Super-enhancer hypermutation alters oncogene expression in B cell lymphoma.

Diffuse large B cell lymphoma (DLBCL) is the most common B cell non-Hodgkin lymphoma and remains incurable in around 40% of patients. Efforts to sequence the coding genome identified several genes and pathways that are altered in this disease, including potential therapeutic targets1-5. However, the non-coding genome of DLBCL remains largely unexplored. Here we show that active super-enhancers are highly and specifically hypermutated in 92% of samples from individuals with DLBCL, display signatures of activation-induced cytidine deaminase activity, and are linked to genes that encode B cell developmental regulators and oncogenes. As evidence of oncogenic relevance, we show that the hypermutated super-enhancers linked to the BCL6, BCL2 and CXCR4 proto-oncogenes prevent the binding and transcriptional downregulation of the corresponding target gene by transcriptional repressors, including BLIMP1 (targeting BCL6) and the steroid receptor NR3C1 (targeting BCL2 and CXCR4). Genetic correction of selected mutations restored repressor DNA binding, downregulated target gene expression and led to the counter-selection of cells containing corrected alleles, indicating an oncogenic dependency on the super-enhancer mutations. This pervasive super-enhancer mutational mechanism reveals a major set of genetic lesions deregulating gene expression, which expands the involvement of known oncogenes in DLBCL pathogenesis and identifies new deregulated gene targets of therapeutic relevance.

MERIT reveals the impact of genomic context on sequencing error rate in ultra-deep applications.

BACKGROUND: Rapid progress in high-throughput sequencing (HTS) and the development of novel library preparation methods have improved the sensitivity of detecting mutations in heterogeneous samples, specifically in high-depth (> 500×) clinical applications. However, HTS methods are bounded by their technical and theoretical limitations and sequencing errors cannot be completely eliminated. Comprehensive quantification of the background noise can highlight both the efficiency and the limitations of any HTS methodology, and help differentiate true mutations at low abundance from artifacts. RESULTS: We introduce MERIT (Mutation Error Rate Inference Toolkit), designed for in-depth quantification of erroneous substitutions and small insertions and deletions. MERIT incorporates an all-inclusive variant caller and considers genomic context, including the nucleotides immediately at 5 'and 3 ', thereby establishing error rates for 96 possible substitutions as well as four single-base and 16 double-base indels. We applied MERIT to ultra-deep sequencing data (1,300,000 ×) obtained from the amplification of multiple clinically relevant loci, and showed a significant relationship between error rates and genomic contexts. In addition to observing significant difference between transversion and transition rates, we identified variations of more than 100-fold within each error type at high sequencing depths. For instance, T >G transversions in trinucleotide GTCs occurred 133.5 ± 65.9 more often than those in ATAs. Similarly, C >T transitions in GCGs were observed at 73.8 ± 10.5 higher rate than those in TCTs. We also devised an in silico approach to determine the optimal sequencing depth, where errors occur at rates similar to those of expected true mutations. Our analyses showed that increasing sequencing depth might improve sensitivity for detecting some mutations based on their genomic context. For example, T >G rate of error in GTCs did not change when sequenced beyond 10,000 ×; in contrast, T >G rate in TTAs consistently improved even at above 500,000 ×. CONCLUSIONS: Our results demonstrate significant variation in nucleotide misincorporation rates, and suggest that genomic context should be considered for comprehensive profiling of specimen-specific and sequencing artifacts in high-depth assays. This data provide strong evidence against assigning a single allele frequency threshold to call mutations, for it can result in substantial false positive as well as false negative variants, with important clinical consequences.

Inference of Germline Mutational Status and Evaluation of Loss of Heterozygosity in High-Depth, Tumor-Only Sequencing Data.

PURPOSE: Inherited germline defects are implicated in up to 10% of human tumors, with particularly well-known roles in breast and ovarian cancers that harbor BRCA1/2-mutated genes. There is also increasing evidence for the role of germline alterations in other malignancies such as colon and pancreatic cancers. Mutations in familial cancer genes can be detected by high throughput sequencing (HTS), when applied to formalin-fixed paraffin-embedded (FFPE) tumor specimens. However, due to often lack of patient-matched control normal DNA and/or low tumor purity, there is limited ability to determine the genomic status of these alterations (germline versus somatic) and to assess the presence of loss of heterozygosity (LOH). These analyses, especially when applied to genes such as BRCA1/2, can have significant clinical implications for patient care. METHODS: LOHGIC (LOH-Germline Inference Calculator) is a statistical model selection method to determine somatic-versus-germline status and predict LOH for mutations identified via clinical grade, high-depth, hybrid-capture tumor-only sequencing. LOHGIC incorporates statistical uncertainties inherent to HTS as well as specimen biases in tumor purity estimates, which we use to assess BRCA1/2 mutations in 1,636 specimens sequenced at Rutgers Cancer Institute of New Jersey. RESULTS: Evaluation of LOHGIC with available germline sequencing from BRCA1/2 testing, demonstrates 93% accuracy, 100% precision, and 96% recall. This analysis highlights a differential tumor spectrum associated with BRCA1/2 mutations. CONCLUSION: LOHGIC can assess LOH status for both germline and somatic mutations. It also can be applied to any gene with candidate, inherited mutations. This approach demonstrates the clinical utility of targeted sequencing in both identifying patients with potential germline alterations in tumor suppressor genes as well as estimating LOH occurrence in cancer cells, which may confer therapeutic relevance.