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.

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.

Electronic Health Records and Genomics: Perspectives from the Association for Molecular Pathology Electronic Health Record (EHR) Interoperability for Clinical Genomics Data Working Group.

The use of genomics in medicine is expanding rapidly, but information systems are lagging in their ability to support genomic workflows both from the laboratory and patient-facing provider perspective. The complexity of genomic data, the lack of needed data standards, and lack of genomic fluency and functionality as well as several other factors have contributed to the gaps between genomic data generation, interoperability, and utilization. These gaps are posing significant challenges to laboratory and pathology professionals, clinicians, and patients in the ability to generate, communicate, consume, and use genomic test results. The Association for Molecular Pathology Electronic Health Record Working Group was convened to assess the challenges and opportunities and to recommend solutions on ways to resolve current problems associated with the display and use of genomic data in electronic health records.

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.

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.

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.

Recurrent XPO1 mutations alter pathogenesis of chronic lymphocytic leukemia.

BACKGROUND: Exportin 1 (XPO1/CRM1) is a key mediator of nuclear export with relevance to multiple cancers, including chronic lymphocytic leukemia (CLL). Whole exome sequencing has identified hot-spot somatic XPO1 point mutations which we found to disrupt highly conserved biophysical interactions in the NES-binding groove, conferring novel cargo-binding abilities and forcing cellular mis-localization of critical regulators. However, the pathogenic role played by change-in-function XPO1 mutations in CLL is not fully understood. METHODS: We performed a large, multi-center retrospective analysis of CLL cases (N = 1286) to correlate nonsynonymous mutations in XPO1 (predominantly E571K or E571G; n = 72) with genetic and epigenetic features contributing to the overall outcomes in these patients. We then established a mouse model with over-expression of wildtype (wt) or mutant (E571K or E571G) XPO1 restricted to the B cell compartment (Eµ-XPO1). Eµ-XPO1 mice were then crossed with the Eµ-TCL1 CLL mouse model. Lastly, we determined crystal structures of XPO1 (wt or E571K) bound to several selective inhibitors of nuclear export (SINE) molecules (KPT-185, KPT-330/Selinexor, and KPT-8602/Eltanexor). RESULTS: We report that nonsynonymous mutations in XPO1 associate with high risk genetic and epigenetic features and accelerated CLL progression. Using the newly-generated Eµ-XPO1 mouse model, we found that constitutive B-cell over-expression of wt or mutant XPO1 could affect development of a CLL-like disease in aged mice. Furthermore, concurrent B-cell expression of XPO1 with E571K or E571G mutations and TCL1 accelerated the rate of leukemogenesis relative to that of Eµ-TCL1 mice. Lastly, crystal structures of E571 or E571K-XPO1 bound to SINEs, including Selinexor, are highly similar, suggesting that the activity of this class of compounds will not be affected by XPO1 mutations at E571 in patients with CLL. CONCLUSIONS: These findings indicate that mutations in XPO1 at E571 can drive leukemogenesis by priming the pre-neoplastic lymphocytes for acquisition of additional genetic and epigenetic abnormalities that collectively result in neoplastic transformation.

CytoGPS: A large-scale karyotype analysis of CML data.

Karyotyping, the practice of visually examining and recording chromosomal abnormalities, is commonly used to diagnose diseases of genetic origin, including cancers. Karyotypes are recorded as text written in the International System for Human Cytogenetic Nomenclature (ISCN). Downstream analysis of karyotypes is conducted manually, due to the visual nature of analysis and the linguistic structure of the ISCN. The ISCN has not been computer-readable and, as such, prevents the full potential of these genomic data from being realized. In response, we developed CytoGPS, a platform to analyze large volumes of cytogenetic data using a Loss-Gain-Fusion model that converts the human-readable ISCN karyotypes into a machine-readable binary format. As proof of principle, we applied CytoGPS to cytogenetic data from the Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer, a National Cancer Institute hosted database of over 69,000 karyotypes of human cancers. Using the Jaccard coefficient to determine similarity between karyotypes structured as binary vectors, we were able to identify novel patterns from 4,968 Mitelman CML karyotypes, such as the co-occurrence of trisomy 19 and 21. The CytoGPS platform unlocks the potential for large-scale, comparative analysis of cytogenetic data. This methodological platform is freely available at CytoGPS.org.

Phase II Study of Combination Obinutuzumab, Ibrutinib, and Venetoclax in Treatment-Naïve and Relapsed or Refractory Chronic Lymphocytic Leukemia.

PURPOSE: The development of highly effective targeted agents for chronic lymphocytic leukemia offers the potential for fixed-duration combinations that achieve deep remissions without cytotoxic chemotherapy. PATIENTS AND METHODS: This phase II study tested a combination regimen of obinutuzumab, ibrutinib, and venetoclax for a total of 14 cycles in both patients with treatment-naïve (n = 25) and relapsed or refractory (n = 25) chronic lymphocytic leukemia to determine the response to therapy and safety. RESULTS: The primary end point was the rate of complete remission with undetectable minimal residual disease by flow cytometry in both the blood and bone marrow 2 months after completion of treatment, which was 28% in both groups. The overall response rate at that time was 84% in treatment-naïve patients and 88% in relapsed or refractory patients. At that time, 67% of treatment-naïve patients and 50% of relapsed or refractory patients had undetectable minimal residual disease in both the blood and marrow. At a median follow-up of 24.2 months in treatment-naïve patients and 21.5 months in relapsed or refractory patients, the median progression-free and overall survival times were not yet reached, with only 1 patient experiencing progression and 1 death. Neutropenia and thrombocytopenia were the most frequent adverse events, followed by hypertension. Grade 3 or 4 neutropenia was experienced by 66% of patients, with more events in the relapsed or refractory cohort. There was only 1 episode of neutropenic fever. A favorable impact on both perceived and objective cognitive performance during treatment was observed. CONCLUSION: The combination regimen of obinutuzumab, ibrutinib, and venetoclax offers time-limited treatment that results in deep remissions and is now being studied in phase III cooperative group trials.

Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia.

OBJECTIVE: Unsupervised machine learning approaches hold promise for large-scale clinical data. However, the heterogeneity of clinical data raises new methodological challenges in feature selection, choosing a distance metric that captures biological meaning, and visualization. We hypothesized that clustering could discover prognostic groups from patients with chronic lymphocytic leukemia, a disease that provides biological validation through well-understood outcomes. METHODS: To address this challenge, we applied k-medoids clustering with 10 distance metrics to 2 experiments ("A" and "B") with mixed clinical features collapsed to binary vectors and visualized with both multidimensional scaling and t-stochastic neighbor embedding. To assess prognostic utility, we performed survival analysis using a Cox proportional hazard model, log-rank test, and Kaplan-Meier curves. RESULTS: In both experiments, survival analysis revealed a statistically significant association between clusters and survival outcomes (A: overall survival, P = .0164; B: time from diagnosis to treatment, P = .0039). Multidimensional scaling separated clusters along a gradient mirroring the order of overall survival. Longer survival was associated with mutated immunoglobulin heavy-chain variable region gene (IGHV) status, absent Zap 70 expression, female sex, and younger age. CONCLUSIONS: This approach to mixed-type data handling and selection of distance metric captured well-understood, binary, prognostic markers in chronic lymphocytic leukemia (sex, IGHV mutation status, ZAP70 expression status) with high fidelity.

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.

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.

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.

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.

MYC protein expression is an important prognostic factor in acute myeloid leukemia.

As new drugs targeting MYC show clinical activity in acute myeloid leukemia (AML), understanding MYC expression in AML is of critical importance. We assessed MYC protein expression by immunohistochemistry in bone marrow of patients with untreated AML (n = 265). Overall, 90% of patients demonstrated MYC overexpression and MYC immunopositivity ≤6% was associated with superior complete remission (CR) duration of 23 months versus 12 months for MYC immunopositivity >6% (p = .028). Among 241 patients at higher risk for relapse, including those ≥55 years of age and patients with intermediate- and high-risk AML, MYC immunopositivity ≤6% conferred significantly superior median overall survival (OS) (24 versus 13 months; p = .042), event-free survival (EFS) (14 versus 6 months; p = .048), and relapse-free survival (RFS) (25 versus 12 months; p = .024). The prognostic impact of MYC-immunopositivity was retained on multivariate analysis of OS, EFS, and RFS. We conclude that MYC immunopositivity is an important prognostic factor in patients with untreated AML, particularly those at higher risk for relapse.

Correction: Acute myeloid leukemia with t(4;12)(q12;p13): an aggressive disease with frequent involvement of PDGFRA and ETV6.

[This corrects the article DOI: 10.18632/oncotarget.23743.].

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.