Dataset Information

Comparison of tumor mutation burden of 300 various non-Hodgkin lymphomas using panel based massively parallel sequencing.

ABSTRACT:

Background

Tumor mutation burden is an emerging biomarker for immunotherapy. Although several clinical trials for immunotherapy in lymphoma have been carried out, the mutation burden of various lymphomas is not well known yet. Thus, the objective of this study was to compare tumor mutation burden of various non-Hodgkin lymphomas using panel based massively parallel sequencing.

Methods

We conducted 405 gene panel based massively parallel sequencing of 300 non-Hodgkin lymphomas and investigate the number of SNV/Indel in each lymphoma.

Results

The number of SNV/Indel was higher in mature B-cell lymphoma than in mature T- and NK-cell lymphoma. (P < 0.001) The number of SNV/Indel in primary mediastinal large B-cell lymphoma and primary diffuse large B-cell lymphoma of the central nervous system was the highest, which was significantly higher than that in diffuse large B-cell lymphoma, not otherwise specified (DLBCL NOS).(P = 0.030 and P = 0.008, respectively) The SNV/Indel number in EBV-positive DLBCL NOS was significantly lower than that in DLBCL NOS. (P = 0.048) Peripheral T-cell lymphoma, NOS showed no significant difference in the number of SNV/Indel from extranodal NK/T-cell lymphoma, nasal type (P = 0.942) or angioimmunoblastic T-cell lymphoma (P = 0.739). The number of SNV/Indel in anaplastic large cell lymphoma, ALK-positive was significantly lower than that in anaplastic large cell lymphoma, ALK-negative (P = 0.049). It was the lowest among all the lymphomas considered.

Conclusion

Various lymphomas have different mutation burdens. Thus, tumor mutation burden can be used as a promising biomarker for immunotherapy in lymphomas.

SUBMITTER: Cho J

PROVIDER: S-EPMC8404326 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundRNA-Seq exploits the rapid generation of gigabases of sequence data by Massively Parallel Nucleotide Sequencing, allowing for the mapping and digital quantification of whole transcriptomes. Whilst previous comparisons between RNA-Seq and microarrays have been performed at the level of gene expression, in this study we adopt a more fine-grained approach. Using RNA samples from a normal human breast epithelial cell line (MCF-10a) and a breast cancer cell line (MCF-7), we present a comprehensive comparison between RNA-Seq data generated on the Applied Biosystems SOLiD platform and data from Affymetrix Exon 1.0ST arrays. The use of Exon arrays makes it possible to assess the performance of RNA-Seq in two key areas: detection of expression at the granularity of individual exons, and discovery of transcription outside annotated loci.ResultsWe found a high degree of correspondence between the two platforms in terms of exon-level fold changes and detection. For example, over 80% of exons detected as expressed in RNA-Seq were also detected on the Exon array, and 91% of exons flagged as changing from Absent to Present on at least one platform had fold-changes in the same direction. The greatest detection correspondence was seen when the read count threshold at which to flag exons Absent in the SOLiD data was set to t<1 suggesting that the background error rate is extremely low in RNA-Seq. We also found RNA-Seq more sensitive to detecting differentially expressed exons than the Exon array, reflecting the wider dynamic range achievable on the SOLiD platform. In addition, we find significant evidence of novel protein coding regions outside known exons, 93% of which map to Exon array probesets, and are able to infer the presence of thousands of novel transcripts through the detection of previously unreported exon-exon junctions.ConclusionsBy focusing on exon-level expression, we present the most fine-grained comparison between RNA-Seq and microarrays to date. Overall, our study demonstrates that data from a SOLiD RNA-Seq experiment are sufficient to generate results comparable to those produced from Affymetrix Exon arrays, even using only a single replicate from each platform, and when presented with a large genome.