Baseline radiomics features and MYC rearrangement status predict progression in aggressive B-cell lymphoma

We investigated whether the outcome prediction of patients with aggressive B-cell lymphoma can be improved by combining clinical, molecular genotype, and radiomics features. MYC, BCL2, and BCL6 rearrangements were assessed using fluorescence in situ hybridization. Seventeen radiomics features were extracted from the baseline positron emission tomography–computed tomography of 323 patients, which included maximum standardized uptake value (SUV(max)), SUV(peak), SUV(mean), metabolic tumor volume (MTV), total lesion glycolysis, and 12 dissemination features pertaining to distance, differences in uptake and volume between lesions, respectively. Logistic regression with backward feature selection was used to predict progression after 2 years. The predictive value of (1) International Prognostic Index (IPI); (2) IPI plus MYC; (3) IPI, MYC, and MTV; (4) radiomics; and (5) MYC plus radiomics models were tested using the cross-validated area under the curve (CV-AUC) and positive predictive values (PPVs). IPI yielded a CV-AUC of 0.65 ± 0.07 with a PPV of 29.6%. The IPI plus MYC model yielded a CV-AUC of 0.68 ± 0.08. IPI, MYC, and MTV yielded a CV-AUC of 0.74 ± 0.08. The highest model performance of the radiomics model was observed for MTV combined with the maximum distance between the largest lesion and another lesion, the maximum difference in SUV(peak) between 2 lesions, and the sum of distances between all lesions, yielding an improved CV-AUC of 0.77 ± 0.07. The same radiomics features were retained when adding MYC (CV-AUC, 0.77 ± 0.07). PPV was highest for the MYC plus radiomics model (50.0%) and increased by 20% compared with the IPI (29.6%). Adding radiomics features improved model performance and PPV and can, therefore, aid in identifying poor prognosis patients

Computational solutions in genomic pathology of non-Hodgkin Lymphomas

This thesis focusses on computational approaches to study the genome of non-Hodgkin lymphomas. According to the World Health Organization, there are more than 30 types of non-Hodgkin's lymphoma. These types are distinguished from each other on the basis of morphology (cell and tissue structure), immunophenotype (proteins in the cell and on the cell surface) and genomics (DNA alterations). The disease course of different types of lymphoma also varies in aggressiveness. This difference in clinical outcome is reflected in the two most common types: diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL). These two types of lymphomas are studied in this thesis through two main aims. The first aim is to develop a comprehensive assay for simultaneous screening of all genomic alterations using a limited amount of input DNA derived from standard diagnostic biopsy material without the need of patient-matched control DNA as reference, optimized to be implemented in clinical practice for lymphoma diagnostics. The second aim is to improve our understanding of the biological basis of the clinical heterogeneity of DLBCL and FL and thereby enable improved risk stratification for these patients. We intend to achieve this by applying the assays developed under aim 1, to large, selected patient cohorts of DLBCL and FL. This thesis describes how we successfully developed an “all-in-one” next-generation sequencing assay for diagnostic biopsy material, with a bioinformatics pipeline to detect all DNA alterations relevant for lymphoma. Despite the challenges in the clinical setting, including the frequent lack of matched-normal reference samples and the suboptimal DNA quality of standard diagnostic biopsy material, somatic mutations, copy number aberrations and translocations were identified. Therefore, adaptations were customized to the specific needs of DNA derived from diagnostic biopsy material for wet- and drylab procedures. In addition, a new algorithm was introduced, ACE, that allows for an accurate measure of tumor cell percentage, which in turn had been applied to quality select samples on basis of tumor cell percentage. The various drylab implementations have been converted to pipelines and were publicly made available for reuse. The application of the NGS assay, as described in the first aim, has led to improved insights into the molecular basis of these diseases with improved risk stratification, and clues for molecular-informed clinical trial designs and tailored treatment approaches as consequences. An in-depth molecular characterization of HHV8-negative effusion-based lymphoma has contributed to a more refined definition of the disease and has found its way into the recent 5th edition of the WHO Classification. Our studies of larger patient populations in DLBCL and in selected patients with uncommon presentations of FL show that the a priori recognition of a heterogeneous disease course of patients can be improved by molecular profiling. This allows us to better distinguish between DLBCL and FL patients with a good and poor prognosis. Moreover, these DNA profiles offer possibilities for personalized therapy. In the future, new medicines or treatment methods can be tested in patients based on these new insights. This offers hope for patients, especially those who now fall into a high-risk group. There is however still room for improvement to disentangle the underlying complex oncogenesis of malignant lymphoma that may go beyond DNA alterations and to eventually tailor personalized treatment options accordingly

ACE: Absolute Copy number Estimation from low-coverage whole-genome sequencing data

Summary: Chromosomal copy number aberrations can be efficiently detected and quantified using low-coverage whole-genome sequencing (lcWGS), but analysis is hampered by the lack of knowledge on absolute DNA copy numbers and tumor purity. Here we describe an analytical tool for Absolute Copy number Estimation, ACE, which scales relative copy number signals from chromosomal segments to optimally fit absolute copy numbers, without the need for additional genetic information, such as SNP data. In doing so, ACE derives an estimate of tumor purity as well. ACE facilitates analysis of large numbers of samples, while maintaining the flexibility to customize models and generate output of single samples. Availability and implementation: ACE is freely available via www.bioconductor.org and at www.github.com/tgac-vumc/ACE. Supplementary information: Supplementary methods and data are available at Bioinformatics online. Documentation, example data, and a vignette, are included in the R package of ACE

The path towards consensus genome classification of diffuse large B-cell lymphoma for use in clinical practice

Diffuse large B-cell lymphoma (DLBCL) is a widely heterogeneous disease in presentation, treatment response and outcome that results from a broad biological heterogeneity. Various stratification approaches have been proposed over time but failed to sufficiently capture the heterogeneous biology and behavior of the disease in a clinically relevant manner. The most recent DNA-based genomic subtyping studies are a major step forward by offering a level of refinement that could serve as a basis for exploration of personalized and targeted treatment for the years to come. To enable consistent trial designs and allow meaningful comparisons between studies, harmonization of the currently available knowledge into a single genomic classification widely applicable in daily practice is pivotal. In this review, we investigate potential avenues for harmonization of the presently available genomic subtypes of DLBCL inspired by consensus molecular classifications achieved for other malignancies. Finally, suggestions for laboratory techniques and infrastructure required for successful clinical implementation are described

Chromosome 20 loss is characteristic of breast implant-associated anaplastic large cell lymphoma

Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a very rare type of T-cell lymphoma that is uniquely caused by a single environmental stimulus. Here, we present a comprehensive genetic analysis of a relatively large series of BIA-ALCL (n = 29), for which genome-wide chromosomal copy number aberrations (CNAs) and mutational profiles for a subset (n = 7) were determined. For comparison, CNAs for anaplastic lymphoma kinase (ALK)- nodal anaplastic large cell lymphomas (ALCLs; n = 24) were obtained. CNAs were detected in 94% of BIA-ALCLs, with losses at chromosome 20q13.13 in 66% of the samples. Loss of 20q13.13 is characteristic of BIA-ALCL compared with other classes of ALCL, such as primary cutaneous ALCL and systemic type ALK+ and ALK- ALCL. Mutational patterns confirm that the interleukin-6-JAK1-STAT3 pathway is deregulated. Although this is commonly observed across various types of T-cell lymphomas, the extent of deregulation is significantly higher in BIA-ALCL, as indicated by phosphorylated STAT3 immunohistochemistry. The characteristic loss of chromosome 20 in BIA-ALCL provides further justification to recognize BIA-ALCL as a separate disease entity. Moreover, CNA analysis may serve as a parameter for future diagnostic assays for women with breast implants to distinguish seroma caused by BIA-ALCL from other causes of seroma accumulation, such as infection or trauma

An infrastructure for service oriented sensor networks

Emerging wireless technologies enable ubiquitous access to networked services. Integration of wireless technologies into sensor and actuator nodes provides the means for remote access and control. However, ad hoc deployment of nodes complicates the process of finding, selecting and sing these in a meaningful way. The use of a service discovery framework enables nodes to present themselves and the resources they hold. In this paper, we review the applicability of a number of well-known service discovery protocols in the context of networked nodes. Multicast DNS and Service Discovery (mDNS-SD) stands out with its auto-configuration, distributed architecture,sharing of resources, and wide area access. For wireless battery operated and resource constrained nodes, we seek to integrate SD and power management techniques. This leads us to a standards based infrastructure for service oriented sensor networks where; 1) nodes collaborate in an ad hoc fashion by using SD techniques to discover (and announce) resources locally and over the public Internet, 2) nodes preserve power through aggressive utilization of low power (sleep) modes, while yet being reachable for clients according to defined schemas, and 3) clients may access and configure nodes, and (if possible) access sleeping nodes by implicit wake-up procedures. To demonstrate the proposed infrastructure a complete experimental setup has been devised featuring; Bluetooth enabled nodes, lightweight implementations of mDNS-SD and communication stacks, Internet access through cellular/wired gateways, together with a public DNS server. Our experiments verify that mDNS-SD can be effectively deployed on small wireless sensor and actuator nodes and provides the basis of a service oriented infrastructure for low power sensor networks.Validerad; 2006; 20061206 (jench)</p

Genomic and microenvironmental landscape of stage I follicular lymphoma, compared with stage III/IV

Although the genomic and immunemicroenvironmental landscape of follicular lymphoma (FL) has been extensively investigated, little is known about the potential biological differences between stage I and stage III/IV disease. Using next-generation sequencing and immunohistochemistry, 82 FL nodal stage I caseswere analyzed and comparedwith 139 FL stage III/IV nodal cases.Many similarities in mutations, chromosomal copy number aberrations, and microenvironmental cell populationswere detected. However, therewere also significant differences inmicroenvironmental and genomic features. CD8- T cells (P= .02) and STAT6 mutations (false discovery rate [FDR],0.001)weremore frequent in stage I FL. In contrast, programmed cell death protein 1-positive T cells, CD68-/CD163- macrophages (P<.001), BCL2 translocation (BCL2trl-) (P< .0001), and KMT2D (FDR= 0.003) and CREBBP (FDR= 0.04) mutationswere foundmore frequently in stage III/IV FL. Using clustering,we identified 3 clusters within stage I, and 2 clusterswithin stage III/IV. The BLC2trl- stage I clusterwas comparable to the BCL2trl- cluster in stage III/IV. The two BCL2trl- stage I clusters were unique for stage I. One was enriched for CREBBP (95%) andSTAT6 (64%)mutations,without BLC6 translocation (BCL6trl), whereas the BCL2trl- stage III/IV cluster contained BCL6trl (64%)with fewer CREBBP (45%) andSTAT6 (9%)mutations. The other BCL2trl- stage I clusterwas relatively heterogeneouswith more copy number aberrations and linker histonemutations. This exploratory study shows that stage I FL is genetically heterogeneouswith different underlying oncogenic pathways. Stage I FL BCL2trl- is likely STAT6 driven,whereas BCL2trl- stage III/IV appears to bemore BCL6trl driven