Multi-platform profiling characterizes molecular subgroups and resistance networks in chronic lymphocytic leukemia

Knowledge of the genomic landscape of chronic lymphocytic leukemia (CLL) grows increasingly detailed, providing challenges in contextualizing the accumulated information. To define the underlying networks, we here perform a multi-platform molecular characterization. We identify major subgroups characterized by genomic instability (GI) or activation of epithelial-mesenchymal-transition (EMT)-like programs, which subdivide into non-inflammatory and inflammatory subtypes. GI CLL exhibit disruption of genome integrity, DNA-damage response and are associated with mutagenesis mediated through activation-induced cytidine deaminase or defective mismatch repair. TP53 wild-type and mutated/deleted cases constitute a transcriptionally uniform entity in GI CLL and show similarly poor progression-free survival at relapse. EMT-like CLL exhibit high genomic stability, reduced benefit from the addition of rituximab and EMT-like differentiation is inhibited by induction of DNA damage. This work extends the perspective on CLL biology and risk categories in TP53 wild-type CLL. Furthermore, molecular targets identified within each subgroup provide opportunities for new treatment approaches.


Statistics
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n/a Confirmed The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above. RNA quantification and quality control was performed with the Agilent 2100 Bioanalyzer and 2100 Expert Software Version 2.6 (B.02.07.SI532). Affymetrix Human Exon 1.0 ST Array (HuEx-1_0-st-v2) data files were preprocessed by the robust multichip average (RMA) algorithm using the aroma.affymetrix R package version 2.12.0 (Bengtsson H et al. Methods. 2008;Tech Repor(745):1-9 2008). Statistical procedures on expression data were performed with the R software environment, versions 3.3.3 and 3.4.1 and BRB-ArrayTools Versions 4.2.1 -4.6.1 (available at http://linus.nci.nih.gov/BRB-ArrayTools.html and www.r-project.org). Agglomerative hierarchical clustering and consensus clustering (Monti S et. al. Mach Learn. 2003;52:91-118) was applied (R environment or Genesis, release 1.8.0). Visualizing selected gene sets was conducted using the Genesis platform, release 1.  (6755):788-791). Related statistics were performed with MATLAB 2018b. Signature projections for mutational processes were conducted and analyzed by using the SignatureAnalyzer algorithm (https:// software.broadinstitute.org/cancer/cga/msp). Intensities of individual bands in western blots were analyzed using Fiji ImageJ

October 2018
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Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

Life sciences Behavioural & social sciences Ecological, evolutionary & environmental sciences
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Life sciences study design
All studies must disclose on these points even when the disclosure is negative. Complete data sets are available: For GEP at Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/; GEO accession number: GSE58211 (REACH only); GSE126595 (full clinical data set); GSE126699 (including functional data). For SNP-Microarray raw data at Gene Expression Omnibus (GEO accession number: GSE36908 (CLL8 treatment naive) and GSE83566 (relapsed)). CLL8 WES data is deposited in dbGaP under accession code phs000922.v1.p1. CLL8 RRBS sequencing data is available from the NCBI (GEO accession number: GSE143673). The proteome profiling raw data and processed outputs are available at https:// www.ebi.ac.uk/pride/archive/projects/PXD004608.
Patient samples for multi-platform profiling were used based on availability of material with highest quality from the underlying clinical trial cohorts (CLL8 (GEP:n=426) and the REACH(GEP:n=300) trials). Sample size for the validation cohort closely matched the discovery cohort.
Data was analysed as given, no data exclusion performed.
Gene expression profiles from independent patient samples of the randomized, multicenter phase 3 CLL8 trial were validated on expression profiles from independent patient samples of the randomized, multicenter phase 3 REACH trial. No replicates were used from individual samples for gene expression profiling. Confirmation of biological categories and processes inferred from the multi-platform analysis was conducted in multiple, independent in vitro and in vivo mouse models. Experimental findings were reliably reproduced. All experiments have been performed with appropriate replicates as described in the Figures and Methods sections.
Patient samples were taken from 2 independent randomized trials. In vivo validation was conducted on defined mouse models and therefore no randomization was needed.
Due to the experimental design and sequential analysis workflow sample blinding was not neccessary for multi-platform profiling and confirmatory mouse models. Visual quantifications were performed in a blinded manner as specified in the respective manuscript sections. Validation Samples used in this study were chosen based on availability and sufficient quality of the material. Higher leukocyte counts were observed for the CLL8 discovery cohort of CD19 sorted CLL cases, likely through selection of samples with abundant material for multiple analyses, however, patient characteristics and especially high-risk markers showed a well-balanced distribution representative of the full trial population. Assessment of batch effects imposed through e.g. time point of sampling, location of sampling, time point of labeling/hybridization and other factors could not be observed. Only samples from patients with need for treatment and fulfilling inclusion/exclusion crieria, as defined in the respective study protocol, were collected at enrolment onto the respective trials at participating centers.
Trial participation, genetic testing and data evaluation have been conducted after informed patient consent, with the approval of the respective local ethics committees of participating centers and in accordance with the Declaration of Helsinki. Data analysis and conductance of multi-platform profiling in this study was approved by the Ulm University ethics committee.