Oncogenic role and target properties of the lysine-specific demethylase KDM1A in chronic lymphocytic leukemia.

In chronic lymphocytic leukemia (CLL), epigenetic alterations are considered to centrally shape the transcriptional signatures that drive disease evolution and underlie its biological and clinical subsets. Characterizations of epigenetic regulators, particularly histone-modifying enzymes, are very rudimentary in CLL. In efforts to establish effectors of the CLL-associated oncogene T-cell leukemia 1A (TCL1A), we identified here the lysine-specific histone demethylase KDM1A to interact with the TCL1A protein in B cells in conjunction with an increased catalytic activity of KDM1A. We demonstrate that KDM1A is upregulated in malignant B cells. Elevated KDM1A and associated gene expression signatures correlated with aggressive disease features and adverse clinical outcomes in a large prospective CLL trial cohort. Genetic Kdm1a knockdown in Eµ-TCL1A mice reduced leukemic burden and prolonged animal survival, accompanied by upregulated p53 and proapoptotic pathways. Genetic KDM1A depletion also affected milieu components (T, stromal, and monocytic cells), resulting in significant reductions in their capacity to support CLL-cell survival and proliferation. Integrated analyses of differential global transcriptomes (RNA sequencing) and H3K4me3 marks (chromatin immunoprecipitation sequencing) in Eµ-TCL1A vs iKdm1aKD;Eµ-TCL1A mice (confirmed in human CLL) implicate KDM1A as an oncogenic transcriptional repressor in CLL which alters histone methylation patterns with pronounced effects on defined cell death and motility pathways. Finally, pharmacologic KDM1A inhibition altered H3K4/9 target methylation and revealed marked anti-B-cell leukemic synergisms. Overall, we established the pathogenic role and effector networks of KDM1A in CLL via tumor-cell intrinsic mechanisms and its impacts in cells of the microenvironment. Our data also provide rationales to further investigate therapeutic KDM1A targeting in CLL.

Myeloid neoplasm with histiocytosis and spleen tyrosine kinase fusion responds to fostamatinib.

Not available.

The impact of increasing karyotypic complexity and evolution on survival in patients with CLL treated with ibrutinib.

Complex karyotype, defined as ≥3 cytogenetic abnormalities, is prognostic of survival in patients treated with ibrutinib or venetoclax in relapsed/refractory (RR) chronic lymphocytic leukemia (CLL). Recent studies re-evaluating this dichotomous variable have shown that higher numbers of cytogenetic abnormalities (ie, ≥5) have a worse overall survival in patients treated with chemoimmunotherapy. We sought to determine if increasing karyotypic complexity, treated as a continuous variable, was prognostic of survival for patients treated with ibrutinib for CLL. We conducted a retrospective analysis of all patients with CLL treated with single-agent ibrutinib or in combination with an anti-CD20 antibody at our institution. We included 456 patients with both treatment-naive and RR disease. Median number of prior therapies was 2 (range, 0-13), 30% of patients had presence of del(17p), and 75% expressed unmutated IGHV. Fifty percent had ≥3 cytogenetic abnormalities, including 30% with ≥5. In a multivariable analysis, increasing karyotypic complexity was an independent predictor of shorter progression-free survival (hazard ratio, 1.07; 95% confidence interval, 1.04-1.10; P < .0001) and overall survival (hazard ratio, 1.09; 95% confidence interval, 1.05-1.12; P < .0001). Furthermore, we found that presence of clonal evolution determined by cytogenetic analysis at progression was prognostic of subsequent survival (P = .02). This solidifies karyotypic complexity as an important prognostic factor for patients with CLL treated with ibrutinib. Further research should consider sequential karyotypic analysis as a determination of risk of progression and death in patients with CLL.

Refining prognosis in chronic lymphocytic leukemia with normal Fluorescence in situ hybridization results.

Fluorescence in situ hybridization (FISH) to detect the recurrent cytogenetics abnormalities deletion 13q, trisomy 12, deletion 11q, and deletion 17p is important for prognostication in chronic lymphocytic leukemia (CLL). A subset of patients are negative for each of these abnormalities (normal 12/13/11/17 FISH), and outcomes are heterogenous within this group. To elucidate variables important for prognostication in this subgroup we conducted a retrospective analysis of 280 treatment-naïve CLL patients with normal standard CLL FISH results. In a multivariable model, advanced Rai stage (p = 0.04, hazard ratio [HR] 1.24 (95% confidence interval [CI] 1.01-1.53)), unmutated immunoglobulin heavy chain gene (IGHV) (p < 0.0001, HR 5.59 (95% CI 3.63-8.62)) and IGH rearrangement by FISH (p = 0.02, HR 2.56 (95% CI 1.20-5.48)) were significantly associated with shorter time to first treatment. In a multivariable model for overall survival, increasing age at 5-year increments (p < 0.0001, HR 1.55 (95% CI 1.25-1.93)), unmutated IGHV (p = 0.01, HR 5.28 (95% CI 1.52-18.35)) and gain of REL (p = 0.01, HR 4.08 (5% CI 1.45-11.49)) were significantly associated with shorter survival. Our study identifies variables important for refining prognosis for CLL patients with normal standard CLL FISH results.

RCytoGPS: an R package for reading and visualizing cytogenetics data.

SUMMARY: Cytogenetics data, or karyotypes, are among the most common clinically used forms of genetic data. Karyotypes are stored as standardized text strings using the International System for Human Cytogenomic Nomenclature (ISCN). Historically, these data have not been used in large-scale computational analyses due to limitations in the ISCN text format and structure. Recently developed computational tools such as CytoGPS have enabled large-scale computational analyses of karyotypes. To further enable such analyses, we have now developed RCytoGPS, an R package that takes JSON files generated from CytoGPS.org and converts them into objects in R. This conversion facilitates the analysis and visualizations of karyotype data. In effect this tool streamlines the process of performing large-scale karyotype analyses, thus advancing the field of computational cytogenetic pathology. AVAILABILITY AND IMPLEMENTATION: Freely available at https://CRAN.R-project.org/package=RCytoGPS. The code for the underlying CytoGPS software can be found at https://github.com/i2-wustl/CytoGPS.

Pattern recognition in lymphoid malignancies using CytoGPS and Mercator.

BACKGROUND: There have been many recent breakthroughs in processing and analyzing large-scale data sets in biomedical informatics. For example, the CytoGPS algorithm has enabled the use of text-based karyotypes by transforming them into a binary model. However, such advances are accompanied by new problems of data sparsity, heterogeneity, and noisiness that are magnified by the large-scale multidimensional nature of the data. To address these problems, we developed the Mercator R package, which processes and visualizes binary biomedical data. We use Mercator to address biomedical questions of cytogenetic patterns relating to lymphoid hematologic malignancies, which include a broad set of leukemias and lymphomas. Karyotype data are one of the most common form of genetic data collected on lymphoid malignancies, because karyotyping is part of the standard of care in these cancers. RESULTS: In this paper we combine the analytic power of CytoGPS and Mercator to perform a large-scale multidimensional pattern recognition study on 22,741 karyotype samples in 47 different hematologic malignancies obtained from the public Mitelman database. CONCLUSION: Our findings indicate that Mercator was able to identify both known and novel cytogenetic patterns across different lymphoid malignancies, furthering our understanding of the genetics of these diseases.

Developmental subtypes assessed by DNA methylation-iPLEX forecast the natural history of chronic lymphocytic leukemia.

Alterations in global DNA methylation patterns are a major hallmark of cancer and represent attractive biomarkers for personalized risk stratification. Chronic lymphocytic leukemia (CLL) risk stratification studies typically focus on time to first treatment (TTFT), time to progression (TTP) after treatment, and overall survival (OS). Whereas TTFT risk stratification remains similar over time, TTP and OS have changed dramatically with the introduction of targeted therapies, such as the Bruton tyrosine kinase inhibitor ibrutinib. We have shown that genome-wide DNA methylation patterns in CLL are strongly associated with phenotypic differentiation and patient outcomes. Here, we developed a novel assay, termed methylation-iPLEX (Me-iPLEX), for high-throughput quantification of targeted panels of single cytosine guanine dinucleotides from multiple independent loci. Me-iPLEX was used to classify CLL samples into 1 of 3 known epigenetic subtypes (epitypes). We examined the impact of epitype in 1286 CLL patients from 4 independent cohorts representing a comprehensive view of CLL disease course and therapies. We found that epitype significantly predicted TTFT and OS among newly diagnosed CLL patients. Additionally, epitype predicted TTP and OS with 2 common CLL therapies: chemoimmunotherapy and ibrutinib. Epitype retained significance after stratifying by biologically related biomarkers, immunoglobulin heavy chain mutational status, and ZAP70 expression, as well as other common prognostic markers. Furthermore, among several biological traits enriched between epitypes, we found highly biased immunogenetic features, including IGLV3-21 usage in the poorly characterized intermediate-programmed CLL epitype. In summary, Me-iPLEX is an elegant method to assess epigenetic signatures, including robust classification of CLL epitypes that independently stratify patient risk at diagnosis and time of treatment.

Inferring clonal heterogeneity in cancer using SNP arrays and whole genome sequencing.

MOTIVATION: Clonal heterogeneity is common in many types of cancer, including chronic lymphocytic leukemia (CLL). Previous research suggests that the presence of multiple distinct cancer clones is associated with clinical outcome. Detection of clonal heterogeneity from high throughput data, such as sequencing or single nucleotide polymorphism (SNP) array data, is important for gaining a better understanding of cancer and may improve prediction of clinical outcome or response to treatment. Here, we present a new method, CloneSeeker, for inferring clinical heterogeneity from sequencing data, SNP array data, or both. RESULTS: We generated simulated SNP array and sequencing data and applied CloneSeeker along with two other methods. We demonstrate that CloneSeeker is more accurate than existing algorithms at determining the number of clones, distribution of cancer cells among clones, and mutation and/or copy numbers belonging to each clone. Next, we applied CloneSeeker to SNP array data from samples of 258 previously untreated CLL patients to gain a better understanding of the characteristics of CLL tumors and to elucidate the relationship between clonal heterogeneity and clinical outcome. We found that a significant majority of CLL patients appear to have multiple clones distinguished by copy number alterations alone. We also found that the presence of multiple clones corresponded with significantly worse survival among CLL patients. These findings may prove useful for improving the accuracy of prognosis and design of treatment strategies. AVAILABILITY AND IMPLEMENTATION: Code available on R-Forge: https://r-forge.r-project.org/projects/CloneSeeker/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CytoGPS: a web-enabled karyotype analysis tool for cytogenetics.

SUMMARY: Karyotype data are the most common form of genetic data that is regularly used clinically. They are collected as part of the standard of care in many diseases, particularly in pediatric and cancer medicine contexts. Karyotypes are represented in a unique text-based format, with a syntax defined by the International System for human Cytogenetic Nomenclature (ISCN). While human-readable, ISCN is not intrinsically machine-readable. This limitation has prevented the full use of complex karyotype data in discovery science use cases. To enhance the utility and value of karyotype data, we developed a tool named CytoGPS. CytoGPS first parses ISCN karyotypes into a machine-readable format. It then converts the ISCN karyotype into a binary Loss-Gain-Fusion (LGF) model, which represents all cytogenetic abnormalities as combinations of loss, gain, or fusion events, in a format that is analyzable using modern computational methods. Such data is then made available for comprehensive 'downstream' analyses that previously were not feasible. AVAILABILITY AND IMPLEMENTATION: Freely available at http://cytogps.org.

Time-to-progression after front-line fludarabine, cyclophosphamide, and rituximab chemoimmunotherapy for chronic lymphocytic leukaemia: a retrospective, multicohort study.

BACKGROUND: Fludarabine, cyclophosphamide, and rituximab (FCR) has become a gold-standard chemoimmunotherapy regimen for patients with chronic lymphocytic leukaemia. However, the question remains of how to treat treatment-naive patients with IGHV-unmutated chronic lymphocytic leukaemia. We therefore aimed to develop and validate a gene expression signature to identify which of these patients are likely to achieve durable remissions with FCR chemoimmunotherapy. METHODS: We did a retrospective cohort study in two cohorts of treatment-naive patients (aged ≥18 years) with chronic lymphocytic leukaemia. The discovery and training cohort consisted of peripheral blood samples collected from patients treated at the University of Texas MD Anderson Cancer Center (Houston, TX, USA), who fulfilled the diagnostic criteria of the International Workshop on Chronic Lymphocytic Leukemia, had received at least three cycles of FCR chemoimmunotherapy, and had been treated between Oct 10, 2000, and Oct 26, 2006 (ie, the MDACC cohort). We did transcriptional profiling on samples obtained from the MDACC cohort to identify genes associated with time to progression. We did univariate Cox proportional hazards analyses and used significant genes to cluster IGHV-unmutated samples into two groups (intermediate prognosis and unfavourable prognosis). After using cross-validation to assess robustness, we applied the Lasso method to standardise the gene expression values to find a minimum gene signature. We validated this signature in an external cohort of treatment-naive patients with IGHV-unmutated chronic lymphocytic leukaemia enrolled on the CLL8 trial of the German Chronic Lymphocytic Leukaemia Study Group who were treated between July 21, 2003, and April 4, 2006 (ie, the CLL8 cohort). FINDINGS: The MDACC cohort consisted of 101 patients and the CLL8 cohort consisted of 109 patients. Using the MDACC cohort, we identified and developed a 17-gene expression signature that distinguished IGHV-unmutated patients who were likely to achieve a long-term remission following front-line FCR chemoimmunotherapy from those who might benefit from alternative front-line regimens (hazard ratio 3·83, 95% CI 1·94-7·59; p<0·0001). We validated this gene signature in the CLL8 cohort; patients with an unfavourable prognosis versus those with an intermediate prognosis had a cause-specific hazard ratio of 1·90 (95% CI 1·18-3·06; p=0·008). Median time to progression was 39 months (IQR 22-69) for those with an unfavourable prognosis compared with 59 months (28-84) for those with an intermediate prognosis. INTERPRETATION: We have developed a robust, reproducible 17-gene signature that identifies a subset of treatment-naive patients with IGHV-unmutated chronic lymphocytic leukaemia who might substantially benefit from treatment with FCR chemoimmunotherapy. We recommend testing the value of this gene signature in a prospective study that compares FCR treatment with newer alternative therapies as part of a randomised clinical trial. FUNDING: Chronic Lymphocytic Leukaemia Global Research Foundation and the National Institutes of Health/National Cancer Institute.

Thirty biologically interpretable clusters of transcription factors distinguish cancer type.

BACKGROUND: Transcription factors are essential regulators of gene expression and play critical roles in development, differentiation, and in many cancers. To carry out their regulatory programs, they must cooperate in networks and bind simultaneously to sites in promoter or enhancer regions of genes. We hypothesize that the mRNA co-expression patterns of transcription factors can be used both to learn how they cooperate in networks and to distinguish between cancer types. RESULTS: We recently developed a new algorithm, Thresher, that combines principal component analysis, outlier filtering, and von Mises-Fisher mixture models to cluster genes (in this case, transcription factors) based on expression, determining the optimal number of clusters in the process. We applied Thresher to the RNA-Seq expression data of 486 transcription factors from more than 10,000 samples of 33 kinds of cancer studied in The Cancer Genome Atlas (TCGA). We found that 30 clusters of transcription factors from a 29-dimensional principal component space were able to distinguish between most cancer types, and could separate tumor samples from normal controls. Moreover, each cluster of transcription factors could be either (i) linked to a tissue-specific expression pattern or (ii) associated with a fundamental biological process such as cell cycle, angiogenesis, apoptosis, or cytoskeleton. Clusters of the second type were more likely also to be associated with embryonically lethal mouse phenotypes. CONCLUSIONS: Using our approach, we have shown that the mRNA expression patterns of transcription factors contain most of the information needed to distinguish different cancer types. The Thresher method is capable of discovering biologically interpretable clusters of genes. It can potentially be applied to other gene sets, such as signaling pathways, to decompose them into simpler, yet biologically meaningful, components.

Trisomy 12 chronic lymphocytic leukemia expresses a unique set of activated and targetable pathways.

Although trisomy 12 (+12) chronic lymphocytic leukemia (CLL) comprises about 20% of cases, relatively little is known about its pathophysiology. These cases often demonstrate atypical morphological and immunophenotypic features, high proliferative rates, unmutated immunoglobulin heavy chain variable region genes, and a high frequency of NOTCH1 mutation. Patients with +12 CLL have an intermediate prognosis, and show higher incidences of thrombocytopenia, Richter transformation, and other secondary cancers. Despite these important differences, relatively few transcriptional profiling studies have focused on identifying dysregulated pathways that characterize +12 CLL, and most have used a hierarchical cytogenetic classification in which cases with more than one recurrent abnormality are categorized according to the abnormality with the poorest prognosis. In this study, we sought to identify protein-coding genes whose expression contributes to the unique pathophysiology of +12 CLL. To exclude the likely confounding effects of multiple cytogenetic abnormalities on gene expression, our +12 patient cohort had +12 as the sole abnormality. We profiled samples obtained from 147 treatment-naïve patients. We compared cases with +12 as the only cytogenetic abnormality to cases with only del(13q), del(11q), or diploid cytogenetics using independent discovery (n=97) and validation (n=50) sets. We demonstrate that CLL cases with +12 as the sole abnormality express a unique set of activated pathways compared to other cytogenetic subtypes. Among these pathways, we identify the NFAT signaling pathway and the immune checkpoint molecule, NT5E (CD73), which may represent new therapeutic targets.

Del(20q) in patients with chronic lymphocytic leukemia: a therapy-related abnormality involving lymphoid or myeloid cells.

Deletion 20q (Del(20q)), a common cytogenetic abnormality in myeloid neoplasms, is rare in chronic lymphocytic leukemia. We report 64 patients with chronic lymphocytic leukemia and del(20q), as the sole abnormality in 40, a stemline abnormality in 21, and a secondary abnormality in 3 cases. Fluorescence in situ hybridization (FISH) analysis revealed an additional high-risk abnormality, del(11q) or del(17p), in 25/64 (39%) cases. In most cases, the leukemic cells showed atypical cytologic features, unmutated IGHV (immunoglobulin heavy-chain variable region) genes, and ZAP70 positivity. The del(20q) was detected only after chemotherapy in all 27 cases with initial karyotypes available. With a median follow-up of 90 months, 30 patients (47%) died, most as a direct consequence of chronic lymphocytic leukemia. Eight patients developed a therapy-related myeloid neoplasm, seven with a complex karyotype. Combined morphologic and FISH analysis for del(20q) performed in 12 cases without morphologic evidence of a myeloid neoplasm localized the del(20q) to the chronic lymphocytic leukemia cells in 5 (42%) cases, and to myeloid/erythroid cells in 7 (58)% cases. The del(20q) was detected in myeloid cells in all 4 cases of myelodysplastic syndrome. In aggregate, these data indicate that chronic lymphocytic leukemia with del(20q) acquired after therapy is heterogeneous. In cases with morphologic evidence of dysplasia, the del(20q) likely resides in the myeloid lineage. However, in cases without morphologic evidence of dysplasia, the del(20q) may represent clonal evolution and disease progression. Combining morphologic analysis with FISH for del(20q) or performing FISH on immunomagnetically selected sub-populations to localize the cell population with this abnormality may help guide patient management.

Externally validated predictive clinical model for untreated del(17p13.1) chronic lymphocytic leukemia patients.

Little is known about outcomes of patients with chronic lymphocytic leukemia (CLL) with del(17p13.1) karyotype at diagnosis. We reviewed 114 de novo del(17p13.1) CLL patients seen at our institution. Using proportional hazards models to identify pretreatment clinical variables significantly associated with treatment-free survival (TFS) and overall survival (OS), we developed a simplified risk score for de novo del(17p13.1) CLL patients to predict TFS and OS based on these variables. These scores, particularly the very highest, can be utilized to identify high-risk patients for expedient enrollment on clinical trials. Our data support careful observation for low-risk patients, potentially preventing unnecessary use of aggressive therapies.

B acute lymphoblastic leukemia with t(14;19)(q32;p13.1) involving IGH/EPOR: a clinically aggressive subset of disease.

B acute lymphoblastic leukemia (B-ALL) with t(14;19)(q32;p13.1), in which IGH and EPOR are juxtaposed, has been reported rarely. We describe the clinicopathological features of six patients, three men and three women, with a median age of 39 years. Initial and follow-up bone marrow samples were examined from each patient. The clinical, morphologic, and immunophenotypic results were compared with data obtained from conventional cytogenetic analysis and by using home-brew fluorescence in situ hybridization (FISH) probes for IGH at 14q32 and EPOR at 19p13.1. The bone marrow specimens were hypercellular (median 90%; range 80-100%), with a median blast count of 90% (range 60-93%). Immunophenotypic analysis performed by flow cytometry demonstrated a stable, precursor B-cell immunophenotype. The t(14;19)(q32;p13.1) was present in all cases with morphologic evidence of disease. The translocation was stable and appeared morphologically subtle on conventional karyotypic analysis. Detection was facilitated using FISH, which confirmed IGH/EPOR rearrangement in all cases. All patients received aggressive multiagent chemotherapy as part of a variety of regimens. Four of six patients achieved an initial complete remission, but all relapsed. At last follow-up, five of six patients had died of disease (median survival, 12 months after diagnosis). We conclude that B-ALL associated with t(14;19)(q32;p13.1) is a distinctive form of disease that is associated with younger patient age and an aggressive clinical course.

Mutational profile and prognostic significance of TP53 in diffuse large B-cell lymphoma patients treated with R-CHOP: report from an International DLBCL Rituximab-CHOP Consortium Program Study.

TP53 mutation is an independent marker of poor prognosis in patients with diffuse large B-cell lymphoma (DLBCL) treated with cyclophosphamide, hydroxydaunorubicin, vincristine, and prednisone (CHOP) therapy. However, its prognostic value in the rituximab immunochemotherapy era remains undefined. In the present study of a large cohort of DLBCL patients treated with rituximab plus CHOP (R-CHOP), we show that those with TP53 mutations had worse overall and progression-free survival compared with those without. Unlike earlier studies of patients treated with CHOP, TP53 mutation has predictive value for R-CHOP-treated patients with either the germinal center B-cell or activated B-cell DLBCL subtypes. Furthermore, we identified the loop-sheet-helix and L3 motifs in the DNA-binding domain to be the most critical structures for maintaining p53 function. In contrast, TP53 deletion and loss of heterozygosity did not confer worse survival. If gene mutation data are not available, immunohistochemical analysis showing > 50% cells expressing p53 protein is a useful surrogate and was able to stratify patients with significantly different prognoses. We conclude that assessment of TP53 mutation status is important for stratifying R-CHOP-treated patients into distinct prognostic subsets and has significant value in the design of future therapeutic strategies.

Topological Structures in the Space of Treatment-Naïve Patients with Chronic Lymphocytic Leukemia.

Patients are complex and heterogeneous; clinical data sets are complicated by noise, missing data, and the presence of mixed-type data. Using such data sets requires understanding the high-dimensional "space of patients", composed of all measurements that define all relevant phenotypes. The current state-of-the-art merely defines spatial groupings of patients using cluster analyses. Our goal is to apply topological data analysis (TDA), a new unsupervised technique, to obtain a more complete understanding of patient space. We applied TDA to a space of 266 previously untreated patients with Chronic Lymphocytic Leukemia (CLL), using the "daisy" metric to compute distances between clinical records. We found clear evidence for both loops and voids in the CLL data. To interpret these structures, we developed novel computational and graphical methods. The most persistent loop and the most persistent void can be explained using three dichotomized, prognostically important factors in CLL: IGHV somatic mutation status, beta-2 microglobulin, and Rai stage. In conclusion, patient space turns out to be richer and more complex than current models suggest. TDA could become a powerful tool in a researcher's arsenal for interpreting high-dimensional data by providing novel insights into biological processes and improving our understanding of clinical and biological data sets.