Genetic subdivisions of follicular lymphoma defined by distinct coding and noncoding mutation patterns

Follicular lymphoma (FL) accounts for ∼20% of all new lymphoma cases. Increases in cytological grade are a feature of the clinical progression of this malignancy, and eventual histologic transformation (HT) to the aggressive diffuse large B-cell lymphoma (DLBCL) occurs in up to 15% of patients. Clinical or genetic features to predict the risk and timing of HT have not been described comprehensively. In this study, we analyzed whole-genome sequencing data from 423 patients to compare the protein coding and noncoding mutation landscapes of untransformed FL, transformed FL, and de novo DLBCL. This revealed 2 genetically distinct subgroups of FL, which we have named DLBCL-like (dFL) and constrained FL (cFL). Each subgroup has distinguishing mutational patterns, aberrant somatic hypermutation rates, and biological and clinical characteristics. We implemented a machine learning-derived classification approach to stratify patients with FL into cFL and dFL subgroups based on their genomic features. Using separate validation cohorts, we demonstrate that cFL status, whether assigned with this full classifier or a single-gene approximation, is associated with a reduced rate of HT. This implies distinct biological features of cFL that constrain its evolution, and we highlight the potential for this classification to predict HT from genetic features present at diagnosis.

Genetic subgroups inform on pathobiology in adult and pediatric Burkitt lymphoma

Burkitt lymphoma (BL) accounts for most pediatric non-Hodgkin lymphomas, being less common but significantly more lethal when diagnosed in adults. Much of the knowledge of the genetics of BL thus far has originated from the study of pediatric BL (pBL), leaving its relationship to adult BL (aBL) and other adult lymphomas not fully explored. We sought to more thoroughly identify the somatic changes that underlie lymphomagenesis in aBL and any molecular features that associate with clinical disparities within and between pBL and aBL. Through comprehensive whole-genome sequencing of 230 BL and 295 diffuse large B-cell lymphoma (DLBCL) tumors, we identified additional significantly mutated genes, including more genetic features that associate with tumor Epstein-Barr virus status, and unraveled new distinct subgroupings within BL and DLBCL with 3 predominantly comprising BLs: DGG-BL (DDX3X, GNA13, and GNAI2), IC-BL (ID3 and CCND3), and Q53-BL (quiet TP53). Each BL subgroup is characterized by combinations of common driver and noncoding mutations caused by aberrant somatic hypermutation. The largest subgroups of BL cases, IC-BL and DGG-BL, are further characterized by distinct biological and gene expression differences. IC-BL and DGG-BL and their prototypical genetic features (ID3 and TP53) had significant associations with patient outcomes that were different among aBL and pBL cohorts. These findings highlight shared pathogenesis between aBL and pBL, and establish genetic subtypes within BL that serve to delineate tumors with distinct molecular features, providing a new framework for epidemiologic, diagnostic, and therapeutic strategies.

PRPS-ST: A protocol-agnostic self-training method for gene expression-based classification of blood cancers

Gene expression classifiers are gaining increasing popularity for stratifying tumors into subgroups with distinct biological features. A fundamental limitation shared by current classifiers is the requirement for comparable training and testing data sets. Here, we describe a self-training implementation of our probability ratio-based classification prediction score method (PRPS-ST), which facilitates the porting of existing classification models to other gene expression data sets. In comparison to gold standards, we demonstrate favorable performance of PRPS-ST in gene expression-based classification of DLBCL and B-ALL using a diverse variety of gene expression data types and pre-processing methods, including in classifications with a high degree of class imbalance. Tumors classified by our method were significantly enriched for prototypical genetic features of their respective subgroups. Interestingly, this included cases that were unclassifiable by established methods, implying the potential enhanced sensitivity of PRPS-ST.

Coding and noncoding drivers of mantle cell lymphoma identified through exome and genome sequencing

Mantle cell lymphoma (MCL) is an uncommon B-cell non-Hodgkin lymphoma (NHL) that is incurable with standard therapies. The genetic drivers of this cancer have not been firmly established, and the features that contribute to differences in clinical course remain limited. To extend our understanding of the biological pathways involved in this malignancy, we performed a large-scale genomic analysis of MCL using data from 51 exomes and 34 genomes alongside previously published exome cohorts. To confirm our findings, we resequenced the genes identified in the exome cohort in 191 MCL tumors, each having clinical follow-up data. We confirmed the prognostic association of TP53 and NOTCH1 mutations. Our sequencing revealed novel recurrent noncoding mutations surrounding a single exon of the HNRNPH1gene. In RNA-seq data from 103 of these cases, MCL tumors with these mutations had a distinct imbalance of HNRNPH1 isoforms. This altered splicing of HNRNPH1 was associated with inferior outcomes in MCL and showed a significant increase in protein expression by immunohistochemistry. We describe a functional role for these recurrent noncoding mutations in disrupting an autoregulatory feedback mechanism, thereby deregulating HNRNPH1 protein expression. Taken together, these data strongly imply a role for aberrant regulation of messenger RNA processing in MCL pathobiology.

Identifying, understanding, and correcting technical artifacts on the sex chromosomes in next-generation sequencing data

Background

Mammalian X and Y chromosomes share a common evolutionary origin and retain regions of high sequence similarity. Similar sequence content can confound the mapping of short next-generation sequencing reads to a reference genome. It is therefore possible that the presence of both sex chromosomes in a reference genome can cause technical artifacts in genomic data and affect downstream analyses and applications. Understanding this problem is critical for medical genomics and population genomic inference.

Results

Here, we characterize how sequence homology can affect analyses on the sex chromosomes and present XYalign, a new tool that (1) facilitates the inference of sex chromosome complement from next-generation sequencing data; (2) corrects erroneous read mapping on the sex chromosomes; and (3) tabulates and visualizes important metrics for quality control such as mapping quality, sequencing depth, and allele balance. We find that sequence homology affects read mapping on the sex chromosomes and this has downstream effects on variant calling. However, we show that XYalign can correct mismapping, resulting in more accurate variant calling. We also show how metrics output by XYalign can be used to identify XX and XY individuals across diverse sequencing experiments, including low- and high-coverage whole-genome sequencing, and exome sequencing. Finally, we discuss how the flexibility of the XYalign framework can be leveraged for other uses including the identification of aneuploidy on the autosomes. XYalign is available open source under the GNU General Public License (version 3).

Conclusions

Sex chromsome sequence homology causes the mismapping of short reads, which in turn affects downstream analyses. XYalign provides a reproducible framework to correct mismapping and improve variant calling on the sex chromsomes.

Evaluation of protocols for rRNA depletion-based RNA sequencing of nanogram inputs of mammalian total RNA

Next generation RNA-sequencing (RNA-seq) is a flexible approach that can be applied to a range of applications including global quantification of transcript expression, the characterization of RNA structure such as splicing patterns and profiling of expressed mutations. Many RNA-seq protocols require up to microgram levels of total RNA input amounts to generate high quality data, and thus remain impractical for the limited starting material amounts typically obtained from rare cell populations, such as those from early developmental stages or from laser micro-dissected clinical samples. Here, we present an assessment of the contemporary ribosomal RNA depletion-based protocols, and identify those that are suitable for inputs as low as 1–10 ng of intact total RNA and 100–500 ng of partially degraded RNA from formalin-fixed paraffin-embedded tissues.

SUBSTRA: Supervised Bayesian Patient Stratification

Motivation

Patient stratification methods are key to the vision of precision medicine. Here, we consider transcriptional data to segment the patient population into subsets relevant to a given phenotype. Whereas most existing patient stratification methods focus either on predictive performance or interpretable features, we developed a method striking a balance between these two important goals.

Results

We introduce a Bayesian method called SUBSTRA that uses regularized biclustering to identify patient subtypes and interpretable subtype-specific transcript clusters. The method iteratively re-weights feature importance to optimize phenotype prediction performance by producing more phenotype-relevant patient subtypes. We investigate the performance of SUBSTRA in finding relevant features using simulated data and successfully benchmark it against state-of-the-art unsupervised stratification methods and supervised alternatives. Moreover, SUBSTRA achieves predictive performance competitive with the supervised benchmark methods and provides interpretable transcriptional features in diverse biological settings, such as drug response prediction, cancer diagnosis, or kidney transplant rejection.

Availability and implementation

The R code of SUBSTRA is available at https://github.com/sahandk/SUBSTRA.

Supplementary information

Supplementary data are available at Bioinformatics online.

Integrative genomic analysis of matched primary and metastatic pediatric osteosarcoma

Despite being the most common childhood bone tumor, the genomic characterization of osteosarcoma remains incomplete. In particular, very few osteosarcoma metastases have been sequenced to date, critical to better understand mechanisms of progression and evolution in this tumor. We performed an integrated whole genome and exome sequencing analysis of paired primary and metastatic pediatric osteosarcoma specimens to identify recurrent genomic alterations. Sequencing of 13 osteosarcoma patients including 13 primary, 10 metastatic, and 3 locally recurring tumors revealed a highly heterogeneous mutational landscape, including cases of hypermutation and microsatellite instability positivity, but with virtually no recurrent alterations except for mutations involving the tumor suppressor genes RB1 and TP53. At the germline level, we detected alterations in multiple cancer related genes in the majority of the cohort, including those potentially disrupting DNA damage response pathways. Metastases retained only a minimal number of short variants from their corresponding primary tumors, while copy number alterations showed higher conservation. One recurrently amplified gene, KDR, was highly expressed in advanced cases and associated with poor prognosis. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Genome-wide discovery of somatic coding and noncoding mutations in pediatric endemic and sporadic Burkitt lymphoma

Although generally curable with intensive chemotherapy in resource-rich settings, Burkitt lymphoma (BL) remains a deadly disease in older patients and in sub-Saharan Africa. Epstein-Barr virus (EBV) positivity is a feature in more than 90% of cases in malaria-endemic regions, and up to 30% elsewhere. However, the molecular features of BL have not been comprehensively evaluated when taking into account tumor EBV status or geographic origin. Through an integrative analysis of whole-genome and transcriptome data, we show a striking genome-wide increase in aberrant somatic hypermutation in EBV-positive tumors, supporting a link between EBV and activation-induced cytidine deaminase (AICDA) activity. In addition to identifying novel candidate BL genes such as SIN3A, USP7, and CHD8, we demonstrate that EBV-positive tumors had significantly fewer driver mutations, especially among genes with roles in apoptosis. We also found immunoglobulin variable region genes that were disproportionally used to encode clonal B-cell receptors (BCRs) in the tumors. These include IGHV4-34, known to produce autoreactive antibodies, and IGKV3-20, a feature described in other B-cell malignancies but not yet in BL. Our results suggest that tumor EBV status defines a specific BL phenotype irrespective of geographic origin, with particular molecular properties and distinct pathogenic mechanisms. The novel mutation patterns identified here imply rational use of DNA-damaging chemotherapy in some patients with BL and targeted agents such as the CDK4/6 inhibitor palbociclib in others, whereas the importance of BCR signaling in BL strengthens the potential benefit of inhibitors for PI3K, Syk, and Src family kinases among these patients.

Double-Hit Gene Expression Signature Defines a Distinct Subgroup of Germinal Center B-Cell-Like Diffuse Large B-Cell Lymphoma

PURPOSE

High-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements (HGBL-DH/TH) has a poor outcome after standard chemoimmunotherapy. We sought to understand the biologic underpinnings of HGBL-DH/TH with BCL2 rearrangements (HGBL-DH/TH- BCL2) and diffuse large B-cell lymphoma (DLBCL) morphology through examination of gene expression.

PATIENTS AND METHODS

We analyzed RNA sequencing data from 157 de novo germinal center B-cell-like (GCB)-DLBCLs, including 25 with HGBL-DH/TH- BCL2, to define a gene expression signature that distinguishes HGBL-DH/TH- BCL2 from other GCB-DLBCLs. To assess the genetic, molecular, and phenotypic features associated with this signature, we analyzed targeted resequencing, whole-exome sequencing, RNA sequencing, and immunohistochemistry data.

RESULTS

We developed a 104-gene double-hit signature (DHITsig) that assigned 27% of GCB-DLBCLs to the DHITsig-positive group, with only one half harboring MYC and BCL2 rearrangements (HGBL-DH/TH- BCL2). DHITsig-positive patients had inferior outcomes after rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone immunochemotherapy compared with DHITsig-negative patients (5-year time to progression rate, 57% and 81%, respectively; P < .001), irrespective of HGBL-DH/TH- BCL2 status. The prognostic value of DHITsig was confirmed in an independent validation cohort. DHITsig-positive tumors are biologically characterized by a putative non-light zone germinal center cell of origin and a distinct mutational landscape that comprises genes associated with chromatin modification. A new NanoString assay (DLBCL90) recapitulated the prognostic significance and RNA sequencing assignments. Validating the association with HGBL-DH/TH- BCL2, 11 of 25 DHITsig-positive-transformed follicular lymphomas were classified as HGBL-DH/TH- BCL2 compared with zero of 50 in the DHITsig-negative group. Furthermore, the DHITsig was shared with the majority of B-cell lymphomas with high-grade morphology tested.

CONCLUSION

We have defined a clinically and biologically distinct subgroup of tumors within GCB-DLBCL characterized by a gene expression signature of HGBL-DH/TH- BCL2. This knowledge has been translated into an assay applicable to routinely available biopsy samples, which enables exploration of its utility to guide patient management.

Kronos: a workflow assembler for genome analytics and informatics

Background:

The field of next-generation sequencing informatics has matured to a point where algorithmic advances in sequence alignment and individual feature detection methods have stabilized. Practical and robust implementation of complex analytical workflows (where such tools are structured into “best practices” for automated analysis of next-generation sequencing datasets) still requires significant programming investment and expertise.

Results:

We present Kronos, a software platform for facilitating the development and execution of modular, auditable, and distributable bioinformatics workflows. Kronos obviates the need for explicit coding of workflows by compiling a text configuration file into executable Python applications. Making analysis modules would still require programming. The framework of each workflow includes a run manager to execute the encoded workflows locally (or on a cluster or cloud), parallelize tasks, and log all runtime events. The resulting workflows are highly modular and configurable by construction, facilitating flexible and extensible meta-applications that can be modified easily through configuration file editing. The workflows are fully encoded for ease of distribution and can be instantiated on external systems, a step toward reproducible research and comparative analyses. We introduce a framework for building Kronos components that function as shareable, modular nodes in Kronos workflows.

Conclusions:

The Kronos platform provides a standard framework for developers to implement custom tools, reuse existing tools, and contribute to the community at large. Kronos is shipped with both Docker and Amazon Web Services Machine Images. It is free, open source, and available through the Python Package Index and at https://github.com/jtaghiyar/kronos.

Enhancing knowledge discovery from cancer genomics data with Galaxy

The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on the genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise. To address this, we have produced a collection of Galaxy tools that represent many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We developed new methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability and summarized their runtimes on multiple cloud service providers. Some tools represent extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate these to achieve data integration and visualizations are demonstrated on a cohort of 96 diffuse large B-cell lymphomas and enabled the discovery of multiple candidate lymphoma-related genes. Our toolkit is available from our GitHub repository as Galaxy tool and dependency definitions and has been deployed using virtualization on multiple platforms including Docker.

The genomic landscape of two Burkitt lymphoma cases and derived cell lines: comparison between primary and relapse samples

Relapse occurs in 10-40% of Burkitt lymphoma (BL) patients that have completed intensive chemotherapy regimens and is typically fatal. While treatment-naive BL has been characterized, the genomic landscape of BL at the time of relapse (rBL) has never been reported. Here, we present a genomic characterization of two rBL patients. The diagnostic samples had mutations common in BL, including MYC and CCND3. Additional mutations were detected at relapse, affecting important pathways such as NFκB (IKBKB) and MEK/ERK (NRAS) signaling, glutamine metabolism (SIRT4), and RNA processing (ZFP36L2). Genes implicated in drug resistance were also mutated at relapse (TP53, BAX, ALDH3A1, APAF1, FANCI). This concurrent genomic profiling of samples obtained at diagnosis and relapse has revealed mutations not previously reported in this disease. The patient-derived cell lines will be made available and, along with their detailed genetics, will be a valuable resource to examine the role of specific mutations in therapeutic resistance.

Targeted error-suppressed quantification of circulating tumor DNA using semi-degenerate barcoded adapters and biotinylated baits

Ultrasensitive methods for rare allele detection are critical to leverage the full potential offered by liquid biopsies. Here, we describe a novel molecular barcoding method for the precise detection and quantification of circulating tumor DNA (ctDNA). The major benefits of our design include straightforward and cost-effective production of barcoded adapters to tag individual DNA molecules before PCR and sequencing, and better control over cross-contamination between experiments. We validated our approach in a cohort of 24 patients with a broad spectrum of cancer diagnoses by targeting and quantifying single-nucleotide variants (SNVs), indels and genomic rearrangements in plasma samples. By using personalized panels targeting a priori known mutations, we demonstrate comprehensive error-suppression capabilities for SNVs and detection thresholds for ctDNA below 0.1%. We also show that our semi-degenerate barcoded adapters hold promise for noninvasive genotyping in the absence of tumor biopsies and monitoring of minimal residual disease in longitudinal plasma samples. The benefits demonstrated here include broad applicability, flexibility, affordability and reproducibility in the research and clinical settings.

Genetic Landscapes of Relapsed and Refractory Diffuse Large B-Cell Lymphomas

PURPOSE

Relapsed or refractory diffuse large B-cell lymphoma (rrDLBCL) is fatal in 90% of patients, and yet little is known about its biology.

EXPERIMENTAL DESIGN

Using exome sequencing, we characterized the mutation profiles of 38 rrDLBCL biopsies obtained at the time of progression after immunochemotherapy. To identify genes that may be associated with relapse, we compared the mutation frequency in samples obtained at relapse to an unrelated cohort of 138 diagnostic DLBCLs and separately amplified specific mutations in their matched diagnostic samples to identify clonal expansions.

RESULTS

On the basis of a higher frequency at relapse and evidence for clonal selection, TP53, FOXO1, MLL3 (KMT2C), CCND3, NFKBIZ, and STAT6 emerged as top candidate genes implicated in therapeutic resistance. We observed individual examples of clonal expansions affecting genes whose mutations had not been previously associated with DLBCL including two regulators of NF-κB: NFKBIE and NFKBIZ We detected mutations that may be affect sensitivity to novel therapeutics, such as MYD88 and CD79B mutations, in 31% and 23% of patients with activated B-cell-type of rrDLBCL, respectively. We also identified recurrent STAT6 mutations affecting D419 in 36% of patients with the germinal center B (GCB) cell rrDLBCL. These were associated with activated JAK/STAT signaling, increased phospho-STAT6 protein expression and increased expression of STAT6 target genes.

CONCLUSIONS

This work improves our understanding of therapeutic resistance in rrDLBCL and has identified novel therapeutic opportunities especially for the high-risk patients with GCB-type rrDLBCL. Clin Cancer Res; 22(9); 2290-300. ©2015 AACR.