphamide varied for rituximab, whereas all subtypes were sensitive to cyclophosphamide. This study supports our hypothesis that BAGS-subtyping may be of tangible prognostic and pathogenetic value for CLL patients.

A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia

Primary therapy resistance is a major problem in acute myeloid leukemia treatment. We set out to develop a powerful and robust predictor for therapy resistance for intensively treated adult patients. We used two large gene expression data sets (n=856) to develop a predictor of therapy resistance, which was validated in an independent cohort analyzed by RNA sequencing (n=250). In addition to gene expression markers, standard clinical and laboratory variables as well as the mutation status of 68 genes were considered during construction of the model. The final predictor (PS29MRC) consisted of 29 gene expression markers and a cytogenetic risk classification. A continuous predictor is calculated as a weighted linear sum of the individual variables. In addition, a cut off was defined to divide patients into a high-risk and a low-risk group for resistant disease. PS29MRC was highly significant in the validation set, both as a continuous score (OR=2.39, P=8.63·10⁻⁹, AUC=0.76) and as a dichotomous classifier (OR=8.03, P=4.29·10⁻⁹); accuracy was 77%. In multivariable models, only TP53 mutation, age and PS29MRC (continuous: OR=1.75, P=0.0011; dichotomous: OR=4.44, P=0.00021) were left as significant variables. PS29MRC dominated all models when compared with currently used predictors, and also predicted overall survival independently of established markers. When integrated into the European LeukemiaNet (ELN) 2017 genetic risk stratification, four groups (median survival of 8, 18, 41 months, and not reached) could be defined (P=4.01·10⁻¹⁰). PS29MRC will make it possible to design trials which stratify induction treatment according to the probability of response, and refines the ELN 2017 classification.

Transcriptional coregulator RIP140: an essential regulator of physiology

Transcriptional coregulators drive gene regulatory decisions in the transcriptional space. Although transcription factors including all nuclear receptors provide a docking platform for coregulators to bind, these proteins bring enzymatic capabilities to the gene regulatory sites. RIP140 is a transcriptional coregulator essential for several physiological processes, and aberrations in its function may lead to diseased states. Unlike several other coregulators that are known either for their coactivating or corepressing roles, in gene regulation, RIP140 is capable of acting both as a coactivator and a corepressor. The role of RIP140 in female reproductive axis and recent findings of its role in carcinogenesis and adipose biology have been summarised.

Adults with Philadelphia chromosome-like acute lymphoblastic leukemia frequently have IGH-CRLF2 and JAK2 mutations, persistence of minimal residual disease and poor prognosis

Philadelphia-like B-cell precursor acute lymphoblastic leukemia (Ph-like ALL) is characterized by distinct genetic alterations and inferior prognosis in children and younger adults. The purpose of this study was a genetic and clinical characterization of Ph-like ALL in adults. Twenty-six (13%) of 207 adult patients (median age: 42 years) with B-cell precursor ALL (BCP-ALL) were classified as having Ph-like ALL using gene expression profiling. The frequency of Ph-like ALL was 27% among 95 BCP-ALL patients negative for BCR-ABL1 and KMT2A-rearrangements. IGH-CRLF2 rearrangements (6/16; P=0.002) and mutations in JAK2 (7/16; P<0.001) were found exclusively in the Ph-like ALL subgroup. Clinical and outcome analyses were restricted to patients treated in German Multicenter Study Group for Adult ALL (GMALL) trials 06/99 and 07/03 (n=107). The complete remission rate was 100% among both Ph-like ALL patients (n=19) and the "remaining BCP-ALL" cases (n=40), i.e. patients negative for BCR-ABL1 and KMT2A-rearrangements and the Ph-like subtype. Significantly fewer Ph-like ALL patients reached molecular complete remission (33% versus 79%; P=0.02) and had a lower probability of continuous complete remission (26% versus 60%; P=0.03) and overall survival (22% versus 64%; P=0.006) at 5 years compared to the remaining BCP-ALL patients. The profile of genetic lesions in adults with Ph-like ALL, including older adults, resembles that of pediatric Ph-like ALL and differs from the profile in the remaining BCP-ALL. Our study is the first to demonstrate that Ph-like ALL is associated with inferior outcomes in intensively treated older adult patients. Ph-like adult ALL should be recognized as a distinct, high-risk entity and further research on improved diagnostic and therapeutic approaches is needed. (NCT00199056, NCT00198991).

Genetic signatures of heroin addiction

Heroin addiction is a complex psychiatric disorder with a chronic course and a high relapse rate, which results from the interaction between genetic and environmental factors. Heroin addiction has a substantial heritability in its etiology; hence, identification of individuals with a high genetic propensity to heroin addiction may help prevent the occurrence and relapse of heroin addiction and its complications. The study aimed to identify a small set of genetic signatures that may reliably predict the individuals with a high genetic propensity to heroin addiction. We first measured the transcript level of 13 genes (RASA1, PRKCB, PDK1, JUN, CEBPG, CD74, CEBPB, AUTS2, ENO2, IMPDH2, HAT1, MBD1, and RGS3) in lymphoblastoid cell lines in a sample of 124 male heroin addicts and 124 male control subjects using real-time quantitative PCR. Seven genes (PRKCB, PDK1, JUN, CEBPG, CEBPB, ENO2, and HAT1) showed significant differential expression between the 2 groups. Further analysis using 3 statistical methods including logistic regression analysis, support vector machine learning analysis, and a computer software BIASLESS revealed that a set of 4 genes (JUN, CEBPB, PRKCB, ENO2, or CEBPG) could predict the diagnosis of heroin addiction with the accuracy rate around 85% in our dataset. Our findings support the idea that it is possible to identify genetic signatures of heroin addiction using a small set of expressed genes. However, the study can only be considered as a proof-of-concept study. As the establishment of lymphoblastoid cell line is a laborious and lengthy process, it would be more practical in clinical settings to identify genetic signatures for heroin addiction directly from peripheral blood cells in the future study.

Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example

BACKGROUND

Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches.

METHODS AND FINDINGS

Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived.

CONCLUSIONS

The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases.

Regulatory network reconstruction reveals genes with prognostic value for chronic lymphocytic leukemia

Background

The clinical course of chronic lymphocytic leukemia (CLL) is highly variable; some patients follow an indolent course, but others progress to a more advanced stage. The mutational status of rearranged immunoglobulin heavy chain variable (IGVH) genes in CLL is a feature that is widely recognized for dividing patients into groups that are related to their prognoses. However, the regulatory programs associated with the IGVH statuses are poorly understood, and markers that can precisely predict survival outcomes have yet to be identified.

Methods

In this study, (i) we reconstructed gene regulatory networks in CLL by applying an information-theoretic approach to the expression profiles of 5 cohorts. (ii) We applied master regulator analysis (MRA) to these networks to identify transcription factors (TFs) that regulate an IGVH mutational status signature. The IGVH mutational status signature was developed by searching for differentially expressed genes between the IGVH mutational statuses in numerous CLL cohorts. (iii) To evaluate the biological implication of the inferred regulators, prognostic values were determined using time to treatment (TTT) and overall survival (OS) in two different cohorts.

Results

A robust IGVH expression signature was obtained, and various TFs emerged as regulators of the signature in most of the reconstructed networks. The TF targets expression profiles exhibited significant differences with respect to survival, which allowed the definition of a reduced profile with a high value for OS. TCF7 and its targets stood out for their roles in progression.

Conclusion

TFs and their targets, which were obtained merely from inferred regulatory associations, have prognostic implications and reflect a regulatory context for prognosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2189-6) contains supplementary material, which is available to authorized users.

Gene expression of INPP5F as an independent prognostic marker in fludarabine-based therapy of chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is a heterogeneous disease. Various disease-related and patient-related factors have been shown to influence the course of the disease. The aim of this study was to identify novel biomarkers of significant clinical relevance. Pretreatment CD19-separated lymphocytes (n=237; discovery set) and peripheral blood mononuclear cells (n=92; validation set) from the REACH trial, a randomized phase III trial in relapsed CLL comparing rituximab plus fludarabine plus cyclophosphamide with fludarabine plus cyclophosphamide alone, underwent gene expression profiling. By using Cox regression survival analysis on the discovery set, we identified inositol polyphosphate-5-phosphatase F (INPP5F) as a prognostic factor for progression-free survival (P<0.001; hazard ratio (HR), 1.63; 95% confidence interval (CI), 1.35-1.98) and overall survival (P<0.001; HR, 1.47; 95% CI, 1.18-1.84), regardless of adjusting for known prognostic factors. These findings were confirmed on the validation set, suggesting that INPP5F may serve as a novel, easy-to-assess future prognostic biomarker for fludarabine-based therapy in CLL.

Clustering of Expression Data in Chronic Lymphocytic Leukemia Reveals New Molecular Subdivisions

Although the identification of inherent structure in chronic lymphocytic leukemia (CLL) gene expression data using class discovery approaches has not been extensively explored, the natural clustering of patient samples can reveal molecular subdivisions that have biological and clinical implications. To explore this, we preprocessed raw gene expression data from two published studies, combined the data to increase the statistical power, and performed unsupervised clustering analysis. The clustering analysis was replicated in 4 independent cohorts. To assess the biological significance of the resultant clusters, we evaluated their prognostic value and identified cluster-specific markers. The clustering analysis revealed two robust and stable subgroups of CLL patients in the pooled dataset. The subgroups were confirmed by different methodological approaches (non-negative matrix factorization NMF clustering and hierarchical clustering) and validated in different cohorts. The subdivisions were related with differential clinical outcomes and markers associated with the microenvironment and the MAPK and BCR signaling pathways. It was also found that the cluster markers were independent of the immunoglobulin heavy chain variable (IGVH) genes mutational status. These findings suggest that the microenvironment can influence the clinical behavior of CLL, contributing to prognostic differences. The workflow followed here provides a new perspective on differences in prognosis and highlights new markers that should be explored in this context.

Two distinct molecular subtypes of chronic lymphocytic leukemia give new insights on the pathogenesis of the disease and identify novel therapeutic targets

Biopsy samples of lymph nodes from 38 patients with CLL were analyzed. We found differential expression in 1092 genes in two different subgroups: 418 overexpressed in one subgroup and 674 in another. Molecular pathways identified in one subgroup appear to be characterized by greater dependence of signaling by cytokines and activation of the NFkB pathway, while in the other seem to depend on cell cycle. Despite having found a differential expression between both subgroups, none of these genes reached FDR < 0.25. We have not found significant association with survival or any prognostic factors. Analysis of the differences between normal lymph node and CLL in 253 genes with difference in the intensity of expression revealed upregulated genes different to BCR: CD40, TCL1, IL-7, and PAX5. Using large-scale molecular analysis, we may obtain information about molecular mechanisms of CLL pathogenesis and may contribute to the identification of new therapeutic targets.

APO866 Increases Antitumor Activity of Cyclosporin-A by Inducing Mitochondrial and Endoplasmic Reticulum Stress in Leukemia Cells

PURPOSE

The nicotinamide phosphoribosyltransferase (NAMPT) inhibitor, APO866, has been previously shown to have antileukemic activity in preclinical models, but its cytotoxicity in primary leukemia cells is frequently limited. The success of current antileukemic treatments is reduced by the occurrence of multidrug resistance, which, in turn, is mediated by membrane transport proteins, such as P-glycoprotein-1 (Pgp). Here, we evaluated the antileukemic effects of APO866 in combination with Pgp inhibitors and studied the mechanisms underlying the interaction between these two types of agents.

EXPERIMENTAL DESIGN

The effects of APO866 with or without Pgp inhibitors were tested on the viability of leukemia cell lines, primary leukemia cells (AML, n = 6; B-CLL, n = 19), and healthy leukocytes. Intracellular nicotinamide adenine dinucleotide (NAD(+)) and ATP levels, mitochondrial transmembrane potential (ΔΨ(m)), markers of apoptosis and of endoplasmic reticulum (ER) stress were evaluated.

RESULTS

The combination of APO866 with Pgp inhibitors resulted in a synergistic cytotoxic effect in leukemia cells, while sparing normal CD34(+) progenitor cells and peripheral blood mononuclear cells. Combining Pgp inhibitors with APO866 led to increased intracellular APO866 levels, compounded NAD(+) and ATP shortage, and induced ΔΨ(m) dissipation. Notably, APO866, Pgp inhibitors and, to a much higher extent, their combination induced ER stress and ER stress inhibition strongly reduced the activity of these treatments.

CONCLUSIONS

APO866 and Pgp inhibitors show a strong synergistic cooperation in leukemia cells, including acute myelogenous leukemia (AML) and B-cell chronic lymphocytic leukemia (B-CLL) samples. Further evaluations of the combination of these agents in clinical setting should be considered.

One-step extrapolation of the prediction performance of a gene signature derived from a small study

OBJECTIVE

Microarray-related studies often involve a very large number of genes and small sample size. Cross-validating or bootstrapping is therefore imperative to obtain a fair assessment of the prediction/classification performance of a gene signature. A deficiency of these methods is the reduced training sample size because of the partition process in cross-validation and sampling with replacement in bootstrapping. To address this problem, we aim to obtain a prediction performance estimate that strikes a good balance between bias and variance and has a small root mean squared error.

METHODS

We propose to make a one-step extrapolation from the fitted learning curve to estimate the prediction/classification performance of the model trained by all the samples.

RESULTS

Simulation studies show that the method strikes a good balance between bias and variance and has a small root mean squared error. Three microarray data sets are used for demonstration.

CONCLUSIONS

Our method is advocated to estimate the prediction performance of a gene signature derived from a small study.

Expression and role of RIP140/NRIP1 in chronic lymphocytic leukemia

RIP140 is a transcriptional coregulator, (also known as NRIP1), which finely tunes the activity of various transcription factors and plays very important physiological roles. Noticeably, the RIP140 gene has been implicated in the control of energy expenditure, behavior, cognition, mammary gland development and intestinal homeostasis. RIP140 is also involved in the regulation of various oncogenic signaling pathways and participates in the development and progression of solid tumors. During the past years, several papers have reported evidences linking RIP140 to hematologic malignancies. Among them, two recent studies with correlative data suggested that gene expression signatures including RIP140 can predict survival in chronic lymphocytic leukemia (CLL). This review aims to summarize the literature dealing with the expression of RIP140 in CLL and to explore the potential impact of this factor on transcription pathways which play key roles in this pathology.

ID helix-loop-helix proteins as determinants of cell survival in B-cell chronic lymphocytic leukemia cells in vitro

BACKGROUND

Members of the inhibitor of DNA-binding (ID) family of helix-loop-helix proteins have been causally implicated in the pathogenesis of several types of B-cell lineage malignancy, either on the basis of mutation or by altered expression. B-cell chronic lymphocytic leukemia encompasses a heterogeneous group of disorders and is the commonest leukaemia type in the Western world. In this study, we have investigated the pathobiological functions of the ID2 and ID3 proteins in this disease with an emphasis on their role in regulating leukemic cell death/survival.

METHODS

Bioinformatics analysis of microarray gene expression data was used to investigate expression of ID2/ID3 in leukemic versus normal B cells, their association with clinical course of disease and molecular sub-type and to reconstruct a gene regulatory network using the 'maximum information coefficient' (MIC) for target gene inference. In vitro cultured primary leukemia cells, either in isolation or co-cultured with accessory vascular endothelial cells, were used to investigate ID2/ID3 protein expression by western blotting and to assess the cytotoxic response of different drugs (fludarabine, chlorambucil, ethacrynic acid) by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. ID2/ID3 protein levels in primary leukemia cells and in MEC1 cells were manipulated by transduction with siRNA reagents.

RESULTS

Datamining showed that the expression profiles of ID2 and ID3 are associated with distinct pathobiological features of disease and implicated both genes in regulating cell death/survival by targeting multiple non-overlapping sets of apoptosis effecter genes. Consistent with microarray data, the overall pattern of ID2/ID3 protein expression in relation to cell death/survival responses of primary leukemia cells was suggestive of a pro-survival function for both ID proteins. This was confirmed by siRNA knock-down experiments in MEC1 cells and in primary leukemia cells, but with variability in the dependence of leukemic cells from different patients on ID protein expression for cell survival. Vascular endothelial cells rescued leukemia cells from spontaneous and cytotoxic drug-induced cell death at least in part, via an ID protein-coupled redox-dependent mechanism.

CONCLUSIONS

Our study provides evidence for a pro-survival function of the ID2/ID3 proteins in chronic lymphocytic leukemia cells and also highlights these proteins as potential determinants of the pathobiology of this disorder.

Added predictive value of omics data: specific issues related to validation illustrated by two case studies

Background

In the last years, the importance of independent validation of the prediction ability of a new gene signature has been largely recognized. Recently, with the development of gene signatures which integrate rather than replace the clinical predictors in the prediction rule, the focus has been moved to the validation of the added predictive value of a gene signature, i.e. to the verification that the inclusion of the new gene signature in a prediction model is able to improve its prediction ability.

Methods

The high-dimensional nature of the data from which a new signature is derived raises challenging issues and necessitates the modification of classical methods to adapt them to this framework. Here we show how to validate the added predictive value of a signature derived from high-dimensional data and critically discuss the impact of the choice of methods on the results.

Results

The analysis of the added predictive value of two gene signatures developed in two recent studies on the survival of leukemia patients allows us to illustrate and empirically compare different validation techniques in the high-dimensional framework.

Conclusions

The issues related to the high-dimensional nature of the omics predictors space affect the validation process. An analysis procedure based on repeated cross-validation is suggested.

Isolated trisomy 13 defines a homogeneous AML subgroup with high frequency of mutations in spliceosome genes and poor prognosis

AML patients with isolated trisomy 13 have a very poor clinical outcome Isolated trisomy 13 in AML is associated with a high frequency of mutations in SRSF2 (81%) and RUNX1 (75%)

Investigating the prediction ability of survival models based on both clinical and omics data: two case studies

In biomedical literature, numerous prediction models for clinical outcomes have been developed based either on clinical data or, more recently, on high-throughput molecular data (omics data). Prediction models based on both types of data, however, are less common, although some recent studies suggest that a suitable combination of clinical and molecular information may lead to models with better predictive abilities. This is probably due to the fact that it is not straightforward to combine data with different characteristics and dimensions (poorly characterized high-dimensional omics data, well-investigated low-dimensional clinical data). In this paper, we analyze two publicly available datasets related to breast cancer and neuroblastoma, respectively, in order to show some possible ways to combine clinical and omics data into a prediction model of time-to-event outcome. Different strategies and statistical methods are exploited. The results are compared and discussed according to different criteria, including the discriminative ability of the models, computed on a validation dataset.